diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..7c7bb690
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+# Vim swap files
+*.swp
+*.swo
+
+# Python byte code
+*.pyc
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 00000000..1b330efc
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,39 @@
+language: python
+python:
+  # We don't actually use the Travis Python since we are using Conda, but this
+  # keeps it organized.
+  - "2.7"
+  - "3.5"
+  - "3.6"
+install:
+  - sudo apt-get update
+  # We do this conditionally because it saves us some downloading if the
+  # version is the same.
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
+    else
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+    fi
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  # Useful for debugging any issues with conda
+  - conda info -a
+
+  # Create and activate the environmenbt
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
+  - source activate test-environment
+  # Ensure we will install all pip packages within conda
+  - conda install pip
+  # Install numpy first to avoid headaches
+  - pip install numpy
+  # Install the r_packages
+  - conda install rpy2
+  - ./install_r_packages.sh
+  # Install the packages needed for testing
+  - pip install .['test']
+script: 
+  # Run the tests
+  - pytest -v
diff --git a/README.md b/README.md
index 8bb6b001..137539d1 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # TIGRAMITE – Causal discovery for time series datasets
-Version 3.0 described in http://arxiv.org/abs/1702.07007
+Version 4.0 described in http://arxiv.org/abs/1702.07007v2
 
 (Python Package)
 
@@ -10,7 +10,14 @@ Version 3.0 described in http://arxiv.org/abs/1702.07007
 
 ## General Notes
 
-Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
+Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
+
+- PCMCI: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2
+- Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
+- Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
+- Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829
+- CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. http://proceedings.mlr.press/v84/runge18a.html
+
 
 ## Features
 
@@ -18,26 +25,25 @@ Tigramite is a causal time series analysis python package. It allows to efficien
 - flexible conditional independence test statistics adapted to
   continuously-valued or discrete data, and different assumptions about
   linear or nonlinear dependencies
-- automatic hyperparameter optimization
+- automatic hyperparameter optimization for most tests
 - parallel computing script based on mpi4py
 - handling of missing values and masks
 - p-value correction and confidence interval estimation
 - causal mediation class to analyze causal pathways
 - prediction class based on sklearn models including causal feature selection
+- currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected
 
 
 ## Required python packages
 
-- numpy, tested with Version 1.10
-- scipy, tested with Version 0.17
-- sklearn, tested with Version 0.18 (optional, necessary for GPDC and GPACE tests)
-- ace python package (https://pypi.python.org/pypi/ace/0.3) OR rpy2 and R-package 'acepack' (optional, necessary for GPACE test)
-- matplotlib, tested with Version 1.5
-- networkx, tested with Version 1.10
-- basemap (only if plotting on a map is needed)
-- mpi4py (optional, necessary for using the parallelized implementation)
-- cython (optional, necessary for CMIknn and GPDC tests)
-- statsmodels, tested with Version 0.6 (optional, necessary for p-value corrections)
+- numpy>=1.10.0
+- scipy>=0.17.0
+- scikit-learn>=0.18.1   (optional, necessary for GPDC test)
+- matplotlib>=1.5.1 (optional, only for plotting)
+- networkx=1.10.0 (optional, only for plotting and mediation)
+- cython>=0.26   (optional, necessary for CMIknn and GPDC tests)
+- mpi4py>=2.0.0   (optional, necessary for using the parallelized implementation)
+- rpy2>=2.8   (optional, necessary for RCOT test)
 
 
 ## Installation
@@ -46,31 +52,25 @@ python setup.py install
 
 This will install tigramite in your path.
 
-To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For CMIknn, cython can optionally be used for compilation, otherwise the provided *.c file is used. GPDC also is based on cython, and additionally, sklearn is required for Gaussian Process regression.
+To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For other independence tests more packages are required:
 
-GPACE requires more work: Firstly, sklearn is required for Gaussian Process regression. Secondly, either the python package 'ace' or the R-package 'acepack' are required for the ACE estimator. The R-package version is much faster. 'ace' can be installed via pip install ace. 'acepack' has to be installed in R first, and can then be accessed by tigramite using the rpy2-interface. 
+- CMIknn: cython can optionally be used for compilation, otherwise the provided ``*.c'' file is used 
 
-For GPDC and GPACE we recommend to pre-compute and store the null-distribution for a wide range of expected sample sizes with the function ``generate_and_save_nulldists``. The file containing the null distributions can then be supplied to the class with the keyword null_dist_filename.
+- GPDC: also based on cython, and additionally, scikit-learn is required for Gaussian Process regression
 
+- RCOT requires more work: Firstly, rpy2 is required to access R-packages. The required R-packages can be installed with the script ``install_r_packages.sh''
 
-## User Agreement
-
-By downloading TIGRAMITE you agree with the following points: The toolbox is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application the toolbox.
 
-You commit to cite TIGRAMITE in your reports or publications if used:
-
-1. J. Runge, S. Flaxman, and D. Sejdinovic (2017): Detecting causal associations in large nonlinear time series datasets. https://arxiv.org/abs/1702.07007
-
-2. J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
+## User Agreement
 
-3. J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829
+By downloading TIGRAMITE you agree with the following points: TIGRAMITE is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application of TIGRAMITE.
 
-4. J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths (2012): Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy. Physical Review Letters, 108(25), 258701. http://doi.org/10.1103/PhysRevLett.108.258701
+You commit to cite above papers in your reports or publications.
 
 
 ## License
 
-Copyright (C) Jakob Runge
+Copyright (C) 2014-2019 Jakob Runge
 
 See license.txt for full text.
 
diff --git a/build_tools/conda/build.sh b/build_tools/conda/build.sh
new file mode 100644
index 00000000..22b95cb9
--- /dev/null
+++ b/build_tools/conda/build.sh
@@ -0,0 +1,14 @@
+# NOTE: when installing, conda will include all things under $PREFIX as a part 
+# of your distributed package.  As such, the R libraries are installed to 
+# the correct library location under $PREFIX. This may not be the most robust 
+# solution, but it works for now.
+
+R_LIBS_BUILD=$PREFIX/lib/R/library
+# Install r-MASS package, which is a dependency of RCIT/RCOT
+R -e "install.packages('MASS', '$R_LIBS_BUILD', repos='https://cloud.r-project.org/')"
+# Install momentchi2, whichs is a dependency of RCIT/RCOT
+R -e "install.packages('momentchi2', '$R_LIBS_BUILD', repos = 'https://cloud.r-project.org/')"
+# Install the RCIT package
+R -e "install.packages('external_packages/RCIT', '$R_LIBS_BUILD', repos=NULL, type='source')"
+# Install tigramite
+$PYTHON setup.py install --single-version-externally-managed --record=record.txt
diff --git a/build_tools/conda/meta.yaml b/build_tools/conda/meta.yaml
new file mode 100644
index 00000000..8255c2c5
--- /dev/null
+++ b/build_tools/conda/meta.yaml
@@ -0,0 +1,46 @@
+package:
+  name: tigramite
+  version: 4.0.0
+
+source:
+  path: ../..
+
+requirements:
+  build:
+    - cython
+    - numpy
+    - rpy2
+    - setuptools
+    - python
+  host:
+    - cython
+    - numpy
+    - rpy2
+    - setuptools
+    - python
+  run:
+    - python
+    - numpy 
+    - scipy
+    - six 
+    - scikit-learn>=0.18
+    - matplotlib>=1.5
+    - networkx>=1.10
+    - statsmodels
+    - rpy2
+
+test:
+  source_files:
+    - tests/*py
+  requires:
+    - nose
+    - pytest
+    - rpy2
+  commands:
+    - pytest .
+
+about:
+  home: https://github.com/jakobrunge/tigramite
+  license: GPL-3
+  license_file: license.txt
+  summary: Tigramite is a time series analysis aimed at causal discovery
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index bb0f2938..a78d831d 100644
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diff --git a/docs/_images/math/f75f28dbcbec776fbe031f8ac923f83f9260bd8a.png b/docs/_images/math/f75f28dbcbec776fbe031f8ac923f83f9260bd8a.png
index 61eac71c..01805a45 100644
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diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 489fb4a5..4f6dbada 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -1,33 +1,21 @@
+
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
   "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 
-
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    
-    <title>Overview: module code &#8212; Tigramite 3.0 documentation</title>
-    
+    <title>Overview: module code &#8212; Tigramite 4.0 documentation</title>
     <link rel="stylesheet" href="../_static/sphinxdoc.css" type="text/css" />
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    '../',
-        VERSION:     '3.0',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '.html',
-        HAS_SOURCE:  true,
-        SOURCELINK_SUFFIX: '.txt'
-      };
-    </script>
+    <script type="text/javascript" src="../_static/documentation_options.js"></script>
     <script type="text/javascript" src="../_static/jquery.js"></script>
     <script type="text/javascript" src="../_static/underscore.js"></script>
     <script type="text/javascript" src="../_static/doctools.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
-  </head>
-  <body role="document">
+  </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
       <ul>
@@ -37,19 +25,21 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="../py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
         <div class="sphinxsidebarwrapper">
 <div id="searchbox" style="display: none" role="search">
   <h3>Quick search</h3>
+    <div class="searchformwrapper">
     <form class="search" action="../search.html" method="get">
-      <div><input type="text" name="q" /></div>
-      <div><input type="submit" value="Go" /></div>
+      <input type="text" name="q" />
+      <input type="submit" value="Go" />
       <input type="hidden" name="check_keywords" value="yes" />
       <input type="hidden" name="area" value="default" />
     </form>
+    </div>
 </div>
 <script type="text/javascript">$('#searchbox').show(0);</script>
         </div>
@@ -61,13 +51,12 @@ <h3>Quick search</h3>
           <div class="body" role="main">
             
   <h1>All modules for which code is available</h1>
-<ul><li><a href="tigramite/data_processing.html">tigramite.data_processing</a></li>
+<ul><li><a href="abc.html">abc</a></li>
+<li><a href="tigramite/data_processing.html">tigramite.data_processing</a></li>
 <li><a href="tigramite/independence_tests.html">tigramite.independence_tests</a></li>
-<li><a href="tigramite/linear_mediation.html">tigramite.linear_mediation</a></li>
 <li><a href="tigramite/models.html">tigramite.models</a></li>
 <li><a href="tigramite/pcmci.html">tigramite.pcmci</a></li>
 <li><a href="tigramite/plotting.html">tigramite.plotting</a></li>
-<li><a href="tigramite/prediction.html">tigramite.prediction</a></li>
 </ul>
 
           </div>
@@ -84,12 +73,12 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="../py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
     <div class="footer" role="contentinfo">
-        &#169; Copyright 2017, Jakob Runge.
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.5.6.
+        &#169; Copyright 2018, Jakob Runge.
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.4.
     </div>
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index dd30e87d..de5c5351 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -10,8 +10,8 @@ TIGRAMITE
 
 Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
-1. J. Runge, S. Flaxman, D. Sejdinovic (2017): Detecting causal associations in large nonlinear time series datasets, 
-   https://arxiv.org/abs/1702.07007
+1. J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
+   https://arxiv.org/abs/1702.07007v2
 
 2. J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. 
    Nature Communications, 6, 8502. 
@@ -21,9 +21,13 @@ Tigramite is a causal time series analysis python package. It allows to efficien
    Phys. Rev. E, 92(6), 62829. 
    http://doi.org/10.1103/PhysRevE.92.062829
 
-4. J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths (2012): Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy. 
-   Physical Review Letters, 108(25), 258701. 
-   http://doi.org/10.1103/PhysRevLett.108.258701
+4. J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information.
+   In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics.
+   http://proceedings.mlr.press/v84/runge18a.html
+
+5. J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.
+   Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.   
+   https://aip.scitation.org/doi/10.1063/1.5025050
 
 
 .. toctree::
@@ -35,8 +39,6 @@ Tigramite is a causal time series analysis python package. It allows to efficien
    tigramite.pcmci.PCMCI
    tigramite.independence_tests.CondIndTest
    tigramite.independence_tests.ParCorr
-   tigramite.independence_tests.GP
-   tigramite.independence_tests.GPACE
    tigramite.independence_tests.GPDC
    tigramite.independence_tests.CMIknn
    tigramite.independence_tests.CMIsymb  
@@ -67,12 +69,6 @@ Test statistics:
 .. autoclass:: tigramite.independence_tests.ParCorr
    :members:
 
-.. autoclass:: tigramite.independence_tests.GP
-   :members:
-
-.. autoclass:: tigramite.independence_tests.GPACE
-   :members:
-
 .. autoclass:: tigramite.independence_tests.GPDC
    :members:
 
diff --git a/docs/genindex.html b/docs/genindex.html
index b26649d9..98dba0ec 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -1,34 +1,22 @@
 
+
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
   "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 
-
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    
-    <title>Index &#8212; Tigramite 3.0 documentation</title>
-    
+    <title>Index &#8212; Tigramite 4.0 documentation</title>
     <link rel="stylesheet" href="_static/sphinxdoc.css" type="text/css" />
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    './',
-        VERSION:     '3.0',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '.html',
-        HAS_SOURCE:  true,
-        SOURCELINK_SUFFIX: '.txt'
-      };
-    </script>
+    <script type="text/javascript" src="_static/documentation_options.js"></script>
     <script type="text/javascript" src="_static/jquery.js"></script>
     <script type="text/javascript" src="_static/underscore.js"></script>
     <script type="text/javascript" src="_static/doctools.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" /> 
-  </head>
-  <body role="document">
+  </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
       <ul>
@@ -38,22 +26,21 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
         <div class="sphinxsidebarwrapper">
-
-   
-
 <div id="searchbox" style="display: none" role="search">
   <h3>Quick search</h3>
+    <div class="searchformwrapper">
     <form class="search" action="search.html" method="get">
-      <div><input type="text" name="q" /></div>
-      <div><input type="submit" value="Go" /></div>
+      <input type="text" name="q" />
+      <input type="submit" value="Go" />
       <input type="hidden" name="check_keywords" value="yes" />
       <input type="hidden" name="area" value="default" />
     </form>
+    </div>
 </div>
 <script type="text/javascript">$('#searchbox').show(0);</script>
         </div>
@@ -94,7 +81,7 @@ <h2 id="A">A</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.all_parents">all_parents (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.all_parents">all_parents (tigramite.pcmci.PCMCI attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -104,11 +91,13 @@ <h2 id="C">C</h2>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.independence_tests.CMIknn">CMIknn (class in tigramite.independence_tests)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.independence_tests.CMIsymb">CMIsymb (class in tigramite.independence_tests)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.independence_tests.CondIndTest">CondIndTest (class in tigramite.independence_tests)</a>
+</li>
+      <li><a href="index.html#tigramite.data_processing.DataFrame.construct_array">construct_array() (tigramite.data_processing.DataFrame method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -121,6 +110,8 @@ <h2 id="D">D</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.data_processing.DataFrame">DataFrame (class in tigramite.data_processing)</a>
+</li>
+      <li><a href="index.html#tigramite.data_processing.DataFrame.datatime">datatime (tigramite.data_processing.DataFrame attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -140,9 +131,9 @@ <h2 id="F">F</h2>
 <h2 id="G">G</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#tigramite.independence_tests.CondIndTest.generate_and_save_nulldists">generate_and_save_nulldists() (tigramite.independence_tests.CondIndTest method)</a>
+      <li><a href="index.html#tigramite.independence_tests.GPDC.generate_and_save_nulldists">generate_and_save_nulldists() (tigramite.independence_tests.GPDC method)</a>
 </li>
-      <li><a href="index.html#tigramite.independence_tests.CondIndTest.generate_nulldist">generate_nulldist() (tigramite.independence_tests.CondIndTest method)</a>
+      <li><a href="index.html#tigramite.independence_tests.GPDC.generate_nulldist">generate_nulldist() (tigramite.independence_tests.GPDC method)</a>
 </li>
       <li><a href="index.html#tigramite.models.LinearMediation.get_ace">get_ace() (tigramite.models.LinearMediation method)</a>
 </li>
@@ -156,27 +147,17 @@ <h2 id="G">G</h2>
 </li>
       <li><a href="index.html#tigramite.models.LinearMediation.get_amce">get_amce() (tigramite.models.LinearMediation method)</a>
 </li>
-      <li><a href="index.html#tigramite.independence_tests.CMIknn.get_analytic_confidence">get_analytic_confidence() (tigramite.independence_tests.CMIknn method)</a>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_analytic_confidence">get_analytic_confidence() (tigramite.independence_tests.CondIndTest method)</a>
 
       <ul>
-        <li><a href="index.html#tigramite.independence_tests.CMIsymb.get_analytic_confidence">(tigramite.independence_tests.CMIsymb method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.GPACE.get_analytic_confidence">(tigramite.independence_tests.GPACE method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.GPDC.get_analytic_confidence">(tigramite.independence_tests.GPDC method)</a>
-</li>
         <li><a href="index.html#tigramite.independence_tests.ParCorr.get_analytic_confidence">(tigramite.independence_tests.ParCorr method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.RCOT.get_analytic_confidence">(tigramite.independence_tests.RCOT method)</a>
 </li>
       </ul></li>
-      <li><a href="index.html#tigramite.independence_tests.CMIknn.get_analytic_significance">get_analytic_significance() (tigramite.independence_tests.CMIknn method)</a>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_analytic_significance">get_analytic_significance() (tigramite.independence_tests.CondIndTest method)</a>
 
       <ul>
-        <li><a href="index.html#tigramite.independence_tests.CMIsymb.get_analytic_significance">(tigramite.independence_tests.CMIsymb method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.GPACE.get_analytic_significance">(tigramite.independence_tests.GPACE method)</a>
-</li>
         <li><a href="index.html#tigramite.independence_tests.GPDC.get_analytic_significance">(tigramite.independence_tests.GPDC method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.ParCorr.get_analytic_significance">(tigramite.independence_tests.ParCorr method)</a>
@@ -203,7 +184,7 @@ <h2 id="G">G</h2>
       <ul>
         <li><a href="index.html#tigramite.independence_tests.CMIsymb.get_dependence_measure">(tigramite.independence_tests.CMIsymb method)</a>
 </li>
-        <li><a href="index.html#tigramite.independence_tests.GPACE.get_dependence_measure">(tigramite.independence_tests.GPACE method)</a>
+        <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_dependence_measure">(tigramite.independence_tests.CondIndTest method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.GPDC.get_dependence_measure">(tigramite.independence_tests.GPDC method)</a>
 </li>
@@ -226,12 +207,10 @@ <h2 id="G">G</h2>
 </li>
       <li><a href="index.html#tigramite.models.LinearMediation.get_mediation_graph_data">get_mediation_graph_data() (tigramite.models.LinearMediation method)</a>
 </li>
-      <li><a href="index.html#tigramite.independence_tests.CMIknn.get_model_selection_criterion">get_model_selection_criterion() (tigramite.independence_tests.CMIknn method)</a>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_model_selection_criterion">get_model_selection_criterion() (tigramite.independence_tests.CondIndTest method)</a>
 
       <ul>
-        <li><a href="index.html#tigramite.independence_tests.CMIsymb.get_model_selection_criterion">(tigramite.independence_tests.CMIsymb method)</a>
-</li>
-        <li><a href="index.html#tigramite.independence_tests.GP.get_model_selection_criterion">(tigramite.independence_tests.GP method)</a>
+        <li><a href="index.html#tigramite.independence_tests.GPDC.get_model_selection_criterion">(tigramite.independence_tests.GPDC method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.ParCorr.get_model_selection_criterion">(tigramite.independence_tests.ParCorr method)</a>
 </li>
@@ -245,7 +224,7 @@ <h2 id="G">G</h2>
       <ul>
         <li><a href="index.html#tigramite.independence_tests.CMIsymb.get_shuffle_significance">(tigramite.independence_tests.CMIsymb method)</a>
 </li>
-        <li><a href="index.html#tigramite.independence_tests.GPACE.get_shuffle_significance">(tigramite.independence_tests.GPACE method)</a>
+        <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_shuffle_significance">(tigramite.independence_tests.CondIndTest method)</a>
 </li>
         <li><a href="index.html#tigramite.independence_tests.GPDC.get_shuffle_significance">(tigramite.independence_tests.GPDC method)</a>
 </li>
@@ -254,6 +233,8 @@ <h2 id="G">G</h2>
         <li><a href="index.html#tigramite.independence_tests.RCOT.get_shuffle_significance">(tigramite.independence_tests.RCOT method)</a>
 </li>
       </ul></li>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.get_significance">get_significance() (tigramite.independence_tests.CondIndTest method)</a>
+</li>
       <li><a href="index.html#tigramite.models.Prediction.get_test_array">get_test_array() (tigramite.models.Prediction method)</a>
 </li>
       <li><a href="index.html#tigramite.models.Prediction.get_train_array">get_train_array() (tigramite.models.Prediction method)</a>
@@ -261,10 +242,6 @@ <h2 id="G">G</h2>
       <li><a href="index.html#tigramite.models.LinearMediation.get_tsg">get_tsg() (tigramite.models.LinearMediation method)</a>
 </li>
       <li><a href="index.html#tigramite.models.LinearMediation.get_val_matrix">get_val_matrix() (tigramite.models.LinearMediation method)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.GP">GP (class in tigramite.independence_tests)</a>
-</li>
-      <li><a href="index.html#tigramite.independence_tests.GPACE">GPACE (class in tigramite.independence_tests)</a>
 </li>
       <li><a href="index.html#tigramite.independence_tests.GPDC">GPDC (class in tigramite.independence_tests)</a>
 </li>
@@ -274,7 +251,7 @@ <h2 id="G">G</h2>
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.iterations">iterations (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.iterations">iterations (tigramite.pcmci.PCMCI attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -296,6 +273,20 @@ <h2 id="M">M</h2>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.data_processing.DataFrame.mask">mask (tigramite.data_processing.DataFrame attribute)</a>
 </li>
+      <li><a href="index.html#tigramite.independence_tests.CMIknn.measure">measure (tigramite.independence_tests.CMIknn attribute)</a>
+
+      <ul>
+        <li><a href="index.html#tigramite.independence_tests.CMIsymb.measure">(tigramite.independence_tests.CMIsymb attribute)</a>
+</li>
+        <li><a href="index.html#tigramite.independence_tests.CondIndTest.measure">(tigramite.independence_tests.CondIndTest attribute)</a>
+</li>
+        <li><a href="index.html#tigramite.independence_tests.GPDC.measure">(tigramite.independence_tests.GPDC attribute)</a>
+</li>
+        <li><a href="index.html#tigramite.independence_tests.ParCorr.measure">(tigramite.independence_tests.ParCorr attribute)</a>
+</li>
+        <li><a href="index.html#tigramite.independence_tests.RCOT.measure">(tigramite.independence_tests.RCOT attribute)</a>
+</li>
+      </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.data_processing.DataFrame.missing_flag">missing_flag (tigramite.data_processing.DataFrame attribute)</a>
@@ -308,7 +299,7 @@ <h2 id="M">M</h2>
 <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.N">N (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.N">N (tigramite.pcmci.PCMCI attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -328,7 +319,7 @@ <h2 id="O">O</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.p_max">p_max (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.p_max">p_max (tigramite.pcmci.PCMCI attribute)</a>
 </li>
       <li><a href="index.html#tigramite.independence_tests.ParCorr">ParCorr (class in tigramite.independence_tests)</a>
 </li>
@@ -338,12 +329,12 @@ <h2 id="P">P</h2>
 </li>
       <li><a href="index.html#tigramite.plotting.plot_lagfuncs">plot_lagfuncs() (in module tigramite.plotting)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.plotting.plot_mediation_graph">plot_mediation_graph() (in module tigramite.plotting)</a>
 </li>
       <li><a href="index.html#tigramite.plotting.plot_mediation_time_series_graph">plot_mediation_time_series_graph() (in module tigramite.plotting)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.plotting.plot_time_series_graph">plot_time_series_graph() (in module tigramite.plotting)</a>
 </li>
       <li><a href="index.html#tigramite.plotting.plot_timeseries">plot_timeseries() (in module tigramite.plotting)</a>
@@ -351,6 +342,14 @@ <h2 id="P">P</h2>
       <li><a href="index.html#tigramite.models.Prediction.predict">predict() (tigramite.models.Prediction method)</a>
 </li>
       <li><a href="index.html#tigramite.models.Prediction">Prediction (class in tigramite.models)</a>
+</li>
+      <li><a href="index.html#tigramite.data_processing.DataFrame.print_array_info">print_array_info() (tigramite.data_processing.DataFrame method)</a>
+</li>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.print_info">print_info() (tigramite.independence_tests.CondIndTest method)</a>
+</li>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.print_results">print_results() (tigramite.pcmci.PCMCI method)</a>
+</li>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.print_significant_links">print_significant_links() (tigramite.pcmci.PCMCI method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -367,6 +366,8 @@ <h2 id="R">R</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#tigramite.independence_tests.RCOT">RCOT (class in tigramite.independence_tests)</a>
+</li>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.return_significant_parents">return_significant_parents() (tigramite.pcmci.PCMCI method)</a>
 </li>
       <li><a href="index.html#tigramite.pcmci.PCMCI.run_mci">run_mci() (tigramite.pcmci.PCMCI method)</a>
 </li>
@@ -390,6 +391,8 @@ <h2 id="S">S</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#tigramite.independence_tests.CondIndTest.set_mask_type">set_mask_type() (tigramite.independence_tests.CondIndTest method)</a>
+</li>
       <li><a href="index.html#tigramite.plotting.setup_matrix">setup_matrix (class in tigramite.plotting)</a>
 </li>
       <li><a href="index.html#tigramite.data_processing.smooth">smooth() (in module tigramite.data_processing)</a>
@@ -400,7 +403,7 @@ <h2 id="S">S</h2>
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.T">T (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.T">T (tigramite.pcmci.PCMCI attribute)</a>
 </li>
       <li><a href="index.html#module-tigramite.data_processing">tigramite.data_processing (module)</a>
 </li>
@@ -418,10 +421,12 @@ <h2 id="T">T</h2>
 <h2 id="V">V</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#PCMCI.val_min">val_min (PCMCI attribute)</a>
+      <li><a href="index.html#tigramite.pcmci.PCMCI.val_min">val_min (tigramite.pcmci.PCMCI attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#tigramite.data_processing.DataFrame.var_names">var_names (tigramite.data_processing.DataFrame attribute)</a>
+</li>
       <li><a href="index.html#tigramite.data_processing.var_process">var_process() (in module tigramite.data_processing)</a>
 </li>
   </ul></td>
@@ -451,12 +456,12 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
     <div class="footer" role="contentinfo">
-        &#169; Copyright 2017, Jakob Runge.
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.5.6.
+        &#169; Copyright 2018, Jakob Runge.
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.4.
     </div>
   </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/docs/index.html b/docs/index.html
index 39d06fd3..93f23909 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1,33 +1,21 @@
+
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
   "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 
-
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    
-    <title>TIGRAMITE &#8212; Tigramite 3.0 documentation</title>
-    
+    <title>TIGRAMITE &#8212; Tigramite 4.0 documentation</title>
     <link rel="stylesheet" href="_static/sphinxdoc.css" type="text/css" />
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    './',
-        VERSION:     '3.0',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '.html',
-        HAS_SOURCE:  true,
-        SOURCELINK_SUFFIX: '.txt'
-      };
-    </script>
+    <script type="text/javascript" src="_static/documentation_options.js"></script>
     <script type="text/javascript" src="_static/jquery.js"></script>
     <script type="text/javascript" src="_static/underscore.js"></script>
     <script type="text/javascript" src="_static/doctools.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" /> 
-  </head>
-  <body role="document">
+  </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
       <ul>
@@ -37,7 +25,7 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="#">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="#">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
@@ -45,11 +33,11 @@ <h3>Navigation</h3>
   <h3><a href="#">Table Of Contents</a></h3>
   <ul>
 <li><a class="reference internal" href="#">TIGRAMITE</a></li>
-<li><a class="reference internal" href="#tigramite-pcmci-pcmci"><code class="docutils literal"><span class="pre">tigramite.pcmci</span></code>: PCMCI</a></li>
-<li><a class="reference internal" href="#tigramite-independence-tests-conditional-independence-tests"><code class="docutils literal"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests</a></li>
-<li><a class="reference internal" href="#module-tigramite.data_processing"><code class="docutils literal"><span class="pre">tigramite.data_processing</span></code>: Data processing functions</a></li>
-<li><a class="reference internal" href="#tigramite-models-time-series-modeling-mediation-and-prediction"><code class="docutils literal"><span class="pre">tigramite.models</span></code>: Time series modeling, mediation, and prediction</a></li>
-<li><a class="reference internal" href="#module-tigramite.plotting"><code class="docutils literal"><span class="pre">tigramite.plotting</span></code>: Plotting functions</a></li>
+<li><a class="reference internal" href="#tigramite-pcmci-pcmci"><code class="docutils literal notranslate"><span class="pre">tigramite.pcmci</span></code>: PCMCI</a></li>
+<li><a class="reference internal" href="#tigramite-independence-tests-conditional-independence-tests"><code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests</a></li>
+<li><a class="reference internal" href="#module-tigramite.data_processing"><code class="docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code>: Data processing functions</a></li>
+<li><a class="reference internal" href="#tigramite-models-time-series-modeling-mediation-and-prediction"><code class="docutils literal notranslate"><span class="pre">tigramite.models</span></code>: Time series modeling, mediation, and prediction</a></li>
+<li><a class="reference internal" href="#module-tigramite.plotting"><code class="docutils literal notranslate"><span class="pre">tigramite.plotting</span></code>: Plotting functions</a></li>
 <li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
 </ul>
 
@@ -62,12 +50,14 @@ <h3>This Page</h3>
    </div>
 <div id="searchbox" style="display: none" role="search">
   <h3>Quick search</h3>
+    <div class="searchformwrapper">
     <form class="search" action="search.html" method="get">
-      <div><input type="text" name="q" /></div>
-      <div><input type="submit" value="Go" /></div>
+      <input type="text" name="q" />
+      <input type="submit" value="Go" />
       <input type="hidden" name="check_keywords" value="yes" />
       <input type="hidden" name="area" value="default" />
     </form>
+    </div>
 </div>
 <script type="text/javascript">$('#searchbox').show(0);</script>
         </div>
@@ -83,17 +73,20 @@ <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this he
 <p><a class="reference external" href="https://github.com/jakobrunge/tigramite/">Github repo</a></p>
 <p>Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:</p>
 <ol class="arabic simple">
-<li>J. Runge, S. Flaxman, D. Sejdinovic (2017): Detecting causal associations in large nonlinear time series datasets,
-<a class="reference external" href="https://arxiv.org/abs/1702.07007">https://arxiv.org/abs/1702.07007</a></li>
+<li>J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
+<a class="reference external" href="https://arxiv.org/abs/1702.07007v2">https://arxiv.org/abs/1702.07007v2</a></li>
 <li>J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems.
 Nature Communications, 6, 8502.
 <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></li>
 <li>J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems.
 Phys. Rev. E, 92(6), 62829.
 <a class="reference external" href="http://doi.org/10.1103/PhysRevE.92.062829">http://doi.org/10.1103/PhysRevE.92.062829</a></li>
-<li>J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths (2012): Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy.
-Physical Review Letters, 108(25), 258701.
-<a class="reference external" href="http://doi.org/10.1103/PhysRevLett.108.258701">http://doi.org/10.1103/PhysRevLett.108.258701</a></li>
+<li>J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information.
+In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics.
+<a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></li>
+<li>J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.
+Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.
+<a class="reference external" href="https://aip.scitation.org/doi/10.1063/1.5025050">https://aip.scitation.org/doi/10.1063/1.5025050</a></li>
 </ol>
 <div class="toctree-wrapper compound">
 </div>
@@ -103,56 +96,50 @@ <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this he
 <col width="90%" />
 </colgroup>
 <tbody valign="top">
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.pcmci.PCMCI" title="tigramite.pcmci.PCMCI"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.pcmci.PCMCI</span></code></a>(dataframe,&nbsp;cond_ind_test)</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.pcmci.PCMCI" title="tigramite.pcmci.PCMCI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.pcmci.PCMCI</span></code></a>(dataframe,&nbsp;cond_ind_test)</td>
 <td>PCMCI causal discovery for time series datasets.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.CondIndTest" title="tigramite.independence_tests.CondIndTest"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.CondIndTest</span></code></a>([...])</td>
+<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.CondIndTest" title="tigramite.independence_tests.CondIndTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.CondIndTest</span></code></a>([…])</td>
 <td>Base class of conditional independence tests.</td>
 </tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.ParCorr" title="tigramite.independence_tests.ParCorr"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.ParCorr</span></code></a>(**kwargs)</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.ParCorr" title="tigramite.independence_tests.ParCorr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.ParCorr</span></code></a>(**kwargs)</td>
 <td>Partial correlation test.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.GP" title="tigramite.independence_tests.GP"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.GP</span></code></a>([...])</td>
-<td>Gaussian processes base class.</td>
-</tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.GPACE" title="tigramite.independence_tests.GPACE"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.GPACE</span></code></a>([...])</td>
-<td>GPACE conditional independence test based on Gaussian processes and     maximal correlation.</td>
-</tr>
-<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.GPDC" title="tigramite.independence_tests.GPDC"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.GPDC</span></code></a>([...])</td>
+<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.GPDC" title="tigramite.independence_tests.GPDC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.GPDC</span></code></a>([…])</td>
 <td>GPDC conditional independence test based on Gaussian processes and     distance correlation.</td>
 </tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.CMIknn" title="tigramite.independence_tests.CMIknn"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.CMIknn</span></code></a>([knn,&nbsp;...])</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.CMIknn" title="tigramite.independence_tests.CMIknn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.CMIknn</span></code></a>([knn,&nbsp;…])</td>
 <td>Conditional mutual information test based on nearest-neighbor estimator.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.CMIsymb" title="tigramite.independence_tests.CMIsymb"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.CMIsymb</span></code></a>([...])</td>
+<tr class="row-even"><td><a class="reference internal" href="#tigramite.independence_tests.CMIsymb" title="tigramite.independence_tests.CMIsymb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.CMIsymb</span></code></a>([…])</td>
 <td>Conditional mutual information test based on discrete estimator.</td>
 </tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.RCOT" title="tigramite.independence_tests.RCOT"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.independence_tests.RCOT</span></code></a>([num_f,&nbsp;...])</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.independence_tests.RCOT" title="tigramite.independence_tests.RCOT"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.independence_tests.RCOT</span></code></a>([num_f,&nbsp;…])</td>
 <td>Randomized Conditional Correlation Test.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.data_processing</span></code></a></td>
+<tr class="row-even"><td><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a></td>
 <td>Tigramite data processing functions.</td>
 </tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.models.Models" title="tigramite.models.Models"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.models.Models</span></code></a>(dataframe,&nbsp;model[,&nbsp;...])</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.models.Models" title="tigramite.models.Models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Models</span></code></a>(dataframe,&nbsp;model[,&nbsp;…])</td>
 <td>Base class for time series models.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#tigramite.models.LinearMediation" title="tigramite.models.LinearMediation"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.models.LinearMediation</span></code></a>(dataframe)</td>
+<tr class="row-even"><td><a class="reference internal" href="#tigramite.models.LinearMediation" title="tigramite.models.LinearMediation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.LinearMediation</span></code></a>(dataframe)</td>
 <td>Linear mediation analysis for time series models.</td>
 </tr>
-<tr class="row-odd"><td><a class="reference internal" href="#tigramite.models.Prediction" title="tigramite.models.Prediction"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.models.Prediction</span></code></a>(dataframe,&nbsp;...)</td>
+<tr class="row-odd"><td><a class="reference internal" href="#tigramite.models.Prediction" title="tigramite.models.Prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.models.Prediction</span></code></a>(dataframe,&nbsp;…)</td>
 <td>Prediction class for time series models.</td>
 </tr>
-<tr class="row-even"><td><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-obj docutils literal"><span class="pre">tigramite.plotting</span></code></a></td>
+<tr class="row-even"><td><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a></td>
 <td>Tigramite plotting package.</td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="tigramite-pcmci-pcmci">
-<h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pcmci</span></code>: PCMCI<a class="headerlink" href="#tigramite-pcmci-pcmci" title="Permalink to this headline">¶</a></h1>
+<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.pcmci</span></code>: PCMCI<a class="headerlink" href="#tigramite-pcmci-pcmci" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="tigramite.pcmci.PCMCI">
-<em class="property">class </em><code class="descclassname">tigramite.pcmci.</code><code class="descname">PCMCI</code><span class="sig-paren">(</span><em>dataframe</em>, <em>cond_ind_test</em>, <em>selected_variables=None</em>, <em>var_names=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.pcmci.</code><code class="descname">PCMCI</code><span class="sig-paren">(</span><em>dataframe</em>, <em>cond_ind_test</em>, <em>selected_variables=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI" title="Permalink to this definition">¶</a></dt>
 <dd><p>PCMCI causal discovery for time series datasets.</p>
 <p>PCMCI is a 2-step causal discovery method for large-scale time series
 datasets. The first step is a condition-selection followed by the MCI
@@ -190,7 +177,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 <ol class="arabic">
 <li><p class="first">Condition-selection: For each variable <img class="math" src="_images/math/6b21e0b0899a0d2879d3b8019087fa630bab4ea2.png" alt="j"/>, estimate a
 <em>superset</em>  of parents <img class="math" src="_images/math/f75f28dbcbec776fbe031f8ac923f83f9260bd8a.png" alt="\tilde{\mathcal{P}}(X^j_t)"/> with the
-iterative PC1 algorithm , implemented as <code class="docutils literal"><span class="pre">run_pc_stable</span></code>.</p>
+iterative PC1 algorithm , implemented as <code class="docutils literal notranslate"><span class="pre">run_pc_stable</span></code>.</p>
 </li>
 <li><p class="first"><em>Momentary conditional independence</em> (MCI)</p>
 <div class="math">
@@ -198,19 +185,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 \tilde{\mathcal{P}}(X^i_{t-{\tau}})"/></p>
 </div></li>
 </ol>
-<p>here implemented as <code class="docutils literal"><span class="pre">run_mci</span></code>. The condition-selection step reduces the
+<p>here implemented as <code class="docutils literal notranslate"><span class="pre">run_mci</span></code>. The condition-selection step reduces the
 dimensionality and avoids conditioning on irrelevant variables.</p>
 <p>PCMCI can be flexibly combined with any kind of conditional independence
 test statistic adapted to the kind of data (continuous or discrete) and its
 assumed dependency structure. Currently, implemented in Tigramite are
 ParCorr as a linear test, GPACE allowing nonlinear additive dependencies,
 and CMI with different estimators making no assumptions about the
-dependencies. The classes in <code class="docutils literal"><span class="pre">tigramite.independence_tests</span></code> also handle
+dependencies. The classes in <code class="docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code> also handle
 masked data.</p>
 <p>The main free parameters of PCMCI (in addition to free parameters of the
 conditional independence test statistic) are the maximum time delay
-<img class="math" src="_images/math/12535500db0213985b2cce27db85468b60985da0.png" alt="\tau_{\max}"/> (<code class="docutils literal"><span class="pre">tau_max</span></code>) and the significance threshold in the
-condition- selection step <img class="math" src="_images/math/877d234f4cec6974ce218fc2e975a486a7972dfd.png" alt="\alpha"/> (<code class="docutils literal"><span class="pre">pc_alpha</span></code>). The maximum time
+<img class="math" src="_images/math/12535500db0213985b2cce27db85468b60985da0.png" alt="\tau_{\max}"/> (<code class="docutils literal notranslate"><span class="pre">tau_max</span></code>) and the significance threshold in the
+condition- selection step <img class="math" src="_images/math/877d234f4cec6974ce218fc2e975a486a7972dfd.png" alt="\alpha"/> (<code class="docutils literal notranslate"><span class="pre">pc_alpha</span></code>). The maximum time
 delay depends on the application and should be chosen according to the
 maximum causal time lag expected in the complex system. We recommend a
 rather large choice that includes peaks in the lagged cross-correlation
@@ -222,20 +209,20 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 include the true parents and at the same time be small in size to reduce the
 estimation dimension of the MCI test and improve its power. But including
 the true  parents is typically more important. If a list of values is given
-or <code class="docutils literal"><span class="pre">pc_alpha=None</span></code>, <img class="math" src="_images/math/877d234f4cec6974ce218fc2e975a486a7972dfd.png" alt="\alpha"/> is optimized using model selection
+or <code class="docutils literal notranslate"><span class="pre">pc_alpha=None</span></code>, <img class="math" src="_images/math/877d234f4cec6974ce218fc2e975a486a7972dfd.png" alt="\alpha"/> is optimized using model selection
 criteria.</p>
 <p>Further optional parameters are discussed in <a class="footnote-reference" href="#id5" id="id4">[1]</a>.</p>
 <p class="rubric">References</p>
 <table class="docutils footnote" frame="void" id="id5" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label">[1]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id2">2</a>, <a class="fn-backref" href="#id3">3</a>, <a class="fn-backref" href="#id4">4</a>, <a class="fn-backref" href="#id6">5</a>, <a class="fn-backref" href="#id7">6</a>, <a class="fn-backref" href="#id8">7</a>, <a class="fn-backref" href="#id9">8</a>, <a class="fn-backref" href="#id10">9</a>, <a class="fn-backref" href="#id11">10</a>, <a class="fn-backref" href="#id12">11</a>, <a class="fn-backref" href="#id13">12</a>, <a class="fn-backref" href="#id14">13</a>, <a class="fn-backref" href="#id18">14</a>, <a class="fn-backref" href="#id24">15</a>, <a class="fn-backref" href="#id25">16</a>, <a class="fn-backref" href="#id26">17</a>, <a class="fn-backref" href="#id30">18</a>, <a class="fn-backref" href="#id31">19</a>, <a class="fn-backref" href="#id33">20</a>, <a class="fn-backref" href="#id34">21</a>)</em> J. Runge, D. Sejdinovic, S. Flaxman (2017): Detecting causal
-associations in large nonlinear time series datasets,
-<a class="reference external" href="https://arxiv.org/abs/1702.07007">https://arxiv.org/abs/1702.07007</a></td></tr>
+<tr><td class="label">[1]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id2">2</a>, <a class="fn-backref" href="#id3">3</a>, <a class="fn-backref" href="#id4">4</a>, <a class="fn-backref" href="#id6">5</a>, <a class="fn-backref" href="#id7">6</a>, <a class="fn-backref" href="#id8">7</a>, <a class="fn-backref" href="#id9">8</a>, <a class="fn-backref" href="#id10">9</a>, <a class="fn-backref" href="#id11">10</a>, <a class="fn-backref" href="#id15">11</a>, <a class="fn-backref" href="#id21">12</a>, <a class="fn-backref" href="#id22">13</a>, <a class="fn-backref" href="#id23">14</a>, <a class="fn-backref" href="#id24">15</a>, <a class="fn-backref" href="#id25">16</a>, <a class="fn-backref" href="#id29">17</a>)</em> J. Runge et al. (2018): Detecting Causal Associations in Large
+Nonlinear Time Series Datasets.
+<a class="reference external" href="https://arxiv.org/abs/1702.07007v2">https://arxiv.org/abs/1702.07007v2</a></td></tr>
 </tbody>
 </table>
 <p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.pcmci</span> <span class="k">import</span> <span class="n">PCMCI</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tigramite.independence_tests</span> <span class="k">import</span> <span class="n">ParCorr</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tigramite.data_processing</span> <span class="k">as</span> <span class="nn">pp</span>
@@ -273,14 +260,14 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>dataframe</strong> (<em>data object</em>) &#8211; This is the Tigramite dataframe object. It has the attributes
+<li><strong>dataframe</strong> (<em>data object</em>) – This is the Tigramite dataframe object. It has the attributes
 dataframe.values yielding a numpy array of shape (observations T,
 variables N) and optionally a mask of  the same shape.</li>
-<li><strong>cond_ind_test</strong> (<em>conditional independence test object</em>) &#8211; <p>This can be ParCorr or other classes from the tigramite package or an
+<li><strong>cond_ind_test</strong> (<em>conditional independence test object</em>) – <p>This can be ParCorr or other classes from the tigramite package or an
 external test passed as a callable. This test can be based on the class
 tigramite.independence_tests.CondIndTest. If a callable is passed, it
 must have the signature:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CondIndTest</span><span class="p">():</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CondIndTest</span><span class="p">():</span>
     <span class="c1"># with attributes</span>
     <span class="c1"># * measure : str</span>
     <span class="c1">#   name of the test</span>
@@ -307,76 +294,74 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 </pre></div>
 </div>
 </li>
-<li><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><a class="reference internal" href="#PCMCI.N" title="PCMCI.N"><em>N</em></a><em>)</em><em>)</em>) &#8211; Specify to estimate parents only for selected variables. If None is
+<li><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
 passed, parents are estimated for all variables.</li>
-<li><strong>var_names</strong> (<em>list of strings</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><a class="reference internal" href="#PCMCI.N" title="PCMCI.N"><em>N</em></a><em>)</em><em>)</em>) &#8211; Names of variables, must match the number of variables. If None is
-passed, variables are enumerated as [0, 1, ...]</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Verbose levels 0, 1, ...</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Verbose levels 0, 1, …</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="attribute">
-<dt id="PCMCI.all_parents">
-<code class="descname">all_parents</code><a class="headerlink" href="#PCMCI.all_parents" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>dictionary</em> &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing the
+<dt id="tigramite.pcmci.PCMCI.all_parents">
+<code class="descname">all_parents</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.all_parents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>dictionary</em> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing the
 conditioning-parents estimated with PC algorithm.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="PCMCI.val_min">
-<code class="descname">val_min</code><a class="headerlink" href="#PCMCI.val_min" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>dictionary</em> &#8211; Dictionary of form val_min[j][(i, -tau)] = float
+<dt id="tigramite.pcmci.PCMCI.val_min">
+<code class="descname">val_min</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.val_min" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>dictionary</em> – Dictionary of form val_min[j][(i, -tau)] = float
 containing the minimum test statistic value for each link estimated in
 the PC algorithm.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="PCMCI.p_max">
-<code class="descname">p_max</code><a class="headerlink" href="#PCMCI.p_max" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>dictionary</em> &#8211; Dictionary of form p_max[j][(i, -tau)] = float containing the maximum
+<dt id="tigramite.pcmci.PCMCI.p_max">
+<code class="descname">p_max</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.p_max" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>dictionary</em> – Dictionary of form p_max[j][(i, -tau)] = float containing the maximum
 p-value for each link estimated in the PC algorithm.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="PCMCI.iterations">
-<code class="descname">iterations</code><a class="headerlink" href="#PCMCI.iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>dictionary</em> &#8211; Dictionary containing further information on algorithm steps.</p>
+<dt id="tigramite.pcmci.PCMCI.iterations">
+<code class="descname">iterations</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.iterations" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>dictionary</em> – Dictionary containing further information on algorithm steps.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="PCMCI.N">
-<code class="descname">N</code><a class="headerlink" href="#PCMCI.N" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>int</em> &#8211; Number of variables.</p>
+<dt id="tigramite.pcmci.PCMCI.N">
+<code class="descname">N</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.N" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>int</em> – Number of variables.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="PCMCI.T">
-<code class="descname">T</code><a class="headerlink" href="#PCMCI.T" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>int</em> &#8211; Time series sample length.</p>
+<dt id="tigramite.pcmci.PCMCI.T">
+<code class="descname">T</code><a class="headerlink" href="#tigramite.pcmci.PCMCI.T" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>int</em> – Time series sample length.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.pcmci.PCMCI.get_corrected_pvalues">
-<code class="descname">get_corrected_pvalues</code><span class="sig-paren">(</span><em>p_matrix</em>, <em>fdr_method='fdr_bh'</em>, <em>exclude_contemporaneous=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.get_corrected_pvalues"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_corrected_pvalues" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_corrected_pvalues</code><span class="sig-paren">(</span><em>p_matrix</em>, <em>fdr_method='fdr_bh'</em>, <em>exclude_contemporaneous=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_corrected_pvalues" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-values corrected for multiple testing.</p>
-<p>Wrapper around statsmodels.sandbox.stats.multicomp.multipletests.
-Correction is performed either among all links if
+<p>Currently implemented is Benjamini-Hochberg False Discovery Rate
+method. Correction is performed either among all links if
 exclude_contemporaneous==False, or only among lagged links.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>p_matrix</strong> (<em>array-like</em>) &#8211; Matrix of p-values. Must be of shape (N, N, tau_max + 1).</li>
-<li><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'fdr_bh'</em><em>)</em>) &#8211; Correction method, default is Benjamini-Hochberg False Discovery
-Rate method.</li>
-<li><strong>exclude_contemporaneous</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to include contemporaneous links in correction.</li>
+<li><strong>p_matrix</strong> (<em>array-like</em>) – Matrix of p-values. Must be of shape (N, N, tau_max + 1).</li>
+<li><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'fdr_bh'</em><em>)</em>) – Correction method, currently implemented is Benjamini-Hochberg
+False Discovery Rate method.</li>
+<li><strong>exclude_contemporaneous</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to include contemporaneous links in correction.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>q_matrix</strong> &#8211; Matrix of shape (N, N, tau_max + 1) containing corrected p-values.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>q_matrix</strong> – Matrix of shape (N, N, tau_max + 1) containing corrected p-values.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array-like</p>
@@ -388,29 +373,29 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 
 <dl class="method">
 <dt id="tigramite.pcmci.PCMCI.get_lagged_dependencies">
-<code class="descname">get_lagged_dependencies</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=0</em>, <em>tau_max=1</em>, <em>parents=None</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.get_lagged_dependencies"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_lagged_dependencies" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_lagged_dependencies</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=0</em>, <em>tau_max=1</em>, <em>parents=None</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.get_lagged_dependencies" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns matrix of lagged dependence measure values.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</li>
-<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) &#8211; Minimum time lag.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum time lag. Must be larger or equal to tau_min.</li>
-<li><strong>parents</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</li>
+<li><strong>parents</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying the conditions for each variable. If None is
 passed, no conditions are used.</li>
-<li><strong>max_conds_py</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions of Y to use. If None is passed, this
-number is unrestricted.</li>
-<li><strong>max_conds_px</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions of Z to use. If None is passed, this
-number is unrestricted.</li>
+<li><strong>max_conds_py</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions from parents of Y to use. If None is
+passed, this number is unrestricted.</li>
+<li><strong>max_conds_px</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions from parents of X to use. If None is
+passed, this number is unrestricted.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val_matrix</strong> &#8211; The matrix of shape (N, N, tau_max+1) containing the lagged
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val_matrix</strong> – The matrix of shape (N, N, tau_max+1) containing the lagged
 dependencies.</p>
 </td>
 </tr>
@@ -421,9 +406,89 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="tigramite.pcmci.PCMCI.print_results">
+<code class="descname">print_results</code><span class="sig-paren">(</span><em>return_dict</em>, <em>alpha_level=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.print_results" title="Permalink to this definition">¶</a></dt>
+<dd><p>Prints significant parents from output of MCI or PCMCI algorithms.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>return_dict</strong> (<em>dict</em>) – <dl class="docutils">
+<dt>Dictionary of return values, containing keys</dt>
+<dd><ul class="first last">
+<li>’p_matrix’</li>
+<li>’val_matrix’</li>
+<li>’conf_matrix’</li>
+</ul>
+</dd>
+</dl>
+<p>’q_matrix’ can also be included in keys, but is not necessary.</p>
+</li>
+<li><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.pcmci.PCMCI.print_significant_links">
+<code class="descname">print_significant_links</code><span class="sig-paren">(</span><em>p_matrix</em>, <em>val_matrix</em>, <em>conf_matrix=None</em>, <em>q_matrix=None</em>, <em>alpha_level=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.print_significant_links" title="Permalink to this definition">¶</a></dt>
+<dd><p>Prints significant parents.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level.</li>
+<li><strong>p_matrix</strong> (<em>array-like</em>) – Must be of shape (N, N, tau_max + 1).</li>
+<li><strong>val_matrix</strong> (<em>array-like</em>) – Must be of shape (N, N, tau_max + 1).</li>
+<li><strong>q_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Adjusted p-values. Must be of shape (N, N, tau_max + 1).</li>
+<li><strong>conf_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of confidence intervals of shape (N, N, tau_max+1, 2)</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.pcmci.PCMCI.return_significant_parents">
+<code class="descname">return_significant_parents</code><span class="sig-paren">(</span><em>pq_matrix</em>, <em>val_matrix</em>, <em>alpha_level=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.return_significant_parents" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns list of significant parents as well as a boolean matrix.</p>
+<p>Significance based on p-matrix, or q-value matrix with corrected
+p-values.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>pq_matrix</strong> (<em>array-like</em>) – p-matrix, or q-value matrix with corrected p-values. Must be of
+shape (N, N, tau_max + 1).</li>
+<li><strong>val_matrix</strong> (<em>array-like</em>) – Matrix of test statistic values. Must be of shape (N, N, tau_max +
+1).</li>
+<li><strong>alpha_level</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
+<li><strong>all_parents</strong> (<em>dict</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
+containing estimated parents.</li>
+<li><strong>link_matrix</strong> (<em>array, shape [N, N, tau_max+1]</em>) – Boolean array with True entries for significant links at alpha_level</li>
+</ul>
+</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="tigramite.pcmci.PCMCI.run_mci">
-<code class="descname">run_mci</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=1</em>, <em>tau_max=1</em>, <em>parents=None</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_mci"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_mci" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run_mci</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=0</em>, <em>tau_max=1</em>, <em>parents=None</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_mci" title="Permalink to this definition">¶</a></dt>
 <dd><p>MCI conditional independence tests.</p>
 <p>Implements the MCI test (Algorithm 2 in <a class="footnote-reference" href="#id5" id="id6">[1]</a>). Returns the matrices of
 test statistic values,  p-values, and confidence intervals.</p>
@@ -432,26 +497,26 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...}
+<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:all_parents (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</li>
-<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Minimum time lag to test. Note that zero-lags are undirected.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum time lag. Must be larger or equal to tau_min.</li>
-<li><strong>parents</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 0</em>) – Minimum time lag to test. Note that zero-lags are undirected.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</li>
+<li><strong>parents</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying the conditions for each variable. If None is
 passed, no conditions are used.</li>
-<li><strong>max_conds_py</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions of Y to use. If None is passed, this
+<li><strong>max_conds_py</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions of Y to use. If None is passed, this
 number is unrestricted.</li>
-<li><strong>max_conds_px</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions of Z to use. If None is passed, this
+<li><strong>max_conds_px</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions of Z to use. If None is passed, this
 number is unrestricted.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> &#8211; {&#8216;val_matrix&#8217;:val_matrix, &#8216;p_matrix&#8217;:p_matrix} are always returned
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> – {‘val_matrix’:val_matrix, ‘p_matrix’:p_matrix} are always returned
 and optionally conf_matrix which is of shape [N, N, tau_max+1,2]</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dictionary of arrays of shape [<a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>, <a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>, tau_max+1]</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dictionary of arrays of shape [N, N, tau_max+1]</p>
 </td>
 </tr>
 </tbody>
@@ -460,7 +525,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 
 <dl class="method">
 <dt id="tigramite.pcmci.PCMCI.run_pc_stable">
-<code class="descname">run_pc_stable</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=1</em>, <em>tau_max=1</em>, <em>save_iterations=False</em>, <em>pc_alpha=0.2</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pc_stable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pc_stable" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run_pc_stable</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=1</em>, <em>tau_max=1</em>, <em>save_iterations=False</em>, <em>pc_alpha=0.2</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pc_stable" title="Permalink to this definition">¶</a></dt>
 <dd><p>PC algorithm for estimating parents of all variables.</p>
 <p>Parents are made available as self.all_parents</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -468,26 +533,26 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</li>
-<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Minimum time lag to test. Useful for multi-step ahead predictions.
+<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.
 Must be greater zero.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum time lag. Must be larger or equal to tau_min.</li>
-<li><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>default: False</em>) &#8211; Whether to save iteration step results such as conditions used.</li>
-<li><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.3</em>) &#8211; Significance level in algorithm. If a list or None is passed, the
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</li>
+<li><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>default: False</em>) – Whether to save iteration step results such as conditions used.</li>
+<li><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion()</li>
-<li><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions to test. If None is passed, this number
+<li><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions to test. If None is passed, this number
 is unrestricted.</li>
-<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum number of combinations of conditions of current cardinality
+<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum number of combinations of conditions of current cardinality
 to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
 a larger number, such as 10, can be used.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>all_parents</strong> &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>all_parents</strong> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 containing estimated parents.</p>
 </td>
 </tr>
@@ -500,7 +565,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 
 <dl class="method">
 <dt id="tigramite.pcmci.PCMCI.run_pcmci">
-<code class="descname">run_pcmci</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=1</em>, <em>tau_max=1</em>, <em>save_iterations=False</em>, <em>pc_alpha=0.05</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em>, <em>fdr_method='none'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/pcmci.html#PCMCI.run_pcmci"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcmci" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run_pcmci</code><span class="sig-paren">(</span><em>selected_links=None</em>, <em>tau_min=0</em>, <em>tau_max=1</em>, <em>save_iterations=False</em>, <em>pc_alpha=0.05</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em>, <em>max_conds_py=None</em>, <em>max_conds_px=None</em>, <em>fdr_method='none'</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.pcmci.PCMCI.run_pcmci" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run full PCMCI causal discovery for time series datasets.</p>
 <p>Wrapper around PC-algorithm function and MCI function.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -508,33 +573,33 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...}
+<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:all_parents (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</li>
-<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Minimum time lag to test. Note that zero-lags are undirected.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Maximum time lag. Must be larger or equal to tau_min.</li>
-<li><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to save iteration step results such as conditions used.</li>
-<li><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) &#8211; Significance level in algorithm.</li>
-<li><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum number of conditions to test. If None is passed, this number
+<li><strong>tau_min</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Minimum time lag to test. Note that zero-lags are undirected.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum time lag. Must be larger or equal to tau_min.</li>
+<li><strong>save_iterations</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save iteration step results such as conditions used.</li>
+<li><strong>pc_alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Significance level in algorithm.</li>
+<li><strong>max_conds_dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions to test. If None is passed, this number
 is unrestricted.</li>
-<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Maximum number of combinations of conditions of current cardinality
+<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Maximum number of combinations of conditions of current cardinality
 to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
 a larger number, such as 10, can be used.</li>
-<li><strong>max_conds_py</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum number of conditions of Y to use. If None is passed, this
+<li><strong>max_conds_py</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions of Y to use. If None is passed, this
 number is unrestricted.</li>
-<li><strong>max_conds_px</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum number of conditions of Z to use. If None is passed, this
+<li><strong>max_conds_px</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum number of conditions of Z to use. If None is passed, this
 number is unrestricted.</li>
-<li><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) &#8211; Correction method, default is Benjamini-Hochberg False Discovery
+<li><strong>fdr_method</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'none'</em><em>)</em>) – Correction method, default is Benjamini-Hochberg False Discovery
 Rate method.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> &#8211; {&#8216;val_matrix&#8217;:val_matrix, &#8216;p_matrix&#8217;:p_matrix} are always returned
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> – {‘val_matrix’:val_matrix, ‘p_matrix’:p_matrix} are always returned
 and optionally q_matrix and conf_matrix which is of shape
 [N, N, tau_max+1,2]</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dictionary of arrays of shape [<a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>, <a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>, tau_max+1]</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dictionary of arrays of shape [N, N, tau_max+1]</p>
 </td>
 </tr>
 </tbody>
@@ -545,11 +610,11 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.pc
 
 </div>
 <div class="section" id="tigramite-independence-tests-conditional-independence-tests">
-<h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests<a class="headerlink" href="#tigramite-independence-tests-conditional-independence-tests" title="Permalink to this headline">¶</a></h1>
+<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.independence_tests</span></code>: Conditional independence tests<a class="headerlink" href="#tigramite-independence-tests-conditional-independence-tests" title="Permalink to this headline">¶</a></h1>
 <p>Base class:</p>
 <dl class="class">
 <dt id="tigramite.independence_tests.CondIndTest">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CondIndTest</code><span class="sig-paren">(</span><em>use_mask=False</em>, <em>mask_type=None</em>, <em>significance='analytic'</em>, <em>fixed_thres=0.1</em>, <em>sig_samples=1000</em>, <em>sig_blocklength=None</em>, <em>confidence=False</em>, <em>conf_lev=0.9</em>, <em>conf_samples=100</em>, <em>conf_blocklength=None</em>, <em>recycle_residuals=False</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CondIndTest</code><span class="sig-paren">(</span><em>mask_type=None</em>, <em>significance='analytic'</em>, <em>fixed_thres=0.1</em>, <em>sig_samples=1000</em>, <em>sig_blocklength=None</em>, <em>confidence=None</em>, <em>conf_lev=0.9</em>, <em>conf_samples=100</em>, <em>conf_blocklength=None</em>, <em>recycle_residuals=False</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class of conditional independence tests.</p>
 <p>Provides useful general functions for different independence tests such as
 shuffle significance testing and bootstrap confidence estimation. Also
@@ -559,89 +624,51 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>use_mask</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether a supplied mask should be used.</li>
-<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) &#8211; Masking mode: Indicators for which variables in the dependence measure
-I(X; Y | Z) the samples should be masked. If None, &#8216;y&#8217; is used, which
+<li><strong>mask_type</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default = None</em><em>)</em>) – Must be in {‘y’,’x’,’z’,’xy’,’xz’,’yz’,’xyz’}
+Masking mode: Indicators for which variables in the dependence measure
+I(X; Y | Z) the samples should be masked. If None, ‘y’ is used, which
 excludes all time slices containing masked samples in Y. Explained in
 <a class="footnote-reference" href="#id5" id="id7">[1]</a>.</li>
-<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) &#8211; Type of significance test to use. In this package &#8216;analytic&#8217;,
-&#8216;fixed_thres&#8217; and &#8216;shuffle_test&#8217; are available.</li>
-<li><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) &#8211; If significance is &#8216;fixed_thres&#8217;, this specifies the threshold for the
+<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) – Type of significance test to use. In this package ‘analytic’,
+‘fixed_thres’ and ‘shuffle_test’ are available.</li>
+<li><strong>fixed_thres</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – If significance is ‘fixed_thres’, this specifies the threshold for the
 absolute value of the dependence measure.</li>
-<li><strong>sig_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1000</em><em>)</em>) &#8211; Number of samples for shuffle significance test.</li>
-<li><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Block length for block-shuffle significance test. If None, the
+<li><strong>sig_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1000</em><em>)</em>) – Number of samples for shuffle significance test.</li>
+<li><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-shuffle significance test. If None, the
 block length is determined from the decay of the autocovariance as
 explained in <a class="footnote-reference" href="#id5" id="id8">[1]</a>.</li>
-<li><strong>confidence</strong> (<em>False</em><em> or </em><em>str</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Specify type of confidence estimation. If False, numpy.nan is returned.
-&#8216;bootstrap&#8217; can be used with any test, for ParCorr also &#8216;analytic&#8217; is
+<li><strong>confidence</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Specify type of confidence estimation. If False, numpy.nan is returned.
+‘bootstrap’ can be used with any test, for ParCorr also ‘analytic’ is
 implemented.</li>
-<li><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) &#8211; Two-sided confidence interval.</li>
-<li><strong>conf_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 100</em><em>)</em>) &#8211; Number of samples for bootstrap.</li>
-<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Block length for block-bootstrap. If None, the block length is
+<li><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) – Two-sided confidence interval.</li>
+<li><strong>conf_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 100</em><em>)</em>) – Number of samples for bootstrap.</li>
+<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-bootstrap. If None, the block length is
 determined from the decay of the autocovariance as explained in <a class="footnote-reference" href="#id5" id="id9">[1]</a>.</li>
-<li><strong>recycle_residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Specifies whether residuals should be stored. This may be faster, but
+<li><strong>recycle_residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Specifies whether residuals should be stored. This may be faster, but
 can cost considerable memory.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
-<dt id="tigramite.independence_tests.CondIndTest.generate_and_save_nulldists">
-<code class="descname">generate_and_save_nulldists</code><span class="sig-paren">(</span><em>sample_sizes</em>, <em>null_dist_filename</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.generate_and_save_nulldists"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.generate_and_save_nulldists" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates and saves null distribution for pairwise independence
-tests.</p>
-<p>Generates the null distribution for different sample sizes. Calls
-generate_nulldist. Null dists are saved to disk as
-self.null_dist_filename.npz. Also adds the null distributions to
-self.null_dists.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>sample_sizes</strong> (<em>list</em>) &#8211; List of sample sizes.</li>
-<li><strong>null_dist_filename</strong> (<em>str</em>) &#8211; Name to save file containing null distributions.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dt id="tigramite.independence_tests.CondIndTest.get_analytic_confidence">
+<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
+<dd><p>Base class assumption that this is not implemented.  Concrete classes
+should override when possible.</p>
 </dd></dl>
 
 <dl class="method">
-<dt id="tigramite.independence_tests.CondIndTest.generate_nulldist">
-<code class="descname">generate_nulldist</code><span class="sig-paren">(</span><em>df</em>, <em>add_to_null_dists=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.generate_nulldist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.generate_nulldist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates null distribution for pairwise independence tests.</p>
-<p>Generates the null distribution for sample size df. Assumes pairwise
-samples transformed to uniform marginals. Uses get_dependence_measure
-available in class and generates self.sig_samples random samples. Adds
-the null distributions to self.null_dists.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>df</strong> (<em>int</em>) &#8211; Degrees of freedom / sample size to generate null distribution for.</li>
-<li><strong>add_to_null_dists</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to add the null dist to the dictionary of null dists or
-just return it.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>null_dist</strong> &#8211; Only returned,if add_to_null_dists is False.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape [df,]</p>
-</td>
-</tr>
-</tbody>
-</table>
+<dt id="tigramite.independence_tests.CondIndTest.get_analytic_significance">
+<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
+<dd><p>Base class assumption that this is not implemented.  Concrete classes
+should override when possible.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.get_bootstrap_confidence">
-<code class="descname">get_bootstrap_confidence</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>dependence_measure</em>, <em>conf_samples=100</em>, <em>conf_blocklength=None</em>, <em>conf_lev=0.95</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.get_bootstrap_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_bootstrap_confidence" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_bootstrap_confidence</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>dependence_measure=None</em>, <em>conf_samples=100</em>, <em>conf_blocklength=None</em>, <em>conf_lev=0.95</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_bootstrap_confidence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform bootstrap confidence interval estimation.</p>
 <p>With conf_blocklength &gt; 1 or None a block-bootstrap is performed.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -649,20 +676,20 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>dependence_measure</strong> (<em>object</em>) &#8211; Dependence measure function must be of form
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>dependence_measure</strong> (<em>function</em><em> (</em><em>default = self.get_dependence_measure</em><em>)</em>) – Dependence measure function must be of form
 dependence_measure(array, xyz) and return a numeric value</li>
-<li><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) &#8211; Two-sided confidence interval.</li>
-<li><strong>conf_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 100</em><em>)</em>) &#8211; Number of samples for bootstrap.</li>
-<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Block length for block-bootstrap. If None, the block length is
+<li><strong>conf_lev</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.9</em><em>)</em>) – Two-sided confidence interval.</li>
+<li><strong>conf_samples</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 100</em><em>)</em>) – Number of samples for bootstrap.</li>
+<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Block length for block-bootstrap. If None, the block length is
 determined from the decay of the autocovariance as explained in
 <a class="footnote-reference" href="#id5" id="id10">[1]</a>.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Levelof verbosity.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> &#8211; Upper and lower confidence bound of confidence interval.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> – Upper and lower confidence bound of confidence interval.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Tuple of floats</p>
@@ -674,7 +701,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.get_confidence">
-<code class="descname">get_confidence</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.get_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_confidence" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_confidence</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_confidence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform confidence interval estimation.</p>
 <p>Calls the dependence measure and confidence test functions. The child
 classes can specify a function get_dependence_measure and
@@ -685,14 +712,14 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) &#8211; X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Maximum time lag. This may be used to make sure that estimates for
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> &#8211; Upper and lower confidence bound of confidence interval.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> – Upper and lower confidence bound of confidence interval.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Tuple of floats</p>
@@ -702,9 +729,15 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.get_dependence_measure">
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract function that all concrete classes must instantiate.</p>
+</dd></dl>
+
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.get_fixed_thres_significance">
-<code class="descname">get_fixed_thres_significance</code><span class="sig-paren">(</span><em>value</em>, <em>fixed_thres</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.get_fixed_thres_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_fixed_thres_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_fixed_thres_significance</code><span class="sig-paren">(</span><em>value</em>, <em>fixed_thres</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_fixed_thres_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns signficance for thresholding test.</p>
 <p>Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1 else.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -712,12 +745,12 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
-<li><strong>fixed_thres</strong> (<em>number</em>) &#8211; Fixed threshold, is made positive.</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
+<li><strong>fixed_thres</strong> (<em>number</em>) – Fixed threshold, is made positive.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – Returns 0 if numpy.abs(value) is smaller than fixed_thres and 1
 else.</p>
 </td>
 </tr>
@@ -730,7 +763,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.get_measure">
-<code class="descname">get_measure</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.get_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_measure</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate dependence measure.</p>
 <p>Calls the dependence measure function. The child classes must specify
 a function get_dependence_measure.</p>
@@ -739,14 +772,14 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>Y</strong><strong> [</strong><strong>, </strong><strong>Z</strong><strong>]</strong> (<em>X</em><em>,</em>) &#8211; X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><strong>Y</strong><strong> [</strong><strong>, </strong><strong>Z</strong><strong>]</strong> (<em>X</em><em>,</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Maximum time lag. This may be used to make sure that estimates for
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; The test statistic value.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – The test statistic value.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -756,29 +789,84 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.get_model_selection_criterion">
+<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
+<dd><p>Base class assumption that this is not implemented.  Concrete classes
+should override when possible.</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.get_shuffle_significance">
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<dd><p>Base class assumption that this is not implemented.  Concrete classes
+should override when possible.</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.get_significance">
+<code class="descname">get_significance</code><span class="sig-paren">(</span><em>val</em>, <em>array</em>, <em>xyz</em>, <em>T</em>, <em>dim</em>, <em>sig_override=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.get_significance" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the p-value from whichever significance function is specified
+for this test.  If an override is used, then it will call a different
+function then specified by self.significance</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>val</strong> (<em>float</em>) – Test statistic value.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>T</strong> (<em>int</em>) – Sample length</li>
+<li><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</li>
+<li><strong>sig_override</strong> (<em>string</em>) – Must be in ‘analytic’, ‘shuffle_test’, ‘fixed_thres’</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – P-value.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float or numpy.nan</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="tigramite.independence_tests.CondIndTest.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract property to store the type of independence test.</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.print_info">
+<code class="descname">print_info</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.print_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Print information about the conditional independence test parameters</p>
+</dd></dl>
+
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.run_test">
-<code class="descname">run_test</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.run_test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run_test</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z=None</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.run_test" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform conditional independence test.</p>
 <p>Calls the dependence measure and signficicance test functions. The child
 classes must specify a function get_dependence_measure and either or
 both functions get_analytic_significance and  get_shuffle_significance.
-If recycle_residuals is True, also  _get_single_residuals must be
+If recycle_residuals is True, also _get_single_residuals must be
 available.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) &#8211; X,Y,Z are of the form [(var, -tau)], where var specifies the
+<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) – X,Y,Z are of the form [(var, -tau)], where var specifies the
 variable index and tau the time lag.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Maximum time lag. This may be used to make sure that estimates for
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val, pval</strong> &#8211; The test statistic value and the p-value. These are also made in the
-class as self.val and self.pval.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val, pval</strong> – The test statistic value and the p-value.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Tuple of floats</p>
@@ -790,13 +878,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.CondIndTest.set_dataframe">
-<code class="descname">set_dataframe</code><span class="sig-paren">(</span><em>dataframe</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CondIndTest.set_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.set_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize dataframe.</p>
+<code class="descname">set_dataframe</code><span class="sig-paren">(</span><em>dataframe</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.set_dataframe" title="Permalink to this definition">¶</a></dt>
+<dd><p>Initialize and check the dataframe.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataframe</strong> (<em>data object</em>) &#8211; Set tigramite dataframe object. It must have the attributes
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataframe</strong> (<em>data object</em>) – Set tigramite dataframe object. It must have the attributes
 dataframe.values yielding a numpy array of shape (observations T,
 variables N) and optionally a mask of the same shape and a missing
 values flag.</td>
@@ -805,12 +893,31 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="tigramite.independence_tests.CondIndTest.set_mask_type">
+<code class="descname">set_mask_type</code><span class="sig-paren">(</span><em>mask_type</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CondIndTest.set_mask_type" title="Permalink to this definition">¶</a></dt>
+<dd><p>Setter for mask type to ensure that this option does not clash with
+recycle_residuals.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>mask_type</strong> (<em>str</em>) – Must be in {‘y’,’x’,’z’,’xy’,’xz’,’yz’,’xyz’}
+Masking mode: Indicators for which variables in the dependence
+measure I(X; Y | Z) the samples should be masked. If None, ‘y’ is
+used, which excludes all time slices containing masked samples in Y.
+Explained in <a class="footnote-reference" href="#id5" id="id11">[1]</a>.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </dd></dl>
 
 <p>Test statistics:</p>
 <dl class="class">
 <dt id="tigramite.independence_tests.ParCorr">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">ParCorr</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">ParCorr</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Partial correlation test.</p>
 <p>Partial correlation is estimated through linear ordinary least squares (OLS)
 regression and a test for non-zero linear Pearson correlation on the
@@ -825,33 +932,33 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 the Pearson correlation test.</p>
 <div class="math">
 <p><img src="_images/math/9c7f8f771e36601a75e3520845155d09080f6281.png" alt="\rho\left(r_X, r_Y\right)"/></p>
-</div><p>For the <code class="docutils literal"><span class="pre">significance='analytic'</span></code> Student&#8217;s-<em>t</em> distribution with
+</div><p>For the <code class="docutils literal notranslate"><span class="pre">significance='analytic'</span></code> Student’s-<em>t</em> distribution with
 <img class="math" src="_images/math/e03ea88dda29ab6daa19c839ff37b8115e0a10e1.png" alt="T-D_Z-2"/> degrees of freedom is implemented.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>**kwargs</strong> &#8211; Arguments passed on to Parent class CondIndTest.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>**kwargs</strong> – Arguments passed on to Parent class CondIndTest.</td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="tigramite.independence_tests.ParCorr.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns analytic confidence interval for correlation coefficient.</p>
-<p>Based on Student&#8217;s t-distribution.</p>
+<p>Based on Student’s t-distribution.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>value</strong> (<em>float</em>) &#8211; Test statistic value.</li>
-<li><strong>df</strong> (<em>int</em>) &#8211; degrees of freedom of the test</li>
-<li><strong>conf_lev</strong> (<em>float</em>) &#8211; Confidence interval, eg, 0.9</li>
+<li><strong>value</strong> (<em>float</em>) – Test statistic value.</li>
+<li><strong>df</strong> (<em>int</em>) – degrees of freedom of the test</li>
+<li><strong>conf_lev</strong> (<em>float</em>) – Confidence interval, eg, 0.9</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> &#8211; Upper and lower confidence bound of confidence interval.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(conf_lower, conf_upper)</strong> – Upper and lower confidence bound of confidence interval.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Tuple of floats</p>
@@ -863,8 +970,8 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.ParCorr.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic p-value from Student&#8217;s t-test for the Pearson
+<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns analytic p-value from Student’s t-test for the Pearson
 correlation coefficient.</p>
 <p>Assumes two-sided correlation. If the degrees of freedom are less than
 1, numpy.nan is returned.</p>
@@ -873,13 +980,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>value</strong> (<em>float</em>) &#8211; Test statistic value.</li>
-<li><strong>T</strong> (<em>int</em>) &#8211; Sample length</li>
-<li><strong>dim</strong> (<em>int</em>) &#8211; Dimensionality, ie, number of features.</li>
+<li><strong>value</strong> (<em>float</em>) – Test statistic value.</li>
+<li><strong>T</strong> (<em>int</em>) – Sample length</li>
+<li><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; P-value.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – P-value.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float or numpy.nan</p>
@@ -891,7 +998,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.ParCorr.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return partial correlation.</p>
 <p>Estimated as the Pearson correlation of the residuals of a linear
 OLS regression.</p>
@@ -900,12 +1007,12 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; Partial correlation coefficient.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – Partial correlation coefficient.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -917,8 +1024,8 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.ParCorr.get_model_selection_criterion">
-<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr.get_model_selection_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns Akaike&#8217;s Information criterion modulo constants.</p>
+<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns Akaike’s Information criterion modulo constants.</p>
 <p>Fits a linear model of the parents to variable j and returns the score.
 I used to determine optimal hyperparameters in PCMCI, in particular
 the pc_alpha value.</p>
@@ -927,12 +1034,12 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of target variable in data array.</li>
-<li><strong>parents</strong> (<em>list</em>) &#8211; List of form [(0, -1), (3, -2), ...] containing parents.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Maximum time lag. This may be used to make sure that estimates for
+<li><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</li>
+<li><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
 different lags in X, Z, all have the same sample size.</li>
-<li><strong>Returns</strong> &#8211; </li>
-<li><strong>score</strong> (<em>float</em>) &#8211; Model score.</li>
+<li><strong>Returns</strong> – </li>
+<li><strong>score</strong> (<em>float</em>) – Model score.</li>
 </ul>
 </td>
 </tr>
@@ -942,7 +1049,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.ParCorr.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#ParCorr.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.ParCorr.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for shuffle significance test.</p>
 <p>For residual-based test statistics only the residuals are shuffled.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -950,13 +1057,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – p-value</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -966,283 +1073,133 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
-</dd></dl>
-
-<dl class="class">
-<dt id="tigramite.independence_tests.GP">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">GP</code><span class="sig-paren">(</span><em>gp_version='new'</em>, <em>gp_params=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GP" title="Permalink to this definition">¶</a></dt>
-<dd><p>Gaussian processes base class.</p>
-<p>GP is estimated with scikit-learn and allows to flexibly specify kernels and
-hyperparameters or let them be optimized automatically. The kernel specifies
-the covariance function of the GP. Parameters can be passed on to
-<code class="docutils literal"><span class="pre">GaussianProcessRegressor</span></code> using the gp_params dictionary. If None is
-passed, the kernel &#8216;1.0 * RBF(1.0) + WhiteKernel()&#8217; is used with alpha=0 as
-default. Note that the kernel&#8217;s hyperparameters are optimized during
-fitting.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>gp_version</strong> (<em>{'new'</em><em>, </em><em>'old'}</em><em>, </em><em>optional</em><em> (</em><em>default: 'new'</em><em>)</em>) &#8211; The older GP version from scikit-learn 0.17 was used for the numerical
-simulations in <a class="footnote-reference" href="#id5" id="id11">[1]</a>. The newer version from scikit-learn 0.19 is faster
-and allows more flexibility regarding kernels etc.</li>
-<li><strong>gp_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Dictionary with parameters for <code class="docutils literal"><span class="pre">GaussianProcessRegressor</span></code>.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="tigramite.independence_tests.GP.get_model_selection_criterion">
-<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GP.get_model_selection_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GP.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns log marginal likelihood for GP regression.</p>
-<p>Fits a GP model of the parents to variable j and returns the negative
-log marginal likelihood as a model selection score. Is used to determine
-optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of target variable in data array.</li>
-<li><strong>parents</strong> (<em>list</em>) &#8211; List of form [(0, -1), (3, -2), ...] containing parents.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Maximum time lag. This may be used to make sure that estimates for
-different lags in X, Z, all have the same sample size.</li>
-<li><strong>Returns</strong> &#8211; </li>
-<li><strong>score</strong> (<em>float</em>) &#8211; Model score.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="attribute">
+<dt id="tigramite.independence_tests.ParCorr.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.ParCorr.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Concrete property to return the measure of the independence test</p>
 </dd></dl>
 
 </dd></dl>
 
 <dl class="class">
-<dt id="tigramite.independence_tests.GPACE">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">GPACE</code><span class="sig-paren">(</span><em>null_dist_filename=None</em>, <em>gp_version='new'</em>, <em>gp_params=None</em>, <em>ace_version='acepack'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPACE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPACE" title="Permalink to this definition">¶</a></dt>
+<dt id="tigramite.independence_tests.GPDC">
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">GPDC</code><span class="sig-paren">(</span><em>null_dist_filename=None</em>, <em>gp_version='new'</em>, <em>gp_params=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="docutils">
-<dt>GPACE conditional independence test based on Gaussian processes and</dt>
-<dd>maximal correlation.</dd>
+<dt>GPDC conditional independence test based on Gaussian processes and</dt>
+<dd>distance correlation.</dd>
 </dl>
-<p>GPACE is based on a Gaussian process (GP) regression and a maximal
-correlation test on the residuals. GP is estimated with scikit-learn and
-allows to flexibly specify kernels and hyperparameters or let them be
-optimized automatically. The maximal correlation test is implemented with
-the ACE estimator either from a pure python implementation (slow) or, if rpy
-is available, using the R-package &#8216;acepack&#8217;. Here the null distribution is
-not analytically available, but can be precomputed with the function
-generate_and_save_nulldists(...) which saves a *.npz file containing the
-null distribution for different sample sizes. This file can then be supplied
-as null_dist_filename.</p>
+<p>GPDC is based on a Gaussian process (GP) regression and a distance
+correlation test on the residuals <a class="footnote-reference" href="#id14" id="id12">[2]</a>. GP is estimated with scikit-learn
+and allows to flexibly specify kernels and hyperparameters or let them be
+optimized automatically. The distance correlation test is implemented with
+cython. Here the null distribution is not analytically available, but can be
+precomputed with the function generate_and_save_nulldists(…) which saves a
+*.npz file containing the null distribution for different sample sizes.
+This file can then be supplied as null_dist_filename.</p>
 <p class="rubric">Notes</p>
-<p>As described in <a class="footnote-reference" href="#id5" id="id12">[1]</a>, GPACE is based on a Gaussian
-process (GP) regression and a maximal correlation test on the residuals. To
-test <img class="math" src="_images/math/d843100cd0a02e1e5184694228a7ac41e19e8af2.png" alt="X \perp Y | Z"/>, first  <img class="math" src="_images/math/bcb2457ac9d8995a4f34d57cadac7ecbbe58f3bd.png" alt="Z"/> is regressed out from <img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/>
-and <img class="math" src="_images/math/0062c26909b3e07ee8f5a6285b2563d69bc979ff.png" alt="Y"/> assuming the  model</p>
+<p>GPDC is based on a Gaussian process (GP) regression and a distance
+correlation test on the residuals. Distance correlation is described in
+<a class="footnote-reference" href="#id14" id="id13">[2]</a>. To test <img class="math" src="_images/math/d843100cd0a02e1e5184694228a7ac41e19e8af2.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/bcb2457ac9d8995a4f34d57cadac7ecbbe58f3bd.png" alt="Z"/> is regressed out from
+<img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/> and <img class="math" src="_images/math/0062c26909b3e07ee8f5a6285b2563d69bc979ff.png" alt="Y"/> assuming the  model</p>
 <div class="math">
 <p><img src="_images/math/70852cea72eb5c4d0a45c0db08b49476f4404426.png" alt="X &amp; =  f_X(Z) + \epsilon_{X} \\
 Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\
 \epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)"/></p>
 </div><p>using GP regression. Here <img class="math" src="_images/math/7a3d0c9264473a58cc3a769f99a662631131377c.png" alt="\sigma^2"/> and the kernel bandwidth are
-optimzed using <code class="docutils literal"><span class="pre">sklearn</span></code>. Then the residuals  are transformed to uniform
+optimzed using <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>. Then the residuals  are transformed to uniform
 marginals yielding <img class="math" src="_images/math/21fc11b715eef698662fe1cc017d7ae2d53320d8.png" alt="r_X,r_Y"/> and their dependency is tested with</p>
 <div class="math">
-<p><img src="_images/math/cfa926670304a8b47058e444fdb150096333a0aa.png" alt="\max_{g,h}\rho\left(g(r_X),h(r_Y)\right)"/></p>
-</div><p>where <img class="math" src="_images/math/72f1bd155675db8a2c1c3e6dab9d80755fd1f04a.png" alt="g,h"/> yielding maximal correlation are obtained using the
-Alternating Conditional Expectation (ACE) algorithm. The null distribution
-of the maximal correlation can be pre-computed.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
+<p><img src="_images/math/7bce831f5eba0d0e0fcb0cced170b2926804500a.png" alt="\mathcal{R}\left(r_X, r_Y\right)"/></p>
+</div><p>The null distribution of the distance correlation should be pre-computed.
+Otherwise it is computed during runtime.</p>
+<p>The cython-code for distance correlation is Copyright (c) 2012, Florian
+Finkernagel (<a class="reference external" href="https://gist.github.com/ffinkernagel/2960386">https://gist.github.com/ffinkernagel/2960386</a>).</p>
+<p class="rubric">References</p>
+<table class="docutils footnote" frame="void" id="id14" rules="none">
+<colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>null_dist_filename</strong> (<em>str</em><em>, </em><em>otional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Path to file containing null distribution.</li>
-<li><strong>gp_version</strong> (<em>{'new'</em><em>, </em><em>'old'}</em><em>, </em><em>optional</em><em> (</em><em>default: 'new'</em><em>)</em>) &#8211; The older GP version from scikit-learn 0.17 was used for the numerical
-simulations in <a class="footnote-reference" href="#id5" id="id13">[1]</a>. The newer version from scikit-learn 0.19 is faster
-and allows more flexibility regarding kernels etc.</li>
-<li><strong>gp_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Dictionary with parameters for <code class="docutils literal"><span class="pre">GaussianProcessRegressor</span></code>.</li>
-<li><strong>ace_version</strong> (<em>{'python'</em><em>, </em><em>'acepack'}</em>) &#8211; Estimator for ACE estimator of maximal correlation to use. &#8216;python&#8217;
-loads the very slow pure python version available from
-<a class="reference external" href="https://pypi.python.org/pypi/ace/0.3">https://pypi.python.org/pypi/ace/0.3</a>. &#8216;acepack&#8217; loads the much faster
-version from the R-package acepack. This requires the R-interface
-rpy2 to be installed and acepack needs to be installed in R beforehand.
-Note that both versions &#8216;python&#8217; and &#8216;acepack&#8217; may result in different
-results. In <a class="footnote-reference" href="#id5" id="id14">[1]</a> the acepack version was used.</li>
-<li><strong>**kwargs</strong> &#8211; Arguments passed on to parent class CondIndTest.</li>
-</ul>
-</td>
-</tr>
+<tr><td class="label">[2]</td><td><em>(<a class="fn-backref" href="#id12">1</a>, <a class="fn-backref" href="#id13">2</a>)</em> Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and
+testing dependence by correlation of distances,
+<a class="reference external" href="https://arxiv.org/abs/0803.4101">https://arxiv.org/abs/0803.4101</a></td></tr>
 </tbody>
 </table>
-<dl class="method">
-<dt id="tigramite.independence_tests.GPACE.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPACE.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPACE.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="tigramite.independence_tests.GPACE.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPACE.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPACE.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for the maximal correlation coefficient.</p>
-<p>The null distribution for necessary degrees of freedom (df) is loaded.
-If not available, the null distribution is generated with the function
-generate_nulldist(). It can be precomputed with the function
-generate_and_save_nulldists(...) which saves a *.npz file containing the
-null distribution for different sample sizes. This file can then be
-supplied as null_dist_filename. The maximal correlation coefficient is
-one-sided. If the degrees of freedom are less than 1, numpy.nan is
-returned.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>value</strong> (<em>float</em>) &#8211; Test statistic value.</li>
-<li><strong>T</strong> (<em>int</em>) &#8211; Sample length</li>
-<li><strong>dim</strong> (<em>int</em>) &#8211; Dimensionality, ie, number of features.</li>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>null_dist_filename</strong> (<em>str</em><em>, </em><em>otional</em><em> (</em><em>default: None</em><em>)</em>) – Path to file containing null distribution.</li>
+<li><strong>gp_version</strong> (<em>{'new'</em><em>, </em><em>'old'}</em><em>, </em><em>optional</em><em> (</em><em>default: 'new'</em><em>)</em>) – The older GP version from scikit-learn 0.17 was used for the numerical
+simulations in <a class="footnote-reference" href="#id5" id="id15">[1]</a>. The newer version from scikit-learn 0.19 is faster
+and allows more flexibility regarding kernels etc.</li>
+<li><strong>gp_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary with parameters for <code class="docutils literal notranslate"><span class="pre">GaussianProcessRegressor</span></code>.</li>
+<li><strong>**kwargs</strong> – Arguments passed on to parent class GaussProcReg.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; P-value.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float or numpy.nan</p>
-</td>
-</tr>
 </tbody>
 </table>
-</dd></dl>
-
 <dl class="method">
-<dt id="tigramite.independence_tests.GPACE.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPACE.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPACE.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return GPACE measure.</p>
-<p>Estimated as the maximal correlation of the residuals of a GP
-regression.</p>
+<dt id="tigramite.independence_tests.GPDC.generate_and_save_nulldists">
+<code class="descname">generate_and_save_nulldists</code><span class="sig-paren">(</span><em>sample_sizes</em>, <em>null_dist_filename</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.generate_and_save_nulldists" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generates and saves null distribution for pairwise independence
+tests.</p>
+<p>Generates the null distribution for different sample sizes. Calls
+generate_nulldist. Null dists are saved to disk as
+self.null_dist_filename.npz. Also adds the null distributions to
+self.gauss_pr.null_dists.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>sample_sizes</strong> (<em>list</em>) – List of sample sizes.</li>
+<li><strong>null_dist_filename</strong> (<em>str</em>) – Name to save file containing null distributions.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; GPACE test statistic.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
-</td>
-</tr>
 </tbody>
 </table>
 </dd></dl>
 
 <dl class="method">
-<dt id="tigramite.independence_tests.GPACE.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPACE.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPACE.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns p-value for shuffle significance test.</p>
-<p>For residual-based test statistics only the residuals are shuffled.</p>
+<dt id="tigramite.independence_tests.GPDC.generate_nulldist">
+<code class="descname">generate_nulldist</code><span class="sig-paren">(</span><em>df</em>, <em>add_to_null_dists=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.generate_nulldist" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generates null distribution for pairwise independence tests.</p>
+<p>Generates the null distribution for sample size df. Assumes pairwise
+samples transformed to uniform marginals. Uses get_dependence_measure
+available in class and generates self.sig_samples random samples. Adds
+the null distributions to self.gauss_pr.null_dists.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>df</strong> (<em>int</em>) – Degrees of freedom / sample size to generate null distribution for.</li>
+<li><strong>add_to_null_dists</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to add the null dist to the dictionary of null dists or
+just return it.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>null_dist</strong> – Only returned,if add_to_null_dists is False.</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="tigramite.independence_tests.GPDC">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">GPDC</code><span class="sig-paren">(</span><em>null_dist_filename=None</em>, <em>gp_version='new'</em>, <em>gp_params=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPDC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPDC" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="docutils">
-<dt>GPDC conditional independence test based on Gaussian processes and</dt>
-<dd>distance correlation.</dd>
-</dl>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals <a class="footnote-reference" href="#id17" id="id15">[2]</a>. GP is estimated with scikit-learn
-and allows to flexibly specify kernels and hyperparameters or let them be
-optimized automatically. The distance correlation test is implemented with
-cython. Here the null distribution is not analytically available, but can be
-precomputed with the function generate_and_save_nulldists(...) which saves a
-*.npz file containing the null distribution for different sample sizes. This
-file can then be supplied as null_dist_filename.</p>
-<p class="rubric">Notes</p>
-<p>GPDC is based on a Gaussian process (GP) regression and a distance
-correlation test on the residuals. Distance correlation is described in
-<a class="footnote-reference" href="#id17" id="id16">[2]</a>. To test <img class="math" src="_images/math/d843100cd0a02e1e5184694228a7ac41e19e8af2.png" alt="X \perp Y | Z"/>, first <img class="math" src="_images/math/bcb2457ac9d8995a4f34d57cadac7ecbbe58f3bd.png" alt="Z"/> is regressed out from
-<img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/> and <img class="math" src="_images/math/0062c26909b3e07ee8f5a6285b2563d69bc979ff.png" alt="Y"/> assuming the  model</p>
-<div class="math">
-<p><img src="_images/math/70852cea72eb5c4d0a45c0db08b49476f4404426.png" alt="X &amp; =  f_X(Z) + \epsilon_{X} \\
-Y &amp; =  f_Y(Z) + \epsilon_{Y}  \\
-\epsilon_{X,Y} &amp;\sim \mathcal{N}(0, \sigma^2)"/></p>
-</div><p>using GP regression. Here <img class="math" src="_images/math/7a3d0c9264473a58cc3a769f99a662631131377c.png" alt="\sigma^2"/> and the kernel bandwidth are
-optimzed using <code class="docutils literal"><span class="pre">sklearn</span></code>. Then the residuals  are transformed to uniform
-marginals yielding <img class="math" src="_images/math/21fc11b715eef698662fe1cc017d7ae2d53320d8.png" alt="r_X,r_Y"/> and their dependency is tested with</p>
-<div class="math">
-<p><img src="_images/math/7bce831f5eba0d0e0fcb0cced170b2926804500a.png" alt="\mathcal{R}\left(r_X, r_Y\right)"/></p>
-</div><p>The null distribution of the distance correlation should be pre-computed.
-Otherwise it is computed during runtime.</p>
-<p>The cython-code for distance correlation is Copyright (c) 2012, Florian
-Finkernagel (<a class="reference external" href="https://gist.github.com/ffinkernagel/2960386">https://gist.github.com/ffinkernagel/2960386</a>).</p>
-<p class="rubric">References</p>
-<table class="docutils footnote" frame="void" id="id17" rules="none">
-<colgroup><col class="label" /><col /></colgroup>
-<tbody valign="top">
-<tr><td class="label">[2]</td><td><em>(<a class="fn-backref" href="#id15">1</a>, <a class="fn-backref" href="#id16">2</a>)</em> Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and
-testing dependence by correlation of distances,
-<a class="reference external" href="https://arxiv.org/abs/0803.4101">https://arxiv.org/abs/0803.4101</a></td></tr>
-</tbody>
-</table>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>null_dist_filename</strong> (<em>str</em><em>, </em><em>otional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Path to file containing null distribution.</li>
-<li><strong>gp_version</strong> (<em>{'new'</em><em>, </em><em>'old'}</em><em>, </em><em>optional</em><em> (</em><em>default: 'new'</em><em>)</em>) &#8211; The older GP version from scikit-learn 0.17 was used for the numerical
-simulations in <a class="footnote-reference" href="#id5" id="id18">[1]</a>. The newer version from scikit-learn 0.19 is faster
-and allows more flexibility regarding kernels etc.</li>
-<li><strong>gp_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Dictionary with parameters for <code class="docutils literal"><span class="pre">GaussianProcessRegressor</span></code>.</li>
-<li><strong>**kwargs</strong> &#8211; Arguments passed on to parent class CondIndTest.</li>
-</ul>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape [df,]</p>
 </td>
 </tr>
 </tbody>
 </table>
-<dl class="method">
-<dt id="tigramite.independence_tests.GPDC.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPDC.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.independence_tests.GPDC.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPDC.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for the distance correlation coefficient.</p>
 <p>The null distribution for necessary degrees of freedom (df) is loaded.
 If not available, the null distribution is generated with the function
 generate_nulldist(). It is recommended to generate the nulldists for a
 wide range of sample sizes beforehand with the function
-generate_and_save_nulldists(...). The distance correlation coefficient
+generate_and_save_nulldists(…). The distance correlation coefficient
 is one-sided. If the degrees of freedom are less than 1, numpy.nan is
 returned.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1250,13 +1207,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>value</strong> (<em>float</em>) &#8211; Test statistic value.</li>
-<li><strong>T</strong> (<em>int</em>) &#8211; Sample length</li>
-<li><strong>dim</strong> (<em>int</em>) &#8211; Dimensionality, ie, number of features.</li>
+<li><strong>value</strong> (<em>float</em>) – Test statistic value.</li>
+<li><strong>T</strong> (<em>int</em>) – Sample length</li>
+<li><strong>dim</strong> (<em>int</em>) – Dimensionality, ie, number of features.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; P-value.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – P-value.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float or numpy.nan</p>
@@ -1268,7 +1225,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.GPDC.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPDC.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return GPDC measure.</p>
 <p>Estimated as the distance correlation of the residuals of a GP
 regression.</p>
@@ -1277,12 +1234,12 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; GPDC test statistic.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – GPDC test statistic.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1292,9 +1249,34 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="tigramite.independence_tests.GPDC.get_model_selection_criterion">
+<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns log marginal likelihood for GP regression.</p>
+<p>Fits a GP model of the parents to variable j and returns the negative
+log marginal likelihood as a model selection score. Is used to determine
+optimal hyperparameters in PCMCI, in particular the pc_alpha value.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>j</strong> (<em>int</em>) – Index of target variable in data array.</li>
+<li><strong>parents</strong> (<em>list</em>) – List of form [(0, -1), (3, -2), …] containing parents.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Maximum time lag. This may be used to make sure that estimates for
+different lags in X, Z, all have the same sample size.</li>
+<li><strong>Returns</strong> – </li>
+<li><strong>score</strong> (<em>float</em>) – Model score.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="tigramite.independence_tests.GPDC.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#GPDC.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.GPDC.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for shuffle significance test.</p>
 <p>For residual-based test statistics only the residuals are shuffled.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1302,13 +1284,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – p-value</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1318,11 +1300,17 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="tigramite.independence_tests.GPDC.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.GPDC.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Concrete property to return the measure of the independence test</p>
+</dd></dl>
+
 </dd></dl>
 
 <dl class="class">
 <dt id="tigramite.independence_tests.CMIknn">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CMIknn</code><span class="sig-paren">(</span><em>knn=0.2</em>, <em>shuffle_neighbors=5</em>, <em>significance='shuffle_test'</em>, <em>transform='standardize'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CMIknn</code><span class="sig-paren">(</span><em>knn=0.2</em>, <em>shuffle_neighbors=5</em>, <em>significance='shuffle_test'</em>, <em>transform='standardize'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIknn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditional mutual information test based on nearest-neighbor estimator.</p>
 <p>Conditional mutual information is the most general dependency measure coming
 from an information-theoretic framework. It makes no assumptions about the
@@ -1330,7 +1318,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 joint density. The test here is based on the estimator in  S. Frenzel and B.
 Pompe, Phys. Rev. Lett. 99, 204101 (2007), combined with a shuffle test to
 generate  the distribution under the null hypothesis of independence first
-used in <a class="footnote-reference" href="#id20" id="id19">[3]</a>. The knn-estimator is suitable only for variables taking a
+used in <a class="footnote-reference" href="#id17" id="id16">[3]</a>. The knn-estimator is suitable only for variables taking a
 continuous range of values. For discrete variables use the CMIsymb class.</p>
 <p class="rubric">Notes</p>
 <p>CMI is given by</p>
@@ -1352,16 +1340,17 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 but lower variance, which is more important for significance testing. Note
 that the estimated CMI values can be slightly negative while CMI is a non-
 negative quantity.</p>
-<p>This class requires the scipy.spatial.cKDTree package and the tigramite
+<p>This method requires the scipy.spatial.cKDTree package and the tigramite
 cython module.</p>
 <p class="rubric">References</p>
-<table class="docutils footnote" frame="void" id="id20" rules="none">
+<table class="docutils footnote" frame="void" id="id17" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id19">[3]</a></td><td>J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths:
-Escaping the Curse of Dimensionality in Estimating Multivariate
-Transfer Entropy. Physical Review Letters, 108(25), 258701.
-<a class="reference external" href="http://doi.org/10.1103/PhysRevLett.108.258701">http://doi.org/10.1103/PhysRevLett.108.258701</a></td></tr>
+<tr><td class="label"><a class="fn-backref" href="#id16">[3]</a></td><td>J. Runge (2018): Conditional Independence Testing Based on a
+Nearest-Neighbor Estimator of Conditional Mutual Information.
+In Proceedings of the 21st International Conference on Artificial
+Intelligence and Statistics.
+<a class="reference external" href="http://proceedings.mlr.press/v84/runge18a.html">http://proceedings.mlr.press/v84/runge18a.html</a></td></tr>
 </tbody>
 </table>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1369,51 +1358,39 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>knn</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) &#8211; Number of nearest-neighbors which determines the size of hyper-cubes
+<li><strong>knn</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Number of nearest-neighbors which determines the size of hyper-cubes
 around each (high-dimensional) sample point. If smaller than 1, this is
 computed as a fraction of T, hence knn=knn*T. For knn larger or equal to
 1, this is the absolute number.</li>
-<li><strong>shuffle_neighbors</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Number of nearest-neighbors within Z for the shuffle surrogates which
+<li><strong>shuffle_neighbors</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Number of nearest-neighbors within Z for the shuffle surrogates which
 determines the size of hyper-cubes around each (high-dimensional) sample
 point.</li>
-<li><strong>transform</strong> (<em>{'standardize'</em><em>, </em><em>'ranks'</em><em>,  </em><em>'uniform'</em><em>, </em><em>False}</em><em>, </em><em>optional</em>) &#8211; (default: &#8216;standardize&#8217;)
+<li><strong>transform</strong> (<em>{'standardize'</em><em>, </em><em>'ranks'</em><em>,  </em><em>'uniform'</em><em>, </em><em>False}</em><em>, </em><em>optional</em>) – (default: ‘standardize’)
 Whether to transform the array beforehand by standardizing
 or transforming to uniform marginals.</li>
-<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) &#8211; Type of significance test to use. For CMIknn only &#8216;fixed_thres&#8217; and
-&#8216;shuffle_test&#8217; are available.</li>
-<li><strong>**kwargs</strong> &#8211; Arguments passed on to parent class CondIndTest.</li>
+<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIknn only ‘fixed_thres’ and
+‘shuffle_test’ are available.</li>
+<li><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIknn.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIknn.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.CMIknn.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns CMI estimate as described in Frenzel and Pompe PRL (2007).</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; Conditional mutual information estimate.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – Conditional mutual information estimate.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1423,15 +1400,9 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIknn.get_model_selection_criterion">
-<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn.get_model_selection_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.CMIknn.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIknn.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIknn.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for nearest-neighbor shuffle significance test.</p>
 <p>For non-empty Z, overwrites get_shuffle_significance from the parent
 class  which is a block shuffle test, which does not preserve
@@ -1444,13 +1415,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – p-value</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1460,11 +1431,17 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="tigramite.independence_tests.CMIknn.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.CMIknn.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Concrete property to return the measure of the independence test</p>
+</dd></dl>
+
 </dd></dl>
 
 <dl class="class">
 <dt id="tigramite.independence_tests.CMIsymb">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CMIsymb</code><span class="sig-paren">(</span><em>n_symbs=None</em>, <em>significance='shuffle_test'</em>, <em>sig_blocklength=1</em>, <em>conf_blocklength=1</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">CMIsymb</code><span class="sig-paren">(</span><em>n_symbs=None</em>, <em>significance='shuffle_test'</em>, <em>sig_blocklength=1</em>, <em>conf_blocklength=1</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIsymb" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditional mutual information test based on discrete estimator.</p>
 <p>Conditional mutual information is the most general dependency measure
 coming from an information-theoretic framework. It makes no assumptions
@@ -1474,7 +1451,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 shuffle test to generate  the distribution under the null hypothesis of
 independence. The knn-estimator is suitable only for discrete variables.
 For continuous variables, either pre-process the data using the functions
-in data_processing or use the CMIknn class.</p>
+in data_processing or, better, use the CMIknn class.</p>
 <p class="rubric">Notes</p>
 <p>CMI and its estimator are given by</p>
 <div class="math">
@@ -1485,44 +1462,32 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>n_symbs</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Number of symbols in input data. If None, n_symbs=data.max()+1</li>
-<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) &#8211; Type of significance test to use. For CMIsymb only &#8216;fixed_thres&#8217; and
-&#8216;shuffle_test&#8217; are available.</li>
-<li><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Block length for block-shuffle significance test.</li>
-<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Block length for block-bootstrap.</li>
-<li><strong>**kwargs</strong> &#8211; Arguments passed on to parent class CondIndTest.</li>
+<li><strong>n_symbs</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Number of symbols in input data. If None, n_symbs=data.max()+1</li>
+<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'shuffle_test'</em><em>)</em>) – Type of significance test to use. For CMIsymb only ‘fixed_thres’ and
+‘shuffle_test’ are available.</li>
+<li><strong>sig_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Block length for block-shuffle significance test.</li>
+<li><strong>conf_blocklength</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Block length for block-bootstrap.</li>
+<li><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIsymb.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIsymb.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>value</em>, <em>T</em>, <em>dim</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.CMIsymb.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns CMI estimate based on bincount histogram.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; Conditional mutual information estimate.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – Conditional mutual information estimate.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1532,15 +1497,9 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
-<dl class="method">
-<dt id="tigramite.independence_tests.CMIsymb.get_model_selection_criterion">
-<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb.get_model_selection_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.CMIsymb.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#CMIsymb.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for shuffle significance test.</p>
 <p>For residual-based test statistics only the residuals are shuffled.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1548,13 +1507,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – p-value</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1564,16 +1523,22 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="tigramite.independence_tests.CMIsymb.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.CMIsymb.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Concrete property to return the measure of the independence test</p>
+</dd></dl>
+
 </dd></dl>
 
 <dl class="class">
 <dt id="tigramite.independence_tests.RCOT">
-<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">RCOT</code><span class="sig-paren">(</span><em>num_f=25</em>, <em>approx='lpd4'</em>, <em>seed=42</em>, <em>significance='analytic'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.independence_tests.</code><code class="descname">RCOT</code><span class="sig-paren">(</span><em>num_f=25</em>, <em>approx='lpd4'</em>, <em>seed=42</em>, <em>significance='analytic'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.RCOT" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomized Conditional Correlation Test.</p>
 <p>Tests conditional independence in the fully non-parametric setting based on
 Kernel measures. For not too small sample sizes, the test can utilize an
 analytic approximation of the null distribution making it very fast. Based
-on r-package <code class="docutils literal"><span class="pre">rcit</span></code>. This test is described in <a class="footnote-reference" href="#id23" id="id21">[5]</a>.</p>
+on r-package <code class="docutils literal notranslate"><span class="pre">rcit</span></code>. This test is described in <a class="footnote-reference" href="#id20" id="id18">[5]</a>.</p>
 <p class="rubric">Notes</p>
 <p>RCOT is a fast variant of the Kernel Conditional Independence Test (KCIT)
 utilizing random Fourier features. Kernel tests measure conditional
@@ -1581,18 +1546,18 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 scale linearly with sample size and return accurate p-values much faster
 than KCIT in the large sample size context. To use the analytical null
 approximation, the sample size should be at least ~1000.</p>
-<p>The method is fully described in <a class="footnote-reference" href="#id23" id="id22">[5]</a> and the r-package documentation. The
+<p>The method is fully described in <a class="footnote-reference" href="#id20" id="id19">[5]</a> and the r-package documentation. The
 free parameters are the approximation of the partial kernel cross-covariance
 matrix and the number of random fourier features for the conditioning set.
 One caveat is that RCOT is, as the name suggests, based on random fourier
 features. To get reproducable results, you should fix the seed (default).</p>
-<p>This class requires the rpy package and the prior installation of <code class="docutils literal"><span class="pre">rcit</span></code>
+<p>This class requires the rpy package and the prior installation of <code class="docutils literal notranslate"><span class="pre">rcit</span></code>
 from <a class="reference external" href="https://github.com/ericstrobl/RCIT">https://github.com/ericstrobl/RCIT</a>.</p>
 <p class="rubric">References</p>
-<table class="docutils footnote" frame="void" id="id23" rules="none">
+<table class="docutils footnote" frame="void" id="id20" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label">[5]</td><td><em>(<a class="fn-backref" href="#id21">1</a>, <a class="fn-backref" href="#id22">2</a>)</em> Eric V. Strobl, Kun Zhang, Shyam Visweswaran:
+<tr><td class="label">[5]</td><td><em>(<a class="fn-backref" href="#id18">1</a>, <a class="fn-backref" href="#id19">2</a>)</em> Eric V. Strobl, Kun Zhang, Shyam Visweswaran:
 Approximate Kernel-based Conditional Independence Tests for Fast Non-
 Parametric Causal Discovery.
 <a class="reference external" href="https://arxiv.org/abs/1702.03877">https://arxiv.org/abs/1702.03877</a></td></tr>
@@ -1603,38 +1568,34 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>num_f</strong> (<em>int</em><em>, </em><em>optional</em>) &#8211; Number of random fourier features for conditioning set. More features
+<li><strong>num_f</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of random fourier features for conditioning set. More features
 better approximate highly structured joint densities, but take more
 computational time.</li>
-<li><strong>approx</strong> (<em>str</em><em>, </em><em>optional</em>) &#8211; Which approximation of the partial cross-covariance matrix, options:
-&#8216;lpd4&#8217; the Lindsay-Pilla-Basak method (default), &#8216;gamma&#8217; for the
-Satterthwaite-Welch method, &#8216;hbe&#8217; for the Hall-Buckley-Eagleson method,
-&#8216;chi2&#8217; for a normalized chi-squared statistic, &#8216;perm&#8217; for permutation
+<li><strong>approx</strong> (<em>str</em><em>, </em><em>optional</em>) – Which approximation of the partial cross-covariance matrix, options:
+‘lpd4’ the Lindsay-Pilla-Basak method (default), ‘gamma’ for the
+Satterthwaite-Welch method, ‘hbe’ for the Hall-Buckley-Eagleson method,
+‘chi2’ for a normalized chi-squared statistic, ‘perm’ for permutation
 testing (warning: this one is slow).</li>
-<li><strong>seed</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em>) &#8211; Which random fourier feature seed to use. If None, you won&#8217;t get
+<li><strong>seed</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em>) – Which random fourier feature seed to use. If None, you won’t get
 reproducable results.</li>
-<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) &#8211; Type of significance test to use.</li>
-<li><strong>**kwargs</strong> &#8211; Arguments passed on to parent class CondIndTest.</li>
+<li><strong>significance</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'analytic'</em><em>)</em>) – Type of significance test to use.</li>
+<li><strong>**kwargs</strong> – Arguments passed on to parent class CondIndTest.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
-<dl class="method">
-<dt id="tigramite.independence_tests.RCOT.get_analytic_confidence">
-<code class="descname">get_analytic_confidence</code><span class="sig-paren">(</span><em>value</em>, <em>df</em>, <em>conf_lev</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT.get_analytic_confidence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_analytic_confidence" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.RCOT.get_analytic_significance">
-<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>**args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT.get_analytic_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns analytic p-value from RCIT test statistic.</p>
+<code class="descname">get_analytic_significance</code><span class="sig-paren">(</span><em>**args</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_analytic_significance" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns analytic p-value from RCoT test statistic.
+NOTE: Must first run get_dependence_measure, where p-value is determined
+from RCoT test statistic.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>pval</strong> &#8211; P-value.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>pval</strong> – P-value.</td>
 </tr>
 <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">float or numpy.nan</td>
 </tr>
@@ -1644,19 +1605,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="method">
 <dt id="tigramite.independence_tests.RCOT.get_dependence_measure">
-<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT.get_dependence_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_dependence_measure</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_dependence_measure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns RCOT estimate.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> &#8211; RCOT estimate.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>val</strong> – RCOT estimate.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1666,15 +1627,9 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
-<dl class="method">
-<dt id="tigramite.independence_tests.RCOT.get_model_selection_criterion">
-<code class="descname">get_model_selection_criterion</code><span class="sig-paren">(</span><em>j</em>, <em>parents</em>, <em>tau_max=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT.get_model_selection_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_model_selection_criterion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Placeholder function, not available.</p>
-</dd></dl>
-
 <dl class="method">
 <dt id="tigramite.independence_tests.RCOT.get_shuffle_significance">
-<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/independence_tests.html#RCOT.get_shuffle_significance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_shuffle_significance</code><span class="sig-paren">(</span><em>array</em>, <em>xyz</em>, <em>value</em>, <em>return_null_dist=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.independence_tests.RCOT.get_shuffle_significance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns p-value for shuffle significance test.</p>
 <p>For residual-based test statistics only the residuals are shuffled.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1682,13 +1637,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; data array with X, Y, Z in rows and observations in columns</li>
-<li><strong>xyz</strong> (<em>array of ints</em>) &#8211; XYZ identifier array of shape (dim,).</li>
-<li><strong>value</strong> (<em>number</em>) &#8211; Value of test statistic for unshuffled estimate.</li>
+<li><strong>array</strong> (<em>array-like</em>) – data array with X, Y, Z in rows and observations in columns</li>
+<li><strong>xyz</strong> (<em>array of ints</em>) – XYZ identifier array of shape (dim,).</li>
+<li><strong>value</strong> (<em>number</em>) – Value of test statistic for unshuffled estimate.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> &#8211; p-value</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>pval</strong> – p-value</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -1698,15 +1653,21 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 </table>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="tigramite.independence_tests.RCOT.measure">
+<code class="descname">measure</code><a class="headerlink" href="#tigramite.independence_tests.RCOT.measure" title="Permalink to this definition">¶</a></dt>
+<dd><p>Concrete property to return the measure of the independence test</p>
+</dd></dl>
+
 </dd></dl>
 
 </div>
 <div class="section" id="module-tigramite.data_processing">
-<span id="tigramite-data-processing-data-processing-functions"></span><h1><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-mod docutils literal"><span class="pre">tigramite.data_processing</span></code></a>: Data processing functions<a class="headerlink" href="#module-tigramite.data_processing" title="Permalink to this headline">¶</a></h1>
+<span id="tigramite-data-processing-data-processing-functions"></span><h1><a class="reference internal" href="#module-tigramite.data_processing" title="tigramite.data_processing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.data_processing</span></code></a>: Data processing functions<a class="headerlink" href="#module-tigramite.data_processing" title="Permalink to this headline">¶</a></h1>
 <p>Tigramite data processing functions.</p>
 <dl class="class">
 <dt id="tigramite.data_processing.DataFrame">
-<em class="property">class </em><code class="descclassname">tigramite.data_processing.</code><code class="descname">DataFrame</code><span class="sig-paren">(</span><em>data</em>, <em>mask=None</em>, <em>missing_flag=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#DataFrame"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.data_processing.</code><code class="descname">DataFrame</code><span class="sig-paren">(</span><em>data</em>, <em>mask=None</em>, <em>missing_flag=None</em>, <em>var_names=None</em>, <em>datatime=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.DataFrame" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data object containing time series array and optional mask.</p>
 <p>Alternatively, a panda dataframe can be used.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1714,8 +1675,8 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data</strong> (<em>array-like</em>) &#8211; Numpy array of shape (observations T, variables N)</li>
-<li><strong>mask</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional mask array, must be of same shape as data</li>
+<li><strong>data</strong> (<em>array-like</em>) – Numpy array of shape (observations T, variables N)</li>
+<li><strong>mask</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional mask array, must be of same shape as data</li>
 </ul>
 </td>
 </tr>
@@ -1724,29 +1685,126 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <dl class="attribute">
 <dt id="tigramite.data_processing.DataFrame.data">
 <code class="descname">data</code><a class="headerlink" href="#tigramite.data_processing.DataFrame.data" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>array-like</em> &#8211; Numpy array of shape (observations T, variables N)</p>
+<dd><p><em>array-like</em> – Numpy array of shape (observations T, variables N)</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="tigramite.data_processing.DataFrame.mask">
 <code class="descname">mask</code><a class="headerlink" href="#tigramite.data_processing.DataFrame.mask" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>array-like, optional (default: None)</em> &#8211; Optional mask array, must be of same shape as data</p>
+<dd><p><em>array-like, optional (default: None)</em> – Optional mask array, must be of same shape as data</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="tigramite.data_processing.DataFrame.missing_flag">
 <code class="descname">missing_flag</code><a class="headerlink" href="#tigramite.data_processing.DataFrame.missing_flag" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number, optional (default: None)</em> &#8211; Flag for missing values in dataframe. Dismisses all time slices of
+<dd><p><em>number, optional (default: None)</em> – Flag for missing values in dataframe. Dismisses all time slices of
 samples where missing values occur in any variable and also flags
 samples for all lags up to 2*tau_max. This avoids biases, see
-section on masking in Supplement of <a class="footnote-reference" href="#id5" id="id24">[1]</a>.</p>
+section on masking in Supplement of <a class="footnote-reference" href="#id5" id="id21">[1]</a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="tigramite.data_processing.DataFrame.var_names">
+<code class="descname">var_names</code><a class="headerlink" href="#tigramite.data_processing.DataFrame.var_names" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>list of strings, optional (default: range(N))</em> – Names of variables, must match the number of variables. If None is
+passed, variables are enumerated as [0, 1, …]</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="tigramite.data_processing.DataFrame.datatime">
+<code class="descname">datatime</code><a class="headerlink" href="#tigramite.data_processing.DataFrame.datatime" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>array-like, optional (default: None)</em> – Timelabel array. If None, range(T) is used.</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.data_processing.DataFrame.construct_array">
+<code class="descname">construct_array</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>Z</em>, <em>tau_max</em>, <em>mask=None</em>, <em>mask_type=None</em>, <em>return_cleaned_xyz=False</em>, <em>do_checks=True</em>, <em>cut_off='2xtau_max'</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.DataFrame.construct_array" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs array from variables X, Y, Z from data.</p>
+<p>Data is of shape (T, N), where T is the time series length and N the
+number of variables.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+where var specifies the variable index. X typically is of the form
+[(varX, -tau)] with tau denoting the time lag and Z can be
+multivariate [(var1, -lag), (var2, -lag), …] .</li>
+<li><strong>tau_max</strong> (<em>int</em>) – Maximum time lag. This may be used to make sure that estimates for
+different lags in X and Z all have the same sample size.</li>
+<li><strong>mask</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional mask array, must be of same shape as data.  If it is set,
+then it overrides the self.mask assigned to the dataframe. If it is
+None, then the self.mask is used, if it exists.</li>
+<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
+measure I(X; Y | Z) the samples should be masked. If None, ‘y’ is
+used, which excludes all time slices containing masked samples in Y.
+Explained in <a class="footnote-reference" href="#id5" id="id22">[1]</a>.</li>
+<li><strong>return_cleaned_xyz</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to return cleaned X,Y,Z, where possible duplicates are
+removed.</li>
+<li><strong>do_checks</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to perform sanity checks on input X,Y,Z</li>
+<li><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – How many samples to cutoff at the beginning. The default is
+‘2xtau_max’, which guarantees that MCI tests are all conducted on
+the same samples. For modeling, ‘max_lag_or_tau_max’ can be used,
+which uses the maximum of tau_max and the conditions, which is
+useful to compare multiple models on the same sample.  Last,
+‘max_lag’ uses as much samples as possible.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><p><strong>array, xyz [,XYZ]</strong> – identifier array of shape (dim,) identifying which row in array
+corresponds to X, Y, and Z. For example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)],</span> <span class="n">Y</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">Z</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)]</span>
+<span class="n">yields</span> <span class="n">an</span> <span class="n">array</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span> <span class="ow">and</span> <span class="n">xyz</span> <span class="ow">is</span>
+<span class="n">xyz</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>If return_cleaned_xyz is True, also outputs the cleaned XYZ lists.</p>
+</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Tuple of data array of shape (dim, T) and xyz</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="tigramite.data_processing.DataFrame.print_array_info">
+<code class="descname">print_array_info</code><span class="sig-paren">(</span><em>array</em>, <em>X</em>, <em>Y</em>, <em>Z</em>, <em>missing_flag</em>, <em>mask_type</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.DataFrame.print_array_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Print info about the constructed array</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>array</strong> (<em>Data array of shape</em><em> (</em><em>dim</em><em>, </em><em>T</em><em>)</em>) – </li>
+<li><strong>Y</strong><strong>, </strong><strong>Z</strong> (<em>X</em><em>,</em>) – For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+where var specifies the variable index. X typically is of the form
+[(varX, -tau)] with tau denoting the time lag and Z can be
+multivariate [(var1, -lag), (var2, -lag), …] .</li>
+<li><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Flag for missing values. Dismisses all time slices of samples where
+missing values occur in any variable and also flags samples for all
+lags up to 2*tau_max. This avoids biases, see section on masking in
+Supplement of <a class="footnote-reference" href="#id5" id="id23">[1]</a>.</li>
+<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence
+measure I(X; Y | Z) the samples should be masked. If None, ‘y’ is
+used, which excludes all time slices containing masked samples in Y.
+Explained in <a class="footnote-reference" href="#id5" id="id24">[1]</a>.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
 </dd></dl>
 
 </dd></dl>
 
 <dl class="function">
 <dt id="tigramite.data_processing.lowhighpass_filter">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">lowhighpass_filter</code><span class="sig-paren">(</span><em>data</em>, <em>cutperiod</em>, <em>pass_periods='low'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#lowhighpass_filter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.lowhighpass_filter" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">lowhighpass_filter</code><span class="sig-paren">(</span><em>data</em>, <em>cutperiod</em>, <em>pass_periods='low'</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.lowhighpass_filter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Butterworth low- or high pass filter.</p>
 <p>This function applies a linear filter twice, once forward and once
 backwards. The combined filter has linear phase.</p>
@@ -1755,13 +1813,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<em>array</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>cutperiod</strong> (<em>int</em>) &#8211; Period of cutoff.</li>
-<li><strong>pass_periods</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'low'</em><em>)</em>) &#8211; Either &#8216;low&#8217; or &#8216;high&#8217; to act as a low- or high-pass filter</li>
+<li><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</li>
+<li><strong>cutperiod</strong> (<em>int</em>) – Period of cutoff.</li>
+<li><strong>pass_periods</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'low'</em><em>)</em>) – Either ‘low’ or ‘high’ to act as a low- or high-pass filter</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> &#8211; Filtered data array.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> – Filtered data array.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array</p>
@@ -1773,10 +1831,10 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.ordinal_patt_array">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">ordinal_patt_array</code><span class="sig-paren">(</span><em>array</em>, <em>array_mask=None</em>, <em>dim=2</em>, <em>step=1</em>, <em>weights=False</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#ordinal_patt_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.ordinal_patt_array" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">ordinal_patt_array</code><span class="sig-paren">(</span><em>array</em>, <em>array_mask=None</em>, <em>dim=2</em>, <em>step=1</em>, <em>weights=False</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.ordinal_patt_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns symbolified array of ordinal patterns.</p>
-<p>Each data vector (X_t, ..., X_t+(dim-1)*step) is converted to its rank
-vector. E.g., (0.2, -.6, 1.2) &#8211;&gt; (1,0,2) which is then assigned to a
+<p>Each data vector (X_t, …, X_t+(dim-1)*step) is converted to its rank
+vector. E.g., (0.2, -.6, 1.2) –&gt; (1,0,2) which is then assigned to a
 unique integer (see Article). There are faculty(dim) possible rank vectors.</p>
 <p>Note that the symb_array is step*(dim-1) shorter than the original array!</p>
 <p>Reference: B. Pompe and J. Runge (2011). Momentary information transfer as
@@ -1787,16 +1845,16 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>array</strong> (<em>array-like</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>array_mask</strong> (<em>bool array</em>) &#8211; Data mask where True labels masked samples.</li>
-<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) &#8211; Pattern dimension</li>
-<li><strong>step</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Delay of pattern embedding vector.</li>
-<li><strong>weights</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to return array of variances of embedding vectors as weights.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>array</strong> (<em>array-like</em>) – Data array of shape (time, variables).</li>
+<li><strong>array_mask</strong> (<em>bool array</em>) – Data mask where True labels masked samples.</li>
+<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) – Pattern dimension</li>
+<li><strong>step</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Delay of pattern embedding vector.</li>
+<li><strong>weights</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to return array of variances of embedding vectors as weights.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>patt, patt_mask [, patt_time]</strong> &#8211; Tuple of converted pattern array and new length</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>patt, patt_mask [, patt_time]</strong> – Tuple of converted pattern array and new length</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">tuple of arrays</p>
@@ -1808,19 +1866,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.quantile_bin_array">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">quantile_bin_array</code><span class="sig-paren">(</span><em>data</em>, <em>bins=6</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#quantile_bin_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.quantile_bin_array" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">quantile_bin_array</code><span class="sig-paren">(</span><em>data</em>, <em>bins=6</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.quantile_bin_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns symbolified array with equal-quantile binning.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<em>array</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>bins</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) &#8211; Number of bins.</li>
+<li><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</li>
+<li><strong>bins</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Number of bins.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>symb_array</strong> &#8211; Converted data of integer type.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>symb_array</strong> – Converted data of integer type.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array</p>
@@ -1832,7 +1890,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.smooth">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">smooth</code><span class="sig-paren">(</span><em>data</em>, <em>smooth_width</em>, <em>kernel='gaussian'</em>, <em>mask=None</em>, <em>residuals=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#smooth"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.smooth" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">smooth</code><span class="sig-paren">(</span><em>data</em>, <em>smooth_width</em>, <em>kernel='gaussian'</em>, <em>mask=None</em>, <em>residuals=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.smooth" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns either smoothed time series or its residuals.</p>
 <p>the difference between the original and the smoothed time series
 (=residuals) of a kernel smoothing with gaussian (smoothing kernel width =
@@ -1845,15 +1903,15 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<em>array</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>smooth_width</strong> (<em>float</em>) &#8211; Window width of smoothing, 2*sigma for a gaussian.</li>
-<li><strong>kernel</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'gaussian'</em><em>)</em>) &#8211; Smoothing kernel, &#8216;gaussian&#8217; or &#8216;heaviside&#8217; for a running mean.</li>
-<li><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Data mask where True labels masked samples.</li>
-<li><strong>residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; True if residuals should be returned instead of smoothed data.</li>
+<li><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</li>
+<li><strong>smooth_width</strong> (<em>float</em>) – Window width of smoothing, 2*sigma for a gaussian.</li>
+<li><strong>kernel</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'gaussian'</em><em>)</em>) – Smoothing kernel, ‘gaussian’ or ‘heaviside’ for a running mean.</li>
+<li><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Data mask where True labels masked samples.</li>
+<li><strong>residuals</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – True if residuals should be returned instead of smoothed data.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> &#8211; Smoothed/residual data.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> – Smoothed/residual data.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array-like</p>
@@ -1865,20 +1923,20 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.time_bin_with_mask">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">time_bin_with_mask</code><span class="sig-paren">(</span><em>data</em>, <em>time_bin_length</em>, <em>sample_selector=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#time_bin_with_mask"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.time_bin_with_mask" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">time_bin_with_mask</code><span class="sig-paren">(</span><em>data</em>, <em>time_bin_length</em>, <em>sample_selector=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.time_bin_with_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns time binned data where only about non-masked values is averaged.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<em>array</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>time_bin_length</strong> (<em>int</em>) &#8211; Length of time bin.</li>
-<li><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Data mask where True labels masked samples.</li>
+<li><strong>data</strong> (<em>array</em>) – Data array of shape (time, variables).</li>
+<li><strong>time_bin_length</strong> (<em>int</em>) – Length of time bin.</li>
+<li><strong>mask</strong> (<em>bool array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Data mask where True labels masked samples.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(bindata, T)</strong> &#8211; Tuple of time-binned data array and new length of array.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(bindata, T)</strong> – Tuple of time-binned data array and new length of array.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">tuple of array and int</p>
@@ -1890,31 +1948,37 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.var_process">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">var_process</code><span class="sig-paren">(</span><em>parents_neighbors_coeffs</em>, <em>T=1000</em>, <em>use='inv_inno_cov'</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#var_process"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.var_process" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">var_process</code><span class="sig-paren">(</span><em>parents_neighbors_coeffs</em>, <em>T=1000</em>, <em>use='inv_inno_cov'</em>, <em>verbosity=0</em>, <em>initial_values=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.var_process" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a vector-autoregressive process with correlated innovations.</p>
-<p>Wrapper around var_network with more user-friendly input options.</p>
+<p>Wrapper around var_network with possibly more user-friendly input options.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>parents_neighbors_coeffs</strong> (<em>dict</em>) &#8211; Dictionary of format {..., j:[((var1, lag1), coeff), ...], ...} for all
-variables where vars must be in [0..N-1] and lags &lt;= 0 with number of
-variables N. Coeff refers to the coefficient in the linear model. If
-lag=0, a nonzero value in the covariance matrix (or its inverse) is
-implied. These should be the same for (i, j) and (j, i).</li>
-<li><strong>use</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'inv_inno_cov'</em><em>)</em>) &#8211; Specifier, either &#8216;inno_cov&#8217; or &#8216;inv_inno_cov&#8217;. If &#8216;inno_cov&#8217;, lag=0
-entries in parents_neighbors_coeffs are interpreted as entries of the
-innovation noise term&#8217;s covariance matrix. I &#8216;inv_inno_cov&#8217;, they are
-interpreted as entries in the inverse covariance matrix.</li>
-<li><strong>T</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1000</em><em>)</em>) &#8211; Sample size.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>parents_neighbors_coeffs</strong> (<em>dict</em>) – <dl class="docutils">
+<dt>Dictionary of format:</dt>
+<dd>{…, j:[((var1, lag1), coef1), ((var2, lag2), coef2), …], …}</dd>
+</dl>
+<p>for all variables where vars must be in [0..N-1] and lags &lt;= 0 with
+number of variables N. If lag=0, a nonzero value in the covariance
+matrix (or its inverse) is implied. These should be the same for (i, j)
+and (j, i).</p>
+</li>
+<li><strong>use</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'inv_inno_cov'</em><em>)</em>) – Specifier, either ‘inno_cov’ or ‘inv_inno_cov’.
+Any other specifier will result in non-correlated noise.
+For debugging, ‘no_noise’ can also be specified, in which case random
+noise will be disabled.</li>
+<li><strong>T</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1000</em><em>)</em>) – Sample size.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
+<li><strong>initial_values</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Initial values for each node. Shape must be (N, max_delay+1)</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>X</strong> (<em>array-like</em>) &#8211; Array of realization.</li>
-<li><strong>true_parents_neighbors</strong> (<em>dict</em>) &#8211; Dictionary of true parents and neighbors</li>
+<li><strong>data</strong> (<em>array-like</em>) – Data generated from this process</li>
+<li><strong>true_parent_neighbor</strong> (<em>dict</em>) – Dictionary of lists of tuples.  The dictionary is keyed by node ID, the
+list stores the tuple values (parent_node_id, time_lag)</li>
 </ul>
 </p>
 </td>
@@ -1925,20 +1989,20 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 <dl class="function">
 <dt id="tigramite.data_processing.weighted_avg_and_std">
-<code class="descclassname">tigramite.data_processing.</code><code class="descname">weighted_avg_and_std</code><span class="sig-paren">(</span><em>values</em>, <em>axis</em>, <em>weights</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/data_processing.html#weighted_avg_and_std"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.data_processing.weighted_avg_and_std" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.data_processing.</code><code class="descname">weighted_avg_and_std</code><span class="sig-paren">(</span><em>values</em>, <em>axis</em>, <em>weights</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.data_processing.weighted_avg_and_std" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the weighted average and standard deviation.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>values</strong> (<em>array</em>) &#8211; Data array of shape (time, variables).</li>
-<li><strong>axis</strong> (<em>int</em>) &#8211; Axis to average/std about</li>
-<li><strong>weights</strong> (<em>array</em>) &#8211; Weight array of shape (time, variables).</li>
+<li><strong>values</strong> (<em>array</em>) – Data array of shape (time, variables).</li>
+<li><strong>axis</strong> (<em>int</em>) – Axis to average/std about</li>
+<li><strong>weights</strong> (<em>array</em>) – Weight array of shape (time, variables).</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(average, std)</strong> &#8211; Tuple of weighted average and standard deviation along axis.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(average, std)</strong> – Tuple of weighted average and standard deviation along axis.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">tuple of arrays</p>
@@ -1950,11 +2014,11 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.in
 
 </div>
 <div class="section" id="tigramite-models-time-series-modeling-mediation-and-prediction">
-<h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.models</span></code>: Time series modeling, mediation, and prediction<a class="headerlink" href="#tigramite-models-time-series-modeling-mediation-and-prediction" title="Permalink to this headline">¶</a></h1>
+<h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.models</span></code>: Time series modeling, mediation, and prediction<a class="headerlink" href="#tigramite-models-time-series-modeling-mediation-and-prediction" title="Permalink to this headline">¶</a></h1>
 <p>Base class:</p>
 <dl class="class">
 <dt id="tigramite.models.Models">
-<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">Models</code><span class="sig-paren">(</span><em>dataframe</em>, <em>model</em>, <em>model_params=None</em>, <em>data_transform=None</em>, <em>use_mask=False</em>, <em>mask_type=None</em>, <em>missing_flag=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Models" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">Models</code><span class="sig-paren">(</span><em>dataframe</em>, <em>model</em>, <em>data_transform=StandardScaler(copy=True</em>, <em>with_mean=True</em>, <em>with_std=True)</em>, <em>mask_type=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Models" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for time series models.</p>
 <p>Allows to fit any model from sklearn to the parents of a target variable.
 Also takes care of missing values, masking and preprocessing.</p>
@@ -1963,25 +2027,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>dataframe</strong> (<em>data object</em>) &#8211; Tigramite dataframe object. It must have the attributes dataframe.values
+<li><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
 yielding a numpy array of shape (observations T, variables N) and
 optionally a mask of the same shape and a missing values flag.</li>
-<li><strong>model</strong> (<em>sklearn model object</em>) &#8211; For example, sklearn.linear_model.LinearRegression for a linear
+<li><strong>model</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
 regression model.</li>
-<li><strong>model_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional parameters passed on to sklearn model</li>
-<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Used to transform data prior to fitting. For example,
+<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
 sklearn.preprocessing.StandardScaler for simple standardization. The
 fitted parameters are stored.</li>
-<li><strong>use_mask</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether a supplied mask should be used.</li>
-<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) &#8211; Masking mode: Indicators for which variables in the dependence measure
-I(X; Y | Z) the samples should be masked. If None, &#8216;y&#8217; is used, which
+<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence measure
+I(X; Y | Z) the samples should be masked. If None, ‘y’ is used, which
 excludes all time slices containing masked samples in Y. Explained in
 <a class="footnote-reference" href="#id5" id="id25">[1]</a>.</li>
-<li><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Flag for missing values. Dismisses all time slices of samples where
-missing values occur in any variable and also flags samples for all lags
-up to 2*tau_max. This avoids biases, see section on masking in
-Supplement of <a class="footnote-reference" href="#id5" id="id26">[1]</a>.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
 </ul>
 </td>
 </tr>
@@ -1989,14 +2047,14 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 </table>
 <dl class="method">
 <dt id="tigramite.models.Models.get_coefs">
-<code class="descname">get_coefs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_coefs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Models.get_coefs" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_coefs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Models.get_coefs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns dictionary of coefficients for linear models.</p>
 <p>Only for models from sklearn.linear_model</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>coeffs</strong> &#8211; Dictionary of dictionaries for each variable with keys given by the
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>coeffs</strong> – Dictionary of dictionaries for each variable with keys given by the
 parents and the regression coefficients as values.</td>
 </tr>
 <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">dictionary</td>
@@ -2007,9 +2065,9 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.Models.get_fit">
-<code class="descname">get_fit</code><span class="sig-paren">(</span><em>all_parents</em>, <em>selected_variables=None</em>, <em>tau_max=None</em>, <em>cut_off='max_lag_or_tau_max'</em>, <em>return_data=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Models.get_fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Models.get_fit" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_fit</code><span class="sig-paren">(</span><em>all_parents</em>, <em>selected_variables=None</em>, <em>tau_max=None</em>, <em>cut_off='max_lag_or_tau_max'</em>, <em>return_data=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Models.get_fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit time series model.</p>
-<p>To each variable in selected_variables, the sklearn model is fitted
+<p>For each variable in selected_variables, the sklearn model is fitted
 with <img class="math" src="_images/math/276f7e256cbddeb81eee42e1efc348f3cb4ab5f8.png" alt="y"/> given by the target variable, and <img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/> given by its
 parents. The fitted model class is returned for later use.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2017,22 +2075,22 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>all_parents</strong> (<em>dictionary</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+<li><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
 the parents estimated with PCMCI.</li>
-<li><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><a class="reference internal" href="#PCMCI.N" title="PCMCI.N"><em>N</em></a><em>)</em><em>)</em>) &#8211; Specify to estimate parents only for selected variables. If None is
+<li><strong>selected_variables</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to estimate parents only for selected variables. If None is
 passed, parents are estimated for all variables.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum time lag. If None, the maximum lag in all_parents is used.</li>
-<li><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) &#8211; How many samples to cutoff at the beginning. The default is
-&#8216;2xtau_max&#8217;, which guarantees that MCI tests are all conducted on
-the same samples.  For modeling, &#8216;max_lag_or_tau_max&#8217; can be used,
-which uses the maximum of tau_max and the conditions, which is
-useful to compare multiple models on the same sample. Last,
-&#8216;max_lag&#8217; uses as much samples as possible.</li>
-<li><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to save the data array.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</li>
+<li><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – How many samples to cutoff at the beginning. The default is
+‘max_lag_or_tau_max’, which uses the maximum of tau_max and the
+conditions. This is useful to compare multiple models on the same
+sample. Other options are ‘2xtau_max’, which guarantees that MCI
+tests are all conducted on the same samples.  For modeling, can be
+used, Last, ‘max_lag’ uses as much samples as possible.</li>
+<li><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>fit_results</strong> &#8211; Returns the sklearn model after fitting. Also returns the data
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>fit_results</strong> – Returns the sklearn model after fitting. Also returns the data
 transformation parameters.</p>
 </td>
 </tr>
@@ -2048,14 +2106,14 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <p>Derived classes:</p>
 <dl class="class">
 <dt id="tigramite.models.LinearMediation">
-<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">LinearMediation</code><span class="sig-paren">(</span><em>dataframe</em>, <em>model_params=None</em>, <em>data_transform=None</em>, <em>use_mask=False</em>, <em>mask_type=None</em>, <em>missing_flag=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">LinearMediation</code><span class="sig-paren">(</span><em>dataframe</em>, <em>model_params=None</em>, <em>data_transform=StandardScaler(copy=True</em>, <em>with_mean=True</em>, <em>with_std=True)</em>, <em>mask_type=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation" title="Permalink to this definition">¶</a></dt>
 <dd><p>Linear mediation analysis for time series models.</p>
 <p>Fits linear model to parents and provides functions to return measures such
 as causal effect, mediated causal effect, average causal effect, etc. as
-described in <a class="footnote-reference" href="#id29" id="id27">[4]</a>.</p>
+described in <a class="footnote-reference" href="#id28" id="id26">[4]</a>.</p>
 <p class="rubric">Notes</p>
 <p>This class implements the following causal mediation measures introduced in
-<a class="footnote-reference" href="#id29" id="id28">[4]</a>:</p>
+<a class="footnote-reference" href="#id28" id="id27">[4]</a>:</p>
 <blockquote>
 <div><ul class="simple">
 <li>causal effect (CE)</li>
@@ -2075,7 +2133,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 of the the CE is explained by <img class="math" src="_images/math/0062c26909b3e07ee8f5a6285b2563d69bc979ff.png" alt="Y"/>. ACE from <img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/> is 0.37 since it
 has CE 0.5 on <img class="math" src="_images/math/0062c26909b3e07ee8f5a6285b2563d69bc979ff.png" alt="Y"/> and 0.25 on <img class="math" src="_images/math/bcb2457ac9d8995a4f34d57cadac7ecbbe58f3bd.png" alt="Z"/>.</p>
 <p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">links_coeffs</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">)],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">)]}</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="p">,</span> <span class="n">true_parents</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">var_process</span><span class="p">(</span><span class="n">links_coeffs</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dataframe</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
@@ -2096,10 +2154,10 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 </pre></div>
 </div>
 <p class="rubric">References</p>
-<table class="docutils footnote" frame="void" id="id29" rules="none">
+<table class="docutils footnote" frame="void" id="id28" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label">[4]</td><td><em>(<a class="fn-backref" href="#id27">1</a>, <a class="fn-backref" href="#id28">2</a>, <a class="fn-backref" href="#id32">3</a>)</em> J. Runge et al. (2015): Identifying causal gateways and mediators in
+<tr><td class="label">[4]</td><td><em>(<a class="fn-backref" href="#id26">1</a>, <a class="fn-backref" href="#id27">2</a>, <a class="fn-backref" href="#id30">3</a>)</em> J. Runge et al. (2015): Identifying causal gateways and mediators in
 complex spatio-temporal systems.
 Nature Communications, 6, 8502. <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></td></tr>
 </tbody>
@@ -2109,23 +2167,18 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>dataframe</strong> (<em>data object</em>) &#8211; Tigramite dataframe object. It must have the attributes dataframe.values
+<li><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
 yielding a numpy array of shape (observations T, variables N) and
 optionally a mask of the same shape and a missing values flag.</li>
-<li><strong>model_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional parameters passed on to sklearn model</li>
-<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Used to transform data prior to fitting. For example,
+<li><strong>model_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional parameters passed on to sklearn model</li>
+<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
 sklearn.preprocessing.StandardScaler for simple standardization. The
 fitted parameters are stored.</li>
-<li><strong>use_mask</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether a supplied mask should be used.</li>
-<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) &#8211; Masking mode: Indicators for which variables in the dependence measure
-I(X; Y | Z) the samples should be masked. If None, &#8216;y&#8217; is used, which
+<li><strong>mask_type</strong> (<em>{'y'</em><em>,</em><em>'x'</em><em>,</em><em>'z'</em><em>,</em><em>'xy'</em><em>,</em><em>'xz'</em><em>,</em><em>'yz'</em><em>,</em><em>'xyz'}</em>) – Masking mode: Indicators for which variables in the dependence measure
+I(X; Y | Z) the samples should be masked. If None, ‘y’ is used, which
 excludes all time slices containing masked samples in Y. Explained in
-<a class="footnote-reference" href="#id5" id="id30">[1]</a>.</li>
-<li><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Flag for missing values. Dismisses all time slices of samples where
-missing values occur in any variable and also flags samples for all lags
-up to 2*tau_max. This avoids biases, see section on masking in
-Supplement of <a class="footnote-reference" href="#id5" id="id31">[1]</a>.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<a class="footnote-reference" href="#id5" id="id29">[1]</a>.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
 </ul>
 </td>
 </tr>
@@ -2133,19 +2186,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 </table>
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.fit_model">
-<code class="descname">fit_model</code><span class="sig-paren">(</span><em>all_parents</em>, <em>tau_max=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.fit_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.fit_model" title="Permalink to this definition">¶</a></dt>
+<code class="descname">fit_model</code><span class="sig-paren">(</span><em>all_parents</em>, <em>tau_max=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.fit_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit linear time series model.</p>
 <p>Fits a sklearn.linear_model.LinearRegression model to the parents of
 each variable and computes the coefficient matrices <img class="math" src="_images/math/e486de19dbb61c9a63584f6e4ddd6e92ef03d665.png" alt="\Phi"/> and
-<img class="math" src="_images/math/334b0728f25dd84a28e483181038a307ea6e483e.png" alt="\Psi"/> as described in <a class="footnote-reference" href="#id29" id="id32">[4]</a>.</p>
+<img class="math" src="_images/math/334b0728f25dd84a28e483181038a307ea6e483e.png" alt="\Psi"/> as described in <a class="footnote-reference" href="#id28" id="id30">[4]</a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>all_parents</strong> (<em>dictionary</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+<li><strong>all_parents</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
 the parents estimated with PCMCI.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum time lag. If None, the maximum lag in all_parents is used.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in all_parents is used.</li>
 </ul>
 </td>
 </tr>
@@ -2155,25 +2208,25 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_ace">
-<code class="descname">get_ace</code><span class="sig-paren">(</span><em>i</em>, <em>lag_mode='absmax'</em>, <em>exclude_i=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ace" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_ace</code><span class="sig-paren">(</span><em>i</em>, <em>lag_mode='absmax'</em>, <em>exclude_i=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_ace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average causal effect.</p>
 <p>This is the average causal effect (ACE) emanating from variable i to any
-other variable. With lag_mode=&#8217;absmax&#8217; this is based on the lag of
+other variable. With lag_mode=’absmax’ this is based on the lag of
 maximum CE for each pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects on the variable itself at later
+<li><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at later
 lags.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>ace</strong> &#8211; Average Causal Effect.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>ace</strong> – Average Causal Effect.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -2185,25 +2238,25 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_acs">
-<code class="descname">get_acs</code><span class="sig-paren">(</span><em>j</em>, <em>lag_mode='absmax'</em>, <em>exclude_j=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_acs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_acs" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_acs</code><span class="sig-paren">(</span><em>j</em>, <em>lag_mode='absmax'</em>, <em>exclude_j=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_acs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average causal susceptibility.</p>
 <p>This is the Average Causal Susceptibility (ACS) affecting a variable j
-from any other variable. With lag_mode=&#8217;absmax&#8217; this is based on the lag
+from any other variable. With lag_mode=’absmax’ this is based on the lag
 of maximum CE for each pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of variable.</li>
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>j</strong> (<em>int</em>) – Index of variable.</li>
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects on the variable itself at previous
+<li><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at previous
 lags.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>acs</strong> &#8211; Average Causal Susceptibility.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>acs</strong> – Average Causal Susceptibility.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -2215,27 +2268,27 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_all_ace">
-<code class="descname">get_all_ace</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_i=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_ace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_ace" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_all_ace</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_i=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_ace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average causal effect for all variables.</p>
 <p>This is the average causal effect (ACE) emanating from variable i to any
-other variable. With lag_mode=&#8217;absmax&#8217; this is based on the lag of
+other variable. With lag_mode=’absmax’ this is based on the lag of
 maximum CE for each pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects on the variable itself at later
+<li><strong>exclude_i</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at later
 lags.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>ace</strong> &#8211; Average Causal Effect for each variable.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>ace</strong> – Average Causal Effect for each variable.</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (<a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>,)</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (N,)</p>
 </td>
 </tr>
 </tbody>
@@ -2244,27 +2297,27 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_all_acs">
-<code class="descname">get_all_acs</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_j=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_acs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_acs" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_all_acs</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_j=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_acs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average causal susceptibility.</p>
 <p>This is the Average Causal Susceptibility (ACS) for each variable from
-any other variable. With lag_mode=&#8217;absmax&#8217; this is based on the lag of
+any other variable. With lag_mode=’absmax’ this is based on the lag of
 maximum CE for each pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects on the variable itself at previous
+<li><strong>exclude_j</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects on the variable itself at previous
 lags.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>acs</strong> &#8211; Average Causal Susceptibility.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>acs</strong> – Average Causal Susceptibility.</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (<a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>,)</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (N,)</p>
 </td>
 </tr>
 </tbody>
@@ -2273,28 +2326,28 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_all_amce">
-<code class="descname">get_all_amce</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_k=True</em>, <em>exclude_self_effects=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_all_amce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_amce" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_all_amce</code><span class="sig-paren">(</span><em>lag_mode='absmax'</em>, <em>exclude_k=True</em>, <em>exclude_self_effects=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_all_amce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average mediated causal effect.</p>
 <p>This is the Average Mediated Causal Effect (AMCE) through all variables
-With lag_mode=&#8217;absmax&#8217; this is based on the lag of maximum CE for each
+With lag_mode=’absmax’ this is based on the lag of maximum CE for each
 pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects through the variable itself at
+<li><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects through the variable itself at
 previous lags.</li>
-<li><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal self effects of variables on themselves.</li>
+<li><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal self effects of variables on themselves.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>amce</strong> &#8211; Average Mediated Causal Effect.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>amce</strong> – Average Mediated Causal Effect.</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (<a class="reference internal" href="#PCMCI.N" title="PCMCI.N">N</a>,)</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array of shape (N,)</p>
 </td>
 </tr>
 </tbody>
@@ -2303,26 +2356,26 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_amce">
-<code class="descname">get_amce</code><span class="sig-paren">(</span><em>k</em>, <em>lag_mode='absmax'</em>, <em>exclude_k=True</em>, <em>exclude_self_effects=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_amce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_amce" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_amce</code><span class="sig-paren">(</span><em>k</em>, <em>lag_mode='absmax'</em>, <em>exclude_k=True</em>, <em>exclude_self_effects=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_amce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the average mediated causal effect.</p>
 <p>This is the Average Mediated Causal Effect (AMCE) through a variable k
-With lag_mode=&#8217;absmax&#8217; this is based on the lag of maximum CE for each
+With lag_mode=’absmax’ this is based on the lag of maximum CE for each
 pair.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>k</strong> (<em>int</em>) &#8211; Index of variable.</li>
-<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) &#8211; Lag mode. Either average across all lags between each pair or only
+<li><strong>k</strong> (<em>int</em>) – Index of variable.</li>
+<li><strong>lag_mode</strong> (<em>{'absmax'</em><em>, </em><em>'all_lags'}</em>) – Lag mode. Either average across all lags between each pair or only
 at the lag of maximum absolute causal effect.</li>
-<li><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal effects through the variable itself at
+<li><strong>exclude_k</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal effects through the variable itself at
 previous lags.</li>
-<li><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to exclude causal self effects of variables on themselves.</li>
+<li><strong>exclude_self_effects</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to exclude causal self effects of variables on themselves.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>amce</strong> &#8211; Average Mediated Causal Effect.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>amce</strong> – Average Mediated Causal Effect.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -2334,17 +2387,17 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_ce">
-<code class="descname">get_ce</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_ce</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the causal effect.</p>
-<p>This is the causal effect for  (i, tau) &#8211; &#8211;&gt; j.</p>
+<p>This is the causal effect for  (i, tau) – –&gt; j.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>tau</strong> (<em>int</em>) &#8211; Lag of cause variable.</li>
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of effect variable.</li>
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</li>
+<li><strong>j</strong> (<em>int</em>) – Index of effect variable.</li>
 </ul>
 </td>
 </tr>
@@ -2360,16 +2413,16 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_ce_max">
-<code class="descname">get_ce_max</code><span class="sig-paren">(</span><em>i</em>, <em>j</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_ce_max"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce_max" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_ce_max</code><span class="sig-paren">(</span><em>i</em>, <em>j</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_ce_max" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the causal effect.</p>
-<p>This is the maximum absolute causal effect for  i &#8211;&gt; j across all lags.</p>
+<p>This is the maximum absolute causal effect for  i –&gt; j across all lags.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of effect variable.</li>
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>j</strong> (<em>int</em>) – Index of effect variable.</li>
 </ul>
 </td>
 </tr>
@@ -2385,17 +2438,17 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_coeff">
-<code class="descname">get_coeff</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_coeff"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_coeff" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_coeff</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_coeff" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns link coefficient.</p>
-<p>This is the causal effect for a particular link (i, tau) &#8211;&gt; j.</p>
+<p>This is the causal effect for a particular link (i, tau) –&gt; j.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>tau</strong> (<em>int</em>) &#8211; Lag of cause variable.</li>
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of effect variable.</li>
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</li>
+<li><strong>j</strong> (<em>int</em>) – Index of effect variable.</li>
 </ul>
 </td>
 </tr>
@@ -2411,19 +2464,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_mce">
-<code class="descname">get_mce</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em>, <em>k</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_mce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_mce" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_mce</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em>, <em>k</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_mce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the mediated causal effect.</p>
-<p>This is the causal effect for  i &#8211;&gt; j minus the causal effect not going
+<p>This is the causal effect for  i –&gt; j minus the causal effect not going
 through k.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>tau</strong> (<em>int</em>) &#8211; Lag of cause variable.</li>
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of effect variable.</li>
-<li><strong>k</strong> (<em>int</em>) &#8211; Index of mediator variable.</li>
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</li>
+<li><strong>j</strong> (<em>int</em>) – Index of effect variable.</li>
+<li><strong>k</strong> (<em>int</em>) – Index of mediator variable.</li>
 </ul>
 </td>
 </tr>
@@ -2439,26 +2492,26 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_mediation_graph_data">
-<code class="descname">get_mediation_graph_data</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em>, <em>include_neighbors=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_mediation_graph_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_mediation_graph_data" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_mediation_graph_data</code><span class="sig-paren">(</span><em>i</em>, <em>tau</em>, <em>j</em>, <em>include_neighbors=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_mediation_graph_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns link and node weights for mediation analysis.</p>
 <p>Returns array with non-zero entries for links that are on causal
 paths between <img class="math" src="_images/math/df0deb143e5ac127f00bd248ee8001ecae572adc.png" alt="i"/> and <img class="math" src="_images/math/6b21e0b0899a0d2879d3b8019087fa630bab4ea2.png" alt="j"/> at lag <img class="math" src="_images/math/557d5fad862c9046d26e1a930f45a550c146d592.png" alt="\tau"/>.
-<code class="docutils literal"><span class="pre">path_val_matrix</span></code> contains the corresponding path coefficients and
-<code class="docutils literal"><span class="pre">path_node_array</span></code> the MCE values. <code class="docutils literal"><span class="pre">tsg_path_val_matrix</span></code> contains the
+<code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code> contains the corresponding path coefficients and
+<code class="docutils literal notranslate"><span class="pre">path_node_array</span></code> the MCE values. <code class="docutils literal notranslate"><span class="pre">tsg_path_val_matrix</span></code> contains the
 corresponding values in the time series graph format.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>i</strong> (<em>int</em>) &#8211; Index of cause variable.</li>
-<li><strong>tau</strong> (<em>int</em>) &#8211; Lag of cause variable.</li>
-<li><strong>j</strong> (<em>int</em>) &#8211; Index of effect variable.</li>
-<li><strong>include_neighbors</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to include causal paths emanating from neighbors of i</li>
+<li><strong>i</strong> (<em>int</em>) – Index of cause variable.</li>
+<li><strong>tau</strong> (<em>int</em>) – Lag of cause variable.</li>
+<li><strong>j</strong> (<em>int</em>) – Index of effect variable.</li>
+<li><strong>include_neighbors</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to include causal paths emanating from neighbors of i</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>graph_data</strong> &#8211; Dictionary of matrices for coloring mediation graph plots.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>graph_data</strong> – Dictionary of matrices for coloring mediation graph plots.</p>
 </td>
 </tr>
 <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dictionary</p>
@@ -2470,7 +2523,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_tsg">
-<code class="descname">get_tsg</code><span class="sig-paren">(</span><em>link_matrix</em>, <em>val_matrix=None</em>, <em>include_neighbors=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_tsg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_tsg" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_tsg</code><span class="sig-paren">(</span><em>link_matrix</em>, <em>val_matrix=None</em>, <em>include_neighbors=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_tsg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns time series graph matrix.</p>
 <p>Constructs a matrix of shape (N*tau_max, N*tau_max) from link_matrix.
 This matrix can be used for plotting the time series graph and analyzing
@@ -2488,7 +2541,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>tsg</strong> &#8211; Time series graph matrix.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>tsg</strong> – Time series graph matrix.</td>
 </tr>
 <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">array of shape (N*tau_max, N*tau_max)</td>
 </tr>
@@ -2498,15 +2551,15 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.get_val_matrix">
-<code class="descname">get_val_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.get_val_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.get_val_matrix" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_val_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.get_val_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the matrix of linear coefficients.</p>
 <p>Format is val_matrix[i, j, tau] denotes coefficient of link
-i &#8211;tau&#8211;&gt; j.</p>
+i –tau–&gt; j.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>val_matrix</strong> &#8211; Matrix of linear coefficients, shape (N, N, tau_max + 1).</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>val_matrix</strong> – Matrix of linear coefficients, shape (N, N, tau_max + 1).</td>
 </tr>
 <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">array</td>
 </tr>
@@ -2516,13 +2569,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.net_to_tsg">
-<code class="descname">net_to_tsg</code><span class="sig-paren">(</span><em>row</em>, <em>lag</em>, <em>max_lag</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.net_to_tsg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.net_to_tsg" title="Permalink to this definition">¶</a></dt>
+<code class="descname">net_to_tsg</code><span class="sig-paren">(</span><em>row</em>, <em>lag</em>, <em>max_lag</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.net_to_tsg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper function to translate from network to time series graph.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.models.LinearMediation.tsg_to_net">
-<code class="descname">tsg_to_net</code><span class="sig-paren">(</span><em>node</em>, <em>max_lag</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#LinearMediation.tsg_to_net"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.LinearMediation.tsg_to_net" title="Permalink to this definition">¶</a></dt>
+<code class="descname">tsg_to_net</code><span class="sig-paren">(</span><em>node</em>, <em>max_lag</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.LinearMediation.tsg_to_net" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper function to translate from time series graph to network.</p>
 </dd></dl>
 
@@ -2530,7 +2583,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="class">
 <dt id="tigramite.models.Prediction">
-<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">Prediction</code><span class="sig-paren">(</span><em>dataframe</em>, <em>train_indices</em>, <em>test_indices</em>, <em>prediction_model</em>, <em>prediction_model_params=None</em>, <em>cond_ind_model=None</em>, <em>cond_ind_params=None</em>, <em>data_transform=None</em>, <em>missing_flag=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.models.</code><code class="descname">Prediction</code><span class="sig-paren">(</span><em>dataframe</em>, <em>train_indices</em>, <em>test_indices</em>, <em>prediction_model</em>, <em>cond_ind_test=None</em>, <em>data_transform=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prediction class for time series models.</p>
 <p>Allows to fit and predict from any sklearn model. The optimal predictors can
 be estimated using PCMCI. Also takes care of missing values, masking and
@@ -2540,26 +2593,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>dataframe</strong> (<em>data object</em>) &#8211; Tigramite dataframe object. It must have the attributes dataframe.values
+<li><strong>dataframe</strong> (<em>data object</em>) – Tigramite dataframe object. It must have the attributes dataframe.values
 yielding a numpy array of shape (observations T, variables N) and
 optionally a mask of the same shape and a missing values flag.</li>
-<li><strong>train_indices</strong> (<em>array-like</em>) &#8211; Either boolean array or time indices marking the training data.</li>
-<li><strong>test_indices</strong> (<em>array-like</em>) &#8211; Either boolean array or time indices marking the test data.</li>
-<li><strong>prediction_model</strong> (<em>sklearn model object</em>) &#8211; For example, sklearn.linear_model.LinearRegression for a linear
+<li><strong>train_indices</strong> (<em>array-like</em>) – Either boolean array or time indices marking the training data.</li>
+<li><strong>test_indices</strong> (<em>array-like</em>) – Either boolean array or time indices marking the test data.</li>
+<li><strong>prediction_model</strong> (<em>sklearn model object</em>) – For example, sklearn.linear_model.LinearRegression() for a linear
 regression model.</li>
-<li><strong>prediction_model_params</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional parameters passed on to sklearn model</li>
-<li><strong>cond_ind_model</strong> (<em>Conditional independence test class object</em><em>, </em><em>optional</em>) &#8211; Only needed if predictors are estimated with causal algorithm.
-The class will be initialized with masking set to the training data.
-Further parameters can be supplied by cond_ind_params.</li>
-<li><strong>cond_ind_params</strong> (<em>dictionary</em><em>, </em><em>optional</em>) &#8211; Parameters for conditional independence test.</li>
-<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Used to transform data prior to fitting. For example,
+<li><strong>cond_ind_test</strong> (<em>Conditional independence test object</em><em>, </em><em>optional</em>) – Only needed if predictors are estimated with causal algorithm.
+The class will be initialized with masking set to the training data.</li>
+<li><strong>data_transform</strong> (<em>sklearn preprocessing object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Used to transform data prior to fitting. For example,
 sklearn.preprocessing.StandardScaler for simple standardization. The
 fitted parameters are stored.</li>
-<li><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Flag for missing values. Dismisses all time slices of samples where
-missing values occur in any variable and also flags samples for all lags
-up to 2*tau_max. This avoids biases, see section on masking in
-Supplement of <a class="footnote-reference" href="#id5" id="id33">[1]</a>.</li>
-<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Level of verbosity.</li>
+<li><strong>verbosity</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Level of verbosity.</li>
 </ul>
 </td>
 </tr>
@@ -2567,10 +2613,10 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 </table>
 <dl class="method">
 <dt id="tigramite.models.Prediction.fit">
-<code class="descname">fit</code><span class="sig-paren">(</span><em>target_predictors</em>, <em>selected_targets=None</em>, <em>tau_max=None</em>, <em>return_data=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction.fit" title="Permalink to this definition">¶</a></dt>
+<code class="descname">fit</code><span class="sig-paren">(</span><em>target_predictors</em>, <em>selected_targets=None</em>, <em>tau_max=None</em>, <em>return_data=False</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit time series model.</p>
-<p>Wrapper around <code class="docutils literal"><span class="pre">Models.get_fit()</span></code>. To each variable in
-<code class="docutils literal"><span class="pre">selected_targets</span></code>, the sklearn model is fitted with <img class="math" src="_images/math/276f7e256cbddeb81eee42e1efc348f3cb4ab5f8.png" alt="y"/> given
+<p>Wrapper around <code class="docutils literal notranslate"><span class="pre">Models.get_fit()</span></code>. To each variable in
+<code class="docutils literal notranslate"><span class="pre">selected_targets</span></code>, the sklearn model is fitted with <img class="math" src="_images/math/276f7e256cbddeb81eee42e1efc348f3cb4ab5f8.png" alt="y"/> given
 by the target variable, and <img class="math" src="_images/math/7a7bb470119808e2db2879fc2b2526f467b7a40b.png" alt="X"/> given by its predictors. The
 fitted model class is returned for later use.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2578,13 +2624,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>target_predictors</strong> (<em>dictionary</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+<li><strong>target_predictors</strong> (<em>dictionary</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …} containing
 the predictors estimated with PCMCI.</li>
-<li><strong>selected_targets</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><a class="reference internal" href="#PCMCI.N" title="PCMCI.N"><em>N</em></a><em>)</em><em>)</em>) &#8211; Specify to fit model only for selected targets. If None is
+<li><strong>selected_targets</strong> (<em>list of integers</em><em>, </em><em>optional</em><em> (</em><em>default: range</em><em>(</em><em>N</em><em>)</em><em>)</em>) – Specify to fit model only for selected targets. If None is
 passed, models are estimated for all variables.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Maximum time lag. If None, the maximum lag in target_predictors is
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Maximum time lag. If None, the maximum lag in target_predictors is
 used.</li>
-<li><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to save the data array.</li>
+<li><strong>return_data</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to save the data array.</li>
 </ul>
 </td>
 </tr>
@@ -2600,35 +2646,35 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.Prediction.get_predictors">
-<code class="descname">get_predictors</code><span class="sig-paren">(</span><em>selected_targets=None</em>, <em>selected_links=None</em>, <em>steps_ahead=1</em>, <em>tau_max=1</em>, <em>pc_alpha=0.2</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_predictors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction.get_predictors" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_predictors</code><span class="sig-paren">(</span><em>selected_targets=None</em>, <em>selected_links=None</em>, <em>steps_ahead=1</em>, <em>tau_max=1</em>, <em>pc_alpha=0.2</em>, <em>max_conds_dim=None</em>, <em>max_combinations=1</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction.get_predictors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate predictors using PC1 algorithm.</p>
 <p>Wrapper around PCMCI.run_pc_stable that estimates causal predictors.
-The lead time can be specified by <code class="docutils literal"><span class="pre">steps_ahead</span></code>.</p>
+The lead time can be specified by <code class="docutils literal notranslate"><span class="pre">steps_ahead</span></code>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>selected_targets</strong> (<em>list of ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variables to estimate predictors of. If None, predictors of
+<li><strong>selected_targets</strong> (<em>list of ints</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variables to estimate predictors of. If None, predictors of
 all variables are estimated.</li>
-<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<li><strong>selected_links</strong> (<em>dict</em><em> or </em><em>None</em>) – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 specifying whether only selected links should be tested. If None is
 passed, all links are tested</li>
-<li><strong>steps_ahead</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Minimum time lag to test. Useful for multi-step ahead predictions.</li>
-<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum time lag. Must be larger or equal to tau_min.</li>
-<li><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.2</em>) &#8211; Significance level in algorithm. If a list or None is passed, the
+<li><strong>steps_ahead</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Minimum time lag to test. Useful for multi-step ahead predictions.</li>
+<li><strong>tau_max</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum time lag. Must be larger or equal to tau_min.</li>
+<li><strong>pc_alpha</strong> (<em>float</em><em> or </em><em>list of floats</em><em>, </em><em>default: 0.2</em>) – Significance level in algorithm. If a list or None is passed, the
 pc_alpha level is optimized for every variable across the given
 pc_alpha values using the score computed in
 cond_ind_test.get_model_selection_criterion()</li>
-<li><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; Maximum number of conditions to test. If None is passed, this number
+<li><strong>max_conds_dim</strong> (<em>int</em><em> or </em><em>None</em>) – Maximum number of conditions to test. If None is passed, this number
 is unrestricted.</li>
-<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) &#8211; Maximum number of combinations of conditions of current cardinality
+<li><strong>max_combinations</strong> (<em>int</em><em>, </em><em>default: 1</em>) – Maximum number of combinations of conditions of current cardinality
 to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
 a larger number, such as 10, can be used.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>predictors</strong> &#8211; Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>predictors</strong> – Dictionary of form {0:[(0, -1), (3, -2), …], 1:[], …}
 containing estimated predictors.</p>
 </td>
 </tr>
@@ -2641,19 +2687,19 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.models.Prediction.get_test_array">
-<code class="descname">get_test_array</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_test_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction.get_test_array" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_test_array</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction.get_test_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns test array.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.models.Prediction.get_train_array">
-<code class="descname">get_train_array</code><span class="sig-paren">(</span><em>j</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.get_train_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction.get_train_array" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_train_array</code><span class="sig-paren">(</span><em>j</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction.get_train_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns training array.</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="tigramite.models.Prediction.predict">
-<code class="descname">predict</code><span class="sig-paren">(</span><em>target</em>, <em>new_data=None</em>, <em>pred_params=None</em>, <em>cut_off='max_lag_or_tau_max'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/models.html#Prediction.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.models.Prediction.predict" title="Permalink to this definition">¶</a></dt>
+<code class="descname">predict</code><span class="sig-paren">(</span><em>target</em>, <em>new_data=None</em>, <em>pred_params=None</em>, <em>cut_off='max_lag_or_tau_max'</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.models.Prediction.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Predict target variable with fitted model.</p>
 <p>Uses the model.predict() function of the sklearn model.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2661,15 +2707,15 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>target</strong> (<em>int</em>) &#8211; Index of target variable.</li>
-<li><strong>new_data</strong> (<em>data object</em><em>, </em><em>optional</em>) &#8211; New Tigramite dataframe object with optional new mask.</li>
-<li><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) &#8211; Optional parameters passed on to sklearn prediction function.</li>
-<li><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) &#8211; How many samples to cutoff at the beginning. The default is
-&#8216;2xtau_max&#8217;, which guarantees that MCI tests are all conducted on
-the same samples.  For modeling, &#8216;max_lag_or_tau_max&#8217; can be used,
+<li><strong>target</strong> (<em>int</em>) – Index of target variable.</li>
+<li><strong>new_data</strong> (<em>data object</em><em>, </em><em>optional</em>) – New Tigramite dataframe object with optional new mask.</li>
+<li><strong>pred_params</strong> (<em>dict</em><em>, </em><em>optional</em>) – Optional parameters passed on to sklearn prediction function.</li>
+<li><strong>cut_off</strong> (<em>{'2xtau_max'</em><em>, </em><em>'max_lag'</em><em>, </em><em>'max_lag_or_tau_max'}</em>) – How many samples to cutoff at the beginning. The default is
+‘2xtau_max’, which guarantees that MCI tests are all conducted on
+the same samples.  For modeling, ‘max_lag_or_tau_max’ can be used,
 which uses the maximum of tau_max and the conditions, which is
 useful to compare multiple models on the same sample. Last,
-&#8216;max_lag&#8217; uses as much samples as possible.</li>
+‘max_lag’ uses as much samples as possible.</li>
 </ul>
 </td>
 </tr>
@@ -2687,11 +2733,11 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 </div>
 <div class="section" id="module-tigramite.plotting">
-<span id="tigramite-plotting-plotting-functions"></span><h1><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-mod docutils literal"><span class="pre">tigramite.plotting</span></code></a>: Plotting functions<a class="headerlink" href="#module-tigramite.plotting" title="Permalink to this headline">¶</a></h1>
+<span id="tigramite-plotting-plotting-functions"></span><h1><a class="reference internal" href="#module-tigramite.plotting" title="tigramite.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">tigramite.plotting</span></code></a>: Plotting functions<a class="headerlink" href="#module-tigramite.plotting" title="Permalink to this headline">¶</a></h1>
 <p>Tigramite plotting package.</p>
 <dl class="function">
 <dt id="tigramite.plotting.plot_graph">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_graph</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>sig_thres=None</em>, <em>link_matrix=None</em>, <em>save_name=None</em>, <em>link_colorbar_label='MCI'</em>, <em>node_colorbar_label='auto-MCI'</em>, <em>link_width=None</em>, <em>link_attribute=None</em>, <em>node_pos=None</em>, <em>arrow_linewidth=30.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>vmin_nodes=0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='OrRd'</em>, <em>node_size=20</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>link_label_fontsize=6</em>, <em>lag_array=None</em>, <em>network_lower_bound=0.2</em>, <em>show_colorbar=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_graph</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>sig_thres=None</em>, <em>link_matrix=None</em>, <em>save_name=None</em>, <em>link_colorbar_label='MCI'</em>, <em>node_colorbar_label='auto-MCI'</em>, <em>link_width=None</em>, <em>link_attribute=None</em>, <em>node_pos=None</em>, <em>arrow_linewidth=30.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>vmin_nodes=0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='OrRd'</em>, <em>node_size=20</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>link_label_fontsize=6</em>, <em>lag_array=None</em>, <em>network_lower_bound=0.2</em>, <em>show_colorbar=True</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot.</p>
 <p>This is still in beta. The network is defined either from True values in
 link_matrix, or from thresholding the val_matrix with sig_thres.  Nodes
@@ -2707,42 +2753,42 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure and axes instance. If None they are created.</li>
-<li><strong>figsize</strong> (<em>tuple</em>) &#8211; Size of figure.</li>
-<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of significance thresholds. Must be of same shape as  val_matrix.
+<li><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
+<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</li>
+<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</li>
+<li><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</li>
+<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significance thresholds. Must be of same shape as  val_matrix.
 Either sig_thres or link_matrix has to be provided.</li>
-<li><strong>link_matrix</strong> (<em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of significant links. Must be of same shape as val_matrix. Either
+<li><strong>link_matrix</strong> (<em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significant links. Must be of same shape as val_matrix. Either
 sig_thres or link_matrix has to be provided.</li>
-<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Name of figure file to save figure. If None, figure is shown in window.</li>
-<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) &#8211; Test statistic label.</li>
-<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'auto-MCI'</em><em>)</em>) &#8211; Test statistic label for auto-dependencies.</li>
-<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Array of val_matrix.shape specifying relative link width with maximum
+<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</li>
+<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</li>
+<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'auto-MCI'</em><em>)</em>) – Test statistic label for auto-dependencies.</li>
+<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
 given by arrow_linewidth. If None, all links have same width.</li>
-<li><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; String array of val_matrix.shape specifying link attributes.</li>
-<li><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Dictionary of node positions in axis coordinates of form
-node_pos = {&#8216;x&#8217;:array of shape (N,), &#8216;y&#8217;:array of shape(N)}. These
+<li><strong>link_attribute</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – String array of val_matrix.shape specifying link attributes.</li>
+<li><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
+node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
 coordinates could have been transformed before for basemap plots.</li>
-<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) &#8211; Linewidth.</li>
-<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) &#8211; Link colorbar scale lower bound.</li>
-<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Link colorbar scale upper bound.</li>
-<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Link tick mark interval.</li>
-<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Node colorbar scale lower bound.</li>
-<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Node colorbar scale upper bound.</li>
-<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Node tick mark interval.</li>
-<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Node size.</li>
-<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Size of link arrow head. Passed on to FancyArrowPatch object.</li>
-<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) &#8211; Curvature of links. Passed on to FancyArrowPatch object.</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of colorbar labels.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Opacity.</li>
-<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of node labels.</li>
-<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) &#8211; Fontsize of link labels.</li>
-<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional specification of lags overwriting numpy.arange(0, tau_max+1)</li>
-<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) &#8211; Fraction of vertical space below graph plot.</li>
-<li><strong>show_colorbar</strong> (<em>bool</em>) &#8211; Whether to show colorbars for links and nodes.</li>
+<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</li>
+<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</li>
+<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</li>
+<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</li>
+<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</li>
+<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</li>
+<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</li>
+<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Node size.</li>
+<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</li>
+<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) – Curvature of links. Passed on to FancyArrowPatch object.</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</li>
+<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</li>
+<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</li>
+<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</li>
+<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</li>
+<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</li>
+<li><strong>show_colorbar</strong> (<em>bool</em>) – Whether to show colorbars for links and nodes.</li>
 </ul>
 </td>
 </tr>
@@ -2752,7 +2798,7 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="function">
 <dt id="tigramite.plotting.plot_lagfuncs">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_lagfuncs</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>name=None</em>, <em>setup_args={}</em>, <em>add_lagfunc_args={}</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_lagfuncs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_lagfuncs" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_lagfuncs</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>name=None</em>, <em>setup_args={}</em>, <em>add_lagfunc_args={}</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_lagfuncs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper helper function to plot lag functions.</p>
 <p>Sets up the matrix object and plots the lagfunction, see parameters in setup_matrix
 and add_lagfuncs.</p>
@@ -2761,15 +2807,15 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
-<li><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; File name. If None, figure is shown in window.</li>
-<li><strong>setup_args</strong> (<em>dict</em>) &#8211; Arguments for setting up the lag function matrix, see doc of
+<li><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
+<li><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</li>
+<li><strong>setup_args</strong> (<em>dict</em>) – Arguments for setting up the lag function matrix, see doc of
 setup_matrix.</li>
-<li><strong>add_lagfunc_args</strong> (<em>dict</em>) &#8211; Arguments for adding a lag function matrix, see doc of add_lagfuncs.</li>
+<li><strong>add_lagfunc_args</strong> (<em>dict</em>) – Arguments for adding a lag function matrix, see doc of add_lagfuncs.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>matrix</strong> &#8211; Further lag functions can be overlaid using the matrix.add_lagfuncs(val_matrix)
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>matrix</strong> – Further lag functions can be overlaid using the matrix.add_lagfuncs(val_matrix)
 function.</p>
 </td>
 </tr>
@@ -2782,10 +2828,10 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="function">
 <dt id="tigramite.plotting.plot_mediation_graph">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_mediation_graph</code><span class="sig-paren">(</span><em>path_val_matrix</em>, <em>path_node_array=None</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>save_name=None</em>, <em>link_colorbar_label='link coeff. (edge color)'</em>, <em>node_colorbar_label='MCE (node color)'</em>, <em>link_width=None</em>, <em>node_pos=None</em>, <em>arrow_linewidth=30.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>vmin_nodes=-1.0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='RdBu_r'</em>, <em>node_size=20</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>lag_array=None</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>link_label_fontsize=6</em>, <em>network_lower_bound=0.2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_mediation_graph</code><span class="sig-paren">(</span><em>path_val_matrix</em>, <em>path_node_array=None</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>save_name=None</em>, <em>link_colorbar_label='link coeff. (edge color)'</em>, <em>node_colorbar_label='MCE (node color)'</em>, <em>link_width=None</em>, <em>node_pos=None</em>, <em>arrow_linewidth=30.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>vmin_nodes=-1.0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='RdBu_r'</em>, <em>node_size=20</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>lag_array=None</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>link_label_fontsize=6</em>, <em>network_lower_bound=0.2</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_mediation_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a network plot visualizing the pathways of a mediation analysis.</p>
 <p>This is still in beta. The network is defined from non-zero entries in
-<code class="docutils literal"><span class="pre">path_val_matrix</span></code>.  Nodes denote variables, straight links contemporaneous
+<code class="docutils literal notranslate"><span class="pre">path_val_matrix</span></code>.  Nodes denote variables, straight links contemporaneous
 dependencies and curved arrows lagged dependencies. The node color denotes
 the mediated causal effect (MCE) and the link color the value at the lag
 with maximal link coefficient. The link label lists the lags with
@@ -2797,37 +2843,37 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>path_val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N, N, tau_max+1) containing link weight values.</li>
-<li><strong>path_node_array</strong> (<em>array_like</em>) &#8211; Array of shape (N,) containing node values.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure and axes instance. If None they are created.</li>
-<li><strong>figsize</strong> (<em>tuple</em>) &#8211; Size of figure.</li>
-<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Name of figure file to save figure. If None, figure is shown in window.</li>
-<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) &#8211; Link colorbar label.</li>
-<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) &#8211; Node colorbar label.</li>
-<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Array of val_matrix.shape specifying relative link width with maximum
+<li><strong>path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing link weight values.</li>
+<li><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</li>
+<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</li>
+<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</li>
+<li><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</li>
+<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</li>
+<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</li>
+<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) – Node colorbar label.</li>
+<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
 given by arrow_linewidth. If None, all links have same width.</li>
-<li><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Dictionary of node positions in axis coordinates of form
-node_pos = {&#8216;x&#8217;:array of shape (N,), &#8216;y&#8217;:array of shape(N)}. These
+<li><strong>node_pos</strong> (<em>dictionary</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Dictionary of node positions in axis coordinates of form
+node_pos = {‘x’:array of shape (N,), ‘y’:array of shape(N)}. These
 coordinates could have been transformed before for basemap plots.</li>
-<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) &#8211; Linewidth.</li>
-<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) &#8211; Link colorbar scale lower bound.</li>
-<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Link colorbar scale upper bound.</li>
-<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Link tick mark interval.</li>
-<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Node colorbar scale lower bound.</li>
-<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Node colorbar scale upper bound.</li>
-<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Node tick mark interval.</li>
-<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Node size.</li>
-<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Size of link arrow head. Passed on to FancyArrowPatch object.</li>
-<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) &#8211; Curvature of links. Passed on to FancyArrowPatch object.</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of colorbar labels.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Opacity.</li>
-<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of node labels.</li>
-<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) &#8211; Fontsize of link labels.</li>
-<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) &#8211; Fraction of vertical space below graph plot.</li>
-<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional specification of lags overwriting numpy.arange(0, tau_max+1)</li>
+<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</li>
+<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</li>
+<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</li>
+<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</li>
+<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</li>
+<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</li>
+<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</li>
+<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Node size.</li>
+<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</li>
+<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) – Curvature of links. Passed on to FancyArrowPatch object.</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</li>
+<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</li>
+<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</li>
+<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</li>
+<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</li>
+<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</li>
 </ul>
 </td>
 </tr>
@@ -2837,45 +2883,45 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="function">
 <dt id="tigramite.plotting.plot_mediation_time_series_graph">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_mediation_time_series_graph</code><span class="sig-paren">(</span><em>path_node_array</em>, <em>tsg_path_val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>link_colorbar_label='link coeff. (edge color)'</em>, <em>node_colorbar_label='MCE (node color)'</em>, <em>save_name=None</em>, <em>link_width=None</em>, <em>arrow_linewidth=20.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>order=None</em>, <em>vmin_nodes=-1.0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='RdBu_r'</em>, <em>node_size=10</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.0</em>, <em>network_lower_bound=0.2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_mediation_time_series_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_mediation_time_series_graph</code><span class="sig-paren">(</span><em>path_node_array</em>, <em>tsg_path_val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>link_colorbar_label='link coeff. (edge color)'</em>, <em>node_colorbar_label='MCE (node color)'</em>, <em>save_name=None</em>, <em>link_width=None</em>, <em>arrow_linewidth=20.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>order=None</em>, <em>vmin_nodes=-1.0</em>, <em>vmax_nodes=1.0</em>, <em>node_ticks=0.4</em>, <em>cmap_nodes='RdBu_r'</em>, <em>node_size=10</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.0</em>, <em>network_lower_bound=0.2</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_mediation_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a mediation time series graph plot.</p>
-<p>This is still in beta. The time series graph&#8217;s links are colored by
+<p>This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>tsg_path_val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</li>
-<li><strong>path_node_array</strong> (<em>array_like</em>) &#8211; Array of shape (N,) containing node values.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure and axes instance. If None they are created.</li>
-<li><strong>figsize</strong> (<em>tuple</em>) &#8211; Size of figure.</li>
-<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Name of figure file to save figure. If None, figure is shown in window.</li>
-<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) &#8211; Link colorbar label.</li>
-<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) &#8211; Node colorbar label.</li>
-<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Array of val_matrix.shape specifying relative link width with maximum
+<li><strong>tsg_path_val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N*tau_max, N*tau_max) containing link weight values.</li>
+<li><strong>path_node_array</strong> (<em>array_like</em>) – Array of shape (N,) containing node values.</li>
+<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</li>
+<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</li>
+<li><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</li>
+<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</li>
+<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'link coeff.</em><em> (</em><em>edge color</em><em>)</em><em>'</em><em>)</em>) – Link colorbar label.</li>
+<li><strong>node_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCE</em><em> (</em><em>node color</em><em>)</em><em>'</em><em>)</em>) – Node colorbar label.</li>
+<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
 given by arrow_linewidth. If None, all links have same width.</li>
-<li><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; order of variables from top to bottom.</li>
-<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) &#8211; Linewidth.</li>
-<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) &#8211; Link colorbar scale lower bound.</li>
-<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Link colorbar scale upper bound.</li>
-<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Link tick mark interval.</li>
-<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) &#8211; Node colorbar scale lower bound.</li>
-<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Node colorbar scale upper bound.</li>
-<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Node tick mark interval.</li>
-<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Node size.</li>
-<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Size of link arrow head. Passed on to FancyArrowPatch object.</li>
-<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) &#8211; Curvature of links. Passed on to FancyArrowPatch object.</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of colorbar labels.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Opacity.</li>
-<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of node labels.</li>
-<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) &#8211; Fontsize of link labels.</li>
-<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) &#8211; Fraction of horizontal figure space to allocate left of plot for labels.</li>
-<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) &#8211; Fraction of vertical figure space to allocate top of plot for labels.</li>
-<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) &#8211; Fraction of vertical space below graph plot.</li>
+<li><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – order of variables from top to bottom.</li>
+<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</li>
+<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</li>
+<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</li>
+<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</li>
+<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>vmin_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0</em><em>)</em>) – Node colorbar scale lower bound.</li>
+<li><strong>vmax_nodes</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Node colorbar scale upper bound.</li>
+<li><strong>node_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Node tick mark interval.</li>
+<li><strong>cmap_nodes</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'OrRd'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Node size.</li>
+<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</li>
+<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) – Curvature of links. Passed on to FancyArrowPatch object.</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</li>
+<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</li>
+<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</li>
+<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</li>
+<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</li>
+<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</li>
+<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</li>
 </ul>
 </td>
 </tr>
@@ -2885,44 +2931,45 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="function">
 <dt id="tigramite.plotting.plot_time_series_graph">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_time_series_graph</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>sig_thres=None</em>, <em>link_matrix=None</em>, <em>link_colorbar_label='MCI'</em>, <em>save_name=None</em>, <em>link_width=None</em>, <em>arrow_linewidth=20.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>order=None</em>, <em>node_size=10</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.0</em>, <em>network_lower_bound=0.2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_time_series_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_time_series_graph</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>var_names=None</em>, <em>fig_ax=None</em>, <em>figsize=None</em>, <em>sig_thres=None</em>, <em>link_matrix=None</em>, <em>link_colorbar_label='MCI'</em>, <em>save_name=None</em>, <em>link_width=None</em>, <em>arrow_linewidth=20.0</em>, <em>vmin_edges=-1</em>, <em>vmax_edges=1.0</em>, <em>edge_ticks=0.4</em>, <em>cmap_edges='RdBu_r'</em>, <em>order=None</em>, <em>node_size=10</em>, <em>arrowhead_size=20</em>, <em>curved_radius=0.2</em>, <em>label_fontsize=10</em>, <em>alpha=1.0</em>, <em>node_label_size=10</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.0</em>, <em>network_lower_bound=0.2</em>, <em>undirected_style='dashed'</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_time_series_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a time series graph.</p>
-<p>This is still in beta. The time series graph&#8217;s links are colored by
+<p>This is still in beta. The time series graph’s links are colored by
 val_matrix.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure and axes instance. If None they are created.</li>
-<li><strong>figsize</strong> (<em>tuple</em>) &#8211; Size of figure.</li>
-<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of significance thresholds. Must be of same shape as  val_matrix.
+<li><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
+<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</li>
+<li><strong>fig_ax</strong> (<em>tuple of figure and axis object</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created.</li>
+<li><strong>figsize</strong> (<em>tuple</em>) – Size of figure.</li>
+<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significance thresholds. Must be of same shape as  val_matrix.
 Either sig_thres or link_matrix has to be provided.</li>
-<li><strong>link_matrix</strong> (<em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of significant links. Must be of same shape as val_matrix. Either
+<li><strong>link_matrix</strong> (<em>bool array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significant links. Must be of same shape as val_matrix. Either
 sig_thres or link_matrix has to be provided.</li>
-<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Name of figure file to save figure. If None, figure is shown in window.</li>
-<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) &#8211; Test statistic label.</li>
-<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Array of val_matrix.shape specifying relative link width with maximum
+<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</li>
+<li><strong>link_colorbar_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'MCI'</em><em>)</em>) – Test statistic label.</li>
+<li><strong>link_width</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Array of val_matrix.shape specifying relative link width with maximum
 given by arrow_linewidth. If None, all links have same width.</li>
-<li><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; </li>
-<li><strong>of variables from top to bottom.</strong> (<em>order</em>) &#8211; </li>
-<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) &#8211; Linewidth.</li>
-<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) &#8211; Link colorbar scale lower bound.</li>
-<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Link colorbar scale upper bound.</li>
-<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) &#8211; Link tick mark interval.</li>
-<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) &#8211; Colormap for links.</li>
-<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Node size.</li>
-<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) &#8211; Size of link arrow head. Passed on to FancyArrowPatch object.</li>
-<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) &#8211; Curvature of links. Passed on to FancyArrowPatch object.</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of colorbar labels.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Opacity.</li>
-<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of node labels.</li>
-<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) &#8211; Fontsize of link labels.</li>
-<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) &#8211; Fraction of horizontal figure space to allocate left of plot for labels.</li>
-<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) &#8211; Fraction of vertical figure space to allocate top of plot for labels.</li>
-<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) &#8211; Fraction of vertical space below graph plot.</li>
+<li><strong>order</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – </li>
+<li><strong>of variables from top to bottom.</strong> (<em>order</em>) – </li>
+<li><strong>arrow_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 30</em><em>)</em>) – Linewidth.</li>
+<li><strong>vmin_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Link colorbar scale lower bound.</li>
+<li><strong>vmax_edges</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Link colorbar scale upper bound.</li>
+<li><strong>edge_ticks</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.4</em><em>)</em>) – Link tick mark interval.</li>
+<li><strong>cmap_edges</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'RdBu_r'</em><em>)</em>) – Colormap for links.</li>
+<li><strong>node_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Node size.</li>
+<li><strong>arrowhead_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 20</em><em>)</em>) – Size of link arrow head. Passed on to FancyArrowPatch object.</li>
+<li><strong>float</strong><strong>, </strong><strong>optional</strong><strong> (</strong><strong>default</strong> (<em>curved_radius</em><em>,</em>) – Curvature of links. Passed on to FancyArrowPatch object.</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of colorbar labels.</li>
+<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</li>
+<li><strong>node_label_size</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of node labels.</li>
+<li><strong>link_label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 6</em><em>)</em>) – Fontsize of link labels.</li>
+<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</li>
+<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</li>
+<li><strong>network_lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.2</em><em>)</em>) – Fraction of vertical space below graph plot.</li>
+<li><strong>undirected_style</strong> (<em>string</em><em>, </em><em>optional</em><em> (</em><em>default: 'dashed'</em><em>)</em>) – Style of undirected contemporaneous links.</li>
 </ul>
 </td>
 </tr>
@@ -2932,34 +2979,29 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="function">
 <dt id="tigramite.plotting.plot_timeseries">
-<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_timeseries</code><span class="sig-paren">(</span><em>data</em>, <em>datatime=None</em>, <em>var_names=None</em>, <em>save_name=None</em>, <em>fig_axes=None</em>, <em>figsize=None</em>, <em>var_units=None</em>, <em>time_label='time'</em>, <em>use_mask=False</em>, <em>mask=None</em>, <em>missing_flag=None</em>, <em>grey_masked_samples=False</em>, <em>data_linewidth=1.0</em>, <em>skip_ticks_data_x=1</em>, <em>skip_ticks_data_y=2</em>, <em>label_fontsize=8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#plot_timeseries"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.plot_timeseries" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">tigramite.plotting.</code><code class="descname">plot_timeseries</code><span class="sig-paren">(</span><em>dataframe=None</em>, <em>save_name=None</em>, <em>fig_axes=None</em>, <em>figsize=None</em>, <em>var_units=None</em>, <em>time_label='time'</em>, <em>use_mask=False</em>, <em>grey_masked_samples=False</em>, <em>data_linewidth=1.0</em>, <em>skip_ticks_data_x=1</em>, <em>skip_ticks_data_y=2</em>, <em>label_fontsize=8</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.plot_timeseries" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create and save figure of stacked panels with time series.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data</strong> (<em>array-like</em>) &#8211; Data series array of shape (T, N).</li>
-<li><strong>datatime</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Timelabel array. If None, range(T) is used.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Name of figure file to save figure. If None, figure is shown in window.</li>
-<li><strong>fig_axes</strong> (<em>subplots instance</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure and axes instance. If None they are created as
-fig, axes = pyplot.subplots(N,...)</li>
-<li><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure size if new figure is created. If None, default pyplot figsize
+<li><strong>dataframe</strong> (<em>data object</em><em>, </em><em>optional</em>) – This is the Tigramite dataframe object. It has the attributes
+dataframe.values yielding a np array of shape (observations T,
+variables N) and optionally a mask of the same shape.</li>
+<li><strong>save_name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Name of figure file to save figure. If None, figure is shown in window.</li>
+<li><strong>fig_axes</strong> (<em>subplots instance</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure and axes instance. If None they are created as
+fig, axes = pyplot.subplots(N,…)</li>
+<li><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</li>
-<li><strong>var_units</strong> (<em>list of str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Units of variables.</li>
-<li><strong>time_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) &#8211; Label of time axis.</li>
-<li><strong>use_mask</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to use masked data.</li>
-<li><strong>mask</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Data mask where True labels masked samples.</li>
-<li><strong>missing_flag</strong> (<em>number</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Flag for missing values in dataframe. Dismisses all time slices of
-samples where missing values occur in any variable and also flags
-samples for all lags up to 2*tau_max. This avoids biases, see section on
-masking in Supplement of <a class="footnote-reference" href="#id5" id="id34">[1]</a>.</li>
-<li><strong>grey_masked_samples</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to mark masked samples by grey fills (&#8216;fill&#8217;) or grey data (&#8216;data&#8217;).</li>
-<li><strong>data_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Linewidth.</li>
-<li><strong>skip_ticks_data_x</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Skip every other tickmark.</li>
-<li><strong>skip_ticks_data_y</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) &#8211; Skip every other tickmark.</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of variable labels.</li>
+<li><strong>var_units</strong> (<em>list of str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Units of variables.</li>
+<li><strong>time_label</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – Label of time axis.</li>
+<li><strong>use_mask</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to use masked data.</li>
+<li><strong>grey_masked_samples</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to mark masked samples by grey fills (‘fill’) or grey data (‘data’).</li>
+<li><strong>data_linewidth</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Linewidth.</li>
+<li><strong>skip_ticks_data_x</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Skip every other tickmark.</li>
+<li><strong>skip_ticks_data_y</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 2</em><em>)</em>) – Skip every other tickmark.</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</li>
 </ul>
 </td>
 </tr>
@@ -2969,9 +3011,9 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="class">
 <dt id="tigramite.plotting.setup_matrix">
-<em class="property">class </em><code class="descclassname">tigramite.plotting.</code><code class="descname">setup_matrix</code><span class="sig-paren">(</span><em>N</em>, <em>tau_max</em>, <em>var_names=None</em>, <em>figsize=None</em>, <em>minimum=-1</em>, <em>maximum=1</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.05</em>, <em>legend_width=0.15</em>, <em>legend_fontsize=10</em>, <em>x_base=1.0</em>, <em>y_base=0.5</em>, <em>plot_gridlines=False</em>, <em>lag_units=''</em>, <em>lag_array=None</em>, <em>label_fontsize=10</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">tigramite.plotting.</code><code class="descname">setup_matrix</code><span class="sig-paren">(</span><em>N</em>, <em>tau_max</em>, <em>var_names=None</em>, <em>figsize=None</em>, <em>minimum=-1</em>, <em>maximum=1</em>, <em>label_space_left=0.1</em>, <em>label_space_top=0.05</em>, <em>legend_width=0.15</em>, <em>legend_fontsize=10</em>, <em>x_base=1.0</em>, <em>y_base=0.5</em>, <em>plot_gridlines=False</em>, <em>lag_units=''</em>, <em>lag_array=None</em>, <em>label_fontsize=10</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.setup_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create matrix of lag function panels.</p>
-<p>Class to setup figure object. The function add_lagfuncs(...) allows to plot
+<p>Class to setup figure object. The function add_lagfuncs(…) allows to plot
 the val_matrix of shape (N, N, tau_max+1). Multiple lagfunctions can be
 overlaid for comparison.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2979,23 +3021,23 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>N</strong> (<em>int</em>) &#8211; Number of variables</li>
-<li><strong>tau_max</strong> (<em>int</em>) &#8211; Maximum time lag.</li>
-<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; List of variable names. If None, range(N) is used.</li>
-<li><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Figure size if new figure is created. If None, default pyplot figsize
+<li><strong>N</strong> (<em>int</em>) – Number of variables</li>
+<li><strong>tau_max</strong> (<em>int</em>) – Maximum time lag.</li>
+<li><strong>var_names</strong> (<em>list</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – List of variable names. If None, range(N) is used.</li>
+<li><strong>figsize</strong> (<em>tuple of floats</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Figure size if new figure is created. If None, default pyplot figsize
 is used.</li>
-<li><strong>minimum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) &#8211; Lower y-axis limit.</li>
-<li><strong>maximum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) &#8211; Upper y-axis limit.</li>
-<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) &#8211; Fraction of horizontal figure space to allocate left of plot for labels.</li>
-<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) &#8211; Fraction of vertical figure space to allocate top of plot for labels.</li>
-<li><strong>legend_width</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.15</em><em>)</em>) &#8211; Fraction of horizontal figure space to allocate right of plot for
+<li><strong>minimum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: -1</em><em>)</em>) – Lower y-axis limit.</li>
+<li><strong>maximum</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 1</em><em>)</em>) – Upper y-axis limit.</li>
+<li><strong>label_space_left</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.1</em><em>)</em>) – Fraction of horizontal figure space to allocate left of plot for labels.</li>
+<li><strong>label_space_top</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.05</em><em>)</em>) – Fraction of vertical figure space to allocate top of plot for labels.</li>
+<li><strong>legend_width</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 0.15</em><em>)</em>) – Fraction of horizontal figure space to allocate right of plot for
 legend.</li>
-<li><strong>x_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; x-tick intervals to show.</li>
-<li><strong>y_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: .4</em><em>)</em>) &#8211; y-tick intervals to show.</li>
-<li><strong>plot_gridlines</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) &#8211; Whether to show a grid.</li>
-<li><strong>lag_units</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) &#8211; </li>
-<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Optional specification of lags overwriting numpy.arange(0, tau_max+1)</li>
-<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) &#8211; Fontsize of variable labels.</li>
+<li><strong>x_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – x-tick intervals to show.</li>
+<li><strong>y_base</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: .4</em><em>)</em>) – y-tick intervals to show.</li>
+<li><strong>plot_gridlines</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: False</em><em>)</em>) – Whether to show a grid.</li>
+<li><strong>lag_units</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: ''</em><em>)</em>) – </li>
+<li><strong>lag_array</strong> (<em>array</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Optional specification of lags overwriting np.arange(0, tau_max+1)</li>
+<li><strong>label_fontsize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 10</em><em>)</em>) – Fontsize of variable labels.</li>
 </ul>
 </td>
 </tr>
@@ -3003,23 +3045,23 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 </table>
 <dl class="method">
 <dt id="tigramite.plotting.setup_matrix.add_lagfuncs">
-<code class="descname">add_lagfuncs</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>sig_thres=None</em>, <em>conf_matrix=None</em>, <em>color='black'</em>, <em>label=None</em>, <em>two_sided_thres=True</em>, <em>marker='.'</em>, <em>markersize=5</em>, <em>alpha=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix.add_lagfuncs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix.add_lagfuncs" title="Permalink to this definition">¶</a></dt>
+<code class="descname">add_lagfuncs</code><span class="sig-paren">(</span><em>val_matrix</em>, <em>sig_thres=None</em>, <em>conf_matrix=None</em>, <em>color='black'</em>, <em>label=None</em>, <em>two_sided_thres=True</em>, <em>marker='.'</em>, <em>markersize=5</em>, <em>alpha=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.setup_matrix.add_lagfuncs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add lag function plot from val_matrix array.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>val_matrix</strong> (<em>array_like</em>) &#8211; Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
-<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of significance thresholds. Must be of same shape as
+<li><strong>val_matrix</strong> (<em>array_like</em>) – Matrix of shape (N, N, tau_max+1) containing test statistic values.</li>
+<li><strong>sig_thres</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of significance thresholds. Must be of same shape as
 val_matrix.</li>
-<li><strong>conf_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; Matrix of shape (, N, tau_max+1, 2) containing confidence bounds.</li>
-<li><strong>color</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'black'</em><em>)</em>) &#8211; Line color.</li>
-<li><strong>label</strong> (<em>str</em>) &#8211; Test statistic label.</li>
-<li><strong>two_sided_thres</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) &#8211; Whether to draw sig_thres for pos. and neg. values.</li>
-<li><strong>marker</strong> (<em>matplotlib marker symbol</em><em>, </em><em>optional</em><em> (</em><em>default: '.'</em><em>)</em>) &#8211; Marker.</li>
-<li><strong>markersize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 5</em><em>)</em>) &#8211; Marker size.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) &#8211; Opacity.</li>
+<li><strong>conf_matrix</strong> (<em>array-like</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – Matrix of shape (, N, tau_max+1, 2) containing confidence bounds.</li>
+<li><strong>color</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: 'black'</em><em>)</em>) – Line color.</li>
+<li><strong>label</strong> (<em>str</em>) – Test statistic label.</li>
+<li><strong>two_sided_thres</strong> (<em>bool</em><em>, </em><em>optional</em><em> (</em><em>default: True</em><em>)</em>) – Whether to draw sig_thres for pos. and neg. values.</li>
+<li><strong>marker</strong> (<em>matplotlib marker symbol</em><em>, </em><em>optional</em><em> (</em><em>default: '.'</em><em>)</em>) – Marker.</li>
+<li><strong>markersize</strong> (<em>int</em><em>, </em><em>optional</em><em> (</em><em>default: 5</em><em>)</em>) – Marker size.</li>
+<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em> (</em><em>default: 1.</em><em>)</em>) – Opacity.</li>
 </ul>
 </td>
 </tr>
@@ -3029,13 +3071,13 @@ <h1><code class="xref py py-mod docutils literal"><span class="pre">tigramite.mo
 
 <dl class="method">
 <dt id="tigramite.plotting.setup_matrix.savefig">
-<code class="descname">savefig</code><span class="sig-paren">(</span><em>name=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tigramite/plotting.html#setup_matrix.savefig"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tigramite.plotting.setup_matrix.savefig" title="Permalink to this definition">¶</a></dt>
+<code class="descname">savefig</code><span class="sig-paren">(</span><em>name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tigramite.plotting.setup_matrix.savefig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save matrix figure.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) &#8211; File name. If None, figure is shown in window.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> (<em>str</em><em>, </em><em>optional</em><em> (</em><em>default: None</em><em>)</em>) – File name. If None, figure is shown in window.</td>
 </tr>
 </tbody>
 </table>
@@ -3068,12 +3110,12 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="#">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="#">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
     <div class="footer" role="contentinfo">
-        &#169; Copyright 2017, Jakob Runge.
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.5.6.
+        &#169; Copyright 2018, Jakob Runge.
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.4.
     </div>
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/objects.inv b/docs/objects.inv
index 8fc0e7d1..a5eff445 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 14ca129f..b7e02d74 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -1,26 +1,15 @@
+
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
   "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 
-
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    
-    <title>Python Module Index &#8212; Tigramite 3.0 documentation</title>
-    
+    <title>Python Module Index &#8212; Tigramite 4.0 documentation</title>
     <link rel="stylesheet" href="_static/sphinxdoc.css" type="text/css" />
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    './',
-        VERSION:     '3.0',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '.html',
-        HAS_SOURCE:  true,
-        SOURCELINK_SUFFIX: '.txt'
-      };
-    </script>
+    <script type="text/javascript" src="_static/documentation_options.js"></script>
     <script type="text/javascript" src="_static/jquery.js"></script>
     <script type="text/javascript" src="_static/underscore.js"></script>
     <script type="text/javascript" src="_static/doctools.js"></script>
@@ -29,8 +18,7 @@
  
 
 
-  </head>
-  <body role="document">
+  </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
       <ul>
@@ -40,19 +28,21 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="#" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
         <div class="sphinxsidebarwrapper">
 <div id="searchbox" style="display: none" role="search">
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-      <div><input type="text" name="q" /></div>
-      <div><input type="submit" value="Go" /></div>
+      <input type="text" name="q" />
+      <input type="submit" value="Go" />
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+    </div>
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@@ -107,12 +97,12 @@ <h3>Navigation</h3>
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              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
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index 27c84229..296a2943 100644
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+    <title>Search &#8212; Tigramite 4.0 documentation</title>
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@@ -34,8 +23,7 @@
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+  </head><body>
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@@ -45,7 +33,7 @@ <h3>Navigation</h3>
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-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 4.0 documentation</a> &#187;</li> 
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-        <li class="nav-item nav-item-0"><a href="index.html">Tigramite 3.0 documentation</a> &#187;</li> 
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     <div class="footer" role="contentinfo">
-        &#169; Copyright 2017, Jakob Runge.
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.5.6.
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\ No newline at end of file
diff --git a/environment_py2.yml b/environment_py2.yml
new file mode 100644
index 00000000..4edd17a3
--- /dev/null
+++ b/environment_py2.yml
@@ -0,0 +1,15 @@
+name: tigramite2_conda_tst
+dependencies:
+    - python=2
+    - numpy>=1.10.0
+    - scipy>=0.17.0
+    - scikit-learn>=0.18.1
+    - matplotlib>=1.5.1
+    - networkx=1.10.0
+    - cython>=0.26
+    - basemap>=1.0.0
+    - mpi4py>=2.0.0
+    - pip
+    - pip:
+        - statsmodels >= 0.6.0
+        - ace>=0.3.0
diff --git a/environment_py3.yml b/environment_py3.yml
new file mode 100644
index 00000000..3fbc8a0c
--- /dev/null
+++ b/environment_py3.yml
@@ -0,0 +1,13 @@
+name: tigramite2_conda
+dependencies:
+    - python=3
+    - numpy>=1.10.0
+    - scipy>=0.17.0
+    - scikit-learn>=0.18.1
+    - matplotlib>=1.5.1
+    - networkx=1.10.0
+    - cython>=0.26
+    - pip
+    - pip:
+        - statsmodels >= 0.6.0
+        - ace>=0.3.0
diff --git a/external_packages/RCIT/DESCRIPTION b/external_packages/RCIT/DESCRIPTION
new file mode 100644
index 00000000..7085aa55
--- /dev/null
+++ b/external_packages/RCIT/DESCRIPTION
@@ -0,0 +1,12 @@
+Package: RCIT
+Type: Package
+Title: The Randomized Conditional Independence Test (RCIT) and the Randomized
+    conditional Correlation Test (RCoT)
+Version: 0.1.0
+Author: Eric V. Strobl
+Maintainer:  Eric V. Strobl <ericvonstrobl@gmail.com>
+Description:  RCIT and RCoT are approximate conditional independence tests that use random Fourier features taken from the same class of reproducing kernel Hilbert spaces used by KCIT. RCIT and RCoT are therefore significantly faster than KCIT while at least as accurate.
+Depends: momentchi2, MASS
+License: CC BY-NC 4.0
+LazyData: TRUE
+RoxygenNote: 5.0.1
diff --git a/external_packages/RCIT/Experiments/AUPC_experiments.R b/external_packages/RCIT/Experiments/AUPC_experiments.R
new file mode 100644
index 00000000..4c5aeb39
--- /dev/null
+++ b/external_packages/RCIT/Experiments/AUPC_experiments.R
@@ -0,0 +1,207 @@
+### AUPC EXPERIMENTS ###
+
+### sample size
+
+samps = c(500,1000,2000,5000,1E4,1E5,1E6); # all sample sizes
+
+# matrices to store results
+res_RCIT_d = matrix(0,length(samps),1000);
+res_RCoT_d = matrix(0,length(samps),1000);
+res_RCIT_t_d = matrix(0,length(samps),1000);
+res_RCoT_t_d = matrix(0,length(samps),1000);
+
+res_KCIT_d = matrix(0,length(samps),1000);
+res_KCIT_t_d = matrix(0,length(samps),1000);
+
+res_RCIT_d_perm = matrix(0,length(samps),1000);
+res_RCoT_d_perm = matrix(0,length(samps),1000);
+res_RCIT_t_d_perm = matrix(0,length(samps),1000);
+res_RCoT_t_d_perm = matrix(0,length(samps),1000);
+
+for (n in 1:length(samps)){ # for each sample size
+  print(n)
+  for (t in 1:1000){ # repeat 100 times
+
+    outer_x = sample(c(1:5),1); # sample outer non-linear function
+    outer_y = sample(c(1:5),1);
+
+    ex=rnorm(samps[n]); # sample errors for X
+    ey=rnorm(samps[n]); # sample errors for Y
+    eb=0.25*rnorm(samps[n]); # small dependence inducing error terms
+
+    z=rnorm(samps[n]); #instantiate Z
+
+    x = ex+eb;  #instantiate X
+    y = ey+eb;  #instantiate Y
+
+    #pass X through randomly chosen non-linear function
+    if (outer_x == 2){
+      x = (x)^2;
+    } else if (outer_x == 3){
+      x = (x)^3;
+    } else if (outer_x == 4){
+      x = exp(-sqrt(x^2));
+    } else if (outer_x == 5){
+      x = tanh(x);
+    }
+
+    #pass Y through randomly chosen non-linear function
+    if (outer_y == 2){
+      y = (y)^2;
+    } else if (outer_y == 3){
+      y = (y)^3;
+    } else if (outer_y == 4){
+      y = exp(-sqrt(y^2));
+    } else if (outer_y == 5){
+      y = tanh(y);
+    }
+
+    #run RCIT
+    start=proc.time();
+    out=RCIT(x,y,z);
+    end=proc.time()-start;
+    res_RCIT_d[n,t]=out$p; #store p-value
+    res_RCIT_t_d[n,t]=end[3]; #store timing result
+
+    #run RCoT
+    start=proc.time();
+    out_g = RCoT(x,y,z);
+    end=proc.time()-start;
+    res_RCoT_d[n,t]=out_g$p;
+    res_RCoT_t_d[n,t]=end[3];
+
+    if (samps[n]<=1E4){
+      #run permutation test with S
+      start=proc.time();
+      out=RCIT(x,y,z,approx="perm");
+      end=proc.time()-start;
+      res_RCIT_d_perm[n,t]=out$p;
+      res_RCIT_t_d_perm[n,t]=end[3];
+
+      #run permutation test with S'
+      start=proc.time();
+      out_g = RCoT(x,y,z,approx="perm");
+      end=proc.time()-start;
+      res_RCoT_d_perm[n,t]=out_g$p;
+      res_RCoT_t_d_perm[n,t]=end[3];
+    }
+
+    #run KCIT
+    if (samps[n]<=2000){
+      start=proc.time();
+      res_KCIT_d[n,t]=KCIT(x,y,z);
+      end=proc.time()-start;
+      res_KCIT_t_d[n,t]=end[3];
+    }
+
+  }
+
+  save(res_RCIT_d, res_RCoT_d, res_KCIT_d,
+       res_RCIT_t_d, res_RCoT_t_d, res_KCIT_t_d,
+       res_RCIT_d_perm, res_RCoT_d_perm, res_RCIT_t_d_perm, res_RCoT_t_d_perm,
+       file="Type2_SS.RData")
+
+}
+
+
+### conditioning set size
+
+dims = 1:10; #1-10 dimensions in conditioning set
+samps=1000; #number of samples
+
+## matrices to store results
+res_RCIT_d = matrix(0,length(dims),1000);
+res_RCoT_d = matrix(0,length(dims),1000);
+res_RCIT_t_d = matrix(0,length(dims),1000);
+res_RCoT_t_d = matrix(0,length(dims),1000);
+
+res_KCIT_d = matrix(0,length(dims),1000);
+res_KCIT_t_d = matrix(0,length(dims),1000);
+
+res_RCIT_d_perm = matrix(0,length(dims),1000);
+res_RCoT_d_perm = matrix(0,length(dims),1000);
+res_RCIT_t_d_perm = matrix(0,length(dims),1000);
+res_RCoT_t_d_perm = matrix(0,length(dims),1000);
+
+
+for (n in dims){ # for each dimension
+  print(n)
+  for (t in 1:1000){ # repeat 1000 times
+
+    outer_x = sample(c(1:5),1); # sample outer non-linear function
+    outer_y = sample(c(1:5),1);
+
+    ex=rnorm(samps); #sample errors for X
+    ey=rnorm(samps); #sample errors for Y
+    eb=0.25*rnorm(samps); #small dependence inducing error terms
+
+    z=matrix(rnorm(samps*(dims[n])),ncol=dims[n]); #instantiate Z
+
+    x = ex+eb;  #instantiate X
+    y = ey+eb;  #instantiate Y
+
+    #pass X through randomly chosen non-linear function
+    if (outer_x == 2){
+      x = (x)^2;
+    } else if (outer_x == 3){
+      x = (x)^3;
+    } else if (outer_x == 4){
+      x = exp(-sqrt(x^2));
+    } else if (outer_x == 5){
+      x = tanh(x);
+    }
+
+    #pass Y through randomly chosen non-linear function
+    if (outer_y == 2){
+      y = (y)^2;
+    } else if (outer_y == 3){
+      y = (y)^3;
+    } else if (outer_y == 4){
+      y = exp(-sqrt(y^2));
+    } else if (outer_y == 5){
+      y = tanh(y);
+    }
+
+    #run RCIT
+    start=proc.time();
+    out=RCIT(x,y,z);
+    end=proc.time()-start;
+    res_RCIT_d[n,t]=out$p; #store p-value
+    res_RCIT_t_d[n,t]=end[3]; #store timing result
+
+    #run RCoT
+    start=proc.time();
+    out_g = RCoT(x,y,z);
+    end=proc.time()-start;
+    res_RCoT_d[n,t]=out_g$p;
+    res_RCoT_t_d[n,t]=end[3];
+
+    #run permutation test with S
+    start=proc.time();
+    out=RCIT(x,y,z,approx="perm");
+    end=proc.time()-start;
+    res_RCIT_d_perm[n,t]=out$p;
+    res_RCIT_t_d_perm[n,t]=end[3];
+
+    #run permutation test with S'
+    start=proc.time();
+    out_g = RCoT(x,y,z,approx="perm");
+    end=proc.time()-start;
+    res_RCoT_d_perm[n,t]=out_g$p;
+    res_RCoT_t_d_perm[n,t]=end[3];
+
+    #run KCIT
+    start=proc.time();
+    res_KCIT_d[n,t]=KCIT(x,y,z);
+    end=proc.time()-start;
+    res_KCIT_t_d[n,t]=end[3];
+
+
+  }
+
+  save(res_RCIT_d, res_RCoT_d, res_KCIT_d,
+       res_RCIT_t_d, res_RCoT_t_d, res_KCIT_t_d,
+       res_RCIT_d_perm, res_RCoT_d_perm, res_RCIT_t_d_perm, res_RCoT_t_d_perm,
+       file="Type2_Dim.RData")
+
+}
diff --git a/external_packages/RCIT/Experiments/TypeOne_Experiments.R b/external_packages/RCIT/Experiments/TypeOne_Experiments.R
new file mode 100644
index 00000000..cbaaed04
--- /dev/null
+++ b/external_packages/RCIT/Experiments/TypeOne_Experiments.R
@@ -0,0 +1,196 @@
+### TYPE 1 ERROR RATE EXPERIMENTS ###
+
+### sample size
+
+samps = c(500,1000,2000,5000,1E4,1E5,1E6); # all sample sizes
+
+# matrices to store results
+res_RCIT_d = matrix(0,length(samps),1000);
+res_RCoT_d = matrix(0,length(samps),1000);
+res_RCIT_t_d = matrix(0,length(samps),1000);
+res_RCoT_t_d = matrix(0,length(samps),1000);
+
+res_KCIT_d = matrix(0,length(samps),1000);
+res_KCIT_t_d = matrix(0,length(samps),1000);
+
+res_RCIT_d_perm = matrix(0,length(samps),1000);
+res_RCoT_d_perm = matrix(0,length(samps),1000);
+res_RCIT_t_d_perm = matrix(0,length(samps),1000);
+res_RCoT_t_d_perm = matrix(0,length(samps),1000);
+
+for (n in 1:length(samps)){ # for each sample size
+  print(n)
+  for (t in 1:1000){ # repeat 100 times
+
+    outer_x = sample(c(1:5),1); # sample outer non-linear function
+    outer_y = sample(c(1:5),1);
+
+    x=rnorm(samps[n]); # instantiate X
+    y=rnorm(samps[n]); # instantiate Y
+    z=rnorm(samps[n]); # instantiate Z
+
+    #pass X through randomly chosen non-linear function
+    if (outer_x == 2){
+      x = (x)^2;
+    } else if (outer_x == 3){
+      x = (x)^3;
+    } else if (outer_x == 4){
+      x = exp(-sqrt(x^2));
+    } else if (outer_x == 5){
+      x = tanh(x);
+    }
+
+    #pass Y through randomly chosen non-linear function
+    if (outer_y == 2){
+      y = (y)^2;
+    } else if (outer_y == 3){
+      y = (y)^3;
+    } else if (outer_y == 4){
+      y = exp(-sqrt(y^2));
+    } else if (outer_y == 5){
+      y = tanh(y);
+    }
+
+    #run RCIT
+    start=proc.time();
+    out=RCIT(x,y,z);
+    end=proc.time()-start;
+    res_RCIT_d[n,t]=out$p; #store p-value
+    res_RCIT_t_d[n,t]=end[3]; #store timing result
+
+    #run RCoT
+    start=proc.time();
+    out_g = RCoT(x,y,z);
+    end=proc.time()-start;
+    res_RCoT_d[n,t]=out_g$p;
+    res_RCoT_t_d[n,t]=end[3];
+
+    if (samps[n]<=1E4){
+      #run permutation test with S
+      start=proc.time();
+      out=RCIT(x,y,z,approx="perm");
+      end=proc.time()-start;
+      res_RCIT_d_perm[n,t]=out$p;
+      res_RCIT_t_d_perm[n,t]=end[3];
+
+      #run permutation test with S'
+      start=proc.time();
+      out_g = RCoT(x,y,z,approx="perm");
+      end=proc.time()-start;
+      res_RCoT_d_perm[n,t]=out_g$p;
+      res_RCoT_t_d_perm[n,t]=end[3];
+    }
+
+    #run KCIT
+    if (samps[n]<=2000){
+      start=proc.time();
+      res_KCIT_d[n,t]=KCIT(x,y,z);
+      end=proc.time()-start;
+      res_KCIT_t_d[n,t]=end[3];
+    }
+
+  }
+
+  save(res_RCIT_d, res_RCoT_d, res_KCIT_d,
+       res_RCIT_t_d, res_RCoT_t_d, res_KCIT_t_d,
+       res_RCIT_d_perm, res_RCoT_d_perm, res_RCIT_t_d_perm, res_RCoT_t_d_perm,
+       file="Type1_SS.RData")
+
+}
+
+
+### conditioning set size
+
+dims = 1:10; #1-10 dimensions in conditioning set
+samps=1000; #number of samples
+
+## matrices to store results
+res_RCIT_d = matrix(0,length(dims),1000);
+res_RCoT_d = matrix(0,length(dims),1000);
+res_RCIT_t_d = matrix(0,length(dims),1000);
+res_RCoT_t_d = matrix(0,length(dims),1000);
+
+res_KCIT_d = matrix(0,length(dims),1000);
+res_KCIT_t_d = matrix(0,length(dims),1000);
+
+res_RCIT_d_perm = matrix(0,length(dims),1000);
+res_RCoT_d_perm = matrix(0,length(dims),1000);
+res_RCIT_t_d_perm = matrix(0,length(dims),1000);
+res_RCoT_t_d_perm = matrix(0,length(dims),1000);
+
+
+for (n in dims){ # for each dimension
+  print(n)
+  for (t in 1:1000){ # repeat 1000 times
+    outer_x = sample(c(1:5),1); # sample outer non-linear function
+    outer_y = sample(c(1:5),1);
+
+    x=rnorm(samps[n]); # instantiate X
+    y=rnorm(samps[n]); # instantiate Y
+    z=matrix(rnorm(samps*(dims[n])),ncol=dims[n]); # instantiate Z
+
+    #pass X through randomly chosen non-linear function
+    if (outer_x == 2){
+      x = (x)^2;
+    } else if (outer_x == 3){
+      x = (x)^3;
+    } else if (outer_x == 4){
+      x = exp(-sqrt(x^2));
+    } else if (outer_x == 5){
+      x = tanh(x);
+    }
+
+    #pass Y through randomly chosen non-linear function
+    if (outer_y == 2){
+      y = (y)^2;
+    } else if (outer_y == 3){
+      y = (y)^3;
+    } else if (outer_y == 4){
+      y = exp(-sqrt(y^2));
+    } else if (outer_y == 5){
+      y = tanh(y);
+    }
+
+    #run RCIT
+    start=proc.time();
+    out=RCIT(x,y,z);
+    end=proc.time()-start;
+    res_RCIT_d[n,t]=out$p; #store p-value
+    res_RCIT_t_d[n,t]=end[3]; #store timing result
+
+    #run RCoT
+    start=proc.time();
+    out_g = RCoT(x,y,z);
+    end=proc.time()-start;
+    res_RCoT_d[n,t]=out_g$p;
+    res_RCoT_t_d[n,t]=end[3];
+
+    #run permutation test with S
+    start=proc.time();
+    out=RCIT(x,y,z,approx="perm");
+    end=proc.time()-start;
+    res_RCIT_d_perm[n,t]=out$p;
+    res_RCIT_t_d_perm[n,t]=end[3];
+
+    #run permutation test with S'
+    start=proc.time();
+    out_g = RCoT(x,y,z,approx="perm");
+    end=proc.time()-start;
+    res_RCoT_d_perm[n,t]=out_g$p;
+    res_RCoT_t_d_perm[n,t]=end[3];
+
+    #run KCIT
+    start=proc.time();
+    res_KCIT_d[n,t]=KCIT(x,y,z);
+    end=proc.time()-start;
+    res_KCIT_t_d[n,t]=end[3];
+
+
+  }
+
+  save(res_RCIT_d, res_RCoT_d, res_KCIT_d,
+       res_RCIT_t_d, res_RCoT_t_d, res_KCIT_t_d,
+       res_RCIT_d_perm, res_RCoT_d_perm, res_RCIT_t_d_perm, res_RCoT_t_d_perm,
+       file="Type1_Dim.RData")
+
+}
diff --git a/external_packages/RCIT/NAMESPACE b/external_packages/RCIT/NAMESPACE
new file mode 100644
index 00000000..8982556d
--- /dev/null
+++ b/external_packages/RCIT/NAMESPACE
@@ -0,0 +1,3 @@
+# Generated by roxygen2: do not edit by hand
+
+exportPattern(".")
diff --git a/external_packages/RCIT/R/KCIT.R b/external_packages/RCIT/R/KCIT.R
new file mode 100644
index 00000000..b66a7416
--- /dev/null
+++ b/external_packages/RCIT/R/KCIT.R
@@ -0,0 +1,147 @@
+#' KCIT - tests whether x and y are conditionally independent given z. Calls U_KCI if z is empty.
+#' @param x Random variable x.
+#' @param y Random variable y.
+#' @param z Random variable z.
+#' @param Bootstrap Estimate the null by bootstrap. Default is TRUE. Runs slightly faster when set to FALSE but with less accurate p-values.
+#' @return The p-value \code{p}
+#' @export
+
+KCIT <- function(x,y,z,Bootstrap=TRUE){
+
+  if( length(z)==0 || missing(z) ){
+    p_val = U_KCI(x,y);
+    #print(p_val)
+    return(p_val)
+  }
+  else{
+
+    x=normalize(x);
+    y=normalize(y);
+    z=normalize(z);
+
+    T=length(x);
+
+    lambda = 1E-3;
+    Thresh = 1E-5;
+
+    if (T <= 200) {
+      #width = 0.8;
+      width = 1.2;
+    } else if (T > 1200){
+      #width = 0.3;
+      width = 0.7;
+    }else{
+      #width = 0.5;
+      width = 0.4;
+      }
+
+    if (length(x)>500){
+    width = median(as.vector(dist(cbind(x[1:500],y[1:500]))));}
+    else {
+    width = median(as.vector(dist(cbind(x,y))));}
+
+    if (is.null(dim(z)[2])) { D = 1;}
+    else{D = dim(z)[2];}
+
+    theta = 1/(width^2 * D);
+
+    H =  diag(T) - (1/T)*matrix(1,T,T);
+
+    Kx = RBF_kernel(cbind(x, z/2), theta); Kx = H %*% Kx %*% H;
+
+    Ky = RBF_kernel(y, theta); Ky = H %*% Ky %*% H;
+
+    Kz = RBF_kernel(z, theta); Kz = H %*% Kz %*% H;
+
+    #P1 = (diag(T)-Kz %*% chol2inv(chol(Kz + lambda*diag(T))));
+    P1 = (diag(T)-Kz %*% solve(Kz + lambda*diag(T)));
+    Kxz = P1 %*% Kx %*% t(P1);
+    Kyz = P1 %*% Ky %*% t(P1);
+
+    Sta = sum(diag(Kxz %*% Kyz));
+
+    df = sum(diag(diag(T)-P1));
+
+    listxz = eigen((Kxz+t(Kxz))/2,symmetric=TRUE);
+    eig_Kxz=listxz[[1]]; eivx=listxz[[2]]
+
+    listyz = eigen((Kyz+t(Kyz))/2,symmetric=TRUE);
+    eig_Kyz=listyz[[1]]; eivy=listyz[[2]]
+
+
+    IIx = which(eig_Kxz > max(eig_Kxz) * Thresh);
+    IIy = which(eig_Kyz > max(eig_Kyz) * Thresh);
+    eig_Kxz = eig_Kxz[IIx];
+    eivx = eivx[,IIx];
+    eig_Kyz = eig_Kyz[IIy];
+    eivy = eivy[,IIy];
+
+    eiv_prodx = eivx %*% diag(sqrt(eig_Kxz));
+    eiv_prody = eivy %*% diag(sqrt(eig_Kyz));
+
+    Num_eigx = dim(eiv_prodx)[2];
+    Num_eigy = dim(eiv_prody)[2];
+    Size_u = Num_eigx * Num_eigy;
+
+    uu = matrix(0,T,Size_u);
+
+
+    for (i in 1:Num_eigx){
+      for (j in 1:Num_eigy){
+        uu[,(i-1)*Num_eigy + j] = eiv_prodx[,i] * eiv_prody[,j];
+      }
+    }
+
+    if (Size_u > T){
+      uu_prod = uu %*% t(uu);}
+    else{
+      uu_prod = t(uu) %*% uu;
+    }
+
+    if (Bootstrap){
+      T_BS=5000;
+      IF_unbiased=TRUE;
+
+      list_uu = eigen(uu_prod);
+      eig_uu =list_uu[[1]];
+      II_f = which(eig_uu > max(eig_uu) * Thresh);
+      eig_uu = eig_uu[II_f];
+
+      if (length(eig_uu)*T < 1E6){
+        f_rand1 = matrix(rnorm(length(eig_uu)*T_BS)^2,length(eig_uu),T_BS);
+        Null_dstr = t(eig_uu) %*% f_rand1;
+
+      } else {
+
+        Null_dstr = matrix(0,1,T_BS);
+        Length = max(c(floor(1E6/T),100));
+        Itmax = floor(length(eig_uu)/Length);
+        for (iter in 1:Itmax){
+          f_rand1 = matrix(rnorm(Length*T_BS)^2,Length,T_BS);
+          Null_dstr = Null_dstr + t(eig_uu[((iter-1)*Length+1):(iter*Length)]) %*% f_rand1;
+
+        }
+      }
+
+      sort_Null_dstr = sort(Null_dstr);
+      #Cri = sort_Null_dstr[ceiling((1-alpha)*T_BS)];
+      p_val = sum(Null_dstr>Sta)/T_BS;
+      #print(p_val)
+      return(p_val)
+
+    } else {
+      mean_appr = sum(diag(uu_prod));
+      var_appr = 2*sum(diag(uu_prod^2));
+      k_appr = mean_appr^2/var_appr;
+      theta_appr = var_appr/mean_appr;
+      p_val = 1-pgamma(Sta, shape=k_appr, scale=theta_appr);
+      #print(p_val)
+      return(p_val)
+
+    }
+
+  }
+
+}
+
+
diff --git a/external_packages/RCIT/R/RBF_kernel.R b/external_packages/RCIT/R/RBF_kernel.R
new file mode 100644
index 00000000..8ffea5c8
--- /dev/null
+++ b/external_packages/RCIT/R/RBF_kernel.R
@@ -0,0 +1,11 @@
+RBF_kernel <- function(x,width){
+
+  n2=(dist(cbind(x,x),diag = TRUE, upper = TRUE))^2;
+  n2=as.matrix(n2);
+
+  width=width/2;
+  kx = exp(-n2*width);
+
+  return(kx)
+
+}
diff --git a/external_packages/RCIT/R/RCIT.R b/external_packages/RCIT/R/RCIT.R
new file mode 100644
index 00000000..1ace290d
--- /dev/null
+++ b/external_packages/RCIT/R/RCIT.R
@@ -0,0 +1,144 @@
+#' RCIT and RCoT - tests whether x and y are conditionally independent given z. Calls RIT if z is empty.
+#' @param x Random variable x.
+#' @param y Random variable y.
+#' @param z Random variable z.
+#' @param approx Method for approximating the null distribution. Options include:
+#' "lpd4," the Lindsay-Pilla-Basak method (default),
+#' "gamma" for the Satterthwaite-Welch method,
+#' "hbe" for the Hall-Buckley-Eagleson method,
+#' "chi2" for a normalized chi-squared statistic,
+#' "perm" for permutation testing (warning: this one is slow but recommended for small samples generally <500 )
+#' @param num_f Number of features for conditioning set. Default is 25.
+#' @param seed The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.
+#' @return A list containing the p-value \code{p} and statistic \code{Sta}
+#' @export
+#' @examples
+#' RCIT(rnorm(1000),rnorm(1000),rnorm(1000));
+#'
+#' x=rnorm(10000);
+#' y=(x+rnorm(10000))^2;
+#' z=rnorm(10000);
+#' RCIT(x,y,z,seed=2);
+
+
+RCIT <- function(x,y,z=NULL,approx="lpd4",num_f=25,seed=NULL){
+
+  if (length(z)==0){
+    out=RIT(x,y,approx=approx,seed=seed);
+    return(out)
+  }
+  else{
+
+    x=matrix2(x);
+    y=matrix2(y);
+    z=matrix2(z);
+
+    y = cbind(y,z)
+
+    z=z[,apply(z,2,sd)>0];
+    z=matrix2(z);
+    d=ncol(z);
+
+    if (length(z)==0){
+      out=RIT(x,y,approx=approx, seed=seed);
+      return(out);
+    } else if (sd(x)==0 | sd(y)==0){
+      out=list(p=1,Sta=0);
+      return(out)
+    }
+
+    r=nrow(x);
+    if (r>500){
+      r1=500
+    } else {r1=r;}
+
+    x=normalize(x);
+    y=normalize(y);
+    z=normalize(z);
+
+
+    four_z = random_fourier_features(z[,1:d],num_f=num_f,sigma=median(c(t(dist(z[1:r1,])))), seed = seed );
+    four_x = random_fourier_features(x,num_f=5,sigma=median(c(t(dist(x[1:r1,])))), seed = seed );
+    four_y = random_fourier_features(y,num_f=5,sigma=median(c(t(dist(y[1:r1,])))), seed = seed );
+
+    f_x=normalize(four_x$feat);
+    f_y=normalize(four_y$feat);
+    f_z=normalize(four_z$feat);
+
+    Cxy=cov(f_x,f_y);
+
+    Czz = cov(f_z);
+
+    i_Czz = ginv(Czz+diag(num_f)*1E-10); #requires library(MASS)
+    Cxz=cov(f_x,f_z);
+    Czy=cov(f_z,f_y);
+
+    z_i_Czz=f_z%*%i_Czz;
+    e_x_z = z_i_Czz%*%t(Cxz);
+    e_y_z = z_i_Czz%*%Czy;
+
+    #approximate null distributions
+
+    res_x = f_x-e_x_z;
+    res_y = f_y-e_y_z;
+
+    if (approx == "perm"){
+
+      Cxy_z = cov(res_x, res_y);
+      Sta = r*sum(Cxy_z^2);
+
+      nperm =1000;
+
+      Stas = c();
+      for (ps in 1:nperm){
+        perm = sample(1:r,r);
+        Sta_p = Sta_perm(res_x[perm,],res_y,r)
+        Stas = c(Stas, Sta_p);
+
+      }
+
+      p = 1-(sum(Sta >= Stas)/length(Stas));
+
+    } else {
+
+      Cxy_z=Cxy-Cxz%*%i_Czz%*%Czy; #less accurate for permutation testing
+      Sta = r*sum(Cxy_z^2);
+
+      d =expand.grid(1:ncol(f_x),1:ncol(f_y));
+      res = res_x[,d[,1]]*res_y[,d[,2]];
+      Cov = 1/r * (t(res)%*%res);
+
+      if (approx == "chi2"){
+      i_Cov = ginv(Cov)
+
+      Sta = r * (c(Cxy_z)%*%  i_Cov %*% c(Cxy_z) );
+      p = 1-pchisq(Sta, length(c(Cxy_z)));
+    } else{
+
+      eig_d = eigen(Cov,symmetric=TRUE);
+      eig_d$values=eig_d$values[eig_d$values>0];
+
+      if (approx == "gamma"){
+        p=1-sw(eig_d$values,Sta);
+
+      } else if (approx == "hbe") {
+
+        p=1-hbe(eig_d$values,Sta);
+
+      } else if (approx == "lpd4"){
+        eig_d_values=eig_d$values;
+        p=try(1-lpb4(eig_d_values,Sta),silent=TRUE);
+        if (!is.numeric(p) | is.nan(p)){
+          p=1-hbe(eig_d$values,Sta);
+        }
+      }
+    }
+    }
+
+    if (p<0) p=0;
+
+    out=list(p=p,Sta=Sta);
+    return(out)
+  }
+
+}
diff --git a/external_packages/RCIT/R/RCoT.R b/external_packages/RCIT/R/RCoT.R
new file mode 100644
index 00000000..3c8f7a43
--- /dev/null
+++ b/external_packages/RCIT/R/RCoT.R
@@ -0,0 +1,141 @@
+#' RCoT - tests whether x and y are conditionally independent given z. Calls RIT if z is empty.
+#' @param x Random variable x.
+#' @param y Random variable y.
+#' @param z Random variable z.
+#' @param approx Method for approximating the null distribution. Options include:
+#' "lpd4," the Lindsay-Pilla-Basak method (default),
+#' "gamma" for the Satterthwaite-Welch method,
+#' "hbe" for the Hall-Buckley-Eagleson method,
+#' "chi2" for a normalized chi-squared statistic,
+#' "perm" for permutation testing (warning: this one is slow but recommended for small samples generally <500 )
+#' @param num_f Number of features for conditioning set. Default is 25.
+#' @param seed The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.
+#' @return A list containing the p-value \code{p} and statistic \code{Sta}
+#' @export
+#' @examples
+#' RCIT(rnorm(1000),rnorm(1000),rnorm(1000));
+#'
+#' x=rnorm(10000);
+#' y=(x+rnorm(10000))^2;
+#' z=rnorm(10000);
+#' RCIT(x,y,z,seed=2);
+
+
+RCoT <- function(x,y,z=NULL,approx="lpd4",num_f=25,seed=NULL){
+
+  if (length(z)==0){
+    out=RIT(x,y,approx=approx,seed=seed);
+    return(out)
+  }
+  else{
+
+    x=matrix2(x);
+    y=matrix2(y);
+    z=matrix2(z);
+
+    z=z[,apply(z,2,sd)>0];
+    z=matrix2(z);
+    d=ncol(z);
+
+    if (length(z)==0){
+      out=RIT(x,y,approx=approx, seed=seed);
+      return(out);
+    } else if (sd(x)==0 | sd(y)==0){
+      out=list(p=1,Sta=0);
+      return(out)
+    }
+
+    r=nrow(x);
+    if (r>500){
+      r1=500
+    } else {r1=r;}
+
+    x=normalize(x);
+    y=normalize(y);
+    z=normalize(z);
+
+    four_z = random_fourier_features(z[,1:d],num_f=num_f,sigma=median(c(t(dist(z[1:r1,])))), seed = seed );
+    four_x = random_fourier_features(x,num_f=5,sigma=median(c(t(dist(x[1:r1,])))), seed = seed );
+    four_y = random_fourier_features(y,num_f=5,sigma=median(c(t(dist(y[1:r1,])))), seed = seed );
+
+    f_x=normalize(four_x$feat);
+    f_y=normalize(four_y$feat);
+    f_z=normalize(four_z$feat);
+
+    Cxy=cov(f_x,f_y);
+
+    Czz = cov(f_z);
+
+    i_Czz = ginv(Czz+diag(num_f)*1E-10); #requires library(MASS)
+    Cxz=cov(f_x,f_z);
+    Czy=cov(f_z,f_y);
+
+    z_i_Czz=f_z%*%i_Czz;
+    e_x_z = z_i_Czz%*%t(Cxz);
+    e_y_z = z_i_Czz%*%Czy;
+
+    #approximate null distributions
+
+    res_x = f_x-e_x_z;
+    res_y = f_y-e_y_z;
+
+    if (approx == "perm"){
+
+      Cxy_z = cov(res_x, res_y);
+      Sta = r*sum(Cxy_z^2);
+
+      nperm =1000;
+
+      Stas = c();
+      for (ps in 1:nperm){
+        perm = sample(1:r,r);
+        Sta_p = Sta_perm(res_x[perm,],res_y,r)
+        Stas = c(Stas, Sta_p);
+
+      }
+
+      p = 1-(sum(Sta >= Stas)/length(Stas));
+
+    } else {
+
+      Cxy_z=Cxy-Cxz%*%i_Czz%*%Czy; #less accurate for permutation testing
+      Sta = r*sum(Cxy_z^2);
+
+      d =expand.grid(1:ncol(f_x),1:ncol(f_y));
+      res = res_x[,d[,1]]*res_y[,d[,2]];
+      Cov = 1/r * (t(res)%*%res);
+
+      if (approx == "chi2"){
+        i_Cov = ginv(Cov)
+
+        Sta = r * (c(Cxy_z)%*%  i_Cov %*% c(Cxy_z) );
+        p = 1-pchisq(Sta, length(c(Cxy_z)));
+      } else{
+
+        eig_d = eigen(Cov, symmetric=TRUE);
+        eig_d$values=eig_d$values[eig_d$values>0];
+
+        if (approx == "gamma"){
+          p=1-sw(eig_d$values,Sta);
+
+        } else if (approx == "hbe") {
+
+          p=1-hbe(eig_d$values,Sta);
+
+        } else if (approx == "lpd4"){
+          eig_d_values=eig_d$values;
+          p=try(1-lpb4(eig_d_values,Sta),silent=TRUE);
+          if (!is.numeric(p) | is.nan(p)){
+            p=1-hbe(eig_d$values,Sta);
+          }
+        }
+      }
+    }
+
+    if (p<0) p=0;
+
+    out=list(p=p,Sta=Sta);
+    return(out)
+  }
+
+}
diff --git a/external_packages/RCIT/R/RIT.R b/external_packages/RCIT/R/RIT.R
new file mode 100644
index 00000000..f3f1dc2f
--- /dev/null
+++ b/external_packages/RCIT/R/RIT.R
@@ -0,0 +1,103 @@
+#' Tests whether x and y are unconditionally independent
+#' @param x Random variable x.
+#' @param y Random variable y.
+#' @param approx Method for approximating the null distribution. Options include:
+#' "lpd4," the Lindsay-Pilla-Basak method (default),
+#' "gamma" for the Satterthwaite-Welch method,
+#' "hbe" for the Hall-Buckley-Eagleson method,
+#' "chi2" for a normalized chi-squared statistic,
+#' "perm" for permutation testing (warning: this one is slow but recommended for small samples generally <500 )
+#' @param seed The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.
+#' @return A list containing the p-value \code{p} and statistic \code{Sta}
+#' @examples
+#' RIT(rnorm(1000),rnorm(1000));
+#'
+#' x=rnorm(1000);
+#' y=(x+rnorm(1000))^2;
+#' RIT(x,y);
+
+RIT <- function(x,y,approx="lpd4",seed=NULL){
+
+  if (sd(x)==0 | sd(y)==0){
+    out=list(p=1,Sta=0,w=w,b=b);
+    return(out$p)
+  }
+
+  x=matrix2(x);
+  y=matrix2(y);
+
+
+  r=nrow(x);
+  if (r>500){
+    r1=500
+  } else {r1=r;}
+
+  x=normalize(x);
+  y=normalize(y);
+
+  four_x = random_fourier_features(x,num_f=5,sigma=median(c(t(dist(x[1:r1,])))), seed = seed );
+  four_y = random_fourier_features(y,num_f=5,sigma=median(c(t(dist(y[1:r1,])))), seed = seed );
+
+  f_x=normalize(four_x$feat); #stabilizes computations
+  f_y=normalize(four_y$feat); #stabilizes computations
+
+  Cxy=cov(f_x,f_y);
+
+  Sta = r*sum(Cxy^2);
+
+  #approximate null distributions
+
+  if (approx == "perm"){
+    nperm =100;
+
+    Stas = c();
+    for (ps in 1:nperm){
+      perm = sample(1:r,r);
+      Sta_p = Sta_perm(f_x[perm,],f_y,r)
+      Stas = c(Stas, Sta_p);
+
+    }
+
+    p = 1-(sum(Sta >= Stas)/length(Stas));
+
+  } else{
+
+    res_x = f_x-repmat(t(matrix(colMeans(f_x))),r,1);
+    res_y = f_y-repmat(t(matrix(colMeans(f_y))),r,1);
+
+    d =expand.grid(1:ncol(f_x),1:ncol(f_y));
+    res = res_x[,d[,1]]*res_y[,d[,2]];
+    Cov = 1/r * (t(res)%*%res);
+
+    if (approx == "chi2"){
+    i_Cov = ginv(Cov)
+
+    Sta = r * (c(Cxy)%*%  i_Cov %*% c(Cxy) );
+    p = 1-pchisq(Sta, length(c(Cxy)));
+  } else{
+
+    eig_d = eigen(Cov);
+    eig_d$values=eig_d$values[eig_d$values>0];
+
+    if (approx == "gamma"){
+      p=1-sw(eig_d$values,Sta);
+
+    } else if (approx == "hbe") {
+
+      p=1-hbe(eig_d$values,Sta);
+
+    } else if (approx == "lpd4"){
+      eig_d_values=eig_d$values;
+      p=try(1-lpb4(eig_d_values,Sta), silent=TRUE);
+      if (!is.numeric(p)){
+        p=1-hbe(eig_d$values,Sta);
+      }
+    }
+  }
+  }
+
+  if (p<0) p=0;
+
+  out=list(p=p,Sta=Sta);
+  return(out)
+}
diff --git a/external_packages/RCIT/R/Sta_perm.R b/external_packages/RCIT/R/Sta_perm.R
new file mode 100644
index 00000000..b571731f
--- /dev/null
+++ b/external_packages/RCIT/R/Sta_perm.R
@@ -0,0 +1,10 @@
+
+Sta_perm <- function(f_x,f_y,r){
+
+Cxy=cov(f_x,f_y);
+
+Sta = r*sum(Cxy^2);
+
+return(Sta)
+
+}
diff --git a/external_packages/RCIT/R/U_KCI.R b/external_packages/RCIT/R/U_KCI.R
new file mode 100644
index 00000000..fdc6904d
--- /dev/null
+++ b/external_packages/RCIT/R/U_KCI.R
@@ -0,0 +1,39 @@
+#' RCIT and RCoT - tests whether x and y are unconditionally independent..
+#' @param x Random variable x.
+#' @param y Random variable y.
+#' @return The p-value \code{p}
+#' @export
+
+U_KCI <-function(x,y){
+
+if (sd(x)>0) x=RCIT:::normalize(x);
+if (sd(y)>0) y=RCIT:::normalize(y);
+
+
+T=length(x);
+
+if (length(x)>500){
+  width = sqrt(2)*median(as.vector(dist(cbind(x[1:500],y[1:500]))));}
+else {
+  width = sqrt(2)*median(as.vector(dist(cbind(x,y))));}
+
+theta = 1/(width^2);
+
+H =  diag(T) - (1/T)*matrix(1,T,T);
+
+Kx = RBF_kernel(x, theta); Kx = H %*% Kx %*% H;
+Ky = RBF_kernel(y, theta); Ky = H %*% Ky %*% H;
+
+Sta = sum(diag(Kx %*% Ky));
+
+mean_appr = sum(diag(Kx)) * sum(diag(Ky)) /T;
+var_appr = 2* sum(diag(Kx %*% Kx)) %*% sum(diag(Ky %*% Ky))/T^2;
+k_appr = (mean_appr^2)/var_appr;
+theta_appr = var_appr/mean_appr;
+
+
+p_val = 1-pgamma(Sta, shape=k_appr, scale=theta_appr);
+
+return(p_val)
+
+}
diff --git a/external_packages/RCIT/R/matrix2.R b/external_packages/RCIT/R/matrix2.R
new file mode 100644
index 00000000..4f006d03
--- /dev/null
+++ b/external_packages/RCIT/R/matrix2.R
@@ -0,0 +1,14 @@
+#' Converts a vector to a matrix, or does nothing if the input is a matrix.
+#' @param mat Vector or matrix.
+#' @return Matrix.
+#' @examples
+#' matrix2(rnorm(10));
+#' matrix2(matrix(rnorm(10,2),ncol=2));
+
+matrix2 <- function(mat)
+
+  if (is.matrix(mat)) {
+    return(mat);
+  } else {
+    mat = matrix(mat);
+    return(mat); }
diff --git a/external_packages/RCIT/R/normalize.R b/external_packages/RCIT/R/normalize.R
new file mode 100644
index 00000000..fd54b8f8
--- /dev/null
+++ b/external_packages/RCIT/R/normalize.R
@@ -0,0 +1,15 @@
+#' Normalizes each column of a matrix to mean zero unit variance.
+#' @param mat Matrix
+#' @return A matrix where each column has mean zero and unit variance.
+#' @examples
+#' normalize(matrix(rnorm(10,2),ncol=2))
+
+
+normalize <- function(mat){
+
+  if (is.null(nrow(mat))){mat = matrix(mat);}
+
+  mat = apply(mat, 2, function(x) if (sd(x)>0){(x - mean(x)) / sd(x)} else{x-mean(x);})
+
+
+}
diff --git a/external_packages/RCIT/R/random_fourier_features.R b/external_packages/RCIT/R/random_fourier_features.R
new file mode 100644
index 00000000..e2dc0b36
--- /dev/null
+++ b/external_packages/RCIT/R/random_fourier_features.R
@@ -0,0 +1,37 @@
+#' Generate random Fourier features
+#' @param x Random variable x.
+#' @param w Random coefficients.
+#' @param b Random offsets.
+#' @param num_f Number of random Fourier features.
+#' @param sigma Smooth parameter of RBF kernel.
+#' @param seed The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.
+#' @return A list containing the p-value \code{p} and statistic \code{Sta}
+#' @examples
+#' random_fourier_features(rnorm(1000),num_f=25)
+
+random_fourier_features <- function(x,w=NULL,b=NULL,num_f=NULL,sigma=NULL,seed=NULL){
+
+  if (length(num_f)==0) num_f = 25;
+
+  x = matrix2(x);
+
+  r=nrow(x);
+  c=ncol(x);
+
+  if (sigma==0 | is.na(sigma)){sigma=1};
+
+  if (length(w)==0){
+    if (length(sigma)==0){sigma=1;}
+    set.seed(seed)
+    w=(1/sigma)*matrix(rnorm(num_f*c),num_f,c);
+    set.seed(seed)
+    b=repmat(2*pi*runif(num_f),1,r);
+  } #else if (nrow(w)<num_f){
+  #w=rbind(w,(1/sigma)*matrix(rnorm((num_f-nrow(w))*c),num_f-nrow(w),c));
+  #b=rbind(b,repmat(2*pi*runif(num_f-nrow(b)),1,r));
+  #}
+  feat = sqrt(2)*t(cos(w[1:num_f,1:c]%*%t(x)+b[1:num_f,]));
+
+  out=list(feat=feat,w=w,b=b);
+  return(out)
+}
diff --git a/external_packages/RCIT/R/repmat.R b/external_packages/RCIT/R/repmat.R
new file mode 100644
index 00000000..142a4add
--- /dev/null
+++ b/external_packages/RCIT/R/repmat.R
@@ -0,0 +1,15 @@
+#' Repeat rows or columns of a vector or matrix
+#' @param X Vector or matrix
+#' @param m number of row copies
+#' @param n number of column copies
+#' @return Matrix.
+#' @examples
+#' repmat(matrix(rnorm(10*2),ncol=2),2,2)
+
+repmat = function(X,m,n){
+  ##R equivalent of repmat (matlab)
+  if (!is.matrix(X)){X=matrix(X);}
+  mx = dim(X)[1]
+  nx = dim(X)[2]
+  matrix(t(matrix(X,mx,nx*n)),mx*m,nx*n,byrow=T)
+}
diff --git a/external_packages/RCIT/RCIT.Rproj b/external_packages/RCIT/RCIT.Rproj
new file mode 100644
index 00000000..488ba327
--- /dev/null
+++ b/external_packages/RCIT/RCIT.Rproj
@@ -0,0 +1,21 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace,vignette
diff --git a/external_packages/RCIT/README.md b/external_packages/RCIT/README.md
new file mode 100644
index 00000000..6fe52201
--- /dev/null
+++ b/external_packages/RCIT/README.md
@@ -0,0 +1,17 @@
+# RCIT and RCoT
+This is an R package implementing the Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT).
+
+# Installation
+
+The package depends on the MASS and momentchi2 packages on CRAN, so please install these first. Then:
+
+> library(devtools)
+
+> install_github("ericstrobl/RCIT")
+
+> library(RCIT)
+
+> RCIT(rnorm(1000),rnorm(1000),rnorm(1000))
+
+> RCoT(rnorm(1000),rnorm(1000),rnorm(1000))
+
diff --git a/external_packages/RCIT/Updates.md b/external_packages/RCIT/Updates.md
new file mode 100644
index 00000000..c0abbe9f
--- /dev/null
+++ b/external_packages/RCIT/Updates.md
@@ -0,0 +1,9 @@
+# 6/10/17
+-Added a permutation option to estimate the null (approx="perm"). May be better for smaller sample sizes, although the permutation doesn't change the fact that non-linear regression is difficult with few samples.
+
+-Separated RCIT and RCoT into two different functions. 
+
+-Added KCIT from (Zhang et al., 2011).
+
+# 5/8/17
+Added an additional method for estimating the null distribution called "chi2" which normalizes the empirical partial cross-covariance matrix so that it asymptotically follows a Gaussian with diagonal covariance (after root-n multiplication). The resultant statistic therefore asymptotically obeys a central chi-squared distribution with 25 degrees of freedom rather than a weighted sum of chi-squares. I find that the p-values are slightly less accurate than the default method in general. On the other hand, the new method outputs a normalized statistic, so the statistics are comparable without the same random seed. This method may be useful in algorithms like HITON-PC which initially sort variables according to the test statistic.
diff --git a/external_packages/RCIT/man/KCIT.Rd b/external_packages/RCIT/man/KCIT.Rd
new file mode 100644
index 00000000..b6caa0e6
--- /dev/null
+++ b/external_packages/RCIT/man/KCIT.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/KCIT.R
+\name{KCIT}
+\alias{KCIT}
+\title{RCIT and RCoT - tests whether x and y are conditionally independent given z. Calls U_KCI if z is empty.}
+\usage{
+KCIT(x, y, z, Bootstrap = TRUE)
+}
+\arguments{
+\item{x}{Random variable x.}
+
+\item{y}{Random variable y.}
+
+\item{z}{Random variable z.}
+
+\item{Bootstrap}{Estimate the null by bootstrap. Default is TRUE. Runs slightly faster when set to FALSE but with less accurate p-values.}
+}
+\value{
+The p-value \code{p}
+}
+\description{
+RCIT and RCoT - tests whether x and y are conditionally independent given z. Calls U_KCI if z is empty.
+}
+
diff --git a/external_packages/RCIT/man/RCIT.Rd b/external_packages/RCIT/man/RCIT.Rd
new file mode 100644
index 00000000..8e4abaf8
--- /dev/null
+++ b/external_packages/RCIT/man/RCIT.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/RCIT.R
+\name{RCIT}
+\alias{RCIT}
+\title{RCIT and RCoT - tests whether x and y are conditionally independent given z. Calls RIT if z is empty.}
+\usage{
+RCIT(x, y, z = NULL, approx = "lpd4", corr = TRUE, num_f = 25,
+  seed = NULL)
+}
+\arguments{
+\item{x}{Random variable x.}
+
+\item{y}{Random variable y.}
+
+\item{z}{Random variable z.}
+
+\item{approx}{Method for approximating the null distribution. Options include:
+"lpd4," the Lindsay-Pilla-Basak method (default),
+"gamma" for the Satterthwaite-Welch method,
+"hbe" for the Hall-Buckley-Eagleson method,
+"chi2" for a normalized chi-squared statistic,
+"perm" for permutation testing (warning: this one is slow but recommended for small samples generally <500 )}
+
+\item{corr}{TRUE corresponds to RCoT and FALSE to RCIT. Default is TRUE.}
+
+\item{num_f}{Number of features for conditioning set. Default is 25.}
+
+\item{seed}{The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.}
+}
+\value{
+A list containing the p-value \code{p} and statistic \code{Sta}
+}
+\description{
+RCIT and RCoT - tests whether x and y are conditionally independent given z. Calls RIT if z is empty.
+}
+\examples{
+RCIT(rnorm(1000),rnorm(1000),rnorm(1000));
+
+x=rnorm(10000);
+y=(x+rnorm(10000))^2;
+z=rnorm(10000);
+RCIT(x,y,z,seed=2);
+}
+
diff --git a/external_packages/RCIT/man/RIT.Rd b/external_packages/RCIT/man/RIT.Rd
new file mode 100644
index 00000000..d1b63aaf
--- /dev/null
+++ b/external_packages/RCIT/man/RIT.Rd
@@ -0,0 +1,36 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/RIT.R
+\name{RIT}
+\alias{RIT}
+\title{Tests whether x and y are unconditionally independent}
+\usage{
+RIT(x, y, approx = "lpd4", seed = NULL)
+}
+\arguments{
+\item{x}{Random variable x.}
+
+\item{y}{Random variable y.}
+
+\item{approx}{Method for approximating the null distribution. Options include:
+"lpd4," the Lindsay-Pilla-Basak method (default),
+"gamma" for the Satterthwaite-Welch method,
+"hbe" for the Hall-Buckley-Eagleson method,
+"chi2" for a normalized chi-squared statistic,
+"perm" for permutation testing (warning: this one is slow but recommended for small samples generally <500 )}
+
+\item{seed}{The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.}
+}
+\value{
+A list containing the p-value \code{p} and statistic \code{Sta}
+}
+\description{
+Tests whether x and y are unconditionally independent
+}
+\examples{
+RIT(rnorm(1000),rnorm(1000));
+
+x=rnorm(1000);
+y=(x+rnorm(1000))^2;
+RIT(x,y);
+}
+
diff --git a/external_packages/RCIT/man/U_KCI.Rd b/external_packages/RCIT/man/U_KCI.Rd
new file mode 100644
index 00000000..7c726a38
--- /dev/null
+++ b/external_packages/RCIT/man/U_KCI.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/U_KCI.R
+\name{U_KCI}
+\alias{U_KCI}
+\title{RCIT and RCoT - tests whether x and y are unconditionally independent..}
+\usage{
+U_KCI(x, y)
+}
+\arguments{
+\item{x}{Random variable x.}
+
+\item{y}{Random variable y.}
+}
+\value{
+The p-value \code{p}
+}
+\description{
+RCIT and RCoT - tests whether x and y are unconditionally independent..
+}
+
diff --git a/external_packages/RCIT/man/matrix2.Rd b/external_packages/RCIT/man/matrix2.Rd
new file mode 100644
index 00000000..956b07fa
--- /dev/null
+++ b/external_packages/RCIT/man/matrix2.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/matrix2.R
+\name{matrix2}
+\alias{matrix2}
+\title{Converts a vector to a matrix, or does nothing if the input is a matrix.}
+\usage{
+matrix2(mat)
+}
+\arguments{
+\item{mat}{Vector or matrix.}
+}
+\value{
+Matrix.
+}
+\description{
+Converts a vector to a matrix, or does nothing if the input is a matrix.
+}
+\examples{
+matrix2(rnorm(10));
+matrix2(matrix(rnorm(10,2),ncol=2));
+}
+
diff --git a/external_packages/RCIT/man/normalize.Rd b/external_packages/RCIT/man/normalize.Rd
new file mode 100644
index 00000000..8006cb9f
--- /dev/null
+++ b/external_packages/RCIT/man/normalize.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/normalize.R
+\name{normalize}
+\alias{normalize}
+\title{Normalizes each column of a matrix to mean zero unit variance.}
+\usage{
+normalize(mat)
+}
+\arguments{
+\item{mat}{Matrix}
+}
+\value{
+A matrix where each column has mean zero and unit variance.
+}
+\description{
+Normalizes each column of a matrix to mean zero unit variance.
+}
+\examples{
+normalize(matrix(rnorm(10,2),ncol=2))
+}
+
diff --git a/external_packages/RCIT/man/random_fourier_features.Rd b/external_packages/RCIT/man/random_fourier_features.Rd
new file mode 100644
index 00000000..cba135e7
--- /dev/null
+++ b/external_packages/RCIT/man/random_fourier_features.Rd
@@ -0,0 +1,32 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/random_fourier_features.R
+\name{random_fourier_features}
+\alias{random_fourier_features}
+\title{Generate random Fourier features}
+\usage{
+random_fourier_features(x, w = NULL, b = NULL, num_f = NULL,
+  sigma = NULL, seed = NULL)
+}
+\arguments{
+\item{x}{Random variable x.}
+
+\item{w}{Random coefficients.}
+
+\item{b}{Random offsets.}
+
+\item{num_f}{Number of random Fourier features.}
+
+\item{sigma}{Smooth parameter of RBF kernel.}
+
+\item{seed}{The seed for controlling random number generation. Use if you want to replicate results exactly. Default is NULL.}
+}
+\value{
+A list containing the p-value \code{p} and statistic \code{Sta}
+}
+\description{
+Generate random Fourier features
+}
+\examples{
+random_fourier_features(rnorm(1000),num_f=25)
+}
+
diff --git a/external_packages/RCIT/man/repmat.Rd b/external_packages/RCIT/man/repmat.Rd
new file mode 100644
index 00000000..c550b5d7
--- /dev/null
+++ b/external_packages/RCIT/man/repmat.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/repmat.R
+\name{repmat}
+\alias{repmat}
+\title{Repeat copies of a matrix or a vector just like MATLAB's repmat function}
+\usage{
+repmat(X, m, n)
+}
+\arguments{
+\item{X}{a matrix or a vector}
+
+\item{m}{Number of row repeats}
+
+\item{n}{Number of column repeats}
+}
+\value{
+A matrix with associated row and column repeats
+}
+\description{
+Repeat copies of a matrix or a vector just like MATLAB's repmat function
+}
+\examples{
+repmat(matrix(rnorm(2*2),ncol=2),m=2,n=3)
+}
+
diff --git a/install_r_packages.sh b/install_r_packages.sh
new file mode 100755
index 00000000..da5758f6
--- /dev/null
+++ b/install_r_packages.sh
@@ -0,0 +1,8 @@
+# Install r-MASS package, which is a dependency of RCIT/RCOT
+R -e 'install.packages("MASS", repos = "https://cloud.r-project.org/")' 
+# Install momentchi2, whichs is a dependency of RCIT/RCOT
+R -e 'install.packages("momentchi2", repos = "https://cloud.r-project.org/")' 
+# Install devtools so we can install from github
+R -e 'install.packages("devtools", repos = "https://cloud.r-project.org/")' 
+# Install RCOT/RCIT
+R -e 'devtools::install("external_packages/RCIT")'
diff --git a/run_pcmci_parallel.py b/run_pcmci_parallel.py
index dd03c3a9..86c1fcca 100644
--- a/run_pcmci_parallel.py
+++ b/run_pcmci_parallel.py
@@ -15,7 +15,7 @@
 
 from mpi4py import MPI
 import numpy
-import os, sys, cPickle
+import os, sys, pickle
 
 from tigramite import data_processing as pp
 from tigramite.pcmci import PCMCI
@@ -56,7 +56,6 @@ def run_pc_stable_parallel(j):
         dataframe=dataframe,
         cond_ind_test=cond_ind_test,
         selected_variables=[j],
-        var_names=var_names,
         verbosity=verbosity)
 
     # Run PC condition-selection algorithm. Also here further parameters can be
@@ -120,12 +119,12 @@ def run_mci_parallel(j, pcmci_of_j, all_parents):
 data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
 T, N = data.shape
 
-# Initialize dataframe object
-dataframe = pp.DataFrame(data)
-
 # Optionally specify variable names
 var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']
 
+# Initialize dataframe object
+dataframe = pp.DataFrame(data, var_names=var_names)
+
 # Significance level in condition-selection step. If a list of levels is is
 # provided or pc_alpha=None, the optimal pc_alpha is automatically chosen via
 # model-selection.
@@ -160,7 +159,7 @@ def run_mci_parallel(j, pcmci_of_j, all_parents):
 cond_ind_test = ParCorr()  #confidence='analytic')
 
 # Store results in file
-file_name = os.path.expanduser('~') + '/test/test_results.dat'
+file_name = os.path.expanduser('~') + '/test_results.dat'
 
 
 #
@@ -217,8 +216,8 @@ def run_mci_parallel(j, pcmci_of_j, all_parents):
         print("\n\n## Resulting condition sets:")
         for j in [var for var in all_parents.keys()]:
             pcmci_objects[j]._print_parents_single(j, all_parents[j],
-                                    pcmci_objects[j].val_matrix[j], 
-                                    pcmci_objects[j].p_matrix[j])
+                                    pcmci_objects[j].val_min[j], 
+                                    pcmci_objects[j].p_max[j])
 
     if verbosity > -1:
         print("\n##\n## Running Parallelized Tigramite MCI algorithm\n##"
@@ -314,13 +313,13 @@ def run_mci_parallel(j, pcmci_of_j, all_parents):
                         conf_matrix[p[0], j, abs(p[1])][0], 
                         conf_matrix[p[0], j, abs(p[1])][1])
 
-            print string
+            print (string)
 
 
     if verbosity > -1:
         print("Pickling to "), file_name
     file = open(file_name, 'wb')
-    cPickle.dump(all_results, file, protocol=-1)        
+    pickle.dump(all_results, file, protocol=-1)        
     file.close()
     # PCMCI._print_significant_links(
     #        p_matrix=all_results['p_matrix'],
diff --git a/setup.cfg b/setup.cfg
index 224a7795..b88034e4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,2 +1,2 @@
 [metadata]
-description-file = README.md
\ No newline at end of file
+description-file = README.md
diff --git a/setup.py b/setup.py
index 0a2802b4..ce56f253 100644
--- a/setup.py
+++ b/setup.py
@@ -1,75 +1,102 @@
-# from distutils.core import setup, Extension
-from setuptools import setup, Extension
-
-import numpy
-
+"""
+Install tigramite
+"""
+from __future__ import print_function
 import io
+from setuptools import setup, Extension
+from setuptools.command.build_ext import build_ext
 
-# If cython is available, the included cython *.pyx file
-# is compiled, otherwise the *.c file is used
-try:
-    from Cython.Distutils import build_ext
-except ImportError:
-    use_cython = False
-else:
-    use_cython = True
-
-
-install_requires = [
-    'numpy',
-    'scipy'
-]
-
-extras_require = {
-    'Gaussian Process (GP) Regression':  ['sklearn>=0.18'],
-    'R-based ACE, also requires acepack installed in R':  ['rpy2'],
-    'pure-python ACE':  ['ace>=0.3'],
-    'plotting': ['matplotlib>=1.5', 'networkx>=1.10'],
-    'p-value corrections': ['statsmodels']
-}
+# Handle building against numpy headers before installing numpy
+class UseNumpyHeadersBuildExt(build_ext):
+    """
+    Subclassed build_ext command.
+    Allows for numpy to be imported after it is automatically installed.
+    This lets us use numpy.get_include() while listing numpy as a needed
+    dependency.
+    """
+    def run(self):
+        # Import numpy here, only when headers are needed
+        import numpy
+        # Add numpy headers to include_dirs
+        self.include_dirs.append(numpy.get_include())
+        # Call original build_ext command
+        build_ext.run(self)
 
-tests_require = ['nose']
+# Handle cythonizing code only in development mode
+def define_extension(extension_name, source_files=None):
+    """
+    Will define an extension from the *.c files unless in "setup.py develop"
+    is called.  If this is in develop mode, then it tries to import cython
+    and regenerate the *.c files from the *.pyx files
+    :return: single-element list of needed extension
+    """
+    # Default source file
+    if source_files is None:
+        source_files = [extension_name.replace(".", "/") + ".c"]
+    # If we are, try to import and use cythonize
+    try:
+        from Cython.Build import cythonize
+        # Replace any extension in the source file list with .pyx
+        source_files = [".".join(f.split(".")[:-1] + ["pyx"]) \
+                        for f in source_files]
+        # Return the cythonized extension
+        return cythonize(Extension(extension_name, source_files))
+    except ImportError:
+        print("Cython cannot be found.  Skipping generation of C code from"+\
+              " cython and using pre-compiled C code instead")
+        return [Extension(extension_name, source_files)]
 
+# Define the minimal classes needed to install and run tigramite
+INSTALL_REQUIRES = ["numpy", "scipy", "six"]
+# Define the all the possible extras needed
+EXTRAS_REQUIRE = {
+    'all' : ['scikit-learn>=0.18',#Gaussian Process (GP) Regression
+             'matplotlib>=1.5',   #plotting
+             'networkx>=1.10',    #plotting
+             'rpy2'],             #R-based RCOT
+    'R'   : ['rpy2']              #R-based RCOT
+    }
+# Define the packages needed for testing
+TESTS_REQUIRE = ['nose',
+                 'pytest',
+                 'scikit-learn>=0.18',
+                 'rpy2']
+EXTRAS_REQUIRE['test'] = TESTS_REQUIRE
+# Define the extras needed for development
+EXTRAS_REQUIRE['dev'] = EXTRAS_REQUIRE['all'] + TESTS_REQUIRE + ['cython']
 
-cmdclass = { }
-ext_modules = [ ]
-
-if use_cython:
-    ext_modules += [
-        Extension("tigramite.tigramite_cython_code", 
-                  [ "tigramite/tigramite_cython_code.pyx" ],
-                  include_dirs=[numpy.get_include()]),
-    ]
-    cmdclass.update({ 'build_ext': build_ext })
-else:
-    ext_modules += [
-        Extension("tigramite.tigramite_cython_code", 
-                  ["tigramite/tigramite_cython_code.c"],
-                  include_dirs=[numpy.get_include()]),
-    ]
+# Use a custom build to handle numpy.include_dirs() when building
+CMDCLASS = {'build_ext': UseNumpyHeadersBuildExt}
+# Define the external modules to build
+EXT_MODULES = []
+EXT_MODULES += define_extension("tigramite.tigramite_cython_code")
 
+# Run the setup
 setup(
     name='tigramite',
-    version='3.0b0',
-    packages=['tigramite',],
+    version='4.0.0-beta',
+    packages=['tigramite'],
     license='GNU General Public License v3.0',
     description='Tigramite causal discovery for time series',
     author='Jakob Runge',
-    author_email='jakobrunge@posteo.de',
+    author_email='jakob@jakob-runge.com',
     url='https://github.com/jakobrunge/tigramite/',
     long_description=io.open('README.md', 'r', encoding='utf-8').read(),
-    keywords = 'causality time-series',
-    cmdclass = cmdclass,
-    ext_modules=ext_modules,
-    install_requires=install_requires,
-    test_suite = 'nose.collector',
-    tests_require = tests_require,
+    keywords='causality time-series',
+    cmdclass=CMDCLASS,
+    ext_modules=EXT_MODULES,
+    install_requires=INSTALL_REQUIRES,
+    extras_require=EXTRAS_REQUIRE,
+    test_suite='tests',
+    tests_require=TESTS_REQUIRE,
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
         'Topic :: Scientific/Engineering :: Mathematics',
-        'License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)',
+        'License '\
+            ':: OSI Approved '\
+            ':: GNU General Public License v3 or later (GPLv3+)',
         'Programming Language :: Python :: 2.7',
-    ],
-)
\ No newline at end of file
+    ]
+)
diff --git a/tests/test_construct_array.py b/tests/test_construct_array.py
new file mode 100644
index 00000000..641dade7
--- /dev/null
+++ b/tests/test_construct_array.py
@@ -0,0 +1,125 @@
+"""
+Tests for independence_tests.py.
+"""
+from __future__ import print_function
+from collections import OrderedDict
+import numpy as np
+import pytest
+
+import tigramite.data_processing as pp
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# Define the verbosity at the global scope
+VERBOSITY = 1
+
+# CONSTRUCT ARRAY TESTING FUNCTIONS ############################################
+def rand_node(t_min, n_max, t_max=0, n_min=0):
+    """
+    Generate a random node to test
+    """
+    rand_node = np.random.randint(n_min, n_max)
+    rand_time = np.random.randint(t_min, t_max)
+    return (rand_node, rand_time)
+
+def gen_nodes(n_nodes, seed, t_min, n_max):
+    """
+    Generate some random nodes to tests
+    """
+    # Set the seed if needed
+    np.random.seed(seed)
+    # Y nodes are always at (0, 0)
+    y_nds = [(0, 0)]
+    # X nodes is only one node
+    x_nds = [rand_node(t_min, n_max)]
+    # Z nodes are multiple nodes
+    z_nds = [rand_node(t_min, n_max) for _ in range(n_nodes)]
+    return x_nds, y_nds, z_nds
+
+
+# CONSTRUCT ARRAY TESTING ######################################################
+@pytest.fixture(params=[
+    # Parameterize the array construction
+    #(X, Y, Z) nodes,        t_max, m_val, mask_type
+    (gen_nodes(3, 0, -3, 9), 3,     False, None),           # Few nodes
+    (gen_nodes(7, 1, -3, 9), 3,     False, None),           # More nodes
+    (gen_nodes(7, 2, -3, 3), 3,     False, None),           # Repeated nodes
+    (gen_nodes(7, 3, -3, 9), 3,     True,  None),           # Missing vals
+    (gen_nodes(7, 4, -3, 9), 3,     True,  ['x']),          # M-val + masked x
+    (gen_nodes(7, 4, -3, 9), 3,     True,  ['x','y']),      # M-val + masked xy
+    (gen_nodes(3, 5, -4, 9), 2,     False, ['x']),          # masked x
+    (gen_nodes(3, 6, -4, 9), 2,     False, ['y']),          # masked y
+    (gen_nodes(3, 7, -4, 9), 2,     False, ['z']),          # masked z
+    (gen_nodes(3, 7, -4, 9), 2,     False, ['x','y','z'])]) # mask xyz
+def cstrct_array_params(request):
+    return request.param
+
+def test_construct_array(cstrct_array_params):
+    # Unpack the parameters
+    (x_nds, y_nds, z_nds), tau_max, missing_vals, mask_type =\
+        cstrct_array_params
+    # Make some fake data
+    data = np.arange(1000).reshape(10, 100).T
+    # Get the needed parameters from the data
+    T, N = data.shape
+    max_lag = 2*tau_max
+    n_times = T - max_lag
+
+    # When testing masking and missing value flags, we will remove time slices,
+    # starting with the earliest slice.  This counter keeps track of how many
+    # rows have been masked.
+    n_rows_masked = 0
+
+    # Make a fake mask
+    data_mask = np.zeros_like(data, dtype='bool')
+    if mask_type is not None:
+        for var, nodes in zip(['x', 'y', 'z'], [x_nds, y_nds, z_nds]):
+            if var in mask_type:
+                # Get the first node
+                a_nd, a_tau = nodes[0]
+                # Mask the first value of this node
+                data_mask[a_tau - n_times + n_rows_masked, a_nd] = True
+                n_rows_masked += 1
+
+    # Choose fake missing value as the earliest time entry in the first z-node
+    # from the original (non-shifted) datathat is not cutoff by max_lag or
+    # masked values from the first z-node
+    missing_flag = None
+    if missing_vals:
+        # Get the node index
+        a_nd, _ = z_nds[0]
+        # Select the earliest non-cutoff entry from the unshifted data set
+        earliest_time = max_lag + n_rows_masked
+        missing_flag = data[earliest_time, a_nd]
+        # Record that the row with this value and all rows up to max_lag after
+        # this value have been cut off as well
+        n_rows_masked += max_lag + 1
+
+    # Construct the array
+    data_f = pp.DataFrame(data, data_mask, missing_flag)
+    array, xyz = data_f.construct_array(x_nds, y_nds, z_nds,
+                                        tau_max=tau_max,
+                                        mask_type=mask_type,
+                                        verbosity=VERBOSITY)
+    # Ensure x_nds, y_nds, z_ndes are unique
+    x_nds = list(OrderedDict.fromkeys(x_nds))
+    y_nds = list(OrderedDict.fromkeys(y_nds))
+    z_nds = list(OrderedDict.fromkeys(z_nds))
+    z_nds = [node for node in z_nds
+             if (node not in x_nds) and (node not in y_nds)]
+
+    # Get the expected results
+    expect_array = np.array([list(range(data[time-n_times, node],
+                                        data[time-n_times, node]+n_times))
+                             for node, time in x_nds + y_nds + z_nds])
+    expect_xyz = np.array([0 for _ in x_nds] +\
+                          [1 for _ in y_nds] +\
+                          [2 for _ in z_nds])
+    # Apply the mask, which always blocks the latest time of the 0th node of the
+    # masked variable, which removes the first n time slices in the returned
+    # array
+    expect_array = expect_array[:, n_rows_masked:]
+    # Test the results
+    np.testing.assert_almost_equal(array, expect_array)
+    np.testing.assert_almost_equal(xyz, expect_xyz)
diff --git a/tests/test_independence_tests.py b/tests/test_independence_tests.py
new file mode 100644
index 00000000..3ec773d8
--- /dev/null
+++ b/tests/test_independence_tests.py
@@ -0,0 +1,634 @@
+"""
+Tests for independence_tests.py.
+"""
+from __future__ import print_function
+import numpy as np
+import pytest
+
+from tigramite.independence_tests import ParCorr, GPDC, CMIsymb, CMIknn, RCOT
+import tigramite.data_processing as pp
+
+from test_pcmci_calculations import a_chain, gen_data_frame
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# Define the verbosity at the global scope
+VERBOSITY = 1
+
+def _par_corr_to_cmi(par_corr):
+    """Transformation of partial correlation to CMI scale."""
+    return -0.5 * np.log(1. - par_corr**2)
+
+# INDEPENDENCE TEST DATA GENERATION ############################################
+def gen_data_sample(seed, corr_val, T):
+    # Set the random seed
+    np.random.seed(seed)
+    # Define a symmetric covariance matrix
+    cov = np.array([[1., corr_val],
+                    [corr_val, 1.]])
+    # Generate some random data using the above covariance relation
+    dim = cov.shape[0]
+    array = np.random.multivariate_normal(mean=np.zeros(dim), cov=cov, size=T).T
+    # Get the correlation coefficient
+    val = np.corrcoef(array)[0, 1]
+    # Get a value from the array
+    xyz = np.array([0, 1])
+    # Return the array, the value, and the xyz array
+    return array, val, corr_val, xyz, dim, T
+
+# INDEPENDENCE TEST COMMON TESTS ###############################################
+def check_get_array(ind_test, sample):
+    # Get the data sample values
+    dataframe, true_parents = sample
+    # Set the dataframe of the test object
+    ind_test.set_dataframe(dataframe)
+    # Generate some nodes
+    y_nds = [(0, 0)]
+    x_nds = true_parents[0]
+    z_nds = true_parents[1]
+    tau_max = 3
+    # Get the array using the wrapper function
+    a_array, a_xyz, a_xyz_nodes = \
+            ind_test._get_array(x_nds, y_nds, z_nds, tau_max)
+    # Get the array directly from the dataframe
+    b_array, b_xyz, b_xyz_nodes = \
+            dataframe.construct_array(x_nds, y_nds, z_nds,
+                                      tau_max=tau_max,
+                                      mask_type=ind_test.mask_type,
+                                      return_cleaned_xyz=True,
+                                      do_checks=False,
+                                      verbosity=ind_test.verbosity)
+    # Check the values are the same
+    np.testing.assert_allclose(a_array, b_array)
+    np.testing.assert_allclose(a_xyz, b_xyz)
+    np.testing.assert_allclose(a_xyz_nodes, b_xyz_nodes)
+
+def check_run_test(ind_test, sample):
+    # Get the data sample values
+    dataframe, true_parents = sample
+    # Set the dataframe of the test object
+    ind_test.set_dataframe(dataframe)
+    # Generate some nodes
+    y_nds = [(0, 0)]
+    x_nds = true_parents[0]
+    z_nds = true_parents[1]
+    tau_max = 3
+    # Run the test
+    val, pval = ind_test.run_test(x_nds, y_nds, z_nds, tau_max)
+    # Get the array the test is running on
+    array, xyz, _ = ind_test._get_array(x_nds, y_nds, z_nds, tau_max)
+    dim, T = array.shape
+    # Get the correct dependence measure
+    val_expt = ind_test.get_dependence_measure(array, xyz)
+    pval_expt = ind_test.get_significance(val, array, xyz, T, dim)
+    # Check the values are close
+    np.testing.assert_allclose(np.array(val), np.array(val_expt), atol=1e-2)
+    np.testing.assert_allclose(np.array(pval), np.array(pval_expt), atol=1e-2)
+
+def check_get_measure(ind_test, sample):
+    # Get the data sample values
+    dataframe, true_parents = sample
+    # Set the dataframe of the test object
+    ind_test.set_dataframe(dataframe)
+    # Generate some nodes
+    y_nds = [(0, 0)]
+    x_nds = true_parents[0]
+    z_nds = true_parents[1]
+    tau_max = 3
+    # Run the test
+    val = ind_test.get_measure(x_nds, y_nds, z_nds, tau_max)
+    # Get the array the test is running on
+    array, xyz, _ = ind_test._get_array(x_nds, y_nds, z_nds, tau_max)
+    # Get the correct dependence measure
+    val_expt = ind_test.get_dependence_measure(array, xyz)
+    # Check the values are close
+    np.testing.assert_allclose(np.array(val), np.array(val_expt), atol=1e-2)
+
+def check_get_confidence(ind_test, sample):
+    """
+    Compares results of current confidence interval evaluation method against
+    bootstrapped confidence interval method.  Implicitly tests
+    get_bootstrap_confidence as well.
+
+    NOTE: this is a computationally expensive test
+    """
+    # Get the data sample values
+    dataframe, true_parents = sample
+    # Set the dataframe of the test object
+    ind_test.set_dataframe(dataframe)
+    # Generate some nodes
+    y_nds = [(0, 0)]
+    x_nds = true_parents[0]
+    z_nds = true_parents[1]
+    tau_max = 3
+    # Get the confidence interval
+    conf_a = ind_test.get_confidence(x_nds, y_nds, z_nds, tau_max)
+    # Get the array the test is running on
+    array, xyz, _ = ind_test._get_array(x_nds, y_nds, z_nds, tau_max)
+    # Test current confidence interval against bootstrapped interval
+    conf_b = ind_test.get_bootstrap_confidence(
+        array,
+        xyz,
+        dependence_measure=ind_test.get_dependence_measure,
+        conf_samples=ind_test.conf_samples,
+        conf_blocklength=ind_test.conf_blocklength,
+        conf_lev=ind_test.conf_lev)
+    # Check the values are close
+    np.testing.assert_allclose(np.array(conf_a), np.array(conf_b), atol=1e-2)
+
+# PARTIAL CORRELATION TESTING ##################################################
+@pytest.fixture(params=[
+    # Generate par_corr test instances
+    #sig,            recycle, confidence
+    ('analytic',     True,    'analytic'),
+    ('analytic',     False,   'analytic'),
+    ('analytic',     False,   'bootstrap'),
+    ('shuffle_test', False,   'analytic'),
+    ('fixed_thres',  False,   'analytic')])
+def par_corr(request):
+    # Unpack the parameters
+    sig, recycle, conf = request.param
+    # Generate the par_corr independence test
+    return ParCorr(mask_type=None,
+                   significance=sig,
+                   fixed_thres=0.1,
+                   sig_samples=10000,
+                   sig_blocklength=3,
+                   confidence=conf,
+                   conf_lev=0.9,
+                   conf_samples=10000,
+                   conf_blocklength=1,
+                   recycle_residuals=recycle,
+                   verbosity=0)
+
+@pytest.fixture(params=[
+    # Generate the sample to be used for confidance interval comparison
+    #seed, corr_val, T
+    (5,    0.3,      1000),  # Default
+    (6,    0.3,      1000),  # New Seed
+    (1,    0.9,      1000)]) # Strong Correlation
+def data_sample_a(request):
+    # Unpack the parameters
+    seed, corr_val, T = request.param
+    # Return the data sample
+    return gen_data_sample(seed, corr_val, T)
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.1, 0.9),           1000, 2),
+    (a_chain(0.5, 0.6),           1000, 11),
+    (a_chain(0.5, 0.6, length=5), 1000, 42)])
+def data_frame_a(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+def test_get_array_parcorr(par_corr, data_frame_a):
+    # Check the get_array function
+    check_get_array(par_corr, data_frame_a)
+
+def test_run_test_parcorr(par_corr, data_frame_a):
+    # Check the run_test function
+    check_run_test(par_corr, data_frame_a)
+
+def test_get_measure_parcorr(par_corr, data_frame_a):
+    # Check the get_measure function
+    check_get_measure(par_corr, data_frame_a)
+
+def test_get_confidence_parcorr(par_corr, data_frame_a):
+    # Check the get_confidence function
+    check_get_confidence(par_corr, data_frame_a)
+
+# TODO test null distribution
+def test_shuffle_sig_parcorr(par_corr, data_sample_a):
+    # Get the data sample values
+    array, val, _, xyz, dim, T = data_sample_a
+    # Get the analytic significance
+    pval_a = par_corr.get_analytic_significance(value=val, T=T, dim=dim)
+    # Get the shuffle significance
+    pval_s = par_corr.get_shuffle_significance(array, xyz, val)
+    # Adjust p-value for two-sided measures
+    np.testing.assert_allclose(np.array(pval_a), np.array(pval_s), atol=0.01)
+
+# TODO test standardize, return_means as well
+@pytest.mark.parametrize("seed", [5, 29, 135, 170, 174, 284, 342, 363, 425])
+def test_parcorr_residuals(par_corr, seed):
+    # Set the random seed
+    np.random.seed(seed)
+    # Set the target value and the true residuals
+    target_var = 0
+    true_res = np.random.randn(4, 1000)
+    # Copy the true residuals to a new array
+    array = np.copy(true_res)
+    # Manipulate the array
+    array[0] += 0.5*array[2:].sum(axis=0)
+    # Estimate the residuals
+    est_res = par_corr._get_single_residuals(array, target_var,
+                                             standardize=False,
+                                             return_means=False)
+    np.testing.assert_allclose(est_res, true_res[0], rtol=1e-5, atol=0.02)
+
+def test_parcorr(par_corr, data_sample_a):
+    # Get the data sample values
+    small_array, _, corr_val, xyz, dim, T = data_sample_a
+    # Generate the full array
+    dim = 5
+    array = np.random.randn(dim, T)
+    array[:2, :] = small_array
+    # Generate some confounding
+    array[0] += 0.5* array[2:].sum(axis=0)
+    array[1] += 0.7* array[2:].sum(axis=0)
+    # Reset the dimension
+    xyz = np.array([0, 1, 2, 2, 2])
+    # Get the estimated value
+    val_est = par_corr.get_dependence_measure(array, xyz)
+    # Compare to the true value
+    np.testing.assert_allclose(np.array(corr_val), np.array(val_est), atol=0.02)
+
+# GPDC TESTING #################################################################
+@pytest.fixture()
+def gpdc(request):
+    return GPDC(mask_type=None,
+                significance='analytic',
+                fixed_thres=0.1,
+                sig_samples=1000,
+                sig_blocklength=1,
+                confidence='bootstrap',
+                conf_lev=0.9,
+                conf_samples=100,
+                conf_blocklength=None,
+                recycle_residuals=False,
+                verbosity=0)
+
+@pytest.fixture(params=[
+    # Generate the sample to be used for confidence interval comparison
+    #seed, corr_val, T
+    (5,    0.3,      200),  # Default
+    (6,    0.3,      200),  # New Seed
+    (1,    0.9,      200)]) # Strong Correlation
+def data_sample_b(request):
+    # Unpack the parameters
+    seed, corr_val, T = request.param
+    # Return the data sample
+    return gen_data_sample(seed, corr_val, T)
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.1, 0.9),           250, 2),
+    (a_chain(0.5, 0.6),           250, 11),
+    (a_chain(0.5, 0.6, length=5), 250, 42)])
+def data_frame_b(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+def test_get_array_gpdc(gpdc, data_frame_b):
+    # Check the get_array function
+    check_get_array(gpdc, data_frame_b)
+
+def test_get_measure_gpdc(gpdc, data_frame_b):
+    # Check the get_measure function
+    check_get_measure(gpdc, data_frame_b)
+
+def test_get_confidence_gpdc(gpdc, data_frame_b):
+    # Skip if just checking boostrap vs. bootstrap
+    if not gpdc.confidence == 'bootstrap':
+        # Check the get_confidence function
+        check_get_confidence(gpdc, data_frame_b)
+
+@pytest.mark.parametrize("seed", list(range(10)))
+def test_gpdc_residuals(gpdc, seed):
+    # Set the random seed
+    np.random.seed(seed)
+    c_val = .3
+    T = 1000
+    # Define the function to check against
+    def func(x_arr, c_val=1.):
+        return c_val*x_arr*(1. - 4.*np.exp(-x_arr*x_arr/2.))
+    # Generate the array
+    array = np.random.randn(3, T)
+    # Manipulate the array
+    array[1] += func(array[2], c_val)
+    # Set the target value and the target results
+    target_var = 1
+    target_res = np.copy(array[2])
+    # Calculate the residuals
+    (_, pred) = gpdc._get_single_residuals(array, target_var,
+                                           standardize=False,
+                                           return_means=True)
+    # Testing that the fit matches in the centre
+    cntr = np.where(np.abs(target_res) < .7)[0]
+    np.testing.assert_allclose(pred[cntr],
+                               func(target_res[cntr], c_val),
+                               atol=0.2)
+
+def test_shuffle_sig_gpdc(gpdc, data_sample_b):
+    # Get the data sample
+    array, _, _, xyz, dim, T = data_sample_b
+    # Trim the data sample down, time goes as T^2
+    T = int(T/4.)
+    array = array[:, :T]
+    # Get the value of the dependence measurement
+    val = gpdc.get_dependence_measure(array, xyz)
+    pval_a = gpdc.get_analytic_significance(value=val, T=T, dim=dim)
+    pval_s = gpdc.get_shuffle_significance(array, xyz, val)
+    np.testing.assert_allclose(np.array(pval_a), np.array(pval_s), atol=0.05)
+
+def test_trafo2uniform(gpdc, data_sample_a):
+    # Get the data sample
+    array, _, _, _, _, T = data_sample_a
+    # Make the transformation
+    uniform = gpdc._trafo2uniform(array)
+    # Set the number of bins
+    bins = 10
+    for i in range(array.shape[0]):
+        hist, _ = np.histogram(uniform[i], bins=bins, density=True)
+        np.testing.assert_allclose(np.ones(bins)/float(bins),
+                                   hist/float(bins),
+                                   atol=0.01)
+
+# CMIknn TESTING ###############################################################
+@pytest.fixture()
+def cmi_knn(request):
+    return CMIknn(mask_type=None,
+                  significance='shuffle_test',
+                  fixed_thres=None,
+                  sig_samples=10000,
+                  sig_blocklength=3,
+                  knn=10,
+                  confidence='bootstrap',
+                  conf_lev=0.9,
+                  conf_samples=10000,
+                  conf_blocklength=1,
+                  verbosity=0)
+
+@pytest.fixture(params=[
+    # Generate the sample to be used for confidence interval comparison
+    #seed, corr_val, T
+    (5,    0.3,      10000),  # Default
+    (6,    0.3,      10000),  # New Seed
+    (1,    0.6,      10000)]) # Strong Correlation
+def data_sample_c(request):
+    # Unpack the parameters
+    seed, corr_val, T = request.param
+    # Return the data sample
+    return gen_data_sample(seed, corr_val, T)
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.1, 0.9),           100, 2),
+    (a_chain(0.5, 0.6),           100, 11),
+    (a_chain(0.5, 0.6, length=5), 100, 42)])
+def data_frame_c(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+def test_get_array_cmi_knn(cmi_knn, data_frame_c):
+    # Check the get_array function
+    check_get_array(cmi_knn, data_frame_c)
+
+def test_run_test_cmi_knn(cmi_knn, data_frame_c):
+    # Check the run_test function
+    check_run_test(cmi_knn, data_frame_c)
+
+def test_get_measure_cmi_knn(cmi_knn, data_frame_c):
+    # Check the get_measure function
+    check_get_measure(cmi_knn, data_frame_c)
+
+def test_get_confidence_cmi_knn(cmi_knn, data_frame_c):
+    # Skip if just checking boostrap vs. bootstrap
+    if not cmi_knn.confidence == 'bootstrap':
+        # Check the get_confidence function
+        check_get_confidence(cmi_knn, data_frame_c)
+
+def test_cmi_knn(cmi_knn, data_sample_c):
+    # Get the data sample values
+    small_array, _, corr_val, xyz, dim, T = data_sample_c
+    # Generate the full array
+    dim = 5
+    array = np.random.randn(dim, T)
+    array[:2, :] = small_array
+    # Generate some confounding
+    array[0] += 0.5* array[2:].sum(axis=0)
+    array[1] += 0.7* array[2:].sum(axis=0)
+    # Reset the dimension
+    xyz = np.array([0, 1, 2, 2, 2])
+    # Get the estimated value
+    val_est = cmi_knn.get_dependence_measure(array, xyz)
+    np.testing.assert_allclose(np.array(_par_corr_to_cmi(corr_val)),
+                               np.array(val_est),
+                               atol=0.02)
+
+# CMIsymb TESTING ##############################################################
+@pytest.fixture()
+def cmi_symb(request):
+    return CMIsymb(mask_type=None,
+                   significance='shuffle_test',
+                   fixed_thres=0.1,
+                   sig_samples=10000,
+                   sig_blocklength=3,
+                   confidence='bootstrap',
+                   conf_lev=0.9,
+                   conf_samples=10000,
+                   conf_blocklength=1,
+                   verbosity=0)
+
+@pytest.fixture(params=[
+    # Generate the sample to be used for confidence interval comparison
+    #seed, corr_val, T
+    (5,    0.3,      100000),  # Default
+    (6,    0.3,      100000),  # New Seed
+    (7,    0.6,      100000)]) # Strong Correlation
+def data_sample_d(request):
+    # Unpack the parameters
+    seed, corr_val, T = request.param
+    # Return the data sample
+    return gen_data_sample(seed, corr_val, T)
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.1, 0.9),           100, 2),
+    (a_chain(0.5, 0.6),           100, 11),
+    (a_chain(0.5, 0.6, length=5), 100, 42)])
+def data_frame_d(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+# TODO does not work
+#def test_run_test_cmi_symb(cmi_symb, data_frame_d):
+#    # Make the data frame integer values
+#    df, parents = data_frame_d
+#    df.values = (df.values * 1000).astype(int)
+#    # Check the run_test function
+#    check_run_test(cmi_symb, (df, parents))
+
+# TODO does not work
+#def test_run_test_cmi_symb(cmi_symb, data_frame_d):
+#    # Make the data frame integer values
+#    df, parents = data_frame_d
+#    df.values = (df.values * 1000).astype(int)
+#    # Check the run_test function
+#    check_get_measure(cmi_symb, (df, parents))
+
+def test_cmi_symb(cmi_symb, data_sample_d):
+    # Get the data sample values
+    small_array, _, corr_val, xyz, dim, T = data_sample_d
+    # Generate the full array
+    dim = 3
+    array = np.random.randn(dim, T)
+    array[:2, :] = small_array
+    # Generate some confounding
+    array[0] += 0.5* array[2:].sum(axis=0)
+    array[1] += 0.7* array[2:].sum(axis=0)
+    # Transform to symbolic data
+    array = pp.quantile_bin_array(array.T, bins=16).T
+    # Reset the dimension
+    xyz = np.array([0, 1, 2, 2, 2])
+    # Get the estimated value
+    val_est = cmi_symb.get_dependence_measure(array, xyz)
+    np.testing.assert_allclose(np.array(_par_corr_to_cmi(corr_val)),
+                               np.array(val_est),
+                               atol=0.02)
+
+# RCOT TESTS ###################################################################
+@pytest.fixture()
+def rcot(request):
+    return RCOT(mask_type=None,
+                significance='analytic',
+                fixed_thres=None,
+                sig_samples=500,
+                sig_blocklength=3,
+                confidence='bootstrap',
+                conf_lev=0.9,
+                conf_samples=10000,
+                conf_blocklength=1,
+                num_f=25,
+                approx="lpd4",
+                seed=42)
+
+@pytest.fixture(params=[
+    # Generate the sample to be used for confidence interval comparison
+    #seed, corr_val, T
+    (5,    0.3,      500),  # Default
+    (6,    0.3,      500),  # New Seed
+    (1,    0.6,      500)]) # Strong Correlation
+def data_sample_e(request):
+    # Unpack the parameters
+    seed, corr_val, T = request.param
+    # Return the data sample
+    return gen_data_sample(seed, corr_val, T)
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.1, 0.9),           100, 2),
+    (a_chain(0.5, 0.6),           100, 11),
+    (a_chain(0.5, 0.6, length=5), 100, 42)])
+def data_frame_e(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+def test_get_array_rcot(rcot, data_frame_e):
+    # Check the get_array function
+    check_get_array(rcot, data_frame_e)
+
+def test_run_test_rcot(rcot, data_frame_e):
+    # Check the run_test function
+    check_run_test(rcot, data_frame_e)
+
+def test_get_measure_rcot(rcot, data_frame_e):
+    # Check the get_measure function
+    check_get_measure(rcot, data_frame_e)
+
+def test_get_confidence_rcot(rcot, data_frame_e):
+    # Skip if just checking boostrap vs. bootstrap
+    if not rcot.confidence == 'bootstrap':
+        # Check the get_confidence function
+        check_get_confidence(rcot, data_frame_e)
+
+def test_shuffle_sig_rcot(rcot, data_sample_e):
+    # Get the data sample values
+    array, _, _, xyz, dim, T = data_sample_e
+    # Must call before running get_analytic_significance
+    val = rcot.get_dependence_measure(array, xyz)
+    # Get the analytic significance
+    pval_a = rcot.get_analytic_significance(value=val, T=T, dim=dim)
+    # Get the shuffle significance
+    pval_s = rcot.get_shuffle_significance(array, xyz, val)
+    # Adjust p-value for two-sided measures
+    np.testing.assert_allclose(np.array(pval_a), np.array(pval_s), atol=0.01)
+
+# TODO translate RCoT output to correlation scale
+#def test_rcot(rcot, data_sample_e):
+#    # Get the data sample values
+#    small_array, _, corr_val, xyz, dim, T = data_sample_e
+#    # Generate the full array
+#    dim = 5
+#    array = np.random.randn(dim, T)
+#    array[:2, :] = small_array
+#    # Generate some confounding
+#    array[0] += 0.5* array[2:].sum(axis=0)
+#    array[1] += 0.7* array[2:].sum(axis=0)
+#    # Reset the dimension
+#    xyz = np.array([0, 1, 2, 2, 2])
+#    # Get the estimated value
+#    val_est = rcot.get_dependence_measure(array, xyz)
+#    print(val_est, rcot.get_analytic_significance())
+#    np.testing.assert_allclose(np.array(corr_val),
+#                               np.array(val_est),
+#                               atol=0.02)
+
+# OTHER TESTS ##################################################################
+@pytest.mark.parametrize("mask_type,expected", [
+    ('x', True),    # single item in acceptable list
+    ('y', True),    # single item in acceptable list
+    ('z', True),    # single item in acceptable list
+    ('xy', True),   # multiple items in acceptable list
+    ('xz', True),   # multiple items in acceptable list
+    ('yz', True),   # multiple items in acceptable list
+    ('yx', True),   # multiple items in acceptable list, swapped order
+    ('xyz', True),  # all items in acceptable list
+    ('xzy', True),  # all items in acceptable list, swapped order
+    ('a', False),   # single unacceptable item
+    ('ax', False),  # single unacceptable item and acceptable item
+    ('ab', False)]) # multiple unacceptable items
+def test_check_mask(par_corr, mask_type, expected):
+    # Set the mask type
+    par_corr.mask_type = mask_type
+    # Test the good parameter set
+    if expected:
+        try:
+            par_corr._check_mask_type()
+        # Ensure no exception is raised
+        except:
+            pytest.fail("Acceptable mask type "+mask_type+\
+                        " is incorrectly throwing an error")
+    # Test the bad parameter set
+    else:
+        err_msg = "Unacceptable mask type "+mask_type+\
+                  " is incorrectly NOT throwing an error"
+        with pytest.raises(ValueError, message=err_msg):
+            par_corr._check_mask_type()
diff --git a/tests/test_models.py b/tests/test_models.py
new file mode 100644
index 00000000..d46fc4e5
--- /dev/null
+++ b/tests/test_models.py
@@ -0,0 +1,91 @@
+"""
+Tests for pcmci.py, including tests for run_pc_stable, run_mci, and run_pcmci.
+"""
+from __future__ import print_function
+import numpy as np
+import pytest
+import sklearn.linear_model
+
+from tigramite.models import LinearMediation
+import tigramite.data_processing as pp
+from tigramite.models import Prediction
+from tigramite.independence_tests import ParCorr
+
+from test_pcmci_calculations import a_chain, gen_data_frame
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# TEST LINK GENERATION #########################################################
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time, seed_val
+    (a_chain(0.5, 0.2), 10000, 21),
+    (a_chain(0.5, 0.2), 10000, 42)])
+def data_frame_a(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val), links_coeffs
+
+# TEST MODELS ##################################################################
+def test_linear_mediation_coeffs(data_frame_a):
+    # Build the dataframe and the model
+    (dataframe, true_parents), links_coeffs = data_frame_a
+    med = LinearMediation(dataframe=dataframe)
+    # Fit the model
+    med.fit_model(all_parents=true_parents, tau_max=3)
+    # Ensure the results make sense
+    for j, i, tau, coeff in pp._iter_coeffs(links_coeffs):
+        np.testing.assert_allclose(med.get_coeff(i=i, tau=tau, j=j),
+                                   coeff, rtol=1e-1)
+
+def test_linear_med_cause_coeffs(data_frame_a):
+    # Build the dataframe and the model
+    (dataframe, true_parents), _ = data_frame_a
+    med = LinearMediation(dataframe=dataframe)
+    # Fit the model
+    med.fit_model(all_parents=true_parents, tau_max=3)
+    # Ensure the causal and mediated casual effects make sense
+    for causal_coeff in [med.get_ce(i=0, tau=-2, j=2),
+                         med.get_mce(i=0, tau=-2, j=2, k=1)]:
+        np.testing.assert_allclose(causal_coeff, 0.035, rtol=1e-1)
+
+# TEST PREDICTIONS #############################################################
+def test_predictions(data_frame_a):
+    # TODO NOTE: This doesn't actually test if the predictions make sense, only 
+    # that they work!
+    # Get the data
+    (dataframe, true_parents), links_coeffs = data_frame_a
+    T, _ = dataframe.values.shape
+    # Build the prediction
+    a_cond_ind_test = ParCorr(significance='analytic',
+                              fixed_thres=0.01)
+    pred = Prediction(dataframe=dataframe,
+                      cond_ind_test=a_cond_ind_test,
+                      prediction_model=sklearn.linear_model.LinearRegression(),
+                      train_indices=range(int(0.8*T)),
+                      test_indices=range(int(0.8*T), T),
+                      verbosity=0)
+    # Load some parameters
+    tau_max = 3
+    steps_ahead = 0
+    target = 2
+    # Get the predictors from pc_stable
+    all_predictors = pred.get_predictors(selected_targets=[target],
+                                         selected_links=None,
+                                         steps_ahead=steps_ahead,
+                                         tau_max=tau_max,
+                                         pc_alpha=None,
+                                         max_conds_dim=None,
+                                         max_combinations=1)
+    # Fit the predictors using the ML method
+    pred.fit(target_predictors=all_predictors,
+             selected_targets=[target],
+             tau_max=tau_max,
+             return_data=True)
+    # Predict the values
+    _ = pred.predict(target)
+    # TODO test the values make sense
diff --git a/tests/test_pcmci_calculations.py b/tests/test_pcmci_calculations.py
new file mode 100644
index 00000000..03a8053e
--- /dev/null
+++ b/tests/test_pcmci_calculations.py
@@ -0,0 +1,257 @@
+"""
+Tests for pcmci.py, including tests for run_pc_stable, run_mci, and run_pcmci.
+"""
+from __future__ import print_function
+from collections import Counter, defaultdict
+import numpy as np
+from nose.tools import assert_equal
+import pytest
+
+from tigramite.pcmci import PCMCI
+from tigramite.independence_tests import ParCorr
+import tigramite.data_processing as pp
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# Define the verbosity at the global scope
+VERBOSITY = 1
+
+# CONVENIENCE FUNCTIONS ########################################################
+def assert_graphs_equal(actual, expected):
+    """
+    Check whether lists in dict have the same elements.
+    This ignore the order of the elements in the list.
+    """
+    for j in list(expected):
+        assert_equal(Counter(iter(actual[j])), Counter(iter(expected[j])))
+
+def _get_parent_graph(parents_neighbors_coeffs, exclude=None):
+    """
+    Iterates through the input parent-neighghbour coefficient dictionary to
+    return only parent relations (i.e. where tau != 0)
+    """
+    graph = defaultdict(list)
+    for j, i, tau, _ in pp._iter_coeffs(parents_neighbors_coeffs):
+        if tau != 0 and (i, tau) != exclude:
+            graph[j].append((i, tau))
+    return dict(graph)
+
+def _select_links(link_ids, true_parents):
+    """
+    Select links given by  from the true parents dictionary
+    """
+    if link_ids is None:
+        return None
+    return {par : [true_parents[par][link]] for par in true_parents \
+                                            for link in link_ids}
+
+def _get_parents_from_results(pcmci, results, alpha_level):
+    """
+    Select the significant parents from the MCI-like results at a given
+    alpha_level
+    """
+    significant_parents = \
+        pcmci.return_significant_parents(pq_matrix=results['p_matrix'],
+                                         val_matrix=results['val_matrix'],
+                                         alpha_level=alpha_level)
+    return significant_parents['parents']
+
+def gen_data_frame(links_coeffs, time, seed_val):
+    # Set the random seed
+    np.random.seed(seed_val)
+    # Generate the data
+    data, _ = pp.var_process(links_coeffs, T=time)
+    # Get the true parents
+    true_parents = _get_parent_graph(links_coeffs)
+    return pp.DataFrame(data), true_parents
+
+# TEST LINK GENERATION #########################################################
+def a_chain(auto_corr, coeff, length=3):
+    """
+    Generate a simple chain process with the given auto-correlations and
+    parents with the given coefficient strength.  A length can also be defined
+    to get a longer chain.
+
+    Parameters
+    ----------
+    auto_corr: float
+        Autocorrelation strength for all nodes
+    coeff : float
+        Parent strength for all relations
+    length : int
+        Length of the chain
+    """
+    return_links = dict()
+    return_links[0] = [((0, -1), auto_corr)]
+    for lnk in range(1, length):
+        return_links[lnk] = [((lnk, -1), auto_corr), ((lnk-1, -1), coeff)]
+    return return_links
+
+# TODO implement common_driver: return two variables commonly driven by N common
+# drivers which are random noise, autocorrelation as parameter
+# TODO implement independent drivers, autocorrelated noise
+# TODO check common_driver, independent driver cases for current variable sets
+# TODO implement USER_INPUT dictionary,
+# USER_INPUT = dict()
+
+# TEST DATA GENERATION #########################################################
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,               time,  seed_val
+    (a_chain(0.1, 0.9),           1000,  2),
+    (a_chain(0.5, 0.6),           1000,  11),
+    (a_chain(0.5, 0.6, length=5), 10000, 42)])
+def a_sample(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Generate the dataframe
+    return gen_data_frame(links_coeffs, time, seed_val)
+
+# PCMCI CONSTRUCTION ###########################################################
+@pytest.fixture(params=[
+    # Keep parameters common for all the run_ algorithms here
+    # tau_min, tau_max,  sel_link,
+     (1,       2,        None),
+     (1,       2,        [0])])
+def a_common_params(request):
+    # Return the requested parameters
+    return request.param
+
+@pytest.fixture()
+    # Parameterize and return the independence test.
+    # Currently just a wrapper for ParCorr, but is extendable
+def a_test(request):
+    return ParCorr(verbosity=VERBOSITY)
+
+@pytest.fixture(params=[None, [1]])
+    # Fixture to build and return a parameterized PCMCI.  Different selected
+    # variables can be defined here.
+def a_pcmci(a_sample, a_test, a_common_params, request):
+    # Unpack the test data and true parent graph
+    dataframe, true_parents = a_sample
+    # Unpack the common parameters
+    tau_min, tau_max, sel_link = a_common_params
+    # Get the parameters from this request
+    select_vars = request.param
+    # Build the PCMCI instance
+    pcmci = PCMCI(selected_variables=select_vars,
+                  dataframe=dataframe,
+                  cond_ind_test=a_test,
+                  verbosity=VERBOSITY)
+    # If there are selected variables, edit the true parents to reflect this
+    if select_vars is not None:
+        true_parents = {sel_v : true_parents[sel_v] for sel_v in select_vars}
+    # Select the correct links if they are given
+    select_links = _select_links(sel_link, true_parents)
+    # Ensure we change the true parents to be the same as the selected links
+    if select_links is not None:
+        true_parents = select_links
+    # Return the constructed PCMCI, expected results, and common parameters
+    return pcmci, true_parents, tau_min, tau_max, select_links
+
+# PC_STABLE TESTING ############################################################
+@pytest.fixture(params=[
+    # Keep parameters for the pc_stable algorithm here
+    # pc_alpha,  max_conds_dim,  max_comb, save_iterations
+     (None,      None,           3,        False),
+     (0.05,      None,           1,        False),
+     (0.05,      None,           10,       False),
+     (0.05,      None,           1,        True),
+     (0.05,      3,              1,        False)])
+def a_pc_stable_params(request):
+    # Return the parameters for the pc_stable test
+    return request.param
+
+@pytest.fixture()
+def a_run_pc_stable(a_pcmci, a_pc_stable_params):
+    # Unpack the pcmci, true parents, and common parameters
+    pcmci, true_parents, tau_min, tau_max, select_links = a_pcmci
+    # Unpack the pc_stable parameters
+    pc_alpha, max_conds_dim, max_combinations, save_iter = a_pc_stable_params
+    # Run PC stable
+    pcmci.run_pc_stable(selected_links=select_links,
+                        tau_min=tau_min,
+                        tau_max=tau_max,
+                        save_iterations=save_iter,
+                        pc_alpha=pc_alpha,
+                        max_conds_dim=max_conds_dim,
+                        max_combinations=max_combinations)
+    # Return the calculated and expected results
+    return pcmci.all_parents, true_parents
+
+def test_pc_stable(a_run_pc_stable):
+    """
+    Test the pc_stable algorithm and check it calculates the correct parents.
+    """
+    # Unpack the calculated and true parents
+    parents, true_parents = a_run_pc_stable
+    # Ensure they are the same
+    assert_graphs_equal(parents, true_parents)
+
+# MCI TESTING ##################################################################
+@pytest.fixture(params=[
+    # Keep parameters for the mci algorithm here
+    # alpha_level, max_conds_px, max_conds_py
+     (0.01,        None,         None)])
+def a_mci_params(request):
+    # Return the parameters for the mci test
+    return request.param
+
+@pytest.fixture()
+def a_run_mci(a_pcmci, a_mci_params):
+    # Unpack the pcmci and the true parents, and common parameters
+    pcmci, true_parents, tau_min, tau_max, select_links = a_pcmci
+    # Unpack the MCI parameters
+    alpha_level, max_conds_px, max_conds_py = a_mci_params
+    # Run the MCI algorithm with the given parameters
+    results = pcmci.run_mci(selected_links=select_links,
+                            tau_min=tau_min,
+                            tau_max=tau_max,
+                            parents=true_parents,
+                            max_conds_py=max_conds_px,
+                            max_conds_px=max_conds_py)
+    # Return the calculated and expected results
+    return _get_parents_from_results(pcmci, results, alpha_level), true_parents
+
+def test_mci(a_run_mci):
+    """
+    Test the mci algorithm and check it calculates the correct parents.
+    """
+    # Unpack the calculated and true parents
+    parents, true_parents = a_run_mci
+    # Ensure they are the same
+    assert_graphs_equal(parents, true_parents)
+
+# PCMCI TESTING ################################################################
+@pytest.fixture()
+def a_run_pcmci(a_pcmci, a_pc_stable_params, a_mci_params):
+    # Unpack the pcmci and the true parents, and common parameters
+    pcmci, true_parents, tau_min, tau_max, select_links = a_pcmci
+    # Unpack the pc_stable parameters
+    pc_alpha, max_conds_dim, max_combinations, save_iter = a_pc_stable_params
+    # Unpack the MCI parameters
+    alpha_level, max_conds_px, max_conds_py = a_mci_params
+    # Run the PCMCI algorithm with the given parameters
+    results = pcmci.run_pcmci(selected_links=select_links,
+                              tau_min=tau_min,
+                              tau_max=tau_max,
+                              save_iterations=save_iter,
+                              pc_alpha=pc_alpha,
+                              max_conds_dim=max_conds_dim,
+                              max_combinations=max_combinations,
+                              max_conds_px=max_conds_px,
+                              max_conds_py=max_conds_py)
+    # Return the results and the expected result
+    return _get_parents_from_results(pcmci, results, alpha_level), true_parents
+
+def test_pcmci(a_run_pcmci):
+    """
+    Test the pcmci algorithm and check it calculates the correct parents.
+    """
+    # Unpack the calculated and true parents
+    parents, true_parents = a_run_pcmci
+    # Ensure they are the same
+    assert_graphs_equal(parents, true_parents)
diff --git a/tests/test_pcmci_construction.py b/tests/test_pcmci_construction.py
new file mode 100644
index 00000000..54cb1017
--- /dev/null
+++ b/tests/test_pcmci_construction.py
@@ -0,0 +1,515 @@
+"""
+Tests for pcmci.py, including tests for run_pc_stable, run_mci, and run_pcmci.
+"""
+from __future__ import print_function
+from collections import defaultdict
+from distutils.version import LooseVersion
+import numpy as np
+import pytest
+from scipy.special import comb
+
+from tigramite.pcmci import PCMCI
+from tigramite.independence_tests import ParCorr
+import tigramite.data_processing as pp
+
+from test_pcmci_calculations import a_chain
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# Define the verbosity at the global scope
+VERBOSITY = 1
+
+# CONVENIENCE FUNCTIONS ########################################################
+def _get_parent_graph(parents_neighbors_coeffs, exclude=None):
+    """
+    Iterates through the input parent-neighghbour coefficient dictionary to
+    return only parent relations (i.e. where tau != 0)
+    """
+    graph = defaultdict(list)
+    for j, i, tau, _ in pp._iter_coeffs(parents_neighbors_coeffs):
+        if tau != 0 and (i, tau) != exclude:
+            graph[j].append((i, tau))
+    return dict(graph)
+
+def _select_links(link_ids, true_parents):
+    """
+    Select links given by  from the true parents dictionary
+    """
+    if link_ids is None:
+        return None
+    return {par : [true_parents[par][link]] for par in true_parents \
+                                            for link in link_ids}
+
+# TEST LINK GENERATION #########################################################
+N_NODES = 4
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # Parameterize the sample by setting the autocorrelation value, coefficient
+    # value, total time length, and random seed to different numbers
+    # links_coeffs,     time, seed_val
+    (a_chain(0.5, 0.6, length=N_NODES), 1000, 42)])
+def a_sample(request):
+    # Set the parameters
+    links_coeffs, time, seed_val = request.param
+    # Set the random seed
+    np.random.seed(seed_val)
+    # Generate the data
+    data, _ = pp.var_process(links_coeffs, T=time)
+    # Get the true parents
+    true_parents = _get_parent_graph(links_coeffs)
+    return pp.DataFrame(data), true_parents
+
+# PCMCI CONSTRUCTION ###########################################################
+@pytest.fixture()
+    # Fixture to build and return a parameterized PCMCI.  Different selected
+    # variables can be defined here.
+def a_pcmci(a_sample, request):
+    # Unpack the test data and true parent graph
+    dataframe, true_parents = a_sample
+    # Build the PCMCI instance
+    pcmci = PCMCI(selected_variables=None,
+                  dataframe=dataframe,
+                  cond_ind_test=ParCorr(verbosity=VERBOSITY),
+                  verbosity=VERBOSITY)
+    # Return the constructed PCMCI, expected results, and common parameters
+    return pcmci, true_parents
+
+# TEST VARIABLE SELECTION ######################################################
+def get_select_vars(some_vars):
+    """
+    Yield some subsets of the input variables
+    """
+    for subset in [some_vars[0::2], some_vars[1::2]]:
+        yield subset
+
+def test_select_vars_default(a_pcmci):
+    """
+    Check that _set_selected_variables returns all variables by default.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Test the default selected variables are correct
+    all_vars = pcmci._set_selected_variables(None)
+    err_msg = "Default option for _set_selected_variables should return all"+\
+              " variables"
+    np.testing.assert_array_equal(list(all_vars),
+                                  list(range(pcmci.N)),
+                                  err_msg=err_msg)
+
+def test_select_vars_setting(a_pcmci):
+    """
+    Check that _set_selected_variables returns the correct subset of all variables
+    when requested.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Test that both out of range and negative numbers throw an error while
+    # good sets ones do not
+    for sel_vars in get_select_vars(range(pcmci.N)):
+        # Initialize the list
+        new_vars = pcmci._set_selected_variables(sel_vars)
+        err_msg = "Variables incorrectly selected in _select_variables"
+        # Ensure the correct values are returned
+        np.testing.assert_array_equal(new_vars,
+                                      sorted(list(set(sel_vars))),
+                                      err_msg=err_msg)
+
+def test_select_vars_errors(a_pcmci):
+    """
+    Check that _set_selected_variables throws the correct errors for bad requests
+    but does not crash for good requests.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Test that both out of range and negative numbers throw an error while
+    # good sets ones do not
+    for sel_vars in get_select_vars(range(pcmci.N)):
+        # Initialize the list
+        new_vars = []
+        # Test the good parameter set
+        try:
+            new_vars = pcmci._set_selected_variables(sel_vars)
+        # Ensure no exception is raised
+        except:
+            pytest.fail("Selected variables fail incorrectly!")
+        # Ensure an exception is raised for a bad parameter set
+        for bad_val, message in [(-1, "Negative"),
+                                 (pcmci.N + 1, "Out of range")]:
+            err_msg = message + " selected variables do not fail!"
+            with pytest.raises(ValueError, message=err_msg):
+                bad_vars = np.array(new_vars) * bad_val
+                _ = pcmci._set_selected_variables(bad_vars)
+
+# TEST LINK SELECTION ##########################################################
+TAU_MIN = 1
+TAU_MAX = 3
+def get_expected_links(a_range, b_range, t_range):
+    """
+    Helper function to generate the expected links
+    """
+    return {a_var : [(b_var, -lag) for b_var in b_range for lag in t_range]
+            for a_var in a_range}
+
+def test_select_links_default(a_pcmci):
+    """
+    Check that _set_sel_links returns all links by default.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Test the default selected variables are correct
+    sel_links = pcmci._set_sel_links(None, TAU_MIN, TAU_MAX)
+    err_msg = "Default option for _set_sel_links should return all possible "+\
+              "combinations"
+    good_links = get_expected_links(range(pcmci.N),
+                                    range(pcmci.N),
+                                    range(TAU_MIN, TAU_MAX + 1))
+    np.testing.assert_equal(sel_links, good_links, err_msg=err_msg)
+
+def test_select_links_setting(a_pcmci):
+    """
+    Check that _set_sel_links returns only links of selected variables.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Seleect some of the variables
+    for sel_vars in get_select_vars(range(pcmci.N)):
+        # Initialize the list
+        pcmci.selected_variables = pcmci._set_selected_variables(sel_vars)
+        # Ensure the correct values are returned
+        sel_links = pcmci._set_sel_links(None, TAU_MIN, TAU_MAX)
+        good_links = get_expected_links(sel_vars,
+                                        range(pcmci.N),
+                                        range(TAU_MIN, TAU_MAX + 1))
+        # TODO remove when we remove the empty lists from the code
+        for idx in range(pcmci.N):
+            if idx not in sel_vars:
+                good_links[idx] = []
+        err_msg = "Links incorrectly set in _set_sel_links"
+        np.testing.assert_equal(sel_links, good_links, err_msg=err_msg)
+
+def test_select_links_errors(a_pcmci):
+    """
+    Check that _set_sel_links throws the correct errors when out of range
+    variables are mentioned.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Select some of the variables
+    for sel_vars in get_select_vars(range(pcmci.N)):
+        # Initialize the list
+        pcmci.selected_variables = pcmci._set_selected_variables(sel_vars)
+        # Ensure the correct values are returned
+        test_links = get_expected_links(sel_vars,
+                                        range(pcmci.N),
+                                        range(TAU_MIN, TAU_MAX + 1))
+        # Test the good parameter set
+        try:
+            _ = pcmci._set_sel_links(test_links, TAU_MIN, TAU_MAX)
+        # Ensure no exception is raised
+        except:
+            pytest.fail("Selected links fail incorrectly!")
+        # Ensure an exception is raised for a bad parameter set
+        for bad_val, message in [(pcmci.N + 1, "Out of range")]:
+            err_msg = message + " selected links do not fail!"
+            with pytest.raises(ValueError, message=err_msg):
+                test_links[bad_val] = [(bad_val, TAU_MAX)]
+                _ = pcmci._set_sel_links(test_links, TAU_MIN, TAU_MAX)
+
+# TEST ITERATORS ###############################################################
+@pytest.fixture(params=[
+    # Store some parameters for setting maximum conditions
+    #max_cond_dim, tau_min, tau_max
+    (None,         1,       3),
+    (10,           1,       3),
+    (1,            2,       4)])
+def a_iter_cond_param(request):
+    return request.param
+
+def test_condition_iterator(a_pcmci, a_iter_cond_param):
+    """
+    Test if the correct conditions are returned from the iterator.  Checks that:
+        * Parent node never included in yielded conditions
+        * Yielded conditions of the same dimension are unique
+        * The expected total number of conditions are given
+    """
+    # Unpack the pcmci algorithm
+    pcmci, _ = a_pcmci
+    # Unpack the iterator conditions parameters
+    max_cond_dim, tau_min, tau_max = a_iter_cond_param
+    # Get all possible links
+    sel_links = pcmci._set_sel_links(None, tau_min, tau_max)
+    # Loop over all possible condition dimentions
+    max_cond_dim = pcmci._set_max_condition_dim(max_cond_dim, tau_min, tau_max)
+    # Loop over all nodes
+    for j in range(pcmci.N):
+        # Initialise the list of conditions for this node
+        total_n_conds = 0
+        expected_total_n_conds = 0
+        # Parents for this node
+        parents = sel_links[j]
+        # Loop over all possible dimentionality of conditions
+        for cond_dim in range(max_cond_dim+1):
+            # Skip the case where (cond_dim == num_parents + 1), as is done in 
+            # the main loop of the code
+            if cond_dim > len(parents) - 1:
+                continue
+            # Iterate through all possible pairs (that have not converged yet)
+            for par in parents:
+                # Number of conditions for this dimension
+                expected_n_conds = comb(len(parents) - 1, cond_dim, exact=True)
+                expected_total_n_conds += expected_n_conds
+                # Create an array for these conditions
+                par_conds = list()
+                # Iterate through all possible combinations
+                for cond in pcmci._iter_conditions(par, cond_dim, parents):
+                    # Ensure the condition does not contain the parent
+                    assert par not in cond, "Parent should not be in condition"
+                    # Keep this condition in list of conditions
+                    par_conds.append([par] + cond)
+                    assert len(cond) == cond_dim,\
+                        "Number of conditions should match requested dimension"
+                # Check that at least one condition is found
+                assert len(par_conds) != 0,\
+                    "Expected number of conditions for condition "+\
+                    " dimension {}".format(cond_dim)+\
+                    " and total n_nodes {}".format(len(parents)-1)+\
+                    " is zero!"
+                # Check that this iterations has the expected number of
+                # conditions
+                assert len(par_conds) == expected_n_conds,\
+                    "Expected number of conditions for condition "+\
+                    " dimension {}".format(cond_dim)+\
+                    " and total n_nodes {}".format(len(parents)-1)+\
+                    " does not match"
+                # Record the total number found
+                total_n_conds += len(par_conds)
+                par_conds = np.array(par_conds)
+                # Check that all condition, parent pairs are unique
+                # TODO fix this for 3.4 support
+                if LooseVersion(np.version.version) >= LooseVersion("1.13"):
+                    assert np.unique(par_conds, axis=0).shape == par_conds.shape
+        # Check that the right number of conditions are found
+        assert total_n_conds == expected_total_n_conds, \
+            "Expected number of combinations found"
+
+@pytest.fixture(params=[
+    # Store some parameters for iterating over conditions during the MCI
+    # algorithm
+    #max_cond_dim_X, max_cond_dim_Y, tau_min, tau_max
+    (None,           None,           0,       3), # Default
+    (1,              None,           0,       3), # Maximum conds on X
+    (None,           1,              0,       3), # Maximum conds on Y
+    (None,           None,           1,       3), # Non-trivial tau-min
+    (None,           None,           0,       1)])# Non-trivial tau-max
+def a_iter_indep_cond_param(request):
+    return request.param
+
+def test_iter_indep_conds(a_pcmci, a_iter_indep_cond_param):
+    """
+    Checks that the iteration for the MCI-like part of the algorithm works.
+    This tests that:
+        * The correct number of link-parent pairs are tested
+        * The parent node is not in the condition
+        * The number of conditions is less than the implied maximum
+    """
+    # Unpack the pcmci algorithm
+    pcmci, parents = a_pcmci
+    # Unpack the parameters
+    max_cx, max_cy, tau_min, tau_max = a_iter_indep_cond_param
+    # Set the selected links
+    _int_sel_links = pcmci._set_sel_links(None, tau_min, tau_max)
+    # Set the maximum condition dimension for Y and Z
+    max_conds_py = pcmci._set_max_condition_dim(max_cx, tau_min, tau_max)
+    max_conds_px = pcmci._set_max_condition_dim(max_cy, tau_min, tau_max)
+    # Get the parents that will be checked
+    _int_parents = pcmci._get_int_parents(parents)
+    # Get the conditions as implied by the input arguments
+    n_links = 0
+    # Calculated the expected number of links to test.
+    expect_links = 0
+    # Since a link is never checked as a parent of itself, remove all
+    # (i, tau) == (j, 0) entries from expected testing
+    for j, node_list in _int_sel_links.items():
+        for i, tau in node_list:
+            if not (i == j and  tau == 0):
+                expect_links += 1
+    ## TODO try to check content of Z to ensure it contains parent of y, parents 
+    ## of x lagged.
+    # Iterate over all the returned conditions
+    for j, i, tau, Z in pcmci._iter_indep_conds(_int_parents,
+                                                pcmci.selected_variables,
+                                                _int_sel_links,
+                                                max_conds_py,
+                                                max_conds_px):
+        # Incriment the link count
+        n_links += 1
+        # Ensure the parent link is not in the conditions
+        assert (i, tau) not in Z,\
+            "Parent node must not be in the conditions"
+        # Check that the conditions length are shorter than the maximum
+        # condition length for X and Y
+        total_max_conds = max_conds_px + max_conds_py
+        assert len(Z) <= total_max_conds,\
+            "The number of conditions must be less than the sum of the"+\
+            " maximum number of conditions on X and Y"
+    # Get the total number of tested links
+    assert expect_links == n_links,\
+        "Recoved the expected number of links to test"
+
+# TEST UTILITY FUNCTIONS #######################################################
+def test_sort_parents(a_pcmci):
+    """
+    Test that the function that sorts parents returns the sorted values as
+    desired
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Create some parents to sort
+    orig_parents = []
+    n_parents = 10
+    for i in range(n_parents):
+        orig_parents.append((i, i))
+    # Put them into a dictionary
+    parent_vals = {}
+    sign = 1
+    for val, par in enumerate(orig_parents):
+        # Alternate the sign to ensure sorting is done on abolute value
+        sign *= -1
+        # Use greater values of the test metric for greater values of the parent
+        # indexes
+        parent_vals[par] = val * sign
+    # Sort them
+    sorted_parents = pcmci._sort_parents(parent_vals)
+    # Ensure the sorting works as expected
+    assert sorted_parents == orig_parents[::-1],\
+        "Parents must be sorted by abolute value of the test metric"
+
+@pytest.fixture(params=[
+    # Store some parameters for iterating over conditions during the MCI
+    # algorithm
+    #tau_min, tau_max, should_pass
+    (0,       3,       True),  # 0 = tau_min < tau_max, should pass
+    (2,       3,       True),  # 0 < tau_min < tau_max, should pass
+    (3,       3,       True),  # 0 < tau_min = tau_max, should pass
+    (3,       2,       False), # 0 < tau_max < tau_min, should fail
+    (-1,      3,       False), # tau_min < 0 < tau_min, should fail
+    (0,      -2,       False), # tau_max < 0 < tau_min, should fail
+    (-1,     -2,       False)])# tau_min < tau_max < 0, should fail
+def a_tau_values(request):
+    return request.param
+
+def test_check_tau_limits(a_pcmci, a_tau_values):
+    """
+    Test the tau limit checker fails correctly on:
+        * Negative values
+        * Values where tau_min > tau_max
+    And passes otherwise
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Unpack the tau parameters
+    tau_min, tau_max, should_pass = a_tau_values
+    err_msg = "\ntau_min : {}\ntau_max : {}".format(tau_min, tau_max)
+    # If it should pass, try it
+    if should_pass:
+        try:
+            pcmci._check_tau_limits(tau_min, tau_max)
+        # Ensure no exception is raised
+        except:
+            pytest.fail("Good tau limits failed incorrectly:"+err_msg)
+    # If it should fail, make sure it does
+    else:
+        with pytest.raises(ValueError,
+                           message="Bad tau limits should fail"+err_msg):
+            pcmci._check_tau_limits(tau_min, tau_max)
+
+@pytest.fixture(params=[
+    # Store some parameters for correcting the pvalues
+    #fdr_method, excl,  slice_n,        slice_t,     message
+    ('fdr_bh',   False, range(N_NODES), 0,           "default"),
+    ('fdr_bh',   True,  slice(None),    0,           "exclude contemporaneous"),
+    ('none',     True,  slice(None),    slice(None), "none")])
+def a_correct_pvals_params(request):
+    return request.param
+
+def test_corrected_pvalues(a_pcmci, a_correct_pvals_params):
+    """
+    Test the wrapper functions of the corrected pvalue function.  This means:
+        * check 'none' is a valid mode that does nothing to the input
+        * check that exclude_contemporaneous works
+        * check that autocorrelation elements are ignored
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Unpack the parameters
+    fdr_method, excl, slice_n, slice_t, message = a_correct_pvals_params
+    # Create the p-values
+    pvals = np.linspace(0, 1, num=N_NODES*N_NODES*(TAU_MAX+1))
+    pvals = pvals.reshape(N_NODES, N_NODES, TAU_MAX+1)
+    # Create the corrected p-values
+    qvals = pcmci.get_corrected_pvalues(pvals,
+                                        fdr_method=fdr_method,
+                                        exclude_contemporaneous=excl)
+    err_msg = "get_corrected_pvalues failed on "+message+" mode"
+    np.testing.assert_allclose(pvals[slice_n, slice_n, slice_t],
+                               qvals[slice_n, slice_n, slice_t],
+                               rtol=1e-10,
+                               atol=1e-10,
+                               verbose=True,
+                               err_msg=err_msg)
+
+def test_sig_parents(a_pcmci):
+    """
+    Test that the correct significant parents are returned.
+    """
+    # Unpack the pcmci instance
+    pcmci, _ = a_pcmci
+    # Define the dimensionality
+    dim = N_NODES
+    # Build a p_matrix for 10 x 10 x 10
+    p_matrix = np.arange(dim*dim*dim).reshape(dim, dim, dim) + 1
+    # Build a val matrix as the negative version of this matrix
+    val_matrix = -p_matrix
+    # Define the alpha value
+    alpha = dim*dim*dim/2
+    # Get the significant parents
+    sig_parents = pcmci.return_significant_parents(p_matrix,
+                                                   val_matrix,
+                                                   alpha_level=alpha)
+    # Ensure the link matrix has the correct sum
+    link_matrix = sig_parents['link_matrix']
+    num_links = np.count_nonzero(link_matrix)
+    assert num_links == alpha,\
+        "The correct number of significant parents are found in the returned"+\
+        " link matrix"
+    # Ensure all the parents are in the second half of the returned p_matrix
+    num_links = np.count_nonzero(link_matrix[:5, :, :])
+    assert num_links == alpha,\
+        "The correct links from significant parents are found in the returned"+\
+        " link matrix"
+    # Ensure the correct number of links are returned in the dictionary of
+    # parents
+    parents_dict = sig_parents['parents']
+    all_links = [lnk for links in parents_dict.values() for lnk in links]
+    assert len(all_links) == (dim*dim*(dim - 1))/2.,\
+            "The correct number of links are returned in the dictionary of"+\
+            " parents"
+    # Ensure the correct links are returned:
+    # Expect j to cycle through [0, dim]
+    expect_j = set(range(dim))
+    # Expect i to cycle through [0, dim/2]
+    expect_i = set(range((int)(dim/2)))
+    # Expect tau to cycle through [-3, -1]
+    expect_t = set(range(-dim + 1, 0))
+    for key, links in parents_dict.items():
+        # Ensure the returned keys are correct
+        assert key in expect_j, "Incorrect node/key found in returned parent"+\
+                " dictionary"
+        for (i, tau) in links:
+            # Ensure the returned node values are correct
+            assert i in expect_i, "Incorrect parent found in returned parent"+\
+                    " dictionary"
+            # Ensure the returned tau values are correct
+            assert tau in expect_t, "Incorrect tau found in returned parent"+\
+                    " dictionary"
diff --git a/tests/test_tigramite_independence_tests.py b/tests/test_tigramite_independence_tests.py
deleted file mode 100644
index ab03b944..00000000
--- a/tests/test_tigramite_independence_tests.py
+++ /dev/null
@@ -1,587 +0,0 @@
-import numpy
-
-# Make Python see modules in parent package
-# import sys, os
-# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from tigramite.independence_tests import ParCorr, GPACE, GPDC, CMIknn, CMIsymb, _construct_array
-import tigramite.data_processing as pp
-
-import nose
-import nose.tools as nt
-# import unittest
-
-
-verbosity = 0
-
-def _par_corr_to_cmi(par_corr):
-    """Transformation of partial correlation to CMI scale."""
-
-    return -0.5 * numpy.log(1. - par_corr**2)
-
-#
-#  Start
-#
-class TestCondInd():  #unittest.TestCase):
-    # def __init__(self):
-    #     pass
-
-    def setUp(self):
-
-       auto = 0.6
-       coeff = 0.6
-       T = 1000
-       numpy.random.seed(42)
-       # True graph
-       links_coeffs = {0: [((0, -1), auto)],
-                       1: [((1, -1), auto), ((0, -1), coeff)],
-                       2: [((2, -1), auto), ((1, -1), coeff)]
-                       }
-
-       self.data, self.true_parents_coeffs = pp.var_process(links_coeffs, T=T)
-       T, N = self.data.shape 
-
-       self.ci_par_corr = ParCorr(use_mask=False,
-                           mask_type=None,
-                           significance='analytic',
-                           fixed_thres=None,
-                           sig_samples=10000,
-                           sig_blocklength=3,
-
-                           confidence='analytic',
-                           conf_lev=0.9,
-                           conf_samples=10000,
-                           conf_blocklength=1,
-
-                           recycle_residuals=False,
-                           verbosity=0)
-
-
-       self.ci_gpdc = GPDC(
-                            significance='analytic',
-                            sig_samples=1000,
-                            sig_blocklength=1,
-
-                            confidence='bootstrap', 
-                            conf_lev=0.9,
-                            conf_samples=100,
-                            conf_blocklength=None,
-
-                            use_mask=False,
-                            mask_type='y',
-                            recycle_residuals=False,
-                            verbosity=0)
-
-    def test_construct_array(self):
-
-        data = numpy.array([[0, 10, 20, 30],
-                            [1, 11, 21, 31],
-                            [2, 12, 22, 32],
-                            [3, 13, 23, 33],
-                            [4, 14, 24, 34],
-                            [5, 15, 25, 35],
-                            [6, 16, 26, 36]])
-        data_mask = numpy.array([[0, 1, 1, 0],
-                                 [0, 0, 0, 0],
-                                 [1, 0, 0, 0],
-                                 [0, 0, 1, 1],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0]], dtype='bool')
-
-        X = [(1, -1)]
-        Y = [(0, 0)]
-        Z = [(0, -1), (1, -2), (2, 0)]
-
-        tau_max = 2
-
-        # No masking
-        res = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            use_mask=False,
-            data=data,
-            mask=data_mask,
-            missing_flag=None,
-            mask_type=None, verbosity=verbosity)
-        print res[0]
-        numpy.testing.assert_almost_equal(res[0],
-                                          numpy.array([[13, 14, 15],
-                                                     [ 4,  5,  6],
-                                                     [ 3,  4,  5],
-                                                     [12, 13, 14],
-                                                     [24, 25, 26]]))
-        numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
-
-        # masking y
-        res = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            use_mask=True,
-            data=data,
-            mask=data_mask,
-            mask_type=['y'], verbosity=verbosity)
-        print res[0]
-
-        numpy.testing.assert_almost_equal(res[0],
-                                          numpy.array([[13, 14, 15],
-                                                     [ 4,  5,  6],
-                                                     [ 3,  4,  5],
-                                                     [12, 13, 14],
-                                                     [24, 25, 26]]))
-
-        numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
-
-        # masking all
-        res = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            use_mask=True,
-            data=data,
-            mask=data_mask,
-            mask_type=['x', 'y', 'z'], verbosity=verbosity)
-        print res[0]
-
-        numpy.testing.assert_almost_equal(res[0],
-                                          numpy.array([[13, 14, 15],
-                                                     [ 4,  5,  6],
-                                                     [ 3,  4,  5],
-                                                     [12, 13, 14],
-                                                     [24, 25, 26]]))
-
-        numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
-
-    def test_missing_values(self):
-
-        data = numpy.array([[0, 10, 20, 30],
-                            [1, 11, 21, 31],
-                            [2, 12, 22, 32],
-                            [3, 13, 999, 33],
-                            [4, 14, 24, 34],
-                            [5, 15, 25, 35],
-                            [6, 16, 26, 36],
-                            ])
-        data_mask = numpy.array([[0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0],
-                                 [0, 0, 0, 0]], dtype='bool')
-
-        X = [(1, -2)]
-        Y = [(0, 0)]
-        Z = [(2, -1)]
-
-        tau_max = 1
-
-        # Missing values
-        res = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            use_mask=False,
-            data=data,
-            mask=data_mask,
-            missing_flag=999,
-            mask_type=['y'], verbosity=verbosity)
-
-        # print res[0]
-        numpy.testing.assert_almost_equal(res[0],
-                                          numpy.array([[10, 14],
-                                                     [ 2,  6],
-                                                     [21, 25]]))
-
-
-    def test_bootstrap_vs_analytic_confidence_parcorr(self):
-
-        cov = numpy.array([[1., 0.3],[0.3, 1.]])
-        array = numpy.random.multivariate_normal(mean=numpy.zeros(2),
-                        cov=cov, size=150).T
-
-        val = numpy.corrcoef(array)[0,1]
-        # print val
-        dim, T = array.shape
-        xyz = numpy.array([0,1])
-
-        conf_ana = self.ci_par_corr.get_analytic_confidence(df=T-dim, 
-                                value=val, 
-                                conf_lev=self.ci_par_corr.conf_lev)
-
-        conf_boots = self.ci_par_corr.get_bootstrap_confidence(
-            array, xyz,
-            dependence_measure=self.ci_par_corr.get_dependence_measure,
-            conf_samples=self.ci_par_corr.conf_samples, 
-            conf_blocklength=self.ci_par_corr.conf_blocklength,
-            conf_lev=self.ci_par_corr.conf_lev, 
-            )
-        
-        print conf_ana
-        print conf_boots
-
-        numpy.testing.assert_allclose(numpy.array(conf_ana), 
-                                   numpy.array(conf_boots),
-                                   atol=0.01)
-
-
-    def test_shuffle_vs_analytic_significance_parcorr(self):
-
-        cov = numpy.array([[1., 0.04],[0.04, 1.]])
-        array = numpy.random.multivariate_normal(mean=numpy.zeros(2),
-                        cov=cov, size=250).T
-        # array = numpy.random.randn(3, 10)
-        val = numpy.corrcoef(array)[0,1]
-        # print val
-        dim, T = array.shape
-        xyz = numpy.array([0,1])
-
-        pval_ana = self.ci_par_corr.get_analytic_significance(value=val,
-                                                             T=T, dim=dim)
-
-        pval_shuffle = self.ci_par_corr.get_shuffle_significance(array, xyz,
-                               val)
-        # Adjust p-value for two-sided measures
-        
-        print pval_ana
-        print pval_shuffle
-
-        numpy.testing.assert_allclose(numpy.array(pval_ana), 
-                                   numpy.array(pval_shuffle),
-                                   atol=0.01)
-
-
-    def test__parcorr_get_single_residuals(self):
-
-        target_var = 0  #numpy.array([True, False, False, False])
-        true_residual = numpy.random.randn(4, 1000)
-
-        array = numpy.copy(true_residual)
-
-        array[0] += 0.5*array[2:].sum(axis=0)
-
-        est_residual = self.ci_par_corr._get_single_residuals(array, target_var, 
-                standardize=False, return_means=False)
-
-        # print est_residual[:10]
-        # print true_residual[0, :10]
-        numpy.testing.assert_allclose(est_residual, true_residual[0], atol=0.01)
-
-
-    def test_par_corr(self):
-
-        val_ana = 0.6
-        T = 1000
-        array = numpy.random.randn(5, T)
-
-        cov = numpy.array([[1., val_ana],[val_ana, 1.]])
-        array[:2, :] = numpy.random.multivariate_normal(mean=numpy.zeros(2),
-                        cov=cov, size=T).T
-
-        # Generate some confounding
-        array[0] += 0.5* array[2:].sum(axis=0)
-        array[1] += 0.7* array[2:].sum(axis=0)
-
-        # print numpy.corrcoef(array)[0,1]
-        # print val
-        dim, T = array.shape
-        xyz = numpy.array([0,1,2,2,2])
-
-        val_est = self.ci_par_corr.get_dependence_measure(array, xyz)
-        
-        print val_est
-        print val_ana
-
-        numpy.testing.assert_allclose(numpy.array(val_ana), 
-                                   numpy.array(val_est),
-                                   atol=0.02)
-
-    def test__gpdc_get_single_residuals(self):
-
-
-        ci_test = self.ci_gpdc
-        # ci_test = self.ci_par_corr
-
-        c = .3
-        T = 1000
-
-        numpy.random.seed(42)
-
-        def func(x):
-            return x * (1. - 4. * x**0 * numpy.exp(-x**2 / 2.))
-
-        array = numpy.random.randn(3, T)
-        array[1] += c*func(array[2])   #.sum(axis=0)
-        xyz = numpy.array([0,1] + [2 for i in range(array.shape[0]-2)])
-
-        target_var = 1    
-
-        dim, T = array.shape
-        # array -= array.mean(axis=1).reshape(dim, 1)
-        c_std = c  #/array[1].std()
-        # array /= array.std(axis=1).reshape(dim, 1)
-        array_orig = numpy.copy(array)
-
-        (est_residual, pred) = ci_test._get_single_residuals(
-                        array, target_var, 
-                        standardize=False, 
-                        return_means=True)
-
-        # Testing that in the center the fit is good
-        center = numpy.where(numpy.abs(array_orig[2]) < .7)[0]
-        print (pred[center][:10]).round(2)
-        print (c_std*func(array_orig[2][center])[:10]).round(2)
-        numpy.testing.assert_allclose(pred[center], 
-            c_std*func(array_orig[2][center]), atol=0.2)
-
-    def plot__gpdc_get_single_residuals(self):
-
-
-        #######
-        ci_test = self.ci_gpdc
-        # ci_test = self.ci_par_corr
-
-        a = 0.
-        c = .3
-        T = 500
-        # Each key refers to a variable and the incoming links are supplied as a
-        # list of format [((driver, lag), coeff), ...]
-        links_coeffs = {0: [((0, -1), a)],
-                        1: [((1, -1), a), ((0, -1), c)],
-                        }
-
-        numpy.random.seed(42)
-        data, true_parents_neighbors = pp.var_process(
-            links_coeffs,
-                                                                      use='inv_inno_cov', T=T)
-        dataframe = pp.DataFrame(data)
-        ci_test.set_dataframe(dataframe)
-        # ci_test.set_tau_max(1)
-
-        # X=[(1, -1)]
-        # Y=[(1, 0)]
-        # Z=[(0, -1)] + [(1, -tau) for tau in range(1, 2)]
-        # array, xyz, XYZ = ci_test.get_array(X, Y, Z, 
-        #     verbosity=0)]
-        # ci_test.run_test(X, Y, Z,)
-        def func(x):
-            return x * (1. - 4. * x**0 * numpy.exp(-x**2 / 2.))
-
-        true_residual = numpy.random.randn(3, T)
-        array = numpy.copy(true_residual)
-        array[1] += c*func(array[2])   #.sum(axis=0)
-        xyz = numpy.array([0,1] + [2 for i in range(array.shape[0]-2)])
-
-        print 'xyz ', xyz, numpy.where(xyz==1)
-        target_var = 1    
-
-        dim, T = array.shape
-        # array -= array.mean(axis=1).reshape(dim, 1)
-        c_std = c  #/array[1].std()
-        # array /= array.std(axis=1).reshape(dim, 1)
-        array_orig = numpy.copy(array)
-
-        import matplotlib
-        from matplotlib import pyplot
-        (est_residual, pred) = ci_test._get_single_residuals(
-                        array, target_var, 
-                        standardize=False, 
-                        return_means=True)
-        (resid_, pred_parcorr) = self.ci_par_corr._get_single_residuals(
-                        array, target_var, 
-                        standardize=False, 
-                        return_means=True)
-
-        fig = pyplot.figure()
-        ax = fig.add_subplot(111)
-        ax.scatter(array_orig[2], array_orig[1])
-        ax.scatter(array_orig[2], pred, color='red')
-        ax.scatter(array_orig[2], pred_parcorr, color='green')
-        ax.plot(numpy.sort(array_orig[2]), c_std*func(numpy.sort(array_orig[2])), color='black')
-
-        pyplot.savefig('/home/jakobrunge/test/gpdctest.pdf')
-
-
-    def test_shuffle_vs_analytic_significance_gpdc(self):
-
-        cov = numpy.array([[1., 0.2], [0.2, 1.]])
-        array = numpy.random.multivariate_normal(mean=numpy.zeros(2),
-                        cov=cov, size=245).T
-
-        dim, T = array.shape
-        xyz = numpy.array([0,1])
-
-        val = self.ci_gpdc.get_dependence_measure(array, xyz)
-
-        pval_ana = self.ci_gpdc.get_analytic_significance(value=val,
-                                                             T=T, dim=dim)
-
-        pval_shuffle = self.ci_gpdc.get_shuffle_significance(array, xyz,
-                               val)
-
-        print pval_ana
-        print pval_shuffle
-
-        numpy.testing.assert_allclose(numpy.array(pval_ana), 
-                                   numpy.array(pval_shuffle),
-                                   atol=0.05)
-
-
-    def test_shuffle_vs_analytic_significance_gpdc(self):
-
-        cov = numpy.array([[1., 0.01], [0.01, 1.]])
-        array = numpy.random.multivariate_normal(mean=numpy.zeros(2),
-                        cov=cov, size=300).T
-
-        dim, T = array.shape
-        xyz = numpy.array([0,1])
-
-        val = self.ci_gpdc.get_dependence_measure(array, xyz)
-
-        pval_ana = self.ci_gpdc.get_analytic_significance(value=val,
-                                                             T=T, dim=dim)
-
-        pval_shuffle = self.ci_gpdc.get_shuffle_significance(array, xyz,
-                               val)
-        print pval_ana
-        print pval_shuffle
-
-        numpy.testing.assert_allclose(numpy.array(pval_ana), 
-                                   numpy.array(pval_shuffle),
-                                   atol=0.05)
-
-    def test_cmi_knn(self):
-
-        ci_cmi_knn = CMIknn(use_mask=False,
-                            mask_type=None,
-                            significance='shuffle_test',
-                            fixed_thres=None,
-                            sig_samples=10000,
-                            sig_blocklength=3,
-                            knn=10,
-
-                            confidence='bootstrap',
-                            conf_lev=0.9,
-                            conf_samples=10000,
-                            conf_blocklength=1,
-
-                            verbosity=0)
-
-
-        # ci_cmi_knn._trafo2uniform(self, x)
-
-        val_ana = 0.6
-        T = 10000
-        numpy.random.seed(42)
-        array = numpy.random.randn(5, T)
-
-        cov = numpy.array([[1., val_ana],[val_ana, 1.]])
-        array[:2, :] = numpy.random.multivariate_normal(
-                        mean=numpy.zeros(2),
-                        cov=cov, size=T).T
-
-        # Generate some confounding
-        if len(array) > 2:
-            array[0] += 0.5* array[2:].sum(axis=0)
-            array[1] += 0.7* array[2:].sum(axis=0)
-
-        # print numpy.corrcoef(array)[0,1]
-        # print val
-        dim, T = array.shape
-        xyz = numpy.array([0,1,2,2,2])
-
-        val_est = ci_cmi_knn.get_dependence_measure(array, xyz)
-        
-        print val_est
-        print _par_corr_to_cmi(val_ana)
-
-        numpy.testing.assert_allclose(numpy.array(_par_corr_to_cmi(val_ana)), 
-                                   numpy.array(val_est),
-                                   atol=0.02)
-
-    def test_trafo2uniform(self):
-
-        T = 1000
-        # numpy.random.seed(None)
-        array = numpy.random.randn(2, T)
-
-        bins = 10
-
-        uniform = self.ci_gpdc._trafo2uniform(array)
-        # print uniform
-
-        # import matplotlib
-        # from matplotlib import pylab
-        for i in range(array.shape[0]):
-            print uniform[i].shape
-            hist, edges = numpy.histogram(uniform[i], bins=bins, 
-                                      density=True)
-            # pylab.figure()
-            # pylab.hist(uniform[i], color='grey', alpha=0.3)
-            # pylab.hist(array[i], alpha=0.3)
-            # pylab.show()
-            print hist/float(bins)  #, edges
-            numpy.testing.assert_allclose(numpy.ones(bins)/float(bins), 
-                                          hist/float(bins),
-                                           atol=0.01)
-
-    def test_cmi_symb(self):
-
-        ci_cmi_symb = CMIsymb(use_mask=False,
-                            mask_type=None,
-                            significance='shuffle_test',
-                            fixed_thres=None,
-                            sig_samples=10000,
-                            sig_blocklength=3,
-
-                            confidence='bootstrap',
-                            conf_lev=0.9,
-                            conf_samples=10000,
-                            conf_blocklength=1,
-
-                            verbosity=0)
-
-        val_ana = 0.6
-        T = 100000
-        numpy.random.seed(None)
-        array = numpy.random.randn(3, T)
-
-        cov = numpy.array([[1., val_ana],[val_ana, 1.]])
-        array[:2, :] = numpy.random.multivariate_normal(
-                        mean=numpy.zeros(2),
-                        cov=cov, size=T).T
-
-        # Generate some confounding
-        if len(array) > 2:
-            array[0] += 0.5* array[2:].sum(axis=0)
-            array[1] += 0.7* array[2:].sum(axis=0)
-
-        # Transform to symbolic data
-        array = pp.quantile_bin_array(array.T, bins=16).T
-
-        dim, T = array.shape
-        xyz = numpy.array([0,1,2,2,2])
-
-        val_est = ci_cmi_symb.get_dependence_measure(array, xyz)
-        
-        print val_est
-        print _par_corr_to_cmi(val_ana)
-
-        numpy.testing.assert_allclose(numpy.array(_par_corr_to_cmi(val_ana)), 
-                                   numpy.array(val_est),
-                                   atol=0.02)
-
-if __name__ == "__main__":
-    # unittest.main()
-
-    # tci = TestCondInd()  #unittest.TestCase)
-
-    # tci.setUp()
-    # tci.test_construct_array()
-    # tci.test_missing_values()
-    # tci.test_shuffle_vs_analytic_significance_gpdc()
-    # tci.test_trafo2uniform()
-    # tci.test_cmi_symb()
-    # tci.test_bootstrap_vs_analytic_confidence()
-    # tci.test_shuffle_vs_analytic_significance_gpdc()
-    # tci.test__gpdc_get_single_residuals()
-    # unittest.main()
-    nose.run()
diff --git a/tests/test_tigramite_pcmci.py b/tests/test_tigramite_pcmci.py
deleted file mode 100644
index 8fc4b65c..00000000
--- a/tests/test_tigramite_pcmci.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import numpy
-
-# Make Python see modules in parent package
-# import sys, os
-# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-import tigramite
-from tigramite.pcmci import PCMCI
-from tigramite.independence_tests import ParCorr #, GPACE
-import tigramite.data_processing as pp
-# import tigramite_plotting 
-
-import nose
-import nose.tools as nt
-# import unittest
-
-
-def assert_graphs_equal(actual, expected):
-    """Check whether lists in dict are equal"""
-
-    for j in list(expected):
-        nt.assert_items_equal(actual[j], expected[j])
-
-
-def _get_parent_graph(nodes, exclude=None):
-    """Returns parents"""
-
-    graph = {}
-    for j in list(nodes):
-        graph[j] = []
-        for var, lag in nodes[j]:
-            if lag != 0 and (var, lag) != exclude:
-                graph[j].append((var, lag))
-
-    return graph
-
-
-def _get_neighbor_graph(nodes, exclude=None):
-
-    graph = {}
-    for j in list(nodes):
-        graph[j] = []
-        for var, lag in nodes[j]:
-            if lag == 0 and (var, lag) != exclude:
-                graph[j].append((var, lag))
-
-    return graph
-
-
-def cmi2parcorr_trafo(cmi):
-    return numpy.sqrt(1.-numpy.exp(-2.*cmi))
-
-verbosity = 0
-
-#
-#  Start
-#
-class TestPCMCI():  #unittest.TestCase):
-    # def __init__(self):
-    #     pass
-
-    def setUp(self):
-
-       auto = .5
-       coeff = 0.6
-       T = 1000
-       numpy.random.seed(42)
-       # True graph
-       links_coeffs = {0: [((0, -1), auto)],
-                       1: [((1, -1), auto), ((0, -1), coeff)],
-                       2: [((2, -1), auto), ((1, -1), coeff)]
-                       }
-
-       self.data, self.true_parents_coeffs = pp.var_process(links_coeffs, T=T)
-       T, N = self.data.shape 
-
-       self.true_parents = _get_parent_graph(self.true_parents_coeffs)
-
-    def test_pcmci(self):
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        results = pcmci.run_pcmci(
-            tau_max=tau_max,
-            pc_alpha=pc_alpha,
-        )
-
-        parents = pcmci._return_significant_parents(
-            pq_matrix=results['p_matrix'],
-            val_matrix=results['val_matrix'],
-            alpha_level=alpha_level)['parents']
-
-        # print parents
-        # print self.true_parents
-        assert_graphs_equal(parents, self.true_parents)
-
-    def test_pc_stable(self):
-
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        pcmci.run_pc_stable( selected_links=None,
-                             tau_min=1,
-                             tau_max=tau_max,
-                             save_iterations=False,
-                             pc_alpha=pc_alpha,
-                             max_conds_dim=None,
-                             max_combinations=1,
-                             )
-
-        parents = pcmci.all_parents
-        # print parents
-        # print _get_parent_graph(true_parents)
-        assert_graphs_equal(parents, self.true_parents)
-
-    def test_pc_stable_selected_links(self):
-
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        true_parents_here = {0: [(0, -1)],
-                       1: [(1, -1), (0, -1)],
-                       2: []
-                       }
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            selected_variables=None,
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        pcmci.run_pc_stable( selected_links=true_parents_here,
-                             tau_min=1,
-                             tau_max=tau_max,
-                             save_iterations=False,
-                             pc_alpha=pc_alpha,
-                             max_conds_dim=None,
-                             max_combinations=1,
-                             )
-
-        parents = pcmci.all_parents
-        # print parents
-        # print _get_parent_graph(true_parents)
-        assert_graphs_equal(parents, true_parents_here)
-
-
-    def test_pc_stable_selected_variables(self):
-
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        true_parents_here = {0: [],
-                       1: [(1, -1), (0, -1)],
-                       2: []
-                       }
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            selected_variables=[1],
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        pcmci.run_pc_stable( selected_links=None,
-                             tau_min=1,
-                             tau_max=tau_max,
-                             save_iterations=False,
-                             pc_alpha=pc_alpha,
-                             max_conds_dim=None,
-                             max_combinations=1,
-                             )
-
-        parents = pcmci.all_parents
-        # print parents
-        # print _get_parent_graph(true_parents)
-        assert_graphs_equal(parents, true_parents_here)
-
-
-    def test_pc_stable_max_conds_dim(self):
-
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        # true_parents_here = {0: [],
-        #                1: [(1, -1), (0, -1)],
-        #                2: []
-        #                }
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            selected_variables=None,
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        pcmci.run_pc_stable( selected_links=None,
-                             tau_min=1,
-                             tau_max=tau_max,
-                             save_iterations=False,
-                             pc_alpha=pc_alpha,
-                             max_conds_dim=2,
-                             max_combinations=1,
-                             )
-
-        parents = pcmci.all_parents
-        # print parents
-        # print _get_parent_graph(true_parents)
-        assert_graphs_equal(parents, self.true_parents)
-
-
-    def test_mci(self):
-
-        # Setting up strict test level
-        pc_alpha = 0.05  #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-        tau_max = 2
-        alpha_level = 0.01
-
-        dataframe = pp.DataFrame(self.data)
-
-        cond_ind_test = ParCorr(
-            verbosity=verbosity)
-
-        pcmci = PCMCI(
-            selected_variables=None,
-            dataframe=dataframe,
-            cond_ind_test=cond_ind_test,
-            verbosity=verbosity)
-
-        results = pcmci.run_mci(
-                    selected_links=None,
-                    tau_min=1,
-                    tau_max=tau_max,
-                    parents=self.true_parents,
-                    max_conds_py=None,
-                    max_conds_px=None,
-                    )
-
-        parents = pcmci._return_significant_parents(
-                                    pq_matrix=results['p_matrix'],
-                                  val_matrix=results['val_matrix'],
-                                  alpha_level=alpha_level,
-                                  )['parents']
-        # print parents
-        # print _get_parent_graph(true_parents)
-        assert_graphs_equal(parents, self.true_parents)
-
-
-
-if __name__ == "__main__":
-    # unittest.main()
-
-    ## Individual tests
-    # test_pcmci = TestPCMCI()
-    # test_pcmci.setUp()
-    # test_pcmci.test_pc_stable()
-    # test_pcmci.test_pcmci()
-    nose.main(module=TestPCMCI())
-    # nose.run()  #argv=[sys.argv[0],
-    #                         test_tigramite_pcmci,
-    #                         '-v'])
-
-    # unittest.main()
diff --git a/tests/test_var_process.py b/tests/test_var_process.py
new file mode 100644
index 00000000..652c9a0f
--- /dev/null
+++ b/tests/test_var_process.py
@@ -0,0 +1,315 @@
+"""
+Testing var_process using exponential decay examples.
+"""
+from __future__ import print_function
+import copy
+import pytest
+import numpy as np
+import tigramite.data_processing as pp
+
+# Pylint settings
+# pylint: disable=redefined-outer-name
+
+# TEST DATA GENERATION #########################################################
+def gen_decay(init_val, decay_const, n_times, delay):
+    """
+    Generate the initial values for the exponential decay test cases
+    """
+    init_arr = np.ones(n_times)*init_val
+    for time in range(delay, n_times):
+        init_arr[time] = init_arr[time-delay] * decay_const
+    return init_arr
+
+@pytest.fixture(params=[
+    #Generates <dim> decoupled exponential decays. Decay starts from initial
+    # value <init> with decay constant <decay> appreciating every unit of time
+    # for a total of <n_times>.  The value at time <t> is taken as the:
+    #     [value at (<t> - <delay>)]*<decay>
+    #Define parameters to use for exponential decay cases.
+    #init,  decay, n_times, delay, dim, name
+    (100.,  0.99,  1000,    1,     1,   "Default Exp Decay"),
+    (-100., 0.99,  1000,    1,     1,   "Negative Exp Decay"),
+    (100.,  0.99,  10,      1,     1,   "Short Exp Decay"),
+    (100.,  0.99,  1000,    10,    1,   "Delayed Exp Decay"),
+    (100.,  0.99,  1000,    1,     2,   "2D Exp Decay"),])
+def decoupled_exp_decay_process(request):
+    # Get the parameters
+    init, decay, n_times, delay, dim, name = request.param
+    # Generate the initial values
+    init_vals = np.array([gen_decay(init, decay, delay+1, delay) \
+                          for _ in range(dim)])
+    # Generate the coefficients
+    coefs = {}
+    for node in range(dim):
+        coefs[node] = [((node, -delay), decay)]
+    # Generate the expected values
+    expect = np.vstack([gen_decay(init, decay, n_times, delay)\
+                       for _ in range(dim)]).T
+    return name, init_vals, coefs, expect
+
+@pytest.fixture()
+# Couples all the decays by creating negative versions of each variable, then
+# summing all variables to create a variable that is always zero.
+def coupled_exp_decay_process(decoupled_exp_decay_process):
+    # Get the decoupled version
+    name, init_vals, coefs, expect = decoupled_exp_decay_process
+    # Reflect all conditions and expectations to a negative version
+    init_vals = np.vstack([init_vals, -init_vals])
+    expect = np.hstack([expect, -expect])
+    # Create reflected, decoupled nodes
+    n_nodes = len(coefs)
+    for key in list(coefs.keys()):
+        # Grab the delay and coefficient
+        delay  = coefs[key][0][0][1]
+        a_coef = coefs[key][0][1]
+        # Make a new, decoupled node
+        coefs[key + n_nodes] = [((key+n_nodes, delay), a_coef)]
+    # Couple the last node as the sum of all the other nodes
+    all_nodes = []
+    for coef_val in coefs.values():
+        all_nodes += coef_val
+    # Add in the last node
+    coefs[len(coefs)] = all_nodes
+    # Sum all initial values to get the initial value for the last, coupled node
+    init_vals = np.vstack([init_vals, np.sum(init_vals, axis=0)])
+    # Sum all expected values to get the expected value for the last, coupled
+    # node
+    expect = np.hstack([expect, np.sum(expect, axis=-1).reshape(-1, 1)])
+    return "Coupled "+name, init_vals, coefs, expect
+
+def gen_process(a_process):
+    """
+    Calls var_process for the process fixtures
+    """
+    # Get the initial values and setup for the decay process
+    _, init_vals, coefs, expect = a_process
+    # Deducte the max time from the expected answer shape
+    max_time = expect.shape[0]
+    # Generate the data
+    data, true_parents_neighbors = pp.var_process(coefs,
+                                                  T=max_time,
+                                                  initial_values=init_vals,
+                                                  use="no_noise")
+    return data, true_parents_neighbors
+
+def check_process_data(name, coefs, data, expect):
+    """
+    Checks the data is as expected
+    """
+    # Strip the coefficients from the input parameters
+    error_message = "PARAM SET: " + name + " FAILED\n"+\
+                    "Bad parameter set for process\n"+\
+                    " "+str(coefs)+"\n"\
+                    "Data should match expected value"+\
+                    "\n Data[:5]:\n "+str(data[:5])+\
+                    "\n Data[-5:]:\n "+str(data[-5:])+\
+                    "\n Expect[:5]:\n "+str(expect[:5])+\
+                    "\n Expect[-5:]:\n "+str(expect[-5:])
+    # Check they are the same
+    np.testing.assert_allclose(data,
+                               expect,
+                               rtol=1e-10,
+                               verbose=False,
+                               err_msg=error_message)
+
+def check_process_parent_neighbours(return_links, coefs):
+    """
+    Checks the returned process parent-neighbour graphs
+    """
+    # Strip the coefficients from the input parameters
+    true_node_links = dict()
+    for node_id, all_node_links in coefs.items():
+        true_node_links[node_id] = [link for link, _ in all_node_links]
+    # Check this matches the given parameters
+    assert return_links == true_node_links
+
+def check_process(a_process):
+    """
+    Checks var_process
+    """
+    # Unpack the process
+    name, _, coefs, expect = a_process
+    # Generate the data
+    data, return_links = gen_process(a_process)
+    # Ensure the correct parent links are returned
+    check_process_parent_neighbours(return_links, coefs)
+    # Ensure the data is also correct
+    check_process_data(name, coefs, data, expect)
+
+def test_decoupled_process_data(decoupled_exp_decay_process):
+    """
+    Test that the decoupled processes work
+    """
+    # Generate and check the data
+    check_process(decoupled_exp_decay_process)
+
+def test_coupled_process_data(coupled_exp_decay_process):
+    """
+    Test that the coupled process work
+    """
+    # Generate and check the data
+    check_process(coupled_exp_decay_process)
+
+# TEST PARAMETER CHECKING ######################################################
+@pytest.fixture(params=[
+    #Returns a good parameter set along with a modified, bad parameter set that
+    #should raise an error in var_process
+    #Bad parameter sets are created by defining a link using:
+    #node,  parent, delay, error_message
+    (0,     0,      1,     "Positive time delay"),
+    (100,   0,      -1,    "Non-contiguous node ID"),
+    (-10,   0,      -1,    "Node IDs not starting from zero"),
+    (0,     100,    -1,    "Non-existant node as parent"),
+    (1,     0,      0,     "Non-symmetric instantaneous relation")])
+def bad_parameter_sets(request):
+    # Define a good parameter set
+    default_coef = 0.5
+    good_params = {}
+    good_params[0] = [((0, -1), default_coef)]
+    good_params[1] = [((1, -1), default_coef)] + good_params[0]
+    good_params[2] = [((2, -1), default_coef)] + good_params[1]
+    # Define a bad parameter set
+    node, parent, delay, message = request.param
+    bad_params = copy.deepcopy(good_params)
+    bad_params[node] = [((parent, delay), default_coef)]
+    return good_params, bad_params, message
+
+def test_bad_parameters(bad_parameter_sets):
+    """
+    Test that the correct exceptions are raised for bad input connectivity
+    dictionaries
+    """
+    # Unpack the parameter set fixture
+    good_params, bad_params, message = bad_parameter_sets
+    error_message = message + " should trigger a value error for var_process "+\
+                              "parent-neighbour dictionary"
+    # Test the good parameter set
+    try:
+        pp._check_parent_neighbor(good_params)
+        covar = pp._get_covariance_matrix(good_params)
+        pp._check_symmetric_relations(covar)
+    # Ensure no exception is raised
+    except:
+        pytest.fail("Good parameter set triggers exception incorrectly!")
+    # Ensure an exception is raised for a bad parameter set
+    with pytest.raises(ValueError, message=error_message):
+        pp._check_parent_neighbor(bad_params)
+        covar = pp._get_covariance_matrix(bad_params)
+        pp._check_symmetric_relations(covar)
+
+# TEST STABILITY CHECKING ######################################################
+@pytest.fixture(params=[
+    #Returns a stable parameter set along with a modified, unstable parameter 
+    #set that should raise an error in var_process
+    #Parameter sets are created by autocorrelated nodes, with the node ID equal
+    #to the time delay:
+    #max_delay,  coefficient,  error_message
+    (4,          10.0,         "Low-dimensionality, non-stationary process"),
+    (10,         10.0,         "High-dimensionality, non-stationary process")])
+def unstable_parameter_sets(request):
+    # Define a stable parameter set
+    stab_coef = 0.1
+    stab_params = dict()
+    # Define the unstable parameter set
+    unst_params = dict()
+    # Get the dimensionality and coefficient
+    max_delay, unst_coef, message = request.param
+    # Loop over all possible delays from this max delay
+    for delay in range(max_delay):
+        for a_params, a_coef in zip([stab_params, unst_params],
+                                    [stab_coef, unst_coef]):
+            node_id = delay
+            # Correlate each node with the 0th node, using a stable coefficient
+            a_params[node_id] = [((0, -delay), stab_coef)]
+            # Autocorrelate each node N with delay N and the given coefficient
+            a_params[node_id] = [((node_id, -delay), a_coef)]
+    # Return the stable and unstable coefficients
+    return pp._get_lag_connect_matrix(stab_params),\
+           pp._get_lag_connect_matrix(unst_params),\
+           message
+
+def test_stability_parameters(unstable_parameter_sets):
+    """
+    Test that the correct exceptions are raised for unstable lagged connectivity 
+    matricies
+    """
+    # Unpack the parameter set fixture
+    stab_matrix, unst_matrix, message = unstable_parameter_sets
+    error_message = message + " should trigger an assertion error for "+\
+                              "_var_network graph."
+    # Test the good parameter set
+    try:
+        pp._check_stability(stab_matrix)
+    # Ensure no exception is raised
+    except:
+        pytest.fail("Stable matrix set triggers exception incorrectly!")
+    # Ensure an exception is raised for a bad parameter set
+    with pytest.raises(AssertionError, message=error_message):
+        pp._check_stability(unst_matrix)
+
+# TEST NOISE GENERATION ########################################################
+@pytest.fixture()
+def covariance_parameters(request):
+    """
+    Define a good parameter set with no time delays at all to induce
+    a noise-only sample and return the resulting covariance matrix
+    """
+    default_coef = 0.1
+    good_params = {}
+    good_params[0] = [((1, 0), default_coef * 1.),
+                      ((2, 0), default_coef * 3.)]
+    good_params[1] = [((2, 0), default_coef * 2.),
+                      ((0, 0), default_coef * 1.)]
+    good_params[2] = [((0, 0), default_coef * 3.),
+                      ((1, 0), default_coef * 2.)]
+    good_params[3] = [((3, 0), default_coef * 4.)]
+    # Get the innovation matrix
+    covar_matrix = pp._get_covariance_matrix(good_params)
+    return good_params, covar_matrix
+
+def test_symmetric_covariance(covariance_parameters):
+    """
+    Test the random noise generation in var_process
+    """
+    # Unpack the covariance matrix
+    _, covar_matrix = covariance_parameters
+    # Test the matrix is symmetric
+    err_message = "Covariance matrix must be symmetric!"
+    np.testing.assert_allclose(covar_matrix,
+                               covar_matrix.T,
+                               rtol=1e-10,
+                               verbose=True,
+                               err_msg=err_message)
+
+def test_covariance_construction(covariance_parameters):
+    """
+    Test the random noise covariance matrix construction from a set of
+    parameters
+    """
+    # Unpack the covariance matrix and parameters
+    good_params, covar_matrix = covariance_parameters
+    # Check the values are passed correctly
+    for j, i, _, coeff in pp._iter_coeffs(good_params):
+        covar_coeff = covar_matrix[j, i]
+        err_message = "Node {} and parent node {} have".format(j, i)+\
+                        " coefficient {} for tau == 0,\nbut the".format(coeff)+\
+                        " coefficient in the covariance matrix"+\
+                        " is {} ".format(covar_coeff)
+        np.testing.assert_approx_equal(coeff, covar_coeff, err_msg=err_message)
+
+def test_noise_generation(covariance_parameters):
+    """
+    Ensure the covariance parameters are respected when the noise is generated
+    """
+    # Unpack the parameters and covariance matrix
+    good_params, covar_matrix = covariance_parameters
+    # Generate noise-only from this parameter set
+    data, _ = pp.var_process(good_params, T=10000, use='inno_cov',
+                             verbosity=0, initial_values=None)
+    # Get the covariance of the data set
+    covar_result = np.cov(data.T)
+    err_message = "Covariance of data does not match covariance implied by "+\
+                  " parameter set"
+    np.testing.assert_allclose(covar_matrix, covar_result,
+                               rtol=1e-1, atol=0.025,
+                               verbose=True, err_msg=err_message)
diff --git a/tigramite/data_processing.py b/tigramite/data_processing.py
index 9b21cb8c..bbd3bf73 100644
--- a/tigramite/data_processing.py
+++ b/tigramite/data_processing.py
@@ -1,13 +1,16 @@
 """Tigramite data processing functions."""
 
-# Author: Jakob Runge <jakobrunge@posteo.de>
+# Author: Jakob Runge <jakob@jakob-runge.com>
 #
 # License: GNU General Public License v3.0
-
-
-import numpy
-import sys, warnings
-
+from __future__ import print_function
+from collections import defaultdict, OrderedDict
+import sys
+import warnings
+import copy
+import numpy as np
+import scipy.sparse
+import scipy.sparse.linalg
 
 class DataFrame():
     """Data object containing time series array and optional mask.
@@ -26,7 +29,7 @@ class DataFrame():
     ----------
     data : array-like
         Numpy array of shape (observations T, variables N)
-    
+
     mask : array-like, optional (default: None)
         Optional mask array, must be of same shape as data
 
@@ -36,27 +39,305 @@ class DataFrame():
         samples for all lags up to 2*tau_max. This avoids biases, see
         section on masking in Supplement of [1]_.
 
+    var_names : list of strings, optional (default: range(N))
+        Names of variables, must match the number of variables. If None is
+        passed, variables are enumerated as [0, 1, ...]
+
+    datatime : array-like, optional (default: None)
+        Timelabel array. If None, range(T) is used.
     """
-    def __init__(self, data,
-                 mask = None, missing_flag=None):
+    def __init__(self, data, mask=None, missing_flag=None, var_names=None,
+        datatime=None):
 
         self.values = data
         self.mask = mask
         self.missing_flag = missing_flag
         T, N = data.shape
-
-        # if type(self.values) != numpy.ndarray:
+        # Set the variable names
+        self.var_names = var_names
+        # Set the default variable names if none are set
+        if self.var_names is None:
+            self.var_names = {i: i for i in range(N)}
+
+        # Set datatime
+        self.datatime = datatime
+        if self.datatime is None:
+            self.datatime = np.arange(T)
+
+        # if type(self.values) != np.ndarray:
         #     raise TypeError("data is of type %s, " % type(self.values) +
-        #                     "must be numpy.ndarray")
+        #                     "must be np.ndarray")
         if N > T:
             warnings.warn("data.shape = %s," % str(self.values.shape) +
                           " is it of shape (observations, variables) ?")
-        # if numpy.isnan(data).sum() != 0:
+        # if np.isnan(data).sum() != 0:
         #     raise ValueError("NaNs in the data")
-
+        self._check_mask()
+
+    def _check_mask(self, mask=None, require_mask=False):
+        """Checks that the mask is:
+            * The same shape as the data
+            * Is an numpy ndarray (or subtype)
+            * Does not contain any NaN entrie
+
+        Parameters
+        ----------
+        require_mask : bool (default : False)
+        """
+        # Check that there is a mask if required
+        _use_mask = mask
+        if _use_mask is None:
+            _use_mask = self.mask
+        if require_mask and _use_mask is None:
+            raise ValueError("Expected a mask, but got nothing!")
+        # If we have a mask, check it
+        if _use_mask is not None:
+            # Check the mask inherets from an ndarray
+            if not isinstance(_use_mask, np.ndarray):
+                raise TypeError("mask is of type %s, " %
+                                type(_use_mask) +
+                                "must be numpy.ndarray")
+            # Check if there is an nan-value in the mask
+            if np.isnan(np.sum(_use_mask)):
+                raise ValueError("NaNs in the data mask")
+            # Check the mask and the values have the same shape
+            if self.values.shape != _use_mask.shape:
+                raise ValueError("shape mismatch: dataframe.values.shape = %s"
+                                 % str(self.values.shape) + \
+                                 " but mask.shape = %s,"
+                                 % str(_use_mask.shape)) + \
+                                 "must identical"
+
+    def construct_array(self, X, Y, Z, tau_max,
+                        mask=None,
+                        mask_type=None,
+                        return_cleaned_xyz=False,
+                        do_checks=True,
+                        cut_off='2xtau_max',
+                        verbosity=0):
+        """Constructs array from variables X, Y, Z from data.
+
+        Data is of shape (T, N), where T is the time series length and N the
+        number of variables.
+
+        Parameters
+        ----------
+        X, Y, Z : list of tuples
+            For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+            where var specifies the variable index. X typically is of the form
+            [(varX, -tau)] with tau denoting the time lag and Z can be
+            multivariate [(var1, -lag), (var2, -lag), ...] .
+
+        tau_max : int
+            Maximum time lag. This may be used to make sure that estimates for
+            different lags in X and Z all have the same sample size.
+
+        mask : array-like, optional (default: None)
+            Optional mask array, must be of same shape as data.  If it is set,
+            then it overrides the self.mask assigned to the dataframe. If it is
+            None, then the self.mask is used, if it exists.
+
+        mask_type : {'y','x','z','xy','xz','yz','xyz'}
+            Masking mode: Indicators for which variables in the dependence
+            measure I(X; Y | Z) the samples should be masked. If None, 'y' is
+            used, which excludes all time slices containing masked samples in Y.
+            Explained in [1]_.
+
+        return_cleaned_xyz : bool, optional (default: False)
+            Whether to return cleaned X,Y,Z, where possible duplicates are
+            removed.
+
+        do_checks : bool, optional (default: True)
+            Whether to perform sanity checks on input X,Y,Z
+
+        cut_off : {'2xtau_max', 'max_lag', 'max_lag_or_tau_max'}
+            How many samples to cutoff at the beginning. The default is
+            '2xtau_max', which guarantees that MCI tests are all conducted on
+            the same samples. For modeling, 'max_lag_or_tau_max' can be used,
+            which uses the maximum of tau_max and the conditions, which is
+            useful to compare multiple models on the same sample.  Last,
+            'max_lag' uses as much samples as possible.
+
+        verbosity : int, optional (default: 0)
+            Level of verbosity.
+
+        Returns
+        -------
+        array, xyz [,XYZ] : Tuple of data array of shape (dim, T) and xyz
+            identifier array of shape (dim,) identifying which row in array
+            corresponds to X, Y, and Z. For example::
+                X = [(0, -1)], Y = [(1, 0)], Z = [(1, -1), (0, -2)]
+                yields an array of shape (5, T) and xyz is
+                xyz = numpy.array([0,1,2,2])
+            If return_cleaned_xyz is True, also outputs the cleaned XYZ lists.
+        """
+        # Get the length in time and the number of nodes
+        T, N = self.values.shape
+
+        # Remove duplicates in X, Y, Z
+        X = list(OrderedDict.fromkeys(X))
+        Y = list(OrderedDict.fromkeys(Y))
+        Z = list(OrderedDict.fromkeys(Z))
+
+        # If a node in Z occurs already in X or Y, remove it from Z
+        Z = [node for node in Z if (node not in X) and (node not in Y)]
+
+        # Check that all lags are non-positive and indices are in [0,N-1]
+        XYZ = X + Y + Z
+        dim = len(XYZ)
+
+        # Ensure that XYZ makes sense
+        if do_checks:
+            self._check_nodes(Y, XYZ, N, dim)
+
+        # Figure out what cut off we will be using
+        if cut_off == '2xtau_max':
+            max_lag = 2*tau_max
+        elif cut_off == 'max_lag':
+            max_lag = abs(np.array(XYZ)[:, 1].min())
+        elif cut_off == 'max_lag_or_tau_max':
+            max_lag = max(abs(np.array(XYZ)[:, 1].min()), tau_max)
+
+        # Setup XYZ identifier
+        index_code = {'x' : 0,
+                      'y' : 1,
+                      'z' : 2}
+        xyz = np.array([index_code[name]
+                        for var, name in zip([X, Y, Z], ['x', 'y', 'z'])
+                        for _ in var])
+
+        # Setup and fill array with lagged time series
+        time_length = T - max_lag
+        array = np.zeros((dim, time_length), dtype=self.values.dtype)
+        # Note, lags are negative here
+        for i, (var, lag) in enumerate(XYZ):
+            array[i, :] = self.values[max_lag + lag:T + lag, var]
+
+        # Choose which indices to use
+        use_indices = np.ones(time_length, dtype='int')
+
+        # Remove all values that have missing value flag, as well as the time
+        # slices that occur up to max_lag after
+        if self.missing_flag is not None:
+            missing_anywhere = np.any(self.values == self.missing_flag, axis=1)
+            for tau in range(max_lag+1):
+                use_indices[missing_anywhere[tau:T-max_lag+tau]] = 0
+
+        # Use the mask override if needed
+        _use_mask = mask
+        if _use_mask is None:
+            _use_mask = self.mask
+        else:
+            self._check_mask(mask=_use_mask)
+
+        if _use_mask is not None:
+            # Remove samples with mask == 1 conditional on which mask_type is
+            # used Create an array selector that is the same shape as the output
+            # array
+            array_mask = np.zeros((dim, time_length), dtype='int32')
+            # Iterate over all nodes named in X, Y, or Z
+            for i, (var, lag) in enumerate(XYZ):
+                # Transform the mask into the output array shape, i.e. from data
+                # mask to array mask
+                array_mask[i, :] = (_use_mask[max_lag + lag: T + lag, var] == False)
+            # Iterate over defined mapping from letter index to number index,
+            # i.e. 'x' -> 0, 'y' -> 1, 'z'-> 2
+            for idx, cde in index_code.items():
+                # Check if the letter index is in the mask type
+                if (mask_type is not None) and (idx in mask_type):
+                    # If so, check if any of the data that correspond to the
+                    # letter index is masked by taking the product along the
+                    # node-data to return a time slice selection, where 0 means
+                    # the time slice will not be used
+                    slice_select = np.prod(array_mask[xyz == cde, :], axis=0)
+                    use_indices *= slice_select
+
+        if (self.missing_flag is not None) or (_use_mask is not None):
+            if use_indices.sum() == 0:
+                raise ValueError("No unmasked samples")
+            array = array[:, use_indices == 1]
+
+        # Print information about the constructed array
+        if verbosity > 2:
+            self.print_array_info(array, X, Y, Z, self.missing_flag, mask_type)
+
+        # Return the array and xyz and optionally (X, Y, Z)
+        if return_cleaned_xyz:
+            return array, xyz, (X, Y, Z)
+        return array, xyz
+
+    def _check_nodes(self, Y, XYZ, N, dim):
+        """
+        Checks that:
+            * The requests XYZ nodes have the correct shape
+            * All lags are non-positive
+            * All indices are less than N
+            * One of the Y nodes has zero lag
+
+        Parameters
+        ----------
+            Y : list of tuples
+                Of the form [(var, -tau)], where var specifies the variable
+                index and tau the time lag.
+
+            XYZ : list of tuples
+                List of nodes chosen for current independence test
+
+            N : int
+                Total number of listed nodes
+
+            dim : int
+                Number of nodes excluding repeated nodes
+        """
+        if np.array(XYZ).shape != (dim, 2):
+            raise ValueError("X, Y, Z must be lists of tuples in format"
+                             " [(var, -lag),...], eg., [(2, -2), (1, 0), ...]")
+        if np.any(np.array(XYZ)[:, 1] > 0):
+            raise ValueError("nodes are %s, " % str(XYZ) +
+                             "but all lags must be non-positive")
+        if (np.any(np.array(XYZ)[:, 0] >= N)
+                or np.any(np.array(XYZ)[:, 0] < 0)):
+            raise ValueError("var indices %s," % str(np.array(XYZ)[:, 0]) +
+                             " but must be in [0, %d]" % (N - 1))
+        if np.all(np.array(Y)[:, 1] != 0):
+            raise ValueError("Y-nodes are %s, " % str(Y) +
+                             "but one of the Y-nodes must have zero lag")
+
+    def print_array_info(self, array, X, Y, Z, missing_flag, mask_type):
+        """
+        Print info about the constructed array
+
+        Parameters
+        ----------
+        array : Data array of shape (dim, T)
+
+        X, Y, Z : list of tuples
+            For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
+            where var specifies the variable index. X typically is of the form
+            [(varX, -tau)] with tau denoting the time lag and Z can be
+            multivariate [(var1, -lag), (var2, -lag), ...] .
+
+        missing_flag : number, optional (default: None)
+            Flag for missing values. Dismisses all time slices of samples where
+            missing values occur in any variable and also flags samples for all
+            lags up to 2*tau_max. This avoids biases, see section on masking in
+            Supplement of [1]_.
+
+        mask_type : {'y','x','z','xy','xz','yz','xyz'}
+            Masking mode: Indicators for which variables in the dependence
+            measure I(X; Y | Z) the samples should be masked. If None, 'y' is
+            used, which excludes all time slices containing masked samples in Y.
+            Explained in [1]_.
+        """
+        indt = " " * 12
+        print(indt + "Constructed array of shape %s from"%str(array.shape) +
+              "\n" + indt + "X = %s" % str(X) +
+              "\n" + indt + "Y = %s" % str(Y) +
+              "\n" + indt + "Z = %s" % str(Z))
         if self.mask is not None:
-            if self.mask.shape != self.values.shape:
-                raise ValueError("Mask array must of same shape as data array")
+            print(indt+"with masked samples in %s removed" % mask_type)
+        if self.missing_flag is not None:
+            print(indt+"with missing values = %s removed" % self.missing_flag)
 
 
 
@@ -71,7 +352,7 @@ def lowhighpass_filter(data, cutperiod, pass_periods='low'):
     data : array
         Data array of shape (time, variables).
 
-    cutperiod : int 
+    cutperiod : int
         Period of cutoff.
 
     pass_periods : str, optional (default: 'low')
@@ -91,7 +372,7 @@ def lowhighpass_filter(data, cutperiod, pass_periods='low'):
     order = 3
     ws = 1. / cutperiod / (0.5 * fs)
     b, a = butter(order, ws, pass_periods)
-    if numpy.ndim(data) == 1:
+    if np.ndim(data) == 1:
         data = filtfilt(b, a, data)
     else:
         for i in range(data.shape[1]):
@@ -122,7 +403,7 @@ def smooth(data, smooth_width, kernel='gaussian',
 
     kernel : str, optional (default: 'gaussian')
         Smoothing kernel, 'gaussian' or 'heaviside' for a running mean.
-        
+
     mask : bool array, optional (default: None)
         Data mask where True labels masked samples.
 
@@ -131,7 +412,7 @@ def smooth(data, smooth_width, kernel='gaussian',
 
     Returns
     -------
-    data : array-like    
+    data : array-like
         Smoothed/residual data.
     """
 
@@ -141,29 +422,29 @@ def smooth(data, smooth_width, kernel='gaussian',
 
     totaltime = len(data)
     if kernel == 'gaussian':
-        window = numpy.exp(-(numpy.arange(totaltime).reshape((1, totaltime)) -
-                             numpy.arange(totaltime).reshape((totaltime, 1))
+        window = np.exp(-(np.arange(totaltime).reshape((1, totaltime)) -
+                             np.arange(totaltime).reshape((totaltime, 1))
                              ) ** 2 / ((2. * smooth_width / 2.) ** 2))
     elif kernel == 'heaviside':
         import scipy.linalg
-        wtmp = numpy.zeros(totaltime)
-        wtmp[:numpy.ceil(smooth_width / 2.)] = 1
+        wtmp = np.zeros(totaltime)
+        wtmp[:np.ceil(smooth_width / 2.)] = 1
         window = scipy.linalg.toeplitz(wtmp)
 
     if mask is None:
-        if numpy.ndim(data) == 1:
+        if np.ndim(data) == 1:
             smoothed_data = (data * window).sum(axis=1) / window.sum(axis=1)
         else:
-            smoothed_data = numpy.zeros(data.shape)
+            smoothed_data = np.zeros(data.shape)
             for i in range(data.shape[1]):
                 smoothed_data[:, i] = (
                     data[:, i] * window).sum(axis=1) / window.sum(axis=1)
     else:
-        if numpy.ndim(data) == 1:
+        if np.ndim(data) == 1:
             smoothed_data = ((data * window * (mask==False)).sum(axis=1) /
                              (window * (mask==False)).sum(axis=1))
         else:
-            smoothed_data = numpy.zeros(data.shape)
+            smoothed_data = np.zeros(data.shape)
             for i in range(data.shape[1]):
                 smoothed_data[:, i] = ((
                     data[:, i] * window * (mask==False)[:, i]).sum(axis=1) /
@@ -195,13 +476,13 @@ def weighted_avg_and_std(values, axis, weights):
         Tuple of weighted average and standard deviation along axis.
     """
 
-    values[numpy.isnan(values)] = 0.
-    average = numpy.ma.average(values, axis=axis, weights=weights)
+    values[np.isnan(values)] = 0.
+    average = np.ma.average(values, axis=axis, weights=weights)
 
-    variance = numpy.sum(weights * (values - numpy.expand_dims(average, axis)
+    variance = np.sum(weights * (values - np.expand_dims(average, axis)
                                     ) ** 2, axis=axis) / weights.sum(axis=axis)
 
-    return (average, numpy.sqrt(variance))
+    return (average, np.sqrt(variance))
 
 
 def time_bin_with_mask(data, time_bin_length, sample_selector=None):
@@ -229,13 +510,13 @@ def time_bin_with_mask(data, time_bin_length, sample_selector=None):
     time_bin_length = int(time_bin_length)
 
     if sample_selector is None:
-        sample_selector = numpy.ones(data.shape)
+        sample_selector = np.ones(data.shape)
 
-    if numpy.ndim(data) == 1.:
+    if np.ndim(data) == 1.:
         data.shape = (T, 1)
         sample_selector.shape = (T, 1)
 
-    bindata = numpy.zeros(
+    bindata = np.zeros(
         (T // time_bin_length,) + data.shape[1:], dtype="float32")
     for index, i in enumerate(range(0, T - time_bin_length + 1,
                                     time_bin_length)):
@@ -251,7 +532,7 @@ def time_bin_with_mask(data, time_bin_length, sample_selector=None):
     return (bindata.squeeze(), T)
 
 
-def ordinal_patt_array(array, array_mask=None, dim=2, step=1, 
+def ordinal_patt_array(array, array_mask=None, dim=2, step=1,
                         weights=False, verbosity=0):
     """Returns symbolified array of ordinal patterns.
 
@@ -281,7 +562,7 @@ def ordinal_patt_array(array, array_mask=None, dim=2, step=1,
 
     weights : bool, optional (default: False)
         Whether to return array of variances of embedding vectors as weights.
-        
+
     verbosity : int, optional (default: 0)
         Level of verbosity.
 
@@ -290,12 +571,11 @@ def ordinal_patt_array(array, array_mask=None, dim=2, step=1,
     patt, patt_mask [, patt_time] : tuple of arrays
         Tuple of converted pattern array and new length
     """
-    import scipy
     from scipy.misc import factorial
 
     # Import cython code
     try:
-        import tigramite_cython_code
+        import tigramite.tigramite_cython_code as tigramite_cython_code
     except ImportError:
         raise ImportError("Could not import tigramite_cython_code, please"
                           " compile cython code first as described in Readme.")
@@ -305,17 +585,17 @@ def ordinal_patt_array(array, array_mask=None, dim=2, step=1,
     if array_mask is not None:
         assert array_mask.dtype == 'int32'
     else:
-        array_mask = numpy.zeros(array.shape, dtype='int32')
+        array_mask = np.zeros(array.shape, dtype='int32')
 
 
-    if numpy.ndim(array) == 1:
+    if np.ndim(array) == 1:
         T = len(array)
         array = array.reshape(T, 1)
         array_mask = array_mask.reshape(T, 1)
 
     # Add noise to destroy ties...
     array += (1E-6 * array.std(axis=0)
-              * numpy.random.rand(array.shape[0], array.shape[1]).astype('float64'))
+              * np.random.rand(array.shape[0], array.shape[1]).astype('float64'))
 
 
     patt_time = int(array.shape[0] - step * (dim - 1))
@@ -325,26 +605,28 @@ def ordinal_patt_array(array, array_mask=None, dim=2, step=1,
         raise ValueError("Dim mist be > 1 and length of delay vector smaller "
                          "array length.")
 
-    patt = numpy.zeros((patt_time, N), dtype='int32')
-    weights_array = numpy.zeros((patt_time, N), dtype='float64')
+    patt = np.zeros((patt_time, N), dtype='int32')
+    weights_array = np.zeros((patt_time, N), dtype='float64')
 
-    patt_mask = numpy.zeros((patt_time, N), dtype='int32')
+    patt_mask = np.zeros((patt_time, N), dtype='int32')
 
-    # Precompute factorial for c-code... patterns of dimension 
+    # Precompute factorial for c-code... patterns of dimension
     # larger than 10 are not supported
-    fac = factorial(numpy.arange(10)).astype('int32')
+    fac = factorial(np.arange(10)).astype('int32')
 
     # _get_patterns_cython assumes mask=0 to be a masked value
     array_mask = (array_mask == False).astype('int32')
 
-    (patt, patt_mask, weights_array) = tigramite_cython_code._get_patterns_cython(
-        array, array_mask, patt, patt_mask, weights_array, dim, step, fac, N, 
-        T)
+    (patt, patt_mask, weights_array) = \
+            tigramite_cython_code._get_patterns_cython(array, array_mask,
+                                                       patt, patt_mask,
+                                                       weights_array, dim,
+                                                       step, fac, N, T)
 
-    weights_array = numpy.asarray(weights_array)
-    patt = numpy.asarray(patt)
+    weights_array = np.asarray(weights_array)
+    patt = np.asarray(patt)
     # Transform back to mask=1 implying a masked value
-    patt_mask = numpy.asarray(patt_mask) == False
+    patt_mask = np.asarray(patt_mask) == False
 
     if weights:
         return (patt, patt_mask, patt_time, weights_array)
@@ -371,12 +653,12 @@ def quantile_bin_array(data, bins=6):
     T, N = data.shape
 
     # get the bin quantile steps
-    bin_edge = int(numpy.ceil(T / float(bins)))
+    bin_edge = int(np.ceil(T / float(bins)))
 
-    symb_array = numpy.zeros((T, N), dtype='int32')
+    symb_array = np.zeros((T, N), dtype='int32')
 
     # get the lower edges of the bins for every time series
-    edges = numpy.sort(data, axis=0)[::bin_edge, :].T
+    edges = np.sort(data, axis=0)[::bin_edge, :].T
     bins = edges.shape[1]
 
     # This gives the symbolic time series
@@ -385,10 +667,114 @@ def quantile_bin_array(data, bins=6):
 
     return symb_array.astype('int32')
 
+def _generate_noise(covar_matrix, time=1000, use_inverse=False):
+    """
+    Generate a multivariate normal distribution using correlated innovations.
+
+    Parameters
+    ----------
+    covar_matrix : array
+        Covariance matrix of the random variables
+
+    time : int
+        Sample size
+
+    use_inverse : bool, optional
+        Negate the off-diagonal elements and invert the covariance matrix
+        before use
+
+    Returns
+    -------
+    noise : array
+        Random noise generated according to covar_matrix
+    """
+    # Pull out the number of nodes from the shape of the covar_matrix
+    n_nodes = covar_matrix.shape[0]
+    # Make a deep copy for use in the inverse case
+    this_covar = covar_matrix
+    # Take the negative inverse if needed
+    if use_inverse:
+        this_covar = copy.deepcopy(covar_matrix)
+        this_covar *= -1
+        this_covar[np.diag_indices_from(this_covar)] *= -1
+        this_covar = np.linalg.inv(this_covar)
+    # Return the noise distribution
+    return np.random.multivariate_normal(mean=np.zeros(n_nodes),
+                                            cov=this_covar,
+                                            size=time)
+
+def _check_stability(graph):
+    """
+    Raises an AssertionError if the input graph corresponds to a non-stationary
+    process.
+
+    Parameters
+    ----------
+    graph : array
+        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)
+    """
+    # Get the shape from the input graph
+    n_nodes, _, period = graph.shape
+    # Set the top section as the horizontally stacked matrix of
+    # shape (n_nodes, n_nodes * period)
+    stability_matrix = \
+        scipy.sparse.hstack([scipy.sparse.lil_matrix(graph[:, :, t_slice])
+                             for t_slice in range(period)])
+    # Extend an identity matrix of shape
+    # (n_nodes * (period - 1), n_nodes * (period - 1)) to shape
+    # (n_nodes * (period - 1), n_nodes * period) and stack the top section on
+    # top to make the stability matrix of shape
+    # (n_nodes * period, n_nodes * period)
+    stability_matrix = \
+        scipy.sparse.vstack([stability_matrix,
+                             scipy.sparse.eye(n_nodes * (period - 1),
+                                              n_nodes * period)])
+    # Check the number of dimensions to see if we can afford to use a dense
+    # matrix
+    n_eigs = stability_matrix.shape[0]
+    if n_eigs <= 25:
+        # If it is relatively low in dimensionality, use a dense array
+        stability_matrix = stability_matrix.todense()
+        eigen_values, _ = scipy.linalg.eig(stability_matrix)
+    else:
+        # If it is a large dimensionality, convert to a compressed row sorted
+        # matrix, as it may be easier for the linear algebra package
+        stability_matrix = stability_matrix.tocsr()
+        # Get the eigen values of the stability matrix
+        eigen_values = scipy.sparse.linalg.eigs(stability_matrix,
+                                                k=(n_eigs - 2),
+                                                return_eigenvectors=False)
+    # Ensure they all have less than one magnitude
+    assert np.all(np.abs(eigen_values) < 1.), \
+        "Values given by time lagged connectivity matrix corresponds to a "+\
+        " non-stationary process!"
+
+def _check_initial_values(initial_values, shape):
+    """
+    Raises a AssertionError if the input initial values:
+        * Are not a numpy array OR
+        * Do not have the shape (n_nodes, max_delay+1)
+
+    Parameters
+    ----------
+    graph : array
+        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)
+    """
+    # Ensure it is a numpy array
+    assert isinstance(initial_values, np.ndarray),\
+        "User must provide initial_values as a numpy.ndarray"
+    # Check the shape is correct
+    assert initial_values.shape == shape,\
+        "Initial values must be of shape (n_nodes, max_delay+1)"+\
+        "\n current shape : " + str(initial_values.shape)+\
+        "\n desired shape : " + str(shape)
 
 def _var_network(graph,
-                inv_inno_cov=None, inno_cov=None, use='inno_cov',
-                T=100):
+                 add_noise=True,
+                 inno_cov=None,
+                 invert_inno=False,
+                 T=100,
+                 initial_values=None):
     """Returns a vector-autoregressive process with correlated innovations.
 
     Useful for testing.
@@ -404,142 +790,383 @@ def _var_network(graph,
 
         with inv_inno_cov being the negative (except for diagonal) inverse
         covariance matrix of (eps_1(t), eps_2(t)) OR inno_cov being
-        the covariance.
+        the covariance. Initial values can also be provided.
+
 
     Parameters
     ----------
     graph : array
-        Lagged connectivity matrices.
+        Lagged connectivity matrices. Shape is (n_nodes, n_nodes, max_delay+1)
 
-    inv_inno_cov : array, optional (default: None)
-        Inverse covariance matrix of innovations.
+    add_noise : bool, optional (default: True)
+        Flag to add random noise or not
 
     inno_cov : array, optional (default: None)
         Covariance matrix of innovations.
 
-    use : str, optional (default: 'inno_cov')
-        Specifier, either 'inno_cov' or 'inv_inno_cov'.
+    invert_inno : bool, optional (defualt : False)
+        Flag to negate off-diagonal elements of inno_cov and invert it before
+        using it as the covariance matrix of innovations
 
     T : int, optional (default: 100)
         Sample size.
 
+    initial_values : array, optional (defult: None)
+        Initial values for each node. Shape is (n_nodes, max_delay+1), i.e. must
+        be of shape (graph.shape[1], graph.shape[2]).
+
     Returns
     -------
     X : array
         Array of realization.
     """
-    N, N, P = graph.shape
+    n_nodes, _, period = graph.shape
 
+    time = T
     # Test stability
-    stabmat = numpy.zeros((N * P, N * P))
-    index = 0
-    for i in range(0, N * P, N):
-        stabmat[:N, i:i + N] = graph[:, :, index]
-        if index < P - 1:
-            stabmat[i + N:i + 2 * N, i:i + N] = numpy.identity(N)
-
-        index += 1
-
-    eig = numpy.linalg.eig(stabmat)[0]
-    assert numpy.all(numpy.abs(eig) < 1.), "Nonstationary process!"
-
-    X = numpy.random.randn(N, T)
-
-    if use == 'inv_inno_cov' and inv_inno_cov is not None:
-        mult = -numpy.ones((N, N))
-        mult[numpy.diag_indices_from(mult)] = 1
-        inv_inno_cov *= mult
-        noise = numpy.random.multivariate_normal(
-            mean=numpy.zeros(N),
-            cov=numpy.linalg.inv(inv_inno_cov),
-            size=T)
-    elif use == 'inno_cov' and inno_cov is not None:
-        noise = numpy.random.multivariate_normal(
-            mean=numpy.zeros(N), cov=inno_cov, size=T)
-    else:
-        noise = numpy.random.randn(T, N)
+    _check_stability(graph)
+
+    # Generate the returned data
+    data = np.random.randn(n_nodes, time)
+    # Load the initial values
+    if initial_values is not None:
+        # Check the shape of the initial values
+        _check_initial_values(initial_values, data[:, :period].shape)
+        # Input the initial values
+        data[:, :period] = initial_values
+
+    # Check if we are adding noise
+    noise = None
+    if add_noise:
+        # Use inno_cov if it was provided
+        if inno_cov is not None:
+            noise = _generate_noise(inno_cov,
+                                    time=time,
+                                    use_inverse=invert_inno)
+        # Otherwise just use uncorrelated random noise
+        else:
+            noise = np.random.randn(time, n_nodes)
 
-    for t in range(P, T):
-        Xpast = numpy.repeat(
-            X[:, t - P:t][:, ::-1].reshape(1, N, P), N, axis=0)
-        X[:, t] = (Xpast * graph).sum(axis=2).sum(axis=1) + noise[t]
+    for a_time in range(period, time):
+        data_past = np.repeat(
+            data[:, a_time-period:a_time][:, ::-1].reshape(1, n_nodes, period),
+            n_nodes, axis=0)
+        data[:, a_time] = (data_past*graph).sum(axis=2).sum(axis=1)
+        if add_noise:
+            data[:, a_time] += noise[a_time]
 
-    return X.transpose()
+    return data.transpose()
 
+def _iter_coeffs(parents_neighbors_coeffs):
+    """
+    Iterator through the current parents_neighbors_coeffs structure.  Mainly to
+    save repeated code and make it easier to change this structure.
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+
+    Yields
+    -------
+    (node_id, parent_id, time_lag, coeff) : tuple
+        Tuple defining the relationship between nodes across time
+    """
+    # Iterate through all defined nodes
+    for node_id in list(parents_neighbors_coeffs):
+        # Iterate over parent nodes and unpack node and coeff
+        for (parent_id, time_lag), coeff in parents_neighbors_coeffs[node_id]:
+            # Yield the entry
+            yield node_id, parent_id, time_lag, coeff
+
+def _check_parent_neighbor(parents_neighbors_coeffs):
+    """
+    Checks to insure input parent-neighbor connectivity input is sane.  This
+    means that:
+        * all time lags are non-positive
+        * all parent nodes are included as nodes themselves
+        * all node indexing is contiguous
+        * all node indexing starts from zero
+    Raises a ValueError if any one of these conditions are not met.
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+    """
+    # Initialize some lists for checking later
+    all_nodes = set()
+    all_parents = set()
+    # Iterate through variables
+    for j in list(parents_neighbors_coeffs):
+        # Cache all node ids to ensure they are contiguous
+        all_nodes.add(j)
+    # Iterate through all nodes
+    for j, i, tau, _ in _iter_coeffs(parents_neighbors_coeffs):
+        # Check all time lags are equal to or less than zero
+        if tau > 0:
+            raise ValueError("Lag between parent {} and node {}".format(i, j)+\
+                             " is {} > 0, must be <= 0!".format(tau))
+        # Cache all parent ids to ensure they are mentioned as node ids
+        all_parents.add(i)
+    # Check that all nodes are contiguous from zero
+    all_nodes_list = sorted(list(all_nodes))
+    if all_nodes_list != list(range(len(all_nodes_list))):
+        raise ValueError("Node IDs in input dictionary must be contiguous"+\
+                         " and start from zero!\n"+\
+                         " Found IDs : [" +\
+                         ",".join(map(str, all_nodes_list))+ "]")
+    # Check that all parent nodes are mentioned as a node ID
+    if not all_parents.issubset(all_nodes):
+        missing_nodes = sorted(list(all_parents - all_nodes))
+        all_parents_list = sorted(list(all_parents))
+        raise ValueError("Parent IDs in input dictionary must also be in set"+\
+                         " of node IDs."+\
+                         "\n Parent IDs "+" ".join(map(str, all_parents_list))+\
+                         "\n Node IDs "+" ".join(map(str, all_nodes_list)) +\
+                         "\n Missing IDs " + " ".join(map(str, missing_nodes)))
+
+def _check_symmetric_relations(a_matrix):
+    """
+    Check if the argument matrix is symmetric.  Raise a value error with details
+    about the offending elements if it is not.  This is useful for checking the
+    instantaneously linked nodes have the same link strength.
+
+    Parameters
+    ----------
+    a_matrix : 2D numpy array
+        Relationships between nodes at tau = 0. Indexed such that first index is
+        node and second is parent, i.e. node j with parent i has strength
+        a_matrix[j,i]
+    """
+    # Check it is symmetric
+    if not np.allclose(a_matrix, a_matrix.T, rtol=1e-10, atol=1e-10):
+        # Store the disagreement elements
+        bad_elems = ~np.isclose(a_matrix, a_matrix.T, rtol=1e-10, atol=1e-10)
+        bad_idxs = np.argwhere(bad_elems)
+        error_message = ""
+        for node, parent in bad_idxs:
+            # Check that we haven't already printed about this pair
+            if bad_elems[node, parent]:
+                error_message += \
+                    "Parent {:d} of node {:d}".format(parent, node)+\
+                    " has coefficient {:f}.\n".format(a_matrix[node, parent])+\
+                    "Parent {:d} of node {:d}".format(node, parent)+\
+                    " has coefficient {:f}.\n".format(a_matrix[parent, node])
+            # Check if we already printed about this one
+            bad_elems[node, parent] = False
+            bad_elems[parent, node] = False
+        raise ValueError("Relationships between nodes at tau=0 are not"+\
+                         " symmetric!\n"+error_message)
+
+def _find_max_time_lag_and_node_id(parents_neighbors_coeffs):
+    """
+    Function to find the maximum time lag in the parent-neighbors-coefficients
+    object, as well as the largest node ID
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+
+    Returns
+    -------
+    (max_time_lag, max_node_id) : tuple
+        Tuple of the maximum time lag and maximum node ID
+    """
+    # Default maximum lag and node ID
+    max_time_lag = 0
+    max_node_id = 0
+    # Iterate through the keys in parents_neighbors_coeffs
+    for j, _, tau, _ in _iter_coeffs(parents_neighbors_coeffs):
+        # Find max lag time
+        max_time_lag = max(max_time_lag, abs(tau))
+        # Find the max node ID
+        max_node_id = max(max_node_id, j)
+    # Return these values
+    return max_time_lag, max_node_id
+
+def _get_true_parent_neighbor_dict(parents_neighbors_coeffs):
+    """
+    Function to return the dictionary of true parent neighbor causal
+    connections in time.
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+
+    Returns
+    -------
+    true_parent_neighbor : dict
+        Dictionary of lists of tuples.  The dictionary is keyed by node ID, the
+        list stores the tuple values (parent_node_id, time_lag)
+    """
+    # Initialize the returned dictionary of lists
+    true_parents_neighbors = defaultdict(list)
+    for j, i, tau, coeff in _iter_coeffs(parents_neighbors_coeffs):
+        # Add parent node id and lag if non-zero coeff
+        if coeff != 0.:
+            true_parents_neighbors[j].append((i, tau))
+    # Return the true relations
+    return true_parents_neighbors
+
+def _get_covariance_matrix(parents_neighbors_coeffs):
+    """
+    Determines the covariance matrix for correlated innovations
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+
+    Returns
+    -------
+    covar_matrix : numpy array
+        Covariance matrix implied by the parents_neighbors_coeffs.  Used to
+        generate correlated innovations.
+    """
+    # Get the total number of nodes
+    _, max_node_id = \
+            _find_max_time_lag_and_node_id(parents_neighbors_coeffs)
+    n_nodes = max_node_id + 1
+    # Initialize the covariance matrix
+    covar_matrix = np.identity(n_nodes)
+    # Iterate through all the node connections
+    for j, i, tau, coeff in _iter_coeffs(parents_neighbors_coeffs):
+        # Add to covar_matrix if node connection is instantaneous
+        if tau == 0:
+            covar_matrix[j, i] = coeff
+    return covar_matrix
+
+def _get_lag_connect_matrix(parents_neighbors_coeffs):
+    """
+    Generates the lagged connectivity matrix from a parent-neighbor
+    connectivity dictionary.  Used to generate the input for _var_network
+
+    Parameters
+    ----------
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+        {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...} for
+        all variables where vars must be in [0..N-1] and lags <= 0 with number
+        of variables N.
+
+    Returns
+    -------
+    connect_matrix : numpy array
+        Lagged connectivity matrix. Shape is (n_nodes, n_nodes, max_delay+1)
+    """
+    # Get the total number of nodes and time lag
+    max_time_lag, max_node_id = \
+            _find_max_time_lag_and_node_id(parents_neighbors_coeffs)
+    n_nodes = max_node_id + 1
+    n_times = max_time_lag + 1
+    # Initialize full time graph
+    connect_matrix = np.zeros((n_nodes, n_nodes, n_times))
+    for j, i, tau, coeff in _iter_coeffs(parents_neighbors_coeffs):
+        # If there is a non-zero time lag, add the connection to the matrix
+        if tau != 0:
+            connect_matrix[j, i, -(tau+1)] = coeff
+    # Return the connectivity matrix
+    return connect_matrix
 
 def var_process(parents_neighbors_coeffs, T=1000, use='inv_inno_cov',
-                verbosity=0):
+                verbosity=0, initial_values=None):
     """Returns a vector-autoregressive process with correlated innovations.
 
-    Wrapper around var_network with more user-friendly input options.
+    Wrapper around var_network with possibly more user-friendly input options.
 
     Parameters
     ----------
-    parents_neighbors_coeffs : dict 
-        Dictionary of format {..., j:[((var1, lag1), coeff), ...], ...} for all
-        variables where vars must be in [0..N-1] and lags <= 0 with number of
-        variables N. Coeff refers to the coefficient in the linear model. If
-        lag=0, a nonzero value in the covariance matrix (or its inverse) is
-        implied. These should be the same for (i, j) and (j, i).
-
-    use : str, optional (default: 'inv_inno_cov') 
-        Specifier, either 'inno_cov' or 'inv_inno_cov'. If 'inno_cov', lag=0
-        entries in parents_neighbors_coeffs are interpreted as entries of the
-        innovation noise term's covariance matrix. I 'inv_inno_cov', they are
-        interpreted as entries in the inverse covariance matrix.
-
-    T : int, optional (default: 1000) 
+    parents_neighbors_coeffs : dict
+        Dictionary of format:
+            {..., j:[((var1, lag1), coef1), ((var2, lag2), coef2), ...], ...}
+        for all variables where vars must be in [0..N-1] and lags <= 0 with
+        number of variables N. If lag=0, a nonzero value in the covariance
+        matrix (or its inverse) is implied. These should be the same for (i, j)
+        and (j, i).
+
+    use : str, optional (default: 'inv_inno_cov')
+        Specifier, either 'inno_cov' or 'inv_inno_cov'.
+        Any other specifier will result in non-correlated noise.
+        For debugging, 'no_noise' can also be specified, in which case random
+        noise will be disabled.
+
+    T : int, optional (default: 1000)
         Sample size.
 
     verbosity : int, optional (default: 0)
         Level of verbosity.
 
+    initial_values : array, optional (default: None)
+        Initial values for each node. Shape must be (N, max_delay+1)
+
     Returns
     -------
-    X : array-like
-        Array of realization.
-
-    true_parents_neighbors : dict
-        Dictionary of true parents and neighbors
+    data : array-like
+        Data generated from this process
+    true_parent_neighbor : dict
+        Dictionary of lists of tuples.  The dictionary is keyed by node ID, the
+        list stores the tuple values (parent_node_id, time_lag)
     """
-
-    max_lag = 0
-    for j in list(parents_neighbors_coeffs):
-        for node, coeff in parents_neighbors_coeffs[j]:
-            i, tau = node[0], -node[1]
-            max_lag = max(max_lag, abs(tau))
-    N = max(list(parents_neighbors_coeffs)) + 1
-    graph = numpy.zeros((N, N, max_lag + 1))
-
-    innos = numpy.zeros((N, N))
-    innos[range(N), range(N)] = 1.
-    true_parents_neighbors = {}
-    # print graph.shape
-    for j in list(parents_neighbors_coeffs):
-        true_parents_neighbors[j] = []
-        for node, coeff in parents_neighbors_coeffs[j]:
-            i, tau = node[0], -node[1]
-            if coeff != 0.:
-                true_parents_neighbors[j].append((i, -tau))
-            if tau == 0:
-                innos[j, i] = innos[i, j] = coeff
-            else:
-                graph[j, i, tau - 1] = coeff
-
-    if verbosity > 0:
-        print("VAR graph =\n%s" % str(graph))
-        if use == 'inno_cov':
+    # Check the input parents_neighbors_coeffs dictionary for sanity
+    _check_parent_neighbor(parents_neighbors_coeffs)
+    # Generate the true parent neighbors graph
+    true_parents_neighbors = \
+        _get_true_parent_neighbor_dict(parents_neighbors_coeffs)
+    # Generate the correlated innovations
+    innos = _get_covariance_matrix(parents_neighbors_coeffs)
+    # Generate the lagged connectivity matrix for _var_network
+    connect_matrix = _get_lag_connect_matrix(parents_neighbors_coeffs)
+    # Default values as per 'inno_cov'
+    add_noise = True
+    invert_inno = False
+    # Use the correlated innovations
+    if use == 'inno_cov':
+        if verbosity > 0:
             print("\nInnovation Cov =\n%s" % str(innos))
-        elif use == 'inv_inno_cov':
+    # Use the inverted correlated innovations
+    elif use == 'inv_inno_cov':
+        invert_inno = True
+        if verbosity > 0:
             print("\nInverse Innovation Cov =\n%s" % str(innos))
-
-    data = _var_network(graph=graph, inv_inno_cov=innos,
-                       inno_cov=innos,
-                       use=use, T=T)
-
+    # Do not use any noise
+    elif use == 'no_noise':
+        add_noise = False
+        if verbosity > 0:
+            print("\nInverse Innovation Cov =\n%s" % str(innos))
+    # Use decorrelated noise
+    else:
+        innos = None
+    # Ensure the innovation matrix is symmetric if it is used
+    if (innos is not None) and add_noise:
+        _check_symmetric_relations(innos)
+    # Generate the data using _var_network
+    data = _var_network(graph=connect_matrix,
+                        add_noise=add_noise,
+                        inno_cov=innos,
+                        invert_inno=invert_inno,
+                        T=T,
+                        initial_values=initial_values)
+    # Return the data
     return data, true_parents_neighbors
 
-
 class _Logger(object):
     """Class to append print output to a string which can be saved"""
     def __init__(self):
@@ -548,4 +1175,4 @@ def __init__(self):
 
     def write(self, message):
         self.terminal.write(message)
-        self.log += message  # .write(message)
\ No newline at end of file
+        self.log += message  # .write(message)
diff --git a/tigramite/independence_tests.py b/tigramite/independence_tests.py
index 102095a4..73390a70 100644
--- a/tigramite/independence_tests.py
+++ b/tigramite/independence_tests.py
@@ -1,251 +1,44 @@
 """Tigramite causal discovery for time series."""
 
-# Author: Jakob Runge <jakobrunge@posteo.de>
+# Author: Jakob Runge <jakob@jakob-runge.com>
 #
 # License: GNU General Public License v3.0
 
+from __future__ import print_function
 import warnings
-import numpy
-import sys, os
 import math
-
-from scipy import linalg, special, stats
+import abc
+from scipy import special, stats, spatial
+import numpy as np
+import six
 
 try:
     from sklearn import gaussian_process
 except:
-    print("Could not import sklearn for GPACE")
+    print("Could not import sklearn for Gaussian process tests")
 
 try:
-    from scipy import spatial
     from tigramite import tigramite_cython_code
 except:
     print("Could not import packages for CMIknn and GPDC estimation")
 
-
-# try:
-#     import rpy2
-#     import rpy2.robjects
-#     rpy2.robjects.r['options'](warn=-1)
-
-#     from rpy2.robjects.packages import importr
-#     import rpy2.robjects.numpy2ri
-#     rpy2.robjects.numpy2ri.activate()
-# except:
-#         print("Could not import rpy package")
-
 try:
-    importr('RCIT')
+    import rpy2
+    import rpy2.robjects
+    rpy2.robjects.r['options'](warn=-1)
+    from rpy2.robjects.packages import importr
+    import rpy2.robjects.numpy2ri
+    rpy2.robjects.numpy2ri.activate()
 except:
-    print("Could not import r-package RCIT")
+    print("Could not import rpy package")
 
 try:
-    importr('acepack')
-except:
-    print("Could not import r-package acepack for GPACE,"
-          " use python ACE package")
-
-try: 
-    import ace
+    importr('RCIT')
 except:
-    print("Could not import python ACE package for GPACE")
-
-
-
-# @staticmethod
-def _construct_array(X, Y, Z, tau_max, data,
-                     use_mask=False,
-                     mask=None, mask_type=None,
-                     missing_flag=None,
-                     return_cleaned_xyz=False,
-                     do_checks=True,
-                     cut_off='2xtau_max',
-                     verbosity=0):
-    """Constructs array from variables X, Y, Z from data.
-
-    Data is of shape (T, N), where T is the time series length and N the
-    number of variables.
-
-    Parameters
-    ----------
-    X, Y, Z : list of tuples
-        For a dependence measure I(X;Y|Z), Y is of the form [(varY, 0)],
-        where var specifies the variable index. X typically is of the form
-        [(varX, -tau)] with tau denoting the time lag and Z can be
-        multivariate [(var1, -lag), (var2, -lag), ...] .
-
-    tau_max : int
-        Maximum time lag. This may be used to make sure that estimates for
-        different lags in X and Z all have the same sample size.
-
-    data : array-like, 
-        This is the data input array of shape = (T, N)
-
-    use_mask : bool, optional (default: False)
-        Whether a supplied mask should be used.
-
-    mask : boolean array, optional (default: False)
-        Mask of data array, marking masked values as 1. Must be of same
-        shape as data.
-
-    missing_flag : number, optional (default: None)
-        Flag for missing values. Dismisses all time slices of samples where
-        missing values occur in any variable and also flags samples for all
-        lags up to 2*tau_max. This avoids biases, see section on masking in
-        Supplement of [1]_.
-
-    mask_type : {'y','x','z','xy','xz','yz','xyz'}
-        Masking mode: Indicators for which variables in the dependence
-        measure I(X; Y | Z) the samples should be masked. If None, 'y' is
-        used, which excludes all time slices containing masked samples in Y.
-        Explained in [1]_.
-
-    return_cleaned_xyz : bool, optional (default: False)
-        Whether to return cleaned X,Y,Z, where possible duplicates are 
-        removed.
-
-    do_checks : bool, optional (default: True)
-        Whether to perform sanity checks on input X,Y,Z
-
-    cut_off : {'2xtau_max', 'max_lag', 'max_lag_or_tau_max'}
-        How many samples to cutoff at the beginning. The default is '2xtau_max',
-        which guarantees that MCI tests are all conducted on the same samples. 
-        For modeling, 'max_lag_or_tau_max' can be used, which uses the maximum
-        of tau_max and the conditions, which is useful to compare multiple
-        models on the same sample. Last, 'max_lag' uses as much samples as
-        possible.
-
-    verbosity : int, optional (default: 0)
-        Level of verbosity.
-
-    Returns
-    -------
-    array, xyz [,XYZ] : Tuple of data array of shape (dim, T) and xyz 
-        identifier array of shape (dim,) identifying which row in array
-        corresponds to X, Y, and Z. For example::
-            X = [(0, -1)], Y = [(1, 0)], Z = [(1, -1), (0, -2)]
-            yields an array of shape (5, T) and xyz is  
-            xyz = numpy.array([0,1,2,2])          
-        If return_cleaned_xyz is True, also outputs the cleaned XYZ lists.
-     
-    """
+    print("Could not import r-package RCIT")
 
-    def uniq(input):
-        output = []
-        for x in input:
-            if x not in output:
-                output.append(x)
-        return output
-
-    data_type = data.dtype
-
-    T, N = data.shape
-
-    # Remove duplicates in X, Y, Z
-    X = uniq(X)
-    Y = uniq(Y)
-    Z = uniq(Z)
-
-    if do_checks:
-        if len(X) == 0:
-            raise ValueError("X must be non-zero")
-        if len(Y) == 0:
-            raise ValueError("Y must be non-zero")
-
-    # If a node in Z occurs already in X or Y, remove it from Z
-    Z = [node for node in Z if (node not in X) and (node not in Y)]
-
-    # Check that all lags are non-positive and indices are in [0,N-1]
-    XYZ = X + Y + Z
-    dim = len(XYZ)
-
-    if do_checks:
-        if numpy.array(XYZ).shape != (dim, 2):
-            raise ValueError("X, Y, Z must be lists of tuples in format"
-                             " [(var, -lag),...], eg., [(2, -2), (1, 0), ...]")
-        if numpy.any(numpy.array(XYZ)[:, 1] > 0):
-            raise ValueError("nodes are %s, " % str(XYZ) +
-                             "but all lags must be non-positive")
-        if (numpy.any(numpy.array(XYZ)[:, 0] >= N)
-                or numpy.any(numpy.array(XYZ)[:, 0] < 0)):
-            raise ValueError("var indices %s," % str(numpy.array(XYZ)[:, 0]) +
-                             " but must be in [0, %d]" % (N - 1))
-        if numpy.all(numpy.array(Y)[:, 1] < 0):
-            raise ValueError("Y-nodes are %s, " % str(Y) +
-                             "but one of the Y-nodes must have zero lag")
-
-    if cut_off == '2xtau_max':
-        max_lag = 2*tau_max
-    elif cut_off == 'max_lag':
-        max_lag = abs(numpy.array(XYZ)[:, 1].min())
-    elif cut_off == 'max_lag_or_tau_max':
-        max_lag = max(abs(numpy.array(XYZ)[:, 1].min()), tau_max)
-
-    # Setup XYZ identifier
-    xyz = numpy.array([0 for i in range(len(X))] +
-                      [1 for i in range(len(Y))] +
-                      [2 for i in range(len(Z))])
-
-    # Setup and fill array with lagged time series
-    array = numpy.zeros((dim, T - max_lag), dtype=data_type)
-    for i, node in enumerate(XYZ):
-        var, lag = node
-        array[i, :] = data[max_lag + lag: T + lag, var]
-
-    if missing_flag is not None or use_mask:
-        use_indices = numpy.ones(T - max_lag, dtype='int')
-    
-    if missing_flag is not None:
-        # Dismiss all samples where missing values occur in any variable
-        # and for any lag up to max_lag
-        missing_anywhere = numpy.any(data==missing_flag, axis=1)
-        for tau in range(max_lag+1):
-            use_indices[missing_anywhere[tau:T-max_lag+tau]] = 0
-   
-    if use_mask:
-        # Remove samples with mask == 1
-        # conditional on which mask_type is used
-        array_selector = numpy.zeros((dim, T - max_lag), dtype='int32')
-        for i, node in enumerate(XYZ):
-            var, lag = node
-            array_selector[i, :] = (
-                mask[max_lag + lag: T + lag, var] == False)
-
-        # use_indices = numpy.ones(T - max_lag, dtype='int')
-        if 'x' in mask_type:
-            use_indices *= numpy.prod(array_selector[xyz == 0, :],
-                                      axis=0)
-        if 'y' in mask_type:
-            use_indices *= numpy.prod(array_selector[xyz == 1, :],
-                                      axis=0)
-        if 'z' in mask_type:
-            use_indices *= numpy.prod(array_selector[xyz == 2, :],
-                                      axis=0)
-    
-    if missing_flag is not None or use_mask:
-        if use_indices.sum() == 0:
-            raise ValueError("No unmasked samples")
-        array = array[:, use_indices == 1]
-
-    if verbosity > 2:
-        print("            Constructed array of shape " +
-              "%s from\n" % str(array.shape) +
-              "            X = %s\n" % str(X) +
-              "            Y = %s\n" % str(Y) +
-              "            Z = %s" % str(Z))
-        if use_mask:
-            print("            with masked samples in "
-                  "%s removed" % mask_type)
-        if missing_flag is not None:
-            print("            with missing values labeled "
-                  "%s removed" % missing_flag)    
-
-    if return_cleaned_xyz:
-        return array, xyz, (X, Y, Z)
-    else:
-        return array, xyz
-
-class CondIndTest(object):
+@six.add_metaclass(abc.ABCMeta)
+class CondIndTest():
     """Base class of conditional independence tests.
 
     Provides useful general functions for different independence tests such as
@@ -254,32 +47,30 @@ class CondIndTest(object):
 
     Parameters
     ----------
-    use_mask : bool, optional (default: False)
-        Whether a supplied mask should be used.
-
-    mask_type : {'y','x','z','xy','xz','yz','xyz'}
+    mask_type : str, optional (default = None)
+        Must be in {'y','x','z','xy','xz','yz','xyz'}
         Masking mode: Indicators for which variables in the dependence measure
         I(X; Y | Z) the samples should be masked. If None, 'y' is used, which
         excludes all time slices containing masked samples in Y. Explained in
         [1]_.
 
     significance : str, optional (default: 'analytic')
-        Type of significance test to use. In this package 'analytic', 
+        Type of significance test to use. In this package 'analytic',
         'fixed_thres' and 'shuffle_test' are available.
 
     fixed_thres : float, optional (default: 0.1)
-        If significance is 'fixed_thres', this specifies the threshold for the 
+        If significance is 'fixed_thres', this specifies the threshold for the
         absolute value of the dependence measure.
 
     sig_samples : int, optional (default: 1000)
-        Number of samples for shuffle significance test. 
+        Number of samples for shuffle significance test.
 
     sig_blocklength : int, optional (default: None)
         Block length for block-shuffle significance test. If None, the
-        block length is determined from the decay of the autocovariance as 
+        block length is determined from the decay of the autocovariance as
         explained in [1]_.
 
-    confidence : False or str, optional (default: False)
+    confidence : str, optional (default: None)
         Specify type of confidence estimation. If False, numpy.nan is returned.
         'bootstrap' can be used with any test, for ParCorr also 'analytic' is
         implemented.
@@ -301,86 +92,173 @@ class CondIndTest(object):
     verbosity : int, optional (default: 0)
         Level of verbosity.
     """
+    @abc.abstractmethod
+    def get_dependence_measure(self, array, xyz):
+        """
+        Abstract function that all concrete classes must instantiate.
+        """
+        pass
 
-    def __init__(self, 
-        use_mask=False,
-        mask_type=None,
-
-        significance='analytic',
-        fixed_thres=0.1,
-        sig_samples=1000,
-        sig_blocklength=None,
-
-        confidence=False,
-        conf_lev=0.9,
-        conf_samples=100,
-        conf_blocklength=None,
-
-        recycle_residuals=False,
-        verbosity=0
-        ):
+    @abc.abstractproperty
+    def measure(self):
+        """
+        Abstract property to store the type of independence test.
+        """
+        pass
 
-        self.use_mask = use_mask
-        self.mask_type = mask_type
+    def __init__(self,
+                 mask_type=None,
+                 significance='analytic',
+                 fixed_thres=0.1,
+                 sig_samples=1000,
+                 sig_blocklength=None,
+                 confidence=None,
+                 conf_lev=0.9,
+                 conf_samples=100,
+                 conf_blocklength=None,
+                 recycle_residuals=False,
+                 verbosity=0):
+        # Set the dataframe to None for now, will be reset during pcmci call
+        self.dataframe = None
+        # Set the options
         self.significance = significance
         self.sig_samples = sig_samples
         self.sig_blocklength = sig_blocklength
         self.fixed_thres = fixed_thres
+        self.verbosity = verbosity
+        # If we recycle residuals, then set up a residual cache
+        self.recycle_residuals = recycle_residuals
+        if self.recycle_residuals:
+            self.residuals = {}
+        # If we use a mask, we cannot recycle residuals
+        self.set_mask_type(mask_type)
 
+        # Set the confidence type and details
         self.confidence = confidence
         self.conf_lev = conf_lev
         self.conf_samples = conf_samples
         self.conf_blocklength = conf_blocklength
 
-        self.verbosity = verbosity
+        # Print information about the
+        if self.verbosity > 0:
+            self.print_info()
 
-        self.recycle_residuals = recycle_residuals
-        if self.recycle_residuals:
-            self.residuals = {}
+    def set_mask_type(self, mask_type):
+        """
+        Setter for mask type to ensure that this option does not clash with
+        recycle_residuals.
 
-        if self.use_mask:
+        Parameters
+        ----------
+        mask_type : str
+            Must be in {'y','x','z','xy','xz','yz','xyz'}
+            Masking mode: Indicators for which variables in the dependence
+            measure I(X; Y | Z) the samples should be masked. If None, 'y' is
+            used, which excludes all time slices containing masked samples in Y.
+            Explained in [1]_.
+        """
+        # Set the mask type
+        self.mask_type = mask_type
+        # Check if this clashes with residual recycling
+        if self.mask_type is not None:
+            if self.recycle_residuals is True:
+                warnings.warn("Using a mask disables recycling residuals.")
             self.recycle_residuals = False
-        if self.mask_type is None:
-            self.mask_type = 'y'
+        # Check the mask type is keyed correctly
+        self._check_mask_type()
 
+    def print_info(self):
+        """
+        Print information about the conditional independence test parameters
+        """
+        info_str = "\n# Initialize conditional independence test\n\nParameters:"
+        info_str += "\nindependence test = %s" % self.measure
+        info_str += "\nsignificance = %s" % self.significance
+        # Check if we are using a shuffle test
+        if self.significance == 'shuffle_test':
+            info_str += "\nsig_samples = %s" % self.sig_samples
+            info_str += "\nsig_blocklength = %s" % self.sig_blocklength
+        # Check if we are using a fixed threshold
+        elif self.significance == 'fixed_thres':
+            info_str += "\nfixed_thres = %s" % self.fixed_thres
+        # Check if we have a confidence type
+        if self.confidence:
+            info_str += "\nconfidence = %s" % self.confidence
+            info_str += "\nconf_lev = %s" % self.conf_lev
+        # Check if this confidence type is boostrapping
+        if self.confidence == 'bootstrap':
+            info_str += "\nconf_samples = %s" % self.conf_samples
+            info_str += "\nconf_blocklength = %s" %self.conf_blocklength
+        # Check if we use a non-trivial mask type
+        if self.mask_type is not None:
+            info_str += "mask_type = %s" % self.mask_type
+        # Check if we are recycling residuals or not
+        if self.recycle_residuals:
+            info_str += "recycle_residuals = %s" % self.recycle_residuals
+        # Print the information string
+        print(info_str)
 
-        if self.verbosity > 0:
-            print("\n# Initialize conditional independence test\n"
-                  "\nParameters:")
-            print("independence test = %s" % self.measure
-                  + "\nsignificance = %s" % self.significance
-                  )
-            if self.significance == 'shuffle_test':
-                print(""
-                + "sig_samples = %s" % self.sig_samples
-                + "\nsig_blocklength = %s" % self.sig_blocklength)
-            elif self.significance == 'fixed_thres':
-                print(""
-                + "fixed_thres = %s" % self.fixed_thres)
-            if self.confidence:
-                print("confidence = %s" % self.confidence
-                + "\nconf_lev = %s" % self.conf_lev)
-                if self.confidence == 'bootstrap':
-                    print(""
-                    + "conf_samples = %s" % self.conf_samples
-                    + "\nconf_blocklength = %s" % self.conf_blocklength)
-            if self.use_mask:
-                print(""
-                  + "use_mask = %s" % self.use_mask
-                  + "\nmask_type = %s" % self.mask_type)
-            if self.recycle_residuals:
-                print("recycle_residuals = %s" % self.recycle_residuals)
-
-            # print("\n")
-        # if use_mask:
-        #     if mask_type is None or len(set(mask_type) -
-        #                                 set(['x', 'y', 'z'])) > 0:
-        #         raise ValueError("mask_type = %s, but must be list containing"
-        #                          % mask_type + " 'x','y','z', or any "
-        #                          "combination")
+    def _check_mask_type(self):
+        """
+        mask_type : str, optional (default = None)
+            Must be in {'y','x','z','xy','xz','yz','xyz'}
+            Masking mode: Indicators for which variables in the dependence
+            measure I(X; Y | Z) the samples should be masked. If None, 'y' is
+            used, which excludes all time slices containing masked samples in Y.
+            Explained in [1]_.
+        """
+        if self.mask_type is not None:
+            mask_set = set(self.mask_type) - set(['x', 'y', 'z'])
+            if mask_set:
+                err_msg = "mask_type = %s," % self.mask_type + " but must be" +\
+                          " list containing 'x','y','z', or any combination"
+                raise ValueError(err_msg)
+
+
+    def get_analytic_confidence(self, value, df, conf_lev):
+        """
+        Base class assumption that this is not implemented.  Concrete classes
+        should override when possible.
+        """
+        raise NotImplementedError("Analytic confidence not"+\
+                                  " implemented for %s" % self.measure)
+
+    def get_model_selection_criterion(self, j, parents, tau_max=0):
+        """
+        Base class assumption that this is not implemented.  Concrete classes
+        should override when possible.
+        """
+        raise NotImplementedError("Model selection not"+\
+                                  " implemented for %s" % self.measure)
+
+    def get_analytic_significance(self, value, T, dim):
+        """
+        Base class assumption that this is not implemented.  Concrete classes
+        should override when possible.
+        """
+        raise NotImplementedError("Analytic significance not"+\
+                                  " implemented for %s" % self.measure)
+
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
+        """
+        Base class assumption that this is not implemented.  Concrete classes
+        should override when possible.
+        """
+        raise NotImplementedError("Shuffle significance not"+\
+                                  " implemented for %s" % self.measure)
+
+    def _get_single_residuals(self, array, target_var,
+                              standardize=True, return_means=False):
+        """
+        Base class assumption that this is not implemented.  Concrete classes
+        should override when possible.
+        """
+        raise NotImplementedError("Residual calculation not"+\
+                                  " implemented for %s" % self.measure)
 
     def set_dataframe(self, dataframe):
-        """Initialize dataframe.
+        """Initialize and check the dataframe.
 
         Parameters
         ----------
@@ -391,9 +269,9 @@ def set_dataframe(self, dataframe):
             values flag.
 
         """
-        self.data = dataframe.values
-        self.mask = dataframe.mask
-        self.missing_flag = dataframe.missing_flag
+        self.dataframe = dataframe
+        if self.mask_type is not None:
+            dataframe._check_mask(require_mask=True)
 
     def _keyfy(self, x, z):
         """Helper function to make lists unique."""
@@ -401,36 +279,35 @@ def _keyfy(self, x, z):
 
     def _get_array(self, X, Y, Z, tau_max=0, verbosity=None):
         """Convencience wrapper around _construct_array."""
-        
+        # Set the verbosity to the default value
         if verbosity is None:
             verbosity=self.verbosity
 
-        return _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            data=self.data,
-            use_mask=self.use_mask,
-            mask=self.mask,
-            mask_type=self.mask_type,
-            missing_flag=self.missing_flag,
-            return_cleaned_xyz=True,
-            do_checks=False,
-            verbosity=verbosity)
-
-    # @profile
+        if self.measure in ['par_corr']:
+            if len(X) > 1 or len(Y) > 1:
+                raise ValueError("X and Y for %s must be univariate." %
+                                        self.measure)
+        # Call the wrapped function
+        return self.dataframe.construct_array(X=X, Y=Y, Z=Z,
+                                              tau_max=tau_max,
+                                              mask_type=self.mask_type,
+                                              return_cleaned_xyz=True,
+                                              do_checks=False,
+                                              verbosity=verbosity)
+
     def run_test(self, X, Y, Z=None, tau_max=0):
         """Perform conditional independence test.
 
         Calls the dependence measure and signficicance test functions. The child
         classes must specify a function get_dependence_measure and either or
         both functions get_analytic_significance and  get_shuffle_significance.
-        If recycle_residuals is True, also  _get_single_residuals must be
+        If recycle_residuals is True, also _get_single_residuals must be
         available.
 
         Parameters
         ----------
         X, Y, Z : list of tuples
-            X,Y,Z are of the form [(var, -tau)], where var specifies the 
+            X,Y,Z are of the form [(var, -tau)], where var specifies the
             variable index and tau the time lag.
 
         tau_max : int, optional (default: 0)
@@ -440,63 +317,149 @@ def run_test(self, X, Y, Z=None, tau_max=0):
         Returns
         -------
         val, pval : Tuple of floats
-        
-            The test statistic value and the p-value. These are also made in the
-            class as self.val and self.pval.
-        
+
+            The test statistic value and the p-value.
         """
 
+        # Get the array to test on
         array, xyz, XYZ = self._get_array(X, Y, Z, tau_max)
         X, Y, Z = XYZ
-
+        # Record the dimensions
         dim, T = array.shape
-
-        if numpy.isnan(array).sum() != 0:
+        # Ensure it is a valid array
+        if np.isnan(array).sum() != 0:
             raise ValueError("nans in the array!")
+        # Get the dependence measure, reycling residuals if need be
+        val = self._get_dependence_measure_recycle(X, Y, Z, xyz, array)
+        # Get the p-value
+        pval = self.get_significance(val, array, xyz, T, dim)
+        # Return the value and the pvalue
+        return val, pval
+
+    def _get_dependence_measure_recycle(self, X, Y, Z, xyz, array):
+        """Get the dependence_measure, optionally recycling residuals
+
+        If self.recycle_residuals is True, also _get_single_residuals must be
+        available.
+
+        Parameters
+        ----------
+        X, Y, Z : list of tuples
+            X,Y,Z are of the form [(var, -tau)], where var specifies the
+            variable index and tau the time lag.
+
+        xyz : array of ints
+            XYZ identifier array of shape (dim,).
 
+        array : array
+            Data array of shape (dim, T)
+
+        Return
+        ------
+        val : float
+            Test statistic
+        """
+        # Check if we are recycling residuals
         if self.recycle_residuals:
-            if self._keyfy(X, Z) in list(self.residuals):
-                x_resid = self.residuals[self._keyfy(X, Z)]
-            else:
-                x_resid = self._get_single_residuals(array, target_var = 0)
-                if len(Z) > 0:
-                    self.residuals[self._keyfy(X, Z)] = x_resid
+            # Get or calculate the cached residuals
+            x_resid = self._get_cached_residuals(X, Z, array, 0)
+            y_resid = self._get_cached_residuals(Y, Z, array, 1)
+            # Make a new residual array
+            array_resid = np.array([x_resid, y_resid])
+            xyz_resid = np.array([0, 1])
+            # Return the dependence measure
+            return self.get_dependence_measure(array_resid, xyz_resid)
+        # If not, return the dependence measure on the array and xyz
+        return self.get_dependence_measure(array, xyz)
+
+    def _get_cached_residuals(self, x_nodes, z_nodes, array, target_var):
+        """
+        Retrieve or calculate the cached residuals for the given node sets.
 
-            if self._keyfy(Y, Z) in list(self.residuals):
-                y_resid = self.residuals[self._keyfy(Y, Z)]
-            else:
-                y_resid = self._get_single_residuals(array, target_var = 1)
-                if len(Z) > 0:
-                    self.residuals[self._keyfy(Y, Z)] = y_resid
+        Parameters
+        ----------
+            x_nodes : list of tuples
+                List of nodes, X or Y normally. Used to key the residual cache
+                during lookup
 
-            array_resid = numpy.array([x_resid, y_resid])
-            xyz_resid = numpy.array([0, 1])
+            z_nodes : list of tuples
+                List of nodes, Z normally
 
-            val = self.get_dependence_measure(array_resid, xyz_resid)
+            target_var : int
+                Key to differentiate X from Y.
+                x_nodes == X => 0, x_nodes == Y => 1
 
+            array : array
+                Data array of shape (dim, T)
+
+        Returns
+        -------
+            x_resid : array
+                Residuals calculated by _get_single_residual
+        """
+        # Check if we have calculated these residuals
+        if self._keyfy(x_nodes, z_nodes) in list(self.residuals):
+            x_resid = self.residuals[self._keyfy(x_nodes, z_nodes)]
+        # If not, calculate the residuals
         else:
-            val = self.get_dependence_measure(array, xyz)
+            x_resid = self._get_single_residuals(array, target_var=target_var)
+            if z_nodes:
+                self.residuals[self._keyfy(x_nodes, z_nodes)] = x_resid
+        # Return these residuals
+        return x_resid
 
-        if self.significance == 'analytic':
-            pval = self.get_analytic_significance(value=val, T=T, dim=dim)
+    def get_significance(self, val, array, xyz, T, dim, sig_override=None):
+        """
+        Returns the p-value from whichever significance function is specified
+        for this test.  If an override is used, then it will call a different
+        function then specified by self.significance
+
+        Parameters
+        ----------
+        val : float
+            Test statistic value.
 
-        elif self.significance == 'shuffle_test':
+        array : array-like
+            data array with X, Y, Z in rows and observations in columns
+
+        xyz : array of ints
+            XYZ identifier array of shape (dim,).
+
+        T : int
+            Sample length
+
+        dim : int
+            Dimensionality, ie, number of features.
+
+        sig_override : string
+            Must be in 'analytic', 'shuffle_test', 'fixed_thres'
+
+        Returns
+        -------
+        pval : float or numpy.nan
+            P-value.
+        """
+        # Defaults to the self.signficance memeber value
+        use_sig = self.significance
+        if sig_override is not None:
+            use_sig = sig_override
+        # Check if we are using the analytic significance
+        if use_sig == 'analytic':
+            pval = self.get_analytic_significance(value=val, T=T, dim=dim)
+        # Check if we are using the shuffle significance
+        elif use_sig == 'shuffle_test':
             pval = self.get_shuffle_significance(array=array,
                                                  xyz=xyz,
                                                  value=val)
-        elif self.significance == 'fixed_thres':
-            pval = self.get_fixed_thres_significance(value=val, 
-                                                fixed_thres=self.fixed_thres)
+        # Check if we are using the fixed_thres significance
+        elif use_sig == 'fixed_thres':
+            pval = self.get_fixed_thres_significance(
+                    value=val,
+                    fixed_thres=self.fixed_thres)
         else:
             raise ValueError("%s not known." % self.significance)
-
-        self.X = X
-        self.Y = Y
-        self.Z = Z
-        self.val = val
-        self.pval = pval
-
-        return val, pval
+        # Return the calculated value
+        return pval
 
     def get_measure(self, X, Y, Z=None, tau_max=0):
         """Estimate dependence measure.
@@ -507,7 +470,7 @@ def get_measure(self, X, Y, Z=None, tau_max=0):
         Parameters
         ----------
         X, Y [, Z] : list of tuples
-            X,Y,Z are of the form [(var, -tau)], where var specifies the 
+            X,Y,Z are of the form [(var, -tau)], where var specifies the
             variable index and tau the time lag.
 
         tau_max : int, optional (default: 0)
@@ -518,41 +481,16 @@ def get_measure(self, X, Y, Z=None, tau_max=0):
         -------
         val : float
             The test statistic value.
-        
-        """
-
-        array, xyz, XYZ = self._get_array(X, Y, Z, tau_max)
-        X, Y, Z = XYZ
 
+        """
+        # Make the array
+        array, xyz, (X, Y, Z) = self._get_array(X, Y, Z, tau_max)
         D, T = array.shape
-
-        if numpy.isnan(array).sum() != 0:
+        # Check it is valid
+        if np.isnan(array).sum() != 0:
             raise ValueError("nans in the array!")
-
-        if self.recycle_residuals:
-            if self._keyfy(X, Z) in list(self.residuals):
-                x_resid = self.residuals[self._keyfy(X, Z)]
-            else:
-                x_resid = self._get_single_residuals(array, target_var = 0)
-                if len(Z) > 0:
-                    self.residuals[self._keyfy(X, Z)] = x_resid
-
-            if self._keyfy(Y, Z) in list(self.residuals):
-                y_resid = self.residuals[self._keyfy(Y, Z)]
-            else:
-                y_resid = self._get_single_residuals(array,target_var = 1)
-                if len(Z) > 0:
-                    self.residuals[self._keyfy(Y, Z)] = y_resid
-
-            array_resid = numpy.array([x_resid, y_resid])
-            xyz_resid = numpy.array([0, 1])
-
-            val = self.get_dependence_measure(array_resid, xyz_resid)
-
-        else:
-            val = self.get_dependence_measure(array, xyz)
-
-        return val
+        # Return the dependence measure
+        return self._get_dependence_measure_recycle(X, Y, Z, xyz, array)
 
     def get_confidence(self, X, Y, Z=None, tau_max=0):
         """Perform confidence interval estimation.
@@ -565,7 +503,7 @@ def get_confidence(self, X, Y, Z=None, tau_max=0):
         Parameters
         ----------
         X, Y, Z : list of tuples
-            X,Y,Z are of the form [(var, -tau)], where var specifies the 
+            X,Y,Z are of the form [(var, -tau)], where var specifies the
             variable index and tau the time lag.
 
         tau_max : int, optional (default: 0)
@@ -577,46 +515,45 @@ def get_confidence(self, X, Y, Z=None, tau_max=0):
         (conf_lower, conf_upper) : Tuple of floats
             Upper and lower confidence bound of confidence interval.
         """
-
+        # Check if a confidence type has been defined
         if self.confidence:
-            if (self.conf_lev < .5 or self.conf_lev >= 1.):
+            # Ensure the confidence level given makes sense
+            if self.conf_lev < .5 or self.conf_lev >= 1.:
                 raise ValueError("conf_lev = %.2f, " % self.conf_lev +
                                  "but must be between 0.5 and 1")
-            if (self.confidence == 'bootstrap'
-                    and self.conf_samples * (1. - self.conf_lev) / 2. < 1.):
+            half_conf = self.conf_samples * (1. - self.conf_lev)/2.
+            if self.confidence == 'bootstrap' and  half_conf < 1.:
                 raise ValueError("conf_samples*(1.-conf_lev)/2 is %.2f"
-                                 % (self.conf_samples * (1. - self.conf_lev) / 2.) +
-                                 ", must be >> 1")
-
-        array, xyz, XYZ = self._get_array(X, Y, Z, tau_max, verbosity=0)
-
+                                 % half_conf + ", must be >> 1")
+        # Make and check the array
+        array, xyz, _ = self._get_array(X, Y, Z, tau_max, verbosity=0)
         dim, T = array.shape
-
-        if numpy.isnan(array).sum() != 0:
+        if np.isnan(array).sum() != 0:
             raise ValueError("nans in the array!")
 
+        # Check if we are using analytic confidence or bootstrapping it
         if self.confidence == 'analytic':
             val = self.get_dependence_measure(array, xyz)
-
-            (conf_lower, conf_upper) = self.get_analytic_confidence(df=T-dim, 
-                                    value=val, conf_lev=self.conf_lev)
-
+            (conf_lower, conf_upper) = \
+                    self.get_analytic_confidence(df=T-dim,
+                                                 value=val,
+                                                 conf_lev=self.conf_lev)
         elif self.confidence == 'bootstrap':
             # Overwrite analytic values
-            (conf_lower, conf_upper) = self.get_bootstrap_confidence(array, xyz,
-                             dependence_measure=self.get_dependence_measure,
-                             conf_samples=self.conf_samples, 
-                             conf_blocklength=self.conf_blocklength,
-                             conf_lev=self.conf_lev, verbosity=self.verbosity)
-        elif self.confidence == False:
+            (conf_lower, conf_upper) = \
+                    self.get_bootstrap_confidence(
+                        array, xyz,
+                        conf_samples=self.conf_samples,
+                        conf_blocklength=self.conf_blocklength,
+                        conf_lev=self.conf_lev, verbosity=self.verbosity)
+        elif not self.confidence:
             return None
-
         else:
-            raise ValueError("%s confidence estimation not implemented" 
+            raise ValueError("%s confidence estimation not implemented"
                              % self.confidence)
-
+        # Cache the confidence interval
         self.conf = (conf_lower, conf_upper)
-
+        # Return the confidence interval
         return (conf_lower, conf_upper)
 
     def _print_cond_ind_results(self, val, pval=None, conf=None):
@@ -645,14 +582,11 @@ def _print_cond_ind_results(self, val, pval=None, conf=None):
             if conf is not None:
                 printstr += " | conf bounds = (%.3f, %.3f)" % (
                     conf[0], conf[1])
-
-
         print(printstr)
 
-    # @profile
-    def get_bootstrap_confidence(self, array, xyz, dependence_measure,
-                             conf_samples=100, conf_blocklength=None,
-                             conf_lev=.95, verbosity=0):
+    def get_bootstrap_confidence(self, array, xyz, dependence_measure=None,
+                                 conf_samples=100, conf_blocklength=None,
+                                 conf_lev=.95, verbosity=0):
         """Perform bootstrap confidence interval estimation.
 
         With conf_blocklength > 1 or None a block-bootstrap is performed.
@@ -665,8 +599,8 @@ def get_bootstrap_confidence(self, array, xyz, dependence_measure,
         xyz : array of ints
             XYZ identifier array of shape (dim,).
 
-        dependence_measure : object
-            Dependence measure function must be of form 
+        dependence_measure : function (default = self.get_dependence_measure)
+            Dependence measure function must be of form
             dependence_measure(array, xyz) and return a numeric value
 
         conf_lev : float, optional (default: 0.9)
@@ -681,54 +615,61 @@ def get_bootstrap_confidence(self, array, xyz, dependence_measure,
             [1]_.
 
         verbosity : int, optional (default: 0)
-            Level of verbosity.
+            Levelof verbosity.
 
         Returns
         -------
         (conf_lower, conf_upper) : Tuple of floats
             Upper and lower confidence bound of confidence interval.
         """
+        # Check if a dependence measure if provided or if to use default
+        if not dependence_measure:
+            dependence_measure = self.get_dependence_measure
 
         # confidence interval is two-sided
-        c_int = (1. - (1. - conf_lev) / 2.)
+        c_int = 1. - (1. - conf_lev)/2.
         dim, T = array.shape
 
+        # If not block length is given, determine the optimal block length.
+        # This has a maximum of 10% of the time sample length
         if conf_blocklength is None:
-            conf_blocklength = self._get_block_length(array, xyz,
-                                                     mode='confidence')
-
-        n_blocks = int(math.ceil(float(T) / float(conf_blocklength)))
+            conf_blocklength = \
+                    self._get_block_length(array, xyz, mode='confidence')
+        # Determine the number of blocks total, rounding up for non-integer
+        # amounts
+        n_blks = int(math.ceil(float(T)/conf_blocklength))
 
+        # Print some information
         if verbosity > 2:
             print("            block_bootstrap confidence intervals"
                   " with block-length = %d ..." % conf_blocklength)
 
-        bootdist = numpy.zeros(conf_samples)
-        for sam in range(conf_samples):
-            rand_block_starts = numpy.random.randint(0,
-                         T - conf_blocklength + 1, n_blocks)
-            array_bootstrap = numpy.zeros((dim, n_blocks*conf_blocklength), 
-                                          dtype = array.dtype)
-
-            # array_bootstrap = array[:, rand_block_starts]
-            for b in range(conf_blocklength):
-                array_bootstrap[:, b::conf_blocklength] = array[:, 
-                                                          rand_block_starts + b]
-
+        # Generate the block bootstrapped distribution
+        bootdist = np.zeros(conf_samples)
+        for smpl in range(conf_samples):
+            # Get the starting indecies for the blocks
+            blk_strt = np.random.randint(0, T - conf_blocklength + 1, n_blks)
+            # Get the empty array of block resampled values
+            array_bootstrap = \
+                    np.zeros((dim, n_blks*conf_blocklength), dtype=array.dtype)
+            # Fill the array of block resamples
+            for i in range(conf_blocklength):
+                array_bootstrap[:, i::conf_blocklength] = array[:, blk_strt + i]
             # Cut to proper length
             array_bootstrap = array_bootstrap[:, :T]
-            bootdist[sam] = dependence_measure(array_bootstrap, xyz)
+
+            bootdist[smpl] = dependence_measure(array_bootstrap, xyz)
 
         # Sort and get quantile
         bootdist.sort()
         conf_lower = bootdist[int((1. - c_int) * conf_samples)]
         conf_upper = bootdist[int(c_int * conf_samples)]
-
+        # Return the confidance limits as a tuple
         return (conf_lower, conf_upper)
 
     def _get_acf(self, series, max_lag=None):
         """Returns autocorrelation function.
-        
+
         Parameters
         ----------
         series : 1D-array
@@ -737,23 +678,24 @@ def _get_acf(self, series, max_lag=None):
         max_lag : int, optional (default: None)
             maximum lag for autocorrelation function. If None is passed, 10% of
             the data series length are used.
-        
+
         Returns
         -------
-        autocorr : array of shape (max_lag + 1,) 
+        autocorr : array of shape (max_lag + 1,)
             Autocorrelation function.
         """
+        # Set the default max lag
         if max_lag is None:
             max_lag = int(max(5, 0.1*len(series)))
-
-        autocorr = numpy.ones(max_lag + 1)
+        # Initialize the result
+        autocorr = np.ones(max_lag + 1)
+        # Iterate over possible lags
         for lag in range(1, max_lag + 1):
-
-            y1 = series[lag:]
-            y2 = series[:len(series) - lag]
-
-            autocorr[lag] = numpy.corrcoef(y1, y2, ddof=0)[0, 1]
-
+            # Set the values
+            y1_vals = series[lag:]
+            y2_vals = series[:len(series) - lag]
+            # Calculate the autocorrelation
+            autocorr[lag] = np.corrcoef(y1_vals, y2_vals, ddof=0)[0, 1]
         return autocorr
 
     def _get_block_length(self, array, xyz, mode):
@@ -763,7 +705,7 @@ def _get_block_length(self, array, xyz, mode):
         improves the method of Pfeifer (2005) with non-overlapping blocks In
         case of multidimensional X, the max is used. Further details in [1]_.
         Two modes are available. For mode='significance', only the indices
-        corresponding to X are shuffled in array. For  mode='confidence' all
+        corresponding to X are shuffled in array. For mode='confidence' all
         variables are jointly shuffled. If the autocorrelation curve fit fails,
         a block length of 5% of T is used. The block length is limited to a
         maximum of 10% of T.
@@ -775,61 +717,52 @@ def _get_block_length(self, array, xyz, mode):
 
         xyz : array of ints
             XYZ identifier array of shape (dim,).
-        
+
         mode : str
             Which mode to use.
-        
+
         Returns
         -------
         block_len : int
             Optimal block length.
         """
-
+        # Inject a dependency on siganal, optimize
         from scipy import signal, optimize
-
+        # Get the shape of the array
         dim, T = array.shape
-
+        # Initiailize the indices
+        indices = range(dim)
         if mode == 'significance':
-            indices = numpy.where(xyz == 0)[0]
-        else:
-            indices = range(dim)
+            indices = np.where(xyz == 0)[0]
 
         # Maximum lag for autocov estimation
         max_lag = int(0.1*T)
+        # Define the function to optimize against
+        def func(x_vals, a_const, decay):
+            return a_const * decay**x_vals
 
-        def func(x, a, decay):
-            return a * decay**x
-
+        # Calculate the block length
         block_len = 1
         for i in indices:
-
             # Get decay rate of envelope of autocorrelation functions
             # via hilbert trafo
             autocov = self._get_acf(series=array[i], max_lag=max_lag)
-
             autocov[0] = 1.
-            hilbert = numpy.abs(signal.hilbert(autocov))
-
+            hilbert = np.abs(signal.hilbert(autocov))
+            # Try to fit the curve
             try:
-                popt, pcov = optimize.curve_fit(
-                    func, range(0, max_lag + 1), hilbert)
+                popt, _ = optimize.curve_fit(func, range(0, max_lag+1), hilbert)
                 phi = popt[1]
-
                 # Formula of Pfeifer (2005) assuming non-overlapping blocks
                 l_opt = (4. * T * (phi / (1. - phi) + phi**2 / (1. - phi)**2)**2
                          / (1. + 2. * phi / (1. - phi))**2)**(1. / 3.)
-
                 block_len = max(block_len, int(l_opt))
-
             except RuntimeError:
-                print(
-                    "Error - curve_fit failed in block_shuffle, using"
-                    " block_len = %d" % (int(.05 * T)))
+                print("Error - curve_fit failed in block_shuffle, using"
+                      " block_len = %d" % (int(.05 * T)))
                 block_len = max(int(.05 * T), 2)
-
         # Limit block length to a maximum of 10% of T
         block_len = min(block_len, int(0.1 * T))
-
         return block_len
 
     def _get_shuffle_dist(self, array, xyz, dependence_measure,
@@ -840,7 +773,6 @@ def _get_shuffle_dist(self, array, xyz, dependence_measure,
         The rows in array corresponding to the X-variable are shuffled using
         a block-shuffle approach.
 
-
         Parameters
         ----------
         array : array-like
@@ -850,73 +782,73 @@ def _get_shuffle_dist(self, array, xyz, dependence_measure,
             XYZ identifier array of shape (dim,).
 
        dependence_measure : object
-           Dependence measure function must be of form 
+           Dependence measure function must be of form
            dependence_measure(array, xyz) and return a numeric value
- 
+
         sig_samples : int, optional (default: 100)
-            Number of samples for shuffle significance test. 
+            Number of samples for shuffle significance test.
 
         sig_blocklength : int, optional (default: None)
             Block length for block-shuffle significance test. If None, the
-            block length is determined from the decay of the autocovariance as 
+            block length is determined from the decay of the autocovariance as
             explained in [1]_.
 
         verbosity : int, optional (default: 0)
             Level of verbosity.
-        
+
         Returns
         -------
         null_dist : array of shape (sig_samples,)
-            Contains the sorted test statistic values estimated from the 
+            Contains the sorted test statistic values estimated from the
             shuffled arrays.
         """
 
         dim, T = array.shape
 
-        x_indices = numpy.where(xyz == 0)[0]
+        x_indices = np.where(xyz == 0)[0]
         dim_x = len(x_indices)
 
         if sig_blocklength is None:
             sig_blocklength = self._get_block_length(array, xyz,
                                                      mode='significance')
 
-        n_blocks = int(math.floor(float(T) / float(sig_blocklength)))
-        # print 'n_blocks ', n_blocks
+        n_blks = int(math.floor(float(T)/sig_blocklength))
+        # print 'n_blks ', n_blks
         if verbosity > 2:
             print("            Significance test with block-length = %d "
                   "..." % (sig_blocklength))
 
-        array_shuffled = numpy.copy(array)
-        block_starts = numpy.arange(0, T - sig_blocklength + 1, sig_blocklength)
+        array_shuffled = np.copy(array)
+        block_starts = np.arange(0, T - sig_blocklength + 1, sig_blocklength)
 
-        # Dividing the array up into n_blocks of length sig_blocklength may
+        # Dividing the array up into n_blks of length sig_blocklength may
         # leave a tail. This tail is later randomly inserted
-        tail = array[x_indices, n_blocks*sig_blocklength:]
+        tail = array[x_indices, n_blks*sig_blocklength:]
 
-        null_dist = numpy.zeros(sig_samples)
+        null_dist = np.zeros(sig_samples)
         for sam in range(sig_samples):
 
-            rand_block_starts = numpy.random.permutation(block_starts)[:n_blocks]
+            blk_starts = np.random.permutation(block_starts)[:n_blks]
 
-            x_shuffled = numpy.zeros((dim_x, n_blocks*sig_blocklength), 
-                                          dtype = array.dtype)
+            x_shuffled = np.zeros((dim_x, n_blks*sig_blocklength),
+                                  dtype=array.dtype)
 
             for i, index in enumerate(x_indices):
-                for b in range(sig_blocklength):
-                    x_shuffled[i, b::sig_blocklength] = array[index, 
-                                            rand_block_starts + b]
+                for blk in range(sig_blocklength):
+                    x_shuffled[i, blk::sig_blocklength] = \
+                            array[index, blk_starts + blk]
 
             # Insert tail randomly somewhere
             if tail.shape[1] > 0:
-                insert_tail_at = numpy.random.choice(block_starts)
-                x_shuffled = numpy.insert(x_shuffled, insert_tail_at, 
-                                          tail.T, axis=1)
+                insert_tail_at = np.random.choice(block_starts)
+                x_shuffled = np.insert(x_shuffled, insert_tail_at,
+                                       tail.T, axis=1)
 
             for i, index in enumerate(x_indices):
                 array_shuffled[index] = x_shuffled[i]
 
             null_dist[sam] = dependence_measure(array=array_shuffled,
-                                           xyz=xyz)
+                                                xyz=xyz)
 
         null_dist.sort()
 
@@ -942,7 +874,7 @@ def get_fixed_thres_significance(self, value, fixed_thres):
             else.
 
         """
-        if numpy.abs(value) < numpy.abs(fixed_thres):
+        if np.abs(value) < np.abs(fixed_thres):
             pval = 1.
         else:
             pval = 0.
@@ -950,7 +882,7 @@ def get_fixed_thres_significance(self, value, fixed_thres):
         return pval
 
     def _trafo2uniform(self, x):
-        """Transforms input array to uniform marginals. 
+        """Transforms input array to uniform marginals.
 
         Assumes x.shape = (dim, T)
 
@@ -966,92 +898,18 @@ def _trafo2uniform(self, x):
         """
 
         def trafo(xi):
-            xisorted = numpy.sort(xi)
-            yi = numpy.linspace(1. / len(xi), 1, len(xi))
-            return numpy.interp(xi, xisorted, yi)
+            xisorted = np.sort(xi)
+            yi = np.linspace(1. / len(xi), 1, len(xi))
+            return np.interp(xi, xisorted, yi)
 
-        if numpy.ndim(x) == 1:
+        if np.ndim(x) == 1:
             u = trafo(x)
         else:
-            u = numpy.empty(x.shape)
+            u = np.empty(x.shape)
             for i in range(x.shape[0]):
                 u[i] = trafo(x[i])
         return u
 
-    def generate_nulldist(self, df, add_to_null_dists=True):
-        """Generates null distribution for pairwise independence tests.
-
-        Generates the null distribution for sample size df. Assumes pairwise
-        samples transformed to uniform marginals. Uses get_dependence_measure
-        available in class and generates self.sig_samples random samples. Adds
-        the null distributions to self.null_dists.
-
-        Parameters
-        ----------
-        df : int
-            Degrees of freedom / sample size to generate null distribution for.
-
-        add_to_null_dists : bool, optional (default: True)
-            Whether to add the null dist to the dictionary of null dists or
-            just return it.
-
-        Returns
-        -------
-        null_dist : array of shape [df,]
-            Only returned,if add_to_null_dists is False.
-        """
-        
-        if self.verbosity > 0:
-            print("Generating null distribution for df = %d. " % df)
-
-            if add_to_null_dists:
-                print("For faster computations, run function "
-                      "generate_and_save_nulldists(...) to "
-                      "precompute null distribution and load *.npz file with "
-                      "argument null_dist_filename")
-
-        xyz = numpy.array([0,1])
-        null_dist = numpy.zeros(self.null_samples)
-        for i in range(self.null_samples):
-            array = numpy.random.rand(2, df)
-
-            null_dist[i] = self.get_dependence_measure(array, xyz)
-
-        null_dist.sort()    
-        if add_to_null_dists:
-            self.null_dists[df] = null_dist
-        else:
-            return null_dist
-
-    def generate_and_save_nulldists(self, sample_sizes, null_dist_filename):
-        """Generates and saves null distribution for pairwise independence 
-        tests.
-
-        Generates the null distribution for different sample sizes. Calls 
-        generate_nulldist. Null dists are saved to disk as
-        self.null_dist_filename.npz. Also adds the null distributions to
-        self.null_dists.
-
-        Parameters
-        ----------
-        sample_sizes : list
-            List of sample sizes.    
-
-        null_dist_filename : str
-            Name to save file containing null distributions.
-        """
-        
-        self.null_dist_filename = null_dist_filename
-
-        null_dists = numpy.zeros((len(sample_sizes), self.sig_samples))
-        
-        for iT, T in enumerate(sample_sizes):
-            null_dists[iT] = self.generate_nulldist(T, add_to_null_dists=False)
-            self.null_dists[T] = null_dists[iT]
-        
-        numpy.savez("%s" % null_dist_filename, exact_dist=null_dists, 
-                            T=numpy.array(sample_sizes))
-
 
 class ParCorr(CondIndTest):
     r"""Partial correlation test.
@@ -1066,7 +924,7 @@ class ParCorr(CondIndTest):
     :math:`X` and :math:`Y` assuming the  model
 
     .. math::  X & =  Z \beta_X + \epsilon_{X} \\
-        Y & =  Z \beta_Y + \epsilon_{Y}  
+        Y & =  Z \beta_Y + \epsilon_{Y}
 
     using OLS regression. Then the dependency of the residuals is tested with
     the Pearson correlation test.
@@ -1078,26 +936,27 @@ class ParCorr(CondIndTest):
 
     Parameters
     ----------
-    **kwargs : 
+    **kwargs :
         Arguments passed on to Parent class CondIndTest.
     """
+    # documentation
+    @property
+    def measure(self):
+        """
+        Concrete property to return the measure of the independence test
+        """
+        return self._measure
 
     def __init__(self, **kwargs):
-
-        # super(ParCorr, self).__init__(
-
-        self.measure = 'par_corr'
+        self._measure = 'par_corr'
         self.two_sided = True
         self.residual_based = True
 
         CondIndTest.__init__(self, **kwargs)
-        if self.verbosity > 0:
-            print("")
 
-    # @profile
-    def _get_single_residuals(self, array, target_var, 
-                standardize = True,
-                return_means=False):
+    def _get_single_residuals(self, array, target_var,
+                              standardize=True,
+                              return_means=False):
         """Returns residuals of linear multiple regression.
 
         Performs a OLS regression of the variable indexed by target_var on the
@@ -1114,7 +973,7 @@ def _get_single_residuals(self, array, target_var,
             Variable to regress out conditions from.
 
         standardize : bool, optional (default: True)
-            Whether to standardize the array beforehand. Must be used for 
+            Whether to standardize the array beforehand. Must be used for
             partial correlation.
 
         return_means : bool, optional (default: False)
@@ -1133,16 +992,16 @@ def _get_single_residuals(self, array, target_var,
         if standardize:
             array -= array.mean(axis=1).reshape(dim, 1)
             array /= array.std(axis=1).reshape(dim, 1)
-            if numpy.isnan(array).sum() != 0:
+            if np.isnan(array).sum() != 0:
                 raise ValueError("nans after standardizing, "
                                  "possibly constant array!")
 
         y = array[target_var, :]
 
         if dim_z > 0:
-            z = numpy.fastCopyAndTranspose(array[2:, :])
-            beta_hat = numpy.linalg.lstsq(z, y)[0]
-            mean = numpy.dot(z, beta_hat)
+            z = np.fastCopyAndTranspose(array[2:, :])
+            beta_hat = np.linalg.lstsq(z, y, rcond=None)[0]
+            mean = np.dot(z, beta_hat)
             resid = y - mean
         else:
             resid = y
@@ -1150,10 +1009,8 @@ def _get_single_residuals(self, array, target_var,
 
         if return_means:
             return (resid, mean)
-        else:
-            return resid
+        return resid
 
-    # @profile
     def get_dependence_measure(self, array, xyz):
         """Return partial correlation.
 
@@ -1171,18 +1028,16 @@ def get_dependence_measure(self, array, xyz):
         Returns
         -------
         val : float
-            Partial correlation coefficient.    
+            Partial correlation coefficient.
         """
 
-        x = self._get_single_residuals(array, target_var = 0)
-        y = self._get_single_residuals(array, target_var = 1)
-
-        val, dummy = stats.pearsonr(x, y)
-
+        x_vals = self._get_single_residuals(array, target_var=0)
+        y_vals = self._get_single_residuals(array, target_var=1)
+        val, _ = stats.pearsonr(x_vals, y_vals)
         return val
 
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
         """Returns p-value for shuffle significance test.
 
         For residual-based test statistics only the residuals are shuffled.
@@ -1197,41 +1052,41 @@ def get_shuffle_significance(self, array, xyz, value,
 
         value : number
             Value of test statistic for unshuffled estimate.
-        
+
         Returns
         -------
         pval : float
             p-value
         """
 
-        x = self._get_single_residuals(array, target_var = 0)
-        y = self._get_single_residuals(array, target_var = 1)
-        array_resid = numpy.array([x, y])
-        xyz_resid = numpy.array([0,1])
+        x_vals = self._get_single_residuals(array, target_var=0)
+        y_vals = self._get_single_residuals(array, target_var=1)
+        array_resid = np.array([x_vals, y_vals])
+        xyz_resid = np.array([0, 1])
 
         null_dist = self._get_shuffle_dist(array_resid, xyz_resid,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+                                           self.get_dependence_measure,
+                                           sig_samples=self.sig_samples,
+                                           sig_blocklength=self.sig_blocklength,
+                                           verbosity=self.verbosity)
+
+        pval = (null_dist >= np.abs(value)).mean()
 
-        pval = (null_dist >= numpy.abs(value)).mean()
-        
         # Adjust p-value for two-sided measures
-        if pval < 1.: pval *= 2.
+        if pval < 1.:
+            pval *= 2.
 
         if return_null_dist:
             return pval, null_dist
-        else:
-            return pval
+        return pval
 
-    def get_analytic_significance(self, value, T, dim): 
+    def get_analytic_significance(self, value, T, dim):
         """Returns analytic p-value from Student's t-test for the Pearson
         correlation coefficient.
-        
+
         Assumes two-sided correlation. If the degrees of freedom are less than
         1, numpy.nan is returned.
-        
+
         Parameters
         ----------
         value : float
@@ -1248,21 +1103,21 @@ def get_analytic_significance(self, value, T, dim):
         pval : float or numpy.nan
             P-value.
         """
+        # Get the number of degrees of freedom
+        deg_f = T - dim
 
-        df = T - dim
-
-        if df < 1:
-            pval = numpy.nan
+        if deg_f < 1:
+            pval = np.nan
         else:
-            trafo_val = value * numpy.sqrt(df / (1. - value**2))
+            trafo_val = value * np.sqrt(deg_f/(1. - value*value))
             # Two sided significance level
-            pval = stats.t.sf(numpy.abs(trafo_val), df) * 2
+            pval = stats.t.sf(np.abs(trafo_val), deg_f) * 2
 
         return pval
 
     def get_analytic_confidence(self, value, df, conf_lev):
         """Returns analytic confidence interval for correlation coefficient.
-        
+
         Based on Student's t-distribution.
 
         Parameters
@@ -1281,27 +1136,26 @@ def get_analytic_confidence(self, value, df, conf_lev):
         (conf_lower, conf_upper) : Tuple of floats
             Upper and lower confidence bound of confidence interval.
         """
-
         # Confidence interval is two-sided
         c_int = (1. - (1. - conf_lev) / 2.)
 
-        value_tdist = value * numpy.sqrt(df) / numpy.sqrt(1. - value**2)
+        value_tdist = value * np.sqrt(df) / np.sqrt(1. - value**2)
         conf_lower = (stats.t.ppf(q=1. - c_int, df=df, loc=value_tdist)
-                      / numpy.sqrt(df + stats.t.ppf(q=1. - c_int, df=df,
-                                                       loc=value_tdist)**2))
+                      / np.sqrt(df + stats.t.ppf(q=1. - c_int, df=df,
+                                                 loc=value_tdist)**2))
         conf_upper = (stats.t.ppf(q=c_int, df=df, loc=value_tdist)
-                      / numpy.sqrt(df + stats.t.ppf(q=c_int, df=df,
-                                                       loc=value_tdist)**2))
+                      / np.sqrt(df + stats.t.ppf(q=c_int, df=df,
+                                                 loc=value_tdist)**2))
         return (conf_lower, conf_upper)
 
 
     def get_model_selection_criterion(self, j, parents, tau_max=0):
         """Returns Akaike's Information criterion modulo constants.
-        
+
         Fits a linear model of the parents to variable j and returns the score.
-        I used to determine optimal hyperparameters in PCMCI, in particular 
+        I used to determine optimal hyperparameters in PCMCI, in particular
         the pc_alpha value.
-        
+
         Parameters
         ----------
         j : int
@@ -1313,7 +1167,7 @@ def get_model_selection_criterion(self, j, parents, tau_max=0):
         tau_max : int, optional (default: 0)
             Maximum time lag. This may be used to make sure that estimates for
             different lags in X, Z, all have the same sample size.
-        
+
         Returns:
         score : float
             Model score.
@@ -1322,32 +1176,26 @@ def get_model_selection_criterion(self, j, parents, tau_max=0):
         Y = [(j, 0)]
         X = [(j, 0)]   # dummy variable here
         Z = parents
-        array, xyz = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            data=self.data,
-            use_mask=self.use_mask,
-            mask=self.mask,
-            mask_type=self.mask_type,
-            return_cleaned_xyz=False,
-            do_checks=False,
-            verbosity=self.verbosity)
+        array, xyz = self.dataframe.construct_array(X=X, Y=Y, Z=Z,
+                                                    tau_max=tau_max,
+                                                    mask_type=self.mask_type,
+                                                    return_cleaned_xyz=False,
+                                                    do_checks=False,
+                                                    verbosity=self.verbosity)
 
         dim, T = array.shape
 
-        y = self._get_single_residuals(
-            array, target_var=1, return_means=False)
+        y = self._get_single_residuals(array, target_var=1, return_means=False)
         # Get RSS
         rss = (y**2).sum()
         # Number of parameters
         p = dim - 1
         # Get AIC
-        score = T * numpy.log(rss) + 2. * p
-
+        score = T * np.log(rss) + 2. * p
         return score
 
-class GP():
-    r"""Gaussian processes base class.
+class GaussProcReg():
+    r"""Gaussian processes abstract base class.
 
     GP is estimated with scikit-learn and allows to flexibly specify kernels and
     hyperparameters or let them be optimized automatically. The kernel specifies
@@ -1357,8 +1205,21 @@ class GP():
     default. Note that the kernel's hyperparameters are optimized during
     fitting.
 
+    When the null distribution is not analytically available, but can be
+    precomputed with the function generate_and_save_nulldists(...) which saves
+    a \*.npz file containing the null distribution for different sample sizes.
+    This file can then be supplied as null_dist_filename.
+
     Parameters
     ----------
+    null_samples : int
+        Number of null samples to use
+
+    cond_ind_test : CondIndTest
+        Conditional independence test that this Gaussian Proccess Regressor will
+        calculate the null distribution for.  This is used to grab the
+        get_dependence_measure function.
+
     gp_version : {'new', 'old'}, optional (default: 'new')
         The older GP version from scikit-learn 0.17 was used for the numerical
         simulations in [1]_. The newer version from scikit-learn 0.19 is faster
@@ -1366,20 +1227,133 @@ class GP():
 
     gp_params : dictionary, optional (default: None)
         Dictionary with parameters for ``GaussianProcessRegressor``.
-    
-    """
-    def __init__(self,
-                gp_version='new',
-                gp_params=None):
 
-        # CondIndTest.__inits__(self,)
+    null_dist_filename : str, otional (default: None)
+        Path to file containing null distribution.
 
+    verbosity : int, optional (default: 0)
+        Level of verbosity.
+    """
+    def __init__(self,
+                 null_samples,
+                 cond_ind_test,
+                 gp_version='new',
+                 gp_params=None,
+                 null_dist_filename=None,
+                 verbosity=0):
+        # Set the dependence measure function
+        self.cond_ind_test = cond_ind_test
+        # Set member variables
         self.gp_version = gp_version
         self.gp_params = gp_params
-    
-    # @profile
+        self.verbosity = verbosity
+        # Set the null distribution defaults
+        self.null_samples = null_samples
+        self.null_dists = {}
+        self.null_dist_filename = null_dist_filename
+        # Check if we are loading a null distrubtion from a cached file
+        if self.null_dist_filename is not None:
+            self.null_dists, self.null_samples = \
+                    self._load_nulldist(self.null_dist_filename)
+
+    def _load_nulldist(self, filename):
+        r"""
+        Load a precomputed null distribution from a \*.npz file.  This
+        distribution can be calculated using generate_and_save_nulldists(...).
+
+        Parameters
+        ----------
+        filename : strng
+            Path to the \*.npz file
+
+        Returns
+        -------
+        null_dists, null_samples : dict, int
+            The null distirbution as a dictionary of distributions keyed by
+            sample size, the number of null samples in total.
+        """
+        null_dist_file = np.load(filename)
+        null_dists = dict(zip(null_dist_file['T'],
+                              null_dist_file['exact_dist']))
+        null_samples = len(null_dist_file['exact_dist'][0])
+        return null_dists, null_samples
+
+    def _generate_nulldist(self, df,
+                           add_to_null_dists=True):
+        """Generates null distribution for pairwise independence tests.
+
+        Generates the null distribution for sample size df. Assumes pairwise
+        samples transformed to uniform marginals. Uses get_dependence_measure
+        available in class and generates self.sig_samples random samples. Adds
+        the null distributions to self.null_dists.
+
+        Parameters
+        ----------
+        df : int
+            Degrees of freedom / sample size to generate null distribution for.
+
+        add_to_null_dists : bool, optional (default: True)
+            Whether to add the null dist to the dictionary of null dists or
+            just return it.
+
+        Returns
+        -------
+        null_dist : array of shape [df,]
+            Only returned,if add_to_null_dists is False.
+        """
+
+        if self.verbosity > 0:
+            print("Generating null distribution for df = %d. " % df)
+            if add_to_null_dists:
+                print("For faster computations, run function "
+                      "generate_and_save_nulldists(...) to "
+                      "precompute null distribution and load *.npz file with "
+                      "argument null_dist_filename")
+
+        xyz = np.array([0,1])
+
+        null_dist = np.zeros(self.null_samples)
+        for i in range(self.null_samples):
+            array = np.random.rand(2, df)
+            null_dist[i] = self.cond_ind_test.get_dependence_measure(array, xyz)
+
+        null_dist.sort()
+        if add_to_null_dists:
+            self.null_dists[df] = null_dist
+        return null_dist
+
+    def _generate_and_save_nulldists(self, sample_sizes, null_dist_filename):
+        """Generates and saves null distribution for pairwise independence
+        tests.
+
+        Generates the null distribution for different sample sizes. Calls
+        generate_nulldist. Null dists are saved to disk as
+        self.null_dist_filename.npz. Also adds the null distributions to
+        self.null_dists.
+
+        Parameters
+        ----------
+        sample_sizes : list
+            List of sample sizes.
+
+        null_dist_filename : str
+            Name to save file containing null distributions.
+        """
+
+        self.null_dist_filename = null_dist_filename
+
+        null_dists = np.zeros((len(sample_sizes), self.null_samples))
+
+        for iT, T in enumerate(sample_sizes):
+            null_dists[iT] = self._generate_nulldist(T, add_to_null_dists=False)
+            self.null_dists[T] = null_dists[iT]
+
+        np.savez("%s" % null_dist_filename,
+                 exact_dist=null_dists,
+                 T=np.array(sample_sizes))
+
     def _get_single_residuals(self, array, target_var,
-                              return_means=False, 
+                              return_means=False,
                               standardize=True,
                               return_likelihood=False):
         """Returns residuals of Gaussian process regression.
@@ -1419,21 +1393,20 @@ def _get_single_residuals(self, array, target_var,
 
         if dim <= 2:
             if return_likelihood:
-                return array[target_var, :], -numpy.inf
-            else:
-                return array[target_var, :]
+                return array[target_var, :], -np.inf
+            return array[target_var, :]
 
         # Standardize
         if standardize:
             array -= array.mean(axis=1).reshape(dim, 1)
             array /= array.std(axis=1).reshape(dim, 1)
-            if numpy.isnan(array).sum() != 0:
+            if np.isnan(array).sum() != 0:
                 raise ValueError("nans after standardizing, "
                                  "possibly constant array!")
 
         target_series = array[target_var, :]
-        z = numpy.fastCopyAndTranspose(array[2:])
-        if numpy.ndim(z) == 1:
+        z = np.fastCopyAndTranspose(array[2:])
+        if np.ndim(z) == 1:
             z = z.reshape(-1, 1)
 
         if self.gp_version == 'old':
@@ -1441,19 +1414,20 @@ def _get_single_residuals(self, array, target_var,
             def remove_ties(series, verbosity=0):
                 # Test whether ties exist and add noise to destroy ties...
                 cnt = 0
-                while len(numpy.unique(series)) < numpy.size(series):
-                    series += 1E-6 * numpy.random.rand(*series.shape)
+                while len(np.unique(series)) < np.size(series):
+                    series += 1E-6 * np.random.rand(*series.shape)
                     cnt += 1
-                    if cnt > 100: break
+                    if cnt > 100:
+                        break
                 return series
 
-            z = remove_ties(z)  #z += 1E-3 * numpy.random.rand(*z.shape)
-            target_series = remove_ties(target_series)  #target_series += 1E-3 * numpy.random.rand(*target_series.shape)
-            
+            z = remove_ties(z)
+            target_series = remove_ties(target_series)
+
             gp = gaussian_process.GaussianProcess(
                 nugget=1E-1,
                 thetaL=1E-16,
-                thetaU=numpy.inf,
+                thetaU=np.inf,
                 corr='squared_exponential',
                 optimizer='fmin_cobyla',
                 regr='constant',
@@ -1461,7 +1435,7 @@ def remove_ties(series, verbosity=0):
                 storage_mode='light')
 
         elif self.gp_version == 'new':
-            # Overwrite default kernel and alpha values 
+            # Overwrite default kernel and alpha values
             params = self.gp_params.copy()
             if 'kernel' not in list(self.gp_params):
                 kernel = gaussian_process.kernels.RBF() +\
@@ -1476,16 +1450,15 @@ def remove_ties(series, verbosity=0):
                 alpha = self.gp_params['alpha']
                 del params['alpha']
 
-            gp = gaussian_process.GaussianProcessRegressor(
-                kernel=kernel,
-                alpha=alpha,
-                 **params)
+            gp = gaussian_process.GaussianProcessRegressor(kernel=kernel,
+                                                           alpha=alpha,
+                                                           **params)
 
         gp.fit(z, target_series.reshape(-1, 1))
 
         if self.verbosity > 3 and self.gp_version == 'new':
             print(kernel, alpha, gp.kernel_, gp.alpha)
-        
+
         if self.verbosity > 3 and self.gp_version == 'old':
             print(gp.get_params)
 
@@ -1496,351 +1469,60 @@ def remove_ties(series, verbosity=0):
 
         resid = target_series - mean
 
-        if return_means and return_likelihood==False:
+        if return_means and not return_likelihood:
             return (resid, mean)
-        elif return_means==False and return_likelihood:
-            return (resid, likelihood)
-        elif return_means and return_likelihood:
-            return resid, mean, likelihood
-        else:
-            return resid
-
-    def get_model_selection_criterion(self, j,
-                                      parents,
-                                      tau_max=0):
-        """Returns log marginal likelihood for GP regression.
-        
-        Fits a GP model of the parents to variable j and returns the negative
-        log marginal likelihood as a model selection score. Is used to determine
-        optimal hyperparameters in PCMCI, in particular the pc_alpha value.
-        
-        Parameters
-        ----------
-        j : int
-            Index of target variable in data array.
-
-        parents : list
-            List of form [(0, -1), (3, -2), ...] containing parents.
-
-        tau_max : int, optional (default: 0)
-            Maximum time lag. This may be used to make sure that estimates for
-            different lags in X, Z, all have the same sample size.
-        
-        Returns:
-        score : float
-            Model score.
-        """
-
-        Y = [(j, 0)]
-        X = [(j, 0)]   # dummy variable here
-        Z = parents
-        array, xyz = _construct_array(
-            X=X, Y=Y, Z=Z,
-            tau_max=tau_max,
-            data=self.data,
-            use_mask=self.use_mask,
-            mask=self.mask,
-            mask_type=self.mask_type,
-            return_cleaned_xyz=False,
-            do_checks=False,
-            verbosity=self.verbosity)
-
-        dim, T = array.shape
-
-        y, logli = self._get_single_residuals(array,
-                            target_var=1, return_likelihood=True)
-
-        score = -logli
-
-        return score
-
-class GPACE(CondIndTest,GP):
-    r"""GPACE conditional independence test based on Gaussian processes and
-        maximal correlation.
-
-    GPACE is based on a Gaussian process (GP) regression and a maximal
-    correlation test on the residuals. GP is estimated with scikit-learn and
-    allows to flexibly specify kernels and hyperparameters or let them be
-    optimized automatically. The maximal correlation test is implemented with
-    the ACE estimator either from a pure python implementation (slow) or, if rpy
-    is available, using the R-package 'acepack'. Here the null distribution is
-    not analytically available, but can be precomputed with the function
-    generate_and_save_nulldists(...) which saves a \*.npz file containing the
-    null distribution for different sample sizes. This file can then be supplied
-    as null_dist_filename.
-
-    Notes
-    -----
-    As described in [1]_, GPACE is based on a Gaussian
-    process (GP) regression and a maximal correlation test on the residuals. To
-    test :math:`X \perp Y | Z`, first  :math:`Z` is regressed out from :math:`X`
-    and :math:`Y` assuming the  model
-
-    .. math::  X & =  f_X(Z) + \epsilon_{X} \\
-        Y & =  f_Y(Z) + \epsilon_{Y}  \\
-        \epsilon_{X,Y} &\sim \mathcal{N}(0, \sigma^2)
-
-    using GP regression. Here :math:`\sigma^2` and the kernel bandwidth are
-    optimzed using ``sklearn``. Then the residuals  are transformed to uniform
-    marginals yielding :math:`r_X,r_Y` and their dependency is tested with
-
-    .. math::  \max_{g,h}\rho\left(g(r_X),h(r_Y)\right)
-
-    where :math:`g,h` yielding maximal correlation are obtained using the
-    Alternating Conditional Expectation (ACE) algorithm. The null distribution
-    of the maximal correlation can be pre-computed.
-
-    Parameters
-    ----------
-    null_dist_filename : str, otional (default: None)
-        Path to file containing null distribution.
-
-    gp_version : {'new', 'old'}, optional (default: 'new')
-        The older GP version from scikit-learn 0.17 was used for the numerical
-        simulations in [1]_. The newer version from scikit-learn 0.19 is faster
-        and allows more flexibility regarding kernels etc.
-
-    gp_params : dictionary, optional (default: None)
-        Dictionary with parameters for ``GaussianProcessRegressor``.
-    
-    ace_version : {'python', 'acepack'}
-        Estimator for ACE estimator of maximal correlation to use. 'python'
-        loads the very slow pure python version available from
-        https://pypi.python.org/pypi/ace/0.3. 'acepack' loads the much faster
-        version from the R-package acepack. This requires the R-interface
-        rpy2 to be installed and acepack needs to be installed in R beforehand.
-        Note that both versions 'python' and 'acepack' may result in different
-        results. In [1]_ the acepack version was used.
-
-    **kwargs : 
-        Arguments passed on to parent class CondIndTest.
-
-    """
-    def __init__(self,
-                null_dist_filename=None,
-                gp_version='new',
-                gp_params=None,
-                ace_version='acepack',
-                **kwargs):
-
-        GP.__init__(self, 
-                    gp_version=gp_version,
-                    gp_params=gp_params,
-                    )
-
-        self.ace_version = ace_version
-
-        self.measure = 'gp_ace'
-        self.two_sided = False
-        self.residual_based = True
-
-        # Load null-dist file, adapt if necessary
-        self.null_dist_filename = null_dist_filename
-
-        CondIndTest.__init__(self, **kwargs)
-
-        if self.verbosity > 0:
-            print("null_dist_filename = %s" % self.null_dist_filename)
-            print("gp_version = %s" % self.gp_version)
-            if self.gp_params is not None:
-                for key in  list(self.gp_params):
-                    print("%s = %s" % (key, self.gp_params[key]))
-            print("ace_version = %s" % self.ace_version)
-            print("")
-
-        if null_dist_filename is None:
-            self.null_samples = self.sig_samples
-            self.null_dists = {}
-        else:
-            null_dist_file = numpy.load(null_dist_filename)
-            # self.sample_sizes = null_dist_file['T']
-            self.null_dists = dict(zip(null_dist_file['T'], 
-                                      null_dist_file['exact_dist']))
-            # print self.null_dist
-            self.null_samples = len(null_dist_file['exact_dist'][0])
-
-    def get_dependence_measure(self, array, xyz):
-        """Return GPACE measure.
-
-        Estimated as the maximal correlation of the residuals of a GP
-        regression.
-
-        Parameters
-        ----------
-        array : array-like
-            data array with X, Y, Z in rows and observations in columns
-
-        xyz : array of ints
-            XYZ identifier array of shape (dim,).
-
-        Returns
-        -------
-        val : float
-            GPACE test statistic.    
-        """
-
-        D, T = array.shape
-
-        x = self._get_single_residuals(array, target_var=0)
-        y = self._get_single_residuals(array, target_var=1)
-
-        val = self._get_maxcorr(numpy.array([x, y]))
-
-        return val
-
-    # @profile
-    def _get_maxcorr(self, array_resid):
-        """Return maximal correlation coefficient estimated by ACE.
-
-        Method is described in [1]_. The maximal correlation test is implemented
-        with the ACE estimator either from a pure python implementation (slow)
-        or, if rpy is available, using the R-package 'acepack'. The variables
-        are transformed to uniform marginals using the empirical cumulative
-        distribution function beforehand. Here the null distribution is not
-        analytically available, but can be precomputed with the function
-        generate_and_save_nulldists(...) which saves a \*.npz file containing the
-        null distribution for different sample sizes. This file can then be
-        supplied as null_dist_filename.
-
-        Parameters 
-        ---------- 
-        array_resid : array-like     
-            data array must be of shape (2, T)
-
-        Returns
-        -------
-        val : float
-            Maximal correlation coefficient.
-        """
-
-        # Remove ties before applying transformation to uniform marginals
-        # array_resid = self._remove_ties(array_resid, verbosity=4)
-
-        x, y = self._trafo2uniform(array_resid)
-
-        if self.ace_version == 'python':
-            class Suppressor(object):
-                """Wrapper class to prevent output from ACESolver."""
-                def __enter__(self):
-                    self.stdout = sys.stdout
-                    sys.stdout = self
-                def __exit__(self, type, value, traceback):
-                    sys.stdout = self.stdout
-                def write(self, x): 
-                    pass
-            myace = ace.ace.ACESolver()
-            myace.specify_data_set([x], y)
-            with Suppressor():
-                myace.solve()
-            val = numpy.corrcoef(myace.x_transforms[0], myace.y_transform)[0,1]
-        
-        elif self.ace_version == 'acepack':
-            ace_rpy = rpy2.robjects.r['ace'](x, y)
-            val = numpy.corrcoef(numpy.asarray(ace_rpy[8]).flatten(), 
-                                 numpy.asarray(ace_rpy[9]))[0, 1]
-        else:
-            raise ValueError("ace_version must be 'python' or 'acepack'")
-        
-        return val
-
-
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
-        """Returns p-value for shuffle significance test.
-
-        For residual-based test statistics only the residuals are shuffled.
-
-        Parameters
-        ----------
-        array : array-like
-            data array with X, Y, Z in rows and observations in columns
-
-        xyz : array of ints
-            XYZ identifier array of shape (dim,).
-
-        value : number
-            Value of test statistic for unshuffled estimate.
-        
-        Returns
-        -------
-        pval : float
-            p-value
-        """
-
-        x = self._get_single_residuals(array, target_var = 0)
-        y = self._get_single_residuals(array, target_var = 1)
-        array_resid = numpy.array([x, y])
-        xyz_resid = numpy.array([0,1])
+        elif return_likelihood and not return_means:
+            return (resid, likelihood)
+        elif return_means and return_likelihood:
+            return resid, mean, likelihood
+        return resid
 
-        null_dist = self._get_shuffle_dist(array_resid, xyz_resid,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+    def _get_model_selection_criterion(self, j, parents, tau_max=0):
+        """Returns log marginal likelihood for GP regression.
 
-        pval = (null_dist >= value).mean()
-        
-        if return_null_dist:
-            return pval, null_dist
-        else:
-            return pval
+        Fits a GP model of the parents to variable j and returns the negative
+        log marginal likelihood as a model selection score. Is used to determine
+        optimal hyperparameters in PCMCI, in particular the pc_alpha value.
 
-    def get_analytic_significance(self, value, T, dim):
-        """Returns p-value for the maximal correlation coefficient.
-        
-        The null distribution for necessary degrees of freedom (df) is loaded.
-        If not available, the null distribution is generated with the function
-        generate_nulldist(). It can be precomputed with the function
-        generate_and_save_nulldists(...) which saves a \*.npz file containing the
-        null distribution for different sample sizes. This file can then be
-        supplied as null_dist_filename. The maximal correlation coefficient is
-        one-sided. If the degrees of freedom are less than 1, numpy.nan is
-        returned.
-        
         Parameters
         ----------
-        value : float
-            Test statistic value.
+        j : int
+            Index of target variable in data array.
 
-        T : int
-            Sample length
+        parents : list
+            List of form [(0, -1), (3, -2), ...] containing parents.
 
-        dim : int
-            Dimensionality, ie, number of features.
+        tau_max : int, optional (default: 0)
+            Maximum time lag. This may be used to make sure that estimates for
+            different lags in X, Z, all have the same sample size.
 
-        Returns
-        -------
-        pval : float or numpy.nan
-            P-value.
+        Returns:
+        score : float
+            Model score.
         """
 
-        # GP regression approximately doesn't cost degrees of freedom
-        df = T
-
-        if df < 1:
-            pval = numpy.nan
-        else:
-            # idx_near = (numpy.abs(self.sample_sizes - df)).argmin()
-
-            if int(df) not in list(self.null_dists):
-            # numpy.abs(self.sample_sizes[idx_near] - df) / float(df) > 0.01:
-                if self.verbosity > 0:
-                    print("Null distribution for GPACE not available "
-                             "for deg. of freed. = %d."
-                             "" % df) 
-                self.generate_nulldist(df)
+        Y = [(j, 0)]
+        X = [(j, 0)]   # dummy variable here
+        Z = parents
+        array, xyz = \
+                self.cond_ind_test.dataframe.construct_array(
+                    X=X, Y=Y, Z=Z,
+                    tau_max=tau_max,
+                    mask_type=self.cond_ind_test.mask_type,
+                    return_cleaned_xyz=False,
+                    do_checks=False,
+                    verbosity=self.verbosity)
 
-            null_dist_here = self.null_dists[df]
-            pval = numpy.mean(null_dist_here > numpy.abs(value))
+        dim, T = array.shape
 
-        return pval
+        _, logli = self._get_single_residuals(array,
+                                              target_var=1,
+                                              return_likelihood=True)
 
-    def get_analytic_confidence(self, value, df, conf_lev):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
+        score = -logli
+        return score
 
-class GPDC(CondIndTest,GP):
+class GPDC(CondIndTest):
     r"""GPDC conditional independence test based on Gaussian processes and
         distance correlation.
 
@@ -1850,12 +1532,12 @@ class GPDC(CondIndTest,GP):
     optimized automatically. The distance correlation test is implemented with
     cython. Here the null distribution is not analytically available, but can be
     precomputed with the function generate_and_save_nulldists(...) which saves a
-    \*.npz file containing the null distribution for different sample sizes. This
-    file can then be supplied as null_dist_filename.
+    \*.npz file containing the null distribution for different sample sizes.
+    This file can then be supplied as null_dist_filename.
 
     Notes
     -----
-    
+
     GPDC is based on a Gaussian process (GP) regression and a distance
     correlation test on the residuals. Distance correlation is described in
     [2]_. To test :math:`X \perp Y | Z`, first :math:`Z` is regressed out from
@@ -1879,7 +1561,7 @@ class GPDC(CondIndTest,GP):
 
     References
     ----------
-    .. [2] Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and 
+    .. [2] Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov: Measuring and
            testing dependence by correlation of distances,
            https://arxiv.org/abs/0803.4101
 
@@ -1896,50 +1578,170 @@ class GPDC(CondIndTest,GP):
     gp_params : dictionary, optional (default: None)
         Dictionary with parameters for ``GaussianProcessRegressor``.
 
-    **kwargs : 
-        Arguments passed on to parent class CondIndTest.
+    **kwargs :
+        Arguments passed on to parent class GaussProcReg.
 
     """
-    def __init__(self,
-                null_dist_filename=None,
-                gp_version='new',
-                gp_params=None,
-                **kwargs):
-
-        GP.__init__(self, 
-                    gp_version=gp_version,
-                    gp_params=gp_params,
-                    )
+    @property
+    def measure(self):
+        """
+        Concrete property to return the measure of the independence test
+        """
+        return self._measure
 
-        self.measure = 'gp_dc'
+    def __init__(self,
+                 null_dist_filename=None,
+                 gp_version='new',
+                 gp_params=None,
+                 **kwargs):
+        self._measure = 'gp_dc'
         self.two_sided = False
         self.residual_based = True
-
-        # Load null-dist file, adapt if necessary
-        self.null_dist_filename = null_dist_filename
-
+        # Call the parent constructor
         CondIndTest.__init__(self, **kwargs)
+        # Build the regressor
+        self.gauss_pr = GaussProcReg(self.sig_samples,
+                                     self,
+                                     gp_version=gp_version,
+                                     gp_params=gp_params,
+                                     null_dist_filename=null_dist_filename,
+                                     verbosity=self.verbosity)
 
         if self.verbosity > 0:
-            print("null_dist_filename = %s" % self.null_dist_filename)
-            print("gp_version = %s" % self.gp_version)
-            if self.gp_params is not None:
-                for key in  list(self.gp_params):
-                    print("%s = %s" % (key, self.gp_params[key]))
+            print("null_dist_filename = %s" % self.gauss_pr.null_dist_filename)
+            print("gp_version = %s" % self.gauss_pr.gp_version)
+            if self.gauss_pr.gp_params is not None:
+                for key in  list(self.gauss_pr.gp_params):
+                    print("%s = %s" % (key, self.gauss_pr.gp_params[key]))
             print("")
 
-        if null_dist_filename is None:
-            self.null_samples = self.sig_samples
-            self.null_dists = {}
-        else:
-            null_dist_file = numpy.load(null_dist_filename)
-            # self.sample_sizes = null_dist_file['T']
-            self.null_dists = dict(zip(null_dist_file['T'], 
-                                      null_dist_file['exact_dist']))
-            # print self.null_dist
-            self.null_samples = len(null_dist_file['exact_dist'][0])
-
-    
+    def _load_nulldist(self, filename):
+        r"""
+        Load a precomputed null distribution from a \*.npz file.  This
+        distribution can be calculated using generate_and_save_nulldists(...).
+
+        Parameters
+        ----------
+        filename : strng
+            Path to the \*.npz file
+
+        Returns
+        -------
+        null_dists, null_samples : dict, int
+            The null distirbution as a dictionary of distributions keyed by
+            sample size, the number of null samples in total.
+        """
+        return self.gauss_pr._load_nulldist(filename)
+
+    def generate_nulldist(self, df, add_to_null_dists=True):
+        """Generates null distribution for pairwise independence tests.
+
+        Generates the null distribution for sample size df. Assumes pairwise
+        samples transformed to uniform marginals. Uses get_dependence_measure
+        available in class and generates self.sig_samples random samples. Adds
+        the null distributions to self.gauss_pr.null_dists.
+
+        Parameters
+        ----------
+        df : int
+            Degrees of freedom / sample size to generate null distribution for.
+
+        add_to_null_dists : bool, optional (default: True)
+            Whether to add the null dist to the dictionary of null dists or
+            just return it.
+
+        Returns
+        -------
+        null_dist : array of shape [df,]
+            Only returned,if add_to_null_dists is False.
+        """
+        return self.gauss_pr._generate_nulldist(df, add_to_null_dists)
+
+    def generate_and_save_nulldists(self, sample_sizes, null_dist_filename):
+        """Generates and saves null distribution for pairwise independence
+        tests.
+
+        Generates the null distribution for different sample sizes. Calls
+        generate_nulldist. Null dists are saved to disk as
+        self.null_dist_filename.npz. Also adds the null distributions to
+        self.gauss_pr.null_dists.
+
+        Parameters
+        ----------
+        sample_sizes : list
+            List of sample sizes.
+
+        null_dist_filename : str
+            Name to save file containing null distributions.
+        """
+        self.gauss_pr._generate_and_save_nulldists(sample_sizes,
+                                                   null_dist_filename)
+
+    def _get_single_residuals(self, array, target_var,
+                              return_means=False,
+                              standardize=True,
+                              return_likelihood=False):
+        """Returns residuals of Gaussian process regression.
+
+        Performs a GP regression of the variable indexed by target_var on the
+        conditions Z. Here array is assumed to contain X and Y as the first two
+        rows with the remaining rows (if present) containing the conditions Z.
+        Optionally returns the estimated mean and the likelihood.
+
+        Parameters
+        ----------
+        array : array-like
+            data array with X, Y, Z in rows and observations in columns
+
+        target_var : {0, 1}
+            Variable to regress out conditions from.
+
+        standardize : bool, optional (default: True)
+            Whether to standardize the array beforehand.
+
+        return_means : bool, optional (default: False)
+            Whether to return the estimated regression line.
+
+        return_likelihood : bool, optional (default: False)
+            Whether to return the log_marginal_likelihood of the fitted GP
+
+        Returns
+        -------
+        resid [, mean, likelihood] : array-like
+            The residual of the regression and optionally the estimated mean
+            and/or the likelihood.
+        """
+        return self.gauss_pr._get_single_residuals(
+            array, target_var,
+            return_means,
+            standardize,
+            return_likelihood)
+
+    def get_model_selection_criterion(self, j, parents, tau_max=0):
+        """Returns log marginal likelihood for GP regression.
+
+        Fits a GP model of the parents to variable j and returns the negative
+        log marginal likelihood as a model selection score. Is used to determine
+        optimal hyperparameters in PCMCI, in particular the pc_alpha value.
+
+        Parameters
+        ----------
+        j : int
+            Index of target variable in data array.
+
+        parents : list
+            List of form [(0, -1), (3, -2), ...] containing parents.
+
+        tau_max : int, optional (default: 0)
+            Maximum time lag. This may be used to make sure that estimates for
+            different lags in X, Z, all have the same sample size.
+
+        Returns:
+        score : float
+            Model score.
+        """
+        return self.gauss_pr._get_model_selection_criterion(j, parents, tau_max)
+
     def get_dependence_measure(self, array, xyz):
         """Return GPDC measure.
 
@@ -1957,33 +1759,28 @@ def get_dependence_measure(self, array, xyz):
         Returns
         -------
         val : float
-            GPDC test statistic.    
+            GPDC test statistic.
         """
 
-        D, T = array.shape
-
-        x = self._get_single_residuals(array, target_var=0)
-        y = self._get_single_residuals(array, target_var=1)
-
-        val = self._get_dcorr(numpy.array([x, y]))
-
+        x_vals = self._get_single_residuals(array, target_var=0)
+        y_vals = self._get_single_residuals(array, target_var=1)
+        val = self._get_dcorr(np.array([x_vals, y_vals]))
         return val
 
 
-    # @profile
     def _get_dcorr(self, array_resid):
         """Return distance correlation coefficient.
-        
+
         The variables are transformed to uniform marginals using the empirical
         cumulative distribution function beforehand. Here the null distribution
         is not analytically available, but can be precomputed with the function
-        generate_and_save_nulldists(...) which saves a \*.npz file containing the
-        null distribution for different sample sizes. This file can then be
+        generate_and_save_nulldists(...) which saves a \*.npz file containing
+        the null distribution for different sample sizes. This file can then be
         supplied as null_dist_filename.
 
-        Parameters 
-        ---------- 
-        array_resid : array-like     
+        Parameters
+        ----------
+        array_resid : array-like
             data array must be of shape (2, T)
 
         Returns
@@ -1991,19 +1788,15 @@ def _get_dcorr(self, array_resid):
         val : float
             Distance correlation coefficient.
         """
-
         # Remove ties before applying transformation to uniform marginals
         # array_resid = self._remove_ties(array_resid, verbosity=4)
+        x_vals, y_vals = self._trafo2uniform(array_resid)
 
-        x, y = self._trafo2uniform(array_resid)
-
-        dc, val, dvx, dvy = tigramite_cython_code.dcov_all(x, y)
-        
+        _, val, _, _ = tigramite_cython_code.dcov_all(x_vals, y_vals)
         return val
 
-
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
         """Returns p-value for shuffle significance test.
 
         For residual-based test statistics only the residuals are shuffled.
@@ -2018,34 +1811,33 @@ def get_shuffle_significance(self, array, xyz, value,
 
         value : number
             Value of test statistic for unshuffled estimate.
-        
+
         Returns
         -------
         pval : float
             p-value
         """
 
-        x = self._get_single_residuals(array, target_var = 0)
-        y = self._get_single_residuals(array, target_var = 1)
-        array_resid = numpy.array([x, y])
-        xyz_resid = numpy.array([0,1])
+        x_vals = self._get_single_residuals(array, target_var=0)
+        y_vals = self._get_single_residuals(array, target_var=1)
+        array_resid = np.array([x_vals, y_vals])
+        xyz_resid = np.array([0, 1])
 
         null_dist = self._get_shuffle_dist(array_resid, xyz_resid,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+                                           self.get_dependence_measure,
+                                           sig_samples=self.sig_samples,
+                                           sig_blocklength=self.sig_blocklength,
+                                           verbosity=self.verbosity)
 
         pval = (null_dist >= value).mean()
-        
+
         if return_null_dist:
             return pval, null_dist
-        else:
-            return pval
+        return pval
 
     def get_analytic_significance(self, value, T, dim):
         """Returns p-value for the distance correlation coefficient.
-        
+
         The null distribution for necessary degrees of freedom (df) is loaded.
         If not available, the null distribution is generated with the function
         generate_nulldist(). It is recommended to generate the nulldists for a
@@ -2053,7 +1845,7 @@ def get_analytic_significance(self, value, T, dim):
         generate_and_save_nulldists(...). The distance correlation coefficient
         is one-sided. If the degrees of freedom are less than 1, numpy.nan is
         returned.
-        
+
         Parameters
         ----------
         value : float
@@ -2075,28 +1867,19 @@ def get_analytic_significance(self, value, T, dim):
         df = T
 
         if df < 1:
-            pval = numpy.nan
+            pval = np.nan
         else:
-            # idx_near = (numpy.abs(self.sample_sizes - df)).argmin()
-            if int(df) not in list(self.null_dists):
-            # if numpy.abs(self.sample_sizes[idx_near] - df) / float(df) > 0.01:
+            # idx_near = (np.abs(self.sample_sizes - df)).argmin()
+            if int(df) not in list(self.gauss_pr.null_dists):
+            # if np.abs(self.sample_sizes[idx_near] - df) / float(df) > 0.01:
                 if self.verbosity > 0:
                     print("Null distribution for GPDC not available "
-                             "for deg. of freed. = %d."
-                             "" % df) 
-                    
+                          "for deg. of freed. = %d." % df)
                 self.generate_nulldist(df)
-
-            null_dist_here = self.null_dists[int(df)]
-            pval = numpy.mean(null_dist_here > numpy.abs(value))
-
+            null_dist_here = self.gauss_pr.null_dists[int(df)]
+            pval = np.mean(null_dist_here > np.abs(value))
         return pval
 
-    def get_analytic_confidence(self, value, df, conf_lev):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
-
 class CMIknn(CondIndTest):
     r"""Conditional mutual information test based on nearest-neighbor estimator.
 
@@ -2113,12 +1896,12 @@ class CMIknn(CondIndTest):
     -----
     CMI is given by
 
-    .. math:: I(X;Y|Z) &= \int p(z)  \iint  p(x,y|z) \log 
+    .. math:: I(X;Y|Z) &= \int p(z)  \iint  p(x,y|z) \log
                 \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz
 
-    Its knn-estimator is given by 
+    Its knn-estimator is given by
 
-    .. math:: \widehat{I}(X;Y|Z)  &=   \psi (k) + \frac{1}{T} \sum_{t=1}^T 
+    .. math:: \widehat{I}(X;Y|Z)  &=   \psi (k) + \frac{1}{T} \sum_{t=1}^T
             \left[ \psi(k_{Z,t}) - \psi(k_{XZ,t}) - \psi(k_{YZ,t}) \right]
 
     where :math:`\psi` is the Digamma function.  This estimator has as a
@@ -2134,15 +1917,16 @@ class CMIknn(CondIndTest):
     that the estimated CMI values can be slightly negative while CMI is a non-
     negative quantity.
 
-    This class requires the scipy.spatial.cKDTree package and the tigramite
+    This method requires the scipy.spatial.cKDTree package and the tigramite
     cython module.
 
     References
     ----------
-    .. [3] J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths: 
-           Escaping the Curse of Dimensionality in Estimating Multivariate 
-           Transfer Entropy. Physical Review Letters, 108(25), 258701. 
-           http://doi.org/10.1103/PhysRevLett.108.258701
+    .. [3] J. Runge (2018): Conditional Independence Testing Based on a 
+           Nearest-Neighbor Estimator of Conditional Mutual Information.
+           In Proceedings of the 21st International Conference on Artificial 
+           Intelligence and Statistics.
+           http://proceedings.mlr.press/v84/runge18a.html
 
     Parameters
     ----------
@@ -2157,44 +1941,48 @@ class CMIknn(CondIndTest):
         determines the size of hyper-cubes around each (high-dimensional) sample
         point.
 
-    transform : {'standardize', 'ranks',  'uniform', False}, optional 
+    transform : {'standardize', 'ranks',  'uniform', False}, optional
         (default: 'standardize')
         Whether to transform the array beforehand by standardizing
         or transforming to uniform marginals.
 
     significance : str, optional (default: 'shuffle_test')
-        Type of significance test to use. For CMIknn only 'fixed_thres' and 
+        Type of significance test to use. For CMIknn only 'fixed_thres' and
         'shuffle_test' are available.
 
-    **kwargs : 
-        Arguments passed on to parent class CondIndTest. 
+    **kwargs :
+        Arguments passed on to parent class CondIndTest.
     """
-    def __init__(self,
-                knn=0.2,
-                shuffle_neighbors=5,
-                significance='shuffle_test',
-                transform='standardize',
-                **kwargs):
-
+    @property
+    def measure(self):
+        """
+        Concrete property to return the measure of the independence test
+        """
+        return self._measure
 
+    def __init__(self,
+                 knn=0.2,
+                 shuffle_neighbors=5,
+                 significance='shuffle_test',
+                 transform='standardize',
+                 **kwargs):
+        # Set the member variables
         self.knn = knn
         self.shuffle_neighbors = shuffle_neighbors
         self.transform = transform
-
-        self.measure = 'cmi_knn'
+        self._measure = 'cmi_knn'
         self.two_sided = False
         self.residual_based = False
         self.recycle_residuals = False
-
+        # Call the parent constructor
         CondIndTest.__init__(self, significance=significance, **kwargs)
-
+        # Print some information about construction
         if self.verbosity > 0:
             if self.knn < 1:
                 print("knn/T = %s" % self.knn)
             else:
                 print("knn = %s" % self.knn)
-            print("shuffle_neighbors = %d" % self.shuffle_neighbors)
-            print("")
+            print("shuffle_neighbors = %d\n" % self.shuffle_neighbors)
 
     def _get_nearest_neighbors(self, array, xyz, knn):
         """Returns nearest neighbors according to Frenzel and Pompe (2007).
@@ -2203,14 +1991,14 @@ def _get_nearest_neighbors(self, array, xyz, knn):
         sample in joint space XYZ and returns the numbers of nearest neighbors
         within eps in subspaces Z, XZ, YZ.
 
-        Parameters 
-        ---------- 
+        Parameters
+        ----------
         array : array-like
             data array with X, Y, Z in rows and observations in columns
 
         xyz : array of ints
             XYZ identifier array of shape (dim,).
-       
+
         knn : int or float
             Number of nearest-neighbors which determines the size of hyper-cubes
             around each (high-dimensional) sample point. If smaller than 1, this
@@ -2225,10 +2013,10 @@ def _get_nearest_neighbors(self, array, xyz, knn):
 
         dim, T = array.shape
         array = array.astype('float')
-        
+
         # Add noise to destroy ties...
         array += (1E-6 * array.std(axis=1).reshape(dim, 1)
-                  * numpy.random.rand(array.shape[0], array.shape[1]))
+                  * np.random.rand(array.shape[0], array.shape[1]))
 
         if self.transform == 'standardize':
             # Standardize
@@ -2237,7 +2025,7 @@ def _get_nearest_neighbors(self, array, xyz, knn):
             array /= array.std(axis=1).reshape(dim, 1)
             # FIXME: If the time series is constant, return nan rather than
             # raising Exception
-            if numpy.isnan(array).sum() != 0:
+            if np.isnan(array).sum() != 0:
                 raise ValueError("nans after standardizing, "
                                  "possibly constant array!")
         elif self.transform == 'uniform':
@@ -2249,17 +2037,21 @@ def _get_nearest_neighbors(self, array, xyz, knn):
         # Use cKDTree to get distances eps to the k-th nearest neighbors for
         # every sample in joint space XYZ with maximum norm
         tree_xyz = spatial.cKDTree(array.T)
-        epsarray = tree_xyz.query(array.T, k=knn+1, p=numpy.inf,
-                                  eps=0.)[0][:,knn].astype('float')
+        epsarray = tree_xyz.query(array.T, k=knn+1, p=np.inf,
+                                  eps=0.)[0][:, knn].astype('float')
 
         # Prepare for fast cython access
-        dim_x = int(numpy.where(xyz == 0)[0][-1] + 1)
-        dim_y = int(numpy.where(xyz == 1)[0][-1] + 1 - dim_x)
+        dim_x = int(np.where(xyz == 0)[0][-1] + 1)
+        dim_y = int(np.where(xyz == 1)[0][-1] + 1 - dim_x)
 
         k_xz, k_yz, k_z = \
-         tigramite_cython_code._get_neighbors_within_eps_cython(array, T, dim_x,
-         dim_y, epsarray, knn, dim)
-
+                tigramite_cython_code._get_neighbors_within_eps_cython(array,
+                                                                       T,
+                                                                       dim_x,
+                                                                       dim_y,
+                                                                       epsarray,
+                                                                       knn,
+                                                                       dim)
         return k_xz, k_yz, k_z
 
     def get_dependence_measure(self, array, xyz):
@@ -2272,7 +2064,7 @@ def get_dependence_measure(self, array, xyz):
 
         xyz : array of ints
             XYZ identifier array of shape (dim,).
-        
+
         Returns
         -------
         val : float
@@ -2286,18 +2078,19 @@ def get_dependence_measure(self, array, xyz):
         else:
             knn_here = max(1, int(self.knn))
 
-        k_xz, k_yz, k_z = self._get_nearest_neighbors(array=array, xyz=xyz,
-                                                 knn=knn_here)
+        k_xz, k_yz, k_z = self._get_nearest_neighbors(array=array,
+                                                      xyz=xyz,
+                                                      knn=knn_here)
 
         val = special.digamma(knn_here) - (special.digamma(k_xz) +
-                                      special.digamma(k_yz) -
-                                      special.digamma(k_z)).mean()
+                                           special.digamma(k_yz) -
+                                           special.digamma(k_z)).mean()
 
         return val
 
 
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
         """Returns p-value for nearest-neighbor shuffle significance test.
 
         For non-empty Z, overwrites get_shuffle_significance from the parent
@@ -2317,7 +2110,7 @@ class  which is a block shuffle test, which does not preserve
 
         value : number
             Value of test statistic for unshuffled estimate.
-        
+
         Returns
         -------
         pval : float
@@ -2333,53 +2126,53 @@ class  which is a block shuffle test, which does not preserve
         #         return 0.
 
         # max_neighbors = max(1, int(max_neighbor_ratio*T))
-        x_indices = numpy.where(xyz == 0)[0]
-        z_indices = numpy.where(xyz == 2)[0]
+        x_indices = np.where(xyz == 0)[0]
+        z_indices = np.where(xyz == 2)[0]
 
         if len(z_indices) > 0 and self.shuffle_neighbors < T:
             if self.verbosity > 2:
                 print("            nearest-neighbor shuffle significance "
                       "test with n = %d and %d surrogates" % (
-                        self.shuffle_neighbors,  self.sig_samples))
+                      self.shuffle_neighbors, self.sig_samples))
 
             # Get nearest neighbors around each sample point in Z
-            z_array = numpy.fastCopyAndTranspose(array[z_indices,:])
+            z_array = np.fastCopyAndTranspose(array[z_indices, :])
             tree_xyz = spatial.cKDTree(z_array)
-            neighbors = tree_xyz.query(z_array, 
-                        k=self.shuffle_neighbors, 
-                        p=numpy.inf, 
-                        eps=0.)[1].astype('int32')  
-            # print neighbors
+            neighbors = tree_xyz.query(z_array,
+                                       k=self.shuffle_neighbors,
+                                       p=np.inf,
+                                       eps=0.)[1].astype('int32')
 
-            null_dist = numpy.zeros(self.sig_samples)
+            null_dist = np.zeros(self.sig_samples)
             for sam in range(self.sig_samples):
 
                 # Generate random order in which to go through indices loop in
                 # next step
-                order = numpy.random.permutation(T).astype('int32')
-
+                order = np.random.permutation(T).astype('int32')
+                # print(order[:5])
                 # Select a series of neighbor indices that contains as few as
                 # possible duplicates
                 restricted_permutation = \
                     tigramite_cython_code._get_restricted_permutation_cython(
-                                T=T, 
-                                shuffle_neighbors=self.shuffle_neighbors, 
-                                neighbors=neighbors, 
-                                order = order)
+                        T=T,
+                        shuffle_neighbors=self.shuffle_neighbors,
+                        neighbors=neighbors,
+                        order=order)
 
-                array_shuffled = numpy.copy(array)
+                array_shuffled = np.copy(array)
                 for i in x_indices:
                     array_shuffled[i] = array[i, restricted_permutation]
 
-                null_dist[sam] = self.get_dependence_measure(array_shuffled, 
+                null_dist[sam] = self.get_dependence_measure(array_shuffled,
                                                              xyz)
-        
+
         else:
-            null_dist = self._get_shuffle_dist(array, xyz,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+            null_dist = \
+                    self._get_shuffle_dist(array, xyz,
+                                           self.get_dependence_measure,
+                                           sig_samples=self.sig_samples,
+                                           sig_blocklength=self.sig_blocklength,
+                                           verbosity=self.verbosity)
 
         # Sort
         null_dist.sort()
@@ -2387,26 +2180,8 @@ class  which is a block shuffle test, which does not preserve
 
         if return_null_dist:
             return pval, null_dist
-        else:
-            return pval
-
-
-    def get_analytic_significance(self, value, T, dim):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic significance not implemented for %s"
-                         "" % self.measure)
-
-    def get_analytic_confidence(self, value, df, conf_lev):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
+        return pval
 
-    def get_model_selection_criterion(self, j,
-                                      parents,
-                                      tau_max=0):
-        """Placeholder function, not available."""
-        raise ValueError("Model selection not implemented for %s"
-                         "" % self.measure)
 
 class CMIsymb(CondIndTest):
     r"""Conditional mutual information test based on discrete estimator.
@@ -2419,13 +2194,13 @@ class CMIsymb(CondIndTest):
     shuffle test to generate  the distribution under the null hypothesis of
     independence. The knn-estimator is suitable only for discrete variables.
     For continuous variables, either pre-process the data using the functions
-    in data_processing or use the CMIknn class.
+    in data_processing or, better, use the CMIknn class.
 
     Notes
     -----
     CMI and its estimator are given by
 
-    .. math:: I(X;Y|Z) &= \sum p(z)  \sum \sum  p(x,y|z) \log 
+    .. math:: I(X;Y|Z) &= \sum p(z)  \sum \sum  p(x,y|z) \log
                 \frac{ p(x,y |z)}{p(x|z)\cdot p(y |z)} \,dx dy dz
 
     Parameters
@@ -2434,7 +2209,7 @@ class CMIsymb(CondIndTest):
         Number of symbols in input data. If None, n_symbs=data.max()+1
 
     significance : str, optional (default: 'shuffle_test')
-        Type of significance test to use. For CMIsymb only 'fixed_thres' and 
+        Type of significance test to use. For CMIsymb only 'fixed_thres' and
         'shuffle_test' are available.
 
     sig_blocklength : int, optional (default: 1)
@@ -2443,26 +2218,31 @@ class CMIsymb(CondIndTest):
     conf_blocklength : int, optional (default: 1)
         Block length for block-bootstrap.
 
-    **kwargs : 
+    **kwargs :
         Arguments passed on to parent class CondIndTest.
     """
-    def __init__(self,
-                n_symbs=None,
-                significance='shuffle_test',
-                sig_blocklength=1,
-                conf_blocklength=1,
-                **kwargs):
-
+    @property
+    def measure(self):
+        """
+        Concrete property to return the measure of the independence test
+        """
+        return self._measure
 
-        self.measure = 'cmi_symb'
+    def __init__(self,
+                 n_symbs=None,
+                 significance='shuffle_test',
+                 sig_blocklength=1,
+                 conf_blocklength=1,
+                 **kwargs):
+        # Setup the member variables
+        self._measure = 'cmi_symb'
         self.two_sided = False
         self.residual_based = False
         self.recycle_residuals = False
-
         self.n_symbs = n_symbs
-
-        CondIndTest.__init__(self, 
-                             significance=significance, 
+        # Call the parent constructor
+        CondIndTest.__init__(self,
+                             significance=significance,
                              sig_blocklength=sig_blocklength,
                              conf_blocklength=conf_blocklength,
                              **kwargs)
@@ -2486,7 +2266,7 @@ def _bincount_hist(self, symb_array, weights=None):
         ----------
         symb_array : integer array
             Data array of shape (dim, T).
-            
+
         weights : float array, optional (default: None)
             Optional weights array of shape (dim, T).
 
@@ -2506,36 +2286,33 @@ def _bincount_hist(self, symb_array, weights=None):
 
         dim, T = symb_array.shape
 
-        # Needed because numpy.bincount cannot process longs
-        if type(self.n_symbs ** dim) != int:
-            raise ValueError("Too many n_symbs and/or dimensions, "
-                             "numpy.bincount cannot process longs")
-        if self.n_symbs ** dim * 16. / 8. / 1024. ** 3 > 3.:
-            raise ValueError("Dimension exceeds 3 GB of necessary "
-                             "memory (change this code line if more...)")
-        if dim * self.n_symbs ** dim > 2 ** 65:
-            raise ValueError("base = %d, D = %d: Histogram failed: "
-                             "dimension D*base**D exceeds int64 data type"
-                             % (self.n_symbs, dim))
-
-        flathist = numpy.zeros((self.n_symbs ** dim), dtype='int16')
-        multisymb = numpy.zeros(T, dtype='int64')
+        # Needed because np.bincount cannot process longs
+        # if not isinstance(self.n_symbs ** dim, int):
+        #     raise ValueError("Too many n_symbs and/or dimensions, "
+        #                      "numpy.bincount cannot process longs")
+        # if self.n_symbs ** dim * 16. / 8. / 1024. ** 3 > 3.:
+        #     raise ValueError("Dimension exceeds 3 GB of necessary "
+        #                      "memory (change this code line if more...)")
+        # if dim * self.n_symbs ** dim > 2 ** 65:
+        #     raise ValueError("base = %d, D = %d: Histogram failed: "
+        #                      "dimension D*base**D exceeds int64 data type"
+        #                      % (self.n_symbs, dim))
+
+        flathist = np.zeros((self.n_symbs ** dim), dtype='int16')
+        multisymb = np.zeros(T, dtype='int64')
         if weights is not None:
-            flathist = numpy.zeros((self.n_symbs ** dim), dtype='float32')
-            multiweights = numpy.ones(T, dtype='float32')
+            flathist = np.zeros((self.n_symbs ** dim), dtype='float32')
+            multiweights = np.ones(T, dtype='float32')
 
-        # print numpy.prod(weights, axis=0)
         for i in range(dim):
             multisymb += symb_array[i, :] * self.n_symbs ** i
             if weights is not None:
                 multiweights *= weights[i, :]
-                # print i, multiweights
 
         if weights is None:
-            result = numpy.bincount(multisymb)
-            # print result
+            result = np.bincount(multisymb)
         else:
-            result = (numpy.bincount(multisymb, weights=multiweights)
+            result = (np.bincount(multisymb, weights=multiweights)
                       / multiweights.sum())
 
         flathist[:len(result)] += result
@@ -2555,55 +2332,37 @@ def get_dependence_measure(self, array, xyz):
 
         xyz : array of ints
             XYZ identifier array of shape (dim,).
-        
+
         Returns
         -------
         val : float
             Conditional mutual information estimate.
         """
 
-        dim, T = array.shape
+        _, T = array.shape
 
-        # High-dimensional Histogram
+        # High-dimensional histogram
         hist = self._bincount_hist(array, weights=None)
 
         def _plogp_vector(T):
             """Precalculation of p*log(p) needed for entropies."""
-            gfunc = numpy.zeros(T + 1, dtype='float')
-            gfunc = numpy.zeros(T + 1)
-            gfunc[1:] = numpy.arange(
-                1, T + 1, 1) * numpy.log(numpy.arange(1, T + 1, 1))
-            def plogp_func(t):
-                return gfunc[t]
-            return numpy.vectorize(plogp_func)
-        
+            gfunc = np.zeros(T + 1)
+            data = np.arange(1, T + 1, 1)
+            gfunc[1:] = data * np.log(data)
+            def plogp_func(time):
+                return gfunc[time]
+            return np.vectorize(plogp_func)
+
         plogp = _plogp_vector(T)
-        
         hxyz = (-(plogp(hist)).sum() + plogp(T)) / float(T)
-        hxz = (-(plogp(hist.sum(axis=1))).sum() + plogp(T)) / \
-            float(T)
-        hyz = (-(plogp(hist.sum(axis=0))).sum() + plogp(T)) / \
-            float(T)
-        hz = (-(plogp(hist.sum(axis=0).sum(axis=0))).sum() +
-              plogp(T)) / float(T)
-
-        # else:
-        #     def plogp_func(p):
-        #         if p == 0.: return 0.
-        #         else: return p*numpy.log(p)
-        #     plogp = numpy.vectorize(plogp_func)
-
-        #     hxyz = -plogp(hist).sum()
-        #     hxz = -plogp(hist.sum(axis=1)).sum()
-        #     hyz = -plogp(hist.sum(axis=0)).sum()
-        #     hz = -plogp(hist.sum(axis=0).sum(axis=0)).sum()
-
+        hxz = (-(plogp(hist.sum(axis=1))).sum() + plogp(T)) / float(T)
+        hyz = (-(plogp(hist.sum(axis=0))).sum() + plogp(T)) / float(T)
+        hz = (-(plogp(hist.sum(axis=0).sum(axis=0))).sum()+plogp(T)) / float(T)
         val = hxz + hyz - hz - hxyz
-
         return val
 
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
         """Returns p-value for shuffle significance test.
 
         For residual-based test statistics only the residuals are shuffled.
@@ -2618,7 +2377,7 @@ def get_shuffle_significance(self, array, xyz, value,
 
         value : number
             Value of test statistic for unshuffled estimate.
-        
+
         Returns
         -------
         pval : float
@@ -2626,35 +2385,16 @@ def get_shuffle_significance(self, array, xyz, value,
         """
 
         null_dist = self._get_shuffle_dist(array, xyz,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+                                           self.get_dependence_measure,
+                                           sig_samples=self.sig_samples,
+                                           sig_blocklength=self.sig_blocklength,
+                                           verbosity=self.verbosity)
 
         pval = (null_dist >= value).mean()
-        
+
         if return_null_dist:
             return pval, null_dist
-        else:
-            return pval
-
-    def get_analytic_significance(self, value, T, dim):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
-
-    def get_analytic_confidence(self, value, df, conf_lev):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
-
-    def get_model_selection_criterion(self, j,
-                                      parents,
-                                      tau_max=0):
-        """Placeholder function, not available."""
-        raise ValueError("Model selection not implemented for %s"
-                         "" % self.measure)
-
+        return pval
 
 class RCOT(CondIndTest):
     r"""Randomized Conditional Correlation Test.
@@ -2666,7 +2406,7 @@ class RCOT(CondIndTest):
 
     Notes
     -----
-    
+
     RCOT is a fast variant of the Kernel Conditional Independence Test (KCIT)
     utilizing random Fourier features. Kernel tests measure conditional
     independence in the fully non-parametric setting. In practice, RCOT tests
@@ -2681,13 +2421,14 @@ class RCOT(CondIndTest):
     features. To get reproducable results, you should fix the seed (default).
 
     This class requires the rpy package and the prior installation of ``rcit``
-    from https://github.com/ericstrobl/RCIT.
+    from https://github.com/ericstrobl/RCIT. This is provided with tigramite
+    as an external package.
 
     References
     ----------
-    .. [5] Eric V. Strobl, Kun Zhang, Shyam Visweswaran: 
+    .. [5] Eric V. Strobl, Kun Zhang, Shyam Visweswaran:
            Approximate Kernel-based Conditional Independence Tests for Fast Non-
-           Parametric Causal Discovery.  
+           Parametric Causal Discovery.
            https://arxiv.org/abs/1702.03877
 
     Parameters
@@ -2705,37 +2446,43 @@ class RCOT(CondIndTest):
         testing (warning: this one is slow).
 
     seed : int or None, optional
-        Which random fourier feature seed to use. If None, you won't get 
+        Which random fourier feature seed to use. If None, you won't get
         reproducable results.
 
     significance : str, optional (default: 'analytic')
         Type of significance test to use.
 
-    **kwargs : 
-        Arguments passed on to parent class CondIndTest. 
+    **kwargs :
+        Arguments passed on to parent class CondIndTest.
     """
-    def __init__(self,
-                num_f=25,
-                approx="lpd4",
-                seed=42,
-                significance='analytic',
-                **kwargs):
+    @property
+    def measure(self):
+        """
+        Concrete property to return the measure of the independence test
+        """
+        return self._measure
 
+    def __init__(self,
+                 num_f=25,
+                 approx="lpd4",
+                 seed=42,
+                 significance='analytic',
+                 **kwargs):
+        # Set the members
         self.num_f = num_f
         self.approx = approx
         self.seed = seed
-
-        self.measure = 'rcot'
+        self._measure = 'rcot'
         self.two_sided = False
         self.residual_based = False
-        self.recycle_residuals = False
-
+        self._pval = None
+        # Call the parent constructor
         CondIndTest.__init__(self, significance=significance, **kwargs)
 
+        # Print some information
         if self.verbosity > 0:
-            print("num_f = %s" % self.num_f)
-            print("approx = %s" % self.approx)
-            print("")
+            print("num_f = %s" % self.num_f + "\n")
+            print("approx = %s" % self.approx + "\n\n")
 
     def get_dependence_measure(self, array, xyz):
         """Returns RCOT estimate.
@@ -2747,44 +2494,40 @@ def get_dependence_measure(self, array, xyz):
 
         xyz : array of ints
             XYZ identifier array of shape (dim,).
-        
+
         Returns
         -------
         val : float
             RCOT estimate.
         """
-
         dim, T = array.shape
-
-        x = array[0]
-        y = array[1]
-        z = numpy.fastCopyAndTranspose(array[2:])
-
-        rcot = numpy.asarray(rpy2.robjects.r['RCIT'](x, y, z, 
-            corr=True, 
-            num_f=self.num_f, 
-            approx=self.approx,
-            seed=self.seed))
-        
+        x_vals = array[0]
+        y_vals = array[1]
+        z_vals = np.fastCopyAndTranspose(array[2:])
+        rcot = np.asarray(rpy2.robjects.r['RCoT'](x_vals, y_vals, z_vals,
+                                                  num_f=self.num_f,
+                                                  approx=self.approx,
+                                                  seed=self.seed))
         val = float(rcot[1])
-        self.pval = float(rcot[0])
-
+        # Cache the p-value for use later
+        self._pval = float(rcot[0])
         return val
 
+    def get_analytic_significance(self, **args):
+        """
+        Returns analytic p-value from RCoT test statistic.
+        NOTE: Must first run get_dependence_measure, where p-value is determined
+        from RCoT test statistic.
 
-    def get_analytic_significance(self, **args): 
-        """Returns analytic p-value from RCIT test statistic.
-        
         Returns
         -------
         pval : float or numpy.nan
             P-value.
         """
+        return self._pval
 
-        return self.pval
-
-    def get_shuffle_significance(self, array, xyz, value, 
-        return_null_dist=False):
+    def get_shuffle_significance(self, array, xyz, value,
+                                 return_null_dist=False):
         """Returns p-value for shuffle significance test.
 
         For residual-based test statistics only the residuals are shuffled.
@@ -2799,7 +2542,7 @@ def get_shuffle_significance(self, array, xyz, value,
 
         value : number
             Value of test statistic for unshuffled estimate.
-        
+
         Returns
         -------
         pval : float
@@ -2807,172 +2550,13 @@ def get_shuffle_significance(self, array, xyz, value,
         """
 
         null_dist = self._get_shuffle_dist(array, xyz,
-                               self.get_dependence_measure,
-                               sig_samples=self.sig_samples, 
-                               sig_blocklength=self.sig_blocklength,
-                               verbosity=self.verbosity)
+                                           self.get_dependence_measure,
+                                           sig_samples=self.sig_samples,
+                                           sig_blocklength=self.sig_blocklength,
+                                           verbosity=self.verbosity)
 
         pval = (null_dist >= value).mean()
-        
+
         if return_null_dist:
             return pval, null_dist
-        else:
-            return pval
-
-    def get_analytic_confidence(self, value, df, conf_lev):
-        """Placeholder function, not available."""
-        raise ValueError("Analytic confidence not implemented for %s"
-                         "" % self.measure)
-
-    def get_model_selection_criterion(self, j,
-                                      parents,
-                                      tau_max=0):
-        """Placeholder function, not available."""
-        raise ValueError("Model selection not implemented for %s"
-                         "" % self.measure)
-
-
-if __name__ == '__main__':
-
-    # Quick test
-    import data_processing as pp
-    numpy.random.seed(44)
-    a = 0.
-    c = 0.6
-    T = 4000
-    # Each key refers to a variable and the incoming links are supplied as a
-    # list of format [((driver, lag), coeff), ...]
-    links_coeffs = {0: [((0, -1), a)],
-                    1: [((1, -1), a), ((0, -1), c)],
-                    2: [((2, -1), a), ((1, -1), c)],
-                    }
-
-    data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
-
-    data_mask = numpy.zeros(data.shape)
-
-    # cond_ind_test = ParCorr(
-    #     significance='analytic',
-    #     sig_samples=100,
-
-    #     confidence='bootstrap', #'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=1,
-
-    #     use_mask=False,
-    #     mask_type='y',
-    #     recycle_residuals=False,
-    #     verbosity=3)
-
-
-    # cond_ind_test = GPACE(
-    #     significance='analytic',
-    #     sig_samples=100,
-
-    #     confidence=False, # False  'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=None,
-
-    #     use_mask=False,
-    #     mask_type='y',
-
-    #     null_dist_filename=None,
-    #     gp_version='new',
-    #     ace_version='acepack',
-    #     recycle_residuals=False,
-    #     verbosity=4)
-
-    # cond_ind_test = GPDC(
-    #     significance='analytic',
-    #     sig_samples=1000,
-    #     sig_blocklength=1,
-
-    #     confidence=False, # False  'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=1,
-
-    #     use_mask=False,
-    #     mask_type='y',
-
-    #     null_dist_filename='/home/jakobrunge/test/test.npz', #'/home/tests/test.npz',
-    #     gp_version='new',
-
-    #     recycle_residuals=False,
-    #     verbosity=4)
-
-    # cond_ind_test.generate_and_save_nulldists( sample_sizes=[100, 250],
-    #     null_dist_filename='/home/jakobrunge/test/test.npz')
-    # cond_ind_test.null_dist_filename = '/home/jakobrunge/test/test.npz'
-
-    cond_ind_test = CMIknn(
-        significance='shuffle_test',
-        sig_samples=1000,
-        knn=.1,
-        transform='ranks',
-        shuffle_neighbors=5,
-        confidence=False, #'bootstrap',
-        conf_lev=0.9,
-        conf_samples=100,
-        conf_blocklength=None,
-
-        use_mask=False,
-        mask_type='y',
-        recycle_residuals=False,
-        verbosity=3,
-        )
-
-    # cond_ind_test = CMIsymb()
-    #     significance='shuffle_test',
-    #     sig_samples=1000,
-
-    #     confidence='bootstrap', #'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=None,
-
-    #     use_mask=False,
-    #     mask_type='y',
-    #     recycle_residuals=False,
-    #     verbosity=3)
-
-
-    # cond_ind_test = RCOT(
-    #     significance='analytic',
-    #     num_f=25,
-    #     confidence=False, #'bootstrap', #'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=None,
-
-    #     use_mask=False,
-    #     mask_type='y',
-    #     recycle_residuals=False,
-    #     verbosity=3,
-    #     )
-
-    if cond_ind_test.measure == 'cmi_symb':
-        data = pp.quantile_bin_array(data, bins=6)
-
-    dataframe = pp.DataFrame(data)
-    cond_ind_test.set_dataframe(dataframe)
-
-    tau_max = 5
-    X = [(0, -2)]
-    Y = [(2, 0)]
-    Z = [(1, -1)]  #(2, -1), (1, -1), (0, -3)]  #[(1, -1)]  #[(2, -1), (1, -1), (0, -3)] # [(2, -1), (1, -1), (2, -3)]   [(1, -1)]
-    
-    # print cond_ind_test._get_shuffle_dist
-
-    val, pval = cond_ind_test.run_test(X, Y, Z, tau_max=tau_max)
-    conf_interval = cond_ind_test.get_confidence(X, Y, Z, tau_max=tau_max)
-
-    # print cond_ind_test.get_model_selection_criterion(2,
-    #                                   [(0, -2)],
-    #                                   tau_max=tau_max)
-
-    print ("I(X,Y|Z) = %.2f | p-value = %.3f " % (val, pval))
-    if conf_interval is not None:
-        print ("[%.2f, %.2f]" % conf_interval)
+        return pval
\ No newline at end of file
diff --git a/tigramite/models.py b/tigramite/models.py
index 2722ba68..d275413f 100644
--- a/tigramite/models.py
+++ b/tigramite/models.py
@@ -1,17 +1,16 @@
 """Tigramite causal discovery for time series."""
 
-# Author: Jakob Runge <jakobrunge@posteo.de>
+# Author: Jakob Runge <jakob@jakob-runge.com>
 #
 # License: GNU General Public License v3.0
 
-import numpy
-import sys, os
-import math
-
-from scipy import linalg, special, stats
+from __future__ import print_function
 from copy import deepcopy
 
-from tigramite.independence_tests import _construct_array
+import numpy as np
+
+from tigramite.data_processing import DataFrame
+from tigramite.pcmci import PCMCI
 
 try:
     import sklearn
@@ -25,7 +24,6 @@
     print("Could not import networkx, LinearMediation plots not possible...")
 
 
-from tigramite.pcmci import PCMCI
 
 class Models():
     """Base class for time series models.
@@ -41,19 +39,13 @@ class Models():
         optionally a mask of the same shape and a missing values flag.
 
     model : sklearn model object
-        For example, sklearn.linear_model.LinearRegression for a linear
+        For example, sklearn.linear_model.LinearRegression() for a linear
         regression model.
 
-    model_params : dictionary, optional (default: None)
-        Optional parameters passed on to sklearn model
-
     data_transform : sklearn preprocessing object, optional (default: None)
-        Used to transform data prior to fitting. For example, 
+        Used to transform data prior to fitting. For example,
         sklearn.preprocessing.StandardScaler for simple standardization. The
         fitted parameters are stored.
-    
-    use_mask : bool, optional (default: False)
-        Whether a supplied mask should be used.
 
     mask_type : {'y','x','z','xy','xz','yz','xyz'}
         Masking mode: Indicators for which variables in the dependence measure
@@ -61,62 +53,49 @@ class Models():
         excludes all time slices containing masked samples in Y. Explained in
         [1]_.
 
-    missing_flag : number, optional (default: None)
-        Flag for missing values. Dismisses all time slices of samples where
-        missing values occur in any variable and also flags samples for all lags
-        up to 2*tau_max. This avoids biases, see section on masking in
-        Supplement of [1]_.
-
     verbosity : int, optional (default: 0)
         Level of verbosity.
     """
 
-    def __init__(self, 
-            dataframe,
-            model,
-            model_params=None,
-            data_transform=None,
-            use_mask=False,
-            mask_type=None,
-            missing_flag=None,
-            verbosity=0,
-            ):
-
-        self.use_mask = use_mask
+    def __init__(self,
+                 dataframe,
+                 model,
+                 data_transform=sklearn.preprocessing.StandardScaler(),
+                 mask_type=None,
+                 verbosity=0):
+        # Set the mask type and dataframe object
         self.mask_type = mask_type
-        self.missing_flag = missing_flag
-
         self.dataframe = dataframe
-        self.data = dataframe.values
-        self.mask = dataframe.mask
-
-        self.N = self.data.shape[1]
-
+        # Get the number of nodes for this dataset
+        self.N = self.dataframe.values.shape[1]
+        # Set the model to be used
         self.model = model
-        self.model_params = model_params
-        if self.model_params is None:
-            self.model_params = {}
-
+        # Set the data_transform object and verbosity
         self.data_transform = data_transform
-
         self.verbosity = verbosity
+        # Initialize the object that will be set later
+        self.all_parents = None
+        self.selected_variables = None
+        self.tau_max = None
+        self.fit_results = None
 
-
-    def get_fit(self, all_parents, selected_variables=None,
-                 tau_max=None, cut_off='max_lag_or_tau_max',
-                 return_data=False):
+    def get_fit(self, all_parents,
+                selected_variables=None,
+                tau_max=None,
+                cut_off='max_lag_or_tau_max',
+                return_data=False):
         """Fit time series model.
 
-        To each variable in selected_variables, the sklearn model is fitted
+        For each variable in selected_variables, the sklearn model is fitted
         with :math:`y` given by the target variable, and :math:`X` given by its
         parents. The fitted model class is returned for later use.
 
         Parameters
         ----------
-        all_parents : dictionary 
+        all_parents : dictionary
             Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
             the parents estimated with PCMCI.
-  
+
         selected_variables : list of integers, optional (default: range(N))
             Specify to estimate parents only for selected variables. If None is
             passed, parents are estimated for all variables.
@@ -126,11 +105,11 @@ def get_fit(self, all_parents, selected_variables=None,
 
         cut_off : {'2xtau_max', 'max_lag', 'max_lag_or_tau_max'}
             How many samples to cutoff at the beginning. The default is
-            '2xtau_max', which guarantees that MCI tests are all conducted on
-            the same samples.  For modeling, 'max_lag_or_tau_max' can be used,
-            which uses the maximum of tau_max and the conditions, which is
-            useful to compare multiple models on the same sample. Last,
-            'max_lag' uses as much samples as possible.
+            'max_lag_or_tau_max', which uses the maximum of tau_max and the
+            conditions. This is useful to compare multiple models on the same
+            sample. Other options are '2xtau_max', which guarantees that MCI
+            tests are all conducted on the same samples.  For modeling, can be
+            used, Last, 'max_lag' uses as much samples as possible.
 
         return_data : bool, optional (default: False)
             Whether to save the data array.
@@ -142,76 +121,66 @@ def get_fit(self, all_parents, selected_variables=None,
             transformation parameters.
 
         """
-
+        # Initialize the fit by setting the instance's all_parents attribute
         self.all_parents = all_parents
-
-        if selected_variables is None:
-            self.selected_variables = range(self.N)
-        else:
+        # Set the default selected variables to all variables and check if this
+        # should be overwritten
+        self.selected_variables = range(self.N)
+        if selected_variables is not None:
             self.selected_variables = selected_variables
-
+        # Find the maximal parents lag
         max_parents_lag = 0
         for j in self.selected_variables:
-            # print self.all_parents[j]
-            if len(all_parents[j]) > 0:
-                max_parents_lag = max(max_parents_lag, numpy.abs(numpy.array(
-                        all_parents[j])[:,1]).max())
-
-        if tau_max is None:
-            self.tau_max = max_parents_lag
-        else:
+            if all_parents[j]:
+                this_parent_lag = np.abs(np.array(all_parents[j])[:, 1]).max()
+                max_parents_lag = max(max_parents_lag, this_parent_lag)
+        # Set the default tau max and check if it shoudl be overwritten
+        self.tau_max = max_parents_lag
+        if tau_max is not None:
             self.tau_max = tau_max
             if self.tau_max < max_parents_lag:
                 raise ValueError("tau_max = %d, but must be at least "
                                  " max_parents_lag = %d"
                                  "" % (self.tau_max, max_parents_lag))
-
+        # Initialize the fit results
         fit_results = {}
         for j in self.selected_variables:
             Y = [(j, 0)]
             X = [(j, 0)] # dummy
             Z = self.all_parents[j]
-            array, xyz = _construct_array(X, Y, Z, 
-                                    tau_max=self.tau_max,
-                                    data=self.data,
-                                    use_mask=self.use_mask,
-                                    mask=self.mask, 
-                                    mask_type=self.mask_type,
-                                    missing_flag=self.missing_flag,
-                                    cut_off=cut_off,
-                                    verbosity=self.verbosity)
-
+            array, xyz = \
+                self.dataframe.construct_array(X, Y, Z,
+                                               tau_max=self.tau_max,
+                                               mask_type=self.mask_type,
+                                               cut_off=cut_off,
+                                               verbosity=self.verbosity)
+            # Get the dimensions out of the constructed array
             dim, T = array.shape
             dim_z = dim - 2
-
+            # Transform the data if needed
             if self.data_transform is not None:
                 array = self.data_transform.fit_transform(X=array.T).T
-
+            # Fit the model if there are any parents for this variable to fit
             if dim_z > 0:
-                model = self.model(**self.model_params)
-
-                # print array[2:].T
-                # print array[1].reshape(T, 1)
-                model.fit(X=array[2:].T, y=array[1])
-
+                # Copy and fit the model
+                a_model = deepcopy(self.model)
+                a_model.fit(X=array[2:].T, y=array[1])
+                # Cache the results
                 fit_results[j] = {}
-                fit_results[j]['model'] = model
-
-                if self.data_transform is not None:
-                    fit_results[j]['data_transform'] = deepcopy(self.data_transform)
-
+                fit_results[j]['model'] = a_model
+                # Cache the data transform
+                fit_results[j]['data_transform'] = deepcopy(self.data_transform)
+                # Cache the data if needed
                 if return_data:
                     fit_results[j]['data'] = array
-                # print j, model.coef_, model.intercept_
-
+            # If there are no parents, skip this variable
             else:
                 fit_results[j] = None
 
+        # Cache and return the fit results
         self.fit_results = fit_results
-
         return fit_results
 
-
     def get_coefs(self):
         """Returns dictionary of coefficients for linear models.
 
@@ -223,15 +192,11 @@ def get_coefs(self):
             Dictionary of dictionaries for each variable with keys given by the
             parents and the regression coefficients as values.
         """
-
         coeffs = {}
-
         for j in self.selected_variables:
             coeffs[j] = {}
             for ipar, par in enumerate(self.all_parents[j]):
-                # print ipar, par, self.fit_results[j].coef_ , self.fit_results[j].coef_.shape
                 coeffs[j][par] = self.fit_results[j]['model'].coef_[ipar]
-
         return coeffs
 
 
@@ -241,7 +206,7 @@ class LinearMediation(Models):
     Fits linear model to parents and provides functions to return measures such
     as causal effect, mediated causal effect, average causal effect, etc. as
     described in [4]_.
-    
+
     Notes
     -----
     This class implements the following causal mediation measures introduced in
@@ -259,8 +224,8 @@ class LinearMediation(Models):
               Y_t &= 0.5 X_{t-1} +  \eta^Y_t \\
               Z_t &= 0.5 Y_{t-1} +  \eta^Z_t
 
-    Here the link coefficient of :math:`X_{t-2} \to Z_t` is zero while the 
-    causal effect is 0.25. MCE through :math:`Y` is 0.25 implying that *all* 
+    Here the link coefficient of :math:`X_{t-2} \to Z_t` is zero while the
+    causal effect is 0.25. MCE through :math:`Y` is 0.25 implying that *all*
     of the the CE is explained by :math:`Y`. ACE from :math:`X` is 0.37 since it
     has CE 0.5 on :math:`Y` and 0.25 on :math:`Z`.
 
@@ -288,7 +253,7 @@ class LinearMediation(Models):
     References
     ----------
     .. [4]  J. Runge et al. (2015): Identifying causal gateways and mediators in
-            complex spatio-temporal systems. 
+            complex spatio-temporal systems.
             Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
 
     Parameters
@@ -302,12 +267,9 @@ class LinearMediation(Models):
         Optional parameters passed on to sklearn model
 
     data_transform : sklearn preprocessing object, optional (default: None)
-        Used to transform data prior to fitting. For example, 
+        Used to transform data prior to fitting. For example,
         sklearn.preprocessing.StandardScaler for simple standardization. The
         fitted parameters are stored.
-    
-    use_mask : bool, optional (default: False)
-        Whether a supplied mask should be used.
 
     mask_type : {'y','x','z','xy','xz','yz','xyz'}
         Masking mode: Indicators for which variables in the dependence measure
@@ -315,76 +277,54 @@ class LinearMediation(Models):
         excludes all time slices containing masked samples in Y. Explained in
         [1]_.
 
-    missing_flag : number, optional (default: None)
-        Flag for missing values. Dismisses all time slices of samples where
-        missing values occur in any variable and also flags samples for all lags
-        up to 2*tau_max. This avoids biases, see section on masking in
-        Supplement of [1]_.
-
     verbosity : int, optional (default: 0)
         Level of verbosity.
     """
-
-    def __init__(self, 
-            dataframe,
-            model_params=None,
-            data_transform=None,
-            use_mask=False,
-            mask_type=None,
-            missing_flag=None,
-            verbosity=0,
-            ):
-
-
-        if data_transform is None:
-            data_transform=sklearn.preprocessing.StandardScaler()
-        elif data_transform == False:
-            class noscaler():
-                def __init__(self):
-                    pass
-                def fit_transform(self, X):
-                    return X
-                def transform(self, X):
-                    return X
-            data_transform = noscaler()
-
-        Models.__init__(self, 
-            dataframe=dataframe,
-            model=sklearn.linear_model.LinearRegression,
-            model_params=model_params,
-            data_transform=data_transform,
-            use_mask=use_mask,
-            mask_type=mask_type,
-            missing_flag=missing_flag,
-            verbosity=verbosity)
-
-
-    def fit_model(self, all_parents,
-                     tau_max=None,):
+    def __init__(self,
+                 dataframe,
+                 model_params=None,
+                 data_transform=sklearn.preprocessing.StandardScaler(),
+                 mask_type=None,
+                 verbosity=0):
+        # Initialize the member variables to None
+        self.phi = None
+        self.psi = None
+        self.all_psi_k = None
+
+        # Build the model using the parameters
+        if model_params is None:
+            model_params = {}
+        this_model = sklearn.linear_model.LinearRegression(**model_params)
+        Models.__init__(self,
+                        dataframe=dataframe,
+                        model=this_model,
+                        data_transform=data_transform,
+                        mask_type=mask_type,
+                        verbosity=verbosity)
+
+    def fit_model(self, all_parents, tau_max=None):
         """Fit linear time series model.
 
         Fits a sklearn.linear_model.LinearRegression model to the parents of
-        each variable and computes the coefficient matrices :math:`\Phi` and 
+        each variable and computes the coefficient matrices :math:`\Phi` and
         :math:`\Psi` as described in [4]_.
 
         Parameters
         ----------
-        all_parents : dictionary 
+        all_parents : dictionary
             Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
             the parents estimated with PCMCI.
-        
+
         tau_max : int, optional (default: None)
             Maximum time lag. If None, the maximum lag in all_parents is used.
 
         """
-
+        # Fit the model using the base class
         self.fit_results = self.get_fit(all_parents=all_parents,
                                         selected_variables=None,
-                                        tau_max=tau_max
-                                        )
-
+                                        tau_max=tau_max)
+        # Cache the results in the member variables
         coeffs = self.get_coefs()
-
         self.phi = self._get_phi(coeffs)
         self.psi = self._get_psi(self.phi)
         self.all_psi_k = self._get_all_psi_k(self.phi)
@@ -395,7 +335,7 @@ def _check_sanity(self, X, Y, k=None):
         if len(X) != 1 or len(Y) != 1:
             raise ValueError("X must be of form [(i, -tau)] and Y = [(j, 0)], "
                              "but are X = %s, Y=%s" % (X, Y))
-        
+
         i, tau = X[0]
 
         if abs(tau) > self.tau_max:
@@ -419,15 +359,15 @@ def _get_phi(self, coeffs):
             Matrices of coefficients for each time lag.
         """
 
-        phi = numpy.zeros((self.tau_max + 1, self.N, self.N))
-        phi[0] = numpy.identity(self.N)
+        phi = np.zeros((self.tau_max + 1, self.N, self.N))
+        phi[0] = np.identity(self.N)
 
         for j in list(coeffs):
             for par in list(coeffs[j]):
                 i, tau = par
                 phi[abs(tau), j, i] = coeffs[j][par]
 
-        return phi 
+        return phi
 
     def _get_psi(self, phi):
         """Returns the linear causal effect matrices for different lags.
@@ -443,13 +383,13 @@ def _get_psi(self, phi):
             Matrices of causal effects for each time lag.
         """
 
-        psi = numpy.zeros((self.tau_max + 1, self.N, self.N))
+        psi = np.zeros((self.tau_max + 1, self.N, self.N))
 
-        psi[0] = numpy.identity(self.N)
+        psi[0] = np.identity(self.N)
         for n in range(1, self.tau_max + 1):
-            psi[n] = numpy.zeros((self.N, self.N)) 
+            psi[n] = np.zeros((self.N, self.N))
             for s in range(1, n+1):
-                psi[n] += numpy.dot(phi[s], psi[n-s])
+                psi[n] += np.dot(phi[s], psi[n-s])
 
         return psi
 
@@ -470,17 +410,15 @@ def _get_psi_k(self, phi, k):
             Matrices of causal effects excluding k.
         """
 
-        psi_k = numpy.zeros((self.tau_max + 1, self.N, self.N))
+        psi_k = np.zeros((self.tau_max + 1, self.N, self.N))
 
-        psi_k[0] = numpy.identity(self.N)
-        phi_k = numpy.copy(phi)
-        # print phi[1]
+        psi_k[0] = np.identity(self.N)
+        phi_k = np.copy(phi)
         phi_k[1:, k, :] = 0.
-        # print phi_k[1]
         for n in range(1, self.tau_max + 1):
-            psi_k[n] = numpy.zeros((self.N, self.N)) 
+            psi_k[n] = np.zeros((self.N, self.N))
             for s in range(1, n+1):
-                psi_k[n] += numpy.dot(phi_k[s], psi_k[n-s])
+                psi_k[n] += np.dot(phi_k[s], psi_k[n-s])
 
         return psi_k
 
@@ -499,7 +437,7 @@ def _get_all_psi_k(self, phi):
             excluded.
         """
 
-        all_psi_k = numpy.zeros((self.N, self.tau_max + 1, self.N, self.N))
+        all_psi_k = np.zeros((self.N, self.tau_max + 1, self.N, self.N))
 
         for k in range(self.N):
             all_psi_k[k] = self._get_psi_k(phi, k)
@@ -509,7 +447,7 @@ def _get_all_psi_k(self, phi):
     def get_val_matrix(self,):
         """Returns the matrix of linear coefficients.
 
-        Format is val_matrix[i, j, tau] denotes coefficient of link 
+        Format is val_matrix[i, j, tau] denotes coefficient of link
         i --tau--> j.
 
         Returns
@@ -520,7 +458,7 @@ def get_val_matrix(self,):
 
         return self.phi.transpose()
 
-    
+
     def net_to_tsg(self, row, lag, max_lag):
         """Helper function to translate from network to time series graph."""
         return row*max_lag + lag
@@ -529,7 +467,7 @@ def tsg_to_net(self, node, max_lag):
         """Helper function to translate from time series graph to network."""
         row = node // max_lag
         lag = node % max_lag
-        return (row, -lag)        
+        return (row, -lag)
 
     def get_tsg(self, link_matrix, val_matrix=None, include_neighbors = False):
         """Returns time series graph matrix.
@@ -558,27 +496,26 @@ def get_tsg(self, link_matrix, val_matrix=None, include_neighbors = False):
         max_lag = link_matrix.shape[2] + 1
 
         # Create TSG
-        tsg = numpy.zeros((N*max_lag, N*max_lag))
-        for i, j, tau in numpy.column_stack(numpy.where(link_matrix)):
+        tsg = np.zeros((N*max_lag, N*max_lag))
+        for i, j, tau in np.column_stack(np.where(link_matrix)):
             if tau > 0 or include_neighbors:
                 for t in range(max_lag):
                     link_start = self.net_to_tsg(i, t-tau, max_lag)
                     link_end = self.net_to_tsg(j, t, max_lag)
-                    if (0 <= link_start and 
+                    if (0 <= link_start and
                         (link_start % max_lag) <= (link_end  % max_lag)):
                         if val_matrix is not None:
                             tsg[link_start, link_end] = val_matrix[i,j,tau]
                         else:
                             tsg[link_start, link_end] = 1
-
         return tsg
 
 
     def get_mediation_graph_data(self,  i, tau, j, include_neighbors=False):
         r"""Returns link and node weights for mediation analysis.
-        
+
         Returns array with non-zero entries for links that are on causal
-        paths between :math:`i` and :math:`j` at lag :math:`\tau`. 
+        paths between :math:`i` and :math:`j` at lag :math:`\tau`.
         ``path_val_matrix`` contains the corresponding path coefficients and
         ``path_node_array`` the MCE values. ``tsg_path_val_matrix`` contains the
         corresponding values in the time series graph format.
@@ -604,11 +541,11 @@ def get_mediation_graph_data(self,  i, tau, j, include_neighbors=False):
 
         """
 
-        path_link_matrix = numpy.zeros((self.N, self.N, self.tau_max + 1))
-        path_val_matrix = numpy.zeros((self.N, self.N, self.tau_max + 1))
+        path_link_matrix = np.zeros((self.N, self.N, self.tau_max + 1))
+        path_val_matrix = np.zeros((self.N, self.N, self.tau_max + 1))
 
         # Get mediation of path variables
-        path_node_array = (self.psi.reshape(1, self.tau_max + 1, self.N, self.N) 
+        path_node_array = (self.psi.reshape(1, self.tau_max + 1, self.N, self.N)
                          - self.all_psi_k)[:,abs(tau), j, i]
 
         # Get involved links
@@ -620,25 +557,25 @@ def get_mediation_graph_data(self,  i, tau, j, include_neighbors=False):
         # include_neighbors = False because True would allow
         # --> o -- motifs in networkx.all_simple_paths as paths, but
         # these are blocked...
-        tsg = self.get_tsg(link_matrix, val_matrix=val_matrix, 
+        tsg = self.get_tsg(link_matrix, val_matrix=val_matrix,
                             include_neighbors = False)
 
         if include_neighbors:
             # Add contemporaneous links only at source node
-            for m, n in zip(*numpy.where(link_matrix[:,:,0])):
+            for m, n in zip(*np.where(link_matrix[:,:,0])):
                 # print m,n
                 if m != n:
                     tsg[self.net_to_tsg(m, max_lag-tau-1, max_lag),
                         self.net_to_tsg(n, max_lag-tau-1, max_lag)
                         ] = val_matrix[m, n, 0]
 
-        tsg_path_val_matrix = numpy.zeros(tsg.shape)
+        tsg_path_val_matrix = np.zeros(tsg.shape)
 
         graph = networkx.DiGraph(tsg)
         pathways = []
 
-        for path in networkx.all_simple_paths(graph, 
-                    source=self.net_to_tsg(i, max_lag-tau-1, max_lag), 
+        for path in networkx.all_simple_paths(graph,
+                    source=self.net_to_tsg(i, max_lag-tau-1, max_lag),
                     target=self.net_to_tsg(j, max_lag-0-1, max_lag)):
             pathways.append([self.tsg_to_net(p, max_lag) for p in path])
             for ip, p in enumerate(path[1:]):
@@ -648,10 +585,10 @@ def get_mediation_graph_data(self,  i, tau, j, include_neighbors=False):
                 link_start = self.tsg_to_net(path[ip], max_lag)
                 link_end = self.tsg_to_net(p, max_lag)
                 delta_tau = abs(link_end[1] - link_start[1])
-                path_val_matrix[link_start[0], 
-                                link_end[0], 
-                                delta_tau] = val_matrix[link_start[0], 
-                                                        link_end[0], 
+                path_val_matrix[link_start[0],
+                                link_end[0],
+                                delta_tau] = val_matrix[link_start[0],
+                                                        link_end[0],
                                                         delta_tau]
 
         graph_data = {'path_node_array':path_node_array,
@@ -663,7 +600,7 @@ def get_mediation_graph_data(self,  i, tau, j, include_neighbors=False):
 
     def get_coeff(self, i, tau, j):
         """Returns link coefficient.
-        
+
         This is the causal effect for a particular link (i, tau) --> j.
 
         Parameters
@@ -681,13 +618,11 @@ def get_coeff(self, i, tau, j):
         -------
         coeff : float
         """
-
         return self.phi[abs(tau), j, i]
 
-
     def get_ce(self, i, tau, j):
         """Returns the causal effect.
-        
+
         This is the causal effect for  (i, tau) -- --> j.
 
         Parameters
@@ -709,7 +644,7 @@ def get_ce(self, i, tau, j):
 
     def get_ce_max(self, i, j):
         """Returns the causal effect.
-        
+
         This is the maximum absolute causal effect for  i --> j across all lags.
 
         Parameters
@@ -724,11 +659,11 @@ def get_ce_max(self, i, j):
         -------
         ce : float
         """
-        return numpy.abs(self.psi[1:, j, i]).max()
+        return np.abs(self.psi[1:, j, i]).max()
 
     def get_mce(self, i, tau, j, k):
         """Returns the mediated causal effect.
-        
+
         This is the causal effect for  i --> j minus the causal effect not going
         through k.
 
@@ -750,14 +685,12 @@ def get_mce(self, i, tau, j, k):
         -------
         mce : float
         """
-        mce = self.psi[abs(tau), j, i] - self.all_psi_k[k, abs(tau), j, i] 
-        
-        return mce 
-            
+        mce = self.psi[abs(tau), j, i] - self.all_psi_k[k, abs(tau), j, i]
+        return mce
 
     def get_ace(self, i, lag_mode='absmax', exclude_i=True):
         """Returns the average causal effect.
-        
+
         This is the average causal effect (ACE) emanating from variable i to any
         other variable. With lag_mode='absmax' this is based on the lag of
         maximum CE for each pair.
@@ -766,7 +699,7 @@ def get_ace(self, i, lag_mode='absmax', exclude_i=True):
         ----------
         i : int
             Index of cause variable.
-        
+
         lag_mode : {'absmax', 'all_lags'}
             Lag mode. Either average across all lags between each pair or only
             at the lag of maximum absolute causal effect.
@@ -781,26 +714,26 @@ def get_ace(self, i, lag_mode='absmax', exclude_i=True):
             Average Causal Effect.
         """
 
-        all_but_i = numpy.ones(self.N, dtype='bool')
+        all_but_i = np.ones(self.N, dtype='bool')
         if exclude_i:
             all_but_i[i] = False
 
         if lag_mode == 'absmax':
-            return numpy.abs(self.psi[1:, all_but_i, i]).max(axis=0).mean()
+            return np.abs(self.psi[1:, all_but_i, i]).max(axis=0).mean()
         elif lag_mode == 'all_lags':
-            return numpy.abs(self.psi[1:, all_but_i, i]).mean()
+            return np.abs(self.psi[1:, all_but_i, i]).mean()
         else:
             raise ValueError("lag_mode = %s not implemented" % lag_mode)
-    
+
     def get_all_ace(self, lag_mode='absmax', exclude_i=True):
         """Returns the average causal effect for all variables.
-        
+
         This is the average causal effect (ACE) emanating from variable i to any
         other variable. With lag_mode='absmax' this is based on the lag of
         maximum CE for each pair.
 
         Parameters
-        ----------       
+        ----------
         lag_mode : {'absmax', 'all_lags'}
             Lag mode. Either average across all lags between each pair or only
             at the lag of maximum absolute causal effect.
@@ -815,7 +748,7 @@ def get_all_ace(self, lag_mode='absmax', exclude_i=True):
             Average Causal Effect for each variable.
         """
 
-        ace = numpy.zeros(self.N)
+        ace = np.zeros(self.N)
         for i in range(self.N):
             ace[i] = self.get_ace(i, lag_mode=lag_mode, exclude_i=exclude_i)
 
@@ -823,7 +756,7 @@ def get_all_ace(self, lag_mode='absmax', exclude_i=True):
 
     def get_acs(self, j, lag_mode='absmax', exclude_j=True):
         """Returns the average causal susceptibility.
-        
+
         This is the Average Causal Susceptibility (ACS) affecting a variable j
         from any other variable. With lag_mode='absmax' this is based on the lag
         of maximum CE for each pair.
@@ -832,7 +765,7 @@ def get_acs(self, j, lag_mode='absmax', exclude_j=True):
         ----------
         j : int
             Index of variable.
-        
+
         lag_mode : {'absmax', 'all_lags'}
             Lag mode. Either average across all lags between each pair or only
             at the lag of maximum absolute causal effect.
@@ -847,26 +780,26 @@ def get_acs(self, j, lag_mode='absmax', exclude_j=True):
             Average Causal Susceptibility.
         """
 
-        all_but_j = numpy.ones(self.N, dtype='bool')
+        all_but_j = np.ones(self.N, dtype='bool')
         if exclude_j:
             all_but_j[j] = False
 
         if lag_mode == 'absmax':
-            return numpy.abs(self.psi[1:, j, all_but_j]).max(axis=0).mean()
+            return np.abs(self.psi[1:, j, all_but_j]).max(axis=0).mean()
         elif lag_mode == 'all_lags':
-            return numpy.abs(self.psi[1:, j, all_but_j]).mean()
+            return np.abs(self.psi[1:, j, all_but_j]).mean()
         else:
             raise ValueError("lag_mode = %s not implemented" % lag_mode)
 
     def get_all_acs(self, lag_mode='absmax', exclude_j=True):
         """Returns the average causal susceptibility.
-        
+
         This is the Average Causal Susceptibility (ACS) for each variable from
         any other variable. With lag_mode='absmax' this is based on the lag of
         maximum CE for each pair.
 
         Parameters
-        ----------        
+        ----------
         lag_mode : {'absmax', 'all_lags'}
             Lag mode. Either average across all lags between each pair or only
             at the lag of maximum absolute causal effect.
@@ -881,16 +814,16 @@ def get_all_acs(self, lag_mode='absmax', exclude_j=True):
             Average Causal Susceptibility.
         """
 
-        acs = numpy.zeros(self.N)
+        acs = np.zeros(self.N)
         for j in range(self.N):
             acs[j] = self.get_acs(j, lag_mode=lag_mode, exclude_j=exclude_j)
 
         return acs
 
-    def get_amce(self, k, lag_mode='absmax', 
+    def get_amce(self, k, lag_mode='absmax',
                     exclude_k=True, exclude_self_effects=True):
         """Returns the average mediated causal effect.
-        
+
         This is the Average Mediated Causal Effect (AMCE) through a variable k
         With lag_mode='absmax' this is based on the lag of maximum CE for each
         pair.
@@ -899,13 +832,13 @@ def get_amce(self, k, lag_mode='absmax',
         ----------
         k : int
             Index of variable.
-        
+
         lag_mode : {'absmax', 'all_lags'}
             Lag mode. Either average across all lags between each pair or only
             at the lag of maximum absolute causal effect.
 
         exclude_k : bool, optional (default: True)
-            Whether to exclude causal effects through the variable itself at 
+            Whether to exclude causal effects through the variable itself at
             previous lags.
 
         exclude_self_effects : bool, optional (default: True)
@@ -917,7 +850,7 @@ def get_amce(self, k, lag_mode='absmax',
             Average Mediated Causal Effect.
         """
 
-        all_but_k = numpy.ones(self.N, dtype='bool')
+        all_but_k = np.ones(self.N, dtype='bool')
         if exclude_k:
             all_but_k[k] = False
             N_new = self.N - 1
@@ -925,30 +858,30 @@ def get_amce(self, k, lag_mode='absmax',
             N_new = self.N
 
         if exclude_self_effects:
-            weights = numpy.identity(N_new) == False
+            weights = np.identity(N_new) == False
         else:
-            weights = numpy.ones((N_new, N_new), dtype = 'bool')
+            weights = np.ones((N_new, N_new), dtype = 'bool')
 
         if self.tau_max < 2:
             raise ValueError("Mediation only nonzero for tau_max >= 2")
 
-        all_mce = self.psi[2:, :, :] - self.all_psi_k[k, 2:, :, :] 
+        all_mce = self.psi[2:, :, :] - self.all_psi_k[k, 2:, :, :]
         # all_mce[:, range(self.N), range(self.N)] = 0.
 
         if lag_mode == 'absmax':
-            return numpy.average(numpy.abs(all_mce[:, all_but_k, :]
+            return np.average(np.abs(all_mce[:, all_but_k, :]
                                                   [:, :, all_but_k]
                                                 ).max(axis=0), weights=weights)
         elif lag_mode == 'all_lags':
-            return numpy.abs(all_mce[:, all_but_k, :][:,:, all_but_k]).mean()
+            return np.abs(all_mce[:, all_but_k, :][:,:, all_but_k]).mean()
         else:
             raise ValueError("lag_mode = %s not implemented" % lag_mode)
 
 
-    def get_all_amce(self, lag_mode='absmax', 
+    def get_all_amce(self, lag_mode='absmax',
                     exclude_k=True, exclude_self_effects=True):
         """Returns the average mediated causal effect.
-        
+
         This is the Average Mediated Causal Effect (AMCE) through all variables
         With lag_mode='absmax' this is based on the lag of maximum CE for each
         pair.
@@ -960,7 +893,7 @@ def get_all_amce(self, lag_mode='absmax',
             at the lag of maximum absolute causal effect.
 
         exclude_k : bool, optional (default: True)
-            Whether to exclude causal effects through the variable itself at 
+            Whether to exclude causal effects through the variable itself at
             previous lags.
 
         exclude_self_effects : bool, optional (default: True)
@@ -971,15 +904,15 @@ def get_all_amce(self, lag_mode='absmax',
         amce : array of shape (N,)
             Average Mediated Causal Effect.
         """
-        amce = numpy.zeros(self.N)
+        amce = np.zeros(self.N)
         for k in range(self.N):
-            amce[k] = self.get_amce(k, 
-                                lag_mode=lag_mode, 
-                                exclude_k=exclude_k, 
-                                exclude_self_effects=exclude_self_effects)
+            amce[k] = self.get_amce(k,
+                                    lag_mode=lag_mode,
+                                    exclude_k=exclude_k,
+                                    exclude_self_effects=exclude_self_effects)
 
         return amce
-       
+
 
 class Prediction(Models, PCMCI):
     r"""Prediction class for time series models.
@@ -1002,112 +935,85 @@ class Prediction(Models, PCMCI):
         Either boolean array or time indices marking the test data.
 
     prediction_model : sklearn model object
-        For example, sklearn.linear_model.LinearRegression for a linear
+        For example, sklearn.linear_model.LinearRegression() for a linear
         regression model.
 
-    prediction_model_params : dictionary, optional (default: None)
-        Optional parameters passed on to sklearn model
-
-    cond_ind_model : Conditional independence test class object, optional
+    cond_ind_test : Conditional independence test object, optional
         Only needed if predictors are estimated with causal algorithm.
-        The class will be initialized with masking set to the training data. 
-        Further parameters can be supplied by cond_ind_params.
-
-    cond_ind_params : dictionary, optional
-        Parameters for conditional independence test.
+        The class will be initialized with masking set to the training data.
 
     data_transform : sklearn preprocessing object, optional (default: None)
-        Used to transform data prior to fitting. For example, 
+        Used to transform data prior to fitting. For example,
         sklearn.preprocessing.StandardScaler for simple standardization. The
         fitted parameters are stored.
 
-    missing_flag : number, optional (default: None)
-        Flag for missing values. Dismisses all time slices of samples where
-        missing values occur in any variable and also flags samples for all lags
-        up to 2*tau_max. This avoids biases, see section on masking in
-        Supplement of [1]_.
-
     verbosity : int, optional (default: 0)
         Level of verbosity.
     """
-
-    def __init__(self, 
-            dataframe,
-            train_indices,
-            test_indices,
-            prediction_model,
-            prediction_model_params=None,
-            cond_ind_model=None,
-            cond_ind_params=None,
-            data_transform=None,
-            missing_flag=None,
-            verbosity=0,
-            ):
-
-
-        Models.__init__(self, 
-            dataframe=dataframe,
-            model=prediction_model,
-            model_params=prediction_model_params,
-            data_transform=data_transform,
-            use_mask=True,
-            mask_type='y',
-            missing_flag=missing_flag,
-            verbosity=verbosity)
-
-        if cond_ind_params is None:
-            cond_ind_params = {}
-
-        if cond_ind_model is not None:
-            cond_ind_test = cond_ind_model(
-                    use_mask=True,
-                    mask_type='y',
-                    verbosity=verbosity,
-                    **cond_ind_params)
-        else:
-            cond_ind_test = None
-
-        if dataframe.mask is None:
-            mask = numpy.zeros(dataframe.values.shape, dtype='bool')
-        else:
-            mask = dataframe.mask
-
+    def __init__(self,
+                 dataframe,
+                 train_indices,
+                 test_indices,
+                 prediction_model,
+                 cond_ind_test=None,
+                 data_transform=None,
+                 verbosity=0):
+
+        # Default value for the mask
+        mask = dataframe.mask
+        if mask is None:
+            mask = np.zeros(dataframe.values.shape, dtype='bool')
+        # Get the dataframe shape
         T = len(dataframe.values)
-
-        self.train_mask = numpy.copy(mask)
-        self.train_mask[[t for t in range(T) if t not in train_indices]] = True
-
-        self.test_mask = numpy.copy(mask)
+        # Have the default dataframe be the training data frame
+        train_mask = np.copy(mask)
+        train_mask[[t for t in range(T) if t not in train_indices]] = True
+        self.dataframe = DataFrame(dataframe.values,
+                                   mask=train_mask,
+                                   missing_flag=dataframe.missing_flag)
+        # Initialize the models baseclass with the training dataframe
+        Models.__init__(self,
+                        dataframe=self.dataframe,
+                        model=prediction_model,
+                        data_transform=data_transform,
+                        mask_type='y',
+                        verbosity=verbosity)
+
+        # Build the testing dataframe as well
+        self.test_mask = np.copy(mask)
         self.test_mask[[t for t in range(T) if t not in test_indices]] = True
 
-        dataframe_here = deepcopy(dataframe)
-        dataframe_here.mask = self.train_mask
-
+        # Setup the PCMCI instance
         if cond_ind_test is not None:
+            # Force the masking
+            cond_ind_test.set_mask_type('y')
+            cond_ind_test.verbosity = verbosity
             PCMCI.__init__(self,
-                dataframe=dataframe_here,
-                cond_ind_test=cond_ind_test,
-                selected_variables=None,
-                var_names=None,
-                verbosity=verbosity)
-
-        self.cond_ind_model = cond_ind_model
-        self.prediction_model = prediction_model
-        self.prediction_model_params = prediction_model_params
+                           dataframe=self.dataframe,
+                           cond_ind_test=cond_ind_test,
+                           selected_variables=None,
+                           verbosity=verbosity)
+
+        # Set the member variables
+        self.cond_ind_test = cond_ind_test
+        # Initialize member varialbes that are set outside
+        self.target_predictors = None
+        self.selected_targets = None
+        self.fitted_model = None
+        self.test_array = None
 
     def get_predictors(self,
-                      selected_targets=None,
-                      selected_links=None,
-                      steps_ahead=1,
-                      tau_max=1,
-                      pc_alpha=0.2,
-                      max_conds_dim=None,
-                      max_combinations=1,
-                      ):
+                       selected_targets=None,
+                       selected_links=None,
+                       steps_ahead=1,
+                       tau_max=1,
+                       pc_alpha=0.2,
+                       max_conds_dim=None,
+                       max_combinations=1):
         """Estimate predictors using PC1 algorithm.
 
-        Wrapper around PCMCI.run_pc_stable that estimates causal predictors. 
-        The lead time can be specified by ``steps_ahead``. 
+        Wrapper around PCMCI.run_pc_stable that estimates causal predictors.
+        The lead time can be specified by ``steps_ahead``.
 
         Parameters
         ----------
@@ -1116,26 +1022,26 @@ def get_predictors(self,
             all variables are estimated.
 
         selected_links : dict or None
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             specifying whether only selected links should be tested. If None is
             passed, all links are tested
 
         steps_ahead : int, default: 1
             Minimum time lag to test. Useful for multi-step ahead predictions.
-        
+
         tau_max : int, default: 1
             Maximum time lag. Must be larger or equal to tau_min.
-                
+
         pc_alpha : float or list of floats, default: 0.2
             Significance level in algorithm. If a list or None is passed, the
-            pc_alpha level is optimized for every variable across the given 
-            pc_alpha values using the score computed in 
+            pc_alpha level is optimized for every variable across the given
+            pc_alpha values using the score computed in
             cond_ind_test.get_model_selection_criterion()
-        
+
         max_conds_dim : int or None
             Maximum number of conditions to test. If None is passed, this number
             is unrestricted.
-        
+
         max_combinations : int, default: 1
             Maximum number of combinations of conditions of current cardinality
             to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
@@ -1144,36 +1050,27 @@ def get_predictors(self,
         Returns
         -------
         predictors : dict
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             containing estimated predictors.
         """
-
-        if self.cond_ind_model is None:
-            raise ValueError("No cond_ind_model given!")
-
-        if selected_targets is None:
-            self.selected_variables = range(self.N)
-        else:      
+        # Ensure an independence model is given
+        if self.cond_ind_test is None:
+            raise ValueError("No cond_ind_test given!")
+        # Set the selected variables
+        self.selected_variables = range(self.N)
+        if selected_targets is not None:
             self.selected_variables = selected_targets
-
-        self.mask = self.train_mask
-
-        predictors = self.run_pc_stable(
-                      selected_links=selected_links,
-                      tau_min=steps_ahead,
-                      tau_max=tau_max,
-                      save_iterations=False,
-                      pc_alpha=pc_alpha,
-                      max_conds_dim=max_conds_dim,
-                      max_combinations=max_combinations,
-                      )
-
+        predictors = self.run_pc_stable(selected_links=selected_links,
+                                        tau_min=steps_ahead,
+                                        tau_max=tau_max,
+                                        save_iterations=False,
+                                        pc_alpha=pc_alpha,
+                                        max_conds_dim=max_conds_dim,
+                                        max_combinations=max_combinations)
         return predictors
 
-
-
-    def fit(self, target_predictors, selected_targets=None,
-                        tau_max=None, return_data=False):
+    def fit(self, target_predictors,
+            selected_targets=None, tau_max=None, return_data=False):
         r"""Fit time series model.
 
         Wrapper around ``Models.get_fit()``. To each variable in
@@ -1183,16 +1080,16 @@ def fit(self, target_predictors, selected_targets=None,
 
         Parameters
         ----------
-        target_predictors : dictionary 
+        target_predictors : dictionary
             Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
             the predictors estimated with PCMCI.
-        
+
         selected_targets : list of integers, optional (default: range(N))
             Specify to fit model only for selected targets. If None is
             passed, models are estimated for all variables.
 
         tau_max : int, optional (default: None)
-            Maximum time lag. If None, the maximum lag in target_predictors is 
+            Maximum time lag. If None, the maximum lag in target_predictors is
             used.
 
         return_data : bool, optional (default: False)
@@ -1214,20 +1111,16 @@ def fit(self, target_predictors, selected_targets=None,
             if target not in list(self.target_predictors):
                 raise ValueError("No predictors given for target %s" % target)
 
-        # Set mask to train_indices
-        self.mask = self.train_mask
-
-        self.fitted_model = self.get_fit(
-                                all_parents=self.target_predictors,
-                                selected_variables=self.selected_targets,
-                                tau_max=tau_max,
-                                return_data=return_data,
-                                )
-
+        self.fitted_model = \
+            self.get_fit(all_parents=self.target_predictors,
+                         selected_variables=self.selected_targets,
+                         tau_max=tau_max,
+                         return_data=return_data)
         return self
 
-    
-    def predict(self, target, new_data=None, pred_params=None,
+    def predict(self, target,
+                new_data=None,
+                pred_params=None,
                 cut_off='max_lag_or_tau_max'):
         r"""Predict target variable with fitted model.
 
@@ -1256,58 +1149,50 @@ def predict(self, target, new_data=None, pred_params=None,
         -------
         Results from prediction.
         """
-
+        # Print message
         if self.verbosity > 0:
             print("\n##\n## Predicting target %s\n##" % target)
             if pred_params is not None:
                 for key in list(pred_params):
                     print("%s = %s" % (key, pred_params[key]))
-
+        # Default value for pred_params
         if pred_params is None:
             pred_params = {}
-
-        if new_data is None:
-            data = self.dataframe.values
-            # Set mask to test_indices
-            mask = self.test_mask
-        else:
-            data = new_data.values
-            if new_data.mask is not None:
-                mask = new_data.mask
-            else:
-                mask = numpy.zeros(data.shape)
-
-
+        # Check this is a valid target
         if target not in self.selected_targets:
             raise ValueError("Target %s not yet fitted" % target)
-
+        # Construct the array form of the data
         Y = [(target, 0)]
         X = [(target, 0)] # dummy
         Z = self.target_predictors[target]
-
-        array, xyz = _construct_array(X, Y, Z, 
-                                tau_max=self.tau_max,
-                                data=data,
-                                use_mask=self.use_mask,
-                                mask=mask, 
-                                mask_type=self.mask_type,
-                                missing_flag=self.missing_flag,
-                                cut_off=cut_off,
-                                verbosity=self.verbosity)
-
-        if self.data_transform is not None:
-            # print array.shape
-            # print self.fitted_model[target]['data_transform'].mean_
-            array = self.fitted_model[target]['data_transform'].transform(X=array.T).T
-
-        self.test_array = array
-        # print array.shape
-
-        pred = self.fitted_model[target]['model'].predict(X=array[2:].T, **pred_params)
-
+        # Check if we've passed a new dataframe object
+        test_array = None
+        if new_data is not None:
+            test_array, _ = new_data.construct_array(X, Y, Z,
+                                                     tau_max=self.tau_max,
+                                                     mask_type=self.mask_type,
+                                                     cut_off=cut_off,
+                                                     verbosity=self.verbosity)
+        # Otherwise use the default values
+        else:
+            test_array, _ = \
+                self.dataframe.construct_array(X, Y, Z,
+                                               tau_max=self.tau_max,
+                                               mask=self.test_mask,
+                                               mask_type=self.mask_type,
+                                               cut_off=cut_off,
+                                               verbosity=self.verbosity)
+        # Transform the data if needed
+        a_transform = self.fitted_model[target]['data_transform']
+        if a_transform is not None:
+            test_array = a_transform.transform(X=test_array.T).T
+        # Cache the test array
+        self.test_array = test_array
+        # Run the predictor
+        pred = self.fitted_model[target]['model'].predict(X=test_array[2:].T,
+                                                          **pred_params)
         return pred
 
-
     def get_train_array(self, j):
         """Returns training array."""
         return self.fitted_model[j]['data']
@@ -1315,222 +1200,3 @@ def get_train_array(self, j):
     def get_test_array(self):
         """Returns test array."""
         return self.test_array
-
-
-if __name__ == '__main__':
-
-
-    import tigramite.data_processing as pp
-
-    # ###
-    # ### Models class
-    # ###
-
-    # numpy.random.seed(44)
-    # a = 0.9
-    # c = 0.5
-    # T = 1000
-
-    # verbosity = 0
-    # # Each key refers to a variable and the incoming links are supplied as a
-    # # list of format [((driver, lag), coeff), ...]
-    # links_coeffs = {0: [((0, -1), a)],
-    #                 1: [((1, -1), a), ((0, -1), c)],
-    #                 2: [((2, -1), a), ((1, -1), c), ((0, -1), c)],
-    #                 }
-
-    # data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
-
-    # # Assume true parents are known
-    # all_parents = true_parents_neighbors
-    # print all_parents
-
-    # # data_mask = numpy.zeros(data.shape)
-    # dataframe = pp.DataFrame(data)
-
-
-    # import sklearn
-    # import sklearn.linear_model
-
-    # model = Models(dataframe=dataframe, 
-    #     model = sklearn.linear_model.LinearRegression,
-    #     # model = sklearn.gaussian_process.GaussianProcessRegressor,
-    #     # model_params={'fit_intercept':False},
-    #     # model_params = {'alpha':0., 'kernel':sklearn.gaussian_process.kernels.RBF() +
-    #     #                                     sklearn.gaussian_process.kernels.WhiteKernel()},
-    #     # data_transform=sklearn.preprocessing.StandardScaler(),
-
-    #     )
-    
-    # results = model.get_fit(all_parents=all_parents, 
-    #                         )
-
-    # for j in list(results):
-    #     print results[j]['model']  #.coef_
-
-
-    ###
-    ### Linear Mediation
-    ###
-    numpy.random.seed(42)
-    links_coeffs = {0: [((0, -1), 0.6), ((1, 0), 0.6)],
-                    1: [((1, -1), 0.8), ((0, -1), 0.5), ((0, 0), 0.6)],
-                    2: [((2, -1), 0.9), ((1, -1), 0.5), ((0, -2), 0.)],
-                    }
-
-    data, true_parents = pp.var_process(links_coeffs, T=1000)
-    dataframe = pp.DataFrame(data)
-    med = LinearMediation(dataframe=dataframe, data_transform=False)
-    med.fit_model(all_parents=true_parents, tau_max=4)
-    print ("Link coefficient (0, -2) --> 2: ", med.get_coeff(i=0, tau=-2, j=2))
-    print ("Causal effect (0, -2) --> 2: ", med.get_ce(i=0, tau=-2, j=2))
-    print ("Mediated Causal effect (0, -2) --> 2 through 1: ", med.get_mce(i=0, tau=-2, j=2, k=1))
-    print ("Average Causal Effect: ", med.get_all_ace())
-    print ("Average Causal Susceptibility: ", med.get_all_acs())
-    print ("Average Mediated Causal Effect: ", med.get_all_amce())
-
-    i=0; tau=4; j=2
-
-    graph_data = med.get_mediation_graph_data(i=i, tau=tau, j=j, 
-                                            include_neighbors=True)
-
-    import plotting as tp
-
-
-    tp.plot_mediation_graph(
-                var_names=['X', 'Y', 'Z'],
-                path_val_matrix=graph_data['path_val_matrix'], 
-               path_node_array=graph_data['path_node_array'],
-               save_name="/home/jakobrunge/test/test_graph.pdf"
-                )
-
-    tp.plot_mediation_time_series_graph(
-        var_names=['X', 'Y', 'Z'],
-        path_node_array=graph_data['path_node_array'],
-        tsg_path_val_matrix=graph_data['tsg_path_val_matrix'],
-        # vmin_edges=-0.5, vmax_edges=0.5, edge_ticks=0.1,
-        # vmin_nodes=-0.5, vmax_nodes=0.5, node_ticks=0.1,
-        save_name="/home/jakobrunge/test/test_tsg_graph.pdf"
-        )
-
-    # ##
-    # ## Prediction
-    # ##
-    # import pylab
-
-    # numpy.random.seed(44)
-    # a = 0.4
-    # c = 0.6
-    # T = 200
-
-    # verbosity = 0
-    # # Each key refers to a variable and the incoming links are supplied as a
-    # # list of format [((driver, lag), coeff), ...]
-    # links_coeffs = {0: [((0, -1), a)],
-    #                 1: [((1, -1), a), ((0, -1), c)],
-    #                 2: [((2, -1), a), ((1, -1), c)],  # ((0, -1), c)],
-    #                 }
-
-    # data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
-
-    # # print data
-    # # print data.mean(axis=0), data.std(axis=0)
-    # data_mask = numpy.zeros(data.shape)
-
-    # dataframe = pp.DataFrame(data)
-
-    # from tigramite.independence_tests import ParCorr
-    # # import sklearn
-    # # import sklearn.preprocessing
-    # # import sklearn.neighbors
-    # # import sklearn.linear_model
-
-    # pred = Prediction(dataframe=dataframe,
-    #         cond_ind_model=ParCorr,
-    #         cond_ind_params = {'significance':'analytic',
-    #                            'fixed_thres':0.01},
-    #         prediction_model = sklearn.linear_model.LinearRegression,
-    #         # prediction_model = sklearn.gaussian_process.GaussianProcessRegressor,
-    #         # prediction_model = sklearn.neighbors.KNeighborsRegressor,
-    #     # prediction_model_params={'fit_intercept':False},
-    #     # prediction_model_params = {'n_neighbors':5},
-    #     # prediction_model_params = {'alpha':0., 'kernel':sklearn.gaussian_process.kernels.RBF() +
-    #     #                                     sklearn.gaussian_process.kernels.WhiteKernel()},
-    #     # data_transform=sklearn.preprocessing.StandardScaler(),
-    #     train_indices= range(int(0.8*T)),
-    #     test_indices= range(int(0.8*T), T),
-    #     verbosity=0
-    #     )
-
-    # tau_max = 25
-    # steps_ahead = 1
-    # target = 2
-
-    # all_predictors = pred.get_predictors(
-    #                   selected_targets=[target],
-    #                   selected_links=None,
-    #                   steps_ahead=steps_ahead,
-    #                   tau_max=tau_max,
-    #                   pc_alpha=None,
-    #                   max_conds_dim=None,
-    #                   max_combinations=1,
-    #                   )
-
-    # print all_predictors
-    
-    # pred.fit(target_predictors=all_predictors, 
-    #                 selected_targets=[target],
-    #                     tau_max=tau_max,
-    #                     return_data=True)
-
-    # # print all_predictors[target]
-
-    # new_data = pp.DataFrame(pp.var_process(links_coeffs, T=100)[0])
-
-    # predicted = pred.predict(target,
-    #                         # new_data=new_data,
-    #                         # cut_off = 'max_lag',
-    #                         # pred_params = {'return_std':True}
-    #                         )
-    # # print predicted[1]
-
-    # true_data = pred.get_test_array()[0]
-    # train_data = pred.get_train_array(target)
-    # print pred.get_test_array()[0, :10]
-    # print train_data[0, :10]
-
-    # print "NRMSE = %.2f" % (numpy.abs(true_data - predicted).mean()/true_data.std())
-    # pylab.scatter(true_data, predicted)
-    # pylab.title("NRMSE = %.2f" % (numpy.abs(true_data - predicted).mean()/true_data.std()))
-
-    # # pylab.errorbar(true_data, predicted[0], yerr=predicted[1], fmt='o')
-
-    # pylab.plot(true_data, true_data, 'k-')
-    # pylab.show()
-
-    # pylab.figure()
-    # pred_bad = Prediction(
-    #     dataframe=dataframe,
-    #     cond_ind_model=ParCorr,
-    #     prediction_model = sklearn.linear_model.LinearRegression,
-    #     data_transform=sklearn.preprocessing.StandardScaler(),
-    #     train_indices= range(int(0.8*T)),
-    #     test_indices= range(int(0.8*T), T),
-    #     verbosity=0
-    #     )
-    # all_predictors = {2:[(i, -tau) for i in range(3) for tau in range(1, tau_max+1)]}
-    # pred_bad.fit(target_predictors=all_predictors, 
-    #                 selected_targets=[target],
-    #                     tau_max=tau_max)
-
-    # predicted = pred_bad.predict(target)
-    # true_data = pred_bad.get_test_array()[0]
-    # print "NRMSE = %.2f" % (numpy.abs(true_data - predicted).mean()/true_data.std())
-
-    # pylab.scatter(true_data, predicted)
-    # pylab.title("\nNRMSE = %.2f" % (numpy.abs(true_data - predicted).mean()/true_data.std()))
-    # pylab.plot(true_data, true_data, 'k-')
-    # pylab.xlabel('True test data')
-    # pylab.ylabel('Predicted test data')
-
-    # pylab.show()
diff --git a/tigramite/pcmci.py b/tigramite/pcmci.py
index 940aab6d..f0c23a89 100644
--- a/tigramite/pcmci.py
+++ b/tigramite/pcmci.py
@@ -1,21 +1,42 @@
 """Tigramite causal discovery for time series."""
 
-# Author: Jakob Runge <jakobrunge@posteo.de>
+# Author: Jakob Runge <jakob@jakob-runge.com>
 #
 # License: GNU General Public License v3.0
 
-
-import numpy
+from __future__ import print_function
 import itertools
+from collections import defaultdict
 from copy import deepcopy
-import pickle
+import numpy as np
 
-try:
-    import statsmodels
-    from statsmodels.sandbox.stats import multicomp
-except:
-    print("Could not import statsmodels, p-value corrections not available.")
+def _create_nested_dictionary(depth=0, lowest_type=dict):
+    """Create a series of nested dictionaries to a maximum depth.  The first
+    depth - 1 nested dictionaries are defaultdicts, the last is a normal
+    dictionary.
 
+    Parameters
+    ----------
+    depth : int
+        Maximum depth argument.
+    lowest_type: callable (optional)
+        Type contained in leaves of tree.  Ex: list, dict, tuple, int, float ...
+    """
+    new_depth = depth - 1
+    if new_depth <= 0:
+        return defaultdict(lowest_type)
+    return defaultdict(lambda: _create_nested_dictionary(new_depth))
+
+def _nested_to_normal(nested_dict):
+    """Transforms the nested default dictionary into a standard dictionaries
+
+    Parameters
+    ----------
+    nested_dict : default dictionary of default dictionaries of ... etc.
+    """
+    if isinstance(nested_dict, defaultdict):
+        nested_dict = {k: _nested_to_normal(v) for k, v in nested_dict.items()}
+    return nested_dict
 
 class PCMCI():
     r"""PCMCI causal discovery for time series datasets.
@@ -36,15 +57,15 @@ class PCMCI():
        * false discovery control and confidence interval estimation
 
 
-    Notes 
-    ----- 
+    Notes
+    -----
 
     .. image:: mci_schematic.*
        :width: 200pt
 
     The PCMCI causal discovery method is comprehensively described in [1]_,
     where also analytical and numerical results are presented. Here we briefly
-    summarize the method. 
+    summarize the method.
 
     In the PCMCI framework, the dependency structure of a set of
     time series variables is represented in a *time series graph* as shown in
@@ -61,15 +82,15 @@ class PCMCI():
     PCMCI estimates causal links by a two-step procedure:
 
     1.  Condition-selection: For each variable :math:`j`, estimate a
-        *superset*  of parents :math:`\tilde{\mathcal{P}}(X^j_t)` with the 
+        *superset*  of parents :math:`\tilde{\mathcal{P}}(X^j_t)` with the
         iterative PC1 algorithm , implemented as ``run_pc_stable``.
 
     2.  *Momentary conditional independence* (MCI)
 
         .. math:: X^i_{t-\tau} ~\perp~ X^j_{t} ~|~ \tilde{\mathcal{P}}(X^j_t),
                                         \tilde{\mathcal{P}}(X^i_{t-{\tau}})
-    
-    here implemented as ``run_mci``. The condition-selection step reduces the 
+
+    here implemented as ``run_mci``. The condition-selection step reduces the
     dimensionality and avoids conditioning on irrelevant variables.
 
     PCMCI can be flexibly combined with any kind of conditional independence
@@ -98,13 +119,13 @@ class PCMCI():
     or ``pc_alpha=None``, :math:`\alpha` is optimized using model selection
     criteria.
 
-    Further optional parameters are discussed in [1]_. 
+    Further optional parameters are discussed in [1]_.
 
     References
     ----------
-    .. [1] J. Runge, D. Sejdinovic, S. Flaxman (2017): Detecting causal
-           associations in large nonlinear time series datasets, 
-           https://arxiv.org/abs/1702.07007
+    .. [1] J. Runge et al. (2018): Detecting Causal Associations in Large 
+           Nonlinear Time Series Datasets.
+           https://arxiv.org/abs/1702.07007v2
 
     Examples
     --------
@@ -161,7 +182,7 @@ class CondIndTest():
                 #   whether the mask should be used
 
                 # and functions
-                # * run_test(X, Y, Z, tau_max) : where X,Y,Z are of the form 
+                # * run_test(X, Y, Z, tau_max) : where X,Y,Z are of the form
                 #   X = [(var, -tau)]  for non-negative integers var and tau
                 #   specifying the variable and time lag
                 #   return (test statistic value, p-value)
@@ -170,10 +191,10 @@ class CondIndTest():
                 # optionally also
 
                 # * get_model_selection_criterion(j, parents) : required if
-                #   pc_alpha parameter is to be optimized. Here j is the 
+                #   pc_alpha parameter is to be optimized. Here j is the
                 #   variable index and parents a list [(var, -tau), ...]
                 #   return score for model selection
-                # * get_confidence(X, Y, Z, tau_max) : required for 
+                # * get_confidence(X, Y, Z, tau_max) : required for
                 #   return_confidence=True
                 #   estimate confidence interval after run_test was called
                 #   return (lower bound, upper bound)
@@ -182,161 +203,168 @@ class CondIndTest():
         Specify to estimate parents only for selected variables. If None is
         passed, parents are estimated for all variables.
 
-    var_names : list of strings, optional (default: range(N))
-        Names of variables, must match the number of variables. If None is
-        passed, variables are enumerated as [0, 1, ...]
-
     verbosity : int, optional (default: 0)
         Verbose levels 0, 1, ...
 
     Attributes
     ----------
-    all_parents : dictionary    
+    all_parents : dictionary
         Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing the
         conditioning-parents estimated with PC algorithm.
-    
+
     val_min : dictionary
         Dictionary of form val_min[j][(i, -tau)] = float
         containing the minimum test statistic value for each link estimated in
         the PC algorithm.
-    
+
     p_max : dictionary
         Dictionary of form p_max[j][(i, -tau)] = float containing the maximum
         p-value for each link estimated in the PC algorithm.
 
     iterations : dictionary
         Dictionary containing further information on algorithm steps.
-    
+
     N : int
         Number of variables.
-    
+
     T : int
         Time series sample length.
 
     """
-
     def __init__(self, dataframe,
                  cond_ind_test,
                  selected_variables=None,
-                 var_names=None,
                  verbosity=0):
-
+        # Set the data for this iteration of the algorithm
         self.dataframe = dataframe
-        self.data = dataframe.values
-        self.verbosity = verbosity
+        # Set the conditional independence test to be used
         self.cond_ind_test = cond_ind_test
+        self.cond_ind_test.set_dataframe(self.dataframe)
+        # Set the verbosity for debugging/logging messages
+        self.verbosity = verbosity
+        # Set the variable names 
+        self.var_names = self.dataframe.var_names
+        
+        # Store the shape of the data in the T and N variables
+        self.T, self.N = self.dataframe.values.shape
+        # Set the selected variables
+        self.selected_variables = \
+            self._set_selected_variables(selected_variables)
 
-        if var_names is None:
-            self.var_names = dict([(i, i) for i in range(len(self.data))])
-        else:
-            self.var_names = var_names
-
-        cond_ind_test.set_dataframe(self.dataframe)
-
-        self.T, self.N = self.data.shape
-        if selected_variables is None:
-            self.selected_variables = range(self.N)
-        else:
-            self.selected_variables = selected_variables
+    def _set_selected_variables(self, selected_variables):
+        """Helper function to set and check the selected variables argument
 
+        Parameters
+        ----------
+        selected_variables : list or None
+            List of variable ID's from the input data set
 
+        Returns
+        -------
+        selected_variables : list
+            Defaults to a list of all given variable IDs [0..N-1]
+        """
+        # Set the default selected variables if none are set
+        _int_selected_variables = deepcopy(selected_variables)
+        if _int_selected_variables is None:
+            _int_selected_variables = range(self.N)
         # Some checks
-        if selected_variables is not None:
-            if (numpy.any(numpy.array(selected_variables) < 0) or
-                    numpy.any(numpy.array(selected_variables) >= self.N)):
-                raise ValueError("selected_variables must be within 0..N-1")
-
-        if cond_ind_test.use_mask:
-            if dataframe.mask is None:
-                raise ValueError("dataframe.mask must be array of same shape"
-                                 " as fulldata.")
-            if type(dataframe.mask) != numpy.ndarray:
-                raise TypeError("dataframe.mask is of type %s, " %
-                                type(dataframe.mask) +
-                                "must be numpy.ndarray")
-            if numpy.isnan(dataframe.mask).sum() != 0:
-                raise ValueError("NaNs in the sample_selector")
-
-            if self.data.shape != dataframe.mask.shape:
-                raise ValueError("shape mismatch: data.shape = %s"
-                                 % str(self.data.shape) +
-                                 " but data_mask.shape = %s, must identical"
-                                 % str(dataframe.mask.shape))
-
-    class _Conditions():
-        """Helper class to keep track of conditions.
-
-        Tracks conditions already used for link (i, -tau) --> j
+        if _int_selected_variables is not None and \
+          (np.any(np.array(_int_selected_variables) < 0) or
+           np.any(np.array(_int_selected_variables) >= self.N)):
+            raise ValueError("selected_variables must be within 0..N-1")
+        # Ensure there are only unique values
+        _int_selected_variables = sorted(list(set(_int_selected_variables)))
+        # Return the selected variables
+        return _int_selected_variables
+
+    def _set_sel_links(self, selected_links, tau_min, tau_max):
+        """Helper function to set and check the selected links argument
 
         Parameters
         ----------
-        j : int
-            Index of current variable.
+        selected_links : dict or None
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+            specifying whether only selected links should be tested. If None is
+            passed, all links are returned
+        tau_mix : int
+            Minimum time delay to test
+        tau_max : int
+            Maximum time delay to test
+
+        Returns
+        -------
+        selected_variables : list
+            Defaults to a list of all given variable IDs [0..N-1]
+        """
+        # Copy and pass into the function
+        _int_sel_links = deepcopy(selected_links)
+        # Set the default selected links if none are set
+        _vars = list(range(self.N))
+        if _int_sel_links is None:
+            _int_sel_links = {}
+            # Set the default as all combinations of the selected variables
+            for j in _vars:
+                # If it is in selected variables, select all possible links
+                if j in self.selected_variables:
+                    _int_sel_links[j] = [(var, -lag) for var in _vars
+                                         for lag in range(tau_min, tau_max + 1)]
+                # If it is not, make it an empty list
+                else:
+                    _int_sel_links[j] = []
+        # Otherwise, check that our selection is sane
+        # Check that the selected links refer to links that are inside the
+        # data range
+        _key_set = set(_int_sel_links.keys())
+        valid_entries = _key_set.issubset(_vars)
+        valid_entries = valid_entries and \
+            set(var for parents in _int_sel_links.values()
+                    for var, _ in parents).issubset(_vars)
+        if not valid_entries:
+            raise ValueError("Out of range variable defined in \n",
+                             _int_sel_links,
+                             "\nMust be in range [0, ", self.N-1, "]")
+        # Warning if keys in selected links not in selected_variables
+        if self.verbosity > 0 and \
+                not _key_set.issubset(set(self.selected_variables)):
+            print("Warning: Link specified in selected links that is outside "+\
+                  "the scope of the selected variables")
+        ## Note: variables are scoped by selected_variables first, and then
+        ## by selected links.  Add to docstring?
+        # Return the selected links
+        return _int_sel_links
+
+    def _iter_conditions(self, parent, conds_dim, all_parents):
+        """Yield next condition.
+
+        Yields next condition from lexicographically ordered conditions.
 
+        Parameters
+        ----------
         parent : tuple
             Tuple of form (i, -tau).
-
         conds_dim : int
             Cardinality in current step.
-
-        parents_j : list
+        all_parents : list
             List of form [(0, -1), (3, -2), ...]
 
-        Attributes
-        ----------
-        parents_j_excl_current : list
-            List of current parents excluding parent being tested.
-        checked_conds : list
-            List of already checked condition sets.
-
-         """
-        def __init__(self, parent, j, conds_dim, parents_j):
-
-            self.j = j
-            self.parent = parent
-            self.conds_dim = conds_dim
-            self.parents_j_excl_current = [p for p in parents_j if p != parent]
-            self.checked_conds = []
-
-        def next_cond(self, check_only=False):
-            """Yield next condition.
-
-            Returns next condition from lexicographically ordered conditions.
-            Returns False if all possible conditions have been tested.
-
-            Parameters
-            ----------
-            check_only : bool, default: False
-                Return only True instead of next condition.
-
-            Returns
-            -------
-            cond :  list or bool
-                List of form [(0, -1), (3, -2), ...] yielding next condition.
-            """
-            if len(self.parents_j_excl_current) < self.conds_dim:
-                return False
-
-            for cond in itertools.combinations(self.parents_j_excl_current, 
-                                                self.conds_dim):
-
-                if set(list(cond)) not in self.checked_conds:
-                    if check_only is False:
-                        self.checked_conds.append(set(list(cond)))
-                        return list(cond)
-                    else:
-                        return True
-
-            return False
-
-    def _sort_parents(self, parents_values):
+        Yields
+        -------
+        cond :  list
+            List of form [(0, -1), (3, -2), ...] for the next condition.
+        """
+        all_parents_excl_current = [p for p in all_parents if p != parent]
+        for cond in itertools.combinations(all_parents_excl_current, conds_dim):
+            yield list(cond)
+
+    def _sort_parents(self, parents_vals):
         """Sort current parents according to test statistic values.
 
         Sorting is from strongest to weakest absolute values.
 
         Parameters
         ---------
-        parents_values : dict
+        parents_vals : dict
             Dictionary of form {(0, -1):float, ...} containing the minimum test
             statistic value of a link
 
@@ -345,28 +373,20 @@ def _sort_parents(self, parents_values):
         parents : list
             List of form [(0, -1), (3, -2), ...] containing sorted parents.
         """
-
         if self.verbosity > 1:
             print("\n    Sorting parents in decreasing order with "
                   "\n    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| ")
+        # Get the absoute value for all the test statistics
+        abs_values = {k : np.abs(parents_vals[k]) for k in list(parents_vals)}
+        return sorted(abs_values, key=abs_values.get, reverse=True)
 
-        abs_values = dict([(key, numpy.abs(parents_values[key]))
-                           for key in list(parents_values)])
-
-        parents = sorted(abs_values,
-                         key=abs_values.get,
-                         reverse=True)
-
-        return parents
-
-    def _dict_to_matrix(self, val_dict, tau_max):
+    def _dict_to_matrix(self, val_dict, tau_max, n_vars):
         """Helper function to convert dictionary to matrix formart.
-        
+
         Parameters
         ---------
         val_dict : dict
             Dictionary of form {0:{(0, -1):float, ...}, 1:{...}, ...}
-
         tau_max : int
             Maximum lag.
 
@@ -375,59 +395,142 @@ def _dict_to_matrix(self, val_dict, tau_max):
         matrix : array of shape (N, N, tau_max+1)
             Matrix format of p-values and test statistic values.
         """
-
-        N = len(val_dict)
-
-        matrix = numpy.ones((N, N, tau_max + 1))
+        matrix = np.ones((n_vars, n_vars, tau_max + 1))
         for j in val_dict.keys():
             for link in val_dict[j].keys():
                 k, tau = link
                 matrix[k, j, abs(tau)] = val_dict[j][link]
-        
         return matrix
 
-    # @profile
+    def _print_link_info(self, j, index_parent, parent, num_parents):
+        """Print info about the current link being tested
+
+        Parameters
+        ----------
+        j : int
+            Index of current node being tested
+        index_parent : int
+            Index of the current parent
+        parent : tuple
+            Standard (i, tau) tuple of parent node id and time delay
+        num_parents : int
+            Total number of parents
+        """
+        print("\n    Link (%s %d) --> %s (%d/%d):" % (
+            self.var_names[parent[0]], parent[1], self.var_names[j],
+            index_parent + 1, num_parents))
+
+    def _print_cond_info(self, Z, comb_index, pval, val):
+        """Print info about the condition
+
+        Parameters
+        ----------
+        Z : list
+            The current condition being tested
+        comb_index : int
+            Index of the combination yielding this condition
+        pval : float
+            p-value from this condition
+        val : float
+            value from this condition
+        """
+        var_name_z = ""
+        for i, tau in Z:
+            var_name_z += "(%s %d) " % (self.var_names[i], tau)
+        print("    Combination %d: %s --> pval = %.5f / val = %.3f" %
+              (comb_index, var_name_z, pval, val))
+
+    def _print_a_pc_result(self, pval, pc_alpha, conds_dim, max_combinations):
+        """
+        Print the results from the current iteration of conditions.
+
+        Parameters
+        ----------
+        pval : float
+            pval to check signficance
+        pc_alpha : float
+            lower bound on what is considered significant
+        conds_dim : int
+            Cardinality of the current step
+        max_combinations : int
+            Maximum number of combinations of conditions of current cardinality
+            to test.
+        """
+        # Start with an indent
+        print_str = "    "
+        # Determine the body of the text
+        if pval > pc_alpha:
+            print_str += "Non-significance detected."
+        elif conds_dim > max_combinations:
+            print_str += "Still conditions of dimension"+\
+                    " %d left," % (conds_dim) +\
+                    " but q_max = %d reached." % (max_combinations)
+        else:
+            print_str += "No conditions of dimension %d left." % (conds_dim)
+        # Print the message
+        print(print_str)
+
+    def _print_converged_pc_single(self, converged, j, max_conds_dim):
+        """
+        Print statement about the convergence of the pc_stable_single algorithm.
+
+        Parameters
+        ----------
+        convergence : bool
+            true if convergence was reacjed
+        j : int
+            Variable index.
+        max_conds_dim : int
+            Maximum number of conditions to test
+        """
+        if converged:
+            print("\nAlgorithm converged for variable %s" %
+                  self.var_names[j])
+        else:
+            print(
+                "\nAlgorithm not yet converged, but max_conds_dim = %d"
+                " reached." % max_conds_dim)
+
     def _run_pc_stable_single(self, j,
-                             selected_links=None,
-                             tau_min=1,
-                             tau_max=1,
-                             save_iterations=False,
-                             pc_alpha=0.2,
-                             max_conds_dim=None,
-                             max_combinations=1,
-                             ):
+                              selected_links=None,
+                              tau_min=1,
+                              tau_max=1,
+                              save_iterations=False,
+                              pc_alpha=0.2,
+                              max_conds_dim=None,
+                              max_combinations=1):
         """PC algorithm for estimating parents of single variable.
 
         Parameters
         ----------
         j : int
             Variable index.
-        
+
         selected_links : list, optional (default: None)
             List of form [(0, -1), (3, -2), ...]
             specifying whether only selected links should be tested. If None is
             passed, all links are tested
-        
+
         tau_min : int, optional (default: 1)
-            Minimum time lag to test. Useful for variable selection in 
+            Minimum time lag to test. Useful for variable selection in
             multi-step ahead predictions. Must be greater zero.
-        
+
         tau_max : int, optional (default: 1)
             Maximum time lag. Must be larger or equal to tau_min.
-        
+
         save_iterations : bool, optional (default: False)
             Whether to save iteration step results such as conditions used.
-        
+
         pc_alpha : float or None, optional (default: 0.2)
             Significance level in algorithm. If a list is given, pc_alpha is
             optimized using model selection criteria provided in the
             cond_ind_test class as get_model_selection_criterion(). If None,
             a default list of values is used.
-        
+
         max_conds_dim : int, optional (default: None)
             Maximum number of conditions to test. If None is passed, this number
             is unrestricted.
-        
+
         max_combinations : int, optional (default: 1)
             Maximum number of combinations of conditions of current cardinality
             to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
@@ -437,169 +540,216 @@ def _run_pc_stable_single(self, j,
         -------
         parents : list
             List of estimated parents.
-        
+
         val_min : dict
             Dictionary of form {(0, -1):float, ...} containing the minimum test
             statistic value of a link.
-        
+
         p_max : dict
-            Dictionary of form {(0, -1):float, ...} containing the maximum 
+            Dictionary of form {(0, -1):float, ...} containing the maximum
             p-value of a link across different conditions.
-        
+
         iterations : dict
             Dictionary containing further information on algorithm steps.
         """
-
-        if pc_alpha is None:
-            pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-
-        p_max = {}
-        val_min = {}
-
-        parents_values = {}
-        parents = selected_links
-
-        iterations = {'iterations': {}}
-
+        # Initialize the dictionaries for the p_max, val_min parents_values
+        # results
+        p_max = dict()
+        val_min = dict()
+        parents_values = dict()
+        # Initialize the parents values from the selected links, copying to
+        # ensure this initial argument is unchagned.
+        parents = deepcopy(selected_links)
+        # Define a nested defaultdict of depth 4 to save all information about
+        # iterations
+        iterations = _create_nested_dictionary(4)
+        # Ensure tau_min is atleast 1
         tau_min = max(1, tau_min)
-        #
-        # Iteration through increasing number of conditions
-        #
-        converged = False
-
-        conds_dim = -1   
-        while (conds_dim < max_conds_dim):
-
-            conds_dim += 1
-
-            if save_iterations:
-                iterations['iterations'][conds_dim] = {}
-
-            # Re-initiate list of non-significant links
-            nonsig_parents = []
 
+        # Loop over all possible condition dimentions
+        max_conds_dim = self._set_max_condition_dim(max_conds_dim,
+                                                    tau_min, tau_max)
+        # Iteration through increasing number of conditions, i.e. from 
+        # [0,max_conds_dim] inclusive
+        converged = False
+        for conds_dim in range(max_conds_dim+1):
+            # (Re)initialize the list of non-significant links
+            nonsig_parents = list()
+            # Check if the algorithm has converged
             if len(parents) - 1 < conds_dim:
                 converged = True
-
-            # if converged:
                 break
-
+            # Print information about
             if self.verbosity > 1:
-                print("\nTesting condition sets of dimension"
-                      " %d:" % conds_dim)
-
-            parents_here = parents
+                print("\nTesting condition sets of dimension %d:" % conds_dim)
 
             # Iterate through all possible pairs (that have not converged yet)
-            for ip, parent in enumerate(parents_here):
-
+            for index_parent, parent in enumerate(parents):
+                # Print info about this link
                 if self.verbosity > 1:
-                    print("\n    Link (%s %d) --> %s (%d/%d):" % (
-                        self.var_names[parent[0]], parent[1], self.var_names[j],
-                        ip + 1, len(parents_here)))
-
-                if save_iterations:
-                    iterations['iterations'][conds_dim][parent] = {}
-
-                # Initiate conditions class
-                conditions = self._Conditions(parent, j, conds_dim, parents)
-
-                comb_index = 0
-                while comb_index < max_combinations and conditions.next_cond(
-                                                        check_only=True):
-
-                    comb_index += 1
-
-                    # Choose next condition
-                    Z = conditions.next_cond()
-
+                    self._print_link_info(j, index_parent, parent, len(parents))
+                # Iterate through all possible combinations
+                for comb_index, Z in \
+                        enumerate(self._iter_conditions(parent, conds_dim,
+                                                        parents)):
+                    # Break if we try too many combinations
+                    if comb_index >= max_combinations:
+                        break
                     # Perform independence test
-                    i, tau = parent
-
-                    val, pval = self.cond_ind_test.run_test(
-                        X=[(i, tau)],
-                        Y=[(j, 0)],
-                        Z=Z,
-                        tau_max=tau_max,
-                    )
-
+                    val, pval = self.cond_ind_test.run_test(X=[parent],
+                                                            Y=[(j, 0)],
+                                                            Z=Z,
+                                                            tau_max=tau_max)
+                    # Print some information if needed
                     if self.verbosity > 1:
-                        var_name_Z = ""
-                        for Zi in Z:
-                            var_name_Z += "(%s %d) " % (
-                                self.var_names[Zi[0]], Zi[1])
-                        print("    Combination %d: %s --> pval = %.5f /"
-                              " val = %.3f" %
-                              (comb_index, var_name_Z, pval, val))
-
+                        self._print_cond_info(Z, comb_index, pval, val)
                     # Keep track of maximum p-value and minimum estimated value
                     # for each pair (across any condition)
-                    if (i, tau) in list(parents_values):
-                        parents_values[(i, tau)] = min(numpy.abs(val),
-                                                parents_values[(i, tau)])
-                    else:
-                        parents_values[(i, tau)] = numpy.abs(val)
-
-                    if (i, tau) in list(p_max):
-                        p_max[(i, tau)] = max(numpy.abs(pval),
-                                              p_max[(i, tau)])
-                        val_min[(i, tau)] = min(numpy.abs(val),
-                                              val_min[(i, tau)])
-                    else:
-                        p_max[(i, tau)] = pval
-                        val_min[(i, tau)] = numpy.abs(val)
-
+                    parents_values[parent] = \
+                        min(np.abs(val), parents_values.get(parent,
+                                                            float("inf")))
+                    p_max[parent] = \
+                        max(np.abs(pval), p_max.get(parent, -float("inf")))
+                    val_min[parent] = \
+                        min(np.abs(val), val_min.get(parent, float("inf")))
+                    # Save the iteration if we need to
                     if save_iterations:
-                        iterations['iterations'][conds_dim][parent][
-                                comb_index]={'conds': deepcopy(Z),
-                                             'val': val, 'pval': pval}
-
+                        a_iter = iterations['iterations'][conds_dim][parent]
+                        a_iter[comb_index]['conds'] = deepcopy(Z)
+                        a_iter[comb_index]['val'] = val
+                        a_iter[comb_index]['pval'] = pval
                     # Delete link later and break while-loop if non-significant
                     if pval > pc_alpha:
                         nonsig_parents.append((j, parent))
                         break
 
+                # Print the results if needed
                 if self.verbosity > 1:
-                    if pval > pc_alpha:
-                        print("    Non-significance detected.")
-                    elif conditions.next_cond(check_only=True) == False:
-                        print("    No conditions of dimension %d left." %
-                              conds_dim)
-                    else:
-                        print("    Still conditions of dimension %d left,"
-                              " but q_max = %d reached." % (
-                            conds_dim, max_combinations))
+                    self._print_a_pc_result(pval, pc_alpha,
+                                            conds_dim, max_combinations)
 
             # Remove non-significant links
-            for j_parent in nonsig_parents:
-                j, parent = j_parent
-
-                # del parents[parents.index(parent)]
+            for _, parent in nonsig_parents:
                 del parents_values[parent]
-
+            # Return the parents list sorted by the test metric so that the
+            # updated parents list is given to the next cond_dim loop
             parents = self._sort_parents(parents_values)
-
+            # Print information about the change in possible parents
             if self.verbosity > 1:
                 print("\nUpdating parents:")
-                self._print_parents_single(
-                    j, parents, parents_values, p_max)
-
-        # if save_iterations:
-        #     iterations['p_max'] = p_max
+                self._print_parents_single(j, parents, parents_values, p_max)
 
+        # Print information about if convergence was reached
         if self.verbosity > 1:
-            if converged:
-                print("\nAlgorithm converged for variable %s" %
-                      self.var_names[j])
-            else:
-                print(
-                    "\nAlgorithm not yet converged, but max_conds_dim = %d"
-                    " reached." % max_conds_dim)
-
-        return {'parents':parents, 
+            self._print_converged_pc_single(converged, j, max_conds_dim)
+        # Return the results
+        return {'parents':parents,
                 'val_min':val_min,
-                'p_max':p_max, 
-                'iterations':iterations}
+                'p_max':p_max,
+                'iterations': _nested_to_normal(iterations)}
+
+    def _print_pc_params(self, selected_links, tau_min, tau_max, pc_alpha,
+                         max_conds_dim, max_combinations):
+        """
+        Print the setup of the current pc_stable run
+
+        Parameters
+        ----------
+        selected_links : dict or None
+            Dictionary of form specifying which links should be tested.
+        tau_min : int, default: 1
+            Minimum time lag to test.
+        tau_max : int, default: 1
+            Maximum time lag to test
+        pc_alpha : float or list of floats
+            Significance level in algorithm.
+        max_conds_dim : int
+            Maximum number of conditions to test.
+        max_combinations : int
+            Maximum number of combinations of conditions to test.
+        """
+        print("\n##\n## Running Tigramite PC algorithm\n##"
+              "\n\nParameters:")
+        if len(self.selected_variables) < self.N:
+            print("selected_variables = %s" % self.selected_variables)
+        if selected_links is not None:
+            print("selected_links = %s" % selected_links)
+        print("independence test = %s" % self.cond_ind_test.measure
+              + "\ntau_min = %d" % tau_min
+              + "\ntau_max = %d" % tau_max
+              + "\npc_alpha = %s" % pc_alpha
+              + "\nmax_conds_dim = %s" % max_conds_dim
+              + "\nmax_combinations = %d" % max_combinations)
+        print("\n")
+
+    def _print_pc_sel_results(self, pc_alpha, results, j, score, optimal_alpha):
+        """
+        Print the results from the pc_alpha selection
+
+        Parameters
+        ----------
+        pc_alpha : list
+            Tested significance levels in algorithm.
+        results : dict
+            Results from the tested pc_alphas
+        score : array of floats
+            scores from each pc_alpha
+        j : int
+            Index of current variable.
+        optimal_alpha : float
+            Optimal value of pc_alpha
+        """
+        print("\n# Condition selection results:")
+        for iscore, pc_alpha_here in enumerate(pc_alpha):
+            names_parents = "[ "
+            for pari in results[pc_alpha_here]['parents']:
+                names_parents += "(%s %d) " % (
+                    self.var_names[pari[0]], pari[1])
+            names_parents += "]"
+            print("    pc_alpha=%s got score %.4f with parents %s" %
+                  (pc_alpha_here, score[iscore], names_parents))
+        print("\n--> optimal pc_alpha for variable %s is %s" %
+              (self.var_names[j], optimal_alpha))
+
+    def _check_tau_limits(self, tau_min, tau_max):
+        """
+        Check the tau limits adhere to 0 <= tau_min <= tau_max
+
+        Parameters
+        ----------
+        tau_min : float
+            Minimum tau value.
+        tau_max : float
+            Maximum tau value.
+        """
+        if not 0 <= tau_min <= tau_max:
+            raise ValueError("tau_max = %d, " % (tau_max) +\
+                             "tau_min = %d, " % (tau_min) +\
+                             "but 0 <= tau_min <= tau_max")
+
+    def _set_max_condition_dim(self, max_conds_dim, tau_min, tau_max):
+        """
+        Set the maximum dimension of the conditions. Defaults to self.N*tau_max
+
+        Parameters
+        ----------
+        max_conds_dim : int
+            Input maximum condition dimension
+        tau_max : int
+            Maximum tau.
+
+        Returns
+        -------
+        max_conds_dim : int
+            Input maximum condition dimension or default
+        """
+        # Check if an input was given
+        if max_conds_dim is None:
+            max_conds_dim = self.N * (tau_max - tau_min + 1)
+        # Check this is a valid
+        if max_conds_dim < 0:
+            raise ValueError("maximum condition dimension must be >= 0")
+        return max_conds_dim
 
     def run_pc_stable(self,
                       selected_links=None,
@@ -608,8 +758,7 @@ def run_pc_stable(self,
                       save_iterations=False,
                       pc_alpha=0.2,
                       max_conds_dim=None,
-                      max_combinations=1,
-                      ):
+                      max_combinations=1):
         """PC algorithm for estimating parents of all variables.
 
         Parents are made available as self.all_parents
@@ -617,30 +766,30 @@ def run_pc_stable(self,
         Parameters
         ----------
         selected_links : dict or None
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             specifying whether only selected links should be tested. If None is
             passed, all links are tested
 
         tau_min : int, default: 1
             Minimum time lag to test. Useful for multi-step ahead predictions.
             Must be greater zero.
-        
+
         tau_max : int, default: 1
             Maximum time lag. Must be larger or equal to tau_min.
-        
+
         save_iterations : bool, default: False
             Whether to save iteration step results such as conditions used.
-        
-        pc_alpha : float or list of floats, default: 0.3
+
+        pc_alpha : float or list of floats, default: [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
             Significance level in algorithm. If a list or None is passed, the
-            pc_alpha level is optimized for every variable across the given 
-            pc_alpha values using the score computed in 
+            pc_alpha level is optimized for every variable across the given
+            pc_alpha values using the score computed in
             cond_ind_test.get_model_selection_criterion()
-        
+
         max_conds_dim : int or None
             Maximum number of conditions to test. If None is passed, this number
             is unrestricted.
-        
+
         max_combinations : int, default: 1
             Maximum number of combinations of conditions of current cardinality
             to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
@@ -649,35 +798,30 @@ def run_pc_stable(self,
         Returns
         -------
         all_parents : dict
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             containing estimated parents.
         """
-
-
-
-        if pc_alpha is None:
-            pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-            self.alpha_selection = True
-        elif type(pc_alpha) == list:
-            self.alpha_selection = True
-        else:
-            self.alpha_selection = False
-
-        if tau_min > tau_max or min(tau_min, tau_max) < 0:
-            raise ValueError("tau_max = %d, tau_min = %d, " % (
-                             tau_max, tau_min)
-                             + "but 0 <= tau_min <= tau_max")
-
+        # Create an internal copy of pc_alpha
+        _int_pc_alpha = deepcopy(pc_alpha)
+        # Check if we are selecting an optimal alpha value
+        select_optimal_alpha = True
+        # Set the default values for pc_alpha
+        if _int_pc_alpha is None:
+            _int_pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
+        elif not isinstance(_int_pc_alpha, (list, tuple, np.ndarray)):
+            _int_pc_alpha = [_int_pc_alpha]
+            select_optimal_alpha = False
+        # Check the limits on tau_min
+        self._check_tau_limits(tau_min, tau_max)
         tau_min = max(1, tau_min)
-
+        # Check that the maximum combinatiosn variable is correct
         if max_combinations <= 0:
             raise ValueError("max_combinations must be > 0")
-
-        p_max = dict([(j, {}) for j in range(self.N)])
-        val_min = dict([(j, {}) for j in range(self.N)])
-
-        iterations = dict([(j, {}) for j in range(self.N)])
-
+        # Implement defaultdict for all p_max, val_max, and iterations
+        p_max = defaultdict(dict)
+        val_min = defaultdict(dict)
+        iterations = defaultdict(dict)
+        # Print information about the selected parameters
         if self.verbosity > 0:
             print("\n##\n## Running Tigramite PC algorithm\n##"
                   "\n\nParameters:")
@@ -712,85 +856,77 @@ def run_pc_stable(self,
         if max_conds_dim < 0:
             raise ValueError("max_conds_dim must be >= 0")
 
+            self._print_pc_params(selected_links, tau_min, tau_max,
+                                  _int_pc_alpha, max_conds_dim,
+                                  max_combinations)
+        # Set the selected links
+        _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)
+        # Initialize all parents
+        all_parents = dict()
+        # Set the maximum condition dimension
+        max_conds_dim = self._set_max_condition_dim(max_conds_dim,
+                                                    tau_min, tau_max)
+
+        # Loop through the selected variables
         for j in self.selected_variables:
-
+            # Print the status of this variable
             if self.verbosity > 0:
                 print("\n## Variable %s" % self.var_names[j])
-
-            if self.alpha_selection == False:
-                result = self._run_pc_stable_single(j, 
-                                            selected_links=selected_links[j],
-                                            tau_min=tau_min,
-                                            tau_max=tau_max,
-                                            save_iterations=save_iterations,
-                                            pc_alpha=pc_alpha,
-                                            max_conds_dim=max_conds_dim,
-                                            max_combinations=max_combinations,
-                                            )
-                all_parents[j] = result['parents']
-                val_min[j] = result['val_min']
-                p_max[j] = result['p_max']
-                iterations[j] = result['iterations']
-
-            else:
                 if self.verbosity > 1:
-                    print("\nIterating through pc_alpha = %s:" % pc_alpha)
-
-                score = numpy.zeros(len(pc_alpha))
-                results = {}
-                for iscore, pc_alpha_here in enumerate(pc_alpha):
-                    if self.verbosity > 1:
-                        print("\n# pc_alpha = %s (%d/%d):" % (pc_alpha_here,
-                                            iscore+1, len(pc_alpha)))
-
-                    results[pc_alpha_here] = self._run_pc_stable_single(j,
-                                       selected_links=selected_links[j],
-                                       tau_min=tau_min,
-                                       tau_max=tau_max,
-                                       save_iterations=save_iterations,
-                                       pc_alpha=pc_alpha_here,
-                                       max_conds_dim=max_conds_dim,
-                                       max_combinations=max_combinations,
-                                       )
-
-                    # Score
-                    parents_here = results[pc_alpha_here]['parents']
-
-                    mscore = self.cond_ind_test.get_model_selection_criterion(j,
-                                                     parents_here, tau_max)
-                    score[iscore] = mscore
-
-                optimal_alpha = pc_alpha[score.argmin()]
-
+                    print("\nIterating through pc_alpha = %s:" % _int_pc_alpha)
+            # Initialize the scores for selecting the optimal alpha
+            score = np.zeros_like(_int_pc_alpha)
+            # Initialize the result
+            results = {}
+            for iscore, pc_alpha_here in enumerate(_int_pc_alpha):
+                # Print statement about the pc_alpha being tested
                 if self.verbosity > 1:
-                    print("\n# Condition selection results:")
-                    for iscore, pc_alpha_here in enumerate(pc_alpha):
-                        names_parents = "[ "
-                        for pari in results[pc_alpha_here]['parents']:
-                            names_parents += "(%s %d) " % (
-                                self.var_names[pari[0]], pari[1])
-                        names_parents += "]"
-                        print("    pc_alpha=%s got score %.4f with parents %s" %
-                              (pc_alpha_here, score[iscore], names_parents))
-                    print("\n--> optimal pc_alpha for variable %s is %s" %
-                          (self.var_names[j], optimal_alpha))
-
-                all_parents[j] = results[optimal_alpha]['parents']
-                val_min[j] = results[optimal_alpha]['val_min']
-                p_max[j] = results[optimal_alpha]['p_max']
-                iterations[j] = results[optimal_alpha]['iterations']
-
+                    print("\n# pc_alpha = %s (%d/%d):" % (pc_alpha_here,
+                                                          iscore+1,
+                                                          score.shape[0]))
+                # Get the results for this alpha value
+                results[pc_alpha_here] = \
+                    self._run_pc_stable_single(j,
+                                               selected_links=_int_sel_links[j],
+                                               tau_min=tau_min,
+                                               tau_max=tau_max,
+                                               save_iterations=save_iterations,
+                                               pc_alpha=pc_alpha_here,
+                                               max_conds_dim=max_conds_dim,
+                                               max_combinations=max_combinations)
+                # Figure out the best score if there is more than one pc_alpha
+                # value
+                if select_optimal_alpha:
+                    score[iscore] = \
+                        self.cond_ind_test.get_model_selection_criterion(
+                            j, results[pc_alpha_here]['parents'], tau_max)
+            # Record the optimal alpha value
+            optimal_alpha = _int_pc_alpha[score.argmin()]
+            # Only print the selection results if there is more than one
+            # pc_alpha
+            if self.verbosity > 1 and select_optimal_alpha:
+                self._print_pc_sel_results(_int_pc_alpha, results, j,
+                                           score, optimal_alpha)
+            # Record the results for this variable
+            all_parents[j] = results[optimal_alpha]['parents']
+            val_min[j] = results[optimal_alpha]['val_min']
+            p_max[j] = results[optimal_alpha]['p_max']
+            iterations[j] = results[optimal_alpha]['iterations']
+            # Only save the optimal alpha if there is more than one pc_alpha
+            if select_optimal_alpha:
                 iterations[j]['optimal_pc_alpha'] = optimal_alpha
-
+        # Save the results in the current status of the algorithm
         self.all_parents = all_parents
-        self.val_matrix = self._dict_to_matrix(val_min, tau_max)
-        self.p_matrix = self._dict_to_matrix(p_max, tau_max)
+        self.val_matrix = self._dict_to_matrix(val_min, tau_max, self.N)
+        self.p_matrix = self._dict_to_matrix(p_max, tau_max, self.N)
         self.iterations = iterations
-
+        self.val_min = val_min
+        self.p_max = p_max
+        # Print the results
         if self.verbosity > 0:
             print("\n## Resulting condition sets:")
             self._print_parents(all_parents, val_min, p_max)
-
+        # Return the parents
         return all_parents
 
     def _print_parents_single(self, j, parents, val_min, p_max):
@@ -800,206 +936,277 @@ def _print_parents_single(self, j, parents, val_min, p_max):
         ----------
         j : int
             Index of current variable.
-        
         parents : list
             List of form [(0, -1), (3, -2), ...]
-        
         val_min : dict
             Dictionary of form {(0, -1):float, ...} containing the minimum test
             statistic value of a link
-
         p_max : dict
-            Dictionary of form {(0, -1):float, ...} containing the maximum 
+            Dictionary of form {(0, -1):float, ...} containing the maximum
             p-value of a link across different conditions.
-
-        Returns
-        -------
-        self : returns an instance of self.
         """
-    
-
         if len(parents) < 20 or hasattr(self, 'iterations'):
             print("\n    Variable %s has %d parent(s):" % (
                             self.var_names[j], len(parents)))
-            # if hasattr(self, 'iterations'):
-            #     print self.iterations
-            if (hasattr(self, 'iterations') 
+            if (hasattr(self, 'iterations')
                 and 'optimal_pc_alpha' in list(self.iterations[j])):
                     print("    [pc_alpha = %s]" % (
-                                    self.iterations[j]['optimal_pc_alpha']))    
+                                    self.iterations[j]['optimal_pc_alpha']))
             for p in parents:
                 print("        (%s %d): max_pval = %.5f, min_val = %.3f" % (
-                    self.var_names[p[0]], p[1], p_max[p], 
+                    self.var_names[p[0]], p[1], p_max[p],
                     val_min[p]))
         else:
             print("\n    Variable %s has %d parent(s):" % (
                 self.var_names[j], len(parents)))
 
-        return self
-
     def _print_parents(self, all_parents, val_min, p_max):
         """Print current parents.
 
         Parameters
         ----------
-        all_parents : dictionary    
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing 
+        all_parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
             the conditioning-parents estimated with PC algorithm.
-
         val_min : dict
-            Dictionary of form {0:{(0, -1):float, ...}} containing the minimum 
+            Dictionary of form {0:{(0, -1):float, ...}} containing the minimum
             test statistic value of a link
-
         p_max : dict
-            Dictionary of form {0:{(0, -1):float, ...}} containing the maximum 
+            Dictionary of form {0:{(0, -1):float, ...}} containing the maximum
             p-value of a link across different conditions.
-
-        Returns
-        -------
-        self : returns an instance of self.
         """
         for j in [var for var in list(all_parents)]:
             self._print_parents_single(j, all_parents[j],
                                        val_min[j], p_max[j])
-        return self
- 
-    def get_lagged_dependencies(self,
-            selected_links=None,
-            tau_min=0,
-            tau_max=1,
-            parents=None,
-            max_conds_py=None,
-            max_conds_px=None,
-            ):
 
-        """Returns matrix of lagged dependence measure values.
+    def _mci_condition_to_string(self, conds):
+        """Convert the list of conditions into a string
 
         Parameters
         ----------
-        selected_links : dict or None
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
-            specifying whether only selected links should be tested. If None is
-            passed, all links are tested
+        conds : list
+            List of conditions
+        """
+        cond_string = "[ "
+        for k, tau_k in conds:
+            cond_string += "(%s %d) " % (self.var_names[k], tau_k)
+        cond_string += "]"
+        return cond_string
 
-        tau_min : int, default: 0
-            Minimum time lag.
+    def _print_mci_conditions(self, conds_y, conds_x_lagged,
+                              j, i, tau, count, n_parents):
+        """Print information about the conditions for the MCI algorithm
 
-        tau_max : int, default: 1
-            Maximum time lag. Must be larger or equal to tau_min.
+        Parameters
+        ----------
+        conds_y : list
+            Conditions on node
+        conds_x_lagged : list
+            Conditions on parent
+        j : int
+            Current node
+        i : int
+            Parent node
+        tau : int
+            Parent time delay
+        count : int
+            Index of current parent
+        n_parents : int
+            Total number of parents
+        """
+        # Remove the current parent from the conditions
+        conds_y_no_i = [node for node in conds_y if node != (i, tau)]
+        # Get the condition string for parent
+        condy_str = self._mci_condition_to_string(conds_y_no_i)
+        # Get the condition string for node
+        condx_str = self._mci_condition_to_string(conds_x_lagged)
+        # Formate and print the information
+        indent = "\n        "
+        print_str = indent + "link (%s %d) " % (self.var_names[i], tau)
+        print_str += "--> %s (%d/%d):" % (self.var_names[j], count+1, n_parents)
+        print_str += indent + "with conds_y = %s" % (condy_str)
+        print_str += indent + "with conds_x = %s" % (condx_str)
+        print(print_str)
+
+    def _get_int_parents(self, parents):
+        """Get the input parents dictionary
 
+        Parameters
+        ----------
         parents : dict or None
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             specifying the conditions for each variable. If None is
             passed, no conditions are used.
 
-        max_conds_py : int or None
-            Maximum number of conditions of Y to use. If None is passed, this 
-            number is unrestricted.
-
-        max_conds_px : int or None
-            Maximum number of conditions of Z to use. If None is passed, this 
-            number is unrestricted.
-
         Returns
         -------
-        val_matrix : array
-            The matrix of shape (N, N, tau_max+1) containing the lagged 
-            dependencies.
+        int_parents : defaultdict of lists
+            Internal copy of parents, respecting default options
         """
+        int_parents = deepcopy(parents)
+        if int_parents is None:
+            int_parents = defaultdict(list)
+        else:
+            int_parents = defaultdict(list, int_parents)
+        return int_parents
+
+    def _iter_indep_conds(self,
+                          parents,
+                          selected_variables,
+                          selected_links,
+                          max_conds_py,
+                          max_conds_px):
+        """Iterate through the conditions dictated by the arguments, yielding
+        the needed arguments for conditional independence functions.
 
-        if tau_min > tau_max or min(tau_min, tau_max) < 0:
-         raise ValueError("tau_max = %d, tau_min = %d, " % (
-                          tau_max, tau_min)
-                          + "but 0 <= tau_min <= tau_max")
-
-        if selected_links is None:
-            selected_links = {}
-        
-            for j in range(self.N):
-                if j in self.selected_variables:
-                    selected_links[j] = [(var, -lag)
-                                      for var in range(self.N)
-                                      for lag in range(tau_min, tau_max + 1)
-                                      ]
-                else:
-                    selected_links[j] = []
-
-        if self.verbosity > 0:
-         print("\n## Estimating lagged dependencies")
-
-        if max_conds_py is None:
-            max_conds_py = self.N * tau_max
-
-        if max_conds_px is None:
-            max_conds_px = self.N * tau_max
-
-        if parents is None:
-            parents = {}
-            for j in range(self.N):
-                parents[j] = []
-
-        val_matrix = numpy.zeros((self.N, self.N, tau_max + 1))
-
-        for j in self.selected_variables:
-
+        Parameters
+        ----------
+        parents : dict
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+            specifying the conditions for each variable.
+        selected_variables : list of integers, optional (default: range(N))
+            Specify to estimate parents only for selected variables.
+        selected_links : dict
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+            specifying whether only selected links should be tested.
+        max_conds_py : int
+            Maximum number of conditions of Y to use.
+        max_conds_px : int
+            Maximum number of conditions of Z to use.
+
+        Yields
+        ------
+        i, j, tau, Z : list of tuples
+            (i, tau) is the parent node, (j, 0) is the current node, and Z is of
+            the form [(var, tau + tau')] and specifies the condition to test
+        """
+        # Loop over the selected variables
+        for j in selected_variables:
+            # Get the conditions for node j
             conds_y = parents[j][:max_conds_py]
-
-            parent_list = [parent for parent in selected_links[j]
-                             if (parent[1] != 0 or parent[0] != j)]
-
+            # Create a parent list from links seperated in time and by node
+            parent_list = [(i, tau) for i, tau in selected_links[j]
+                           if (i, tau) != (j, 0)]
             # Iterate through parents (except those in conditions)
             for cnt, (i, tau) in enumerate(parent_list):
-
+                # Get the conditions for node i
                 conds_x = parents[i][:max_conds_px]
-                # lag = [-tau]
-
+                # Shift the conditions for X by tau
+                conds_x_lagged = [(k, tau + k_tau) for k, k_tau in conds_x]
+                # Print information about the mci conditions if requested
                 if self.verbosity > 1:
-                    var_names_condy = "[ "
-                    for conds_yi in [node for node in conds_y 
-                                     if node != (i, tau)]:
-                        var_names_condy += "(%s %d) " % (
-                         self.var_names[conds_yi[0]], conds_yi[1])
-                    var_names_condy += "]"
-                    var_names_condx = "[ "
-                    for conds_xi in conds_x:
-                        var_names_condx += "(%s %d) " % (
-                         self.var_names[conds_xi[0]], conds_xi[1] + tau)
-                    var_names_condx += "]"
-
-                    print("\n        link (%s %d) --> %s (%d/%d):" % (
-                        self.var_names[i], tau, self.var_names[j],
-                        cnt + 1, len(parent_list)) +
-                        "\n        with conds_y = %s" % (var_names_condy) +
-                        "\n        with conds_x = %s" % (var_names_condx))
-
+                    self._print_mci_conditions(conds_y, conds_x_lagged, j, i,
+                                               tau, cnt, len(parent_list))
                 # Construct lists of tuples for estimating
                 # I(X_t-tau; Y_t | Z^Y_t, Z^X_t-tau)
                 # with conditions for X shifted by tau
-                X = [(i, tau)]
-                Y = [(j, 0)]
-                Z = [node for node in conds_y if node != (i, tau)] + [
-                     (node[0], tau + node[1]) for node in conds_x]
-
-                val = self.cond_ind_test.get_measure(X=X, Y=Y, Z=Z,
-                                                     tau_max=tau_max)
+                Z = [node for node in conds_y if node != (i, tau)]
+                # Remove overlapped nodes between conds_x_lagged and conds_y
+                Z += [node for node in conds_x_lagged if node not in Z]
+                # Yield these list
+                yield j, i, tau, Z
 
-                val_matrix[i, j, abs(tau)] = val
+    def get_lagged_dependencies(self,
+                                selected_links=None,
+                                tau_min=0,
+                                tau_max=1,
+                                parents=None,
+                                max_conds_py=None,
+                                max_conds_px=None):
+        """Returns matrix of lagged dependence measure values.
 
-                if self.verbosity > 1:
-                    self.cond_ind_test._print_cond_ind_results(val=val)
+        Parameters
+        ----------
+        selected_links : dict or None
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+            specifying whether only selected links should be tested. If None is
+            passed, all links are tested
+        tau_min : int, default: 0
+            Minimum time lag.
+        tau_max : int, default: 1
+            Maximum time lag. Must be larger or equal to tau_min.
+        parents : dict or None
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
+            specifying the conditions for each variable. If None is
+            passed, no conditions are used.
+        max_conds_py : int or None
+            Maximum number of conditions from parents of Y to use. If None is
+            passed, this number is unrestricted.
+        max_conds_px : int or None
+            Maximum number of conditions from parents of X to use. If None is
+            passed, this number is unrestricted.
 
+        Returns
+        -------
+        val_matrix : array
+            The matrix of shape (N, N, tau_max+1) containing the lagged
+            dependencies.
+        """
+        # Check the limits on tau
+        self._check_tau_limits(tau_min, tau_max)
+        # Set the selected links
+        _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)
+        # Print status message
+        if self.verbosity > 0:
+            print("\n## Estimating lagged dependencies")
+        # Set the maximum condition dimension for Y and X
+        max_conds_py = self._set_max_condition_dim(max_conds_py,
+                                                   tau_min, tau_max)
+        max_conds_px = self._set_max_condition_dim(max_conds_px,
+                                                   tau_min, tau_max)
+        # Get the parents that will be checked
+        _int_parents = self._get_int_parents(parents)
+        # Initialize the returned val_matrix
+        val_matrix = np.zeros((self.N, self.N, tau_max + 1))
+        # Get the conditions as implied by the input arguments
+        for j, i, tau, Z in self._iter_indep_conds(_int_parents,
+                                                   self.selected_variables,
+                                                   _int_sel_links,
+                                                   max_conds_py,
+                                                   max_conds_px):
+            # Set X and Y (for clarity of code)
+            X = [(i, tau)]
+            Y = [(j, 0)]
+            # Run the independence test
+            val = self.cond_ind_test.get_measure(X, Y, Z=Z, tau_max=tau_max)
+            # Record the value
+            val_matrix[i, j, abs(tau)] = val
+            # Print the results
+            if self.verbosity > 1:
+                self.cond_ind_test._print_cond_ind_results(val=val)
+        # Return the value matrix
         return val_matrix
 
+    def _print_mci_parameters(self, tau_min, tau_max,
+                              max_conds_py, max_conds_px):
+        """Print the parameters for this MCI algorithm
+
+        Parameters
+        ----------
+        tau_min : int
+            Minimum time delay
+        tau_max : int
+            Maximum time delay
+        max_conds_py : int
+            Maximum number of conditions of Y to use.
+        max_conds_px : int
+            Maximum number of conditions of Z to use.
+        """
+        print("\n##\n## Running Tigramite MCI algorithm\n##"
+              "\n\nParameters:")
+        print("\nindependence test = %s" % self.cond_ind_test.measure
+              + "\ntau_min = %d" % tau_min
+              + "\ntau_max = %d" % tau_max
+              + "\nmax_conds_py = %s" % max_conds_py
+              + "\nmax_conds_px = %s" % max_conds_px)
 
     def run_mci(self,
                 selected_links=None,
-                tau_min=1,
+                tau_min=0,
                 tau_max=1,
                 parents=None,
                 max_conds_py=None,
-                max_conds_px=None,
-                ):
-
+                max_conds_px=None):
         """MCI conditional independence tests.
 
         Implements the MCI test (Algorithm 2 in [1]_). Returns the matrices of
@@ -1008,27 +1215,22 @@ def run_mci(self,
         Parameters
         ----------
         selected_links : dict or None
-            Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...}
             specifying whether only selected links should be tested. If None is
             passed, all links are tested
-
-        tau_min : int, default: 1
+        tau_min : int, default: 0
             Minimum time lag to test. Note that zero-lags are undirected.
-        
         tau_max : int, default: 1
             Maximum time lag. Must be larger or equal to tau_min.
-        
         parents : dict or None
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             specifying the conditions for each variable. If None is
             passed, no conditions are used.
-
         max_conds_py : int or None
-            Maximum number of conditions of Y to use. If None is passed, this 
+            Maximum number of conditions of Y to use. If None is passed, this
             number is unrestricted.
-        
         max_conds_px : int or None
-            Maximum number of conditions of Z to use. If None is passed, this 
+            Maximum number of conditions of Z to use. If None is passed, this
             number is unrestricted.
 
         Returns
@@ -1037,137 +1239,75 @@ def run_mci(self,
             {'val_matrix':val_matrix, 'p_matrix':p_matrix} are always returned
             and optionally conf_matrix which is of shape [N, N, tau_max+1,2]
         """
-
-        if tau_min > tau_max or min(tau_min, tau_max) < 0:
-            raise ValueError("tau_max = %d, tau_min = %d, " % (
-                             tau_max, tau_min)
-                             + "but 0 <= tau_min <= tau_max")
-
-        if selected_links is None:
-            selected_links = {}
-            for j in range(self.N):
-                if j in self.selected_variables:
-                    selected_links[j] = [(var, -lag)
-                                         for var in range(self.N)
-                                         for lag in range(tau_min, tau_max + 1)
-                                         ]
-                else:
-                    selected_links[j] = []
-
+        # Check the limits on tau
+        self._check_tau_limits(tau_min, tau_max)
+        # Set the selected links
+        _int_sel_links = self._set_sel_links(selected_links, tau_min, tau_max)
+        # Print information about the input parameters
         if self.verbosity > 0:
-            print("\n##\n## Running Tigramite MCI algorithm\n##"
-                  "\n\nParameters:")
-
-            print("\nindependence test = %s" % self.cond_ind_test.measure
-                  + "\ntau_min = %d" % tau_min
-                  + "\ntau_max = %d" % tau_max
-                  + "\nmax_conds_py = %s" % max_conds_py
-                  + "\nmax_conds_px = %s" % max_conds_px)
-
-        if max_conds_py is None:
-            max_conds_py = self.N * tau_max
-
-        if max_conds_px is None:
-            max_conds_px = self.N * tau_max
-
-        if parents is None:
-            parents = {}
-            for j in range(self.N):
-                parents[j] = []
-        else:
-          for j in range(self.N):
-                if j not in list(parents):
-                    parents[j] = []      
-
-        val_matrix = numpy.zeros((self.N, self.N, tau_max + 1))
-        p_matrix = numpy.ones((self.N, self.N, tau_max + 1))
+            self._print_mci_parameters(tau_min, tau_max,
+                                       max_conds_py, max_conds_px)
+
+        # Set the maximum condition dimension for Y and X
+        max_conds_py = self._set_max_condition_dim(max_conds_py,
+                                                   tau_min, tau_max)
+        max_conds_px = self._set_max_condition_dim(max_conds_px,
+                                                   tau_min, tau_max)
+        # Get the parents that will be checked
+        _int_parents = self._get_int_parents(parents)
+        # Initialize the return values
+        val_matrix = np.zeros((self.N, self.N, tau_max + 1))
+        p_matrix = np.ones((self.N, self.N, tau_max + 1))
+        # Initialize the optional return of the confidance matrix
+        conf_matrix = None
         if self.cond_ind_test.confidence is not False:
-            conf_matrix = numpy.zeros((self.N, self.N, tau_max + 1, 2))
-        else:
-            conf_matrix = None
-
-        for j in self.selected_variables:
-
-            if self.verbosity > 0:
-                print("\n\tVariable %s" % self.var_names[j])
-
-            conds_y = parents[j][:max_conds_py]
-
-            parent_list = [parent for parent in selected_links[j]
-                         if (parent[1] != 0 or parent[0] != j)]
-
-            # Iterate through parents (except those in conditions)
-            for cnt, (i, tau) in enumerate(parent_list):
-
-                conds_x = parents[i][:max_conds_px]
-                # lag = [-tau]
-
-                if self.verbosity > 1:
-                    var_names_condy = "[ "
-                    for conds_yi in [node for node in conds_y 
-                                     if node != (i, tau)]:
-                        var_names_condy += "(%s %d) " % (
-                            self.var_names[conds_yi[0]], conds_yi[1])
-                    var_names_condy += "]"
-                    var_names_condx = "[ "
-                    for conds_xi in conds_x:
-                        var_names_condx += "(%s %d) " % (
-                            self.var_names[conds_xi[0]], conds_xi[1] + tau)
-                    var_names_condx += "]"
-
-                    print("\n        link (%s %d) --> %s (%d/%d):" % (
-                        self.var_names[i], tau, self.var_names[j],
-                        cnt + 1, len(parent_list)) +
-                          "\n        with conds_y = %s" % (var_names_condy) +
-                          "\n        with conds_x = %s" % (var_names_condx))
-
-                # Construct lists of tuples for estimating
-                # I(X_t-tau; Y_t | Z^Y_t, Z^X_t-tau)
-                # with conditions for X shifted by tau
-                X = [(i, tau)]
-                Y = [(j, 0)]
-                Z = [node for node in conds_y if node != (i, tau)] + [
-                     (node[0], tau + node[1]) for node in conds_x]
-
-                val, pval = self.cond_ind_test.run_test(X=X, Y=Y, Z=Z,
-                                                        tau_max=tau_max)
-
-                val_matrix[i, j, abs(tau)] = val
-                p_matrix[i, j, abs(tau)] = pval
-
-                conf = self.cond_ind_test.get_confidence(X=X, Y=Y, Z=Z,
-                                                        tau_max=tau_max)
-                if self.cond_ind_test.confidence is not False:
-                    conf_matrix[i, j, abs(tau)] = conf
-
-                if self.verbosity > 1:
-                    self.cond_ind_test._print_cond_ind_results(val=val, 
-                            pval=pval, conf=conf)
-
-        return {'val_matrix':val_matrix, 
-                'p_matrix':p_matrix, 
+            conf_matrix = np.zeros((self.N, self.N, tau_max + 1, 2))
+
+        # Get the conditions as implied by the input arguments
+        for j, i, tau, Z in self._iter_indep_conds(_int_parents,
+                                                   self.selected_variables,
+                                                   _int_sel_links,
+                                                   max_conds_py,
+                                                   max_conds_px):
+            # Set X and Y (for clarity of code)
+            X = [(i, tau)]
+            Y = [(j, 0)]
+            # Run the independence tests and record the results
+            val, pval = self.cond_ind_test.run_test(X, Y, Z=Z, tau_max=tau_max)
+            val_matrix[i, j, abs(tau)] = val
+            p_matrix[i, j, abs(tau)] = pval
+            # Get the confidance value, returns None if cond_ind_test.confidance
+            # is False
+            conf = self.cond_ind_test.get_confidence(X, Y, Z=Z, tau_max=tau_max)
+            # Record the value if the conditional independence requires it
+            if self.cond_ind_test.confidence:
+                conf_matrix[i, j, abs(tau)] = conf
+            # Print the results if needed
+            if self.verbosity > 1:
+                self.cond_ind_test._print_cond_ind_results(val,
+                                                           pval=pval,
+                                                           conf=conf)
+        # Return the values as a dictionary
+        return {'val_matrix':val_matrix,
+                'p_matrix':p_matrix,
                 'conf_matrix':conf_matrix}
 
-
     def get_corrected_pvalues(self, p_matrix,
                               fdr_method='fdr_bh',
-                              exclude_contemporaneous=True,
-                              ):
+                              exclude_contemporaneous=True):
         """Returns p-values corrected for multiple testing.
-        
-        Wrapper around statsmodels.sandbox.stats.multicomp.multipletests.
-        Correction is performed either among all links if
+
+        Currently implemented is Benjamini-Hochberg False Discovery Rate
+        method. Correction is performed either among all links if
         exclude_contemporaneous==False, or only among lagged links.
 
         Parameters
         ----------
         p_matrix : array-like
             Matrix of p-values. Must be of shape (N, N, tau_max + 1).
-
         fdr_method : str, optional (default: 'fdr_bh')
-            Correction method, default is Benjamini-Hochberg False Discovery
-            Rate method.
-
+            Correction method, currently implemented is Benjamini-Hochberg
+            False Discovery Rate method.     
         exclude_contemporaneous : bool, optional (default: True)
             Whether to include contemporaneous links in correction.
 
@@ -1177,30 +1317,57 @@ def get_corrected_pvalues(self, p_matrix,
             Matrix of shape (N, N, tau_max + 1) containing corrected p-values.
         """
 
-        N, N, tau_max_plusone = p_matrix.shape
-        tau_max = tau_max_plusone - 1
-
+        def _ecdf(x):
+            '''no frills empirical cdf used in fdrcorrection
+            '''
+            nobs = len(x)
+            return np.arange(1,nobs+1)/float(nobs)
+
+        # Get the shape paramters from the p_matrix
+        _, N, tau_max_plusone = p_matrix.shape
+        # Create a mask for these values
+        mask = np.ones((N, N, tau_max_plusone), dtype='bool')
+        # Ignore values from autocorrelation indices
+        mask[range(N), range(N), 0] = False
+        # Exclude all contemporaneous values if requested
         if exclude_contemporaneous:
-            mask = numpy.ones((self.N, self.N, tau_max + 1), dtype='bool')
             mask[:, :, 0] = False
-        else:
-            mask = numpy.ones((self.N, self.N, tau_max + 1), dtype='bool')
-            mask[range(self.N), range(self.N), 0] = False
+        # Create the return value
+        q_matrix = np.array(p_matrix)
+        # Use the multiple tests function
+        if fdr_method is None or fdr_method == 'none':
+            pass
+        elif fdr_method == 'fdr_bh':
+            pvs = p_matrix[mask]
+            # q_matrix[mask] = multicomp.multipletests(pvs, method=fdr_method)[1]
 
-        q_matrix = numpy.array(p_matrix)
+            pvals_sortind = np.argsort(pvs)
+            pvals_sorted = np.take(pvs, pvals_sortind)
 
-        if fdr_method != 'none':
-            pvals = p_matrix[numpy.where(mask)]
-            q_matrix[numpy.where(mask)] = multicomp.multipletests(
-                pvals, method=fdr_method)[1]  # .reshape(N,N,tau_max)
+            ecdffactor = _ecdf(pvals_sorted)
+            # reject = pvals_sorted <= ecdffactor*alpha
+
+            pvals_corrected_raw = pvals_sorted / ecdffactor
+            pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
+            del pvals_corrected_raw
+
+            pvals_corrected[pvals_corrected>1] = 1
+            pvals_corrected_ = np.empty_like(pvals_corrected)
+            pvals_corrected_[pvals_sortind] = pvals_corrected
+            del pvals_corrected
+
+            q_matrix[mask] = pvals_corrected_
 
+        else:
+            raise ValueError('Only FDR method fdr_bh implemented')
+
+        # Return the new matrix
         return q_matrix
 
-    def _return_significant_parents(self,
-                                  pq_matrix,
-                                  val_matrix,
-                                  alpha_level=0.05,
-                                  ):
+    def return_significant_parents(self,
+                                   pq_matrix,
+                                   val_matrix,
+                                   alpha_level=0.05):
         """Returns list of significant parents as well as a boolean matrix.
 
         Significance based on p-matrix, or q-value matrix with corrected
@@ -1208,49 +1375,44 @@ def _return_significant_parents(self,
 
         Parameters
         ----------
-        alpha_level : float, optional (default: 0.05)
-            Significance level.
-
         pq_matrix : array-like
             p-matrix, or q-value matrix with corrected p-values. Must be of
             shape (N, N, tau_max + 1).
-
         val_matrix : array-like
             Matrix of test statistic values. Must be of shape (N, N, tau_max +
             1).
+        alpha_level : float, optional (default: 0.05)
+            Significance level.
 
         Returns
         -------
         all_parents : dict
-            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...}
             containing estimated parents.
 
         link_matrix : array, shape [N, N, tau_max+1]
             Boolean array with True entries for significant links at alpha_level
         """
-
-        link_matrix = (pq_matrix <= alpha_level)
-        all_parents = {}
+        # Initialize the return value
+        all_parents = dict()
         for j in self.selected_variables:
-
-            links = dict([((p[0], -p[1] - 1), numpy.abs(val_matrix[p[0], 
-                            j, abs(p[1]) + 1]))
-                          for p in zip(*numpy.where(link_matrix[:, j, 1:]))])
-
+            # Get the good links
+            good_links = np.argwhere(pq_matrix[:, j, 1:] <= alpha_level)
+            # Build a dictionary from these links to their values
+            links = {(i, -tau-1): np.abs(val_matrix[i, j, abs(tau) + 1])
+                     for i, tau in good_links}
             # Sort by value
-            all_parents[j] = sorted(links, key=links.get, 
-                                                    reverse=True)
-
-        return {'parents':all_parents, 
-                'link_matrix':link_matrix}
-
-    def _print_significant_links(self,
-                                  p_matrix,
-                                  val_matrix,
-                                  conf_matrix=None,
-                                  q_matrix=None,
-                                  alpha_level=0.05,
-                                  ):
+            all_parents[j] = sorted(links, key=links.get, reverse=True)
+        # Return the significant parents
+        return {'parents': all_parents,
+                'link_matrix': pq_matrix <= alpha_level}
+
+    def print_significant_links(self,
+                                p_matrix,
+                                val_matrix,
+                                conf_matrix=None,
+                                q_matrix=None,
+                                alpha_level=0.05):
         """Prints significant parents.
 
         Parameters
@@ -1262,7 +1424,7 @@ def _print_significant_links(self,
             Must be of shape (N, N, tau_max + 1).
 
         val_matrix : array-like
-            Must be of shape (N, N, tau_max + 1). 
+            Must be of shape (N, N, tau_max + 1).
 
         q_matrix : array-like, optional (default: None)
             Adjusted p-values. Must be of shape (N, N, tau_max + 1).
@@ -1270,49 +1432,72 @@ def _print_significant_links(self,
         conf_matrix : array-like, optional (default: None)
             Matrix of confidence intervals of shape (N, N, tau_max+1, 2)
         """
-
         if q_matrix is not None:
             sig_links = (q_matrix <= alpha_level)
         else:
             sig_links = (p_matrix <= alpha_level)
-
         print("\n## Significant links at alpha = %s:" % alpha_level)
         for j in self.selected_variables:
-
-            links = dict([((p[0], -p[1] ), numpy.abs(val_matrix[p[0], 
-                            j, abs(p[1])]))
-                          for p in zip(*numpy.where(sig_links[:, j, :]))])
-
+            links = {(p[0], -p[1]): np.abs(val_matrix[p[0], j, abs(p[1])])
+                     for p in zip(*np.where(sig_links[:, j, :]))}
             # Sort by value
             sorted_links = sorted(links, key=links.get, reverse=True)
-
             n_links = len(links)
-
             string = ("\n    Variable %s has %d "
                       "link(s):" % (self.var_names[j], n_links))
             for p in sorted_links:
                 string += ("\n        (%s %d): pval = %.5f" %
-                           (self.var_names[p[0]], p[1], 
+                           (self.var_names[p[0]], p[1],
                             p_matrix[p[0], j, abs(p[1])]))
-
                 if q_matrix is not None:
                     string += " | qval = %.5f" % (
                         q_matrix[p[0], j, abs(p[1])])
-
                 string += " | val = %.3f" % (
                     val_matrix[p[0], j, abs(p[1])])
-
                 if conf_matrix is not None:
                     string += " | conf = (%.3f, %.3f)" % (
-                        conf_matrix[p[0], j, abs(p[1])][0], 
+                        conf_matrix[p[0], j, abs(p[1])][0],
                         conf_matrix[p[0], j, abs(p[1])][1])
-
             print(string)
 
+    def print_results(self, 
+                      return_dict, 
+                      alpha_level=0.05):
+        """Prints significant parents from output of MCI or PCMCI algorithms.
+
+        Parameters
+        ----------
+        return_dict : dict
+            Dictionary of return values, containing keys
+                * 'p_matrix'
+                * 'val_matrix'
+                * 'conf_matrix'
+            'q_matrix' can also be included in keys, but is not necessary.
+
+        alpha_level : float, optional (default: 0.05)
+            Significance level.
+        """
+        # Check if q_matrix is defined.  It is returned for PCMCI but not for
+        # MCI
+        q_matrix = None
+        q_key = 'q_matrix'
+        if q_key in return_dict:
+            q_matrix = return_dict[q_key]
+        # Check if conf_matrix is defined
+        conf_matrix = None
+        conf_key = 'conf_matrix'
+        if conf_key in return_dict:
+            conf_matrix = return_dict[conf_key]
+        # Wrap the already defined function
+        self.print_significant_links(return_dict['p_matrix'],
+                                     return_dict['val_matrix'],
+                                     conf_matrix=conf_matrix,
+                                     q_matrix=q_matrix,
+                                     alpha_level=alpha_level)
 
     def run_pcmci(self,
                   selected_links=None,
-                  tau_min=1,
+                  tau_min=0,
                   tau_max=1,
                   save_iterations=False,
                   pc_alpha=0.05,
@@ -1320,9 +1505,7 @@ def run_pcmci(self,
                   max_combinations=1,
                   max_conds_py=None,
                   max_conds_px=None,
-                  fdr_method='none',
-                  ):
-
+                  fdr_method='none'):
         """Run full PCMCI causal discovery for time series datasets.
 
         Wrapper around PC-algorithm function and MCI function.
@@ -1330,12 +1513,12 @@ def run_pcmci(self,
         Parameters
         ----------
         selected_links : dict or None
-            Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...} 
+            Dictionary of form {0:all_parents (3, -2), ...], 1:[], ...}
             specifying whether only selected links should be tested. If None is
             passed, all links are tested
 
-        tau_min : int, optional (default: 1)
-          Minimum time lag to test. Note that zero-lags are undirected. 
+        tau_min : int, optional (default: 0)
+          Minimum time lag to test. Note that zero-lags are undirected.
 
         tau_max : int, optional (default: 1)
           Maximum time lag. Must be larger or equal to tau_min.
@@ -1343,8 +1526,8 @@ def run_pcmci(self,
         save_iterations : bool, optional (default: False)
           Whether to save iteration step results such as conditions used.
 
-        pc_alpha : float, optional (default: 0.1)
-          Significance level in algorithm. 
+        pc_alpha : float, optional (default: 0.05)
+          Significance level in algorithm.
 
         max_conds_dim : int, optional (default: None)
           Maximum number of conditions to test. If None is passed, this number
@@ -1354,13 +1537,13 @@ def run_pcmci(self,
           Maximum number of combinations of conditions of current cardinality
           to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
           a larger number, such as 10, can be used.
-      
+
         max_conds_py : int, optional (default: None)
-            Maximum number of conditions of Y to use. If None is passed, this 
+            Maximum number of conditions of Y to use. If None is passed, this
             number is unrestricted.
 
         max_conds_px : int, optional (default: None)
-            Maximum number of conditions of Z to use. If None is passed, this 
+            Maximum number of conditions of Z to use. If None is passed, this
             number is unrestricted.
 
         fdr_method : str, optional (default: 'none')
@@ -1371,206 +1554,55 @@ def run_pcmci(self,
         -------
         results : dictionary of arrays of shape [N, N, tau_max+1]
             {'val_matrix':val_matrix, 'p_matrix':p_matrix} are always returned
-            and optionally q_matrix and conf_matrix which is of shape 
+            and optionally q_matrix and conf_matrix which is of shape
             [N, N, tau_max+1,2]
         """
-
-        all_parents = self.run_pc_stable(
-            selected_links=selected_links,
-            tau_min=tau_min,
-            tau_max=tau_max,
-            save_iterations=save_iterations,
-            pc_alpha=pc_alpha,
-            max_conds_dim=max_conds_dim,
-            max_combinations=max_combinations,
-            )
-
-        results = self.run_mci(
-            selected_links=selected_links,
-            tau_min=tau_min,
-            tau_max=tau_max,
-            parents=all_parents,
-            max_conds_py=max_conds_py,
-            max_conds_px=max_conds_px,
-            )
-
+        # Get the parents from run_pc_stable
+        all_parents = self.run_pc_stable(selected_links=selected_links,
+                                         tau_min=tau_min,
+                                         tau_max=tau_max,
+                                         save_iterations=save_iterations,
+                                         pc_alpha=pc_alpha,
+                                         max_conds_dim=max_conds_dim,
+                                         max_combinations=max_combinations)
+        # Get the results from run_mci, using the parents as the input
+        results = self.run_mci(selected_links=selected_links,
+                               tau_min=tau_min,
+                               tau_max=tau_max,
+                               parents=all_parents,
+                               max_conds_py=max_conds_py,
+                               max_conds_px=max_conds_px)
+        # Get the values and p-values
         val_matrix = results['val_matrix']
         p_matrix = results['p_matrix']
+        # Initialize and fill the the confidance matrix if the confidance test
+        # says it should be returned
+
+        conf_matrix = None
         if self.cond_ind_test.confidence is not False:
             conf_matrix = results['conf_matrix']
-        else:
-            conf_matrix = None
-
+        # Initialize and fill the q_matrix if there is a fdr_method
+        q_matrix = None
         if fdr_method != 'none':
-            q_matrix = self.get_corrected_pvalues(p_matrix=p_matrix,
+            q_matrix = self.get_corrected_pvalues(p_matrix,
                                                   fdr_method=fdr_method)
-        else:
-            q_matrix = None
-
+        # Store the parents in the pcmci member
         self.all_parents = all_parents
-        return {'val_matrix':val_matrix, 
-                'p_matrix':p_matrix, 
-                'q_matrix':q_matrix,
-                'conf_matrix':conf_matrix}
-
+        # Cache the resulting values in the return dictionary
+        return_dict = {'val_matrix': val_matrix,
+                       'p_matrix': p_matrix,
+                       'q_matrix': q_matrix,
+                       'conf_matrix': conf_matrix}
+        # Print the information
+        if self.verbosity > 0:
+            self.print_results(return_dict)
+        # Return the dictionary
+        return return_dict
 
 if __name__ == '__main__':
+    from tigramite.independence_tests import ParCorr
+    import tigramite.data_processing as pp
+    dataframe = pp.DataFrame(np.random.randn(100,3),)
+    pcmci = PCMCI(dataframe, ParCorr())
 
-    import data_processing as pp
-    from independence_tests import ParCorr, GPACE, GPDC, CMIknn, CMIsymb
-
-    numpy.random.seed(42)
-    # Example process to play around with
-    a = 0.8
-    c1 = .8
-    c2 = -.8
-    c3 = .8
-    T = 500
-
-    # Each key refers to a variable and the incoming links are supplied as a
-    # list of format [((driver, lag), coeff), ...]
-    links_coeffs = {0: [((0, -1), a), ((1, -1), c1)],
-                    1: [((1, -1), a), ((3, -1), c1)],
-                    2: [((2, -1), a), ((1, -2), c2), ((3, -3), c3)],
-                    3: [((3, -1), a)],
-                    }
-
-    data, true_parents_neighbors = pp.var_process(links_coeffs,
-                                                  use='inv_inno_cov', T=T)
-
-    data_mask = numpy.zeros(data.shape)
-
-    T, N = data.shape
-
-    var_names = range(N)  # ['X', 'Y', 'Z', 'W']
-
-    pc_alpha = 0.2  # [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-    selected_variables = None  #[2] # [2]  # [2]
-
-    tau_max = 3
-    alpha_level = 0.01
-
-    dataframe = pp.DataFrame(data,
-        mask=data_mask,
-        )
-    verbosity = 2
-
-    cond_ind_test = ParCorr(
-        significance='analytic',
-        fixed_thres=0.05,
-        sig_samples=100,
-
-        use_mask=False,
-        mask_type=['x','y', 'z'],  #  ['x','y','z'],
-
-        confidence='analytic',
-        conf_lev=0.9,
-        conf_samples=200,
-        conf_blocklength=10,
-
-        recycle_residuals=False,
-        verbosity=verbosity)
-
-    # cond_ind_test = GPACE(
-    #     significance='analytic',
-    #     fixed_thres=0.05,
-    #     sig_samples=2000,
-
-    #     use_mask=False,
-    #     mask_type=['y'],
-
-    #     confidence=False,
-    #     conf_lev=0.9,
-    #     conf_samples=200,
-    #     conf_blocklength=None,
-
-    #     gp_version='new',
-    #     gp_alpha=None,
-    #     ace_version='acepack',
-    #     recycle_residuals=False,
-    #     verbosity=verbosity)
-
-    # cond_ind_test = GPDC(
-    #     significance='analytic',
-    #     fixed_thres=0.05,
-    #     sig_samples=2000,
-
-    #     use_mask=False,
-    #     mask_type=['y'],
-
-    #     confidence=False,
-    #     conf_lev=0.9,
-    #     conf_samples=200,
-    #     conf_blocklength=None,
-
-    #     gp_version='new',
-    #     gp_alpha=1.,
-    #     recycle_residuals=False,
-    #     verbosity=verbosity)
-
-    # cond_ind_test = CMIsymb(
-    #     significance='shuffle_test',
-    #     sig_samples=1000,
-    #     sig_blocklength=10,
-
-    #     confidence='bootstrap', #'bootstrap',
-    #     conf_lev=0.9,
-    #     conf_samples=100,
-    #     conf_blocklength=10,
-
-    #     use_mask=False,
-    #     mask_type=['y'],
-    #     recycle_residuals=False,
-    #     verbosity=3)
-
-    if cond_ind_test.measure == 'cmi_symb':
-        dataframe.values = pp.quantile_bin_array(dataframe.values, bins=3)
-
-    pcmci = PCMCI(
-        dataframe=dataframe,
-        cond_ind_test=cond_ind_test,
-        selected_variables=selected_variables,
-        var_names=var_names,
-        verbosity=verbosity)
-
-    # results = pcmci.run_pcmci(
-    #     selected_links=None,
-    #     tau_min=1,
-    #     tau_max=tau_max,
-    #     save_iterations=False,
-
-    #     pc_alpha=pc_alpha,
-    #     max_conds_dim=None,
-    #     max_combinations=1,
-
-    #     max_conds_py=None,
-    #     max_conds_px=None,
-
-    #     fdr_method='fdr_bh',
-    # )
-    results = pcmci.run_pc_stable( 
-                      tau_max=tau_max,
-                      save_iterations=True,
-                      pc_alpha=0.2,
-                      max_conds_dim=None,
-                      max_combinations=1000,
-                      )
-
-    # pcmci._print_significant_links(
-    #                p_matrix=results['p_matrix'],
-    #                q_matrix=results['q_matrix'], 
-    #                val_matrix=results['val_matrix'],
-    #                alpha_level=alpha_level,
-    #                conf_matrix=results['conf_matrix'])
-
-    # pcmci.run_mci(
-    #     selected_links=None,
-    #     tau_min=1,
-    #     tau_max=tau_max,
-    #     parents = None,
-
-    #     max_conds_py=None,
-    #     max_conds_px=None,
-    # )
-    
-
+    pcmci.get_corrected_pvalues(np.random.rand(2,2,2))
diff --git a/tigramite/plotting.py b/tigramite/plotting.py
index 80590e0e..9aeb4f87 100644
--- a/tigramite/plotting.py
+++ b/tigramite/plotting.py
@@ -4,7 +4,7 @@
 #
 # License: GNU General Public License v3.0
 
-import numpy
+import numpy as np
 import matplotlib.transforms as transforms
 from matplotlib import pyplot, ticker
 from matplotlib.ticker import FormatStrFormatter
@@ -20,19 +20,19 @@ def _par_corr_trafo(cmi):
 
     # Set negative values to small positive number
     # (zero would be interpreted as non-significant in some functions)
-    if numpy.ndim(cmi) == 0:
+    if np.ndim(cmi) == 0:
         if cmi < 0.:
             cmi = 1E-8
     else:
         cmi[cmi < 0.] = 1E-8
 
-    return numpy.sqrt(1. - numpy.exp(-2. * cmi))
+    return np.sqrt(1. - np.exp(-2. * cmi))
 
 
 def _par_corr_to_cmi(par_corr):
     """Transformation of partial correlation to CMI scale."""
 
-    return -0.5 * numpy.log(1. - par_corr**2)
+    return -0.5 * np.log(1. - par_corr**2)
 
 
 def _myround(x, base=5, round_mode='updown'):
@@ -41,9 +41,9 @@ def _myround(x, base=5, round_mode='updown'):
     if round_mode == 'updown':
         return base * round(float(x) / base)
     elif round_mode == 'down':
-        return base * numpy.floor(float(x) / base)
+        return base * np.floor(float(x) / base)
     elif round_mode == 'up':
-        return base * numpy.ceil(float(x) / base)
+        return base * np.ceil(float(x) / base)
 
     return base * round(float(x) / base)
 
@@ -111,8 +111,8 @@ def _get_absmax(val_matrix):
     along the tau-axis and stores the (positive or negative) value in
     the (N,N)-array absmax."""
 
-    absmax_indices = numpy.abs(val_matrix).argmax(axis=2)
-    i, j = numpy.indices(val_matrix.shape[:2])
+    absmax_indices = np.abs(val_matrix).argmax(axis=2)
+    i, j = np.indices(val_matrix.shape[:2])
 
     return val_matrix[i, j, absmax_indices]
 
@@ -208,16 +208,16 @@ def _add_timeseries(fig, axes, i, time, dataseries, label,
         ax = axes
 
     if missing_flag is not None:
-        dataseries_nomissing = numpy.ma.masked_where(dataseries==missing_flag, 
+        dataseries_nomissing = np.ma.masked_where(dataseries==missing_flag, 
                                                  dataseries)
     else:
-        dataseries_nomissing = numpy.ma.masked_where(
-                                                 numpy.zeros(dataseries.shape), 
+        dataseries_nomissing = np.ma.masked_where(
+                                                 np.zeros(dataseries.shape), 
                                                  dataseries)
 
     if use_mask:
 
-        maskdata = numpy.ma.masked_where(mask, dataseries_nomissing)
+        maskdata = np.ma.masked_where(mask, dataseries_nomissing)
 
         if grey_masked_samples == 'fill':
             ax.fill_between(time, maskdata.min(), maskdata.max(),
@@ -268,17 +268,13 @@ def _add_timeseries(fig, axes, i, time, dataseries, label,
     pyplot.tight_layout()
 
 
-def plot_timeseries(data, 
-                    datatime=None, 
-                    var_names=None, 
+def plot_timeseries(dataframe=None, 
                     save_name=None,
                     fig_axes=None,
                     figsize=None,
                     var_units=None,
                     time_label='time',
                     use_mask=False,
-                    mask=None,
-                    missing_flag=None,
                     grey_masked_samples=False,
                     data_linewidth=1.,
                     skip_ticks_data_x=1,
@@ -289,14 +285,10 @@ def plot_timeseries(data,
     
     Parameters
     ----------
-    data : array-like
-        Data series array of shape (T, N).
-
-    datatime : array-like, optional (default: None)
-        Timelabel array. If None, range(T) is used.
-
-    var_names : list, optional (default: None)
-        List of variable names. If None, range(N) is used.
+    dataframe : data object, optional
+        This is the Tigramite dataframe object. It has the attributes
+        dataframe.values yielding a np array of shape (observations T,
+        variables N) and optionally a mask of the same shape.
 
     save_name : str, optional (default: None)
         Name of figure file to save figure. If None, figure is shown in window.
@@ -318,15 +310,6 @@ def plot_timeseries(data,
     use_mask : bool, optional (default: False)
         Whether to use masked data.
 
-    mask : array-like, optional (default: None)
-        Data mask where True labels masked samples.
-
-    missing_flag : number, optional (default: None)
-        Flag for missing values in dataframe. Dismisses all time slices of
-        samples where missing values occur in any variable and also flags
-        samples for all lags up to 2*tau_max. This avoids biases, see section on
-        masking in Supplement of [1]_.
-
     grey_masked_samples : bool, optional (default: False)
         Whether to mark masked samples by grey fills ('fill') or grey data ('data').
 
@@ -343,13 +326,14 @@ def plot_timeseries(data,
         Fontsize of variable labels.
     """
 
-    T, N = data.shape
-
-    if var_names is None:
-        var_names = range(N)
+    # Read in all attributes from dataframe
+    data = dataframe.values
+    mask = dataframe.mask
+    var_names = dataframe.var_names
+    missing_flag = dataframe.missing_flag
+    datatime = dataframe.datatime
 
-    if datatime is None:
-        datatime = numpy.arange(T)
+    T, N = data.shape
 
     if var_units is None:
         var_units = ['' for i in range(N)]
@@ -388,7 +372,7 @@ def plot_timeseries(data,
     if save_name is not None:
         fig.savefig(save_name)
     else:
-        pyplot.show()
+        return fig, axes
 
 def plot_lagfuncs(val_matrix, name=None, setup_args={}, add_lagfunc_args={}):
     """Wrapper helper function to plot lag functions.
@@ -485,7 +469,7 @@ class setup_matrix():
     lag_units : str, optional (default: '')
 
     lag_array : array, optional (default: None)
-        Optional specification of lags overwriting numpy.arange(0, tau_max+1)
+        Optional specification of lags overwriting np.arange(0, tau_max+1)
 
     label_fontsize : int, optional (default: 10)   
         Fontsize of variable labels.
@@ -511,7 +495,7 @@ def __init__(self, N, tau_max,
         self.labels = []
         self.lag_units = lag_units
         # if lag_array is None:
-        #     self.lag_array = numpy.arange(0, self.tau_max + 1)
+        #     self.lag_array = np.arange(0, self.tau_max + 1)
         # else:
         self.lag_array = lag_array
         if x_base is None:
@@ -564,29 +548,30 @@ def __init__(self, N, tau_max,
                     skip=(1, 1))
                 if x_base is not None:
                     self.axes_dict[(i, j)].xaxis.set_major_locator(
-                        ticker.FixedLocator(numpy.arange(0, self.tau_max + 1,
+                        ticker.FixedLocator(np.arange(0, self.tau_max + 1,
                                                          x_base)))
                     if x_base / 2. % 1 == 0:
                         self.axes_dict[(i, j)].xaxis.set_minor_locator(
-                            ticker.FixedLocator(numpy.arange(0, self.tau_max +
+                            ticker.FixedLocator(np.arange(0, self.tau_max +
                                                              1,
                                                              x_base / 2.)))
                 if y_base is not None:
                     self.axes_dict[(i, j)].yaxis.set_major_locator(
                         ticker.FixedLocator(
-                            numpy.arange(_myround(minimum, y_base, 'down'),
+                            np.arange(_myround(minimum, y_base, 'down'),
                                          _myround(maximum, y_base, 'up') +
                                          y_base, y_base)))
                     self.axes_dict[(i, j)].yaxis.set_minor_locator(
                         ticker.FixedLocator(
-                            numpy.arange(_myround(minimum, y_base, 'down'),
+                            np.arange(_myround(minimum, y_base, 'down'),
                                          _myround(maximum, y_base, 'up') +
                                          y_base, y_base / 2.)))
 
                     self.axes_dict[(i, j)].set_ylim(
                         _myround(minimum, y_base, 'down'),
                         _myround(maximum, y_base, 'up'))
-                self.axes_dict[(i, j)].label_outer()
+                if j != 0:    
+                    self.axes_dict[(i, j)].get_yaxis().set_ticklabels([]) #label_outer()
                 self.axes_dict[(i, j)].set_xlim(0, self.tau_max)
                 if plot_gridlines:
                     self.axes_dict[(i, j)].grid(True, which='major',
@@ -648,14 +633,14 @@ def add_lagfuncs(self, val_matrix,
         for ij in list(self.axes_dict):
             i = ij[0]
             j = ij[1]
-            maskedres = numpy.copy(val_matrix[i, j, int(i == j):])
+            maskedres = np.copy(val_matrix[i, j, int(i == j):])
             self.axes_dict[(i, j)].plot(range(int(i == j), self.tau_max + 1),
                                         maskedres,
                                         linestyle='', color=color,
                                         marker=marker, markersize=markersize,
                                         alpha=alpha, clip_on=False)
             if conf_matrix is not None:
-                maskedconfres = numpy.copy(conf_matrix[i, j, int(i == j):])
+                maskedconfres = np.copy(conf_matrix[i, j, int(i == j):])
                 self.axes_dict[(i, j)].plot(range(int(i == j),
                                                   self.tau_max + 1),
                                             maskedconfres[:, 0],
@@ -672,7 +657,7 @@ def add_lagfuncs(self, val_matrix,
                                             alpha=alpha, clip_on=False)
 
             self.axes_dict[(i, j)].plot(range(int(i == j), self.tau_max + 1),
-                                        numpy.zeros(self.tau_max + 1 -
+                                        np.zeros(self.tau_max + 1 -
                                                     int(i == j)),
                                         color='black', linestyle='dotted',
                                         linewidth=.1)
@@ -745,7 +730,7 @@ def savefig(self, name=None):
                 horizontalalignment='center', fontsize=self.label_fontsize)
 
         if self.lag_array is not None:
-            assert self.lag_array.shape == numpy.arange(self.tau_max + 1).shape
+            assert self.lag_array.shape == np.arange(self.tau_max + 1).shape
             for ij in list(self.axes_dict):
                 i = ij[0]
                 j = ij[1]            
@@ -797,7 +782,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
         n2 = G.node[v]['patch']
 
         if directed:
-            rad = curved_radius
+            rad = -1.*curved_radius
 #            facecolor = d['directed_color']
 #            edgecolor = d['directed_edgecolor']
             if cmap_links is not None:
@@ -819,12 +804,12 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
             alpha = d['directed_alpha']
             if (u, v) in seen:
                 rad = seen.get((u, v))
-                rad = (rad + numpy.sign(rad) * 0.1) * -1.
+                rad = (rad + np.sign(rad) * 0.1) * -1.
             arrowstyle = arrowstyle
             link_edge = d['directed_edge']
             linestyle = 'solid'
             linewidth = 0.
-            if d['directed_attribute'] == 'spurious':
+            if d.get('directed_attribute', None) == 'spurious':
                 facecolor = 'grey'
             #     linestyle = 'dashed'
 
@@ -850,17 +835,10 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
             alpha = d['undirected_alpha']
             arrowstyle = 'simple,head_length=0.0001'
             link_edge = d['undirected_edge']
-            try:
-                linestyle = d['undirected_style']
-            except:
-                linestyle = 'solid'
+            linestyle = undirected_style
             linewidth = 0.
-            try:
-                if d['undirected_attribute'] == 'spurious':
-                    facecolor = 'grey'
-            except:
-                pass
-                # facecolor = 
+            if d.get('undirected_attribute', None) == 'spurious':
+                facecolor = 'grey'
                 # linestyle = 'dashed'
 
             # print d['undirected_attribute']
@@ -890,7 +868,8 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
                             linestyle=linestyle,
                             color=facecolor,
                             clip_on=False,
-                            patchA=n1, patchB=n2)
+                            patchA=n1, patchB=n2
+                            )
         ax.add_patch(e)
 
         if d['label'] is not None and directed:
@@ -921,7 +900,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
     ##
     # Draw nodes
     ##
-    node_sizes = numpy.zeros((len(node_rings), N))
+    node_sizes = np.zeros((len(node_rings), N))
     for ring in list(node_rings):  # iterate through to get all node sizes
         if node_rings[ring]['sizes'] is not None:
             node_sizes[ring] = node_rings[ring]['sizes']
@@ -973,7 +952,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
                 cb_n = pyplot.colorbar(
                     data_to_rgb, cax=cax_n, orientation='horizontal')
                 try:
-                    cb_n.set_ticks(numpy.arange(_myround(vmin,
+                    cb_n.set_ticks(np.arange(_myround(vmin,
                                 node_rings[ring]['ticks'], 'down'), _myround(
                         vmax, node_rings[ring]['ticks'], 'up') +
                         node_rings[ring]['ticks'], node_rings[ring]['ticks']))
@@ -1024,7 +1003,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
 
             if log_sizes:
                 ax.text(0., 0., '         ' * ring + '%.2f' %
-                        (numpy.exp(max_sizes[ring]) - 1.),
+                        (np.exp(max_sizes[ring]) - 1.),
                         fontsize=node_label_size,
                         horizontalalignment='left', verticalalignment='center')
             else:
@@ -1059,12 +1038,12 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
 
     if cmap_links is not None and len(all_links_weights) > 0:
         if links_vmin is None:
-            links_vmin = numpy.array(all_links_weights).min()
+            links_vmin = np.array(all_links_weights).min()
         if links_vmax is None:
-            links_vmax = numpy.array(all_links_weights).max()
+            links_vmax = np.array(all_links_weights).max()
         data_to_rgb_links = pyplot.cm.ScalarMappable(
             norm=None, cmap=pyplot.get_cmap(cmap_links))
-        data_to_rgb_links.set_array(numpy.array(all_links_weights))
+        data_to_rgb_links.set_array(np.array(all_links_weights))
         data_to_rgb_links.set_clim(vmin=links_vmin, vmax=links_vmax)
         # Create colorbars for links
 # cax_e = pyplot.axes([.8, ax.figbox.bounds[1]+0.5, 0.025, 0.35],
@@ -1078,8 +1057,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
             cb_e = pyplot.colorbar(
                 data_to_rgb_links, cax=cax_e, orientation='horizontal')
             try:
-                cb_e.set_ticks(numpy.arange(_myround(links_vmin, links_ticks,
-                                                    'down'),
+                cb_e.set_ticks(np.arange(_myround(links_vmin, links_ticks, 'down'),
                                          _myround(links_vmax, links_ticks, 'up') +
                                          links_ticks, links_ticks))
             except:
@@ -1092,12 +1070,12 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
 
     if cmap_links_edges is not None and len(all_links_edge_weights) > 0:
         if links_edges_vmin is None:
-            links_edges_vmin = numpy.array(all_links_edge_weights).min()
+            links_edges_vmin = np.array(all_links_edge_weights).min()
         if links_edges_vmax is None:
-            links_edges_vmax = numpy.array(all_links_edge_weights).max()
+            links_edges_vmax = np.array(all_links_edge_weights).max()
         data_to_rgb_links_edges = pyplot.cm.ScalarMappable(
             norm=None, cmap=pyplot.get_cmap(cmap_links_edges))
-        data_to_rgb_links_edges.set_array(numpy.array(all_links_edge_weights))
+        data_to_rgb_links_edges.set_array(np.array(all_links_edge_weights))
         data_to_rgb_links_edges.set_clim(
             vmin=links_edges_vmin, vmax=links_edges_vmax)
 
@@ -1112,7 +1090,7 @@ def draw_edge(ax, u, v, d, seen, arrowstyle='simple', directed=True):
         cb_e = pyplot.colorbar(
             data_to_rgb_links_edges, cax=cax_e, orientation='horizontal')
         try:
-            cb_e.set_ticks(numpy.arange(_myround(links_edges_vmin,
+            cb_e.set_ticks(np.arange(_myround(links_edges_vmin,
                                                 links_edges_ticks, 'down'),
                                         _myround(links_edges_vmax,
                                                 links_edges_ticks, 'up') +
@@ -1273,7 +1251,7 @@ def plot_graph(val_matrix,
         Fontsize of link labels.
 
     lag_array : array, optional (default: None)
-        Optional specification of lags overwriting numpy.arange(0, tau_max+1)
+        Optional specification of lags overwriting np.arange(0, tau_max+1)
 
     network_lower_bound : float, optional (default: 0.2)
         Fraction of vertical space below graph plot.
@@ -1294,10 +1272,10 @@ def plot_graph(val_matrix,
         raise ValueError("Need to specify either sig_thres or link_matrix")
 
     elif sig_thres is not None and link_matrix is None:
-        link_matrix = numpy.abs(val_matrix) >= sig_thres
+        link_matrix = np.abs(val_matrix) >= sig_thres
 
 
-    if link_width is not None and not numpy.all(link_width >= 0.):
+    if link_width is not None and not np.all(link_width >= 0.):
         raise ValueError("link_width must be non-negative")
 
     N, N, dummy = val_matrix.shape
@@ -1308,12 +1286,12 @@ def plot_graph(val_matrix,
 
     # Define graph links by absolute maximum (positive or negative like for
     # partial correlation)
-    # val_matrix[numpy.abs(val_matrix) < sig_thres] = 0.
+    # val_matrix[np.abs(val_matrix) < sig_thres] = 0.
 
     net = _get_absmax(val_matrix * link_matrix)  
     G = networkx.DiGraph(net)
 
-    node_color = numpy.zeros(N)
+    node_color = np.zeros(N)
     # list of all strengths for color map
     all_strengths = []
     # Add attributes, contemporaneous and directed links are handled separately
@@ -1321,7 +1299,7 @@ def plot_graph(val_matrix,
         # average lagfunc for link u --> v ANDOR u -- v
         if tau_max > 0:
             # argmax of absolute maximum
-            argmax = numpy.abs(val_matrix[u, v][1:]).argmax() + 1
+            argmax = np.abs(val_matrix[u, v][1:]).argmax() + 1
         else:
             argmax = 0
         if u != v:
@@ -1330,21 +1308,21 @@ def plot_graph(val_matrix,
             # Here we use the  maximum for the width and weight (=color)
             # of the link
             # Draw link if u--v OR v--u at lag 0 is nonzero
-            # dic['undirected'] = ((numpy.abs(val_matrix[u, v][0]) >=
+            # dic['undirected'] = ((np.abs(val_matrix[u, v][0]) >=
             #                       sig_thres[u, v][0]) or
-            #                      (numpy.abs(val_matrix[v, u][0]) >=
+            #                      (np.abs(val_matrix[v, u][0]) >=
             #                       sig_thres[v, u][0]))
             dic['undirected'] = (link_matrix[u,v,0] or link_matrix[v,u,0])
             dic['undirected_alpha'] = alpha
             # value at argmax of average
-            if numpy.abs(val_matrix[u, v][0] - val_matrix[v, u][0]) > .0001:
+            if np.abs(val_matrix[u, v][0] - val_matrix[v, u][0]) > .0001:
                 print("Contemporaneous I(%d; %d)=%.3f != I(%d; %d)=%.3f" % (
                       u, v, val_matrix[u, v][0], v, u, val_matrix[v, u][0]) +
                       " due to conditions, finite sample effects or "
                       "masking, here edge color = "
                       "larger (absolute) value.")
             dic['undirected_color'] = _get_absmax(
-                numpy.array([[[val_matrix[u, v][0],
+                np.array([[[val_matrix[u, v][0],
                                val_matrix[v, u][0]]]])).squeeze()
             if link_width is None:
                 dic['undirected_width'] = arrow_linewidth
@@ -1368,9 +1346,9 @@ def plot_graph(val_matrix,
 
             if tau_max > 0:
                 # True if ensemble mean at lags > 0 is nonzero
-                # dic['directed'] = numpy.any(
-                #     numpy.abs(val_matrix[u, v][1:]) >= sig_thres[u, v][1:])
-                dic['directed'] = numpy.any(link_matrix[u,v,1:])
+                # dic['directed'] = np.any(
+                #     np.abs(val_matrix[u, v][1:]) >= sig_thres[u, v][1:])
+                dic['directed'] = np.any(link_matrix[u,v,1:])
             else:
                 dic['directed'] = False
 
@@ -1396,8 +1374,8 @@ def plot_graph(val_matrix,
             # Sorted list of significant lags (only if robust wrt
             # d['min_ensemble_frac'])
             if tau_max > 0:
-                lags = numpy.abs(val_matrix[u, v][1:]).argsort()[::-1] + 1
-                sig_lags = (numpy.where(link_matrix[u, v,1:])[0] + 1).tolist()
+                lags = np.abs(val_matrix[u, v][1:]).argsort()[::-1] + 1
+                sig_lags = (np.where(link_matrix[u, v,1:])[0] + 1).tolist()
             else:
                 lags, sig_lags = [], []
             if lag_array is not None:
@@ -1470,7 +1448,7 @@ def plot_graph(val_matrix,
     if save_name is not None:
         pyplot.savefig(save_name)
     else:
-        pyplot.show()
+        return fig, ax
 
 
 def plot_time_series_graph(val_matrix, 
@@ -1496,7 +1474,8 @@ def plot_time_series_graph(val_matrix,
         node_label_size=10,
         label_space_left=0.1,
         label_space_top=0.,
-        network_lower_bound=0.2
+        network_lower_bound=0.2,
+        undirected_style='dashed'
                            ):
     """Creates a time series graph.
 
@@ -1581,6 +1560,9 @@ def plot_time_series_graph(val_matrix,
 
     network_lower_bound : float, optional (default: 0.2)
         Fraction of vertical space below graph plot.
+
+    undirected_style : string, optional (default: 'dashed')
+        Style of undirected contemporaneous links.
     """
 
     import networkx
@@ -1596,10 +1578,10 @@ def plot_time_series_graph(val_matrix,
         raise ValueError("Need to specify either sig_thres or link_matrix")
 
     elif sig_thres is not None and link_matrix is None:
-        link_matrix = numpy.abs(val_matrix) >= sig_thres
+        link_matrix = np.abs(val_matrix) >= sig_thres
 
 
-    if link_width is not None and not numpy.all(link_width >= 0.):
+    if link_width is not None and not np.all(link_width >= 0.):
         raise ValueError("link_width must be non-negative")
 
     N, N, dummy = val_matrix.shape
@@ -1621,12 +1603,12 @@ def translate(row, lag):
 
     # Define graph links by absolute maximum (positive or negative like for
     # partial correlation)
-    tsg = numpy.zeros((N * max_lag, N * max_lag))
-    tsg_attr = numpy.zeros((N * max_lag, N * max_lag))
+    tsg = np.zeros((N * max_lag, N * max_lag))
+    tsg_attr = np.zeros((N * max_lag, N * max_lag))
 
-    for i, j, tau in numpy.column_stack(numpy.where(link_matrix)):
+    for i, j, tau in np.column_stack(np.where(link_matrix)):
         #                    print '\n',i, j, tau
-        #                    print numpy.where(nonmasked[:,j])[0]
+        #                    print np.where(nonmasked[:,j])[0]
 
         for t in range(max_lag):
             if (0 <= translate(i, t - tau) and
@@ -1639,7 +1621,7 @@ def translate(row, lag):
 
     G = networkx.DiGraph(tsg)
 
-    # node_color = numpy.zeros(N)
+    # node_color = np.zeros(N)
     # list of all strengths for color map
     all_strengths = []
     # Add attributes, contemporaneous and directed links are handled separately
@@ -1658,7 +1640,7 @@ def translate(row, lag):
 
             dic['undirected_alpha'] = alpha
             dic['undirected_color'] = _get_absmax(
-                numpy.array([[[tsg_attr[u, v],
+                np.array([[[tsg_attr[u, v],
                                tsg_attr[v, u]]]])
             ).squeeze()
             dic['undirected_width'] = arrow_linewidth
@@ -1682,17 +1664,17 @@ def translate(row, lag):
     if len(all_strengths) == 0:
         all_strengths = [0.]
 
-    posarray = numpy.zeros((N * max_lag, 2))
+    posarray = np.zeros((N * max_lag, 2))
     for i in range(N * max_lag):
 
-        posarray[i] = numpy.array([(i % max_lag), (1. - i // max_lag)])
+        posarray[i] = np.array([(i % max_lag), (1. - i // max_lag)])
 
     pos_tmp = {}
     for i in range(N * max_lag):
         # for n in range(N):
         #     for tau in range(max_lag):
         #         i = n*N + tau
-        pos_tmp[i] = numpy.array([((i % max_lag) - posarray.min(axis=0)[0]) /
+        pos_tmp[i] = np.array([((i % max_lag) - posarray.min(axis=0)[0]) /
                                   (posarray.max(axis=0)[0] -
                                    posarray.min(axis=0)[0]),
                                   ((1. - i // max_lag) -
@@ -1734,8 +1716,8 @@ def translate(row, lag):
         curved_radius=curved_radius, label_fontsize=label_fontsize,
         label_fraction=.5,
         link_colorbar_label=link_colorbar_label, undirected_curved=True,
-        network_lower_bound=network_lower_bound
-        # undirected_style=undirected_style
+        network_lower_bound=network_lower_bound,
+        undirected_style=undirected_style
         )
 
     for i in range(N):
@@ -1746,7 +1728,7 @@ def translate(row, lag):
                 horizontalalignment='left', verticalalignment='center',
                 transform=trans)
 
-    for tau in numpy.arange(max_lag - 1, -1, -1):
+    for tau in np.arange(max_lag - 1, -1, -1):
         trans = transforms.blended_transform_factory(
             ax.transData, fig.transFigure)
         if tau == max_lag - 1:
@@ -1909,7 +1891,7 @@ def plot_mediation_time_series_graph(
     else:
         fig, ax = fig_ax
 
-    if link_width is not None and not numpy.all(link_width >= 0.):
+    if link_width is not None and not np.all(link_width >= 0.):
         raise ValueError("link_width must be non-negative")
 
     if order is None:
@@ -1924,11 +1906,11 @@ def translate(row, lag):
     # Define graph links by absolute maximum (positive or negative like for
     # partial correlation)
     tsg = tsg_path_val_matrix
-    tsg_attr = numpy.zeros((N * max_lag, N * max_lag))
+    tsg_attr = np.zeros((N * max_lag, N * max_lag))
 
     G = networkx.DiGraph(tsg)
 
-    # node_color = numpy.zeros(N)
+    # node_color = np.zeros(N)
     # list of all strengths for color map
     all_strengths = []
     # Add attributes, contemporaneous and directed links are handled separately
@@ -1947,7 +1929,7 @@ def translate(row, lag):
 
             dic['undirected_alpha'] = alpha
             dic['undirected_color'] = _get_absmax(
-                numpy.array([[[tsg[u, v],
+                np.array([[[tsg[u, v],
                                tsg[v, u]]]])
             ).squeeze()
             dic['undirected_width'] = arrow_linewidth
@@ -1971,17 +1953,17 @@ def translate(row, lag):
     if len(all_strengths) == 0:
         all_strengths = [0.]
 
-    posarray = numpy.zeros((N * max_lag, 2))
+    posarray = np.zeros((N * max_lag, 2))
     for i in range(N * max_lag):
 
-        posarray[i] = numpy.array([(i % max_lag), (1. - i // max_lag)])
+        posarray[i] = np.array([(i % max_lag), (1. - i // max_lag)])
 
     pos_tmp = {}
     for i in range(N * max_lag):
         # for n in range(N):
         #     for tau in range(max_lag):
         #         i = n*N + tau
-        pos_tmp[i] = numpy.array([((i % max_lag) - posarray.min(axis=0)[0]) /
+        pos_tmp[i] = np.array([((i % max_lag) - posarray.min(axis=0)[0]) /
                                   (posarray.max(axis=0)[0] -
                                    posarray.min(axis=0)[0]),
                                   ((1. - i // max_lag) -
@@ -1994,7 +1976,7 @@ def translate(row, lag):
         for tau in range(max_lag):
             pos[n * max_lag + tau] = pos_tmp[order[n] * max_lag + tau]
 
-    node_color = numpy.zeros(N * max_lag)
+    node_color = np.zeros(N * max_lag)
     for inet, n in enumerate(range(0, N * max_lag, max_lag)):
         node_color[n:n+max_lag] = path_node_array[inet]
 
@@ -2046,7 +2028,7 @@ def translate(row, lag):
                 horizontalalignment='left', verticalalignment='center',
                 transform=trans)
 
-    for tau in numpy.arange(max_lag - 1, -1, -1):
+    for tau in np.arange(max_lag - 1, -1, -1):
         trans = transforms.blended_transform_factory(
             ax.transData, fig.transFigure)
         if tau == max_lag - 1:
@@ -2196,7 +2178,7 @@ def plot_mediation_graph(
         Fraction of vertical space below graph plot.
 
     lag_array : array, optional (default: None)
-        Optional specification of lags overwriting numpy.arange(0, tau_max+1)
+        Optional specification of lags overwriting np.arange(0, tau_max+1)
     """
     import networkx
 
@@ -2208,7 +2190,7 @@ def plot_mediation_graph(
     else:
         fig, ax = fig_ax
 
-    if link_width is not None and not numpy.all(link_width >= 0.):
+    if link_width is not None and not np.all(link_width >= 0.):
         raise ValueError("link_width must be non-negative")
 
     N, N, dummy = val_matrix.shape
@@ -2219,12 +2201,12 @@ def plot_mediation_graph(
 
     # Define graph links by absolute maximum (positive or negative like for
     # partial correlation)
-    # val_matrix[numpy.abs(val_matrix) < sig_thres] = 0.
+    # val_matrix[np.abs(val_matrix) < sig_thres] = 0.
     link_matrix = val_matrix != 0.
     net = _get_absmax(val_matrix)  
     G = networkx.DiGraph(net)
 
-    node_color = numpy.zeros(N)
+    node_color = np.zeros(N)
     # list of all strengths for color map
     all_strengths = []
     # Add attributes, contemporaneous and directed links are handled separately
@@ -2234,7 +2216,7 @@ def plot_mediation_graph(
         # average lagfunc for link u --> v ANDOR u -- v
         if tau_max > 0:
             # argmax of absolute maximum
-            argmax = numpy.abs(val_matrix[u, v][1:]).argmax() + 1
+            argmax = np.abs(val_matrix[u, v][1:]).argmax() + 1
         else:
             argmax = 0
         if u != v:
@@ -2243,21 +2225,21 @@ def plot_mediation_graph(
             # Here we use the  maximum for the width and weight (=color)
             # of the link
             # Draw link if u--v OR v--u at lag 0 is nonzero
-            # dic['undirected'] = ((numpy.abs(val_matrix[u, v][0]) >=
+            # dic['undirected'] = ((np.abs(val_matrix[u, v][0]) >=
             #                       sig_thres[u, v][0]) or
-            #                      (numpy.abs(val_matrix[v, u][0]) >=
+            #                      (np.abs(val_matrix[v, u][0]) >=
             #                       sig_thres[v, u][0]))
             dic['undirected'] = (link_matrix[u,v,0] or link_matrix[v,u,0])
             dic['undirected_alpha'] = alpha
             # value at argmax of average
-            if numpy.abs(val_matrix[u, v][0] - val_matrix[v, u][0]) > .0001:
+            if np.abs(val_matrix[u, v][0] - val_matrix[v, u][0]) > .0001:
                 print("Contemporaneous I(%d; %d)=%.3f != I(%d; %d)=%.3f" % (
                       u, v, val_matrix[u, v][0], v, u, val_matrix[v, u][0]) +
                       " due to conditions, finite sample effects or "
                       "masking, here edge color = "
                       "larger (absolute) value.")
             dic['undirected_color'] = _get_absmax(
-                numpy.array([[[val_matrix[u, v][0],
+                np.array([[[val_matrix[u, v][0],
                                val_matrix[v, u][0]]]])).squeeze()
             if link_width is None:
                 dic['undirected_width'] = arrow_linewidth
@@ -2269,9 +2251,9 @@ def plot_mediation_graph(
 
             if tau_max > 0:
                 # True if ensemble mean at lags > 0 is nonzero
-                # dic['directed'] = numpy.any(
-                #     numpy.abs(val_matrix[u, v][1:]) >= sig_thres[u, v][1:])
-                dic['directed'] = numpy.any(link_matrix[u,v,1:])
+                # dic['directed'] = np.any(
+                #     np.abs(val_matrix[u, v][1:]) >= sig_thres[u, v][1:])
+                dic['directed'] = np.any(link_matrix[u,v,1:])
             else:
                 dic['directed'] = False
             dic['directed_alpha'] = alpha
@@ -2289,8 +2271,8 @@ def plot_mediation_graph(
             # Sorted list of significant lags (only if robust wrt
             # d['min_ensemble_frac'])
             if tau_max > 0:
-                lags = numpy.abs(val_matrix[u, v][1:]).argsort()[::-1] + 1
-                sig_lags = (numpy.where(link_matrix[u, v,1:])[0] + 1).tolist()
+                lags = np.abs(val_matrix[u, v][1:]).argsort()[::-1] + 1
+                sig_lags = (np.where(link_matrix[u, v,1:])[0] + 1).tolist()
             else:
                 lags, sig_lags = [], []
             if lag_array is not None:
@@ -2363,46 +2345,49 @@ def plot_mediation_graph(
 if __name__ == '__main__':
 
 
-    numpy.random.seed(42)
-    val_matrix = numpy.random.rand(3,3,4)
-    link_matrix = numpy.abs(val_matrix) > .7
+    from tigramite.independence_tests import ParCorr
+    import tigramite.data_processing as pp
+    np.random.seed(42)
+    val_matrix = np.random.rand(3,3,4)
+    link_matrix = np.abs(val_matrix) > .7
     # print link_matrix
+    data = np.random.randn(100,3)
+    mask = np.random.randint(0, 2, size=(100,3))
+    dataframe = pp.DataFrame(data, mask=mask)
+
+
+    # data = np.random.randn(100, 3)
+    # datatime = np.arange(100)
+    # mask = np.zeros(data.shape)
 
+    # mask[:int(len(data)/2)]=True
 
-    data = numpy.random.randn(100, 3)
-    datatime = numpy.arange(100)
-    mask = numpy.zeros(data.shape)
-
-    mask[:int(len(data)/2)]=True
-
-    data[:,0] = -99.
-    plot_lagfuncs(val_matrix=val_matrix, 
-        setup_args={'figsize':(10,10),
-     'label_space_top':0.05,
-     'label_space_left':0.1,
-      'x_base':1, 'y_base':5,
-        'var_names':range(3), 
-        'lag_array':numpy.array(['a%d' % i for  i in range(4)])},
-        name='test.pdf',
- )
-
-
-    # plot_timeseries(data,  
-    #                 # datatime=None,
-    #                 # save_name=None,
-    #                 # fig_axes=None,
-    #                 # var_units=None,
-    #                 # time_label='years',
-    #                 # use_mask=False,
-    #                 # missing_flag=-99.,
-    #                 # mask=mask,
-    #                 # grey_masked_samples='fill',
-    #                 # data_linewidth=1.,
-    #                 # skip_ticks_data_x=1,
-    #                 # skip_ticks_data_y=1,
-    #                 # label_fontsize=8,
-    #                 # figsize=(3.375, 3.),
-    #                 )
+    # data[:,0] = -99.
+ #    plot_lagfuncs(val_matrix=val_matrix, 
+ #        setup_args={'figsize':(10,10),
+ #     'label_space_top':0.05,
+ #     'label_space_left':0.1,
+ #      'x_base':1, 'y_base':5,
+ #        'var_names':range(3), 
+ #        'lag_array':np.array(['a%d' % i for  i in range(4)])},
+ #        name='test.pdf',
+ # )
+
+
+    plot_timeseries(
+                    dataframe=dataframe,
+                    save_name='/home/rung_ja/Downloads/test.pdf',
+                    fig_axes=None,
+                    var_units=None,
+                    time_label='years',
+                    use_mask=True,
+                    grey_masked_samples='data',
+                    data_linewidth=1.,
+                    skip_ticks_data_x=1,
+                    skip_ticks_data_y=1,
+                    label_fontsize=8,
+                    figsize=(3.375, 3.),
+                    )
 
     # lagmat = setup_matrix(3, 3, range(3), lag_units = 'months')
 
@@ -2417,18 +2402,20 @@ def plot_mediation_graph(
     # ax = fig.add_subplot(111, frame_on=False)
 
     # plot_graph(
+    #     figsize=(3, 3),
     #     val_matrix=val_matrix,
     #     sig_thres=None,
     #     link_matrix=link_matrix,
     #     var_names=range(len(val_matrix)),
+    #     save_name='/home/rung_ja/Downloads/test.pdf',
     # )
 
 
-    plot_time_series_graph(
-        val_matrix=val_matrix,
-        sig_thres=None,
-        link_matrix=link_matrix,
-        var_names=range(len(val_matrix)),
-
-    )
-    pyplot.show()
\ No newline at end of file
+    # plot_time_series_graph(
+    #     val_matrix=val_matrix,
+    #     sig_thres=None,
+    #     link_matrix=link_matrix,
+    #     var_names=range(len(val_matrix)),
+    #     undirected_style='dashed',
+    # )
+    # pyplot.show()
diff --git a/tigramite/tigramite_cython_code.c b/tigramite/tigramite_cython_code.c
index b084a7b5..ce9e4eb3 100644
--- a/tigramite/tigramite_cython_code.c
+++ b/tigramite/tigramite_cython_code.c
@@ -1,13 +1,27 @@
-/* Generated by Cython 0.25.2 */
+/* Generated by Cython 0.28.5 */
+
+/* BEGIN: Cython Metadata
+{
+    "distutils": {
+        "depends": [],
+        "name": "tigramite.tigramite_cython_code",
+        "sources": [
+            "tigramite/tigramite_cython_code.pyx"
+        ]
+    },
+    "module_name": "tigramite.tigramite_cython_code"
+}
+END: Cython Metadata */
 
 #define PY_SSIZE_T_CLEAN
 #include "Python.h"
 #ifndef Py_PYTHON_H
     #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000)
-    #error Cython requires Python 2.6+ or Python 3.2+.
+#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
+    #error Cython requires Python 2.6+ or Python 3.3+.
 #else
-#define CYTHON_ABI "0_25_2"
+#define CYTHON_ABI "0_28_5"
+#define CYTHON_FUTURE_DIVISION 0
 #include <stddef.h>
 #ifndef offsetof
   #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
@@ -29,8 +43,9 @@
 #ifndef DL_EXPORT
   #define DL_EXPORT(t) t
 #endif
+#define __PYX_COMMA ,
 #ifndef HAVE_LONG_LONG
-  #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000)
+  #if PY_VERSION_HEX >= 0x02070000
     #define HAVE_LONG_LONG
   #endif
 #endif
@@ -46,8 +61,14 @@
   #define CYTHON_COMPILING_IN_CPYTHON 0
   #undef CYTHON_USE_TYPE_SLOTS
   #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #if PY_VERSION_HEX < 0x03050000
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
   #undef CYTHON_USE_PYLIST_INTERNALS
   #define CYTHON_USE_PYLIST_INTERNALS 0
   #undef CYTHON_USE_UNICODE_INTERNALS
@@ -66,6 +87,10 @@
   #define CYTHON_FAST_THREAD_STATE 0
   #undef CYTHON_FAST_PYCALL
   #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
 #elif defined(PYSTON_VERSION)
   #define CYTHON_COMPILING_IN_PYPY 0
   #define CYTHON_COMPILING_IN_PYSTON 1
@@ -73,6 +98,8 @@
   #ifndef CYTHON_USE_TYPE_SLOTS
     #define CYTHON_USE_TYPE_SLOTS 1
   #endif
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
   #undef CYTHON_USE_ASYNC_SLOTS
   #define CYTHON_USE_ASYNC_SLOTS 0
   #undef CYTHON_USE_PYLIST_INTERNALS
@@ -97,6 +124,10 @@
   #define CYTHON_FAST_THREAD_STATE 0
   #undef CYTHON_FAST_PYCALL
   #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
 #else
   #define CYTHON_COMPILING_IN_PYPY 0
   #define CYTHON_COMPILING_IN_PYSTON 0
@@ -104,6 +135,12 @@
   #ifndef CYTHON_USE_TYPE_SLOTS
     #define CYTHON_USE_TYPE_SLOTS 1
   #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYTYPE_LOOKUP
+    #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
+    #define CYTHON_USE_PYTYPE_LOOKUP 1
+  #endif
   #if PY_MAJOR_VERSION < 3
     #undef CYTHON_USE_ASYNC_SLOTS
     #define CYTHON_USE_ASYNC_SLOTS 0
@@ -143,6 +180,12 @@
   #ifndef CYTHON_FAST_PYCALL
     #define CYTHON_FAST_PYCALL 1
   #endif
+  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
+    #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000)
+  #endif
+  #ifndef CYTHON_USE_TP_FINALIZE
+    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
+  #endif
 #endif
 #if !defined(CYTHON_FAST_PYCCALL)
 #define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
@@ -153,6 +196,103 @@
   #undef BASE
   #undef MASK
 #endif
+#ifndef __has_attribute
+  #define __has_attribute(x) 0
+#endif
+#ifndef __has_cpp_attribute
+  #define __has_cpp_attribute(x) 0
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+#ifndef CYTHON_MAYBE_UNUSED_VAR
+#  if defined(__cplusplus)
+     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
+#  else
+#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
+#  endif
+#endif
+#ifndef CYTHON_NCP_UNUSED
+# if CYTHON_COMPILING_IN_CPYTHON
+#  define CYTHON_NCP_UNUSED
+# else
+#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
+# endif
+#endif
+#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
+#ifdef _MSC_VER
+    #ifndef _MSC_STDINT_H_
+        #if _MSC_VER < 1300
+           typedef unsigned char     uint8_t;
+           typedef unsigned int      uint32_t;
+        #else
+           typedef unsigned __int8   uint8_t;
+           typedef unsigned __int32  uint32_t;
+        #endif
+    #endif
+#else
+   #include <stdint.h>
+#endif
+#ifndef CYTHON_FALLTHROUGH
+  #if defined(__cplusplus) && __cplusplus >= 201103L
+    #if __has_cpp_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH [[fallthrough]]
+    #elif __has_cpp_attribute(clang::fallthrough)
+      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
+    #elif __has_cpp_attribute(gnu::fallthrough)
+      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
+    #endif
+  #endif
+  #ifndef CYTHON_FALLTHROUGH
+    #if __has_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
+    #else
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+  #if defined(__clang__ ) && defined(__apple_build_version__)
+    #if __apple_build_version__ < 7000000
+      #undef  CYTHON_FALLTHROUGH
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+#endif
+
+#ifndef CYTHON_INLINE
+  #if defined(__clang__)
+    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
+  #elif defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+
 #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
   #define Py_OptimizeFlag 0
 #endif
@@ -181,19 +321,91 @@
 #ifndef Py_TPFLAGS_HAVE_FINALIZE
   #define Py_TPFLAGS_HAVE_FINALIZE 0
 #endif
-#ifndef METH_FASTCALL
-  #define METH_FASTCALL 0x80
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args,
-                                              Py_ssize_t nargs, PyObject *kwnames);
+#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
+  #ifndef METH_FASTCALL
+     #define METH_FASTCALL 0x80
+  #endif
+  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
+  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
+                                                          Py_ssize_t nargs, PyObject *kwnames);
 #else
   #define __Pyx_PyCFunctionFast _PyCFunctionFast
+  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
 #endif
 #if CYTHON_FAST_PYCCALL
 #define __Pyx_PyFastCFunction_Check(func)\
-    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)))))
+    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)))))
 #else
 #define __Pyx_PyFastCFunction_Check(func) 0
 #endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
+  #define PyObject_Malloc(s)   PyMem_Malloc(s)
+  #define PyObject_Free(p)     PyMem_Free(p)
+  #define PyObject_Realloc(p)  PyMem_Realloc(p)
+#endif
+#if CYTHON_COMPILING_IN_PYSTON
+  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
+#else
+  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
+#endif
+#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#elif PY_VERSION_HEX >= 0x03060000
+  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
+#elif PY_VERSION_HEX >= 0x03000000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#else
+  #define __Pyx_PyThreadState_Current _PyThreadState_Current
+#endif
+#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
+#include "pythread.h"
+#define Py_tss_NEEDS_INIT 0
+typedef int Py_tss_t;
+static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
+  *key = PyThread_create_key();
+  return 0; // PyThread_create_key reports success always
+}
+static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
+  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
+  *key = Py_tss_NEEDS_INIT;
+  return key;
+}
+static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
+  PyObject_Free(key);
+}
+static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
+  return *key != Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
+  PyThread_delete_key(*key);
+  *key = Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
+  return PyThread_set_key_value(*key, value);
+}
+static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
+  return PyThread_get_key_value(*key);
+}
+#endif // TSS (Thread Specific Storage) API
+#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
+#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
+#else
+#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
+#endif
+#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
+#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
+#else
+#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
+#endif
 #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
   #define CYTHON_PEP393_ENABLED 1
   #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
@@ -238,18 +450,6 @@
 #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
   #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
 #endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_PYSTON
-  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
 #define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
 #define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
 #if PY_MAJOR_VERSION >= 3
@@ -266,6 +466,7 @@
   #define PyString_Type                PyUnicode_Type
   #define PyString_Check               PyUnicode_Check
   #define PyString_CheckExact          PyUnicode_CheckExact
+  #define PyObject_Unicode             PyObject_Str
 #endif
 #if PY_MAJOR_VERSION >= 3
   #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
@@ -277,8 +478,11 @@
 #ifndef PySet_CheckExact
   #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
 #endif
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+#if CYTHON_ASSUME_SAFE_MACROS
+  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
+#else
+  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
+#endif
 #if PY_MAJOR_VERSION >= 3
   #define PyIntObject                  PyLongObject
   #define PyInt_Type                   PyLong_Type
@@ -313,7 +517,7 @@
   #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
 #endif
 #if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
+  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func))
 #else
   #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
 #endif
@@ -322,68 +526,17 @@
     #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
     #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
   #else
-    typedef struct {
-        unaryfunc am_await;
-        unaryfunc am_aiter;
-        unaryfunc am_anext;
-    } __Pyx_PyAsyncMethodsStruct;
     #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
   #endif
 #else
   #define __Pyx_PyType_AsAsync(obj) NULL
 #endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
+#ifndef __Pyx_PyAsyncMethodsStruct
+    typedef struct {
+        unaryfunc am_await;
+        unaryfunc am_aiter;
+        unaryfunc am_anext;
+    } __Pyx_PyAsyncMethodsStruct;
 #endif
 
 #if defined(WIN32) || defined(MS_WINDOWS)
@@ -411,14 +564,6 @@ static CYTHON_INLINE float __PYX_NAN() {
   __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
 }
 
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-
 #ifndef __PYX_EXTERN_C
   #ifdef __cplusplus
     #define __PYX_EXTERN_C extern "C"
@@ -429,19 +574,20 @@ static CYTHON_INLINE float __PYX_NAN() {
 
 #define __PYX_HAVE__tigramite__tigramite_cython_code
 #define __PYX_HAVE_API__tigramite__tigramite_cython_code
+/* Early includes */
 #include <string.h>
 #include <stdio.h>
-#include <stdlib.h>
 #include "numpy/arrayobject.h"
 #include "numpy/ufuncobject.h"
 #include <math.h>
 #include "pythread.h"
+#include <stdlib.h>
 #include "pystate.h"
 #ifdef _OPENMP
 #include <omp.h>
 #endif /* _OPENMP */
 
-#ifdef PYREX_WITHOUT_ASSERTIONS
+#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
 #define CYTHON_WITHOUT_ASSERTIONS
 #endif
 
@@ -472,8 +618,8 @@ typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* enc
     #define __Pyx_sst_abs(value) abs(value)
 #elif SIZEOF_LONG >= SIZEOF_SIZE_T
     #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER) && defined (_M_X64)
-    #define __Pyx_sst_abs(value) _abs64(value)
+#elif defined (_MSC_VER)
+    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
 #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
     #define __Pyx_sst_abs(value) llabs(value)
 #elif defined (__GNUC__)
@@ -481,8 +627,8 @@ typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* enc
 #else
     #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
 #endif
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
 #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
 #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
 #define __Pyx_PyBytes_FromString        PyBytes_FromString
@@ -495,31 +641,37 @@ static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
     #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
     #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
 #endif
-#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
 #define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
 #define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
 #define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
 #define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
 #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if PY_MAJOR_VERSION < 3
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
     const Py_UNICODE *u_end = u;
     while (*u_end++) ;
     return (size_t)(u_end - u - 1);
 }
-#else
-#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
-#endif
 #define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
 #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
 #define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
 #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
 #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
+static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
 static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
 static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
+#define __Pyx_PySequence_Tuple(obj)\
+    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
 static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
 static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
 #if CYTHON_ASSUME_SAFE_MACROS
@@ -618,10 +770,12 @@ static int __Pyx_init_sys_getdefaultencoding_params(void) {
   #define likely(x)   (x)
   #define unlikely(x) (x)
 #endif /* __GNUC__ */
+static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
 
-static PyObject *__pyx_m;
+static PyObject *__pyx_m = NULL;
 static PyObject *__pyx_d;
 static PyObject *__pyx_b;
+static PyObject *__pyx_cython_runtime = NULL;
 static PyObject *__pyx_empty_tuple;
 static PyObject *__pyx_empty_bytes;
 static PyObject *__pyx_empty_unicode;
@@ -668,42 +822,7 @@ typedef struct {
   Py_ssize_t strides[8];
   Py_ssize_t suboffsets[8];
 } __Pyx_memviewslice;
-
-/* BufferFormatStructs.proto */
-#define IS_UNSIGNED(type) (((type) -1) > 0)
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
+#define __Pyx_MemoryView_Len(m)  (m.shape[0])
 
 /* Atomics.proto */
 #include <pythread.h>
@@ -754,18 +873,66 @@ typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
             __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
 #endif
 
-
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725
- * # in Cython to enable them only on the right systems.
- * 
- * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- */
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726
- * 
+/* BufferFormatStructs.proto */
+#define IS_UNSIGNED(type) (((type) -1) > 0)
+struct __Pyx_StructField_;
+#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
+typedef struct {
+  const char* name;
+  struct __Pyx_StructField_* fields;
+  size_t size;
+  size_t arraysize[8];
+  int ndim;
+  char typegroup;
+  char is_unsigned;
+  int flags;
+} __Pyx_TypeInfo;
+typedef struct __Pyx_StructField_ {
+  __Pyx_TypeInfo* type;
+  const char* name;
+  size_t offset;
+} __Pyx_StructField;
+typedef struct {
+  __Pyx_StructField* field;
+  size_t parent_offset;
+} __Pyx_BufFmt_StackElem;
+typedef struct {
+  __Pyx_StructField root;
+  __Pyx_BufFmt_StackElem* head;
+  size_t fmt_offset;
+  size_t new_count, enc_count;
+  size_t struct_alignment;
+  int is_complex;
+  char enc_type;
+  char new_packmode;
+  char enc_packmode;
+  char is_valid_array;
+} __Pyx_BufFmt_Context;
+
+/* ForceInitThreads.proto */
+#ifndef __PYX_FORCE_INIT_THREADS
+  #define __PYX_FORCE_INIT_THREADS 0
+#endif
+
+/* NoFastGil.proto */
+#define __Pyx_PyGILState_Ensure PyGILState_Ensure
+#define __Pyx_PyGILState_Release PyGILState_Release
+#define __Pyx_FastGIL_Remember()
+#define __Pyx_FastGIL_Forget()
+#define __Pyx_FastGilFuncInit()
+
+
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":730
+ * # in Cython to enable them only on the right systems.
+ * 
+ * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ */
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":731
+ * 
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
  * ctypedef npy_int32      int32_t
@@ -773,7 +940,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t;
  */
 typedef npy_int16 __pyx_t_5numpy_int16_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":732
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
@@ -782,7 +949,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t;
  */
 typedef npy_int32 __pyx_t_5numpy_int32_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":733
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t
  * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
@@ -791,7 +958,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t;
  */
 typedef npy_int64 __pyx_t_5numpy_int64_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":737
  * #ctypedef npy_int128     int128_t
  * 
  * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
@@ -800,7 +967,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t;
  */
 typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":738
  * 
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
@@ -809,7 +976,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t;
  */
 typedef npy_uint16 __pyx_t_5numpy_uint16_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":739
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
@@ -818,7 +985,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t;
  */
 typedef npy_uint32 __pyx_t_5numpy_uint32_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":740
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t
  * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
@@ -827,7 +994,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t;
  */
 typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":744
  * #ctypedef npy_uint128    uint128_t
  * 
  * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
@@ -836,7 +1003,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t;
  */
 typedef npy_float32 __pyx_t_5numpy_float32_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":745
  * 
  * ctypedef npy_float32    float32_t
  * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
@@ -845,7 +1012,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t;
  */
 typedef npy_float64 __pyx_t_5numpy_float64_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":754
  * # The int types are mapped a bit surprising --
  * # numpy.int corresponds to 'l' and numpy.long to 'q'
  * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
@@ -854,7 +1021,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t;
  */
 typedef npy_long __pyx_t_5numpy_int_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":755
  * # numpy.int corresponds to 'l' and numpy.long to 'q'
  * ctypedef npy_long       int_t
  * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
@@ -863,7 +1030,7 @@ typedef npy_long __pyx_t_5numpy_int_t;
  */
 typedef npy_longlong __pyx_t_5numpy_long_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":756
  * ctypedef npy_long       int_t
  * ctypedef npy_longlong   long_t
  * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
@@ -872,7 +1039,7 @@ typedef npy_longlong __pyx_t_5numpy_long_t;
  */
 typedef npy_longlong __pyx_t_5numpy_longlong_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":758
  * ctypedef npy_longlong   longlong_t
  * 
  * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
@@ -881,7 +1048,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t;
  */
 typedef npy_ulong __pyx_t_5numpy_uint_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":759
  * 
  * ctypedef npy_ulong      uint_t
  * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
@@ -890,7 +1057,7 @@ typedef npy_ulong __pyx_t_5numpy_uint_t;
  */
 typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":760
  * ctypedef npy_ulong      uint_t
  * ctypedef npy_ulonglong  ulong_t
  * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
@@ -899,7 +1066,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
  */
 typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":757
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":762
  * ctypedef npy_ulonglong  ulonglong_t
  * 
  * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
@@ -908,7 +1075,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
  */
 typedef npy_intp __pyx_t_5numpy_intp_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":763
  * 
  * ctypedef npy_intp       intp_t
  * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
@@ -917,7 +1084,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t;
  */
 typedef npy_uintp __pyx_t_5numpy_uintp_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":765
  * ctypedef npy_uintp      uintp_t
  * 
  * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
@@ -926,7 +1093,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t;
  */
 typedef npy_double __pyx_t_5numpy_float_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":761
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":766
  * 
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
@@ -935,7 +1102,7 @@ typedef npy_double __pyx_t_5numpy_float_t;
  */
 typedef npy_double __pyx_t_5numpy_double_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":767
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t
  * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
@@ -944,7 +1111,7 @@ typedef npy_double __pyx_t_5numpy_double_t;
  */
 typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
 
-/* "tigramite/tigramite_cython_code.pyx":213
+/* "tigramite/tigramite_cython_code.pyx":214
  * import cython
  * 
  * ctypedef np.double_t DTYPE_t             # <<<<<<<<<<<<<<
@@ -983,7 +1150,7 @@ struct __pyx_MemviewEnum_obj;
 struct __pyx_memoryview_obj;
 struct __pyx_memoryviewslice_obj;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":769
  * ctypedef npy_longdouble longdouble_t
  * 
  * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
@@ -992,7 +1159,7 @@ struct __pyx_memoryviewslice_obj;
  */
 typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":770
  * 
  * ctypedef npy_cfloat      cfloat_t
  * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
@@ -1001,7 +1168,7 @@ typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
  */
 typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":771
  * ctypedef npy_cfloat      cfloat_t
  * ctypedef npy_cdouble     cdouble_t
  * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
@@ -1010,7 +1177,7 @@ typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
  */
 typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":768
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":773
  * ctypedef npy_clongdouble clongdouble_t
  * 
  * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
@@ -1019,7 +1186,7 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
  */
 typedef npy_cdouble __pyx_t_5numpy_complex_t;
 
-/* "View.MemoryView":103
+/* "View.MemoryView":104
  * 
  * @cname("__pyx_array")
  * cdef class array:             # <<<<<<<<<<<<<<
@@ -1044,7 +1211,7 @@ struct __pyx_array_obj {
 };
 
 
-/* "View.MemoryView":275
+/* "View.MemoryView":278
  * 
  * @cname('__pyx_MemviewEnum')
  * cdef class Enum(object):             # <<<<<<<<<<<<<<
@@ -1057,7 +1224,7 @@ struct __pyx_MemviewEnum_obj {
 };
 
 
-/* "View.MemoryView":326
+/* "View.MemoryView":329
  * 
  * @cname('__pyx_memoryview')
  * cdef class memoryview(object):             # <<<<<<<<<<<<<<
@@ -1080,7 +1247,7 @@ struct __pyx_memoryview_obj {
 };
 
 
-/* "View.MemoryView":951
+/* "View.MemoryView":960
  * 
  * @cname('__pyx_memoryviewslice')
  * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
@@ -1097,7 +1264,7 @@ struct __pyx_memoryviewslice_obj {
 
 
 
-/* "View.MemoryView":103
+/* "View.MemoryView":104
  * 
  * @cname("__pyx_array")
  * cdef class array:             # <<<<<<<<<<<<<<
@@ -1111,7 +1278,7 @@ struct __pyx_vtabstruct_array {
 static struct __pyx_vtabstruct_array *__pyx_vtabptr_array;
 
 
-/* "View.MemoryView":326
+/* "View.MemoryView":329
  * 
  * @cname('__pyx_memoryview')
  * cdef class memoryview(object):             # <<<<<<<<<<<<<<
@@ -1131,7 +1298,7 @@ struct __pyx_vtabstruct_memoryview {
 static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;
 
 
-/* "View.MemoryView":951
+/* "View.MemoryView":960
  * 
  * @cname('__pyx_memoryviewslice')
  * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
@@ -1210,16 +1377,7 @@ static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
 
 /* PyObjectGetAttrStr.proto */
 #if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro))
-        return tp->tp_getattro(obj, attr_name);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_getattr))
-        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
-#endif
-    return PyObject_GetAttr(obj, attr_name);
-}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name);
 #else
 #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
 #endif
@@ -1275,15 +1433,6 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject
 /* PyObjectCallOneArg.proto */
 static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
 
-/* BufferFormatCheck.proto */
-static CYTHON_INLINE int  __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
-    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
-static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type); // PROTO
-
 /* MemviewSliceInit.proto */
 #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
 #define __Pyx_MEMVIEW_DIRECT   1
@@ -1316,23 +1465,35 @@ static void __Pyx_RaiseBufferIndexError(int axis);
 /* PyThreadStateGet.proto */
 #if CYTHON_FAST_THREAD_STATE
 #define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;
-#define __Pyx_PyThreadState_assign  __pyx_tstate = PyThreadState_GET();
+#define __Pyx_PyThreadState_assign  __pyx_tstate = __Pyx_PyThreadState_Current;
+#define __Pyx_PyErr_Occurred()  __pyx_tstate->curexc_type
 #else
 #define __Pyx_PyThreadState_declare
 #define __Pyx_PyThreadState_assign
+#define __Pyx_PyErr_Occurred()  PyErr_Occurred()
 #endif
 
 /* PyErrFetchRestore.proto */
 #if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
 #define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
 #define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
 #define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
 #define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
 static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
 static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
+#else
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#endif
 #else
+#define __Pyx_PyErr_Clear() PyErr_Clear()
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
 #define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
 #define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
 #define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
 #define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
 #endif
@@ -1364,17 +1525,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
 
 /* PyObjectSetAttrStr.proto */
 #if CYTHON_USE_TYPE_SLOTS
-#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_setattro))
-        return tp->tp_setattro(obj, attr_name, value);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_setattr))
-        return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
-#endif
-    return PyObject_SetAttr(obj, attr_name, value);
-}
+#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o, n, NULL)
+static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value);
 #else
 #define __Pyx_PyObject_DelAttrStr(o,n)   PyObject_DelAttr(o,n)
 #define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
@@ -1398,36 +1550,47 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_
     (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                               int wraparound, int boundscheck);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
                                                      int is_list, int wraparound, int boundscheck);
 
 /* ExtTypeTest.proto */
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
 
+/* IsLittleEndian.proto */
+static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
+
+/* BufferFormatCheck.proto */
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type);
+
+/* BufferGetAndValidate.proto */
+#define __Pyx_GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)\
+    ((obj == Py_None || obj == NULL) ?\
+    (__Pyx_ZeroBuffer(buf), 0) :\
+    __Pyx__GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack))
+static int  __Pyx__GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
+    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
+static void __Pyx_ZeroBuffer(Py_buffer* buf);
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
+static Py_ssize_t __Pyx_minusones[] = { -1, -1, -1, -1, -1, -1, -1, -1 };
+static Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 };
+
 #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
 /* RaiseException.proto */
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
 
 /* DictGetItem.proto */
 #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
-    PyObject *value;
-    value = PyDict_GetItemWithError(d, key);
-    if (unlikely(!value)) {
-        if (!PyErr_Occurred()) {
-            PyObject* args = PyTuple_Pack(1, key);
-            if (likely(args))
-                PyErr_SetObject(PyExc_KeyError, args);
-            Py_XDECREF(args);
-        }
-        return NULL;
-    }
-    Py_INCREF(value);
-    return value;
-}
+static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key);
+#define __Pyx_PyObject_Dict_GetItem(obj, name)\
+    (likely(PyDict_CheckExact(obj)) ?\
+     __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name))
 #else
-    #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
+#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
+#define __Pyx_PyObject_Dict_GetItem(obj, name)  PyObject_GetItem(obj, name)
 #endif
 
 /* RaiseTooManyValuesToUnpack.proto */
@@ -1467,8 +1630,10 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
 #endif
 
 /* ArgTypeTest.proto */
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
-    const char *name, int exact);
+#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
+    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
+        __Pyx__ArgTypeTest(obj, type, name, exact))
+static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
 
 /* IncludeStringH.proto */
 #include <string.h>
@@ -1498,12 +1663,36 @@ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/
 /* GetAttr.proto */
 static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
 
+/* ObjectGetItem.proto */
+#if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
+#else
+#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
+#endif
+
+/* decode_c_string_utf16.proto */
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = 0;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = -1;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = 1;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+
 /* decode_c_string.proto */
 static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
          const char* cstring, Py_ssize_t start, Py_ssize_t stop,
          const char* encoding, const char* errors,
          PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
 
+/* GetAttr3.proto */
+static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
+
 /* SwapException.proto */
 #if CYTHON_FAST_THREAD_STATE
 #define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
@@ -1515,6 +1704,19 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
 /* Import.proto */
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
 
+/* FastTypeChecks.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
+#else
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
+#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
+#endif
+#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+
 static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
 /* ListCompAppend.proto */
 #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
@@ -1574,23 +1776,40 @@ static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
 /* None.proto */
 static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
 
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
 /* None.proto */
 static CYTHON_INLINE long __Pyx_div_long(long, long);
 
+/* ImportFrom.proto */
+static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
+
+/* HasAttr.proto */
+static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
+
+/* PyObject_GenericGetAttrNoDict.proto */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
+#endif
+
+/* PyObject_GenericGetAttr.proto */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
+#endif
+
 /* SetVTable.proto */
 static int __Pyx_SetVtable(PyObject *dict, void *vtable);
 
+/* SetupReduce.proto */
+static int __Pyx_setup_reduce(PyObject* type_obj);
+
 /* FetchCommonType.proto */
 static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
 
 /* CythonFunction.proto */
 #define __Pyx_CyFunction_USED 1
-#include <structmember.h>
 #define __Pyx_CYFUNCTION_STATICMETHOD  0x01
 #define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
 #define __Pyx_CYFUNCTION_CCLASS        0x04
@@ -1642,6 +1861,17 @@ static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
                                                               PyObject *dict);
 static int __pyx_CyFunction_init(void);
 
+/* SetNameInClass.proto */
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
+#define __Pyx_SetNameInClass(ns, name, value)\
+    (likely(PyDict_CheckExact(ns)) ? _PyDict_SetItem_KnownHash(ns, name, value, ((PyASCIIObject *) name)->hash) : PyObject_SetItem(ns, name, value))
+#elif CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_SetNameInClass(ns, name, value)\
+    (likely(PyDict_CheckExact(ns)) ? PyDict_SetItem(ns, name, value) : PyObject_SetItem(ns, name, value))
+#else
+#define __Pyx_SetNameInClass(ns, name, value)  PyObject_SetItem(ns, name, value)
+#endif
+
 /* CalculateMetaclass.proto */
 static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases);
 
@@ -1651,6 +1881,13 @@ static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases,
 static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict,
                                       PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass);
 
+/* CLineInTraceback.proto */
+#ifdef CYTHON_CLINE_IN_TRACEBACK
+#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
+#else
+static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
+#endif
+
 /* CodeObjectCache.proto */
 typedef struct {
     PyCodeObject* code_object;
@@ -1693,13 +1930,8 @@ typedef struct {
   __Pyx_Buf_DimInfo diminfo[8];
 } __Pyx_LocalBuf_ND;
 
-/* None.proto */
-static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0};
-static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1};
-
 /* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs,
-                                        char order, int ndim);
+static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
 
 /* OverlappingSlices.proto */
 static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
@@ -1724,16 +1956,16 @@ static int __Pyx_ValidateAndInit_memviewslice(
                 PyObject *original_obj);
 
 /* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *);
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *, int writable_flag);
 
 /* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *);
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag);
 
 /* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_int(PyObject *);
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_int(PyObject *, int writable_flag);
 
 /* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *);
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *, int writable_flag);
 
 /* CIntToPy.proto */
 static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
@@ -1867,10 +2099,10 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
 static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *);
 
 /* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
 
 /* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
 
 /* CheckBinaryVersion.proto */
 static int __Pyx_check_binary_version(void);
@@ -1921,7 +2153,7 @@ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
 
 /* Module declarations from 'cpython.ref' */
 
-/* Module declarations from 'libc.stdlib' */
+/* Module declarations from 'cpython.mem' */
 
 /* Module declarations from 'numpy' */
 
@@ -1986,22 +2218,23 @@ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize
 static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
 static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
 static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
+static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
 static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
 static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 };
 static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
 #define __Pyx_MODULE_NAME "tigramite.tigramite_cython_code"
+extern int __pyx_module_is_main_tigramite__tigramite_cython_code;
 int __pyx_module_is_main_tigramite__tigramite_cython_code = 0;
 
 /* Implementation of 'tigramite.tigramite_cython_code' */
 static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_map;
 static PyObject *__pyx_builtin_ValueError;
 static PyObject *__pyx_builtin_RuntimeError;
 static PyObject *__pyx_builtin_ImportError;
 static PyObject *__pyx_builtin_MemoryError;
 static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_Ellipsis;
 static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_builtin_Ellipsis;
 static PyObject *__pyx_builtin_id;
 static PyObject *__pyx_builtin_IndexError;
 static const char __pyx_k_N[] = "N";
@@ -2040,13 +2273,14 @@ static const char __pyx_k_fac[] = "fac";
 static const char __pyx_k_k_z[] = "k_z";
 static const char __pyx_k_kxz[] = "kxz";
 static const char __pyx_k_kyz[] = "kyz";
-static const char __pyx_k_map[] = "map";
+static const char __pyx_k_new[] = "__new__";
 static const char __pyx_k_obj[] = "obj";
 static const char __pyx_k_sys[] = "sys";
 static const char __pyx_k_tau[] = "tau";
 static const char __pyx_k_use[] = "use";
 static const char __pyx_k_var[] = "var";
 static const char __pyx_k_base[] = "base";
+static const char __pyx_k_dict[] = "__dict__";
 static const char __pyx_k_dist[] = "dist";
 static const char __pyx_k_init[] = "__init__";
 static const char __pyx_k_k_xz[] = "k_xz";
@@ -2099,10 +2333,13 @@ static const char __pyx_k_mean_0[] = "mean_0";
 static const char __pyx_k_mean_1[] = "mean_1";
 static const char __pyx_k_module[] = "__module__";
 static const char __pyx_k_name_2[] = "__name__";
+static const char __pyx_k_pickle[] = "pickle";
 static const char __pyx_k_random[] = "random";
+static const char __pyx_k_reduce[] = "__reduce__";
 static const char __pyx_k_struct[] = "struct";
 static const char __pyx_k_timeit[] = "timeit";
 static const char __pyx_k_unpack[] = "unpack";
+static const char __pyx_k_update[] = "update";
 static const char __pyx_k_asarray[] = "asarray";
 static const char __pyx_k_d_there[] = "d_there";
 static const char __pyx_k_fortran[] = "fortran";
@@ -2113,8 +2350,11 @@ static const char __pyx_k_weights[] = "weights";
 static const char __pyx_k_Ellipsis[] = "Ellipsis";
 static const char __pyx_k_dcov_all[] = "dcov_all";
 static const char __pyx_k_epsarray[] = "epsarray";
+static const char __pyx_k_getstate[] = "__getstate__";
 static const char __pyx_k_itemsize[] = "itemsize";
+static const char __pyx_k_pyx_type[] = "__pyx_type";
 static const char __pyx_k_qualname[] = "__qualname__";
+static const char __pyx_k_setstate[] = "__setstate__";
 static const char __pyx_k_unittest[] = "unittest";
 static const char __pyx_k_TypeError[] = "TypeError";
 static const char __pyx_k_enumerate[] = "enumerate";
@@ -2122,15 +2362,19 @@ static const char __pyx_k_mean_here[] = "mean_here";
 static const char __pyx_k_metaclass[] = "__metaclass__";
 static const char __pyx_k_neighbors[] = "neighbors";
 static const char __pyx_k_patt_mask[] = "patt_mask";
+static const char __pyx_k_pyx_state[] = "__pyx_state";
+static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
 static const char __pyx_k_sum_total[] = "sum_total";
 static const char __pyx_k_D_N___init[] = "D_N.__init__";
 static const char __pyx_k_IndexError[] = "IndexError";
 static const char __pyx_k_ValueError[] = "ValueError";
 static const char __pyx_k_array_mask[] = "array_mask";
 static const char __pyx_k_mean_there[] = "mean_there";
+static const char __pyx_k_pyx_result[] = "__pyx_result";
 static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
 static const char __pyx_k_ImportError[] = "ImportError";
 static const char __pyx_k_MemoryError[] = "MemoryError";
+static const char __pyx_k_PickleError[] = "PickleError";
 static const char __pyx_k_mean_0_here[] = "mean_0_here";
 static const char __pyx_k_mean_1_here[] = "mean_1_here";
 static const char __pyx_k_product_sum[] = "product_sum";
@@ -2138,13 +2382,21 @@ static const char __pyx_k_squared_sum[] = "squared_sum";
 static const char __pyx_k_RuntimeError[] = "RuntimeError";
 static const char __pyx_k_mean_0_there[] = "mean_0_there";
 static const char __pyx_k_mean_1_there[] = "mean_1_there";
+static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
+static const char __pyx_k_stringsource[] = "stringsource";
 static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
+static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
 static const char __pyx_k_D_N_product_sum[] = "D_N.product_sum";
 static const char __pyx_k_D_N_squared_sum[] = "D_N.squared_sum";
+static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
 static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
 static const char __pyx_k_calculate_means[] = "calculate_means";
 static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
+static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
+static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
+static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
 static const char __pyx_k_shuffle_neighbors[] = "shuffle_neighbors";
+static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
 static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
 static const char __pyx_k_D_N_calculate_means[] = "D_N.calculate_means";
 static const char __pyx_k_get_patterns_cython[] = "_get_patterns_cython";
@@ -2162,14 +2414,16 @@ static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate
 static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
 static const char __pyx_k_get_neighbors_within_eps_cython[] = "_get_neighbors_within_eps_cython";
 static const char __pyx_k_get_restricted_permutation_cyth[] = "_get_restricted_permutation_cython";
-static const char __pyx_k_home_jakobrunge_work_code_pytho[] = "/home/jakobrunge/work/code/python_code/tigramite/tigramite_v3/tigramite/tigramite_cython_code.pyx";
 static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
-static const char __pyx_k_tigramite_tigramite_cython_code[] = "tigramite.tigramite_cython_code";
+static const char __pyx_k_tigramite_tigramite_cython_code[] = "tigramite/tigramite_cython_code.pyx";
 static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
 static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
 static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
+static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
+static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
 static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
 static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
+static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
 static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
 static const char __pyx_k_Inner_helper_of_dcov_all_Cache_d[] = "Inner helper of dcov_all. Cache different means that are required for calculating \n    the matrix members on the fly";
 static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
@@ -2178,12 +2432,16 @@ static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on
 static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
 static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
 static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
+static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
 static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
 static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
+static const char __pyx_k_tigramite_tigramite_cython_code_2[] = "tigramite.tigramite_cython_code";
 static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
 static PyObject *__pyx_n_s_ASCII;
 static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
 static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
+static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
+static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
 static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
 static PyObject *__pyx_n_s_D_N;
 static PyObject *__pyx_n_s_D_N___init;
@@ -2195,6 +2453,7 @@ static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
 static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
 static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
 static PyObject *__pyx_n_s_ImportError;
+static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
 static PyObject *__pyx_n_s_IndexError;
 static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
 static PyObject *__pyx_kp_s_Inner_helper_of_dcov_all_Cache_d;
@@ -2207,11 +2466,13 @@ static PyObject *__pyx_n_s_N;
 static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
 static PyObject *__pyx_n_b_O;
 static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
+static PyObject *__pyx_n_s_PickleError;
 static PyObject *__pyx_n_s_RuntimeError;
 static PyObject *__pyx_n_s_T;
 static PyObject *__pyx_n_s_TypeError;
 static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
 static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_n_s_View_MemoryView;
 static PyObject *__pyx_n_s_allocate_buffer;
 static PyObject *__pyx_n_s_array;
 static PyObject *__pyx_n_s_array_mask;
@@ -2222,6 +2483,7 @@ static PyObject *__pyx_n_s_c;
 static PyObject *__pyx_n_u_c;
 static PyObject *__pyx_n_s_calculate_means;
 static PyObject *__pyx_n_s_class;
+static PyObject *__pyx_n_s_cline_in_traceback;
 static PyObject *__pyx_kp_s_contiguous_and_direct;
 static PyObject *__pyx_kp_s_contiguous_and_indirect;
 static PyObject *__pyx_n_s_count;
@@ -2231,6 +2493,7 @@ static PyObject *__pyx_n_s_d_there;
 static PyObject *__pyx_n_s_dc;
 static PyObject *__pyx_n_s_dcov_all;
 static PyObject *__pyx_n_s_denom;
+static PyObject *__pyx_n_s_dict;
 static PyObject *__pyx_n_s_dim;
 static PyObject *__pyx_n_s_dim_x;
 static PyObject *__pyx_n_s_dim_y;
@@ -2261,8 +2524,8 @@ static PyObject *__pyx_n_u_fortran;
 static PyObject *__pyx_n_s_get_neighbors_within_eps_cython;
 static PyObject *__pyx_n_s_get_patterns_cython;
 static PyObject *__pyx_n_s_get_restricted_permutation_cyth;
+static PyObject *__pyx_n_s_getstate;
 static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
-static PyObject *__pyx_kp_s_home_jakobrunge_work_code_pytho;
 static PyObject *__pyx_n_s_i;
 static PyObject *__pyx_n_s_id;
 static PyObject *__pyx_n_s_ii;
@@ -2282,7 +2545,6 @@ static PyObject *__pyx_n_s_kxz;
 static PyObject *__pyx_n_s_kyz;
 static PyObject *__pyx_n_s_kz;
 static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_map;
 static PyObject *__pyx_n_s_mask;
 static PyObject *__pyx_n_s_mean;
 static PyObject *__pyx_n_s_mean_0;
@@ -2304,6 +2566,8 @@ static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
 static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
 static PyObject *__pyx_n_s_ndim;
 static PyObject *__pyx_n_s_neighbors;
+static PyObject *__pyx_n_s_new;
+static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
 static PyObject *__pyx_n_s_np;
 static PyObject *__pyx_n_s_numpy;
 static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;
@@ -2315,15 +2579,27 @@ static PyObject *__pyx_n_s_p;
 static PyObject *__pyx_n_s_pack;
 static PyObject *__pyx_n_s_patt;
 static PyObject *__pyx_n_s_patt_mask;
+static PyObject *__pyx_n_s_pickle;
 static PyObject *__pyx_n_s_prepare;
 static PyObject *__pyx_n_s_product_sum;
+static PyObject *__pyx_n_s_pyx_PickleError;
+static PyObject *__pyx_n_s_pyx_checksum;
 static PyObject *__pyx_n_s_pyx_getbuffer;
+static PyObject *__pyx_n_s_pyx_result;
+static PyObject *__pyx_n_s_pyx_state;
+static PyObject *__pyx_n_s_pyx_type;
+static PyObject *__pyx_n_s_pyx_unpickle_Enum;
 static PyObject *__pyx_n_s_pyx_vtable;
 static PyObject *__pyx_n_s_qualname;
 static PyObject *__pyx_n_s_random;
 static PyObject *__pyx_n_s_range;
+static PyObject *__pyx_n_s_reduce;
+static PyObject *__pyx_n_s_reduce_cython;
+static PyObject *__pyx_n_s_reduce_ex;
 static PyObject *__pyx_n_s_restricted_permutation;
 static PyObject *__pyx_n_s_self;
+static PyObject *__pyx_n_s_setstate;
+static PyObject *__pyx_n_s_setstate_cython;
 static PyObject *__pyx_n_s_shape;
 static PyObject *__pyx_n_s_shuffle;
 static PyObject *__pyx_n_s_shuffle_neighbors;
@@ -2336,6 +2612,7 @@ static PyObject *__pyx_n_s_stop;
 static PyObject *__pyx_kp_s_strided_and_direct;
 static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
 static PyObject *__pyx_kp_s_strided_and_indirect;
+static PyObject *__pyx_kp_s_stringsource;
 static PyObject *__pyx_n_s_struct;
 static PyObject *__pyx_n_s_sum_0;
 static PyObject *__pyx_n_s_sum_1;
@@ -2344,13 +2621,15 @@ static PyObject *__pyx_n_s_sys;
 static PyObject *__pyx_n_s_t;
 static PyObject *__pyx_n_s_tau;
 static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_n_s_tigramite_tigramite_cython_code;
+static PyObject *__pyx_kp_s_tigramite_tigramite_cython_code;
+static PyObject *__pyx_n_s_tigramite_tigramite_cython_code_2;
 static PyObject *__pyx_n_s_timeit;
 static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
 static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
 static PyObject *__pyx_n_s_unittest;
 static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
 static PyObject *__pyx_n_s_unpack;
+static PyObject *__pyx_n_s_update;
 static PyObject *__pyx_n_s_use;
 static PyObject *__pyx_n_s_used;
 static PyObject *__pyx_n_s_v;
@@ -2374,11 +2653,16 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
 static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
 static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
 static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
+static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
+static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
+static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
 static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
 static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
 static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
 static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
 static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
@@ -2400,8 +2684,13 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16
 static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
 static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
 static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
 static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
 static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
 static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
 static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
 static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
@@ -2409,6 +2698,7 @@ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyO
 static PyObject *__pyx_int_0;
 static PyObject *__pyx_int_1;
 static PyObject *__pyx_int_2;
+static PyObject *__pyx_int_184977713;
 static PyObject *__pyx_int_neg_1;
 static PyObject *__pyx_tuple_;
 static PyObject *__pyx_tuple__2;
@@ -2419,9 +2709,9 @@ static PyObject *__pyx_tuple__6;
 static PyObject *__pyx_tuple__7;
 static PyObject *__pyx_tuple__8;
 static PyObject *__pyx_tuple__9;
-static PyObject *__pyx_slice__19;
-static PyObject *__pyx_slice__20;
-static PyObject *__pyx_slice__21;
+static PyObject *__pyx_slice__25;
+static PyObject *__pyx_slice__26;
+static PyObject *__pyx_slice__27;
 static PyObject *__pyx_tuple__10;
 static PyObject *__pyx_tuple__11;
 static PyObject *__pyx_tuple__12;
@@ -2431,28 +2721,39 @@ static PyObject *__pyx_tuple__15;
 static PyObject *__pyx_tuple__16;
 static PyObject *__pyx_tuple__17;
 static PyObject *__pyx_tuple__18;
+static PyObject *__pyx_tuple__19;
+static PyObject *__pyx_tuple__20;
+static PyObject *__pyx_tuple__21;
 static PyObject *__pyx_tuple__22;
 static PyObject *__pyx_tuple__23;
-static PyObject *__pyx_tuple__25;
-static PyObject *__pyx_tuple__27;
+static PyObject *__pyx_tuple__24;
+static PyObject *__pyx_tuple__28;
 static PyObject *__pyx_tuple__29;
+static PyObject *__pyx_tuple__30;
 static PyObject *__pyx_tuple__31;
 static PyObject *__pyx_tuple__33;
 static PyObject *__pyx_tuple__35;
 static PyObject *__pyx_tuple__37;
 static PyObject *__pyx_tuple__39;
-static PyObject *__pyx_tuple__40;
 static PyObject *__pyx_tuple__41;
-static PyObject *__pyx_tuple__42;
 static PyObject *__pyx_tuple__43;
-static PyObject *__pyx_codeobj__24;
-static PyObject *__pyx_codeobj__26;
-static PyObject *__pyx_codeobj__28;
-static PyObject *__pyx_codeobj__30;
+static PyObject *__pyx_tuple__45;
+static PyObject *__pyx_tuple__47;
+static PyObject *__pyx_tuple__48;
+static PyObject *__pyx_tuple__49;
+static PyObject *__pyx_tuple__50;
+static PyObject *__pyx_tuple__51;
+static PyObject *__pyx_tuple__52;
 static PyObject *__pyx_codeobj__32;
 static PyObject *__pyx_codeobj__34;
 static PyObject *__pyx_codeobj__36;
 static PyObject *__pyx_codeobj__38;
+static PyObject *__pyx_codeobj__40;
+static PyObject *__pyx_codeobj__42;
+static PyObject *__pyx_codeobj__44;
+static PyObject *__pyx_codeobj__46;
+static PyObject *__pyx_codeobj__53;
+/* Late includes */
 
 /* "tigramite/tigramite_cython_code.pyx":6
  * from libc.math cimport abs
@@ -2511,47 +2812,60 @@ static PyObject *__pyx_pw_9tigramite_21tigramite_cython_code_1_get_neighbors_wit
       const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
       switch (pos_args) {
         case  7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6);
+        CYTHON_FALLTHROUGH;
         case  6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5);
+        CYTHON_FALLTHROUGH;
         case  5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
+        CYTHON_FALLTHROUGH;
         case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
+        CYTHON_FALLTHROUGH;
         case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
+        CYTHON_FALLTHROUGH;
         case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        CYTHON_FALLTHROUGH;
         case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        CYTHON_FALLTHROUGH;
         case  0: break;
         default: goto __pyx_L5_argtuple_error;
       }
       kw_args = PyDict_Size(__pyx_kwds);
       switch (pos_args) {
         case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_array)) != 0)) kw_args--;
+        if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_array)) != 0)) kw_args--;
         else goto __pyx_L5_argtuple_error;
+        CYTHON_FALLTHROUGH;
         case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_T)) != 0)) kw_args--;
+        if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_T)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 1); __PYX_ERR(0, 10, __pyx_L3_error)
         }
+        CYTHON_FALLTHROUGH;
         case  2:
-        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dim_x)) != 0)) kw_args--;
+        if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dim_x)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 2); __PYX_ERR(0, 10, __pyx_L3_error)
         }
+        CYTHON_FALLTHROUGH;
         case  3:
-        if (likely((values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dim_y)) != 0)) kw_args--;
+        if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dim_y)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 3); __PYX_ERR(0, 10, __pyx_L3_error)
         }
+        CYTHON_FALLTHROUGH;
         case  4:
-        if (likely((values[4] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_epsarray)) != 0)) kw_args--;
+        if (likely((values[4] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_epsarray)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 4); __PYX_ERR(0, 10, __pyx_L3_error)
         }
+        CYTHON_FALLTHROUGH;
         case  5:
-        if (likely((values[5] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_k)) != 0)) kw_args--;
+        if (likely((values[5] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_k)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 5); __PYX_ERR(0, 10, __pyx_L3_error)
         }
+        CYTHON_FALLTHROUGH;
         case  6:
-        if (likely((values[6] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dim)) != 0)) kw_args--;
+        if (likely((values[6] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dim)) != 0)) kw_args--;
         else {
           __Pyx_RaiseArgtupleInvalid("_get_neighbors_within_eps_cython", 1, 7, 7, 6); __PYX_ERR(0, 10, __pyx_L3_error)
         }
@@ -2570,11 +2884,11 @@ static PyObject *__pyx_pw_9tigramite_21tigramite_cython_code_1_get_neighbors_wit
       values[5] = PyTuple_GET_ITEM(__pyx_args, 5);
       values[6] = PyTuple_GET_ITEM(__pyx_args, 6);
     }
-    __pyx_v_array = __Pyx_PyObject_to_MemoryviewSlice_dsds_double(values[0]); if (unlikely(!__pyx_v_array.memview)) __PYX_ERR(0, 11, __pyx_L3_error)
+    __pyx_v_array = __Pyx_PyObject_to_MemoryviewSlice_dsds_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_array.memview)) __PYX_ERR(0, 11, __pyx_L3_error)
     __pyx_v_T = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_T == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 12, __pyx_L3_error)
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-    __pyx_v_epsarray = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[4]); if (unlikely(!__pyx_v_epsarray.memview)) __PYX_ERR(0, 15, __pyx_L3_error)
+    __pyx_v_epsarray = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[4], PyBUF_WRITABLE); if (unlikely(!__pyx_v_epsarray.memview)) __PYX_ERR(0, 15, __pyx_L3_error)
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@@ -2616,19 +2930,19 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } };
   int __pyx_t_6;
   int __pyx_t_7;
-  Py_ssize_t __pyx_t_8;
-  int __pyx_t_9;
+  int __pyx_t_8;
+  Py_ssize_t __pyx_t_9;
   int __pyx_t_10;
   int __pyx_t_11;
   int __pyx_t_12;
-  Py_ssize_t __pyx_t_13;
-  Py_ssize_t __pyx_t_14;
-  Py_ssize_t __pyx_t_15;
+  int __pyx_t_13;
+  int __pyx_t_14;
+  int __pyx_t_15;
   Py_ssize_t __pyx_t_16;
-  int __pyx_t_17;
+  Py_ssize_t __pyx_t_17;
   Py_ssize_t __pyx_t_18;
   Py_ssize_t __pyx_t_19;
-  Py_ssize_t __pyx_t_20;
+  int __pyx_t_20;
   Py_ssize_t __pyx_t_21;
   Py_ssize_t __pyx_t_22;
   Py_ssize_t __pyx_t_23;
@@ -2645,9 +2959,12 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   Py_ssize_t __pyx_t_34;
   Py_ssize_t __pyx_t_35;
   Py_ssize_t __pyx_t_36;
-  PyObject *__pyx_t_37 = NULL;
-  PyObject *__pyx_t_38 = NULL;
-  PyObject *__pyx_t_39 = NULL;
+  Py_ssize_t __pyx_t_37;
+  Py_ssize_t __pyx_t_38;
+  Py_ssize_t __pyx_t_39;
+  PyObject *__pyx_t_40 = NULL;
+  PyObject *__pyx_t_41 = NULL;
+  PyObject *__pyx_t_42 = NULL;
   __Pyx_RefNannySetupContext("_get_neighbors_within_eps_cython", 0);
 
   /* "tigramite/tigramite_cython_code.pyx":19
@@ -2669,7 +2986,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_GIVEREF(__pyx_t_1);
   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1);
   __pyx_t_1 = 0;
-  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 19, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 19, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
   if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, __pyx_n_s_int32) < 0) __PYX_ERR(0, 19, __pyx_L1_error)
   __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 19, __pyx_L1_error)
@@ -2677,8 +2994,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
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   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_4);
-  if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 19, __pyx_L1_error)
+  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 19, __pyx_L1_error)
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
   __pyx_v_k_xz = __pyx_t_5;
   __pyx_t_5.memview = NULL;
@@ -2703,7 +3019,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_GIVEREF(__pyx_t_4);
   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4);
   __pyx_t_4 = 0;
-  __pyx_t_4 = PyDict_New(); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 20, __pyx_L1_error)
+  __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 20, __pyx_L1_error)
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   __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 20, __pyx_L1_error)
@@ -2711,8 +3027,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_2);
-  if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 20, __pyx_L1_error)
+  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 20, __pyx_L1_error)
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   __pyx_v_k_yz = __pyx_t_5;
   __pyx_t_5.memview = NULL;
@@ -2737,7 +3052,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_GIVEREF(__pyx_t_2);
   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2);
   __pyx_t_2 = 0;
-  __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 21, __pyx_L1_error)
+  __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 21, __pyx_L1_error)
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@@ -2745,8 +3060,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_1);
-  if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 21, __pyx_L1_error)
+  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_int(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 21, __pyx_L1_error)
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   __pyx_v_k_z = __pyx_t_5;
   __pyx_t_5.memview = NULL;
@@ -2760,8 +3074,9 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *         # Epsilon of k-th nearest neighbor in joint space
  */
   __pyx_t_6 = __pyx_v_T;
-  for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
-    __pyx_v_i = __pyx_t_7;
+  __pyx_t_7 = __pyx_t_6;
+  for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
+    __pyx_v_i = __pyx_t_8;
 
     /* "tigramite/tigramite_cython_code.pyx":30
  * 
@@ -2770,8 +3085,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  *         # Count neighbors within epsmax in subspaces, since the reference
  */
-    __pyx_t_8 = __pyx_v_i;
-    __pyx_v_epsmax = (*((double *) ( /* dim=0 */ (__pyx_v_epsarray.data + __pyx_t_8 * __pyx_v_epsarray.strides[0]) )));
+    __pyx_t_9 = __pyx_v_i;
+    __pyx_v_epsmax = (*((double *) ( /* dim=0 */ (__pyx_v_epsarray.data + __pyx_t_9 * __pyx_v_epsarray.strides[0]) )));
 
     /* "tigramite/tigramite_cython_code.pyx":34
  *         # Count neighbors within epsmax in subspaces, since the reference
@@ -2807,9 +3122,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  *             # Z-subspace, if empty, dz stays 0
  */
-    __pyx_t_9 = __pyx_v_T;
-    for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) {
-      __pyx_v_j = __pyx_t_10;
+    __pyx_t_10 = __pyx_v_T;
+    __pyx_t_11 = __pyx_t_10;
+    for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {
+      __pyx_v_j = __pyx_t_12;
 
       /* "tigramite/tigramite_cython_code.pyx":40
  * 
@@ -2827,9 +3143,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                 dz = max( abs(array[d, i] - array[d, j]), dz)
  * 
  */
-      __pyx_t_11 = __pyx_v_dim;
-      for (__pyx_t_12 = (__pyx_v_dim_x + __pyx_v_dim_y); __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {
-        __pyx_v_d = __pyx_t_12;
+      __pyx_t_13 = __pyx_v_dim;
+      __pyx_t_14 = __pyx_t_13;
+      for (__pyx_t_15 = (__pyx_v_dim_x + __pyx_v_dim_y); __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) {
+        __pyx_v_d = __pyx_t_15;
 
         /* "tigramite/tigramite_cython_code.pyx":42
  *             dz = 0.
@@ -2838,11 +3155,11 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  *             # For no conditions, kz is counted up to T
  */
-        __pyx_t_13 = __pyx_v_d;
-        __pyx_t_14 = __pyx_v_i;
-        __pyx_t_15 = __pyx_v_d;
-        __pyx_t_16 = __pyx_v_j;
-        __pyx_v_dz = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_13 * __pyx_v_array.strides[0]) ) + __pyx_t_14 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_15 * __pyx_v_array.strides[0]) ) + __pyx_t_16 * __pyx_v_array.strides[1]) ))))), __pyx_v_dz);
+        __pyx_t_16 = __pyx_v_d;
+        __pyx_t_17 = __pyx_v_i;
+        __pyx_t_18 = __pyx_v_d;
+        __pyx_t_19 = __pyx_v_j;
+        __pyx_v_dz = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_16 * __pyx_v_array.strides[0]) ) + __pyx_t_17 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_18 * __pyx_v_array.strides[0]) ) + __pyx_t_19 * __pyx_v_array.strides[1]) ))))), __pyx_v_dz);
       }
 
       /* "tigramite/tigramite_cython_code.pyx":45
@@ -2852,8 +3169,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                 kz += 1
  * 
  */
-      __pyx_t_17 = ((__pyx_v_dz < __pyx_v_epsmax) != 0);
-      if (__pyx_t_17) {
+      __pyx_t_20 = ((__pyx_v_dz < __pyx_v_epsmax) != 0);
+      if (__pyx_t_20) {
 
         /* "tigramite/tigramite_cython_code.pyx":46
  *             # For no conditions, kz is counted up to T
@@ -2871,11 +3188,11 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                 for d in range(dim_x+1, dim_x+dim_y):
  *                     dy = max( abs(array[d, i] - array[d, j]), dy)
  */
-        __pyx_t_18 = __pyx_v_dim_x;
-        __pyx_t_19 = __pyx_v_i;
-        __pyx_t_20 = __pyx_v_dim_x;
-        __pyx_t_21 = __pyx_v_j;
-        __pyx_v_dy = fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_18 * __pyx_v_array.strides[0]) ) + __pyx_t_19 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_20 * __pyx_v_array.strides[0]) ) + __pyx_t_21 * __pyx_v_array.strides[1]) )))));
+        __pyx_t_21 = __pyx_v_dim_x;
+        __pyx_t_22 = __pyx_v_i;
+        __pyx_t_23 = __pyx_v_dim_x;
+        __pyx_t_24 = __pyx_v_j;
+        __pyx_v_dy = fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_21 * __pyx_v_array.strides[0]) ) + __pyx_t_22 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_23 * __pyx_v_array.strides[0]) ) + __pyx_t_24 * __pyx_v_array.strides[1]) )))));
 
         /* "tigramite/tigramite_cython_code.pyx":52
  *                 # Y-subspace, the loop is only entered for dim_y > 1
@@ -2884,9 +3201,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                     dy = max( abs(array[d, i] - array[d, j]), dy)
  * 
  */
-        __pyx_t_11 = (__pyx_v_dim_x + __pyx_v_dim_y);
-        for (__pyx_t_12 = (__pyx_v_dim_x + 1); __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {
-          __pyx_v_d = __pyx_t_12;
+        __pyx_t_13 = (__pyx_v_dim_x + __pyx_v_dim_y);
+        __pyx_t_14 = __pyx_t_13;
+        for (__pyx_t_15 = (__pyx_v_dim_x + 1); __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) {
+          __pyx_v_d = __pyx_t_15;
 
           /* "tigramite/tigramite_cython_code.pyx":53
  *                 dy = abs(array[dim_x, i] - array[dim_x, j])
@@ -2895,11 +3213,11 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  *                 if (dy < epsmax):
  */
-          __pyx_t_22 = __pyx_v_d;
-          __pyx_t_23 = __pyx_v_i;
-          __pyx_t_24 = __pyx_v_d;
-          __pyx_t_25 = __pyx_v_j;
-          __pyx_v_dy = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_22 * __pyx_v_array.strides[0]) ) + __pyx_t_23 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_24 * __pyx_v_array.strides[0]) ) + __pyx_t_25 * __pyx_v_array.strides[1]) ))))), __pyx_v_dy);
+          __pyx_t_25 = __pyx_v_d;
+          __pyx_t_26 = __pyx_v_i;
+          __pyx_t_27 = __pyx_v_d;
+          __pyx_t_28 = __pyx_v_j;
+          __pyx_v_dy = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_25 * __pyx_v_array.strides[0]) ) + __pyx_t_26 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_27 * __pyx_v_array.strides[0]) ) + __pyx_t_28 * __pyx_v_array.strides[1]) ))))), __pyx_v_dy);
         }
 
         /* "tigramite/tigramite_cython_code.pyx":55
@@ -2909,8 +3227,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                     kyz += 1
  * 
  */
-        __pyx_t_17 = ((__pyx_v_dy < __pyx_v_epsmax) != 0);
-        if (__pyx_t_17) {
+        __pyx_t_20 = ((__pyx_v_dy < __pyx_v_epsmax) != 0);
+        if (__pyx_t_20) {
 
           /* "tigramite/tigramite_cython_code.pyx":56
  * 
@@ -2937,11 +3255,11 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                 for d in range(1, dim_x):
  *                     dx = max( abs(array[d, i] - array[d, j]), dx)
  */
-        __pyx_t_26 = 0;
-        __pyx_t_27 = __pyx_v_i;
-        __pyx_t_28 = 0;
-        __pyx_t_29 = __pyx_v_j;
-        __pyx_v_dx = fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_26 * __pyx_v_array.strides[0]) ) + __pyx_t_27 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_28 * __pyx_v_array.strides[0]) ) + __pyx_t_29 * __pyx_v_array.strides[1]) )))));
+        __pyx_t_29 = 0;
+        __pyx_t_30 = __pyx_v_i;
+        __pyx_t_31 = 0;
+        __pyx_t_32 = __pyx_v_j;
+        __pyx_v_dx = fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_29 * __pyx_v_array.strides[0]) ) + __pyx_t_30 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_31 * __pyx_v_array.strides[0]) ) + __pyx_t_32 * __pyx_v_array.strides[1]) )))));
 
         /* "tigramite/tigramite_cython_code.pyx":60
  *                 # X-subspace, the loop is only entered for dim_x > 1
@@ -2950,9 +3268,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                     dx = max( abs(array[d, i] - array[d, j]), dx)
  * 
  */
-        __pyx_t_11 = __pyx_v_dim_x;
-        for (__pyx_t_12 = 1; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {
-          __pyx_v_d = __pyx_t_12;
+        __pyx_t_13 = __pyx_v_dim_x;
+        __pyx_t_14 = __pyx_t_13;
+        for (__pyx_t_15 = 1; __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) {
+          __pyx_v_d = __pyx_t_15;
 
           /* "tigramite/tigramite_cython_code.pyx":61
  *                 dx = abs(array[0, i] - array[0, j])
@@ -2961,11 +3280,11 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  *                 if (dx < epsmax):
  */
-          __pyx_t_30 = __pyx_v_d;
-          __pyx_t_31 = __pyx_v_i;
-          __pyx_t_32 = __pyx_v_d;
-          __pyx_t_33 = __pyx_v_j;
-          __pyx_v_dx = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_30 * __pyx_v_array.strides[0]) ) + __pyx_t_31 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_32 * __pyx_v_array.strides[0]) ) + __pyx_t_33 * __pyx_v_array.strides[1]) ))))), __pyx_v_dx);
+          __pyx_t_33 = __pyx_v_d;
+          __pyx_t_34 = __pyx_v_i;
+          __pyx_t_35 = __pyx_v_d;
+          __pyx_t_36 = __pyx_v_j;
+          __pyx_v_dx = __pyx_f_9tigramite_21tigramite_cython_code_max(fabs(((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_33 * __pyx_v_array.strides[0]) ) + __pyx_t_34 * __pyx_v_array.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_35 * __pyx_v_array.strides[0]) ) + __pyx_t_36 * __pyx_v_array.strides[1]) ))))), __pyx_v_dx);
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         /* "tigramite/tigramite_cython_code.pyx":63
@@ -2975,8 +3294,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *                     kxz += 1
  * 
  */
-        __pyx_t_17 = ((__pyx_v_dx < __pyx_v_epsmax) != 0);
-        if (__pyx_t_17) {
+        __pyx_t_20 = ((__pyx_v_dx < __pyx_v_epsmax) != 0);
+        if (__pyx_t_20) {
 
           /* "tigramite/tigramite_cython_code.pyx":64
  * 
@@ -3013,8 +3332,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *         k_yz[i] = kyz
  *         k_z[i] = kz
  */
-    __pyx_t_34 = __pyx_v_i;
-    *((int *) ( /* dim=0 */ (__pyx_v_k_xz.data + __pyx_t_34 * __pyx_v_k_xz.strides[0]) )) = __pyx_v_kxz;
+    __pyx_t_37 = __pyx_v_i;
+    *((int *) ( /* dim=0 */ (__pyx_v_k_xz.data + __pyx_t_37 * __pyx_v_k_xz.strides[0]) )) = __pyx_v_kxz;
 
     /* "tigramite/tigramite_cython_code.pyx":68
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@@ -3023,8 +3342,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  *         k_z[i] = kz
  * 
  */
-    __pyx_t_35 = __pyx_v_i;
-    *((int *) ( /* dim=0 */ (__pyx_v_k_yz.data + __pyx_t_35 * __pyx_v_k_yz.strides[0]) )) = __pyx_v_kyz;
+    __pyx_t_38 = __pyx_v_i;
+    *((int *) ( /* dim=0 */ (__pyx_v_k_yz.data + __pyx_t_38 * __pyx_v_k_yz.strides[0]) )) = __pyx_v_kyz;
 
     /* "tigramite/tigramite_cython_code.pyx":69
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@@ -3033,8 +3352,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
  * 
  * 
  */
-    __pyx_t_36 = __pyx_v_i;
-    *((int *) ( /* dim=0 */ (__pyx_v_k_z.data + __pyx_t_36 * __pyx_v_k_z.strides[0]) )) = __pyx_v_kz;
+    __pyx_t_39 = __pyx_v_i;
+    *((int *) ( /* dim=0 */ (__pyx_v_k_z.data + __pyx_t_39 * __pyx_v_k_z.strides[0]) )) = __pyx_v_kz;
   }
 
   /* "tigramite/tigramite_cython_code.pyx":72
@@ -3086,25 +3405,25 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code__get_neighbors_with
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-  Py_ssize_t __pyx_t_12;
-  Py_ssize_t __pyx_t_13;
-  Py_ssize_t __pyx_t_14;
+  int __pyx_t_12;
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+  int __pyx_t_14;
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-  Py_ssize_t __pyx_t_27;
-  Py_ssize_t __pyx_t_28;
-  Py_ssize_t __pyx_t_29;
+  Py_ssize_t __pyx_t_23;
+  Py_ssize_t __pyx_t_24;
+  Py_ssize_t __pyx_t_25;
+  int __pyx_t_26;
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+  int __pyx_t_28;
+  int __pyx_t_29;
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+  Py_ssize_t __pyx_t_33;
+  Py_ssize_t __pyx_t_34;
+  Py_ssize_t __pyx_t_35;
+  Py_ssize_t __pyx_t_36;
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@@ -3457,7 +3799,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
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-  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 91, __pyx_L1_error)
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@@ -3465,8 +3807,7 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
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-  if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 91, __pyx_L1_error)
+  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 91, __pyx_L1_error)
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@@ -3489,8 +3830,9 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
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-  for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
-    __pyx_v_n = __pyx_t_7;
+  __pyx_t_7 = __pyx_t_6;
+  for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
+    __pyx_v_n = __pyx_t_8;
 
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@@ -3499,9 +3841,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *             mask = 1
  *             ave = 0.
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-    __pyx_t_8 = __pyx_v_T;
-    for (__pyx_t_9 = __pyx_v_start; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
-      __pyx_v_t = __pyx_t_9;
+    __pyx_t_9 = __pyx_v_T;
+    __pyx_t_10 = __pyx_t_9;
+    for (__pyx_t_11 = __pyx_v_start; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
+      __pyx_v_t = __pyx_t_11;
 
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@@ -3528,9 +3871,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *                 tau = k*step
  *                 v[k] = array[t - tau, n]
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-      for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
-        __pyx_v_k = __pyx_t_11;
+      __pyx_t_12 = __pyx_v_dim;
+      __pyx_t_13 = __pyx_t_12;
+      for (__pyx_t_14 = 0; __pyx_t_14 < __pyx_t_13; __pyx_t_14+=1) {
+        __pyx_v_k = __pyx_t_14;
 
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@@ -3548,10 +3892,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *                 ave += v[k]
  *                 mask *= array_mask[t - tau, n]
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-        __pyx_t_13 = __pyx_v_n;
-        __pyx_t_14 = __pyx_v_k;
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+        __pyx_t_15 = (__pyx_v_t - __pyx_v_tau);
+        __pyx_t_16 = __pyx_v_n;
+        __pyx_t_17 = __pyx_v_k;
+        *((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_17 * __pyx_v_v.strides[0]) )) = (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array.data + __pyx_t_15 * __pyx_v_array.strides[0]) ) + __pyx_t_16 * __pyx_v_array.strides[1]) )));
 
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@@ -3560,8 +3904,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *                 mask *= array_mask[t - tau, n]
  *             ave /= dim
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-        __pyx_v_ave = (__pyx_v_ave + (*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_15 * __pyx_v_v.strides[0]) ))));
+        __pyx_t_18 = __pyx_v_k;
+        __pyx_v_ave = (__pyx_v_ave + (*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_18 * __pyx_v_v.strides[0]) ))));
 
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@@ -3570,9 +3914,9 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *             ave /= dim
  *             var = 0.
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-        __pyx_t_17 = __pyx_v_n;
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+        __pyx_t_19 = (__pyx_v_t - __pyx_v_tau);
+        __pyx_t_20 = __pyx_v_n;
+        __pyx_v_mask = (__pyx_v_mask * (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_array_mask.data + __pyx_t_19 * __pyx_v_array_mask.strides[0]) ) + __pyx_t_20 * __pyx_v_array_mask.strides[1]) ))));
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@@ -3604,9 +3948,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *                 var += (v[k] - ave)**2
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-      for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
-        __pyx_v_k = __pyx_t_11;
+      __pyx_t_12 = __pyx_v_dim;
+      __pyx_t_13 = __pyx_t_12;
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+        __pyx_v_k = __pyx_t_14;
 
         /* "tigramite/tigramite_cython_code.pyx":106
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@@ -3615,8 +3960,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *             var /= dim
  *             weights[t-start, n] = var
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+        __pyx_t_21 = __pyx_v_k;
+        __pyx_v_var = (__pyx_v_var + pow(((*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_21 * __pyx_v_v.strides[0]) ))) - __pyx_v_ave), 2.0));
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-      __pyx_t_20 = __pyx_v_n;
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+      __pyx_t_22 = (__pyx_v_t - __pyx_v_start);
+      __pyx_t_23 = __pyx_v_n;
+      *((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_weights.data + __pyx_t_22 * __pyx_v_weights.strides[0]) ) + __pyx_t_23 * __pyx_v_weights.strides[1]) )) = __pyx_v_var;
 
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-      __pyx_t_22 = 1;
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-      if (__pyx_t_23) {
+      __pyx_t_24 = 0;
+      __pyx_t_25 = 1;
+      __pyx_t_26 = (((*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_24 * __pyx_v_v.strides[0]) ))) < (*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_25 * __pyx_v_v.strides[0]) )))) != 0);
+      if (__pyx_t_26) {
 
         /* "tigramite/tigramite_cython_code.pyx":110
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@@ -3693,9 +4038,10 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
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-      for (__pyx_t_11 = 2; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
-        __pyx_v_i = __pyx_t_11;
+      __pyx_t_12 = __pyx_v_dim;
+      __pyx_t_13 = __pyx_t_12;
+      for (__pyx_t_14 = 2; __pyx_t_14 < __pyx_t_13; __pyx_t_14+=1) {
+        __pyx_v_i = __pyx_t_14;
 
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-        for (__pyx_t_25 = 0; __pyx_t_25 < __pyx_t_24; __pyx_t_25+=1) {
-          __pyx_v_j = __pyx_t_25;
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           /* "tigramite/tigramite_cython_code.pyx":115
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-          __pyx_t_27 = __pyx_v_i;
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-          if (__pyx_t_23) {
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+          __pyx_t_31 = __pyx_v_i;
+          __pyx_t_26 = (((*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_30 * __pyx_v_v.strides[0]) ))) < (*((double *) ( /* dim=0 */ (__pyx_v_v.data + __pyx_t_31 * __pyx_v_v.strides[0]) )))) != 0);
+          if (__pyx_t_26) {
 
             /* "tigramite/tigramite_cython_code.pyx":116
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@@ -3727,8 +4074,8 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *             patt[t-start, n] = p
  *             patt_mask[t-start, n] = mask
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-            __pyx_v_p = (__pyx_v_p + (*((int *) ( /* dim=0 */ (__pyx_v_fac.data + __pyx_t_28 * __pyx_v_fac.strides[0]) ))));
+            __pyx_t_32 = __pyx_v_i;
+            __pyx_v_p = (__pyx_v_p + (*((int *) ( /* dim=0 */ (__pyx_v_fac.data + __pyx_t_32 * __pyx_v_fac.strides[0]) ))));
 
             /* "tigramite/tigramite_cython_code.pyx":115
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@@ -3748,9 +4095,9 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  *             patt_mask[t-start, n] = mask
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-      __pyx_t_30 = __pyx_v_n;
-      *((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_patt.data + __pyx_t_29 * __pyx_v_patt.strides[0]) ) + __pyx_t_30 * __pyx_v_patt.strides[1]) )) = __pyx_v_p;
+      __pyx_t_33 = (__pyx_v_t - __pyx_v_start);
+      __pyx_t_34 = __pyx_v_n;
+      *((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_patt.data + __pyx_t_33 * __pyx_v_patt.strides[0]) ) + __pyx_t_34 * __pyx_v_patt.strides[1]) )) = __pyx_v_p;
 
       /* "tigramite/tigramite_cython_code.pyx":118
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@@ -3759,9 +4106,9 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_2_get_patterns_cyth
  * 
  *     return (patt, patt_mask, weights)
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+      __pyx_t_35 = (__pyx_v_t - __pyx_v_start);
+      __pyx_t_36 = __pyx_v_n;
+      *((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_patt_mask.data + __pyx_t_35 * __pyx_v_patt_mask.strides[0]) ) + __pyx_t_36 * __pyx_v_patt_mask.strides[1]) )) = __pyx_v_mask;
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@@ -3838,9 +4185,10 @@ static CYTHON_INLINE int __pyx_f_9tigramite_21tigramite_cython_code_isvalueinarr
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-  int __pyx_t_4;
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+ * 
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+ */
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-    *((int *) ( /* dim=0 */ (__pyx_v_used.data + __pyx_t_16 * __pyx_v_used.strides[0]) )) = __pyx_v_use;
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-  /* "tigramite/tigramite_cython_code.pyx":169
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-  /* "tigramite/tigramite_cython_code.pyx":266
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-  /* "tigramite/tigramite_cython_code.pyx":267
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  *         cdef DTYPE_t squared_sum = 0
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+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self, __pyx_n_s_mean); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 268, __pyx_L1_error)
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+  __pyx_t_4 = __pyx_PyFloat_AsDouble(__pyx_t_1); if (unlikely((__pyx_t_4 == ((npy_double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 268, __pyx_L1_error)
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-  /* "tigramite/tigramite_cython_code.pyx":268
+  /* "tigramite/tigramite_cython_code.pyx":269
  *         cdef np.ndarray[DTYPE_t, ndim=1] mean_1 = self.mean_1
  *         cdef DTYPE_t mean = self.mean
  *         cdef DTYPE_t squared_sum = 0             # <<<<<<<<<<<<<<
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-  /* "tigramite/tigramite_cython_code.pyx":271
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-      __PYX_ERR(0, 271, __pyx_L1_error)
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-  /* "tigramite/tigramite_cython_code.pyx":272
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  *         cdef unsigned int ii
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self, __pyx_n_s_dim); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 272, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self, __pyx_n_s_dim); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 273, __pyx_L1_error)
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-  __pyx_t_6 = __Pyx_PyInt_As_unsigned_int(__pyx_t_1); if (unlikely((__pyx_t_6 == (unsigned int)-1) && PyErr_Occurred())) __PYX_ERR(0, 272, __pyx_L1_error)
+  __pyx_t_6 = __Pyx_PyInt_As_unsigned_int(__pyx_t_1); if (unlikely((__pyx_t_6 == (unsigned int)-1) && PyErr_Occurred())) __PYX_ERR(0, 273, __pyx_L1_error)
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-  /* "tigramite/tigramite_cython_code.pyx":275
+  /* "tigramite/tigramite_cython_code.pyx":276
  *         cdef unsigned int ii
  *         cdef unsigned int jj
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@@ -5697,43 +6136,45 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_4squared_sum(C
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-  for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
-    __pyx_v_ii = __pyx_t_7;
+  __pyx_t_7 = __pyx_t_6;
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-    /* "tigramite/tigramite_cython_code.pyx":276
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-    __pyx_t_8 = __pyx_v_dim;
-    for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
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+    __pyx_t_10 = __pyx_t_9;
+    for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
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-      /* "tigramite/tigramite_cython_code.pyx":277
+      /* "tigramite/tigramite_cython_code.pyx":278
  *         for ii in range(dim):
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  *                 d = dist - mean_0[ii] - mean_1[jj] + mean
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-      __pyx_t_10 = __pyx_v_jj;
-      __pyx_t_11 = __pyx_v_ii;
-      __pyx_v_dist = __pyx_f_9tigramite_21tigramite_cython_code_myabs(((*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_x.rcbuffer->pybuffer.buf, __pyx_t_10, __pyx_pybuffernd_x.diminfo[0].strides)) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_x.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_x.diminfo[0].strides))));
+      __pyx_t_12 = __pyx_v_jj;
+      __pyx_t_13 = __pyx_v_ii;
+      __pyx_v_dist = __pyx_f_9tigramite_21tigramite_cython_code_myabs(((*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_x.rcbuffer->pybuffer.buf, __pyx_t_12, __pyx_pybuffernd_x.diminfo[0].strides)) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_x.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_x.diminfo[0].strides))));
 
-      /* "tigramite/tigramite_cython_code.pyx":278
+      /* "tigramite/tigramite_cython_code.pyx":279
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-      __pyx_t_13 = __pyx_v_jj;
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+      __pyx_t_14 = __pyx_v_ii;
+      __pyx_t_15 = __pyx_v_jj;
+      __pyx_v_d = (((__pyx_v_dist - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_mean_0.rcbuffer->pybuffer.buf, __pyx_t_14, __pyx_pybuffernd_mean_0.diminfo[0].strides))) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_mean_1.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_mean_1.diminfo[0].strides))) + __pyx_v_mean);
 
-      /* "tigramite/tigramite_cython_code.pyx":279
+      /* "tigramite/tigramite_cython_code.pyx":280
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     }
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-  /* "tigramite/tigramite_cython_code.pyx":280
+  /* "tigramite/tigramite_cython_code.pyx":281
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@@ -5752,13 +6193,13 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_4squared_sum(C
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   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_squared_sum); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 280, __pyx_L1_error)
+  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_squared_sum); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 281, __pyx_L1_error)
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-  /* "tigramite/tigramite_cython_code.pyx":264
+  /* "tigramite/tigramite_cython_code.pyx":265
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  *     @cython.boundscheck(False)
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   return __pyx_r;
 }
 
-/* "tigramite/tigramite_cython_code.pyx":283
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       const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
       switch (pos_args) {
         case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        CYTHON_FALLTHROUGH;
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+        CYTHON_FALLTHROUGH;
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       switch (pos_args) {
         case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_self)) != 0)) kw_args--;
+        if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_self)) != 0)) kw_args--;
         else goto __pyx_L5_argtuple_error;
+        CYTHON_FALLTHROUGH;
         case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_other)) != 0)) kw_args--;
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       if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "product_sum") < 0)) __PYX_ERR(0, 283, __pyx_L3_error)
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-  __Pyx_RaiseArgtupleInvalid("product_sum", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 283, __pyx_L3_error)
+  __Pyx_RaiseArgtupleInvalid("product_sum", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 284, __pyx_L3_error)
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-  size_t __pyx_t_14;
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   size_t __pyx_t_17;
   size_t __pyx_t_18;
   size_t __pyx_t_19;
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+  size_t __pyx_t_21;
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@@ -5935,22 +6381,22 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
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-  /* "tigramite/tigramite_cython_code.pyx":286
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-  /* "tigramite/tigramite_cython_code.pyx":287
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  *         cdef DTYPE_t mean_here = self.mean
  *         cdef np.ndarray[DTYPE_t, ndim=1] mean_0_there = other.mean_0             # <<<<<<<<<<<<<<
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-  /* "tigramite/tigramite_cython_code.pyx":288
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  *         cdef np.ndarray[DTYPE_t, ndim=1] mean_0_there = other.mean_0
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-      __PYX_ERR(0, 288, __pyx_L1_error)
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   __pyx_t_1 = 0;
 
-  /* "tigramite/tigramite_cython_code.pyx":289
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-  /* "tigramite/tigramite_cython_code.pyx":292
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  *         cdef DTYPE_t d_there
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   __pyx_v_product_sum = 0.0;
 
-  /* "tigramite/tigramite_cython_code.pyx":293
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@@ -6085,22 +6531,22 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
   __pyx_v_x = ((PyArrayObject *)__pyx_t_1);
   __pyx_t_1 = 0;
 
-  /* "tigramite/tigramite_cython_code.pyx":294
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  *         cdef np.ndarray[DTYPE_t, ndim=1] x = self.x
  *         cdef np.ndarray[DTYPE_t, ndim=1] y = other.x             # <<<<<<<<<<<<<<
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_other, __pyx_n_s_x); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 294, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_other, __pyx_n_s_x); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 295, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
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-      __PYX_ERR(0, 294, __pyx_L1_error)
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@@ -6108,20 +6554,20 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
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   __pyx_t_1 = 0;
 
-  /* "tigramite/tigramite_cython_code.pyx":296
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  *         cdef unsigned int ii
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self, __pyx_n_s_dim); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 296, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_self, __pyx_n_s_dim); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 297, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
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-  /* "tigramite/tigramite_cython_code.pyx":299
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@@ -6129,47 +6575,49 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
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   __pyx_t_9 = __pyx_v_dim;
-  for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) {
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-    /* "tigramite/tigramite_cython_code.pyx":300
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-    for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {
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-      /* "tigramite/tigramite_cython_code.pyx":301
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-      __pyx_t_14 = __pyx_v_ii;
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+      __pyx_t_15 = __pyx_v_jj;
+      __pyx_t_16 = __pyx_v_ii;
+      __pyx_t_17 = __pyx_v_ii;
+      __pyx_t_18 = __pyx_v_jj;
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-      /* "tigramite/tigramite_cython_code.pyx":302
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+      __pyx_t_19 = __pyx_v_jj;
+      __pyx_t_20 = __pyx_v_ii;
+      __pyx_t_21 = __pyx_v_ii;
+      __pyx_t_22 = __pyx_v_jj;
+      __pyx_v_d_there = (((__pyx_f_9tigramite_21tigramite_cython_code_myabs(((*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_y.rcbuffer->pybuffer.buf, __pyx_t_19, __pyx_pybuffernd_y.diminfo[0].strides)) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_y.rcbuffer->pybuffer.buf, __pyx_t_20, __pyx_pybuffernd_y.diminfo[0].strides)))) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_mean_0_there.rcbuffer->pybuffer.buf, __pyx_t_21, __pyx_pybuffernd_mean_0_there.diminfo[0].strides))) - (*__Pyx_BufPtrStrided1d(__pyx_t_9tigramite_21tigramite_cython_code_DTYPE_t *, __pyx_pybuffernd_mean_1_there.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_mean_1_there.diminfo[0].strides))) + __pyx_v_mean_there);
 
-      /* "tigramite/tigramite_cython_code.pyx":303
+      /* "tigramite/tigramite_cython_code.pyx":304
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@@ -6179,19 +6627,19 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
     }
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-  /* "tigramite/tigramite_cython_code.pyx":304
+  /* "tigramite/tigramite_cython_code.pyx":305
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-  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_product_sum); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 304, __pyx_L1_error)
+  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_product_sum); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 305, __pyx_L1_error)
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-  /* "tigramite/tigramite_cython_code.pyx":283
+  /* "tigramite/tigramite_cython_code.pyx":284
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@@ -6235,12 +6683,12 @@ static PyObject *__pyx_pf_9tigramite_21tigramite_cython_code_3D_N_6product_sum(C
   return __pyx_r;
 }
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":197
+/* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":215
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  *         def __getbuffer__(ndarray self, Py_buffer* info, int flags):             # <<<<<<<<<<<<<<
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- *             # requirements, and does not yet fullfill the PEP.
+ *             # requirements, and does not yet fulfill the PEP.
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 /* Python wrapper */
@@ -6257,7 +6705,6 @@ static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx
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 static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
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   int __pyx_v_ndim;
   int __pyx_v_endian_detector;
@@ -6266,7 +6713,6 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
   char *__pyx_v_f;
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-  PyObject *__pyx_t_6 = NULL;
-  char *__pyx_t_7;
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":203
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- *             cdef int copy_shape, i, ndim
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-  if (__pyx_t_1) {
-    __pyx_r = 0;
-    goto __pyx_L0;
+  int __pyx_t_6;
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+  char *__pyx_t_8;
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+    PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete");
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":206
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":222
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- *             cdef int copy_shape, i, ndim
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":207
- *             cdef int copy_shape, i, ndim
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":223
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  *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)             # <<<<<<<<<<<<<<
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":209
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":225
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  * 
  *             ndim = PyArray_NDIM(self)             # <<<<<<<<<<<<<<
  * 
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
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   __pyx_v_ndim = PyArray_NDIM(__pyx_v_self);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":211
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":227
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-  if (__pyx_t_1) {
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":212
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":211
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- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
- */
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-  __pyx_L4:;
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@@ -6374,10 +6769,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":217
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":228
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-  __pyx_L6_bool_binop_done:;
+  __pyx_L4_bool_binop_done:;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216
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-  if (__pyx_t_1) {
+  if (unlikely(__pyx_t_1)) {
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":218
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":231
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-    goto __pyx_L9_bool_binop_done;
+    goto __pyx_L7_bool_binop_done;
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":221
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":232
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-  __pyx_L9_bool_binop_done:;
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220
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-  if (__pyx_t_1) {
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":222
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":233
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":231
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  */
   }
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":224
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":235
  *                 raise ValueError(u"ndarray is not Fortran contiguous")
  * 
  *             info.buf = PyArray_DATA(self)             # <<<<<<<<<<<<<<
  *             info.ndim = ndim
- *             if copy_shape:
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
  */
   __pyx_v_info->buf = PyArray_DATA(__pyx_v_self);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":225
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":236
  * 
  *             info.buf = PyArray_DATA(self)
  *             info.ndim = ndim             # <<<<<<<<<<<<<<
- *             if copy_shape:
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
  *                 # Allocate new buffer for strides and shape info.
  */
   __pyx_v_info->ndim = __pyx_v_ndim;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":237
  *             info.buf = PyArray_DATA(self)
  *             info.ndim = ndim
- *             if copy_shape:             # <<<<<<<<<<<<<<
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
  *                 # Allocate new buffer for strides and shape info.
  *                 # This is allocated as one block, strides first.
  */
-  __pyx_t_1 = (__pyx_v_copy_shape != 0);
+  __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0);
   if (__pyx_t_1) {
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":229
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":240
  *                 # Allocate new buffer for strides and shape info.
  *                 # This is allocated as one block, strides first.
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)             # <<<<<<<<<<<<<<
+ *                 info.strides = <Py_ssize_t*>PyObject_Malloc(sizeof(Py_ssize_t) * 2 * <size_t>ndim)             # <<<<<<<<<<<<<<
  *                 info.shape = info.strides + ndim
  *                 for i in range(ndim):
  */
-    __pyx_v_info->strides = ((Py_ssize_t *)malloc((((sizeof(Py_ssize_t)) * ((size_t)__pyx_v_ndim)) * 2)));
+    __pyx_v_info->strides = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * 2) * ((size_t)__pyx_v_ndim))));
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":230
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":241
  *                 # This is allocated as one block, strides first.
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
+ *                 info.strides = <Py_ssize_t*>PyObject_Malloc(sizeof(Py_ssize_t) * 2 * <size_t>ndim)
  *                 info.shape = info.strides + ndim             # <<<<<<<<<<<<<<
  *                 for i in range(ndim):
  *                     info.strides[i] = PyArray_STRIDES(self)[i]
  */
     __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim);
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":231
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":242
+ *                 info.strides = <Py_ssize_t*>PyObject_Malloc(sizeof(Py_ssize_t) * 2 * <size_t>ndim)
  *                 info.shape = info.strides + ndim
  *                 for i in range(ndim):             # <<<<<<<<<<<<<<
  *                     info.strides[i] = PyArray_STRIDES(self)[i]
  *                     info.shape[i] = PyArray_DIMS(self)[i]
  */
     __pyx_t_4 = __pyx_v_ndim;
-    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-      __pyx_v_i = __pyx_t_5;
+    __pyx_t_5 = __pyx_t_4;
+    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+      __pyx_v_i = __pyx_t_6;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":232
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":243
  *                 info.shape = info.strides + ndim
  *                 for i in range(ndim):
  *                     info.strides[i] = PyArray_STRIDES(self)[i]             # <<<<<<<<<<<<<<
@@ -6541,7 +6937,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
       (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]);
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":233
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":244
  *                 for i in range(ndim):
  *                     info.strides[i] = PyArray_STRIDES(self)[i]
  *                     info.shape[i] = PyArray_DIMS(self)[i]             # <<<<<<<<<<<<<<
@@ -6551,17 +6947,17 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]);
     }
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":237
  *             info.buf = PyArray_DATA(self)
  *             info.ndim = ndim
- *             if copy_shape:             # <<<<<<<<<<<<<<
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
  *                 # Allocate new buffer for strides and shape info.
  *                 # This is allocated as one block, strides first.
  */
-    goto __pyx_L11;
+    goto __pyx_L9;
   }
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":235
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":246
  *                     info.shape[i] = PyArray_DIMS(self)[i]
  *             else:
  *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
@@ -6571,7 +6967,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
   /*else*/ {
     __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self));
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":236
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":247
  *             else:
  *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
  *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)             # <<<<<<<<<<<<<<
@@ -6580,9 +6976,9 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
     __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(__pyx_v_self));
   }
-  __pyx_L11:;
+  __pyx_L9:;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":237
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":248
  *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
  *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
  *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
@@ -6591,7 +6987,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
   __pyx_v_info->suboffsets = NULL;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":238
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":249
  *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
  *             info.suboffsets = NULL
  *             info.itemsize = PyArray_ITEMSIZE(self)             # <<<<<<<<<<<<<<
@@ -6600,7 +6996,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
   __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":239
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":250
  *             info.suboffsets = NULL
  *             info.itemsize = PyArray_ITEMSIZE(self)
  *             info.readonly = not PyArray_ISWRITEABLE(self)             # <<<<<<<<<<<<<<
@@ -6609,7 +7005,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
   __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0));
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":242
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":253
  * 
  *             cdef int t
  *             cdef char* f = NULL             # <<<<<<<<<<<<<<
@@ -6618,7 +7014,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
   __pyx_v_f = NULL;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":243
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":254
  *             cdef int t
  *             cdef char* f = NULL
  *             cdef dtype descr = self.descr             # <<<<<<<<<<<<<<
@@ -6630,85 +7026,32 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
   __pyx_v_descr = ((PyArray_Descr *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":246
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":257
  *             cdef int offset
  * 
- *             cdef bint hasfields = PyDataType_HASFIELDS(descr)             # <<<<<<<<<<<<<<
- * 
- *             if not hasfields and not copy_shape:
- */
-  __pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr);
-
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248
- *             cdef bint hasfields = PyDataType_HASFIELDS(descr)
- * 
- *             if not hasfields and not copy_shape:             # <<<<<<<<<<<<<<
- *                 # do not call releasebuffer
- *                 info.obj = None
- */
-  __pyx_t_2 = ((!(__pyx_v_hasfields != 0)) != 0);
-  if (__pyx_t_2) {
-  } else {
-    __pyx_t_1 = __pyx_t_2;
-    goto __pyx_L15_bool_binop_done;
-  }
-  __pyx_t_2 = ((!(__pyx_v_copy_shape != 0)) != 0);
-  __pyx_t_1 = __pyx_t_2;
-  __pyx_L15_bool_binop_done:;
-  if (__pyx_t_1) {
-
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":250
- *             if not hasfields and not copy_shape:
- *                 # do not call releasebuffer
- *                 info.obj = None             # <<<<<<<<<<<<<<
- *             else:
- *                 # need to call releasebuffer
- */
-    __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(Py_None);
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj);
-    __pyx_v_info->obj = Py_None;
-
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248
- *             cdef bint hasfields = PyDataType_HASFIELDS(descr)
- * 
- *             if not hasfields and not copy_shape:             # <<<<<<<<<<<<<<
- *                 # do not call releasebuffer
- *                 info.obj = None
- */
-    goto __pyx_L14;
-  }
-
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":253
- *             else:
- *                 # need to call releasebuffer
- *                 info.obj = self             # <<<<<<<<<<<<<<
+ *             info.obj = self             # <<<<<<<<<<<<<<
  * 
- *             if not hasfields:
+ *             if not PyDataType_HASFIELDS(descr):
  */
-  /*else*/ {
-    __Pyx_INCREF(((PyObject *)__pyx_v_self));
-    __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj);
-    __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
-  }
-  __pyx_L14:;
+  __Pyx_INCREF(((PyObject *)__pyx_v_self));
+  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+  __Pyx_GOTREF(__pyx_v_info->obj);
+  __Pyx_DECREF(__pyx_v_info->obj);
+  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":255
- *                 info.obj = self
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":259
+ *             info.obj = self
  * 
- *             if not hasfields:             # <<<<<<<<<<<<<<
+ *             if not PyDataType_HASFIELDS(descr):             # <<<<<<<<<<<<<<
  *                 t = descr.type_num
  *                 if ((descr.byteorder == c'>' and little_endian) or
  */
-  __pyx_t_1 = ((!(__pyx_v_hasfields != 0)) != 0);
+  __pyx_t_1 = ((!(PyDataType_HASFIELDS(__pyx_v_descr) != 0)) != 0);
   if (__pyx_t_1) {
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":256
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":260
  * 
- *             if not hasfields:
+ *             if not PyDataType_HASFIELDS(descr):
  *                 t = descr.type_num             # <<<<<<<<<<<<<<
  *                 if ((descr.byteorder == c'>' and little_endian) or
  *                     (descr.byteorder == c'<' and not little_endian)):
@@ -6716,8 +7059,8 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
     __pyx_t_4 = __pyx_v_descr->type_num;
     __pyx_v_t = __pyx_t_4;
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257
- *             if not hasfields:
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":261
+ *             if not PyDataType_HASFIELDS(descr):
  *                 t = descr.type_num
  *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
  *                     (descr.byteorder == c'<' and not little_endian)):
@@ -6725,18 +7068,18 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
     __pyx_t_2 = ((__pyx_v_descr->byteorder == '>') != 0);
     if (!__pyx_t_2) {
-      goto __pyx_L20_next_or;
+      goto __pyx_L15_next_or;
     } else {
     }
     __pyx_t_2 = (__pyx_v_little_endian != 0);
     if (!__pyx_t_2) {
     } else {
       __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L19_bool_binop_done;
+      goto __pyx_L14_bool_binop_done;
     }
-    __pyx_L20_next_or:;
+    __pyx_L15_next_or:;
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":258
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":262
  *                 t = descr.type_num
  *                 if ((descr.byteorder == c'>' and little_endian) or
  *                     (descr.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<
@@ -6747,36 +7090,36 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
     if (__pyx_t_2) {
     } else {
       __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L19_bool_binop_done;
+      goto __pyx_L14_bool_binop_done;
     }
     __pyx_t_2 = ((!(__pyx_v_little_endian != 0)) != 0);
     __pyx_t_1 = __pyx_t_2;
-    __pyx_L19_bool_binop_done:;
+    __pyx_L14_bool_binop_done:;
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257
- *             if not hasfields:
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":261
+ *             if not PyDataType_HASFIELDS(descr):
  *                 t = descr.type_num
  *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
  *                     (descr.byteorder == c'<' and not little_endian)):
  *                     raise ValueError(u"Non-native byte order not supported")
  */
-    if (__pyx_t_1) {
+    if (unlikely(__pyx_t_1)) {
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":259
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":263
  *                 if ((descr.byteorder == c'>' and little_endian) or
  *                     (descr.byteorder == c'<' and not little_endian)):
  *                     raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
  *                 if   t == NPY_BYTE:        f = "b"
  *                 elif t == NPY_UBYTE:       f = "B"
  */
-      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 259, __pyx_L1_error)
+      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 263, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_3);
       __Pyx_Raise(__pyx_t_3, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __PYX_ERR(1, 259, __pyx_L1_error)
+      __PYX_ERR(1, 263, __pyx_L1_error)
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257
- *             if not hasfields:
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":261
+ *             if not PyDataType_HASFIELDS(descr):
  *                 t = descr.type_num
  *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
  *                     (descr.byteorder == c'<' and not little_endian)):
@@ -6784,7 +7127,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  */
     }
 
-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":260
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":264
  *                     (descr.byteorder == c'<' and not little_endian)):
  *                     raise ValueError(u"Non-native byte order not supported")
  *                 if   t == NPY_BYTE:        f = "b"             # <<<<<<<<<<<<<<
@@ -6796,7 +7139,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"b");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":261
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":265
  *                     raise ValueError(u"Non-native byte order not supported")
  *                 if   t == NPY_BYTE:        f = "b"
  *                 elif t == NPY_UBYTE:       f = "B"             # <<<<<<<<<<<<<<
@@ -6807,7 +7150,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"B");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":262
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":266
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       __pyx_v_f = ((char *)"h");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":263
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":267
  *                 elif t == NPY_UBYTE:       f = "B"
  *                 elif t == NPY_SHORT:       f = "h"
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@@ -6829,7 +7172,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"H");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":264
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":268
  *                 elif t == NPY_SHORT:       f = "h"
  *                 elif t == NPY_USHORT:      f = "H"
  *                 elif t == NPY_INT:         f = "i"             # <<<<<<<<<<<<<<
@@ -6840,7 +7183,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"i");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":265
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":269
  *                 elif t == NPY_USHORT:      f = "H"
  *                 elif t == NPY_INT:         f = "i"
  *                 elif t == NPY_UINT:        f = "I"             # <<<<<<<<<<<<<<
@@ -6851,7 +7194,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"I");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":266
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":270
  *                 elif t == NPY_INT:         f = "i"
  *                 elif t == NPY_UINT:        f = "I"
  *                 elif t == NPY_LONG:        f = "l"             # <<<<<<<<<<<<<<
@@ -6862,7 +7205,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"l");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":267
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":271
  *                 elif t == NPY_UINT:        f = "I"
  *                 elif t == NPY_LONG:        f = "l"
  *                 elif t == NPY_ULONG:       f = "L"             # <<<<<<<<<<<<<<
@@ -6873,7 +7216,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"L");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":268
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":272
  *                 elif t == NPY_LONG:        f = "l"
  *                 elif t == NPY_ULONG:       f = "L"
  *                 elif t == NPY_LONGLONG:    f = "q"             # <<<<<<<<<<<<<<
@@ -6884,7 +7227,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"q");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":269
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":273
  *                 elif t == NPY_ULONG:       f = "L"
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  *                 elif t == NPY_ULONGLONG:   f = "Q"             # <<<<<<<<<<<<<<
@@ -6895,7 +7238,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"Q");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":270
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":274
  *                 elif t == NPY_LONGLONG:    f = "q"
  *                 elif t == NPY_ULONGLONG:   f = "Q"
  *                 elif t == NPY_FLOAT:       f = "f"             # <<<<<<<<<<<<<<
@@ -6906,7 +7249,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"f");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":271
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":275
  *                 elif t == NPY_ULONGLONG:   f = "Q"
  *                 elif t == NPY_FLOAT:       f = "f"
  *                 elif t == NPY_DOUBLE:      f = "d"             # <<<<<<<<<<<<<<
@@ -6917,7 +7260,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"d");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":272
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":276
  *                 elif t == NPY_FLOAT:       f = "f"
  *                 elif t == NPY_DOUBLE:      f = "d"
  *                 elif t == NPY_LONGDOUBLE:  f = "g"             # <<<<<<<<<<<<<<
@@ -6928,7 +7271,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       __pyx_v_f = ((char *)"g");
       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":273
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":277
  *                 elif t == NPY_DOUBLE:      f = "d"
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@@ -6939,7 +7282,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
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       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":274
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":278
  *                 elif t == NPY_LONGDOUBLE:  f = "g"
  *                 elif t == NPY_CFLOAT:      f = "Zf"
  *                 elif t == NPY_CDOUBLE:     f = "Zd"             # <<<<<<<<<<<<<<
@@ -6950,7 +7293,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
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       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":275
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":279
  *                 elif t == NPY_CFLOAT:      f = "Zf"
  *                 elif t == NPY_CDOUBLE:     f = "Zd"
  *                 elif t == NPY_CLONGDOUBLE: f = "Zg"             # <<<<<<<<<<<<<<
@@ -6961,7 +7304,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
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       break;
 
-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":276
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":280
  *                 elif t == NPY_CDOUBLE:     f = "Zd"
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@@ -6973,33 +7316,28 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
       break;
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-      /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":278
+      /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":282
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-      __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 278, __pyx_L1_error)
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-      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 278, __pyx_L1_error)
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-      __Pyx_GOTREF(__pyx_t_6);
+      __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
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-      __Pyx_Raise(__pyx_t_6, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-      __PYX_ERR(1, 278, __pyx_L1_error)
+      __PYX_ERR(1, 282, __pyx_L1_error)
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":279
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":283
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  *                 info.format = f             # <<<<<<<<<<<<<<
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":280
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":284
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  *                 info.format = f
  *                 return             # <<<<<<<<<<<<<<
  *             else:
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+ *                 info.format = <char*>PyObject_Malloc(_buffer_format_string_len)
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":255
- *                 info.obj = self
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":259
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":282
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":286
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- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)             # <<<<<<<<<<<<<<
+ *                 info.format = <char*>PyObject_Malloc(_buffer_format_string_len)             # <<<<<<<<<<<<<<
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-    __pyx_v_info->format = ((char *)malloc(0xFF));
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":283
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":287
  *             else:
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+ *                 info.format = <char*>PyObject_Malloc(_buffer_format_string_len)
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":284
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":288
+ *                 info.format = <char*>PyObject_Malloc(_buffer_format_string_len)
  *                 info.format[0] = c'^' # Native data types, manual alignment
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":285
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":289
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-    __pyx_v_f = __pyx_t_7;
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":288
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":292
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@@ -7075,12 +7413,12 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":197
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":215
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@@ -7088,18 +7426,18 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
   goto __pyx_L0;
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_7);
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   __pyx_r = -1;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
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-    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
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-    __Pyx_GOTREF(Py_None);
-    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
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 }
 
-/* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":290
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":291
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":292
+    /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":296
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-    /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":291
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-      /* "View.MemoryView":150
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@@ -9168,7 +9575,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
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-    /* "View.MemoryView":152
+    /* "View.MemoryView":153
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-    /* "View.MemoryView":156
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-    /* "View.MemoryView":157
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-  /* "View.MemoryView":158
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-  if (__pyx_t_4) {
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-    /* "View.MemoryView":159
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-    /* "View.MemoryView":160
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-  /* "View.MemoryView":162
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-  /* "View.MemoryView":164
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-  /* "View.MemoryView":168
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-  __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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-  /* "View.MemoryView":169
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-    /* "View.MemoryView":172
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-    /* "View.MemoryView":173
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-    if (__pyx_t_4) {
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-      /* "View.MemoryView":174
+      /* "View.MemoryView":175
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-      __PYX_ERR(2, 174, __pyx_L1_error)
+      __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 175, __pyx_L1_error)
+      __Pyx_GOTREF(__pyx_t_10);
+      __Pyx_Raise(__pyx_t_10, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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-      /* "View.MemoryView":173
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@@ -9376,7 +9778,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
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-    /* "View.MemoryView":176
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@@ -9386,7 +9788,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
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-      /* "View.MemoryView":177
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@@ -9395,7 +9797,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
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-      /* "View.MemoryView":178
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-      for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_1; __pyx_t_8+=1) {
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-        /* "View.MemoryView":179
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-        /* "View.MemoryView":180
+        /* "View.MemoryView":181
  *                 for i in range(self.len / itemsize):
  *                     p[i] = Py_None
  *                     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
@@ -9433,7 +9836,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
         Py_INCREF(Py_None);
       }
 
-      /* "View.MemoryView":176
+      /* "View.MemoryView":177
  *                 raise MemoryError("unable to allocate array data.")
  * 
  *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
@@ -9442,7 +9845,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
  */
     }
 
-    /* "View.MemoryView":169
+    /* "View.MemoryView":170
  *         self.free_data = allocate_buffer
  *         self.dtype_is_object = format == b'O'
  *         if allocate_buffer:             # <<<<<<<<<<<<<<
@@ -9451,7 +9854,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
  */
   }
 
-  /* "View.MemoryView":120
+  /* "View.MemoryView":121
  *         cdef bint dtype_is_object
  * 
  *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
@@ -9475,7 +9878,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __
   return __pyx_r;
 }
 
-/* "View.MemoryView":183
+/* "View.MemoryView":184
  * 
  *     @cname('getbuffer')
  *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
@@ -9507,13 +9910,15 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   Py_ssize_t __pyx_t_5;
   int __pyx_t_6;
   Py_ssize_t *__pyx_t_7;
-  __Pyx_RefNannySetupContext("__getbuffer__", 0);
-  if (__pyx_v_info != NULL) {
-    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(__pyx_v_info->obj);
+  if (__pyx_v_info == NULL) {
+    PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete");
+    return -1;
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+  __Pyx_RefNannySetupContext("__getbuffer__", 0);
+  __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
+  __Pyx_GIVEREF(__pyx_v_info->obj);
 
-  /* "View.MemoryView":184
+  /* "View.MemoryView":185
  *     @cname('getbuffer')
  *     def __getbuffer__(self, Py_buffer *info, int flags):
  *         cdef int bufmode = -1             # <<<<<<<<<<<<<<
@@ -9522,18 +9927,18 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
   __pyx_v_bufmode = -1;
 
-  /* "View.MemoryView":185
+  /* "View.MemoryView":186
  *     def __getbuffer__(self, Py_buffer *info, int flags):
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  *         if self.mode == u"c":             # <<<<<<<<<<<<<<
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  *         elif self.mode == u"fortran":
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-  __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 185, __pyx_L1_error)
+  __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 186, __pyx_L1_error)
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":186
+    /* "View.MemoryView":187
  *         cdef int bufmode = -1
  *         if self.mode == u"c":
  *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
@@ -9542,7 +9947,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
     __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
 
-    /* "View.MemoryView":185
+    /* "View.MemoryView":186
  *     def __getbuffer__(self, Py_buffer *info, int flags):
  *         cdef int bufmode = -1
  *         if self.mode == u"c":             # <<<<<<<<<<<<<<
@@ -9552,18 +9957,18 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":187
+  /* "View.MemoryView":188
  *         if self.mode == u"c":
  *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         elif self.mode == u"fortran":             # <<<<<<<<<<<<<<
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         if not (flags & bufmode):
  */
-  __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 187, __pyx_L1_error)
+  __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 188, __pyx_L1_error)
   __pyx_t_1 = (__pyx_t_2 != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":188
+    /* "View.MemoryView":189
  *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         elif self.mode == u"fortran":
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
@@ -9572,7 +9977,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
     __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
 
-    /* "View.MemoryView":187
+    /* "View.MemoryView":188
  *         if self.mode == u"c":
  *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         elif self.mode == u"fortran":             # <<<<<<<<<<<<<<
@@ -9582,7 +9987,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":189
+  /* "View.MemoryView":190
  *         elif self.mode == u"fortran":
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         if not (flags & bufmode):             # <<<<<<<<<<<<<<
@@ -9590,22 +9995,22 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  *         info.buf = self.data
  */
   __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0);
-  if (__pyx_t_1) {
+  if (unlikely(__pyx_t_1)) {
 
-    /* "View.MemoryView":190
+    /* "View.MemoryView":191
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         if not (flags & bufmode):
  *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
  *         info.buf = self.data
  *         info.len = self.len
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-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 190, __pyx_L1_error)
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 191, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
     __Pyx_Raise(__pyx_t_3, 0, 0, 0);
     __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __PYX_ERR(2, 190, __pyx_L1_error)
+    __PYX_ERR(2, 191, __pyx_L1_error)
 
-    /* "View.MemoryView":189
+    /* "View.MemoryView":190
  *         elif self.mode == u"fortran":
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         if not (flags & bufmode):             # <<<<<<<<<<<<<<
@@ -9614,7 +10019,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
   }
 
-  /* "View.MemoryView":191
+  /* "View.MemoryView":192
  *         if not (flags & bufmode):
  *             raise ValueError("Can only create a buffer that is contiguous in memory.")
  *         info.buf = self.data             # <<<<<<<<<<<<<<
@@ -9624,7 +10029,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_4 = __pyx_v_self->data;
   __pyx_v_info->buf = __pyx_t_4;
 
-  /* "View.MemoryView":192
+  /* "View.MemoryView":193
  *             raise ValueError("Can only create a buffer that is contiguous in memory.")
  *         info.buf = self.data
  *         info.len = self.len             # <<<<<<<<<<<<<<
@@ -9634,7 +10039,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_5 = __pyx_v_self->len;
   __pyx_v_info->len = __pyx_t_5;
 
-  /* "View.MemoryView":193
+  /* "View.MemoryView":194
  *         info.buf = self.data
  *         info.len = self.len
  *         info.ndim = self.ndim             # <<<<<<<<<<<<<<
@@ -9644,7 +10049,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_6 = __pyx_v_self->ndim;
   __pyx_v_info->ndim = __pyx_t_6;
 
-  /* "View.MemoryView":194
+  /* "View.MemoryView":195
  *         info.len = self.len
  *         info.ndim = self.ndim
  *         info.shape = self._shape             # <<<<<<<<<<<<<<
@@ -9654,7 +10059,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_7 = __pyx_v_self->_shape;
   __pyx_v_info->shape = __pyx_t_7;
 
-  /* "View.MemoryView":195
+  /* "View.MemoryView":196
  *         info.ndim = self.ndim
  *         info.shape = self._shape
  *         info.strides = self._strides             # <<<<<<<<<<<<<<
@@ -9664,7 +10069,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_7 = __pyx_v_self->_strides;
   __pyx_v_info->strides = __pyx_t_7;
 
-  /* "View.MemoryView":196
+  /* "View.MemoryView":197
  *         info.shape = self._shape
  *         info.strides = self._strides
  *         info.suboffsets = NULL             # <<<<<<<<<<<<<<
@@ -9673,7 +10078,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
   __pyx_v_info->suboffsets = NULL;
 
-  /* "View.MemoryView":197
+  /* "View.MemoryView":198
  *         info.strides = self._strides
  *         info.suboffsets = NULL
  *         info.itemsize = self.itemsize             # <<<<<<<<<<<<<<
@@ -9683,7 +10088,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_5 = __pyx_v_self->itemsize;
   __pyx_v_info->itemsize = __pyx_t_5;
 
-  /* "View.MemoryView":198
+  /* "View.MemoryView":199
  *         info.suboffsets = NULL
  *         info.itemsize = self.itemsize
  *         info.readonly = 0             # <<<<<<<<<<<<<<
@@ -9692,7 +10097,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
  */
   __pyx_v_info->readonly = 0;
 
-  /* "View.MemoryView":200
+  /* "View.MemoryView":201
  *         info.readonly = 0
  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
@@ -9702,7 +10107,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":201
+    /* "View.MemoryView":202
  * 
  *         if flags & PyBUF_FORMAT:
  *             info.format = self.format             # <<<<<<<<<<<<<<
@@ -9712,7 +10117,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
     __pyx_t_4 = __pyx_v_self->format;
     __pyx_v_info->format = __pyx_t_4;
 
-    /* "View.MemoryView":200
+    /* "View.MemoryView":201
  *         info.readonly = 0
  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
@@ -9722,7 +10127,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
     goto __pyx_L5;
   }
 
-  /* "View.MemoryView":203
+  /* "View.MemoryView":204
  *             info.format = self.format
  *         else:
  *             info.format = NULL             # <<<<<<<<<<<<<<
@@ -9734,7 +10139,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   }
   __pyx_L5:;
 
-  /* "View.MemoryView":205
+  /* "View.MemoryView":206
  *             info.format = NULL
  * 
  *         info.obj = self             # <<<<<<<<<<<<<<
@@ -9747,7 +10152,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __Pyx_DECREF(__pyx_v_info->obj);
   __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
 
-  /* "View.MemoryView":183
+  /* "View.MemoryView":184
  * 
  *     @cname('getbuffer')
  *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
@@ -9762,22 +10167,22 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru
   __Pyx_XDECREF(__pyx_t_3);
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   __pyx_r = -1;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
+  if (__pyx_v_info->obj != NULL) {
     __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
+    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0;
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   __pyx_L0:;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
-    __Pyx_GOTREF(Py_None);
-    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
+  if (__pyx_v_info->obj == Py_None) {
+    __Pyx_GOTREF(__pyx_v_info->obj);
+    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0;
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   __pyx_L2:;
   __Pyx_RefNannyFinishContext();
   return __pyx_r;
 }
 
-/* "View.MemoryView":209
+/* "View.MemoryView":210
  *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
  * 
  *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
@@ -9801,7 +10206,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
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-  /* "View.MemoryView":210
+  /* "View.MemoryView":211
  * 
  *     def __dealloc__(array self):
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@@ -9811,7 +10216,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
   __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":211
+    /* "View.MemoryView":212
  *     def __dealloc__(array self):
  *         if self.callback_free_data != NULL:
  *             self.callback_free_data(self.data)             # <<<<<<<<<<<<<<
@@ -9820,7 +10225,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
  */
     __pyx_v_self->callback_free_data(__pyx_v_self->data);
 
-    /* "View.MemoryView":210
+    /* "View.MemoryView":211
  * 
  *     def __dealloc__(array self):
  *         if self.callback_free_data != NULL:             # <<<<<<<<<<<<<<
@@ -9830,7 +10235,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":212
+  /* "View.MemoryView":213
  *         if self.callback_free_data != NULL:
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  *         elif self.free_data:             # <<<<<<<<<<<<<<
@@ -9840,7 +10245,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
   __pyx_t_1 = (__pyx_v_self->free_data != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":213
+    /* "View.MemoryView":214
  *             self.callback_free_data(self.data)
  *         elif self.free_data:
  *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
@@ -9850,7 +10255,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
     __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":214
+      /* "View.MemoryView":215
  *         elif self.free_data:
  *             if self.dtype_is_object:
  *                 refcount_objects_in_slice(self.data, self._shape,             # <<<<<<<<<<<<<<
@@ -9859,7 +10264,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
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       __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0);
 
-      /* "View.MemoryView":213
+      /* "View.MemoryView":214
  *             self.callback_free_data(self.data)
  *         elif self.free_data:
  *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
@@ -9868,7 +10273,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
  */
     }
 
-    /* "View.MemoryView":216
+    /* "View.MemoryView":217
  *                 refcount_objects_in_slice(self.data, self._shape,
  *                                           self._strides, self.ndim, False)
  *             free(self.data)             # <<<<<<<<<<<<<<
@@ -9877,7 +10282,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
  */
     free(__pyx_v_self->data);
 
-    /* "View.MemoryView":212
+    /* "View.MemoryView":213
  *         if self.callback_free_data != NULL:
  *             self.callback_free_data(self.data)
  *         elif self.free_data:             # <<<<<<<<<<<<<<
@@ -9887,7 +10292,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":217
+  /* "View.MemoryView":218
  *                                           self._strides, self.ndim, False)
  *             free(self.data)
  *         PyObject_Free(self._shape)             # <<<<<<<<<<<<<<
@@ -9896,7 +10301,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
  */
   PyObject_Free(__pyx_v_self->_shape);
 
-  /* "View.MemoryView":209
+  /* "View.MemoryView":210
  *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
  * 
  *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
@@ -9908,7 +10313,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc
   __Pyx_RefNannyFinishContext();
 }
 
-/* "View.MemoryView":220
+/* "View.MemoryView":221
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  *     @property
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@@ -9935,7 +10340,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct _
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   __Pyx_RefNannySetupContext("__get__", 0);
 
-  /* "View.MemoryView":221
+  /* "View.MemoryView":222
  *     @property
  *     def memview(self):
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@@ -9943,13 +10348,13 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct _
  *     @cname('get_memview')
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 221, __pyx_L1_error)
+  __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 222, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":220
+  /* "View.MemoryView":221
  * 
  *     @property
  *     def memview(self):             # <<<<<<<<<<<<<<
@@ -9968,7 +10373,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct _
   return __pyx_r;
 }
 
-/* "View.MemoryView":224
+/* "View.MemoryView":225
  * 
  *     @cname('get_memview')
  *     cdef get_memview(self):             # <<<<<<<<<<<<<<
@@ -9985,7 +10390,7 @@ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) {
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   __Pyx_RefNannySetupContext("get_memview", 0);
 
-  /* "View.MemoryView":225
+  /* "View.MemoryView":226
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-  /* "View.MemoryView":294
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 }
 
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-  __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 341, __pyx_L3_error)
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-  /* "View.MemoryView":342
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   __Pyx_DECREF(__pyx_v_self->obj);
   __pyx_v_self->obj = __pyx_v_obj;
 
-  /* "View.MemoryView":343
+  /* "View.MemoryView":346
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  */
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-  /* "View.MemoryView":344
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   __pyx_L4_bool_binop_done:;
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-    /* "View.MemoryView":345
+    /* "View.MemoryView":348
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-    /* "View.MemoryView":346
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@@ -10782,7 +11638,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
     __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0);
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-      /* "View.MemoryView":347
+      /* "View.MemoryView":350
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-      /* "View.MemoryView":348
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-    /* "View.MemoryView":344
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@@ -10818,7 +11674,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
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-  /* "View.MemoryView":351
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-    /* "View.MemoryView":352
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-    /* "View.MemoryView":353
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@@ -10846,7 +11702,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
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-    /* "View.MemoryView":351
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  *         global __pyx_memoryview_thread_locks_used
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@@ -10855,7 +11711,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
   }
 
-  /* "View.MemoryView":354
+  /* "View.MemoryView":357
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  *             __pyx_memoryview_thread_locks_used += 1
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@@ -10865,7 +11721,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
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   if (__pyx_t_1) {
 
-    /* "View.MemoryView":355
+    /* "View.MemoryView":358
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     __pyx_v_self->lock = PyThread_allocate_lock();
 
-    /* "View.MemoryView":356
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@@ -10882,18 +11738,18 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  * 
  */
     __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);
-    if (__pyx_t_1) {
+    if (unlikely(__pyx_t_1)) {
 
-      /* "View.MemoryView":357
+      /* "View.MemoryView":360
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  * 
  *         if flags & PyBUF_FORMAT:
  */
-      PyErr_NoMemory(); __PYX_ERR(2, 357, __pyx_L1_error)
+      PyErr_NoMemory(); __PYX_ERR(2, 360, __pyx_L1_error)
 
-      /* "View.MemoryView":356
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@@ -10902,7 +11758,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
     }
 
-    /* "View.MemoryView":354
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@@ -10911,7 +11767,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
   }
 
-  /* "View.MemoryView":359
+  /* "View.MemoryView":362
  *                 raise MemoryError
  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
@@ -10921,7 +11777,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":360
+    /* "View.MemoryView":363
  * 
  *         if flags & PyBUF_FORMAT:
  *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')             # <<<<<<<<<<<<<<
@@ -10939,7 +11795,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
     __pyx_L11_bool_binop_done:;
     __pyx_v_self->dtype_is_object = __pyx_t_1;
 
-    /* "View.MemoryView":359
+    /* "View.MemoryView":362
  *                 raise MemoryError
  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
@@ -10949,7 +11805,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
     goto __pyx_L10;
   }
 
-  /* "View.MemoryView":362
+  /* "View.MemoryView":365
  *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')
  *         else:
  *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<
@@ -10961,7 +11817,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
   }
   __pyx_L10:;
 
-  /* "View.MemoryView":364
+  /* "View.MemoryView":367
  *             self.dtype_is_object = dtype_is_object
  * 
  *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<
@@ -10970,7 +11826,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
   __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int))));
 
-  /* "View.MemoryView":366
+  /* "View.MemoryView":369
  *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
  *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
  *         self.typeinfo = NULL             # <<<<<<<<<<<<<<
@@ -10979,7 +11835,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
  */
   __pyx_v_self->typeinfo = NULL;
 
-  /* "View.MemoryView":341
+  /* "View.MemoryView":344
  *     cdef __Pyx_TypeInfo *typeinfo
  * 
  *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
@@ -10998,7 +11854,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_
   return __pyx_r;
 }
 
-/* "View.MemoryView":368
+/* "View.MemoryView":371
  *         self.typeinfo = NULL
  * 
  *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
@@ -11024,11 +11880,12 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
   int __pyx_t_2;
   int __pyx_t_3;
   int __pyx_t_4;
-  PyThread_type_lock __pyx_t_5;
+  int __pyx_t_5;
   PyThread_type_lock __pyx_t_6;
+  PyThread_type_lock __pyx_t_7;
   __Pyx_RefNannySetupContext("__dealloc__", 0);
 
-  /* "View.MemoryView":369
+  /* "View.MemoryView":372
  * 
  *     def __dealloc__(memoryview self):
  *         if self.obj is not None:             # <<<<<<<<<<<<<<
@@ -11039,7 +11896,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":370
+    /* "View.MemoryView":373
  *     def __dealloc__(memoryview self):
  *         if self.obj is not None:
  *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<
@@ -11048,7 +11905,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
     __Pyx_ReleaseBuffer((&__pyx_v_self->view));
 
-    /* "View.MemoryView":369
+    /* "View.MemoryView":372
  * 
  *     def __dealloc__(memoryview self):
  *         if self.obj is not None:             # <<<<<<<<<<<<<<
@@ -11057,7 +11914,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
   }
 
-  /* "View.MemoryView":374
+  /* "View.MemoryView":377
  *         cdef int i
  *         global __pyx_memoryview_thread_locks_used
  *         if self.lock != NULL:             # <<<<<<<<<<<<<<
@@ -11067,7 +11924,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
   __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":375
+    /* "View.MemoryView":378
  *         global __pyx_memoryview_thread_locks_used
  *         if self.lock != NULL:
  *             for i in range(__pyx_memoryview_thread_locks_used):             # <<<<<<<<<<<<<<
@@ -11075,10 +11932,11 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  *                     __pyx_memoryview_thread_locks_used -= 1
  */
     __pyx_t_3 = __pyx_memoryview_thread_locks_used;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_i = __pyx_t_4;
+    __pyx_t_4 = __pyx_t_3;
+    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
+      __pyx_v_i = __pyx_t_5;
 
-      /* "View.MemoryView":376
+      /* "View.MemoryView":379
  *         if self.lock != NULL:
  *             for i in range(__pyx_memoryview_thread_locks_used):
  *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<
@@ -11088,7 +11946,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
       __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":377
+        /* "View.MemoryView":380
  *             for i in range(__pyx_memoryview_thread_locks_used):
  *                 if __pyx_memoryview_thread_locks[i] is self.lock:
  *                     __pyx_memoryview_thread_locks_used -= 1             # <<<<<<<<<<<<<<
@@ -11097,7 +11955,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
         __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1);
 
-        /* "View.MemoryView":378
+        /* "View.MemoryView":381
  *                 if __pyx_memoryview_thread_locks[i] is self.lock:
  *                     __pyx_memoryview_thread_locks_used -= 1
  *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<
@@ -11107,27 +11965,27 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
         __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0);
         if (__pyx_t_2) {
 
-          /* "View.MemoryView":380
+          /* "View.MemoryView":383
  *                     if i != __pyx_memoryview_thread_locks_used:
  *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
  *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])             # <<<<<<<<<<<<<<
  *                     break
  *             else:
  */
-          __pyx_t_5 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);
-          __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_v_i]);
+          __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);
+          __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]);
 
-          /* "View.MemoryView":379
+          /* "View.MemoryView":382
  *                     __pyx_memoryview_thread_locks_used -= 1
  *                     if i != __pyx_memoryview_thread_locks_used:
  *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (             # <<<<<<<<<<<<<<
  *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
  *                     break
  */
-          (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_5;
-          (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_6;
+          (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6;
+          (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7;
 
-          /* "View.MemoryView":378
+          /* "View.MemoryView":381
  *                 if __pyx_memoryview_thread_locks[i] is self.lock:
  *                     __pyx_memoryview_thread_locks_used -= 1
  *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<
@@ -11136,7 +11994,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
         }
 
-        /* "View.MemoryView":381
+        /* "View.MemoryView":384
  *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
  *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
  *                     break             # <<<<<<<<<<<<<<
@@ -11145,7 +12003,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
         goto __pyx_L6_break;
 
-        /* "View.MemoryView":376
+        /* "View.MemoryView":379
  *         if self.lock != NULL:
  *             for i in range(__pyx_memoryview_thread_locks_used):
  *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<
@@ -11156,7 +12014,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
     }
     /*else*/ {
 
-      /* "View.MemoryView":383
+      /* "View.MemoryView":386
  *                     break
  *             else:
  *                 PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<
@@ -11167,7 +12025,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
     }
     __pyx_L6_break:;
 
-    /* "View.MemoryView":374
+    /* "View.MemoryView":377
  *         cdef int i
  *         global __pyx_memoryview_thread_locks_used
  *         if self.lock != NULL:             # <<<<<<<<<<<<<<
@@ -11176,7 +12034,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
  */
   }
 
-  /* "View.MemoryView":368
+  /* "View.MemoryView":371
  *         self.typeinfo = NULL
  * 
  *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
@@ -11188,7 +12046,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal
   __Pyx_RefNannyFinishContext();
 }
 
-/* "View.MemoryView":385
+/* "View.MemoryView":388
  *                 PyThread_free_lock(self.lock)
  * 
  *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
@@ -11211,7 +12069,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
   char *__pyx_t_7;
   __Pyx_RefNannySetupContext("get_item_pointer", 0);
 
-  /* "View.MemoryView":387
+  /* "View.MemoryView":390
  *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
  *         cdef Py_ssize_t dim
  *         cdef char *itemp = <char *> self.view.buf             # <<<<<<<<<<<<<<
@@ -11220,7 +12078,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
  */
   __pyx_v_itemp = ((char *)__pyx_v_self->view.buf);
 
-  /* "View.MemoryView":389
+  /* "View.MemoryView":392
  *         cdef char *itemp = <char *> self.view.buf
  * 
  *         for dim, idx in enumerate(index):             # <<<<<<<<<<<<<<
@@ -11232,26 +12090,26 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
     __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0;
     __pyx_t_4 = NULL;
   } else {
-    __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 389, __pyx_L1_error)
+    __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 392, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_2);
-    __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 389, __pyx_L1_error)
+    __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 392, __pyx_L1_error)
   }
   for (;;) {
     if (likely(!__pyx_t_4)) {
       if (likely(PyList_CheckExact(__pyx_t_2))) {
         if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break;
         #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 389, __pyx_L1_error)
+        __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 392, __pyx_L1_error)
         #else
-        __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 389, __pyx_L1_error)
+        __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 392, __pyx_L1_error)
         __Pyx_GOTREF(__pyx_t_5);
         #endif
       } else {
         if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break;
         #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 389, __pyx_L1_error)
+        __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 392, __pyx_L1_error)
         #else
-        __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 389, __pyx_L1_error)
+        __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 392, __pyx_L1_error)
         __Pyx_GOTREF(__pyx_t_5);
         #endif
       }
@@ -11260,8 +12118,8 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
       if (unlikely(!__pyx_t_5)) {
         PyObject* exc_type = PyErr_Occurred();
         if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else __PYX_ERR(2, 389, __pyx_L1_error)
+          if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else __PYX_ERR(2, 392, __pyx_L1_error)
         }
         break;
       }
@@ -11272,18 +12130,18 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
     __pyx_v_dim = __pyx_t_1;
     __pyx_t_1 = (__pyx_t_1 + 1);
 
-    /* "View.MemoryView":390
+    /* "View.MemoryView":393
  * 
  *         for dim, idx in enumerate(index):
  *             itemp = pybuffer_index(&self.view, itemp, idx, dim)             # <<<<<<<<<<<<<<
  * 
  *         return itemp
  */
-    __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 390, __pyx_L1_error)
-    __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == NULL)) __PYX_ERR(2, 390, __pyx_L1_error)
+    __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 393, __pyx_L1_error)
+    __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(2, 393, __pyx_L1_error)
     __pyx_v_itemp = __pyx_t_7;
 
-    /* "View.MemoryView":389
+    /* "View.MemoryView":392
  *         cdef char *itemp = <char *> self.view.buf
  * 
  *         for dim, idx in enumerate(index):             # <<<<<<<<<<<<<<
@@ -11293,7 +12151,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
   }
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
 
-  /* "View.MemoryView":392
+  /* "View.MemoryView":395
  *             itemp = pybuffer_index(&self.view, itemp, idx, dim)
  * 
  *         return itemp             # <<<<<<<<<<<<<<
@@ -11303,7 +12161,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
   __pyx_r = __pyx_v_itemp;
   goto __pyx_L0;
 
-  /* "View.MemoryView":385
+  /* "View.MemoryView":388
  *                 PyThread_free_lock(self.lock)
  * 
  *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
@@ -11323,7 +12181,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py
   return __pyx_r;
 }
 
-/* "View.MemoryView":395
+/* "View.MemoryView":398
  * 
  * 
  *     def __getitem__(memoryview self, object index):             # <<<<<<<<<<<<<<
@@ -11358,7 +12216,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
   char *__pyx_t_6;
   __Pyx_RefNannySetupContext("__getitem__", 0);
 
-  /* "View.MemoryView":396
+  /* "View.MemoryView":399
  * 
  *     def __getitem__(memoryview self, object index):
  *         if index is Ellipsis:             # <<<<<<<<<<<<<<
@@ -11369,7 +12227,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":397
+    /* "View.MemoryView":400
  *     def __getitem__(memoryview self, object index):
  *         if index is Ellipsis:
  *             return self             # <<<<<<<<<<<<<<
@@ -11381,7 +12239,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
     __pyx_r = ((PyObject *)__pyx_v_self);
     goto __pyx_L0;
 
-    /* "View.MemoryView":396
+    /* "View.MemoryView":399
  * 
  *     def __getitem__(memoryview self, object index):
  *         if index is Ellipsis:             # <<<<<<<<<<<<<<
@@ -11390,26 +12248,22 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
  */
   }
 
-  /* "View.MemoryView":399
+  /* "View.MemoryView":402
  *             return self
  * 
  *         have_slices, indices = _unellipsify(index, self.view.ndim)             # <<<<<<<<<<<<<<
  * 
  *         cdef char *itemp
  */
-  __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 399, __pyx_L1_error)
+  __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 402, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_3);
   if (likely(__pyx_t_3 != Py_None)) {
     PyObject* sequence = __pyx_t_3;
-    #if !CYTHON_COMPILING_IN_PYPY
-    Py_ssize_t size = Py_SIZE(sequence);
-    #else
-    Py_ssize_t size = PySequence_Size(sequence);
-    #endif
+    Py_ssize_t size = __Pyx_PySequence_SIZE(sequence);
     if (unlikely(size != 2)) {
       if (size > 2) __Pyx_RaiseTooManyValuesError(2);
       else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-      __PYX_ERR(2, 399, __pyx_L1_error)
+      __PYX_ERR(2, 402, __pyx_L1_error)
     }
     #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
     __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); 
@@ -11417,31 +12271,31 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
     __Pyx_INCREF(__pyx_t_4);
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-  /* "View.MemoryView":402
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-    /* "View.MemoryView":403
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-    /* "View.MemoryView":402
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@@ -11464,7 +12318,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
  */
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-  /* "View.MemoryView":405
+  /* "View.MemoryView":408
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@@ -11472,10 +12326,10 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
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-    /* "View.MemoryView":406
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@@ -11483,14 +12337,14 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
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-  /* "View.MemoryView":395
+  /* "View.MemoryView":398
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@@ -11513,12 +12367,12 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_
   return __pyx_r;
 }
 
-/* "View.MemoryView":408
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- * 
+ *         if self.view.readonly:
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@@ -11539,111 +12393,139 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setit
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-  /* "View.MemoryView":409
+  /* "View.MemoryView":412
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+ *             raise TypeError("Cannot assign to read-only memoryview")
+ * 
+ */
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+    /* "View.MemoryView":413
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-    /* "View.MemoryView":413
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+      /* "View.MemoryView":420
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-      /* "View.MemoryView":413
+      /* "View.MemoryView":419
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  *             if obj:             # <<<<<<<<<<<<<<
  *                 self.setitem_slice_assignment(self[index], obj)
  *             else:
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-      goto __pyx_L4;
+      goto __pyx_L5;
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-    /* "View.MemoryView":416
+    /* "View.MemoryView":422
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@@ -11651,27 +12533,27 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setit
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-    __pyx_L4:;
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-    /* "View.MemoryView":411
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  *         if have_slices:             # <<<<<<<<<<<<<<
  *             obj = self.is_slice(value)
  *             if obj:
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-    goto __pyx_L3;
+    goto __pyx_L4;
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-  /* "View.MemoryView":418
+  /* "View.MemoryView":424
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-    __Pyx_GOTREF(__pyx_t_1);
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-  /* "View.MemoryView":408
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- * 
+ *         if self.view.readonly:
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   /* function exit code */
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   return __pyx_r;
 }
 
-/* "View.MemoryView":420
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-    /* "View.MemoryView":422
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  *             try:             # <<<<<<<<<<<<<<
@@ -11760,34 +12642,34 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
       __Pyx_XGOTREF(__pyx_t_5);
       /*try:*/ {
 
-        /* "View.MemoryView":423
+        /* "View.MemoryView":429
  *         if not isinstance(obj, memoryview):
  *             try:
  *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
  *                                  self.dtype_is_object)
  *             except TypeError:
  */
-        __pyx_t_6 = __Pyx_PyInt_From_int((__pyx_v_self->flags | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 423, __pyx_L4_error)
+        __pyx_t_6 = __Pyx_PyInt_From_int((__pyx_v_self->flags | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 429, __pyx_L4_error)
         __Pyx_GOTREF(__pyx_t_6);
 
-        /* "View.MemoryView":424
+        /* "View.MemoryView":430
  *             try:
  *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
  *                                  self.dtype_is_object)             # <<<<<<<<<<<<<<
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-        __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 424, __pyx_L4_error)
+        __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 430, __pyx_L4_error)
         __Pyx_GOTREF(__pyx_t_7);
 
-        /* "View.MemoryView":423
+        /* "View.MemoryView":429
  *         if not isinstance(obj, memoryview):
  *             try:
  *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
  *                                  self.dtype_is_object)
  *             except TypeError:
  */
-        __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 423, __pyx_L4_error)
+        __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 429, __pyx_L4_error)
         __Pyx_GOTREF(__pyx_t_8);
         __Pyx_INCREF(__pyx_v_obj);
         __Pyx_GIVEREF(__pyx_v_obj);
@@ -11798,13 +12680,13 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
         PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7);
         __pyx_t_6 = 0;
         __pyx_t_7 = 0;
-        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 423, __pyx_L4_error)
+        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 429, __pyx_L4_error)
         __Pyx_GOTREF(__pyx_t_7);
         __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
         __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7);
         __pyx_t_7 = 0;
 
-        /* "View.MemoryView":422
+        /* "View.MemoryView":428
  *     cdef is_slice(self, obj):
  *         if not isinstance(obj, memoryview):
  *             try:             # <<<<<<<<<<<<<<
@@ -11815,14 +12697,13 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
       __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
       __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
       __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
-      goto __pyx_L11_try_end;
+      goto __pyx_L9_try_end;
       __pyx_L4_error:;
-      __Pyx_PyThreadState_assign
       __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
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       __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
 
-      /* "View.MemoryView":425
+      /* "View.MemoryView":431
  *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
  *                                  self.dtype_is_object)
  *             except TypeError:             # <<<<<<<<<<<<<<
@@ -11832,12 +12713,12 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
       __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError);
       if (__pyx_t_9) {
         __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-        if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 425, __pyx_L6_except_error)
+        if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 431, __pyx_L6_except_error)
         __Pyx_GOTREF(__pyx_t_7);
         __Pyx_GOTREF(__pyx_t_8);
         __Pyx_GOTREF(__pyx_t_6);
 
-        /* "View.MemoryView":426
+        /* "View.MemoryView":432
  *                                  self.dtype_is_object)
  *             except TypeError:
  *                 return None             # <<<<<<<<<<<<<<
@@ -11845,8 +12726,7 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
  *         return obj
  */
         __Pyx_XDECREF(__pyx_r);
-        __Pyx_INCREF(Py_None);
-        __pyx_r = Py_None;
+        __pyx_r = Py_None; __Pyx_INCREF(Py_None);
         __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
         __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
         __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
@@ -11855,30 +12735,28 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
       goto __pyx_L6_except_error;
       __pyx_L6_except_error:;
 
-      /* "View.MemoryView":422
+      /* "View.MemoryView":428
  *     cdef is_slice(self, obj):
  *         if not isinstance(obj, memoryview):
  *             try:             # <<<<<<<<<<<<<<
  *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
  *                                  self.dtype_is_object)
  */
-      __Pyx_PyThreadState_assign
       __Pyx_XGIVEREF(__pyx_t_3);
       __Pyx_XGIVEREF(__pyx_t_4);
       __Pyx_XGIVEREF(__pyx_t_5);
       __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
       goto __pyx_L1_error;
       __pyx_L7_except_return:;
-      __Pyx_PyThreadState_assign
       __Pyx_XGIVEREF(__pyx_t_3);
       __Pyx_XGIVEREF(__pyx_t_4);
       __Pyx_XGIVEREF(__pyx_t_5);
       __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
       goto __pyx_L0;
-      __pyx_L11_try_end:;
+      __pyx_L9_try_end:;
     }
 
-    /* "View.MemoryView":421
+    /* "View.MemoryView":427
  * 
  *     cdef is_slice(self, obj):
  *         if not isinstance(obj, memoryview):             # <<<<<<<<<<<<<<
@@ -11887,7 +12765,7 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
  */
   }
 
-  /* "View.MemoryView":428
+  /* "View.MemoryView":434
  *                 return None
  * 
  *         return obj             # <<<<<<<<<<<<<<
@@ -11899,7 +12777,7 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
   __pyx_r = __pyx_v_obj;
   goto __pyx_L0;
 
-  /* "View.MemoryView":420
+  /* "View.MemoryView":426
  *             self.setitem_indexed(index, value)
  * 
  *     cdef is_slice(self, obj):             # <<<<<<<<<<<<<<
@@ -11921,7 +12799,7 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_
   return __pyx_r;
 }
 
-/* "View.MemoryView":430
+/* "View.MemoryView":436
  *         return obj
  * 
  *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
@@ -11940,50 +12818,50 @@ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryvi
   int __pyx_t_4;
   __Pyx_RefNannySetupContext("setitem_slice_assignment", 0);
 
-  /* "View.MemoryView":434
+  /* "View.MemoryView":440
  *         cdef __Pyx_memviewslice src_slice
  * 
  *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
  *                                  get_slice_from_memview(dst, &dst_slice)[0],
  *                                  src.ndim, dst.ndim, self.dtype_is_object)
  */
-  if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 434, __pyx_L1_error)
+  if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 440, __pyx_L1_error)
 
-  /* "View.MemoryView":435
+  /* "View.MemoryView":441
  * 
  *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
  *                                  get_slice_from_memview(dst, &dst_slice)[0],             # <<<<<<<<<<<<<<
  *                                  src.ndim, dst.ndim, self.dtype_is_object)
  * 
  */
-  if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 435, __pyx_L1_error)
+  if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 441, __pyx_L1_error)
 
-  /* "View.MemoryView":436
+  /* "View.MemoryView":442
  *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
  *                                  get_slice_from_memview(dst, &dst_slice)[0],
  *                                  src.ndim, dst.ndim, self.dtype_is_object)             # <<<<<<<<<<<<<<
  * 
  *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
  */
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 436, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 442, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 436, __pyx_L1_error)
+  __pyx_t_2 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 442, __pyx_L1_error)
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 436, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 442, __pyx_L1_error)
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-  __pyx_t_3 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 436, __pyx_L1_error)
+  __pyx_t_3 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 442, __pyx_L1_error)
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
 
-  /* "View.MemoryView":434
+  /* "View.MemoryView":440
  *         cdef __Pyx_memviewslice src_slice
  * 
  *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
  *                                  get_slice_from_memview(dst, &dst_slice)[0],
  *                                  src.ndim, dst.ndim, self.dtype_is_object)
  */
-  __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == -1)) __PYX_ERR(2, 434, __pyx_L1_error)
+  __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 440, __pyx_L1_error)
 
-  /* "View.MemoryView":430
+  /* "View.MemoryView":436
  *         return obj
  * 
  *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
@@ -12004,7 +12882,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryvi
   return __pyx_r;
 }
 
-/* "View.MemoryView":438
+/* "View.MemoryView":444
  *                                  src.ndim, dst.ndim, self.dtype_is_object)
  * 
  *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):             # <<<<<<<<<<<<<<
@@ -12033,7 +12911,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
   PyObject *__pyx_t_11 = NULL;
   __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0);
 
-  /* "View.MemoryView":440
+  /* "View.MemoryView":446
  *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
  *         cdef int array[128]
  *         cdef void *tmp = NULL             # <<<<<<<<<<<<<<
@@ -12042,7 +12920,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
   __pyx_v_tmp = NULL;
 
-  /* "View.MemoryView":445
+  /* "View.MemoryView":451
  *         cdef __Pyx_memviewslice *dst_slice
  *         cdef __Pyx_memviewslice tmp_slice
  *         dst_slice = get_slice_from_memview(dst, &tmp_slice)             # <<<<<<<<<<<<<<
@@ -12051,7 +12929,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
   __pyx_v_dst_slice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice));
 
-  /* "View.MemoryView":447
+  /* "View.MemoryView":453
  *         dst_slice = get_slice_from_memview(dst, &tmp_slice)
  * 
  *         if <size_t>self.view.itemsize > sizeof(array):             # <<<<<<<<<<<<<<
@@ -12061,7 +12939,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
   __pyx_t_1 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":448
+    /* "View.MemoryView":454
  * 
  *         if <size_t>self.view.itemsize > sizeof(array):
  *             tmp = PyMem_Malloc(self.view.itemsize)             # <<<<<<<<<<<<<<
@@ -12070,7 +12948,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
     __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize);
 
-    /* "View.MemoryView":449
+    /* "View.MemoryView":455
  *         if <size_t>self.view.itemsize > sizeof(array):
  *             tmp = PyMem_Malloc(self.view.itemsize)
  *             if tmp == NULL:             # <<<<<<<<<<<<<<
@@ -12078,18 +12956,18 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  *             item = tmp
  */
     __pyx_t_1 = ((__pyx_v_tmp == NULL) != 0);
-    if (__pyx_t_1) {
+    if (unlikely(__pyx_t_1)) {
 
-      /* "View.MemoryView":450
+      /* "View.MemoryView":456
  *             tmp = PyMem_Malloc(self.view.itemsize)
  *             if tmp == NULL:
  *                 raise MemoryError             # <<<<<<<<<<<<<<
  *             item = tmp
  *         else:
  */
-      PyErr_NoMemory(); __PYX_ERR(2, 450, __pyx_L1_error)
+      PyErr_NoMemory(); __PYX_ERR(2, 456, __pyx_L1_error)
 
-      /* "View.MemoryView":449
+      /* "View.MemoryView":455
  *         if <size_t>self.view.itemsize > sizeof(array):
  *             tmp = PyMem_Malloc(self.view.itemsize)
  *             if tmp == NULL:             # <<<<<<<<<<<<<<
@@ -12098,7 +12976,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
     }
 
-    /* "View.MemoryView":451
+    /* "View.MemoryView":457
  *             if tmp == NULL:
  *                 raise MemoryError
  *             item = tmp             # <<<<<<<<<<<<<<
@@ -12107,7 +12985,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
     __pyx_v_item = __pyx_v_tmp;
 
-    /* "View.MemoryView":447
+    /* "View.MemoryView":453
  *         dst_slice = get_slice_from_memview(dst, &tmp_slice)
  * 
  *         if <size_t>self.view.itemsize > sizeof(array):             # <<<<<<<<<<<<<<
@@ -12117,7 +12995,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":453
+  /* "View.MemoryView":459
  *             item = tmp
  *         else:
  *             item = <void *> array             # <<<<<<<<<<<<<<
@@ -12129,7 +13007,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":455
+  /* "View.MemoryView":461
  *             item = <void *> array
  * 
  *         try:             # <<<<<<<<<<<<<<
@@ -12138,7 +13016,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
   /*try:*/ {
 
-    /* "View.MemoryView":456
+    /* "View.MemoryView":462
  * 
  *         try:
  *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
@@ -12148,7 +13026,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
     __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":457
+      /* "View.MemoryView":463
  *         try:
  *             if self.dtype_is_object:
  *                 (<PyObject **> item)[0] = <PyObject *> value             # <<<<<<<<<<<<<<
@@ -12157,7 +13035,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  */
       (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value);
 
-      /* "View.MemoryView":456
+      /* "View.MemoryView":462
  * 
  *         try:
  *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
@@ -12167,7 +13045,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
       goto __pyx_L8;
     }
 
-    /* "View.MemoryView":459
+    /* "View.MemoryView":465
  *                 (<PyObject **> item)[0] = <PyObject *> value
  *             else:
  *                 self.assign_item_from_object(<char *> item, value)             # <<<<<<<<<<<<<<
@@ -12175,13 +13053,13 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
  * 
  */
     /*else*/ {
-      __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 459, __pyx_L6_error)
+      __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 465, __pyx_L6_error)
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
     }
     __pyx_L8:;
 
-    /* "View.MemoryView":463
+    /* "View.MemoryView":469
  * 
  * 
  *             if self.view.suboffsets != NULL:             # <<<<<<<<<<<<<<
@@ -12191,18 +13069,18 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor
     __pyx_t_1 = ((__pyx_v_self->view.suboffsets != NULL) != 0);
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":464
+      /* "View.MemoryView":470
  * 
  *             if self.view.suboffsets != NULL:
  *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)             # <<<<<<<<<<<<<<
  *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,
  *                                 item, self.dtype_is_object)
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+  /* "View.MemoryView":507
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+      __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 507, __pyx_L1_error)
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-      __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 501, __pyx_L1_error)
+      __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 507, __pyx_L1_error)
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@@ -12770,18 +13647,18 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
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+    if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 507, __pyx_L1_error)
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-  /* "View.MemoryView":503
+  /* "View.MemoryView":509
  *             bytesvalue = struct.pack(self.view.format, value)
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@@ -12791,7 +13668,7 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
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-    __PYX_ERR(2, 503, __pyx_L1_error)
+    __PYX_ERR(2, 509, __pyx_L1_error)
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-    /* "View.MemoryView":504
+    /* "View.MemoryView":510
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@@ -12810,7 +13687,7 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
  */
     __pyx_v_i = __pyx_t_9;
 
-    /* "View.MemoryView":503
+    /* "View.MemoryView":509
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  *         for i, c in enumerate(bytesvalue):             # <<<<<<<<<<<<<<
@@ -12819,7 +13696,7 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
  */
     __pyx_t_9 = (__pyx_t_9 + 1);
 
-    /* "View.MemoryView":504
+    /* "View.MemoryView":510
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  *         for i, c in enumerate(bytesvalue):
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@@ -12830,7 +13707,7 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
   }
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-  /* "View.MemoryView":490
+  /* "View.MemoryView":496
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@@ -12858,12 +13735,12 @@ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryvie
   return __pyx_r;
 }
 
-/* "View.MemoryView":507
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  *     @cname('getbuffer')
  *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
- *         if flags & PyBUF_STRIDES:
- *             info.shape = self.view.shape
+ *         if flags & PyBUF_WRITABLE and self.view.readonly:
+ *             raise ValueError("Cannot create writable memory view from read-only memoryview")
  */
 
 /* Python wrapper */
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-  char *__pyx_t_3;
-  void *__pyx_t_4;
-  int __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  Py_ssize_t *__pyx_t_4;
+  char *__pyx_t_5;
+  void *__pyx_t_6;
+  int __pyx_t_7;
+  Py_ssize_t __pyx_t_8;
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+    PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete");
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+  }
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-    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(__pyx_v_info->obj);
+  __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
+  __Pyx_GIVEREF(__pyx_v_info->obj);
+
+  /* "View.MemoryView":514
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         if flags & PyBUF_WRITABLE and self.view.readonly:             # <<<<<<<<<<<<<<
+ *             raise ValueError("Cannot create writable memory view from read-only memoryview")
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0);
+  if (__pyx_t_2) {
+  } else {
+    __pyx_t_1 = __pyx_t_2;
+    goto __pyx_L4_bool_binop_done;
   }
+  __pyx_t_2 = (__pyx_v_self->view.readonly != 0);
+  __pyx_t_1 = __pyx_t_2;
+  __pyx_L4_bool_binop_done:;
+  if (unlikely(__pyx_t_1)) {
+
+    /* "View.MemoryView":515
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         if flags & PyBUF_WRITABLE and self.view.readonly:
+ *             raise ValueError("Cannot create writable memory view from read-only memoryview")             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_STRIDES:
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 515, __pyx_L1_error)
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __PYX_ERR(2, 515, __pyx_L1_error)
 
-  /* "View.MemoryView":508
+    /* "View.MemoryView":514
  *     @cname('getbuffer')
  *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         if flags & PyBUF_WRITABLE and self.view.readonly:             # <<<<<<<<<<<<<<
+ *             raise ValueError("Cannot create writable memory view from read-only memoryview")
+ * 
+ */
+  }
+
+  /* "View.MemoryView":517
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+ * 
  *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
  *             info.shape = self.view.shape
  *         else:
@@ -12904,27 +13825,27 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":509
- *     def __getbuffer__(self, Py_buffer *info, int flags):
+    /* "View.MemoryView":518
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  *         if flags & PyBUF_STRIDES:
  *             info.shape = self.view.shape             # <<<<<<<<<<<<<<
  *         else:
  *             info.shape = NULL
  */
-    __pyx_t_2 = __pyx_v_self->view.shape;
-    __pyx_v_info->shape = __pyx_t_2;
+    __pyx_t_4 = __pyx_v_self->view.shape;
+    __pyx_v_info->shape = __pyx_t_4;
 
-    /* "View.MemoryView":508
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):
+    /* "View.MemoryView":517
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+ * 
  *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
  *             info.shape = self.view.shape
  *         else:
  */
-    goto __pyx_L3;
+    goto __pyx_L6;
   }
 
-  /* "View.MemoryView":511
+  /* "View.MemoryView":520
  *             info.shape = self.view.shape
  *         else:
  *             info.shape = NULL             # <<<<<<<<<<<<<<
@@ -12934,9 +13855,9 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   /*else*/ {
     __pyx_v_info->shape = NULL;
   }
-  __pyx_L3:;
+  __pyx_L6:;
 
-  /* "View.MemoryView":513
+  /* "View.MemoryView":522
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  *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
@@ -12946,27 +13867,27 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":514
+    /* "View.MemoryView":523
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  *         if flags & PyBUF_STRIDES:
  *             info.strides = self.view.strides             # <<<<<<<<<<<<<<
  *         else:
  *             info.strides = NULL
  */
-    __pyx_t_2 = __pyx_v_self->view.strides;
-    __pyx_v_info->strides = __pyx_t_2;
+    __pyx_t_4 = __pyx_v_self->view.strides;
+    __pyx_v_info->strides = __pyx_t_4;
 
-    /* "View.MemoryView":513
+    /* "View.MemoryView":522
  *             info.shape = NULL
  * 
  *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
  *             info.strides = self.view.strides
  *         else:
  */
-    goto __pyx_L4;
+    goto __pyx_L7;
   }
 
-  /* "View.MemoryView":516
+  /* "View.MemoryView":525
  *             info.strides = self.view.strides
  *         else:
  *             info.strides = NULL             # <<<<<<<<<<<<<<
@@ -12976,9 +13897,9 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   /*else*/ {
     __pyx_v_info->strides = NULL;
   }
-  __pyx_L4:;
+  __pyx_L7:;
 
-  /* "View.MemoryView":518
+  /* "View.MemoryView":527
  *             info.strides = NULL
  * 
  *         if flags & PyBUF_INDIRECT:             # <<<<<<<<<<<<<<
@@ -12988,27 +13909,27 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":519
+    /* "View.MemoryView":528
  * 
  *         if flags & PyBUF_INDIRECT:
  *             info.suboffsets = self.view.suboffsets             # <<<<<<<<<<<<<<
  *         else:
  *             info.suboffsets = NULL
  */
-    __pyx_t_2 = __pyx_v_self->view.suboffsets;
-    __pyx_v_info->suboffsets = __pyx_t_2;
+    __pyx_t_4 = __pyx_v_self->view.suboffsets;
+    __pyx_v_info->suboffsets = __pyx_t_4;
 
-    /* "View.MemoryView":518
+    /* "View.MemoryView":527
  *             info.strides = NULL
  * 
  *         if flags & PyBUF_INDIRECT:             # <<<<<<<<<<<<<<
  *             info.suboffsets = self.view.suboffsets
  *         else:
  */
-    goto __pyx_L5;
+    goto __pyx_L8;
   }
 
-  /* "View.MemoryView":521
+  /* "View.MemoryView":530
  *             info.suboffsets = self.view.suboffsets
  *         else:
  *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
@@ -13018,9 +13939,9 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   /*else*/ {
     __pyx_v_info->suboffsets = NULL;
   }
-  __pyx_L5:;
+  __pyx_L8:;
 
-  /* "View.MemoryView":523
+  /* "View.MemoryView":532
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  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
@@ -13030,27 +13951,27 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu
   __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":524
+    /* "View.MemoryView":533
  * 
  *         if flags & PyBUF_FORMAT:
  *             info.format = self.view.format             # <<<<<<<<<<<<<<
  *         else:
  *             info.format = NULL
  */
-    __pyx_t_3 = __pyx_v_self->view.format;
-    __pyx_v_info->format = __pyx_t_3;
+    __pyx_t_5 = __pyx_v_self->view.format;
+    __pyx_v_info->format = __pyx_t_5;
 
-    /* "View.MemoryView":523
+    /* "View.MemoryView":532
  *             info.suboffsets = NULL
  * 
  *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
  *             info.format = self.view.format
  *         else:
  */
-    goto __pyx_L6;
+    goto __pyx_L9;
   }
 
-  /* "View.MemoryView":526
+  /* "View.MemoryView":535
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  *         info.itemsize = self.view.itemsize             # <<<<<<<<<<<<<<
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-  __pyx_v_info->len = __pyx_t_6;
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-  __pyx_v_info->readonly = 0;
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+  __pyx_v_info->readonly = __pyx_t_1;
 
-  /* "View.MemoryView":533
+  /* "View.MemoryView":542
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-  /* "View.MemoryView":550
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+  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 653, __pyx_L1_error)
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   __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2);
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-  /* "View.MemoryView":645
+  /* "View.MemoryView":654
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@@ -14550,7 +15585,7 @@ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, in
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   __pyx_v_result->typeinfo = __pyx_v_typeinfo;
 
-  /* "View.MemoryView":646
+  /* "View.MemoryView":655
  *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
  *     result.typeinfo = typeinfo
  *     return result             # <<<<<<<<<<<<<<
@@ -14562,7 +15597,7 @@ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, in
   __pyx_r = ((PyObject *)__pyx_v_result);
   goto __pyx_L0;
 
-  /* "View.MemoryView":643
+  /* "View.MemoryView":652
  * 
  * @cname('__pyx_memoryview_new')
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@@ -14584,7 +15619,7 @@ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, in
   return __pyx_r;
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-/* "View.MemoryView":649
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-  /* "View.MemoryView":650
+  /* "View.MemoryView":659
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@@ -14609,7 +15644,7 @@ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) {
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-  /* "View.MemoryView":649
+  /* "View.MemoryView":658
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-/* "View.MemoryView":652
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-  /* "View.MemoryView":657
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-    /* "View.MemoryView":658
+    /* "View.MemoryView":667
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  */
-    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 658, __pyx_L1_error)
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     __pyx_t_3 = 0;
 
-    /* "View.MemoryView":657
+    /* "View.MemoryView":666
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     goto __pyx_L3;
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-  /* "View.MemoryView":660
+  /* "View.MemoryView":669
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@@ -14703,19 +15738,19 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":662
+  /* "View.MemoryView":671
  *         tup = index
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  *     result = []             # <<<<<<<<<<<<<<
  *     have_slices = False
  *     seen_ellipsis = False
  */
-  __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 662, __pyx_L1_error)
+  __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 671, __pyx_L1_error)
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-  /* "View.MemoryView":663
+  /* "View.MemoryView":672
  * 
  *     result = []
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@@ -14724,7 +15759,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
  */
   __pyx_v_have_slices = 0;
 
-  /* "View.MemoryView":664
+  /* "View.MemoryView":673
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@@ -14733,7 +15768,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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   __pyx_v_seen_ellipsis = 0;
 
-  /* "View.MemoryView":665
+  /* "View.MemoryView":674
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-    __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 665, __pyx_L1_error)
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+        __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 674, __pyx_L1_error)
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@@ -14774,8 +15809,8 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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         PyObject* exc_type = PyErr_Occurred();
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-          else __PYX_ERR(2, 665, __pyx_L1_error)
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@@ -14785,13 +15820,13 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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-    /* "View.MemoryView":666
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@@ -14802,7 +15837,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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-      /* "View.MemoryView":667
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@@ -14812,27 +15847,27 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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       if (__pyx_t_1) {
 
-        /* "View.MemoryView":668
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-            __Pyx_GIVEREF(__pyx_slice__19);
-            PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__19);
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+        __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 677, __pyx_L1_error)
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-        /* "View.MemoryView":669
+        /* "View.MemoryView":678
  *             if not seen_ellipsis:
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@@ -14841,7 +15876,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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         __pyx_v_seen_ellipsis = 1;
 
-        /* "View.MemoryView":667
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-      /* "View.MemoryView":671
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-      /* "View.MemoryView":672
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@@ -14872,7 +15907,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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       __pyx_v_have_slices = 1;
 
-      /* "View.MemoryView":666
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  *     seen_ellipsis = False
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@@ -14882,7 +15917,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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-    /* "View.MemoryView":674
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@@ -14900,30 +15935,25 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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       __pyx_L9_bool_binop_done:;
-      if (__pyx_t_1) {
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-        /* "View.MemoryView":675
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  *             have_slices = have_slices or isinstance(item, slice)
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-        __pyx_t_11 = PyTuple_New(1); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 675, __pyx_L1_error)
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         __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-        __PYX_ERR(2, 675, __pyx_L1_error)
+        __Pyx_Raise(__pyx_t_11, 0, 0, 0);
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-        /* "View.MemoryView":674
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@@ -14932,7 +15962,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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       }
 
-      /* "View.MemoryView":677
+      /* "View.MemoryView":686
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  *             have_slices = have_slices or isinstance(item, slice)             # <<<<<<<<<<<<<<
@@ -14951,18 +15981,18 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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       __pyx_v_have_slices = __pyx_t_1;
 
-      /* "View.MemoryView":678
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  *             have_slices = have_slices or isinstance(item, slice)
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  *     nslices = ndim - len(result)
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-      __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(2, 678, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 687, __pyx_L1_error)
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-    /* "View.MemoryView":665
+    /* "View.MemoryView":674
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  *     for idx, item in enumerate(tup):             # <<<<<<<<<<<<<<
@@ -14973,17 +16003,17 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
 
-  /* "View.MemoryView":680
+  /* "View.MemoryView":689
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  *     nslices = ndim - len(result)             # <<<<<<<<<<<<<<
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-  __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == -1)) __PYX_ERR(2, 680, __pyx_L1_error)
+  __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(2, 689, __pyx_L1_error)
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-  /* "View.MemoryView":681
+  /* "View.MemoryView":690
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-    /* "View.MemoryView":682
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-        __Pyx_GIVEREF(__pyx_slice__21);
-        PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__21);
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+        __Pyx_GIVEREF(__pyx_slice__27);
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+    __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 691, __pyx_L1_error)
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-    /* "View.MemoryView":681
+    /* "View.MemoryView":690
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  */
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-  /* "View.MemoryView":684
+  /* "View.MemoryView":693
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@@ -15031,32 +16061,32 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
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-  __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 684, __pyx_L1_error)
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-  __Pyx_GOTREF(__pyx_t_7);
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+  PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3);
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-  PyTuple_SET_ITEM(__pyx_t_7, 1, __pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4);
   __pyx_t_3 = 0;
   __pyx_t_4 = 0;
-  __pyx_r = ((PyObject*)__pyx_t_7);
-  __pyx_t_7 = 0;
+  __pyx_r = ((PyObject*)__pyx_t_11);
+  __pyx_t_11 = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":652
+  /* "View.MemoryView":661
  *     return isinstance(o, memoryview)
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  * cdef tuple _unellipsify(object index, int ndim):             # <<<<<<<<<<<<<<
@@ -15082,7 +16112,7 @@ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
   return __pyx_r;
 }
 
-/* "View.MemoryView":686
+/* "View.MemoryView":695
  *     return have_slices or nslices, tuple(result)
  * 
  * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
@@ -15101,7 +16131,7 @@ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __
   PyObject *__pyx_t_5 = NULL;
   __Pyx_RefNannySetupContext("assert_direct_dimensions", 0);
 
-  /* "View.MemoryView":687
+  /* "View.MemoryView":696
  * 
  * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
  *     for suboffset in suboffsets[:ndim]:             # <<<<<<<<<<<<<<
@@ -15113,7 +16143,7 @@ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __
     __pyx_t_1 = __pyx_t_3;
     __pyx_v_suboffset = (__pyx_t_1[0]);
 
-    /* "View.MemoryView":688
+    /* "View.MemoryView":697
  * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
  *     for suboffset in suboffsets[:ndim]:
  *         if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -15121,22 +16151,22 @@ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __
  * 
  */
     __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0);
-    if (__pyx_t_4) {
+    if (unlikely(__pyx_t_4)) {
 
-      /* "View.MemoryView":689
+      /* "View.MemoryView":698
  *     for suboffset in suboffsets[:ndim]:
  *         if suboffset >= 0:
  *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
  * 
  * 
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-      __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 689, __pyx_L1_error)
+      __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__28, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 698, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_5);
       __Pyx_Raise(__pyx_t_5, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-      __PYX_ERR(2, 689, __pyx_L1_error)
+      __PYX_ERR(2, 698, __pyx_L1_error)
 
-      /* "View.MemoryView":688
+      /* "View.MemoryView":697
  * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
  *     for suboffset in suboffsets[:ndim]:
  *         if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -15146,7 +16176,7 @@ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __
     }
   }
 
-  /* "View.MemoryView":686
+  /* "View.MemoryView":695
  *     return have_slices or nslices, tuple(result)
  * 
  * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
@@ -15167,7 +16197,7 @@ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __
   return __pyx_r;
 }
 
-/* "View.MemoryView":696
+/* "View.MemoryView":705
  * 
  * @cname('__pyx_memview_slice')
  * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
@@ -15208,7 +16238,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   Py_ssize_t __pyx_t_12;
   __Pyx_RefNannySetupContext("memview_slice", 0);
 
-  /* "View.MemoryView":697
+  /* "View.MemoryView":706
  * @cname('__pyx_memview_slice')
  * cdef memoryview memview_slice(memoryview memview, object indices):
  *     cdef int new_ndim = 0, suboffset_dim = -1, dim             # <<<<<<<<<<<<<<
@@ -15218,16 +16248,16 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   __pyx_v_new_ndim = 0;
   __pyx_v_suboffset_dim = -1;
 
-  /* "View.MemoryView":704
+  /* "View.MemoryView":713
  * 
  * 
  *     memset(&dst, 0, sizeof(dst))             # <<<<<<<<<<<<<<
  * 
  *     cdef _memoryviewslice memviewsliceobj
  */
-  memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)));
+  (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst))));
 
-  /* "View.MemoryView":708
+  /* "View.MemoryView":717
  *     cdef _memoryviewslice memviewsliceobj
  * 
  *     assert memview.view.ndim > 0             # <<<<<<<<<<<<<<
@@ -15238,12 +16268,12 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   if (unlikely(!Py_OptimizeFlag)) {
     if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) {
       PyErr_SetNone(PyExc_AssertionError);
-      __PYX_ERR(2, 708, __pyx_L1_error)
+      __PYX_ERR(2, 717, __pyx_L1_error)
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   }
   #endif
 
-  /* "View.MemoryView":710
+  /* "View.MemoryView":719
  *     assert memview.view.ndim > 0
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -15254,20 +16284,20 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":711
+    /* "View.MemoryView":720
  * 
  *     if isinstance(memview, _memoryviewslice):
  *         memviewsliceobj = memview             # <<<<<<<<<<<<<<
  *         p_src = &memviewsliceobj.from_slice
  *     else:
  */
-    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 711, __pyx_L1_error)
+    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 720, __pyx_L1_error)
     __pyx_t_3 = ((PyObject *)__pyx_v_memview);
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     __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
     __pyx_t_3 = 0;
 
-    /* "View.MemoryView":712
+    /* "View.MemoryView":721
  *     if isinstance(memview, _memoryviewslice):
  *         memviewsliceobj = memview
  *         p_src = &memviewsliceobj.from_slice             # <<<<<<<<<<<<<<
@@ -15276,7 +16306,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
     __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice);
 
-    /* "View.MemoryView":710
+    /* "View.MemoryView":719
  *     assert memview.view.ndim > 0
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -15286,7 +16316,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":714
+  /* "View.MemoryView":723
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@@ -15296,7 +16326,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   /*else*/ {
     __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src));
 
-    /* "View.MemoryView":715
+    /* "View.MemoryView":724
  *     else:
  *         slice_copy(memview, &src)
  *         p_src = &src             # <<<<<<<<<<<<<<
@@ -15307,7 +16337,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":721
+  /* "View.MemoryView":730
  * 
  * 
  *     dst.memview = p_src.memview             # <<<<<<<<<<<<<<
@@ -15317,7 +16347,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   __pyx_t_4 = __pyx_v_p_src->memview;
   __pyx_v_dst.memview = __pyx_t_4;
 
-  /* "View.MemoryView":722
+  /* "View.MemoryView":731
  * 
  *     dst.memview = p_src.memview
  *     dst.data = p_src.data             # <<<<<<<<<<<<<<
@@ -15327,7 +16357,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   __pyx_t_5 = __pyx_v_p_src->data;
   __pyx_v_dst.data = __pyx_t_5;
 
-  /* "View.MemoryView":727
+  /* "View.MemoryView":736
  * 
  * 
  *     cdef __Pyx_memviewslice *p_dst = &dst             # <<<<<<<<<<<<<<
@@ -15336,7 +16366,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
   __pyx_v_p_dst = (&__pyx_v_dst);
 
-  /* "View.MemoryView":728
+  /* "View.MemoryView":737
  * 
  *     cdef __Pyx_memviewslice *p_dst = &dst
  *     cdef int *p_suboffset_dim = &suboffset_dim             # <<<<<<<<<<<<<<
@@ -15345,7 +16375,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
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   __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim);
 
-  /* "View.MemoryView":732
+  /* "View.MemoryView":741
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  * 
  *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
@@ -15357,26 +16387,26 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
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-    __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 732, __pyx_L1_error)
+    __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 741, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 732, __pyx_L1_error)
+    __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 741, __pyx_L1_error)
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   for (;;) {
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         #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 732, __pyx_L1_error)
+        __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 741, __pyx_L1_error)
         #else
-        __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 732, __pyx_L1_error)
+        __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 741, __pyx_L1_error)
         __Pyx_GOTREF(__pyx_t_9);
         #endif
       } else {
         if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break;
         #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 732, __pyx_L1_error)
+        __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 741, __pyx_L1_error)
         #else
-        __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 732, __pyx_L1_error)
+        __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 741, __pyx_L1_error)
         __Pyx_GOTREF(__pyx_t_9);
         #endif
       }
@@ -15385,8 +16415,8 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       if (unlikely(!__pyx_t_9)) {
         PyObject* exc_type = PyErr_Occurred();
         if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else __PYX_ERR(2, 732, __pyx_L1_error)
+          if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else __PYX_ERR(2, 741, __pyx_L1_error)
         }
         break;
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@@ -15397,7 +16427,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
     __pyx_v_dim = __pyx_t_6;
     __pyx_t_6 = (__pyx_t_6 + 1);
 
-    /* "View.MemoryView":733
+    /* "View.MemoryView":742
  * 
  *     for dim, index in enumerate(indices):
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@@ -15407,25 +16437,25 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
     __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":737
+      /* "View.MemoryView":746
  *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
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-      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 737, __pyx_L1_error)
+      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 746, __pyx_L1_error)
 
-      /* "View.MemoryView":734
+      /* "View.MemoryView":743
  *     for dim, index in enumerate(indices):
  *         if PyIndex_Check(index):
  *             slice_memviewslice(             # <<<<<<<<<<<<<<
  *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
  *                 dim, new_ndim, p_suboffset_dim,
  */
-      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == -1)) __PYX_ERR(2, 734, __pyx_L1_error)
+      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 743, __pyx_L1_error)
 
-      /* "View.MemoryView":733
+      /* "View.MemoryView":742
  * 
  *     for dim, index in enumerate(indices):
  *         if PyIndex_Check(index):             # <<<<<<<<<<<<<<
@@ -15435,7 +16465,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       goto __pyx_L6;
     }
 
-    /* "View.MemoryView":740
+    /* "View.MemoryView":749
  *                 0, 0, 0, # have_{start,stop,step}
  *                 False)
  *         elif index is None:             # <<<<<<<<<<<<<<
@@ -15446,7 +16476,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
     __pyx_t_1 = (__pyx_t_2 != 0);
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":741
+      /* "View.MemoryView":750
  *                 False)
  *         elif index is None:
  *             p_dst.shape[new_ndim] = 1             # <<<<<<<<<<<<<<
@@ -15455,7 +16485,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
       (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1;
 
-      /* "View.MemoryView":742
+      /* "View.MemoryView":751
  *         elif index is None:
  *             p_dst.shape[new_ndim] = 1
  *             p_dst.strides[new_ndim] = 0             # <<<<<<<<<<<<<<
@@ -15464,7 +16494,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
       (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0;
 
-      /* "View.MemoryView":743
+      /* "View.MemoryView":752
  *             p_dst.shape[new_ndim] = 1
  *             p_dst.strides[new_ndim] = 0
  *             p_dst.suboffsets[new_ndim] = -1             # <<<<<<<<<<<<<<
@@ -15473,7 +16503,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
       (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L;
 
-      /* "View.MemoryView":744
+      /* "View.MemoryView":753
  *             p_dst.strides[new_ndim] = 0
  *             p_dst.suboffsets[new_ndim] = -1
  *             new_ndim += 1             # <<<<<<<<<<<<<<
@@ -15482,7 +16512,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
       __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
 
-      /* "View.MemoryView":740
+      /* "View.MemoryView":749
  *                 0, 0, 0, # have_{start,stop,step}
  *                 False)
  *         elif index is None:             # <<<<<<<<<<<<<<
@@ -15492,7 +16522,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       goto __pyx_L6;
     }
 
-    /* "View.MemoryView":746
+    /* "View.MemoryView":755
  *             new_ndim += 1
  *         else:
  *             start = index.start or 0             # <<<<<<<<<<<<<<
@@ -15500,13 +16530,13 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  *             step = index.step or 0
  */
     /*else*/ {
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 755, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 746, __pyx_L1_error)
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 755, __pyx_L1_error)
       if (!__pyx_t_1) {
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       } else {
-        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 746, __pyx_L1_error)
+        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 755, __pyx_L1_error)
         __pyx_t_10 = __pyx_t_12;
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
         goto __pyx_L7_bool_binop_done;
@@ -15515,20 +16545,20 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       __pyx_L7_bool_binop_done:;
       __pyx_v_start = __pyx_t_10;
 
-      /* "View.MemoryView":747
+      /* "View.MemoryView":756
  *         else:
  *             start = index.start or 0
  *             stop = index.stop or 0             # <<<<<<<<<<<<<<
  *             step = index.step or 0
  * 
  */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 747, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 756, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 747, __pyx_L1_error)
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 756, __pyx_L1_error)
       if (!__pyx_t_1) {
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       } else {
-        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 747, __pyx_L1_error)
+        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 756, __pyx_L1_error)
         __pyx_t_10 = __pyx_t_12;
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
         goto __pyx_L9_bool_binop_done;
@@ -15537,20 +16567,20 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       __pyx_L9_bool_binop_done:;
       __pyx_v_stop = __pyx_t_10;
 
-      /* "View.MemoryView":748
+      /* "View.MemoryView":757
  *             start = index.start or 0
  *             stop = index.stop or 0
  *             step = index.step or 0             # <<<<<<<<<<<<<<
  * 
  *             have_start = index.start is not None
  */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 748, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 757, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 748, __pyx_L1_error)
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 757, __pyx_L1_error)
       if (!__pyx_t_1) {
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       } else {
-        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 748, __pyx_L1_error)
+        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 757, __pyx_L1_error)
         __pyx_t_10 = __pyx_t_12;
         __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
         goto __pyx_L11_bool_binop_done;
@@ -15559,55 +16589,55 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
       __pyx_L11_bool_binop_done:;
       __pyx_v_step = __pyx_t_10;
 
-      /* "View.MemoryView":750
+      /* "View.MemoryView":759
  *             step = index.step or 0
  * 
  *             have_start = index.start is not None             # <<<<<<<<<<<<<<
  *             have_stop = index.stop is not None
  *             have_step = index.step is not None
  */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 750, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 759, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
       __pyx_t_1 = (__pyx_t_9 != Py_None);
       __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       __pyx_v_have_start = __pyx_t_1;
 
-      /* "View.MemoryView":751
+      /* "View.MemoryView":760
  * 
  *             have_start = index.start is not None
  *             have_stop = index.stop is not None             # <<<<<<<<<<<<<<
  *             have_step = index.step is not None
  * 
  */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 751, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 760, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
       __pyx_t_1 = (__pyx_t_9 != Py_None);
       __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       __pyx_v_have_stop = __pyx_t_1;
 
-      /* "View.MemoryView":752
+      /* "View.MemoryView":761
  *             have_start = index.start is not None
  *             have_stop = index.stop is not None
  *             have_step = index.step is not None             # <<<<<<<<<<<<<<
  * 
  *             slice_memviewslice(
  */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 752, __pyx_L1_error)
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_9);
       __pyx_t_1 = (__pyx_t_9 != Py_None);
       __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
       __pyx_v_have_step = __pyx_t_1;
 
-      /* "View.MemoryView":754
+      /* "View.MemoryView":763
  *             have_step = index.step is not None
  * 
  *             slice_memviewslice(             # <<<<<<<<<<<<<<
  *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
  *                 dim, new_ndim, p_suboffset_dim,
  */
-      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == -1)) __PYX_ERR(2, 754, __pyx_L1_error)
+      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 763, __pyx_L1_error)
 
-      /* "View.MemoryView":760
+      /* "View.MemoryView":769
  *                 have_start, have_stop, have_step,
  *                 True)
  *             new_ndim += 1             # <<<<<<<<<<<<<<
@@ -15618,7 +16648,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
     }
     __pyx_L6:;
 
-    /* "View.MemoryView":732
+    /* "View.MemoryView":741
  *     cdef bint have_start, have_stop, have_step
  * 
  *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
@@ -15628,7 +16658,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   }
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
 
-  /* "View.MemoryView":762
+  /* "View.MemoryView":771
  *             new_ndim += 1
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -15639,7 +16669,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":763
+    /* "View.MemoryView":772
  * 
  *     if isinstance(memview, _memoryviewslice):
  *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
@@ -15648,39 +16678,39 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
     __Pyx_XDECREF(((PyObject *)__pyx_r));
 
-    /* "View.MemoryView":764
+    /* "View.MemoryView":773
  *     if isinstance(memview, _memoryviewslice):
  *         return memoryview_fromslice(dst, new_ndim,
  *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
  *                                     memviewsliceobj.to_dtype_func,
  *                                     memview.dtype_is_object)
  */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 764, __pyx_L1_error) }
+    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 773, __pyx_L1_error) }
 
-    /* "View.MemoryView":765
+    /* "View.MemoryView":774
  *         return memoryview_fromslice(dst, new_ndim,
  *                                     memviewsliceobj.to_object_func,
  *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
  *                                     memview.dtype_is_object)
  *     else:
  */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 765, __pyx_L1_error) }
+    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 774, __pyx_L1_error) }
 
-    /* "View.MemoryView":763
+    /* "View.MemoryView":772
  * 
  *     if isinstance(memview, _memoryviewslice):
  *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
  *                                     memviewsliceobj.to_object_func,
  *                                     memviewsliceobj.to_dtype_func,
  */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 763, __pyx_L1_error)
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 772, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 763, __pyx_L1_error)
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 772, __pyx_L1_error)
     __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
     __pyx_t_3 = 0;
     goto __pyx_L0;
 
-    /* "View.MemoryView":762
+    /* "View.MemoryView":771
  *             new_ndim += 1
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -15689,7 +16719,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
  */
   }
 
-  /* "View.MemoryView":768
+  /* "View.MemoryView":777
  *                                     memview.dtype_is_object)
  *     else:
  *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
@@ -15699,30 +16729,30 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   /*else*/ {
     __Pyx_XDECREF(((PyObject *)__pyx_r));
 
-    /* "View.MemoryView":769
+    /* "View.MemoryView":778
  *     else:
  *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,
  *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
  * 
  * 
  */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 768, __pyx_L1_error)
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
 
-    /* "View.MemoryView":768
+    /* "View.MemoryView":777
  *                                     memview.dtype_is_object)
  *     else:
  *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
  *                                     memview.dtype_is_object)
  * 
  */
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 768, __pyx_L1_error)
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error)
     __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
     __pyx_t_3 = 0;
     goto __pyx_L0;
   }
 
-  /* "View.MemoryView":696
+  /* "View.MemoryView":705
  * 
  * @cname('__pyx_memview_slice')
  * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
@@ -15744,7 +16774,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_
   return __pyx_r;
 }
 
-/* "View.MemoryView":793
+/* "View.MemoryView":802
  * 
  * @cname('__pyx_memoryview_slice_memviewslice')
  * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
@@ -15760,7 +16790,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   int __pyx_t_2;
   int __pyx_t_3;
 
-  /* "View.MemoryView":813
+  /* "View.MemoryView":822
  *     cdef bint negative_step
  * 
  *     if not is_slice:             # <<<<<<<<<<<<<<
@@ -15770,7 +16800,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":815
+    /* "View.MemoryView":824
  *     if not is_slice:
  * 
  *         if start < 0:             # <<<<<<<<<<<<<<
@@ -15780,7 +16810,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_1 = ((__pyx_v_start < 0) != 0);
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":816
+      /* "View.MemoryView":825
  * 
  *         if start < 0:
  *             start += shape             # <<<<<<<<<<<<<<
@@ -15789,7 +16819,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
       __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
 
-      /* "View.MemoryView":815
+      /* "View.MemoryView":824
  *     if not is_slice:
  * 
  *         if start < 0:             # <<<<<<<<<<<<<<
@@ -15798,7 +16828,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":817
+    /* "View.MemoryView":826
  *         if start < 0:
  *             start += shape
  *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
@@ -15812,16 +16842,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":818
+      /* "View.MemoryView":827
  *             start += shape
  *         if not 0 <= start < shape:
  *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
  *     else:
  * 
  */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(2, 818, __pyx_L1_error)
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 827, __pyx_L1_error)
 
-      /* "View.MemoryView":817
+      /* "View.MemoryView":826
  *         if start < 0:
  *             start += shape
  *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
@@ -15830,7 +16860,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":813
+    /* "View.MemoryView":822
  *     cdef bint negative_step
  * 
  *     if not is_slice:             # <<<<<<<<<<<<<<
@@ -15840,7 +16870,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":821
+  /* "View.MemoryView":830
  *     else:
  * 
  *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
@@ -15859,7 +16889,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_L6_bool_binop_done:;
     __pyx_v_negative_step = __pyx_t_2;
 
-    /* "View.MemoryView":823
+    /* "View.MemoryView":832
  *         negative_step = have_step != 0 and step < 0
  * 
  *         if have_step and step == 0:             # <<<<<<<<<<<<<<
@@ -15877,16 +16907,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_L9_bool_binop_done:;
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":824
+      /* "View.MemoryView":833
  * 
  *         if have_step and step == 0:
  *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
  * 
  * 
  */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(2, 824, __pyx_L1_error)
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 833, __pyx_L1_error)
 
-      /* "View.MemoryView":823
+      /* "View.MemoryView":832
  *         negative_step = have_step != 0 and step < 0
  * 
  *         if have_step and step == 0:             # <<<<<<<<<<<<<<
@@ -15895,7 +16925,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":827
+    /* "View.MemoryView":836
  * 
  * 
  *         if have_start:             # <<<<<<<<<<<<<<
@@ -15905,7 +16935,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = (__pyx_v_have_start != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":828
+      /* "View.MemoryView":837
  * 
  *         if have_start:
  *             if start < 0:             # <<<<<<<<<<<<<<
@@ -15915,7 +16945,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = ((__pyx_v_start < 0) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":829
+        /* "View.MemoryView":838
  *         if have_start:
  *             if start < 0:
  *                 start += shape             # <<<<<<<<<<<<<<
@@ -15924,7 +16954,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
 
-        /* "View.MemoryView":830
+        /* "View.MemoryView":839
  *             if start < 0:
  *                 start += shape
  *                 if start < 0:             # <<<<<<<<<<<<<<
@@ -15934,7 +16964,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         __pyx_t_2 = ((__pyx_v_start < 0) != 0);
         if (__pyx_t_2) {
 
-          /* "View.MemoryView":831
+          /* "View.MemoryView":840
  *                 start += shape
  *                 if start < 0:
  *                     start = 0             # <<<<<<<<<<<<<<
@@ -15943,7 +16973,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
           __pyx_v_start = 0;
 
-          /* "View.MemoryView":830
+          /* "View.MemoryView":839
  *             if start < 0:
  *                 start += shape
  *                 if start < 0:             # <<<<<<<<<<<<<<
@@ -15952,7 +16982,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         }
 
-        /* "View.MemoryView":828
+        /* "View.MemoryView":837
  * 
  *         if have_start:
  *             if start < 0:             # <<<<<<<<<<<<<<
@@ -15962,7 +16992,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         goto __pyx_L12;
       }
 
-      /* "View.MemoryView":832
+      /* "View.MemoryView":841
  *                 if start < 0:
  *                     start = 0
  *             elif start >= shape:             # <<<<<<<<<<<<<<
@@ -15972,7 +17002,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":833
+        /* "View.MemoryView":842
  *                     start = 0
  *             elif start >= shape:
  *                 if negative_step:             # <<<<<<<<<<<<<<
@@ -15982,7 +17012,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         __pyx_t_2 = (__pyx_v_negative_step != 0);
         if (__pyx_t_2) {
 
-          /* "View.MemoryView":834
+          /* "View.MemoryView":843
  *             elif start >= shape:
  *                 if negative_step:
  *                     start = shape - 1             # <<<<<<<<<<<<<<
@@ -15991,7 +17021,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
           __pyx_v_start = (__pyx_v_shape - 1);
 
-          /* "View.MemoryView":833
+          /* "View.MemoryView":842
  *                     start = 0
  *             elif start >= shape:
  *                 if negative_step:             # <<<<<<<<<<<<<<
@@ -16001,7 +17031,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
           goto __pyx_L14;
         }
 
-        /* "View.MemoryView":836
+        /* "View.MemoryView":845
  *                     start = shape - 1
  *                 else:
  *                     start = shape             # <<<<<<<<<<<<<<
@@ -16013,7 +17043,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         }
         __pyx_L14:;
 
-        /* "View.MemoryView":832
+        /* "View.MemoryView":841
  *                 if start < 0:
  *                     start = 0
  *             elif start >= shape:             # <<<<<<<<<<<<<<
@@ -16023,7 +17053,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       }
       __pyx_L12:;
 
-      /* "View.MemoryView":827
+      /* "View.MemoryView":836
  * 
  * 
  *         if have_start:             # <<<<<<<<<<<<<<
@@ -16033,7 +17063,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       goto __pyx_L11;
     }
 
-    /* "View.MemoryView":838
+    /* "View.MemoryView":847
  *                     start = shape
  *         else:
  *             if negative_step:             # <<<<<<<<<<<<<<
@@ -16044,7 +17074,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = (__pyx_v_negative_step != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":839
+        /* "View.MemoryView":848
  *         else:
  *             if negative_step:
  *                 start = shape - 1             # <<<<<<<<<<<<<<
@@ -16053,7 +17083,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_start = (__pyx_v_shape - 1);
 
-        /* "View.MemoryView":838
+        /* "View.MemoryView":847
  *                     start = shape
  *         else:
  *             if negative_step:             # <<<<<<<<<<<<<<
@@ -16063,7 +17093,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         goto __pyx_L15;
       }
 
-      /* "View.MemoryView":841
+      /* "View.MemoryView":850
  *                 start = shape - 1
  *             else:
  *                 start = 0             # <<<<<<<<<<<<<<
@@ -16077,7 +17107,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     }
     __pyx_L11:;
 
-    /* "View.MemoryView":843
+    /* "View.MemoryView":852
  *                 start = 0
  * 
  *         if have_stop:             # <<<<<<<<<<<<<<
@@ -16087,7 +17117,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = (__pyx_v_have_stop != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":844
+      /* "View.MemoryView":853
  * 
  *         if have_stop:
  *             if stop < 0:             # <<<<<<<<<<<<<<
@@ -16097,7 +17127,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":845
+        /* "View.MemoryView":854
  *         if have_stop:
  *             if stop < 0:
  *                 stop += shape             # <<<<<<<<<<<<<<
@@ -16106,7 +17136,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
 
-        /* "View.MemoryView":846
+        /* "View.MemoryView":855
  *             if stop < 0:
  *                 stop += shape
  *                 if stop < 0:             # <<<<<<<<<<<<<<
@@ -16116,7 +17146,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
         if (__pyx_t_2) {
 
-          /* "View.MemoryView":847
+          /* "View.MemoryView":856
  *                 stop += shape
  *                 if stop < 0:
  *                     stop = 0             # <<<<<<<<<<<<<<
@@ -16125,7 +17155,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
           __pyx_v_stop = 0;
 
-          /* "View.MemoryView":846
+          /* "View.MemoryView":855
  *             if stop < 0:
  *                 stop += shape
  *                 if stop < 0:             # <<<<<<<<<<<<<<
@@ -16134,7 +17164,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         }
 
-        /* "View.MemoryView":844
+        /* "View.MemoryView":853
  * 
  *         if have_stop:
  *             if stop < 0:             # <<<<<<<<<<<<<<
@@ -16144,7 +17174,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         goto __pyx_L17;
       }
 
-      /* "View.MemoryView":848
+      /* "View.MemoryView":857
  *                 if stop < 0:
  *                     stop = 0
  *             elif stop > shape:             # <<<<<<<<<<<<<<
@@ -16154,7 +17184,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":849
+        /* "View.MemoryView":858
  *                     stop = 0
  *             elif stop > shape:
  *                 stop = shape             # <<<<<<<<<<<<<<
@@ -16163,7 +17193,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_stop = __pyx_v_shape;
 
-        /* "View.MemoryView":848
+        /* "View.MemoryView":857
  *                 if stop < 0:
  *                     stop = 0
  *             elif stop > shape:             # <<<<<<<<<<<<<<
@@ -16173,7 +17203,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       }
       __pyx_L17:;
 
-      /* "View.MemoryView":843
+      /* "View.MemoryView":852
  *                 start = 0
  * 
  *         if have_stop:             # <<<<<<<<<<<<<<
@@ -16183,7 +17213,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       goto __pyx_L16;
     }
 
-    /* "View.MemoryView":851
+    /* "View.MemoryView":860
  *                 stop = shape
  *         else:
  *             if negative_step:             # <<<<<<<<<<<<<<
@@ -16194,7 +17224,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = (__pyx_v_negative_step != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":852
+        /* "View.MemoryView":861
  *         else:
  *             if negative_step:
  *                 stop = -1             # <<<<<<<<<<<<<<
@@ -16203,7 +17233,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_stop = -1L;
 
-        /* "View.MemoryView":851
+        /* "View.MemoryView":860
  *                 stop = shape
  *         else:
  *             if negative_step:             # <<<<<<<<<<<<<<
@@ -16213,7 +17243,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         goto __pyx_L19;
       }
 
-      /* "View.MemoryView":854
+      /* "View.MemoryView":863
  *                 stop = -1
  *             else:
  *                 stop = shape             # <<<<<<<<<<<<<<
@@ -16227,7 +17257,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     }
     __pyx_L16:;
 
-    /* "View.MemoryView":856
+    /* "View.MemoryView":865
  *                 stop = shape
  * 
  *         if not have_step:             # <<<<<<<<<<<<<<
@@ -16237,7 +17267,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":857
+      /* "View.MemoryView":866
  * 
  *         if not have_step:
  *             step = 1             # <<<<<<<<<<<<<<
@@ -16246,7 +17276,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
       __pyx_v_step = 1;
 
-      /* "View.MemoryView":856
+      /* "View.MemoryView":865
  *                 stop = shape
  * 
  *         if not have_step:             # <<<<<<<<<<<<<<
@@ -16255,7 +17285,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":861
+    /* "View.MemoryView":870
  * 
  *         with cython.cdivision(True):
  *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
@@ -16264,7 +17294,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
 
-    /* "View.MemoryView":863
+    /* "View.MemoryView":872
  *             new_shape = (stop - start) // step
  * 
  *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
@@ -16274,7 +17304,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":864
+      /* "View.MemoryView":873
  * 
  *             if (stop - start) - step * new_shape:
  *                 new_shape += 1             # <<<<<<<<<<<<<<
@@ -16283,7 +17313,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
       __pyx_v_new_shape = (__pyx_v_new_shape + 1);
 
-      /* "View.MemoryView":863
+      /* "View.MemoryView":872
  *             new_shape = (stop - start) // step
  * 
  *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
@@ -16292,7 +17322,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":866
+    /* "View.MemoryView":875
  *                 new_shape += 1
  * 
  *         if new_shape < 0:             # <<<<<<<<<<<<<<
@@ -16302,7 +17332,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":867
+      /* "View.MemoryView":876
  * 
  *         if new_shape < 0:
  *             new_shape = 0             # <<<<<<<<<<<<<<
@@ -16311,7 +17341,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
       __pyx_v_new_shape = 0;
 
-      /* "View.MemoryView":866
+      /* "View.MemoryView":875
  *                 new_shape += 1
  * 
  *         if new_shape < 0:             # <<<<<<<<<<<<<<
@@ -16320,7 +17350,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     }
 
-    /* "View.MemoryView":870
+    /* "View.MemoryView":879
  * 
  * 
  *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
@@ -16329,7 +17359,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
 
-    /* "View.MemoryView":871
+    /* "View.MemoryView":880
  * 
  *         dst.strides[new_ndim] = stride * step
  *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
@@ -16338,7 +17368,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
 
-    /* "View.MemoryView":872
+    /* "View.MemoryView":881
  *         dst.strides[new_ndim] = stride * step
  *         dst.shape[new_ndim] = new_shape
  *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
@@ -16349,7 +17379,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":875
+  /* "View.MemoryView":884
  * 
  * 
  *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
@@ -16359,7 +17389,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":876
+    /* "View.MemoryView":885
  * 
  *     if suboffset_dim[0] < 0:
  *         dst.data += start * stride             # <<<<<<<<<<<<<<
@@ -16368,7 +17398,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
     __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
 
-    /* "View.MemoryView":875
+    /* "View.MemoryView":884
  * 
  * 
  *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
@@ -16378,7 +17408,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     goto __pyx_L23;
   }
 
-  /* "View.MemoryView":878
+  /* "View.MemoryView":887
  *         dst.data += start * stride
  *     else:
  *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
@@ -16391,7 +17421,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   }
   __pyx_L23:;
 
-  /* "View.MemoryView":880
+  /* "View.MemoryView":889
  *         dst.suboffsets[suboffset_dim[0]] += start * stride
  * 
  *     if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -16401,7 +17431,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":881
+    /* "View.MemoryView":890
  * 
  *     if suboffset >= 0:
  *         if not is_slice:             # <<<<<<<<<<<<<<
@@ -16411,7 +17441,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":882
+      /* "View.MemoryView":891
  *     if suboffset >= 0:
  *         if not is_slice:
  *             if new_ndim == 0:             # <<<<<<<<<<<<<<
@@ -16421,7 +17451,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":883
+        /* "View.MemoryView":892
  *         if not is_slice:
  *             if new_ndim == 0:
  *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
@@ -16430,7 +17460,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
         __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);
 
-        /* "View.MemoryView":882
+        /* "View.MemoryView":891
  *     if suboffset >= 0:
  *         if not is_slice:
  *             if new_ndim == 0:             # <<<<<<<<<<<<<<
@@ -16440,7 +17470,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
         goto __pyx_L26;
       }
 
-      /* "View.MemoryView":885
+      /* "View.MemoryView":894
  *                 dst.data = (<char **> dst.data)[0] + suboffset
  *             else:
  *                 _err_dim(IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
@@ -16449,18 +17479,18 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
       /*else*/ {
 
-        /* "View.MemoryView":886
+        /* "View.MemoryView":895
  *             else:
  *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
  *                                      "must be indexed and not sliced", dim)             # <<<<<<<<<<<<<<
  *         else:
  *             suboffset_dim[0] = new_ndim
  */
-        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(2, 885, __pyx_L1_error)
+        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 894, __pyx_L1_error)
       }
       __pyx_L26:;
 
-      /* "View.MemoryView":881
+      /* "View.MemoryView":890
  * 
  *     if suboffset >= 0:
  *         if not is_slice:             # <<<<<<<<<<<<<<
@@ -16470,7 +17500,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
       goto __pyx_L25;
     }
 
-    /* "View.MemoryView":888
+    /* "View.MemoryView":897
  *                                      "must be indexed and not sliced", dim)
  *         else:
  *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
@@ -16482,7 +17512,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
     }
     __pyx_L25:;
 
-    /* "View.MemoryView":880
+    /* "View.MemoryView":889
  *         dst.suboffsets[suboffset_dim[0]] += start * stride
  * 
  *     if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -16491,7 +17521,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
  */
   }
 
-  /* "View.MemoryView":890
+  /* "View.MemoryView":899
  *             suboffset_dim[0] = new_ndim
  * 
  *     return 0             # <<<<<<<<<<<<<<
@@ -16501,7 +17531,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   __pyx_r = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":793
+  /* "View.MemoryView":802
  * 
  * @cname('__pyx_memoryview_slice_memviewslice')
  * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
@@ -16513,11 +17543,11 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   __pyx_L1_error:;
   {
     #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
     #endif
     __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
     #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
+    __Pyx_PyGILState_Release(__pyx_gilstate_save);
     #endif
   }
   __pyx_r = -1;
@@ -16525,7 +17555,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst,
   return __pyx_r;
 }
 
-/* "View.MemoryView":896
+/* "View.MemoryView":905
  * 
  * @cname('__pyx_pybuffer_index')
  * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
@@ -16547,7 +17577,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   PyObject *__pyx_t_4 = NULL;
   __Pyx_RefNannySetupContext("pybuffer_index", 0);
 
-  /* "View.MemoryView":898
+  /* "View.MemoryView":907
  * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,
  *                           Py_ssize_t dim) except NULL:
  *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<
@@ -16556,7 +17586,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
   __pyx_v_suboffset = -1L;
 
-  /* "View.MemoryView":899
+  /* "View.MemoryView":908
  *                           Py_ssize_t dim) except NULL:
  *     cdef Py_ssize_t shape, stride, suboffset = -1
  *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<
@@ -16566,7 +17596,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   __pyx_t_1 = __pyx_v_view->itemsize;
   __pyx_v_itemsize = __pyx_t_1;
 
-  /* "View.MemoryView":902
+  /* "View.MemoryView":911
  *     cdef char *resultp
  * 
  *     if view.ndim == 0:             # <<<<<<<<<<<<<<
@@ -16576,7 +17606,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":903
+    /* "View.MemoryView":912
  * 
  *     if view.ndim == 0:
  *         shape = view.len / itemsize             # <<<<<<<<<<<<<<
@@ -16585,15 +17615,15 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     if (unlikely(__pyx_v_itemsize == 0)) {
       PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
-      __PYX_ERR(2, 903, __pyx_L1_error)
+      __PYX_ERR(2, 912, __pyx_L1_error)
     }
     else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {
       PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
-      __PYX_ERR(2, 903, __pyx_L1_error)
+      __PYX_ERR(2, 912, __pyx_L1_error)
     }
     __pyx_v_shape = __Pyx_div_Py_ssize_t(__pyx_v_view->len, __pyx_v_itemsize);
 
-    /* "View.MemoryView":904
+    /* "View.MemoryView":913
  *     if view.ndim == 0:
  *         shape = view.len / itemsize
  *         stride = itemsize             # <<<<<<<<<<<<<<
@@ -16602,7 +17632,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     __pyx_v_stride = __pyx_v_itemsize;
 
-    /* "View.MemoryView":902
+    /* "View.MemoryView":911
  *     cdef char *resultp
  * 
  *     if view.ndim == 0:             # <<<<<<<<<<<<<<
@@ -16612,7 +17642,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":906
+  /* "View.MemoryView":915
  *         stride = itemsize
  *     else:
  *         shape = view.shape[dim]             # <<<<<<<<<<<<<<
@@ -16622,7 +17652,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   /*else*/ {
     __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);
 
-    /* "View.MemoryView":907
+    /* "View.MemoryView":916
  *     else:
  *         shape = view.shape[dim]
  *         stride = view.strides[dim]             # <<<<<<<<<<<<<<
@@ -16631,7 +17661,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);
 
-    /* "View.MemoryView":908
+    /* "View.MemoryView":917
  *         shape = view.shape[dim]
  *         stride = view.strides[dim]
  *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
@@ -16641,7 +17671,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
     __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":909
+      /* "View.MemoryView":918
  *         stride = view.strides[dim]
  *         if view.suboffsets != NULL:
  *             suboffset = view.suboffsets[dim]             # <<<<<<<<<<<<<<
@@ -16650,7 +17680,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
       __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]);
 
-      /* "View.MemoryView":908
+      /* "View.MemoryView":917
  *         shape = view.shape[dim]
  *         stride = view.strides[dim]
  *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
@@ -16661,7 +17691,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":911
+  /* "View.MemoryView":920
  *             suboffset = view.suboffsets[dim]
  * 
  *     if index < 0:             # <<<<<<<<<<<<<<
@@ -16671,7 +17701,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   __pyx_t_2 = ((__pyx_v_index < 0) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":912
+    /* "View.MemoryView":921
  * 
  *     if index < 0:
  *         index += view.shape[dim]             # <<<<<<<<<<<<<<
@@ -16680,7 +17710,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim]));
 
-    /* "View.MemoryView":913
+    /* "View.MemoryView":922
  *     if index < 0:
  *         index += view.shape[dim]
  *         if index < 0:             # <<<<<<<<<<<<<<
@@ -16688,33 +17718,28 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  * 
  */
     __pyx_t_2 = ((__pyx_v_index < 0) != 0);
-    if (__pyx_t_2) {
+    if (unlikely(__pyx_t_2)) {
 
-      /* "View.MemoryView":914
+      /* "View.MemoryView":923
  *         index += view.shape[dim]
  *         if index < 0:
  *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
  * 
  *     if index >= shape:
  */
-      __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 914, __pyx_L1_error)
+      __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 923, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 914, __pyx_L1_error)
+      __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 923, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_4);
       __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 914, __pyx_L1_error)
+      __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 923, __pyx_L1_error)
       __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_GIVEREF(__pyx_t_4);
-      PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4);
-      __pyx_t_4 = 0;
-      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 914, __pyx_L1_error)
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __PYX_ERR(2, 914, __pyx_L1_error)
+      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __PYX_ERR(2, 923, __pyx_L1_error)
 
-      /* "View.MemoryView":913
+      /* "View.MemoryView":922
  *     if index < 0:
  *         index += view.shape[dim]
  *         if index < 0:             # <<<<<<<<<<<<<<
@@ -16723,7 +17748,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     }
 
-    /* "View.MemoryView":911
+    /* "View.MemoryView":920
  *             suboffset = view.suboffsets[dim]
  * 
  *     if index < 0:             # <<<<<<<<<<<<<<
@@ -16732,7 +17757,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
   }
 
-  /* "View.MemoryView":916
+  /* "View.MemoryView":925
  *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
  * 
  *     if index >= shape:             # <<<<<<<<<<<<<<
@@ -16740,33 +17765,28 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  * 
  */
   __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0);
-  if (__pyx_t_2) {
+  if (unlikely(__pyx_t_2)) {
 
-    /* "View.MemoryView":917
+    /* "View.MemoryView":926
  * 
  *     if index >= shape:
  *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
  * 
  *     resultp = bufp + index * stride
  */
-    __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 917, __pyx_L1_error)
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 917, __pyx_L1_error)
+    __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 926, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 917, __pyx_L1_error)
+    __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 926, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_4);
-    __Pyx_GIVEREF(__pyx_t_3);
-    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
-    __pyx_t_3 = 0;
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 917, __pyx_L1_error)
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 926, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
     __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
     __Pyx_Raise(__pyx_t_3, 0, 0, 0);
     __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __PYX_ERR(2, 917, __pyx_L1_error)
+    __PYX_ERR(2, 926, __pyx_L1_error)
 
-    /* "View.MemoryView":916
+    /* "View.MemoryView":925
  *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
  * 
  *     if index >= shape:             # <<<<<<<<<<<<<<
@@ -16775,7 +17795,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
   }
 
-  /* "View.MemoryView":919
+  /* "View.MemoryView":928
  *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
  * 
  *     resultp = bufp + index * stride             # <<<<<<<<<<<<<<
@@ -16784,7 +17804,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
   __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride));
 
-  /* "View.MemoryView":920
+  /* "View.MemoryView":929
  * 
  *     resultp = bufp + index * stride
  *     if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -16794,7 +17814,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":921
+    /* "View.MemoryView":930
  *     resultp = bufp + index * stride
  *     if suboffset >= 0:
  *         resultp = (<char **> resultp)[0] + suboffset             # <<<<<<<<<<<<<<
@@ -16803,7 +17823,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
     __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset);
 
-    /* "View.MemoryView":920
+    /* "View.MemoryView":929
  * 
  *     resultp = bufp + index * stride
  *     if suboffset >= 0:             # <<<<<<<<<<<<<<
@@ -16812,7 +17832,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
  */
   }
 
-  /* "View.MemoryView":923
+  /* "View.MemoryView":932
  *         resultp = (<char **> resultp)[0] + suboffset
  * 
  *     return resultp             # <<<<<<<<<<<<<<
@@ -16822,7 +17842,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   __pyx_r = __pyx_v_resultp;
   goto __pyx_L0;
 
-  /* "View.MemoryView":896
+  /* "View.MemoryView":905
  * 
  * @cname('__pyx_pybuffer_index')
  * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
@@ -16841,7 +17861,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P
   return __pyx_r;
 }
 
-/* "View.MemoryView":929
+/* "View.MemoryView":938
  * 
  * @cname('__pyx_memslice_transpose')
  * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
@@ -16859,13 +17879,14 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   int __pyx_t_1;
   Py_ssize_t *__pyx_t_2;
   long __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
+  long __pyx_t_4;
   Py_ssize_t __pyx_t_5;
-  int __pyx_t_6;
+  Py_ssize_t __pyx_t_6;
   int __pyx_t_7;
   int __pyx_t_8;
+  int __pyx_t_9;
 
-  /* "View.MemoryView":930
+  /* "View.MemoryView":939
  * @cname('__pyx_memslice_transpose')
  * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:
  *     cdef int ndim = memslice.memview.view.ndim             # <<<<<<<<<<<<<<
@@ -16875,7 +17896,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   __pyx_t_1 = __pyx_v_memslice->memview->view.ndim;
   __pyx_v_ndim = __pyx_t_1;
 
-  /* "View.MemoryView":932
+  /* "View.MemoryView":941
  *     cdef int ndim = memslice.memview.view.ndim
  * 
  *     cdef Py_ssize_t *shape = memslice.shape             # <<<<<<<<<<<<<<
@@ -16885,7 +17906,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   __pyx_t_2 = __pyx_v_memslice->shape;
   __pyx_v_shape = __pyx_t_2;
 
-  /* "View.MemoryView":933
+  /* "View.MemoryView":942
  * 
  *     cdef Py_ssize_t *shape = memslice.shape
  *     cdef Py_ssize_t *strides = memslice.strides             # <<<<<<<<<<<<<<
@@ -16895,7 +17916,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   __pyx_t_2 = __pyx_v_memslice->strides;
   __pyx_v_strides = __pyx_t_2;
 
-  /* "View.MemoryView":937
+  /* "View.MemoryView":946
  * 
  *     cdef int i, j
  *     for i in range(ndim / 2):             # <<<<<<<<<<<<<<
@@ -16903,10 +17924,11 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
  *         strides[i], strides[j] = strides[j], strides[i]
  */
   __pyx_t_3 = __Pyx_div_long(__pyx_v_ndim, 2);
-  for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_3; __pyx_t_1+=1) {
+  __pyx_t_4 = __pyx_t_3;
+  for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) {
     __pyx_v_i = __pyx_t_1;
 
-    /* "View.MemoryView":938
+    /* "View.MemoryView":947
  *     cdef int i, j
  *     for i in range(ndim / 2):
  *         j = ndim - 1 - i             # <<<<<<<<<<<<<<
@@ -16915,58 +17937,58 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
  */
     __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i);
 
-    /* "View.MemoryView":939
+    /* "View.MemoryView":948
  *     for i in range(ndim / 2):
  *         j = ndim - 1 - i
  *         strides[i], strides[j] = strides[j], strides[i]             # <<<<<<<<<<<<<<
  *         shape[i], shape[j] = shape[j], shape[i]
  * 
  */
-    __pyx_t_4 = (__pyx_v_strides[__pyx_v_j]);
-    __pyx_t_5 = (__pyx_v_strides[__pyx_v_i]);
-    (__pyx_v_strides[__pyx_v_i]) = __pyx_t_4;
-    (__pyx_v_strides[__pyx_v_j]) = __pyx_t_5;
+    __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]);
+    __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]);
+    (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5;
+    (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6;
 
-    /* "View.MemoryView":940
+    /* "View.MemoryView":949
  *         j = ndim - 1 - i
  *         strides[i], strides[j] = strides[j], strides[i]
  *         shape[i], shape[j] = shape[j], shape[i]             # <<<<<<<<<<<<<<
  * 
  *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
  */
-    __pyx_t_5 = (__pyx_v_shape[__pyx_v_j]);
-    __pyx_t_4 = (__pyx_v_shape[__pyx_v_i]);
-    (__pyx_v_shape[__pyx_v_i]) = __pyx_t_5;
-    (__pyx_v_shape[__pyx_v_j]) = __pyx_t_4;
+    __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]);
+    __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]);
+    (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6;
+    (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5;
 
-    /* "View.MemoryView":942
+    /* "View.MemoryView":951
  *         shape[i], shape[j] = shape[j], shape[i]
  * 
  *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:             # <<<<<<<<<<<<<<
  *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
  * 
  */
-    __pyx_t_7 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (!__pyx_t_7) {
+    __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0);
+    if (!__pyx_t_8) {
     } else {
-      __pyx_t_6 = __pyx_t_7;
+      __pyx_t_7 = __pyx_t_8;
       goto __pyx_L6_bool_binop_done;
     }
-    __pyx_t_7 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0);
-    __pyx_t_6 = __pyx_t_7;
+    __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0);
+    __pyx_t_7 = __pyx_t_8;
     __pyx_L6_bool_binop_done:;
-    if (__pyx_t_6) {
+    if (__pyx_t_7) {
 
-      /* "View.MemoryView":943
+      /* "View.MemoryView":952
  * 
  *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
  *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")             # <<<<<<<<<<<<<<
  * 
  *     return 1
  */
-      __pyx_t_8 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_8 == -1)) __PYX_ERR(2, 943, __pyx_L1_error)
+      __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 952, __pyx_L1_error)
 
-      /* "View.MemoryView":942
+      /* "View.MemoryView":951
  *         shape[i], shape[j] = shape[j], shape[i]
  * 
  *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:             # <<<<<<<<<<<<<<
@@ -16976,7 +17998,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
     }
   }
 
-  /* "View.MemoryView":945
+  /* "View.MemoryView":954
  *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
  * 
  *     return 1             # <<<<<<<<<<<<<<
@@ -16986,7 +18008,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   __pyx_r = 1;
   goto __pyx_L0;
 
-  /* "View.MemoryView":929
+  /* "View.MemoryView":938
  * 
  * @cname('__pyx_memslice_transpose')
  * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
@@ -16998,11 +18020,11 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   __pyx_L1_error:;
   {
     #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
     #endif
     __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
     #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
+    __Pyx_PyGILState_Release(__pyx_gilstate_save);
     #endif
   }
   __pyx_r = 0;
@@ -17010,7 +18032,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
   return __pyx_r;
 }
 
-/* "View.MemoryView":962
+/* "View.MemoryView":971
  *     cdef int (*to_dtype_func)(char *, object) except 0
  * 
  *     def __dealloc__(self):             # <<<<<<<<<<<<<<
@@ -17033,7 +18055,7 @@ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewsl
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("__dealloc__", 0);
 
-  /* "View.MemoryView":963
+  /* "View.MemoryView":972
  * 
  *     def __dealloc__(self):
  *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)             # <<<<<<<<<<<<<<
@@ -17042,7 +18064,7 @@ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewsl
  */
   __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1);
 
-  /* "View.MemoryView":962
+  /* "View.MemoryView":971
  *     cdef int (*to_dtype_func)(char *, object) except 0
  * 
  *     def __dealloc__(self):             # <<<<<<<<<<<<<<
@@ -17054,7 +18076,7 @@ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewsl
   __Pyx_RefNannyFinishContext();
 }
 
-/* "View.MemoryView":965
+/* "View.MemoryView":974
  *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
  * 
  *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
@@ -17069,7 +18091,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
   PyObject *__pyx_t_2 = NULL;
   __Pyx_RefNannySetupContext("convert_item_to_object", 0);
 
-  /* "View.MemoryView":966
+  /* "View.MemoryView":975
  * 
  *     cdef convert_item_to_object(self, char *itemp):
  *         if self.to_object_func != NULL:             # <<<<<<<<<<<<<<
@@ -17079,7 +18101,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
   __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":967
+    /* "View.MemoryView":976
  *     cdef convert_item_to_object(self, char *itemp):
  *         if self.to_object_func != NULL:
  *             return self.to_object_func(itemp)             # <<<<<<<<<<<<<<
@@ -17087,13 +18109,13 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
  *             return memoryview.convert_item_to_object(self, itemp)
  */
     __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 967, __pyx_L1_error)
+    __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 976, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_2);
     __pyx_r = __pyx_t_2;
     __pyx_t_2 = 0;
     goto __pyx_L0;
 
-    /* "View.MemoryView":966
+    /* "View.MemoryView":975
  * 
  *     cdef convert_item_to_object(self, char *itemp):
  *         if self.to_object_func != NULL:             # <<<<<<<<<<<<<<
@@ -17102,7 +18124,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
  */
   }
 
-  /* "View.MemoryView":969
+  /* "View.MemoryView":978
  *             return self.to_object_func(itemp)
  *         else:
  *             return memoryview.convert_item_to_object(self, itemp)             # <<<<<<<<<<<<<<
@@ -17111,14 +18133,14 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
  */
   /*else*/ {
     __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 969, __pyx_L1_error)
+    __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 978, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_2);
     __pyx_r = __pyx_t_2;
     __pyx_t_2 = 0;
     goto __pyx_L0;
   }
 
-  /* "View.MemoryView":965
+  /* "View.MemoryView":974
  *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
  * 
  *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
@@ -17137,7 +18159,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor
   return __pyx_r;
 }
 
-/* "View.MemoryView":971
+/* "View.MemoryView":980
  *             return memoryview.convert_item_to_object(self, itemp)
  * 
  *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
@@ -17153,7 +18175,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo
   PyObject *__pyx_t_3 = NULL;
   __Pyx_RefNannySetupContext("assign_item_from_object", 0);
 
-  /* "View.MemoryView":972
+  /* "View.MemoryView":981
  * 
  *     cdef assign_item_from_object(self, char *itemp, object value):
  *         if self.to_dtype_func != NULL:             # <<<<<<<<<<<<<<
@@ -17163,16 +18185,16 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo
   __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":973
+    /* "View.MemoryView":982
  *     cdef assign_item_from_object(self, char *itemp, object value):
  *         if self.to_dtype_func != NULL:
  *             self.to_dtype_func(itemp, value)             # <<<<<<<<<<<<<<
  *         else:
  *             memoryview.assign_item_from_object(self, itemp, value)
  */
-    __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == 0)) __PYX_ERR(2, 973, __pyx_L1_error)
+    __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 982, __pyx_L1_error)
 
-    /* "View.MemoryView":972
+    /* "View.MemoryView":981
  * 
  *     cdef assign_item_from_object(self, char *itemp, object value):
  *         if self.to_dtype_func != NULL:             # <<<<<<<<<<<<<<
@@ -17182,7 +18204,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":975
+  /* "View.MemoryView":984
  *             self.to_dtype_func(itemp, value)
  *         else:
  *             memoryview.assign_item_from_object(self, itemp, value)             # <<<<<<<<<<<<<<
@@ -17190,13 +18212,13 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo
  *     @property
  */
   /*else*/ {
-    __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 975, __pyx_L1_error)
+    __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 984, __pyx_L1_error)
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   }
   __pyx_L3:;
 
-  /* "View.MemoryView":971
+  /* "View.MemoryView":980
  *             return memoryview.convert_item_to_object(self, itemp)
  * 
  *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
@@ -17217,7 +18239,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo
   return __pyx_r;
 }
 
-/* "View.MemoryView":978
+/* "View.MemoryView":987
  * 
  *     @property
  *     def base(self):             # <<<<<<<<<<<<<<
@@ -17243,7 +18265,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("__get__", 0);
 
-  /* "View.MemoryView":979
+  /* "View.MemoryView":988
  *     @property
  *     def base(self):
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@@ -17255,7 +18277,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__
   __pyx_r = __pyx_v_self->from_object;
   goto __pyx_L0;
 
-  /* "View.MemoryView":978
+  /* "View.MemoryView":987
  * 
  *     @property
  *     def base(self):             # <<<<<<<<<<<<<<
@@ -17270,72 +18292,178 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__
   return __pyx_r;
 }
 
-/* "View.MemoryView":985
- * 
- * @cname('__pyx_memoryview_fromslice')
- * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
- *                           int ndim,
- *                           object (*to_object_func)(char *),
+/* "(tree fragment)":1
+ * def __reduce_cython__(self):             # <<<<<<<<<<<<<<
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
+ * def __setstate_cython__(self, __pyx_state):
  */
 
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) {
-  struct __pyx_memoryviewslice_obj *__pyx_v_result = 0;
-  Py_ssize_t __pyx_v_suboffset;
-  PyObject *__pyx_v_length = NULL;
+/* Python wrapper */
+static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0);
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+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) {
   PyObject *__pyx_r = NULL;
   __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  __Pyx_TypeInfo *__pyx_t_4;
-  Py_buffer __pyx_t_5;
-  Py_ssize_t *__pyx_t_6;
-  Py_ssize_t *__pyx_t_7;
-  Py_ssize_t *__pyx_t_8;
-  Py_ssize_t __pyx_t_9;
-  __Pyx_RefNannySetupContext("memoryview_fromslice", 0);
+  PyObject *__pyx_t_1 = NULL;
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-  /* "View.MemoryView":993
- *     cdef _memoryviewslice result
- * 
- *     if <PyObject *> memviewslice.memview == Py_None:             # <<<<<<<<<<<<<<
- *         return None
- * 
+  /* "(tree fragment)":2
+ * def __reduce_cython__(self):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
  */
-  __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0);
-  if (__pyx_t_1) {
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-    /* "View.MemoryView":994
- * 
- *     if <PyObject *> memviewslice.memview == Py_None:
- *         return None             # <<<<<<<<<<<<<<
- * 
- * 
+  /* "(tree fragment)":1
+ * def __reduce_cython__(self):             # <<<<<<<<<<<<<<
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- * 
- *     if <PyObject *> memviewslice.memview == Py_None:             # <<<<<<<<<<<<<<
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- */
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+ *                           int ndim,
+ *                           object (*to_object_func)(char *),
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+static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) {
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+ * 
+ */
+  __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0);
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+    /* "View.MemoryView":1003
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+ *     if <PyObject *> memviewslice.memview == Py_None:
+ *         return None             # <<<<<<<<<<<<<<
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+ * 
+ */
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+    __pyx_r = Py_None; __Pyx_INCREF(Py_None);
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+
+    /* "View.MemoryView":1002
+ *     cdef _memoryviewslice result
+ * 
+ *     if <PyObject *> memviewslice.memview == Py_None:             # <<<<<<<<<<<<<<
+ *         return None
+ * 
+ */
+  }
 
-  /* "View.MemoryView":999
+  /* "View.MemoryView":1008
  * 
  * 
  *     result = _memoryviewslice(None, 0, dtype_is_object)             # <<<<<<<<<<<<<<
  * 
  *     result.from_slice = memviewslice
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-  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 999, __pyx_L1_error)
+  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1008, __pyx_L1_error)
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-  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 999, __pyx_L1_error)
+  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1008, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_3);
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@@ -17346,13 +18474,13 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
   __Pyx_GIVEREF(__pyx_t_2);
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-  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 999, __pyx_L1_error)
+  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1008, __pyx_L1_error)
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-  /* "View.MemoryView":1001
+  /* "View.MemoryView":1010
  *     result = _memoryviewslice(None, 0, dtype_is_object)
  * 
  *     result.from_slice = memviewslice             # <<<<<<<<<<<<<<
@@ -17361,7 +18489,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
  */
   __pyx_v_result->from_slice = __pyx_v_memviewslice;
 
-  /* "View.MemoryView":1002
+  /* "View.MemoryView":1011
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  *     result.from_slice = memviewslice
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  */
   __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1);
 
-  /* "View.MemoryView":1004
+  /* "View.MemoryView":1013
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  * 
  *     result.from_object = (<memoryview> memviewslice.memview).base             # <<<<<<<<<<<<<<
  *     result.typeinfo = memviewslice.memview.typeinfo
  * 
  */
-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1004, __pyx_L1_error)
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error)
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@@ -17385,7 +18513,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
   __pyx_v_result->from_object = __pyx_t_2;
   __pyx_t_2 = 0;
 
-  /* "View.MemoryView":1005
+  /* "View.MemoryView":1014
  * 
  *     result.from_object = (<memoryview> memviewslice.memview).base
  *     result.typeinfo = memviewslice.memview.typeinfo             # <<<<<<<<<<<<<<
@@ -17395,7 +18523,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
   __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo;
   __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4;
 
-  /* "View.MemoryView":1007
+  /* "View.MemoryView":1016
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  * 
  *     result.view = memviewslice.memview.view             # <<<<<<<<<<<<<<
@@ -17405,7 +18533,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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   __pyx_v_result->__pyx_base.view = __pyx_t_5;
 
-  /* "View.MemoryView":1008
+  /* "View.MemoryView":1017
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  *     result.view = memviewslice.memview.view
  *     result.view.buf = <void *> memviewslice.data             # <<<<<<<<<<<<<<
@@ -17414,7 +18542,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
  */
   __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data);
 
-  /* "View.MemoryView":1009
+  /* "View.MemoryView":1018
  *     result.view = memviewslice.memview.view
  *     result.view.buf = <void *> memviewslice.data
  *     result.view.ndim = ndim             # <<<<<<<<<<<<<<
@@ -17423,7 +18551,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
  */
   __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim;
 
-  /* "View.MemoryView":1010
+  /* "View.MemoryView":1019
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  *     (<__pyx_buffer *> &result.view).obj = Py_None             # <<<<<<<<<<<<<<
@@ -17432,26 +18560,58 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
  */
   ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None;
 
-  /* "View.MemoryView":1011
+  /* "View.MemoryView":1020
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  *     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
  * 
- *     result.flags = PyBUF_RECORDS
+ *     if (<memoryview>memviewslice.memview).flags & PyBUF_WRITABLE:
  */
   Py_INCREF(Py_None);
 
-  /* "View.MemoryView":1013
+  /* "View.MemoryView":1022
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+ * 
+ *     if (<memoryview>memviewslice.memview).flags & PyBUF_WRITABLE:             # <<<<<<<<<<<<<<
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+ *     else:
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+
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  * 
- *     result.flags = PyBUF_RECORDS             # <<<<<<<<<<<<<<
+ *     if (<memoryview>memviewslice.memview).flags & PyBUF_WRITABLE:             # <<<<<<<<<<<<<<
+ *         result.flags = PyBUF_RECORDS
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+  }
+
+  /* "View.MemoryView":1025
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+ *     else:
+ *         result.flags = PyBUF_RECORDS_RO             # <<<<<<<<<<<<<<
  * 
  *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
  */
-  __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS;
+  /*else*/ {
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-  /* "View.MemoryView":1015
- *     result.flags = PyBUF_RECORDS
+  /* "View.MemoryView":1027
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  * 
  *     result.view.shape = <Py_ssize_t *> result.from_slice.shape             # <<<<<<<<<<<<<<
  *     result.view.strides = <Py_ssize_t *> result.from_slice.strides
@@ -17459,7 +18619,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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   __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape);
 
-  /* "View.MemoryView":1016
+  /* "View.MemoryView":1028
  * 
  *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
  *     result.view.strides = <Py_ssize_t *> result.from_slice.strides             # <<<<<<<<<<<<<<
@@ -17468,7 +18628,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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-  /* "View.MemoryView":1019
+  /* "View.MemoryView":1031
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  * 
  *     result.view.suboffsets = NULL             # <<<<<<<<<<<<<<
@@ -17477,7 +18637,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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-  /* "View.MemoryView":1020
+  /* "View.MemoryView":1032
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  *     for suboffset in result.from_slice.suboffsets[:ndim]:             # <<<<<<<<<<<<<<
@@ -17489,7 +18649,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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-    /* "View.MemoryView":1021
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@@ -17499,7 +18659,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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-      /* "View.MemoryView":1022
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@@ -17508,16 +18668,16 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
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-      /* "View.MemoryView":1023
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  * 
  *     result.view.len = result.view.itemsize
  */
-      goto __pyx_L5_break;
+      goto __pyx_L6_break;
 
-      /* "View.MemoryView":1021
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@@ -17526,9 +18686,9 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
  */
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-  /* "View.MemoryView":1025
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-  /* "View.MemoryView":1026
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-    /* "View.MemoryView":1027
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+    __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1039, __pyx_L1_error)
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-  /* "View.MemoryView":1029
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  * 
  *     result.to_object_func = to_object_func             # <<<<<<<<<<<<<<
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-  /* "View.MemoryView":1030
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   __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func;
 
-  /* "View.MemoryView":1032
+  /* "View.MemoryView":1044
  *     result.to_dtype_func = to_dtype_func
  * 
  *     return result             # <<<<<<<<<<<<<<
@@ -17600,7 +18760,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
   __pyx_r = ((PyObject *)__pyx_v_result);
   goto __pyx_L0;
 
-  /* "View.MemoryView":985
+  /* "View.MemoryView":994
  * 
  * @cname('__pyx_memoryview_fromslice')
  * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
@@ -17622,7 +18782,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl
   return __pyx_r;
 }
 
-/* "View.MemoryView":1035
+/* "View.MemoryView":1047
  * 
  * @cname('__pyx_memoryview_get_slice_from_memoryview')
  * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
@@ -17639,7 +18799,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
   PyObject *__pyx_t_3 = NULL;
   __Pyx_RefNannySetupContext("get_slice_from_memview", 0);
 
-  /* "View.MemoryView":1038
+  /* "View.MemoryView":1050
  *                                                    __Pyx_memviewslice *mslice):
  *     cdef _memoryviewslice obj
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -17650,20 +18810,20 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1039
+    /* "View.MemoryView":1051
  *     cdef _memoryviewslice obj
  *     if isinstance(memview, _memoryviewslice):
  *         obj = memview             # <<<<<<<<<<<<<<
  *         return &obj.from_slice
  *     else:
  */
-    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1039, __pyx_L1_error)
+    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1051, __pyx_L1_error)
     __pyx_t_3 = ((PyObject *)__pyx_v_memview);
     __Pyx_INCREF(__pyx_t_3);
     __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
     __pyx_t_3 = 0;
 
-    /* "View.MemoryView":1040
+    /* "View.MemoryView":1052
  *     if isinstance(memview, _memoryviewslice):
  *         obj = memview
  *         return &obj.from_slice             # <<<<<<<<<<<<<<
@@ -17673,7 +18833,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
     __pyx_r = (&__pyx_v_obj->from_slice);
     goto __pyx_L0;
 
-    /* "View.MemoryView":1038
+    /* "View.MemoryView":1050
  *                                                    __Pyx_memviewslice *mslice):
  *     cdef _memoryviewslice obj
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -17682,7 +18842,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
  */
   }
 
-  /* "View.MemoryView":1042
+  /* "View.MemoryView":1054
  *         return &obj.from_slice
  *     else:
  *         slice_copy(memview, mslice)             # <<<<<<<<<<<<<<
@@ -17692,7 +18852,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
   /*else*/ {
     __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice);
 
-    /* "View.MemoryView":1043
+    /* "View.MemoryView":1055
  *     else:
  *         slice_copy(memview, mslice)
  *         return mslice             # <<<<<<<<<<<<<<
@@ -17703,7 +18863,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
     goto __pyx_L0;
   }
 
-  /* "View.MemoryView":1035
+  /* "View.MemoryView":1047
  * 
  * @cname('__pyx_memoryview_get_slice_from_memoryview')
  * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
@@ -17714,7 +18874,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
   /* function exit code */
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_WriteUnraisable("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename, 0, 0);
+  __Pyx_WriteUnraisable("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0);
   __pyx_r = 0;
   __pyx_L0:;
   __Pyx_XDECREF((PyObject *)__pyx_v_obj);
@@ -17722,7 +18882,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p
   return __pyx_r;
 }
 
-/* "View.MemoryView":1046
+/* "View.MemoryView":1058
  * 
  * @cname('__pyx_memoryview_slice_copy')
  * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
@@ -17739,10 +18899,11 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
   Py_ssize_t *__pyx_t_1;
   int __pyx_t_2;
   int __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
+  int __pyx_t_4;
+  Py_ssize_t __pyx_t_5;
   __Pyx_RefNannySetupContext("slice_copy", 0);
 
-  /* "View.MemoryView":1050
+  /* "View.MemoryView":1062
  *     cdef (Py_ssize_t*) shape, strides, suboffsets
  * 
  *     shape = memview.view.shape             # <<<<<<<<<<<<<<
@@ -17752,7 +18913,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
   __pyx_t_1 = __pyx_v_memview->view.shape;
   __pyx_v_shape = __pyx_t_1;
 
-  /* "View.MemoryView":1051
+  /* "View.MemoryView":1063
  * 
  *     shape = memview.view.shape
  *     strides = memview.view.strides             # <<<<<<<<<<<<<<
@@ -17762,7 +18923,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
   __pyx_t_1 = __pyx_v_memview->view.strides;
   __pyx_v_strides = __pyx_t_1;
 
-  /* "View.MemoryView":1052
+  /* "View.MemoryView":1064
  *     shape = memview.view.shape
  *     strides = memview.view.strides
  *     suboffsets = memview.view.suboffsets             # <<<<<<<<<<<<<<
@@ -17772,7 +18933,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
   __pyx_t_1 = __pyx_v_memview->view.suboffsets;
   __pyx_v_suboffsets = __pyx_t_1;
 
-  /* "View.MemoryView":1054
+  /* "View.MemoryView":1066
  *     suboffsets = memview.view.suboffsets
  * 
  *     dst.memview = <__pyx_memoryview *> memview             # <<<<<<<<<<<<<<
@@ -17781,7 +18942,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  */
   __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview);
 
-  /* "View.MemoryView":1055
+  /* "View.MemoryView":1067
  * 
  *     dst.memview = <__pyx_memoryview *> memview
  *     dst.data = <char *> memview.view.buf             # <<<<<<<<<<<<<<
@@ -17790,7 +18951,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  */
   __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf);
 
-  /* "View.MemoryView":1057
+  /* "View.MemoryView":1069
  *     dst.data = <char *> memview.view.buf
  * 
  *     for dim in range(memview.view.ndim):             # <<<<<<<<<<<<<<
@@ -17798,10 +18959,11 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  *         dst.strides[dim] = strides[dim]
  */
   __pyx_t_2 = __pyx_v_memview->view.ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_dim = __pyx_t_3;
+  __pyx_t_3 = __pyx_t_2;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_dim = __pyx_t_4;
 
-    /* "View.MemoryView":1058
+    /* "View.MemoryView":1070
  * 
  *     for dim in range(memview.view.ndim):
  *         dst.shape[dim] = shape[dim]             # <<<<<<<<<<<<<<
@@ -17810,7 +18972,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  */
     (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]);
 
-    /* "View.MemoryView":1059
+    /* "View.MemoryView":1071
  *     for dim in range(memview.view.ndim):
  *         dst.shape[dim] = shape[dim]
  *         dst.strides[dim] = strides[dim]             # <<<<<<<<<<<<<<
@@ -17819,7 +18981,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  */
     (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]);
 
-    /* "View.MemoryView":1060
+    /* "View.MemoryView":1072
  *         dst.shape[dim] = shape[dim]
  *         dst.strides[dim] = strides[dim]
  *         dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1             # <<<<<<<<<<<<<<
@@ -17827,14 +18989,14 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
  * @cname('__pyx_memoryview_copy_object')
  */
     if ((__pyx_v_suboffsets != 0)) {
-      __pyx_t_4 = (__pyx_v_suboffsets[__pyx_v_dim]);
+      __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]);
     } else {
-      __pyx_t_4 = -1L;
+      __pyx_t_5 = -1L;
     }
-    (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_4;
+    (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5;
   }
 
-  /* "View.MemoryView":1046
+  /* "View.MemoryView":1058
  * 
  * @cname('__pyx_memoryview_slice_copy')
  * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
@@ -17846,7 +19008,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem
   __Pyx_RefNannyFinishContext();
 }
 
-/* "View.MemoryView":1063
+/* "View.MemoryView":1075
  * 
  * @cname('__pyx_memoryview_copy_object')
  * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
@@ -17861,7 +19023,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx
   PyObject *__pyx_t_1 = NULL;
   __Pyx_RefNannySetupContext("memoryview_copy", 0);
 
-  /* "View.MemoryView":1066
+  /* "View.MemoryView":1078
  *     "Create a new memoryview object"
  *     cdef __Pyx_memviewslice memviewslice
  *     slice_copy(memview, &memviewslice)             # <<<<<<<<<<<<<<
@@ -17870,7 +19032,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx
  */
   __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice));
 
-  /* "View.MemoryView":1067
+  /* "View.MemoryView":1079
  *     cdef __Pyx_memviewslice memviewslice
  *     slice_copy(memview, &memviewslice)
  *     return memoryview_copy_from_slice(memview, &memviewslice)             # <<<<<<<<<<<<<<
@@ -17878,13 +19040,13 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx
  * @cname('__pyx_memoryview_copy_object_from_slice')
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1067, __pyx_L1_error)
+  __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1079, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1063
+  /* "View.MemoryView":1075
  * 
  * @cname('__pyx_memoryview_copy_object')
  * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
@@ -17903,7 +19065,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx
   return __pyx_r;
 }
 
-/* "View.MemoryView":1070
+/* "View.MemoryView":1082
  * 
  * @cname('__pyx_memoryview_copy_object_from_slice')
  * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
@@ -17923,7 +19085,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
   PyObject *__pyx_t_5 = NULL;
   __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0);
 
-  /* "View.MemoryView":1077
+  /* "View.MemoryView":1089
  *     cdef int (*to_dtype_func)(char *, object) except 0
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -17934,7 +19096,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
   __pyx_t_2 = (__pyx_t_1 != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1078
+    /* "View.MemoryView":1090
  * 
  *     if isinstance(memview, _memoryviewslice):
  *         to_object_func = (<_memoryviewslice> memview).to_object_func             # <<<<<<<<<<<<<<
@@ -17944,7 +19106,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
     __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func;
     __pyx_v_to_object_func = __pyx_t_3;
 
-    /* "View.MemoryView":1079
+    /* "View.MemoryView":1091
  *     if isinstance(memview, _memoryviewslice):
  *         to_object_func = (<_memoryviewslice> memview).to_object_func
  *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func             # <<<<<<<<<<<<<<
@@ -17954,7 +19116,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
     __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func;
     __pyx_v_to_dtype_func = __pyx_t_4;
 
-    /* "View.MemoryView":1077
+    /* "View.MemoryView":1089
  *     cdef int (*to_dtype_func)(char *, object) except 0
  * 
  *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
@@ -17964,7 +19126,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":1081
+  /* "View.MemoryView":1093
  *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
  *     else:
  *         to_object_func = NULL             # <<<<<<<<<<<<<<
@@ -17974,7 +19136,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
   /*else*/ {
     __pyx_v_to_object_func = NULL;
 
-    /* "View.MemoryView":1082
+    /* "View.MemoryView":1094
  *     else:
  *         to_object_func = NULL
  *         to_dtype_func = NULL             # <<<<<<<<<<<<<<
@@ -17985,7 +19147,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":1084
+  /* "View.MemoryView":1096
  *         to_dtype_func = NULL
  * 
  *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,             # <<<<<<<<<<<<<<
@@ -17994,20 +19156,20 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
  */
   __Pyx_XDECREF(__pyx_r);
 
-  /* "View.MemoryView":1086
+  /* "View.MemoryView":1098
  *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
  *                                 to_object_func, to_dtype_func,
  *                                 memview.dtype_is_object)             # <<<<<<<<<<<<<<
  * 
  * 
  */
-  __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1084, __pyx_L1_error)
+  __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1096, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_5);
   __pyx_r = __pyx_t_5;
   __pyx_t_5 = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1070
+  /* "View.MemoryView":1082
  * 
  * @cname('__pyx_memoryview_copy_object_from_slice')
  * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
@@ -18026,7 +19188,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview
   return __pyx_r;
 }
 
-/* "View.MemoryView":1092
+/* "View.MemoryView":1104
  * 
  * 
  * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
@@ -18038,7 +19200,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
   Py_ssize_t __pyx_r;
   int __pyx_t_1;
 
-  /* "View.MemoryView":1093
+  /* "View.MemoryView":1105
  * 
  * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
  *     if arg < 0:             # <<<<<<<<<<<<<<
@@ -18048,7 +19210,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
   __pyx_t_1 = ((__pyx_v_arg < 0) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":1094
+    /* "View.MemoryView":1106
  * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
  *     if arg < 0:
  *         return -arg             # <<<<<<<<<<<<<<
@@ -18058,7 +19220,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
     __pyx_r = (-__pyx_v_arg);
     goto __pyx_L0;
 
-    /* "View.MemoryView":1093
+    /* "View.MemoryView":1105
  * 
  * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
  *     if arg < 0:             # <<<<<<<<<<<<<<
@@ -18067,7 +19229,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
  */
   }
 
-  /* "View.MemoryView":1096
+  /* "View.MemoryView":1108
  *         return -arg
  *     else:
  *         return arg             # <<<<<<<<<<<<<<
@@ -18079,7 +19241,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
     goto __pyx_L0;
   }
 
-  /* "View.MemoryView":1092
+  /* "View.MemoryView":1104
  * 
  * 
  * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
@@ -18092,7 +19254,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
   return __pyx_r;
 }
 
-/* "View.MemoryView":1099
+/* "View.MemoryView":1111
  * 
  * @cname('__pyx_get_best_slice_order')
  * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
@@ -18108,8 +19270,9 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
   int __pyx_t_1;
   int __pyx_t_2;
   int __pyx_t_3;
+  int __pyx_t_4;
 
-  /* "View.MemoryView":1104
+  /* "View.MemoryView":1116
  *     """
  *     cdef int i
  *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<
@@ -18118,7 +19281,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
   __pyx_v_c_stride = 0;
 
-  /* "View.MemoryView":1105
+  /* "View.MemoryView":1117
  *     cdef int i
  *     cdef Py_ssize_t c_stride = 0
  *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<
@@ -18127,17 +19290,17 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
   __pyx_v_f_stride = 0;
 
-  /* "View.MemoryView":1107
+  /* "View.MemoryView":1119
  *     cdef Py_ssize_t f_stride = 0
  * 
  *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
  *         if mslice.shape[i] > 1:
  *             c_stride = mslice.strides[i]
  */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1L; __pyx_t_1-=1) {
+  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
     __pyx_v_i = __pyx_t_1;
 
-    /* "View.MemoryView":1108
+    /* "View.MemoryView":1120
  * 
  *     for i in range(ndim - 1, -1, -1):
  *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
@@ -18147,7 +19310,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
     __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1109
+      /* "View.MemoryView":1121
  *     for i in range(ndim - 1, -1, -1):
  *         if mslice.shape[i] > 1:
  *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
@@ -18156,7 +19319,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
       __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
 
-      /* "View.MemoryView":1110
+      /* "View.MemoryView":1122
  *         if mslice.shape[i] > 1:
  *             c_stride = mslice.strides[i]
  *             break             # <<<<<<<<<<<<<<
@@ -18165,7 +19328,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
       goto __pyx_L4_break;
 
-      /* "View.MemoryView":1108
+      /* "View.MemoryView":1120
  * 
  *     for i in range(ndim - 1, -1, -1):
  *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
@@ -18176,7 +19339,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
   }
   __pyx_L4_break:;
 
-  /* "View.MemoryView":1112
+  /* "View.MemoryView":1124
  *             break
  * 
  *     for i in range(ndim):             # <<<<<<<<<<<<<<
@@ -18184,10 +19347,11 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  *             f_stride = mslice.strides[i]
  */
   __pyx_t_1 = __pyx_v_ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
+  __pyx_t_3 = __pyx_t_1;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
 
-    /* "View.MemoryView":1113
+    /* "View.MemoryView":1125
  * 
  *     for i in range(ndim):
  *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
@@ -18197,7 +19361,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
     __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1114
+      /* "View.MemoryView":1126
  *     for i in range(ndim):
  *         if mslice.shape[i] > 1:
  *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
@@ -18206,7 +19370,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
       __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
 
-      /* "View.MemoryView":1115
+      /* "View.MemoryView":1127
  *         if mslice.shape[i] > 1:
  *             f_stride = mslice.strides[i]
  *             break             # <<<<<<<<<<<<<<
@@ -18215,7 +19379,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
       goto __pyx_L7_break;
 
-      /* "View.MemoryView":1113
+      /* "View.MemoryView":1125
  * 
  *     for i in range(ndim):
  *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
@@ -18226,7 +19390,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
   }
   __pyx_L7_break:;
 
-  /* "View.MemoryView":1117
+  /* "View.MemoryView":1129
  *             break
  * 
  *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
@@ -18236,7 +19400,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
   __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1118
+    /* "View.MemoryView":1130
  * 
  *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
  *         return 'C'             # <<<<<<<<<<<<<<
@@ -18246,7 +19410,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
     __pyx_r = 'C';
     goto __pyx_L0;
 
-    /* "View.MemoryView":1117
+    /* "View.MemoryView":1129
  *             break
  * 
  *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
@@ -18255,7 +19419,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
  */
   }
 
-  /* "View.MemoryView":1120
+  /* "View.MemoryView":1132
  *         return 'C'
  *     else:
  *         return 'F'             # <<<<<<<<<<<<<<
@@ -18267,7 +19431,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
     goto __pyx_L0;
   }
 
-  /* "View.MemoryView":1099
+  /* "View.MemoryView":1111
  * 
  * @cname('__pyx_get_best_slice_order')
  * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
@@ -18280,7 +19444,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _
   return __pyx_r;
 }
 
-/* "View.MemoryView":1123
+/* "View.MemoryView":1135
  * 
  * @cython.cdivision(True)
  * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
@@ -18299,8 +19463,9 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
   int __pyx_t_3;
   Py_ssize_t __pyx_t_4;
   Py_ssize_t __pyx_t_5;
+  Py_ssize_t __pyx_t_6;
 
-  /* "View.MemoryView":1130
+  /* "View.MemoryView":1142
  * 
  *     cdef Py_ssize_t i
  *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
@@ -18309,7 +19474,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
   __pyx_v_src_extent = (__pyx_v_src_shape[0]);
 
-  /* "View.MemoryView":1131
+  /* "View.MemoryView":1143
  *     cdef Py_ssize_t i
  *     cdef Py_ssize_t src_extent = src_shape[0]
  *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
@@ -18318,7 +19483,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
   __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
 
-  /* "View.MemoryView":1132
+  /* "View.MemoryView":1144
  *     cdef Py_ssize_t src_extent = src_shape[0]
  *     cdef Py_ssize_t dst_extent = dst_shape[0]
  *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
@@ -18327,7 +19492,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
   __pyx_v_src_stride = (__pyx_v_src_strides[0]);
 
-  /* "View.MemoryView":1133
+  /* "View.MemoryView":1145
  *     cdef Py_ssize_t dst_extent = dst_shape[0]
  *     cdef Py_ssize_t src_stride = src_strides[0]
  *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
@@ -18336,7 +19501,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
   __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
 
-  /* "View.MemoryView":1135
+  /* "View.MemoryView":1147
  *     cdef Py_ssize_t dst_stride = dst_strides[0]
  * 
  *     if ndim == 1:             # <<<<<<<<<<<<<<
@@ -18346,7 +19511,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
   __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":1136
+    /* "View.MemoryView":1148
  * 
  *     if ndim == 1:
  *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
@@ -18366,7 +19531,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
       goto __pyx_L5_bool_binop_done;
     }
 
-    /* "View.MemoryView":1137
+    /* "View.MemoryView":1149
  *     if ndim == 1:
  *        if (src_stride > 0 and dst_stride > 0 and
  *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
@@ -18381,7 +19546,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
     __pyx_t_1 = __pyx_t_3;
     __pyx_L5_bool_binop_done:;
 
-    /* "View.MemoryView":1136
+    /* "View.MemoryView":1148
  * 
  *     if ndim == 1:
  *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
@@ -18390,16 +19555,16 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
     if (__pyx_t_1) {
 
-      /* "View.MemoryView":1138
+      /* "View.MemoryView":1150
  *        if (src_stride > 0 and dst_stride > 0 and
  *            <size_t> src_stride == itemsize == <size_t> dst_stride):
  *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
  *        else:
  *            for i in range(dst_extent):
  */
-      memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent));
+      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
 
-      /* "View.MemoryView":1136
+      /* "View.MemoryView":1148
  * 
  *     if ndim == 1:
  *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
@@ -18409,7 +19574,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
       goto __pyx_L4;
     }
 
-    /* "View.MemoryView":1140
+    /* "View.MemoryView":1152
  *            memcpy(dst_data, src_data, itemsize * dst_extent)
  *        else:
  *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
@@ -18418,19 +19583,20 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
     /*else*/ {
       __pyx_t_4 = __pyx_v_dst_extent;
-      for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-        __pyx_v_i = __pyx_t_5;
+      __pyx_t_5 = __pyx_t_4;
+      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+        __pyx_v_i = __pyx_t_6;
 
-        /* "View.MemoryView":1141
+        /* "View.MemoryView":1153
  *        else:
  *            for i in range(dst_extent):
  *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
  *                src_data += src_stride
  *                dst_data += dst_stride
  */
-        memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize);
+        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
 
-        /* "View.MemoryView":1142
+        /* "View.MemoryView":1154
  *            for i in range(dst_extent):
  *                memcpy(dst_data, src_data, itemsize)
  *                src_data += src_stride             # <<<<<<<<<<<<<<
@@ -18439,7 +19605,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
         __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
 
-        /* "View.MemoryView":1143
+        /* "View.MemoryView":1155
  *                memcpy(dst_data, src_data, itemsize)
  *                src_data += src_stride
  *                dst_data += dst_stride             # <<<<<<<<<<<<<<
@@ -18451,7 +19617,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
     }
     __pyx_L4:;
 
-    /* "View.MemoryView":1135
+    /* "View.MemoryView":1147
  *     cdef Py_ssize_t dst_stride = dst_strides[0]
  * 
  *     if ndim == 1:             # <<<<<<<<<<<<<<
@@ -18461,7 +19627,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":1145
+  /* "View.MemoryView":1157
  *                dst_data += dst_stride
  *     else:
  *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
@@ -18470,10 +19636,11 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
   /*else*/ {
     __pyx_t_4 = __pyx_v_dst_extent;
-    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-      __pyx_v_i = __pyx_t_5;
+    __pyx_t_5 = __pyx_t_4;
+    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+      __pyx_v_i = __pyx_t_6;
 
-      /* "View.MemoryView":1146
+      /* "View.MemoryView":1158
  *     else:
  *         for i in range(dst_extent):
  *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
@@ -18482,7 +19649,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
       _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
 
-      /* "View.MemoryView":1150
+      /* "View.MemoryView":1162
  *                                      src_shape + 1, dst_shape + 1,
  *                                      ndim - 1, itemsize)
  *             src_data += src_stride             # <<<<<<<<<<<<<<
@@ -18491,7 +19658,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
  */
       __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
 
-      /* "View.MemoryView":1151
+      /* "View.MemoryView":1163
  *                                      ndim - 1, itemsize)
  *             src_data += src_stride
  *             dst_data += dst_stride             # <<<<<<<<<<<<<<
@@ -18503,7 +19670,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":1123
+  /* "View.MemoryView":1135
  * 
  * @cython.cdivision(True)
  * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
@@ -18514,7 +19681,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
   /* function exit code */
 }
 
-/* "View.MemoryView":1153
+/* "View.MemoryView":1165
  *             dst_data += dst_stride
  * 
  * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
@@ -18524,7 +19691,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v
 
 static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
 
-  /* "View.MemoryView":1156
+  /* "View.MemoryView":1168
  *                                   __Pyx_memviewslice *dst,
  *                                   int ndim, size_t itemsize) nogil:
  *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
@@ -18533,7 +19700,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi
  */
   _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
 
-  /* "View.MemoryView":1153
+  /* "View.MemoryView":1165
  *             dst_data += dst_stride
  * 
  * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
@@ -18544,7 +19711,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi
   /* function exit code */
 }
 
-/* "View.MemoryView":1160
+/* "View.MemoryView":1172
  * 
  * @cname('__pyx_memoryview_slice_get_size')
  * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
@@ -18559,8 +19726,9 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
   Py_ssize_t __pyx_t_1;
   int __pyx_t_2;
   int __pyx_t_3;
+  int __pyx_t_4;
 
-  /* "View.MemoryView":1163
+  /* "View.MemoryView":1175
  *     "Return the size of the memory occupied by the slice in number of bytes"
  *     cdef int i
  *     cdef Py_ssize_t size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
@@ -18570,7 +19738,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
   __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
   __pyx_v_size = __pyx_t_1;
 
-  /* "View.MemoryView":1165
+  /* "View.MemoryView":1177
  *     cdef Py_ssize_t size = src.memview.view.itemsize
  * 
  *     for i in range(ndim):             # <<<<<<<<<<<<<<
@@ -18578,10 +19746,11 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
  * 
  */
   __pyx_t_2 = __pyx_v_ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
+  __pyx_t_3 = __pyx_t_2;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
 
-    /* "View.MemoryView":1166
+    /* "View.MemoryView":1178
  * 
  *     for i in range(ndim):
  *         size *= src.shape[i]             # <<<<<<<<<<<<<<
@@ -18591,7 +19760,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
     __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i]));
   }
 
-  /* "View.MemoryView":1168
+  /* "View.MemoryView":1180
  *         size *= src.shape[i]
  * 
  *     return size             # <<<<<<<<<<<<<<
@@ -18601,7 +19770,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
   __pyx_r = __pyx_v_size;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1160
+  /* "View.MemoryView":1172
  * 
  * @cname('__pyx_memoryview_slice_get_size')
  * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
@@ -18614,7 +19783,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr
   return __pyx_r;
 }
 
-/* "View.MemoryView":1171
+/* "View.MemoryView":1183
  * 
  * @cname('__pyx_fill_contig_strides_array')
  * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
@@ -18628,8 +19797,9 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
   int __pyx_t_1;
   int __pyx_t_2;
   int __pyx_t_3;
+  int __pyx_t_4;
 
-  /* "View.MemoryView":1180
+  /* "View.MemoryView":1192
  *     cdef int idx
  * 
  *     if order == 'F':             # <<<<<<<<<<<<<<
@@ -18639,7 +19809,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
   __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":1181
+    /* "View.MemoryView":1193
  * 
  *     if order == 'F':
  *         for idx in range(ndim):             # <<<<<<<<<<<<<<
@@ -18647,10 +19817,11 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
  *             stride = stride * shape[idx]
  */
     __pyx_t_2 = __pyx_v_ndim;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_idx = __pyx_t_3;
+    __pyx_t_3 = __pyx_t_2;
+    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+      __pyx_v_idx = __pyx_t_4;
 
-      /* "View.MemoryView":1182
+      /* "View.MemoryView":1194
  *     if order == 'F':
  *         for idx in range(ndim):
  *             strides[idx] = stride             # <<<<<<<<<<<<<<
@@ -18659,7 +19830,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
  */
       (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
 
-      /* "View.MemoryView":1183
+      /* "View.MemoryView":1195
  *         for idx in range(ndim):
  *             strides[idx] = stride
  *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
@@ -18669,7 +19840,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
       __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
     }
 
-    /* "View.MemoryView":1180
+    /* "View.MemoryView":1192
  *     cdef int idx
  * 
  *     if order == 'F':             # <<<<<<<<<<<<<<
@@ -18679,7 +19850,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":1185
+  /* "View.MemoryView":1197
  *             stride = stride * shape[idx]
  *     else:
  *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
@@ -18687,10 +19858,10 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
  *             stride = stride * shape[idx]
  */
   /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1L; __pyx_t_2-=1) {
+    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
       __pyx_v_idx = __pyx_t_2;
 
-      /* "View.MemoryView":1186
+      /* "View.MemoryView":1198
  *     else:
  *         for idx in range(ndim - 1, -1, -1):
  *             strides[idx] = stride             # <<<<<<<<<<<<<<
@@ -18699,7 +19870,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
  */
       (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
 
-      /* "View.MemoryView":1187
+      /* "View.MemoryView":1199
  *         for idx in range(ndim - 1, -1, -1):
  *             strides[idx] = stride
  *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
@@ -18711,7 +19882,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":1189
+  /* "View.MemoryView":1201
  *             stride = stride * shape[idx]
  * 
  *     return stride             # <<<<<<<<<<<<<<
@@ -18721,7 +19892,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
   __pyx_r = __pyx_v_stride;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1171
+  /* "View.MemoryView":1183
  * 
  * @cname('__pyx_fill_contig_strides_array')
  * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
@@ -18734,7 +19905,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_
   return __pyx_r;
 }
 
-/* "View.MemoryView":1192
+/* "View.MemoryView":1204
  * 
  * @cname('__pyx_memoryview_copy_data_to_temp')
  * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
@@ -18753,8 +19924,9 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   int __pyx_t_3;
   struct __pyx_memoryview_obj *__pyx_t_4;
   int __pyx_t_5;
+  int __pyx_t_6;
 
-  /* "View.MemoryView":1203
+  /* "View.MemoryView":1215
  *     cdef void *result
  * 
  *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
@@ -18764,7 +19936,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
   __pyx_v_itemsize = __pyx_t_1;
 
-  /* "View.MemoryView":1204
+  /* "View.MemoryView":1216
  * 
  *     cdef size_t itemsize = src.memview.view.itemsize
  *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
@@ -18773,7 +19945,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
   __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
 
-  /* "View.MemoryView":1206
+  /* "View.MemoryView":1218
  *     cdef size_t size = slice_get_size(src, ndim)
  * 
  *     result = malloc(size)             # <<<<<<<<<<<<<<
@@ -18782,7 +19954,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
   __pyx_v_result = malloc(__pyx_v_size);
 
-  /* "View.MemoryView":1207
+  /* "View.MemoryView":1219
  * 
  *     result = malloc(size)
  *     if not result:             # <<<<<<<<<<<<<<
@@ -18792,16 +19964,16 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1208
+    /* "View.MemoryView":1220
  *     result = malloc(size)
  *     if not result:
  *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
  * 
  * 
  */
-    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(2, 1208, __pyx_L1_error)
+    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1220, __pyx_L1_error)
 
-    /* "View.MemoryView":1207
+    /* "View.MemoryView":1219
  * 
  *     result = malloc(size)
  *     if not result:             # <<<<<<<<<<<<<<
@@ -18810,7 +19982,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
   }
 
-  /* "View.MemoryView":1211
+  /* "View.MemoryView":1223
  * 
  * 
  *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
@@ -18819,7 +19991,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
   __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
 
-  /* "View.MemoryView":1212
+  /* "View.MemoryView":1224
  * 
  *     tmpslice.data = <char *> result
  *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
@@ -18829,7 +20001,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_t_4 = __pyx_v_src->memview;
   __pyx_v_tmpslice->memview = __pyx_t_4;
 
-  /* "View.MemoryView":1213
+  /* "View.MemoryView":1225
  *     tmpslice.data = <char *> result
  *     tmpslice.memview = src.memview
  *     for i in range(ndim):             # <<<<<<<<<<<<<<
@@ -18837,10 +20009,11 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  *         tmpslice.suboffsets[i] = -1
  */
   __pyx_t_3 = __pyx_v_ndim;
-  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
-    __pyx_v_i = __pyx_t_5;
+  __pyx_t_5 = __pyx_t_3;
+  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+    __pyx_v_i = __pyx_t_6;
 
-    /* "View.MemoryView":1214
+    /* "View.MemoryView":1226
  *     tmpslice.memview = src.memview
  *     for i in range(ndim):
  *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
@@ -18849,7 +20022,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
     (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
 
-    /* "View.MemoryView":1215
+    /* "View.MemoryView":1227
  *     for i in range(ndim):
  *         tmpslice.shape[i] = src.shape[i]
  *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
@@ -18859,16 +20032,16 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
     (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;
   }
 
-  /* "View.MemoryView":1217
+  /* "View.MemoryView":1229
  *         tmpslice.suboffsets[i] = -1
  * 
  *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
  *                               ndim, order)
  * 
  */
-  __pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order);
+  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));
 
-  /* "View.MemoryView":1221
+  /* "View.MemoryView":1233
  * 
  * 
  *     for i in range(ndim):             # <<<<<<<<<<<<<<
@@ -18876,10 +20049,11 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  *             tmpslice.strides[i] = 0
  */
   __pyx_t_3 = __pyx_v_ndim;
-  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
-    __pyx_v_i = __pyx_t_5;
+  __pyx_t_5 = __pyx_t_3;
+  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+    __pyx_v_i = __pyx_t_6;
 
-    /* "View.MemoryView":1222
+    /* "View.MemoryView":1234
  * 
  *     for i in range(ndim):
  *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
@@ -18889,7 +20063,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
     __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1223
+      /* "View.MemoryView":1235
  *     for i in range(ndim):
  *         if tmpslice.shape[i] == 1:
  *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
@@ -18898,7 +20072,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
  */
       (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;
 
-      /* "View.MemoryView":1222
+      /* "View.MemoryView":1234
  * 
  *     for i in range(ndim):
  *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
@@ -18908,7 +20082,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
     }
   }
 
-  /* "View.MemoryView":1225
+  /* "View.MemoryView":1237
  *             tmpslice.strides[i] = 0
  * 
  *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
@@ -18918,16 +20092,16 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1226
+    /* "View.MemoryView":1238
  * 
  *     if slice_is_contig(src[0], order, ndim):
  *         memcpy(result, src.data, size)             # <<<<<<<<<<<<<<
  *     else:
  *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
  */
-    memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size);
+    (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size));
 
-    /* "View.MemoryView":1225
+    /* "View.MemoryView":1237
  *             tmpslice.strides[i] = 0
  * 
  *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
@@ -18937,7 +20111,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
     goto __pyx_L9;
   }
 
-  /* "View.MemoryView":1228
+  /* "View.MemoryView":1240
  *         memcpy(result, src.data, size)
  *     else:
  *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)             # <<<<<<<<<<<<<<
@@ -18949,7 +20123,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   }
   __pyx_L9:;
 
-  /* "View.MemoryView":1230
+  /* "View.MemoryView":1242
  *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
  * 
  *     return result             # <<<<<<<<<<<<<<
@@ -18959,7 +20133,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_r = __pyx_v_result;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1192
+  /* "View.MemoryView":1204
  * 
  * @cname('__pyx_memoryview_copy_data_to_temp')
  * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
@@ -18971,11 +20145,11 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   __pyx_L1_error:;
   {
     #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
     #endif
     __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename);
     #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
+    __Pyx_PyGILState_Release(__pyx_gilstate_save);
     #endif
   }
   __pyx_r = NULL;
@@ -18983,7 +20157,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src,
   return __pyx_r;
 }
 
-/* "View.MemoryView":1235
+/* "View.MemoryView":1247
  * 
  * @cname('__pyx_memoryview_err_extents')
  * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
@@ -18999,24 +20173,24 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent
   PyObject *__pyx_t_3 = NULL;
   PyObject *__pyx_t_4 = NULL;
   #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
   #endif
   __Pyx_RefNannySetupContext("_err_extents", 0);
 
-  /* "View.MemoryView":1238
+  /* "View.MemoryView":1250
  *                              Py_ssize_t extent2) except -1 with gil:
  *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
  *                                                         (i, extent1, extent2))             # <<<<<<<<<<<<<<
  * 
  * @cname('__pyx_memoryview_err_dim')
  */
-  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1238, __pyx_L1_error)
+  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1250, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1238, __pyx_L1_error)
+  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1250, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1238, __pyx_L1_error)
+  __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1250, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_3);
-  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1238, __pyx_L1_error)
+  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1250, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_4);
   __Pyx_GIVEREF(__pyx_t_1);
   PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
@@ -19028,29 +20202,24 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent
   __pyx_t_2 = 0;
   __pyx_t_3 = 0;
 
-  /* "View.MemoryView":1237
+  /* "View.MemoryView":1249
  * cdef int _err_extents(int i, Py_ssize_t extent1,
  *                              Py_ssize_t extent2) except -1 with gil:
  *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %             # <<<<<<<<<<<<<<
  *                                                         (i, extent1, extent2))
  * 
  */
-  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1237, __pyx_L1_error)
+  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1249, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_3);
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1237, __pyx_L1_error)
+  __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1249, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_GIVEREF(__pyx_t_3);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
-  __pyx_t_3 = 0;
-  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1237, __pyx_L1_error)
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __Pyx_Raise(__pyx_t_3, 0, 0, 0);
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __PYX_ERR(2, 1237, __pyx_L1_error)
+  __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __PYX_ERR(2, 1249, __pyx_L1_error)
 
-  /* "View.MemoryView":1235
+  /* "View.MemoryView":1247
  * 
  * @cname('__pyx_memoryview_err_extents')
  * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
@@ -19068,12 +20237,12 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent
   __pyx_r = -1;
   __Pyx_RefNannyFinishContext();
   #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
+  __Pyx_PyGILState_Release(__pyx_gilstate_save);
   #endif
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 }
 
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-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
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-    /* "View.MemoryView":1247
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-  /* "View.MemoryView":1249
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+  int __pyx_t_6;
+  void *__pyx_t_7;
+  int __pyx_t_8;
 
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-  /* "View.MemoryView":1261
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-  /* "View.MemoryView":1263
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-  /* "View.MemoryView":1264
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-  /* "View.MemoryView":1265
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-  /* "View.MemoryView":1268
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-    /* "View.MemoryView":1269
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-    /* "View.MemoryView":1268
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-  /* "View.MemoryView":1270
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-    /* "View.MemoryView":1271
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-    /* "View.MemoryView":1270
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-  /* "View.MemoryView":1273
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-  /* "View.MemoryView":1275
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-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_5; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
+  __pyx_t_3 = __pyx_t_5;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
 
-    /* "View.MemoryView":1276
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  *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
@@ -19492,7 +20663,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1277
+      /* "View.MemoryView":1289
  *     for i in range(ndim):
  *         if src.shape[i] != dst.shape[i]:
  *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
@@ -19502,7 +20673,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
       __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
       if (__pyx_t_2) {
 
-        /* "View.MemoryView":1278
+        /* "View.MemoryView":1290
  *         if src.shape[i] != dst.shape[i]:
  *             if src.shape[i] == 1:
  *                 broadcasting = True             # <<<<<<<<<<<<<<
@@ -19511,7 +20682,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
         __pyx_v_broadcasting = 1;
 
-        /* "View.MemoryView":1279
+        /* "View.MemoryView":1291
  *             if src.shape[i] == 1:
  *                 broadcasting = True
  *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
@@ -19520,7 +20691,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
         (__pyx_v_src.strides[__pyx_v_i]) = 0;
 
-        /* "View.MemoryView":1277
+        /* "View.MemoryView":1289
  *     for i in range(ndim):
  *         if src.shape[i] != dst.shape[i]:
  *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
@@ -19530,7 +20701,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
         goto __pyx_L7;
       }
 
-      /* "View.MemoryView":1281
+      /* "View.MemoryView":1293
  *                 src.strides[i] = 0
  *             else:
  *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
@@ -19538,11 +20709,11 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  *         if src.suboffsets[i] >= 0:
  */
       /*else*/ {
-        __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) __PYX_ERR(2, 1281, __pyx_L1_error)
+        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1293, __pyx_L1_error)
       }
       __pyx_L7:;
 
-      /* "View.MemoryView":1276
+      /* "View.MemoryView":1288
  * 
  *     for i in range(ndim):
  *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
@@ -19551,7 +20722,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
     }
 
-    /* "View.MemoryView":1283
+    /* "View.MemoryView":1295
  *                 _err_extents(i, dst.shape[i], src.shape[i])
  * 
  *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
@@ -19561,16 +20732,16 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1284
+      /* "View.MemoryView":1296
  * 
  *         if src.suboffsets[i] >= 0:
  *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
  * 
  *     if slices_overlap(&src, &dst, ndim, itemsize):
  */
-      __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) __PYX_ERR(2, 1284, __pyx_L1_error)
+      __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1296, __pyx_L1_error)
 
-      /* "View.MemoryView":1283
+      /* "View.MemoryView":1295
  *                 _err_extents(i, dst.shape[i], src.shape[i])
  * 
  *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
@@ -19580,7 +20751,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     }
   }
 
-  /* "View.MemoryView":1286
+  /* "View.MemoryView":1298
  *             _err_dim(ValueError, "Dimension %d is not direct", i)
  * 
  *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
@@ -19590,7 +20761,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1288
+    /* "View.MemoryView":1300
  *     if slices_overlap(&src, &dst, ndim, itemsize):
  * 
  *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
@@ -19600,7 +20771,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1289
+      /* "View.MemoryView":1301
  * 
  *         if not slice_is_contig(src, order, ndim):
  *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
@@ -19609,7 +20780,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
 
-      /* "View.MemoryView":1288
+      /* "View.MemoryView":1300
  *     if slices_overlap(&src, &dst, ndim, itemsize):
  * 
  *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
@@ -19618,17 +20789,17 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
     }
 
-    /* "View.MemoryView":1291
+    /* "View.MemoryView":1303
  *             order = get_best_order(&dst, ndim)
  * 
  *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
  *         src = tmp
  * 
  */
-    __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) __PYX_ERR(2, 1291, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_6;
+    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1303, __pyx_L1_error)
+    __pyx_v_tmpdata = __pyx_t_7;
 
-    /* "View.MemoryView":1292
+    /* "View.MemoryView":1304
  * 
  *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
  *         src = tmp             # <<<<<<<<<<<<<<
@@ -19637,7 +20808,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
     __pyx_v_src = __pyx_v_tmp;
 
-    /* "View.MemoryView":1286
+    /* "View.MemoryView":1298
  *             _err_dim(ValueError, "Dimension %d is not direct", i)
  * 
  *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
@@ -19646,7 +20817,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   }
 
-  /* "View.MemoryView":1294
+  /* "View.MemoryView":1306
  *         src = tmp
  * 
  *     if not broadcasting:             # <<<<<<<<<<<<<<
@@ -19656,7 +20827,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
   if (__pyx_t_2) {
 
-    /* "View.MemoryView":1297
+    /* "View.MemoryView":1309
  * 
  * 
  *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
@@ -19666,7 +20837,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1298
+      /* "View.MemoryView":1310
  * 
  *         if slice_is_contig(src, 'C', ndim):
  *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
@@ -19675,7 +20846,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
 
-      /* "View.MemoryView":1297
+      /* "View.MemoryView":1309
  * 
  * 
  *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
@@ -19685,7 +20856,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
       goto __pyx_L12;
     }
 
-    /* "View.MemoryView":1299
+    /* "View.MemoryView":1311
  *         if slice_is_contig(src, 'C', ndim):
  *             direct_copy = slice_is_contig(dst, 'C', ndim)
  *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
@@ -19695,7 +20866,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1300
+      /* "View.MemoryView":1312
  *             direct_copy = slice_is_contig(dst, 'C', ndim)
  *         elif slice_is_contig(src, 'F', ndim):
  *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
@@ -19704,7 +20875,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
 
-      /* "View.MemoryView":1299
+      /* "View.MemoryView":1311
  *         if slice_is_contig(src, 'C', ndim):
  *             direct_copy = slice_is_contig(dst, 'C', ndim)
  *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
@@ -19714,7 +20885,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     }
     __pyx_L12:;
 
-    /* "View.MemoryView":1302
+    /* "View.MemoryView":1314
  *             direct_copy = slice_is_contig(dst, 'F', ndim)
  * 
  *         if direct_copy:             # <<<<<<<<<<<<<<
@@ -19724,7 +20895,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
     __pyx_t_2 = (__pyx_v_direct_copy != 0);
     if (__pyx_t_2) {
 
-      /* "View.MemoryView":1304
+      /* "View.MemoryView":1316
  *         if direct_copy:
  * 
  *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
@@ -19733,16 +20904,16 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
 
-      /* "View.MemoryView":1305
+      /* "View.MemoryView":1317
  * 
  *             refcount_copying(&dst, dtype_is_object, ndim, False)
  *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
  *             refcount_copying(&dst, dtype_is_object, ndim, True)
  *             free(tmpdata)
  */
-      memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim));
+      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
 
-      /* "View.MemoryView":1306
+      /* "View.MemoryView":1318
  *             refcount_copying(&dst, dtype_is_object, ndim, False)
  *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
  *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
@@ -19751,7 +20922,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
 
-      /* "View.MemoryView":1307
+      /* "View.MemoryView":1319
  *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
  *             refcount_copying(&dst, dtype_is_object, ndim, True)
  *             free(tmpdata)             # <<<<<<<<<<<<<<
@@ -19760,7 +20931,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
       free(__pyx_v_tmpdata);
 
-      /* "View.MemoryView":1308
+      /* "View.MemoryView":1320
  *             refcount_copying(&dst, dtype_is_object, ndim, True)
  *             free(tmpdata)
  *             return 0             # <<<<<<<<<<<<<<
@@ -19770,7 +20941,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
       __pyx_r = 0;
       goto __pyx_L0;
 
-      /* "View.MemoryView":1302
+      /* "View.MemoryView":1314
  *             direct_copy = slice_is_contig(dst, 'F', ndim)
  * 
  *         if direct_copy:             # <<<<<<<<<<<<<<
@@ -19779,7 +20950,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
     }
 
-    /* "View.MemoryView":1294
+    /* "View.MemoryView":1306
  *         src = tmp
  * 
  *     if not broadcasting:             # <<<<<<<<<<<<<<
@@ -19788,7 +20959,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   }
 
-  /* "View.MemoryView":1310
+  /* "View.MemoryView":1322
  *             return 0
  * 
  *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
@@ -19799,28 +20970,28 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   if (__pyx_t_2) {
     __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
   }
-  __pyx_t_7 = (__pyx_t_2 != 0);
-  if (__pyx_t_7) {
+  __pyx_t_8 = (__pyx_t_2 != 0);
+  if (__pyx_t_8) {
 
-    /* "View.MemoryView":1313
+    /* "View.MemoryView":1325
  * 
  * 
  *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
  *         transpose_memslice(&dst)
  * 
  */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) __PYX_ERR(2, 1313, __pyx_L1_error)
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1325, __pyx_L1_error)
 
-    /* "View.MemoryView":1314
+    /* "View.MemoryView":1326
  * 
  *         transpose_memslice(&src)
  *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
  * 
  *     refcount_copying(&dst, dtype_is_object, ndim, False)
  */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) __PYX_ERR(2, 1314, __pyx_L1_error)
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1326, __pyx_L1_error)
 
-    /* "View.MemoryView":1310
+    /* "View.MemoryView":1322
  *             return 0
  * 
  *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
@@ -19829,7 +21000,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   }
 
-  /* "View.MemoryView":1316
+  /* "View.MemoryView":1328
  *         transpose_memslice(&dst)
  * 
  *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
@@ -19838,7 +21009,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
 
-  /* "View.MemoryView":1317
+  /* "View.MemoryView":1329
  * 
  *     refcount_copying(&dst, dtype_is_object, ndim, False)
  *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
@@ -19847,7 +21018,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
 
-  /* "View.MemoryView":1318
+  /* "View.MemoryView":1330
  *     refcount_copying(&dst, dtype_is_object, ndim, False)
  *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
  *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
@@ -19856,7 +21027,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
 
-  /* "View.MemoryView":1320
+  /* "View.MemoryView":1332
  *     refcount_copying(&dst, dtype_is_object, ndim, True)
  * 
  *     free(tmpdata)             # <<<<<<<<<<<<<<
@@ -19865,7 +21036,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
  */
   free(__pyx_v_tmpdata);
 
-  /* "View.MemoryView":1321
+  /* "View.MemoryView":1333
  * 
  *     free(tmpdata)
  *     return 0             # <<<<<<<<<<<<<<
@@ -19875,7 +21046,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   __pyx_r = 0;
   goto __pyx_L0;
 
-  /* "View.MemoryView":1252
+  /* "View.MemoryView":1264
  * 
  * @cname('__pyx_memoryview_copy_contents')
  * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
@@ -19887,11 +21058,11 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   __pyx_L1_error:;
   {
     #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
     #endif
     __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
     #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
+    __Pyx_PyGILState_Release(__pyx_gilstate_save);
     #endif
   }
   __pyx_r = -1;
@@ -19899,7 +21070,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_
   return __pyx_r;
 }
 
-/* "View.MemoryView":1324
+/* "View.MemoryView":1336
  * 
  * @cname('__pyx_memoryview_broadcast_leading')
  * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
@@ -19912,8 +21083,9 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
   int __pyx_v_offset;
   int __pyx_t_1;
   int __pyx_t_2;
+  int __pyx_t_3;
 
-  /* "View.MemoryView":1328
+  /* "View.MemoryView":1340
  *                             int ndim_other) nogil:
  *     cdef int i
  *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
@@ -19922,17 +21094,17 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  */
   __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
 
-  /* "View.MemoryView":1330
+  /* "View.MemoryView":1342
  *     cdef int offset = ndim_other - ndim
  * 
  *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
  *         mslice.shape[i + offset] = mslice.shape[i]
  *         mslice.strides[i + offset] = mslice.strides[i]
  */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1L; __pyx_t_1-=1) {
+  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
     __pyx_v_i = __pyx_t_1;
 
-    /* "View.MemoryView":1331
+    /* "View.MemoryView":1343
  * 
  *     for i in range(ndim - 1, -1, -1):
  *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<
@@ -19941,7 +21113,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  */
     (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);
 
-    /* "View.MemoryView":1332
+    /* "View.MemoryView":1344
  *     for i in range(ndim - 1, -1, -1):
  *         mslice.shape[i + offset] = mslice.shape[i]
  *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<
@@ -19950,7 +21122,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  */
     (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);
 
-    /* "View.MemoryView":1333
+    /* "View.MemoryView":1345
  *         mslice.shape[i + offset] = mslice.shape[i]
  *         mslice.strides[i + offset] = mslice.strides[i]
  *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<
@@ -19960,7 +21132,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
     (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
   }
 
-  /* "View.MemoryView":1335
+  /* "View.MemoryView":1347
  *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
  * 
  *     for i in range(offset):             # <<<<<<<<<<<<<<
@@ -19968,10 +21140,11 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  *         mslice.strides[i] = mslice.strides[0]
  */
   __pyx_t_1 = __pyx_v_offset;
-  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
-    __pyx_v_i = __pyx_t_2;
+  __pyx_t_2 = __pyx_t_1;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
 
-    /* "View.MemoryView":1336
+    /* "View.MemoryView":1348
  * 
  *     for i in range(offset):
  *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<
@@ -19980,7 +21153,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  */
     (__pyx_v_mslice->shape[__pyx_v_i]) = 1;
 
-    /* "View.MemoryView":1337
+    /* "View.MemoryView":1349
  *     for i in range(offset):
  *         mslice.shape[i] = 1
  *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<
@@ -19989,7 +21162,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
  */
     (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);
 
-    /* "View.MemoryView":1338
+    /* "View.MemoryView":1350
  *         mslice.shape[i] = 1
  *         mslice.strides[i] = mslice.strides[0]
  *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
@@ -19999,7 +21172,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
     (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;
   }
 
-  /* "View.MemoryView":1324
+  /* "View.MemoryView":1336
  * 
  * @cname('__pyx_memoryview_broadcast_leading')
  * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
@@ -20010,7 +21183,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
   /* function exit code */
 }
 
-/* "View.MemoryView":1346
+/* "View.MemoryView":1358
  * 
  * @cname('__pyx_memoryview_refcount_copying')
  * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
@@ -20021,7 +21194,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic
 static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
   int __pyx_t_1;
 
-  /* "View.MemoryView":1350
+  /* "View.MemoryView":1362
  * 
  * 
  *     if dtype_is_object:             # <<<<<<<<<<<<<<
@@ -20031,7 +21204,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i
   __pyx_t_1 = (__pyx_v_dtype_is_object != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":1351
+    /* "View.MemoryView":1363
  * 
  *     if dtype_is_object:
  *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<
@@ -20040,7 +21213,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i
  */
     __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);
 
-    /* "View.MemoryView":1350
+    /* "View.MemoryView":1362
  * 
  * 
  *     if dtype_is_object:             # <<<<<<<<<<<<<<
@@ -20049,7 +21222,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i
  */
   }
 
-  /* "View.MemoryView":1346
+  /* "View.MemoryView":1358
  * 
  * @cname('__pyx_memoryview_refcount_copying')
  * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
@@ -20060,7 +21233,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i
   /* function exit code */
 }
 
-/* "View.MemoryView":1355
+/* "View.MemoryView":1367
  * 
  * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
  * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
@@ -20071,11 +21244,11 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i
 static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
   __Pyx_RefNannyDeclarations
   #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
   #endif
   __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0);
 
-  /* "View.MemoryView":1358
+  /* "View.MemoryView":1370
  *                                              Py_ssize_t *strides, int ndim,
  *                                              bint inc) with gil:
  *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<
@@ -20084,7 +21257,7 @@ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_da
  */
   __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);
 
-  /* "View.MemoryView":1355
+  /* "View.MemoryView":1367
  * 
  * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
  * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
@@ -20095,11 +21268,11 @@ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_da
   /* function exit code */
   __Pyx_RefNannyFinishContext();
   #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
+  __Pyx_PyGILState_Release(__pyx_gilstate_save);
   #endif
 }
 
-/* "View.MemoryView":1361
+/* "View.MemoryView":1373
  * 
  * @cname('__pyx_memoryview_refcount_objects_in_slice')
  * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
@@ -20112,10 +21285,11 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
   __Pyx_RefNannyDeclarations
   Py_ssize_t __pyx_t_1;
   Py_ssize_t __pyx_t_2;
-  int __pyx_t_3;
+  Py_ssize_t __pyx_t_3;
+  int __pyx_t_4;
   __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0);
 
-  /* "View.MemoryView":1365
+  /* "View.MemoryView":1377
  *     cdef Py_ssize_t i
  * 
  *     for i in range(shape[0]):             # <<<<<<<<<<<<<<
@@ -20123,30 +21297,31 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
  *             if inc:
  */
   __pyx_t_1 = (__pyx_v_shape[0]);
-  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
-    __pyx_v_i = __pyx_t_2;
+  __pyx_t_2 = __pyx_t_1;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
 
-    /* "View.MemoryView":1366
+    /* "View.MemoryView":1378
  * 
  *     for i in range(shape[0]):
  *         if ndim == 1:             # <<<<<<<<<<<<<<
  *             if inc:
  *                 Py_INCREF((<PyObject **> data)[0])
  */
-    __pyx_t_3 = ((__pyx_v_ndim == 1) != 0);
-    if (__pyx_t_3) {
+    __pyx_t_4 = ((__pyx_v_ndim == 1) != 0);
+    if (__pyx_t_4) {
 
-      /* "View.MemoryView":1367
+      /* "View.MemoryView":1379
  *     for i in range(shape[0]):
  *         if ndim == 1:
  *             if inc:             # <<<<<<<<<<<<<<
  *                 Py_INCREF((<PyObject **> data)[0])
  *             else:
  */
-      __pyx_t_3 = (__pyx_v_inc != 0);
-      if (__pyx_t_3) {
+      __pyx_t_4 = (__pyx_v_inc != 0);
+      if (__pyx_t_4) {
 
-        /* "View.MemoryView":1368
+        /* "View.MemoryView":1380
  *         if ndim == 1:
  *             if inc:
  *                 Py_INCREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
@@ -20155,7 +21330,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
  */
         Py_INCREF((((PyObject **)__pyx_v_data)[0]));
 
-        /* "View.MemoryView":1367
+        /* "View.MemoryView":1379
  *     for i in range(shape[0]):
  *         if ndim == 1:
  *             if inc:             # <<<<<<<<<<<<<<
@@ -20165,7 +21340,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
         goto __pyx_L6;
       }
 
-      /* "View.MemoryView":1370
+      /* "View.MemoryView":1382
  *                 Py_INCREF((<PyObject **> data)[0])
  *             else:
  *                 Py_DECREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
@@ -20177,7 +21352,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
       }
       __pyx_L6:;
 
-      /* "View.MemoryView":1366
+      /* "View.MemoryView":1378
  * 
  *     for i in range(shape[0]):
  *         if ndim == 1:             # <<<<<<<<<<<<<<
@@ -20187,7 +21362,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
       goto __pyx_L5;
     }
 
-    /* "View.MemoryView":1372
+    /* "View.MemoryView":1384
  *                 Py_DECREF((<PyObject **> data)[0])
  *         else:
  *             refcount_objects_in_slice(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
@@ -20196,7 +21371,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
  */
     /*else*/ {
 
-      /* "View.MemoryView":1373
+      /* "View.MemoryView":1385
  *         else:
  *             refcount_objects_in_slice(data, shape + 1, strides + 1,
  *                                       ndim - 1, inc)             # <<<<<<<<<<<<<<
@@ -20207,7 +21382,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
     }
     __pyx_L5:;
 
-    /* "View.MemoryView":1375
+    /* "View.MemoryView":1387
  *                                       ndim - 1, inc)
  * 
  *         data += strides[0]             # <<<<<<<<<<<<<<
@@ -20217,7 +21392,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
     __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0]));
   }
 
-  /* "View.MemoryView":1361
+  /* "View.MemoryView":1373
  * 
  * @cname('__pyx_memoryview_refcount_objects_in_slice')
  * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
@@ -20229,7 +21404,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
   __Pyx_RefNannyFinishContext();
 }
 
-/* "View.MemoryView":1381
+/* "View.MemoryView":1393
  * 
  * @cname('__pyx_memoryview_slice_assign_scalar')
  * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
@@ -20239,7 +21414,7 @@ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ss
 
 static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) {
 
-  /* "View.MemoryView":1384
+  /* "View.MemoryView":1396
  *                               size_t itemsize, void *item,
  *                               bint dtype_is_object) nogil:
  *     refcount_copying(dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
@@ -20248,7 +21423,7 @@ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst
  */
   __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
 
-  /* "View.MemoryView":1385
+  /* "View.MemoryView":1397
  *                               bint dtype_is_object) nogil:
  *     refcount_copying(dst, dtype_is_object, ndim, False)
  *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,             # <<<<<<<<<<<<<<
@@ -20257,7 +21432,7 @@ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst
  */
   __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item);
 
-  /* "View.MemoryView":1387
+  /* "View.MemoryView":1399
  *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
  *                          itemsize, item)
  *     refcount_copying(dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
@@ -20266,7 +21441,7 @@ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst
  */
   __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
 
-  /* "View.MemoryView":1381
+  /* "View.MemoryView":1393
  * 
  * @cname('__pyx_memoryview_slice_assign_scalar')
  * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
@@ -20277,7 +21452,7 @@ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst
   /* function exit code */
 }
 
-/* "View.MemoryView":1391
+/* "View.MemoryView":1403
  * 
  * @cname('__pyx_memoryview__slice_assign_scalar')
  * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
@@ -20292,8 +21467,9 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
   int __pyx_t_1;
   Py_ssize_t __pyx_t_2;
   Py_ssize_t __pyx_t_3;
+  Py_ssize_t __pyx_t_4;
 
-  /* "View.MemoryView":1395
+  /* "View.MemoryView":1407
  *                               size_t itemsize, void *item) nogil:
  *     cdef Py_ssize_t i
  *     cdef Py_ssize_t stride = strides[0]             # <<<<<<<<<<<<<<
@@ -20302,7 +21478,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
  */
   __pyx_v_stride = (__pyx_v_strides[0]);
 
-  /* "View.MemoryView":1396
+  /* "View.MemoryView":1408
  *     cdef Py_ssize_t i
  *     cdef Py_ssize_t stride = strides[0]
  *     cdef Py_ssize_t extent = shape[0]             # <<<<<<<<<<<<<<
@@ -20311,7 +21487,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
  */
   __pyx_v_extent = (__pyx_v_shape[0]);
 
-  /* "View.MemoryView":1398
+  /* "View.MemoryView":1410
  *     cdef Py_ssize_t extent = shape[0]
  * 
  *     if ndim == 1:             # <<<<<<<<<<<<<<
@@ -20321,7 +21497,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
   __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
   if (__pyx_t_1) {
 
-    /* "View.MemoryView":1399
+    /* "View.MemoryView":1411
  * 
  *     if ndim == 1:
  *         for i in range(extent):             # <<<<<<<<<<<<<<
@@ -20329,19 +21505,20 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
  *             data += stride
  */
     __pyx_t_2 = __pyx_v_extent;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_i = __pyx_t_3;
+    __pyx_t_3 = __pyx_t_2;
+    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+      __pyx_v_i = __pyx_t_4;
 
-      /* "View.MemoryView":1400
+      /* "View.MemoryView":1412
  *     if ndim == 1:
  *         for i in range(extent):
  *             memcpy(data, item, itemsize)             # <<<<<<<<<<<<<<
  *             data += stride
  *     else:
  */
-      memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize);
+      (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize));
 
-      /* "View.MemoryView":1401
+      /* "View.MemoryView":1413
  *         for i in range(extent):
  *             memcpy(data, item, itemsize)
  *             data += stride             # <<<<<<<<<<<<<<
@@ -20351,7 +21528,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
       __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
     }
 
-    /* "View.MemoryView":1398
+    /* "View.MemoryView":1410
  *     cdef Py_ssize_t extent = shape[0]
  * 
  *     if ndim == 1:             # <<<<<<<<<<<<<<
@@ -20361,7 +21538,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
     goto __pyx_L3;
   }
 
-  /* "View.MemoryView":1403
+  /* "View.MemoryView":1415
  *             data += stride
  *     else:
  *         for i in range(extent):             # <<<<<<<<<<<<<<
@@ -20370,10 +21547,11 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
  */
   /*else*/ {
     __pyx_t_2 = __pyx_v_extent;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_i = __pyx_t_3;
-
-      /* "View.MemoryView":1404
+    __pyx_t_3 = __pyx_t_2;
+    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+      __pyx_v_i = __pyx_t_4;
+
+      /* "View.MemoryView":1416
  *     else:
  *         for i in range(extent):
  *             _slice_assign_scalar(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
@@ -20382,7 +21560,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
  */
       __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item);
 
-      /* "View.MemoryView":1406
+      /* "View.MemoryView":1418
  *             _slice_assign_scalar(data, shape + 1, strides + 1,
  *                                 ndim - 1, itemsize, item)
  *             data += stride             # <<<<<<<<<<<<<<
@@ -20394,7 +21572,7 @@ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t
   }
   __pyx_L3:;
 
-  /* "View.MemoryView":1391
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+    __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
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+static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) {
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+
+static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) {
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   #endif
   #if PY_MAJOR_VERSION < 3
   0, /*bf_getwritebuffer*/
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 static void __pyx_tp_dealloc_Enum(PyObject *o) {
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-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
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   #endif
@@ -20626,6 +22284,8 @@ static int __pyx_tp_clear_Enum(PyObject *o) {
 }
 
 static PyMethodDef __pyx_methods_Enum[] = {
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+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},
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 };
 
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 static void __pyx_tp_dealloc_memoryview(PyObject *o) {
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-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+  #if CYTHON_USE_TP_FINALIZE
+  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
     if (PyObject_CallFinalizerFromDealloc(o)) return;
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   {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
   {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
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 };
 
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 static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
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-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+  #if CYTHON_USE_TP_FINALIZE
+  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
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 }
 
 static PyMethodDef __pyx_methods__memoryviewslice[] = {
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
   {0, 0, 0, 0}
 };
 
@@ -21073,17 +22737,31 @@ static PyMethodDef __pyx_methods[] = {
 };
 
 #if PY_MAJOR_VERSION >= 3
+#if CYTHON_PEP489_MULTI_PHASE_INIT
+static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/
+static int __pyx_pymod_exec_tigramite_cython_code(PyObject* module); /*proto*/
+static PyModuleDef_Slot __pyx_moduledef_slots[] = {
+  {Py_mod_create, (void*)__pyx_pymod_create},
+  {Py_mod_exec, (void*)__pyx_pymod_exec_tigramite_cython_code},
+  {0, NULL}
+};
+#endif
+
 static struct PyModuleDef __pyx_moduledef = {
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-    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
-  #else
     PyModuleDef_HEAD_INIT,
-  #endif
     "tigramite_cython_code",
     0, /* m_doc */
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+    0, /* m_size */
+  #else
     -1, /* m_size */
+  #endif
     __pyx_methods /* m_methods */,
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+    __pyx_moduledef_slots, /* m_slots */
+  #else
     NULL, /* m_reload */
+  #endif
     NULL, /* m_traverse */
     NULL, /* m_clear */
     NULL /* m_free */
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+  {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0},
+  {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0},
   {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0},
   {&__pyx_n_s_D_N, __pyx_k_D_N, sizeof(__pyx_k_D_N), 0, 0, 1, 1},
   {&__pyx_n_s_D_N___init, __pyx_k_D_N___init, sizeof(__pyx_k_D_N___init), 0, 0, 1, 1},
@@ -21105,6 +22785,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_kp_u_Format_string_allocated_too_shor, __pyx_k_Format_string_allocated_too_shor, sizeof(__pyx_k_Format_string_allocated_too_shor), 0, 1, 0, 0},
   {&__pyx_kp_u_Format_string_allocated_too_shor_2, __pyx_k_Format_string_allocated_too_shor_2, sizeof(__pyx_k_Format_string_allocated_too_shor_2), 0, 1, 0, 0},
   {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1},
+  {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0},
   {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
   {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0},
   {&__pyx_kp_s_Inner_helper_of_dcov_all_Cache_d, __pyx_k_Inner_helper_of_dcov_all_Cache_d, sizeof(__pyx_k_Inner_helper_of_dcov_all_Cache_d), 0, 0, 1, 0},
@@ -21117,11 +22798,13 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_kp_u_Non_native_byte_order_not_suppor, __pyx_k_Non_native_byte_order_not_suppor, sizeof(__pyx_k_Non_native_byte_order_not_suppor), 0, 1, 0, 0},
   {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1},
   {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0},
+  {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1},
   {&__pyx_n_s_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 0, 0, 1, 1},
   {&__pyx_n_s_T, __pyx_k_T, sizeof(__pyx_k_T), 0, 0, 1, 1},
   {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
   {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0},
   {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
+  {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1},
   {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1},
   {&__pyx_n_s_array, __pyx_k_array, sizeof(__pyx_k_array), 0, 0, 1, 1},
   {&__pyx_n_s_array_mask, __pyx_k_array_mask, sizeof(__pyx_k_array_mask), 0, 0, 1, 1},
@@ -21132,6 +22815,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_calculate_means, __pyx_k_calculate_means, sizeof(__pyx_k_calculate_means), 0, 0, 1, 1},
   {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1},
+  {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1},
   {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0},
   {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0},
   {&__pyx_n_s_count, __pyx_k_count, sizeof(__pyx_k_count), 0, 0, 1, 1},
@@ -21141,6 +22825,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_dcov_all, __pyx_k_dcov_all, sizeof(__pyx_k_dcov_all), 0, 0, 1, 1},
   {&__pyx_n_s_denom, __pyx_k_denom, sizeof(__pyx_k_denom), 0, 0, 1, 1},
+  {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1},
   {&__pyx_n_s_dim, __pyx_k_dim, sizeof(__pyx_k_dim), 0, 0, 1, 1},
   {&__pyx_n_s_dim_x, __pyx_k_dim_x, sizeof(__pyx_k_dim_x), 0, 0, 1, 1},
   {&__pyx_n_s_dim_y, __pyx_k_dim_y, sizeof(__pyx_k_dim_y), 0, 0, 1, 1},
@@ -21171,8 +22856,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_get_patterns_cython, __pyx_k_get_patterns_cython, sizeof(__pyx_k_get_patterns_cython), 0, 0, 1, 1},
   {&__pyx_n_s_get_restricted_permutation_cyth, __pyx_k_get_restricted_permutation_cyth, sizeof(__pyx_k_get_restricted_permutation_cyth), 0, 0, 1, 1},
+  {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1},
   {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0},
-  {&__pyx_kp_s_home_jakobrunge_work_code_pytho, __pyx_k_home_jakobrunge_work_code_pytho, sizeof(__pyx_k_home_jakobrunge_work_code_pytho), 0, 0, 1, 0},
   {&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1},
   {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1},
   {&__pyx_n_s_ii, __pyx_k_ii, sizeof(__pyx_k_ii), 0, 0, 1, 1},
@@ -21192,7 +22877,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_kz, __pyx_k_kz, sizeof(__pyx_k_kz), 0, 0, 1, 1},
   {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
-  {&__pyx_n_s_map, __pyx_k_map, sizeof(__pyx_k_map), 0, 0, 1, 1},
   {&__pyx_n_s_mask, __pyx_k_mask, sizeof(__pyx_k_mask), 0, 0, 1, 1},
   {&__pyx_n_s_mean, __pyx_k_mean, sizeof(__pyx_k_mean), 0, 0, 1, 1},
   {&__pyx_n_s_mean_0, __pyx_k_mean_0, sizeof(__pyx_k_mean_0), 0, 0, 1, 1},
@@ -21214,6 +22898,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
   {&__pyx_n_s_neighbors, __pyx_k_neighbors, sizeof(__pyx_k_neighbors), 0, 0, 1, 1},
+  {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1},
+  {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0},
   {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1},
   {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
   {&__pyx_kp_s_numpy_core_multiarray_failed_to, __pyx_k_numpy_core_multiarray_failed_to, sizeof(__pyx_k_numpy_core_multiarray_failed_to), 0, 0, 1, 0},
@@ -21225,15 +22911,27 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_patt, __pyx_k_patt, sizeof(__pyx_k_patt), 0, 0, 1, 1},
   {&__pyx_n_s_patt_mask, __pyx_k_patt_mask, sizeof(__pyx_k_patt_mask), 0, 0, 1, 1},
+  {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1},
   {&__pyx_n_s_prepare, __pyx_k_prepare, sizeof(__pyx_k_prepare), 0, 0, 1, 1},
   {&__pyx_n_s_product_sum, __pyx_k_product_sum, sizeof(__pyx_k_product_sum), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1},
   {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1},
   {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1},
   {&__pyx_n_s_qualname, __pyx_k_qualname, sizeof(__pyx_k_qualname), 0, 0, 1, 1},
   {&__pyx_n_s_random, __pyx_k_random, sizeof(__pyx_k_random), 0, 0, 1, 1},
   {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
+  {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1},
+  {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1},
+  {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1},
   {&__pyx_n_s_restricted_permutation, __pyx_k_restricted_permutation, sizeof(__pyx_k_restricted_permutation), 0, 0, 1, 1},
   {&__pyx_n_s_self, __pyx_k_self, sizeof(__pyx_k_self), 0, 0, 1, 1},
+  {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1},
+  {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1},
   {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
   {&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
   {&__pyx_n_s_shuffle_neighbors, __pyx_k_shuffle_neighbors, sizeof(__pyx_k_shuffle_neighbors), 0, 0, 1, 1},
@@ -21246,6 +22944,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0},
   {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0},
+  {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0},
   {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1},
   {&__pyx_n_s_sum_0, __pyx_k_sum_0, sizeof(__pyx_k_sum_0), 0, 0, 1, 1},
   {&__pyx_n_s_sum_1, __pyx_k_sum_1, sizeof(__pyx_k_sum_1), 0, 0, 1, 1},
@@ -21254,13 +22953,15 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
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   {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
-  {&__pyx_n_s_tigramite_tigramite_cython_code, __pyx_k_tigramite_tigramite_cython_code, sizeof(__pyx_k_tigramite_tigramite_cython_code), 0, 0, 1, 1},
+  {&__pyx_kp_s_tigramite_tigramite_cython_code, __pyx_k_tigramite_tigramite_cython_code, sizeof(__pyx_k_tigramite_tigramite_cython_code), 0, 0, 1, 0},
+  {&__pyx_n_s_tigramite_tigramite_cython_code_2, __pyx_k_tigramite_tigramite_cython_code_2, sizeof(__pyx_k_tigramite_tigramite_cython_code_2), 0, 0, 1, 1},
   {&__pyx_n_s_timeit, __pyx_k_timeit, sizeof(__pyx_k_timeit), 0, 0, 1, 1},
   {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0},
   {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0},
   {&__pyx_n_s_unittest, __pyx_k_unittest, sizeof(__pyx_k_unittest), 0, 0, 1, 1},
   {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0},
   {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1},
+  {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1},
   {&__pyx_n_s_use, __pyx_k_use, sizeof(__pyx_k_use), 0, 0, 1, 1},
   {&__pyx_n_s_used, __pyx_k_used, sizeof(__pyx_k_used), 0, 0, 1, 1},
   {&__pyx_n_s_v, __pyx_k_v, sizeof(__pyx_k_v), 0, 0, 1, 1},
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 static int __Pyx_InitCachedBuiltins(void) {
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":218
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":229
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":222
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":233
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  * 
  *             info.buf = PyArray_DATA(self)
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-  __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_Fortran_contiguou); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(1, 222, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__2);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":259
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":263
  *                 if ((descr.byteorder == c'>' and little_endian) or
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-  __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(1, 259, __pyx_L1_error)
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":799
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":810
  * 
  *         if (end - f) - <int>(new_offset - offset[0]) < 15:
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-  __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(1, 799, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__4);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":803
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-  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(1, 803, __pyx_L1_error)
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":823
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":834
  *             t = child.type_num
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-  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor_2); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(1, 823, __pyx_L1_error)
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-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":989
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1000
  *         _import_array()
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  *         raise ImportError("numpy.core.multiarray failed to import")             # <<<<<<<<<<<<<<
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  * cdef inline int import_umath() except -1:
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-  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_numpy_core_multiarray_failed_to); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(1, 989, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__7);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":995
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  *         _import_umath()
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  *         raise ImportError("numpy.core.umath failed to import")             # <<<<<<<<<<<<<<
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  * cdef inline int import_ufunc() except -1:
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-  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(1, 995, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__8);
 
-  /* "../../../../../anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":1001
+  /* "../../../../../../anaconda/envs/py36/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1012
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-  __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(1, 1001, __pyx_L1_error)
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   __Pyx_GOTREF(__pyx_tuple__9);
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-  /* "View.MemoryView":131
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   __Pyx_GOTREF(__pyx_tuple__10);
   __Pyx_GIVEREF(__pyx_tuple__10);
 
-  /* "View.MemoryView":134
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   __Pyx_GOTREF(__pyx_tuple__11);
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-  /* "View.MemoryView":137
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-  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_n_s_ASCII); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(2, 137, __pyx_L1_error)
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   __Pyx_GOTREF(__pyx_tuple__12);
   __Pyx_GIVEREF(__pyx_tuple__12);
 
-  /* "View.MemoryView":146
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  * 
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-  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(2, 146, __pyx_L1_error)
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   __Pyx_GOTREF(__pyx_tuple__13);
   __Pyx_GIVEREF(__pyx_tuple__13);
 
-  /* "View.MemoryView":174
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-  __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(2, 174, __pyx_L1_error)
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   __Pyx_GOTREF(__pyx_tuple__14);
   __Pyx_GIVEREF(__pyx_tuple__14);
 
-  /* "View.MemoryView":190
+  /* "View.MemoryView":191
  *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
  *         if not (flags & bufmode):
  *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
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  *         info.len = self.len
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-  __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(2, 190, __pyx_L1_error)
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   __Pyx_GOTREF(__pyx_tuple__15);
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-  /* "View.MemoryView":484
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+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
+ */
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+  /* "(tree fragment)":4
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+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
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+ *         if self.view.readonly:
+ *             raise TypeError("Cannot assign to read-only memoryview")             # <<<<<<<<<<<<<<
+ * 
+ *         have_slices, index = _unellipsify(index, self.view.ndim)
+ */
+  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(2, 413, __pyx_L1_error)
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+  __Pyx_GIVEREF(__pyx_tuple__18);
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  *             result = struct.unpack(self.view.format, bytesitem)
  *         except struct.error:
  *             raise ValueError("Unable to convert item to object")             # <<<<<<<<<<<<<<
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-  __Pyx_GOTREF(__pyx_tuple__16);
-  __Pyx_GIVEREF(__pyx_tuple__16);
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-  /* "View.MemoryView":556
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+ *         if flags & PyBUF_WRITABLE and self.view.readonly:
+ *             raise ValueError("Cannot create writable memory view from read-only memoryview")             # <<<<<<<<<<<<<<
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+ *         if flags & PyBUF_STRIDES:
+ */
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  *             raise ValueError("Buffer view does not expose strides")             # <<<<<<<<<<<<<<
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-  __Pyx_GOTREF(__pyx_tuple__17);
-  __Pyx_GIVEREF(__pyx_tuple__17);
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-  __Pyx_GOTREF(__pyx_tuple__18);
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-  __Pyx_GIVEREF(__pyx_tuple__18);
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+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
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+  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(2, 2, __pyx_L1_error)
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+  __Pyx_GIVEREF(__pyx_tuple__23);
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+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
+ */
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-  /* "View.MemoryView":668
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-  __Pyx_GOTREF(__pyx_slice__19);
-  __Pyx_GIVEREF(__pyx_slice__19);
+  __pyx_slice__25 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__25)) __PYX_ERR(2, 677, __pyx_L1_error)
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-  /* "View.MemoryView":671
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  *                 result.append(slice(None))             # <<<<<<<<<<<<<<
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-  __Pyx_GOTREF(__pyx_slice__20);
-  __Pyx_GIVEREF(__pyx_slice__20);
+  __pyx_slice__26 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__26)) __PYX_ERR(2, 680, __pyx_L1_error)
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+  __Pyx_GIVEREF(__pyx_slice__26);
 
-  /* "View.MemoryView":682
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  *     if nslices:
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  * 
  *     return have_slices or nslices, tuple(result)
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-  __Pyx_GOTREF(__pyx_slice__21);
-  __Pyx_GIVEREF(__pyx_slice__21);
+  __pyx_slice__27 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__27)) __PYX_ERR(2, 691, __pyx_L1_error)
+  __Pyx_GOTREF(__pyx_slice__27);
+  __Pyx_GIVEREF(__pyx_slice__27);
 
-  /* "View.MemoryView":689
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  *         if suboffset >= 0:
  *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
  * 
  * 
  */
-  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(2, 689, __pyx_L1_error)
-  __Pyx_GOTREF(__pyx_tuple__22);
-  __Pyx_GIVEREF(__pyx_tuple__22);
+  __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__28)) __PYX_ERR(2, 698, __pyx_L1_error)
+  __Pyx_GOTREF(__pyx_tuple__28);
+  __Pyx_GIVEREF(__pyx_tuple__28);
+
+  /* "(tree fragment)":2
+ * def __reduce_cython__(self):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
+ */
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+  /* "(tree fragment)":4
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")
+ * def __setstate_cython__(self, __pyx_state):
+ *     raise TypeError("no default __reduce__ due to non-trivial __cinit__")             # <<<<<<<<<<<<<<
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+  __Pyx_GOTREF(__pyx_tuple__35);
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-  /* "tigramite/tigramite_cython_code.pyx":218
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-  /* "tigramite/tigramite_cython_code.pyx":237
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-  /* "tigramite/tigramite_cython_code.pyx":243
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-  /* "tigramite/tigramite_cython_code.pyx":283
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  *     @cython.boundscheck(False)
  *     def product_sum(self, other):             # <<<<<<<<<<<<<<
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 #if PY_MAJOR_VERSION < 3
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+#ifdef CYTHON_NO_PYINIT_EXPORT
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 #else
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+#ifdef CYTHON_NO_PYINIT_EXPORT
+#define __Pyx_PyMODINIT_FUNC PyObject *
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+#if defined(__clang__)
+    #define CYTHON_SMALL_CODE
+#elif defined(__GNUC__) && (!(defined(__cplusplus)) || (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 4)))
+    #define CYTHON_SMALL_CODE __attribute__((cold))
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+    #define CYTHON_SMALL_CODE
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+#if PY_MAJOR_VERSION < 3
+__Pyx_PyMODINIT_FUNC inittigramite_cython_code(void) CYTHON_SMALL_CODE; /*proto*/
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+#else
+__Pyx_PyMODINIT_FUNC PyInit_tigramite_cython_code(void) CYTHON_SMALL_CODE; /*proto*/
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 #endif
 {
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+__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
+                        int ndim,
+                        __Pyx_memviewslice *memviewslice,
+                        int memview_is_new_reference)
+{
+    __Pyx_RefNannyDeclarations
+    int i, retval=-1;
+    Py_buffer *buf = &memview->view;
+    __Pyx_RefNannySetupContext("init_memviewslice", 0);
+    if (!buf) {
+        PyErr_SetString(PyExc_ValueError,
+            "buf is NULL.");
+        goto fail;
+    } else if (memviewslice->memview || memviewslice->data) {
+        PyErr_SetString(PyExc_ValueError,
+            "memviewslice is already initialized!");
+        goto fail;
+    }
+    if (buf->strides) {
+        for (i = 0; i < ndim; i++) {
+            memviewslice->strides[i] = buf->strides[i];
+        }
     } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t < '9') {
-            count *= 10;
-            count += *t++ - '0';
+        Py_ssize_t stride = buf->itemsize;
+        for (i = ndim - 1; i >= 0; i--) {
+            memviewslice->strides[i] = stride;
+            stride *= buf->shape[i];
         }
     }
-    *ts = t;
-    return count;
+    for (i = 0; i < ndim; i++) {
+        memviewslice->shape[i]   = buf->shape[i];
+        if (buf->suboffsets) {
+            memviewslice->suboffsets[i] = buf->suboffsets[i];
+        } else {
+            memviewslice->suboffsets[i] = -1;
+        }
+    }
+    memviewslice->memview = memview;
+    memviewslice->data = (char *)buf->buf;
+    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
+        Py_INCREF(memview);
+    }
+    retval = 0;
+    goto no_fail;
+fail:
+    memviewslice->memview = 0;
+    memviewslice->data = 0;
+    retval = -1;
+no_fail:
+    __Pyx_RefNannyFinishContext();
+    return retval;
 }
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
+#ifndef Py_NO_RETURN
+#define Py_NO_RETURN
+#endif
+static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
+    va_list vargs;
+    char msg[200];
+#ifdef HAVE_STDARG_PROTOTYPES
+    va_start(vargs, fmt);
+#else
+    va_start(vargs);
+#endif
+    vsnprintf(msg, 200, fmt, vargs);
+    va_end(vargs);
+    Py_FatalError(msg);
 }
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
+static CYTHON_INLINE int
+__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)++;
+    PyThread_release_lock(lock);
+    return result;
 }
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
+static CYTHON_INLINE int
+__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)--;
+    PyThread_release_lock(lock);
+    return result;
 }
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
+static CYTHON_INLINE void
+__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
+{
+    int first_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (!memview || (PyObject *) memview == Py_None)
+        return;
+    if (__pyx_get_slice_count(memview) < 0)
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    first_time = __pyx_add_acquisition_count(memview) == 0;
+    if (first_time) {
+        if (have_gil) {
+            Py_INCREF((PyObject *) memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_INCREF((PyObject *) memview);
+            PyGILState_Release(_gilstate);
+        }
     }
 }
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
+static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
+                                             int have_gil, int lineno) {
+    int last_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (!memview ) {
+        return;
+    } else if ((PyObject *) memview == Py_None) {
+        memslice->memview = NULL;
+        return;
     }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
+    if (__pyx_get_slice_count(memview) <= 0)
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    last_time = __pyx_sub_acquisition_count(memview) == 1;
+    memslice->data = NULL;
+    if (last_time) {
+        if (have_gil) {
+            Py_CLEAR(memslice->memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_CLEAR(memslice->memview);
+            PyGILState_Release(_gilstate);
+        }
+    } else {
+        memslice->memview = NULL;
     }
 }
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
+
+/* BufferIndexError */
+    static void __Pyx_RaiseBufferIndexError(int axis) {
+  PyErr_Format(PyExc_IndexError,
+     "Out of bounds on buffer access (axis %d)", axis);
+}
+
+/* PyErrFetchRestore */
+    #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    tmp_type = tstate->curexc_type;
+    tmp_value = tstate->curexc_value;
+    tmp_tb = tstate->curexc_traceback;
+    tstate->curexc_type = type;
+    tstate->curexc_value = value;
+    tstate->curexc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    *type = tstate->curexc_type;
+    *value = tstate->curexc_value;
+    *tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+}
 #endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+
+/* WriteUnraisableException */
+    static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
+                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
+                                  int full_traceback, CYTHON_UNUSED int nogil) {
+    PyObject *old_exc, *old_val, *old_tb;
+    PyObject *ctx;
+    __Pyx_PyThreadState_declare
+#ifdef WITH_THREAD
+    PyGILState_STATE state;
+    if (nogil)
+        state = PyGILState_Ensure();
+#ifdef _MSC_VER
+    else state = (PyGILState_STATE)-1;
 #endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
+#endif
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
+    if (full_traceback) {
+        Py_XINCREF(old_exc);
+        Py_XINCREF(old_val);
+        Py_XINCREF(old_tb);
+        __Pyx_ErrRestore(old_exc, old_val, old_tb);
+        PyErr_PrintEx(1);
     }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
+    #if PY_MAJOR_VERSION < 3
+    ctx = PyString_FromString(name);
+    #else
+    ctx = PyUnicode_FromString(name);
+    #endif
+    __Pyx_ErrRestore(old_exc, old_val, old_tb);
+    if (!ctx) {
+        PyErr_WriteUnraisable(Py_None);
+    } else {
+        PyErr_WriteUnraisable(ctx);
+        Py_DECREF(ctx);
     }
-  }
+#ifdef WITH_THREAD
+    if (nogil)
+        PyGILState_Release(state);
+#endif
 }
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
+
+/* pyobject_as_double */
+    static double __Pyx__PyObject_AsDouble(PyObject* obj) {
+    PyObject* float_value;
+#if !CYTHON_USE_TYPE_SLOTS
+    float_value = PyNumber_Float(obj);  if (0) goto bad;
+#else
+    PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
+    if (likely(nb) && likely(nb->nb_float)) {
+        float_value = nb->nb_float(obj);
+        if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
+            PyErr_Format(PyExc_TypeError,
+                "__float__ returned non-float (type %.200s)",
+                Py_TYPE(float_value)->tp_name);
+            Py_DECREF(float_value);
+            goto bad;
+        }
+    } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
+#if PY_MAJOR_VERSION >= 3
+        float_value = PyFloat_FromString(obj);
+#else
+        float_value = PyFloat_FromString(obj, 0);
+#endif
     } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
+        PyObject* args = PyTuple_New(1);
+        if (unlikely(!args)) goto bad;
+        PyTuple_SET_ITEM(args, 0, obj);
+        float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
+        PyTuple_SET_ITEM(args, 0, 0);
+        Py_DECREF(args);
     }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
+#endif
+    if (likely(float_value)) {
+        double value = PyFloat_AS_DOUBLE(float_value);
+        Py_DECREF(float_value);
+        return value;
+    }
+bad:
+    return (double)-1;
 }
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
+
+/* PyObjectCallNoArg */
+    #if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
+#if CYTHON_FAST_PYCALL
+    if (PyFunction_Check(func)) {
+        return __Pyx_PyFunction_FastCall(func, NULL, 0);
+    }
+#endif
+#ifdef __Pyx_CyFunction_USED
+    if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) {
+#else
+    if (likely(PyCFunction_Check(func))) {
+#endif
+        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
+            return __Pyx_PyObject_CallMethO(func, NULL);
         }
     }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
+    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
+}
+#endif
+
+/* PyObjectSetAttrStr */
+      #if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_setattro))
+        return tp->tp_setattro(obj, attr_name, value);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_setattr))
+        return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
+#endif
+    return PyObject_SetAttr(obj, attr_name, value);
+}
+#endif
+
+/* GetItemInt */
+      static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+    PyObject *r;
+    if (!j) return NULL;
+    r = PyObject_GetItem(o, j);
+    Py_DECREF(j);
+    return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    Py_ssize_t wrapped_i = i;
+    if (wraparound & unlikely(i < 0)) {
+        wrapped_i += PyList_GET_SIZE(o);
     }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
+    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {
+        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
+        Py_INCREF(r);
+        return r;
     }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    Py_ssize_t wrapped_i = i;
+    if (wraparound & unlikely(i < 0)) {
+        wrapped_i += PyTuple_GET_SIZE(o);
     }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
+    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {
+        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
+        Py_INCREF(r);
+        return r;
     }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
+                                                     CYTHON_NCP_UNUSED int wraparound,
+                                                     CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
+    if (is_list || PyList_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
     }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    } else {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+                Py_ssize_t l = m->sq_length(o);
+                if (likely(l >= 0)) {
+                    i += l;
+                } else {
+                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
+                        return NULL;
+                    PyErr_Clear();
+                }
+            }
+            return m->sq_item(o, i);
         }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
     }
-  } while (ctx->enc_count);
+#else
+    if (is_list || PySequence_Check(o)) {
+        return PySequence_GetItem(o, i);
+    }
+#endif
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+/* ExtTypeTest */
+      static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (likely(__Pyx_TypeCheck(obj, type)))
+        return 1;
+    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+                 Py_TYPE(obj)->tp_name, type->tp_name);
+    return 0;
+}
+
+/* IsLittleEndian */
+      static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
+{
+  union {
+    uint32_t u32;
+    uint8_t u8[4];
+  } S;
+  S.u32 = 0x01020304;
+  return S.u8[0] == 4;
+}
+
+/* BufferFormatCheck */
+      static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type) {
+  stack[0].field = &ctx->root;
+  stack[0].parent_offset = 0;
+  ctx->root.type = type;
+  ctx->root.name = "buffer dtype";
+  ctx->root.offset = 0;
+  ctx->head = stack;
+  ctx->head->field = &ctx->root;
+  ctx->fmt_offset = 0;
+  ctx->head->parent_offset = 0;
+  ctx->new_packmode = '@';
+  ctx->enc_packmode = '@';
+  ctx->new_count = 1;
+  ctx->enc_count = 0;
   ctx->enc_type = 0;
   ctx->is_complex = 0;
-  return 0;
+  ctx->is_valid_array = 0;
+  ctx->struct_alignment = 0;
+  while (type->typegroup == 'S') {
+    ++ctx->head;
+    ctx->head->field = type->fields;
+    ctx->head->parent_offset = 0;
+    type = type->fields->type;
+  }
 }
-static CYTHON_INLINE PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number;
-    int ndim = ctx->head->field->type->ndim;
-;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
+static int __Pyx_BufFmt_ParseNumber(const char** ts) {
+    int count;
+    const char* t = *ts;
+    if (*t < '0' || *t > '9') {
+      return -1;
+    } else {
+        count = *t++ - '0';
+        while (*t >= '0' && *t < '9') {
+            count *= 10;
+            count += *t++ - '0';
         }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
     }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
+    *ts = t;
+    return count;
+}
+static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
+    int number = __Pyx_BufFmt_ParseNumber(ts);
+    if (number == -1)
+        PyErr_Format(PyExc_ValueError,\
+                     "Does not understand character buffer dtype format string ('%c')", **ts);
+    return number;
+}
+static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
+  PyErr_Format(PyExc_ValueError,
+               "Unexpected format string character: '%c'", ch);
+}
+static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': return "'char'";
+    case 'b': return "'signed char'";
+    case 'B': return "'unsigned char'";
+    case 'h': return "'short'";
+    case 'H': return "'unsigned short'";
+    case 'i': return "'int'";
+    case 'I': return "'unsigned int'";
+    case 'l': return "'long'";
+    case 'L': return "'unsigned long'";
+    case 'q': return "'long long'";
+    case 'Q': return "'unsigned long long'";
+    case 'f': return (is_complex ? "'complex float'" : "'float'");
+    case 'd': return (is_complex ? "'complex double'" : "'double'");
+    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
+    case 'T': return "a struct";
+    case 'O': return "Python object";
+    case 'P': return "a pointer";
+    case 's': case 'p': return "a string";
+    case 0: return "end";
+    default: return "unparseable format string";
+  }
+}
+static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return 2;
+    case 'i': case 'I': case 'l': case 'L': return 4;
+    case 'q': case 'Q': return 8;
+    case 'f': return (is_complex ? 8 : 4);
+    case 'd': return (is_complex ? 16 : 8);
+    case 'g': {
+      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
+      return 0;
+    }
+    case 'O': case 'P': return sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
     }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
 }
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_IsLittleEndian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_IsLittleEndian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
-            ctx->enc_packmode == ctx->new_packmode) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
+static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(short);
+    case 'i': case 'I': return sizeof(int);
+    case 'l': case 'L': return sizeof(long);
+    #ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
+    #endif
+    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
+    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
+    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
+    case 'O': case 'P': return sizeof(void*);
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
     }
   }
 }
-static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
-  buf->buf = NULL;
-  buf->obj = NULL;
-  buf->strides = __Pyx_zeros;
-  buf->shape = __Pyx_zeros;
-  buf->suboffsets = __Pyx_minusones;
-}
-static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
-        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
-        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
-{
-  if (obj == Py_None || obj == NULL) {
-    __Pyx_ZeroBuffer(buf);
-    return 0;
-  }
-  buf->buf = NULL;
-  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
-  if (buf->ndim != nd) {
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer has wrong number of dimensions (expected %d, got %d)",
-                 nd, buf->ndim);
-    goto fail;
-  }
-  if (!cast) {
-    __Pyx_BufFmt_Context ctx;
-    __Pyx_BufFmt_Init(&ctx, stack, dtype);
-    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
-  }
-  if ((unsigned)buf->itemsize != dtype->size) {
-    PyErr_Format(PyExc_ValueError,
-      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
-      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
-      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
-    goto fail;
-  }
-  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
-  return 0;
-fail:;
-  __Pyx_ZeroBuffer(buf);
-  return -1;
-}
-static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
-  if (info->buf == NULL) return;
-  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
-  __Pyx_ReleaseBuffer(info);
-}
-
-/* MemviewSliceInit */
-      static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (!buf) {
-        PyErr_SetString(PyExc_ValueError,
-            "buf is NULL.");
-        goto fail;
-    } else if (memviewslice->memview || memviewslice->data) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
+typedef struct { char c; short x; } __Pyx_st_short;
+typedef struct { char c; int x; } __Pyx_st_int;
+typedef struct { char c; long x; } __Pyx_st_long;
+typedef struct { char c; float x; } __Pyx_st_float;
+typedef struct { char c; double x; } __Pyx_st_double;
+typedef struct { char c; long double x; } __Pyx_st_longdouble;
+typedef struct { char c; void *x; } __Pyx_st_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
     }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
 }
-static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) {
-    va_list vargs;
-    char msg[200];
-#ifdef HAVE_STDARG_PROTOTYPES
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
+/* These are for computing the padding at the end of the struct to align
+   on the first member of the struct. This will probably the same as above,
+   but we don't have any guarantees.
+ */
+typedef struct { short x; char c; } __Pyx_pad_short;
+typedef struct { int x; char c; } __Pyx_pad_int;
+typedef struct { long x; char c; } __Pyx_pad_long;
+typedef struct { float x; char c; } __Pyx_pad_float;
+typedef struct { double x; char c; } __Pyx_pad_double;
+typedef struct { long double x; char c; } __Pyx_pad_longdouble;
+typedef struct { void *x; char c; } __Pyx_pad_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
 #endif
-    vsnprintf(msg, 200, fmt, vargs);
-    Py_FatalError(msg);
-    va_end(vargs);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    int first_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (!memview || (PyObject *) memview == Py_None)
-        return;
-    if (__pyx_get_slice_count(memview) < 0)
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    first_time = __pyx_add_acquisition_count(memview) == 0;
-    if (first_time) {
-        if (have_gil) {
-            Py_INCREF((PyObject *) memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_INCREF((PyObject *) memview);
-            PyGILState_Release(_gilstate);
-        }
+static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
     }
 }
-static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    int last_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (!memview ) {
-        return;
-    } else if ((PyObject *) memview == Py_None) {
-        memslice->memview = NULL;
-        return;
-    }
-    if (__pyx_get_slice_count(memview) <= 0)
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    last_time = __pyx_sub_acquisition_count(memview) == 1;
-    memslice->data = NULL;
-    if (last_time) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        memslice->memview = NULL;
+static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
+  switch (ch) {
+    case 'c':
+        return 'H';
+    case 'b': case 'h': case 'i':
+    case 'l': case 'q': case 's': case 'p':
+        return 'I';
+    case 'B': case 'H': case 'I': case 'L': case 'Q':
+        return 'U';
+    case 'f': case 'd': case 'g':
+        return (is_complex ? 'C' : 'R');
+    case 'O':
+        return 'O';
+    case 'P':
+        return 'P';
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
     }
+  }
 }
-
-/* BufferIndexError */
-      static void __Pyx_RaiseBufferIndexError(int axis) {
-  PyErr_Format(PyExc_IndexError,
-     "Out of bounds on buffer access (axis %d)", axis);
-}
-
-/* PyErrFetchRestore */
-      #if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-#endif
-
-/* WriteUnraisableException */
-      static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
-                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
-                                  int full_traceback, CYTHON_UNUSED int nogil) {
-    PyObject *old_exc, *old_val, *old_tb;
-    PyObject *ctx;
-    __Pyx_PyThreadState_declare
-#ifdef WITH_THREAD
-    PyGILState_STATE state;
-    if (nogil)
-        state = PyGILState_Ensure();
-#ifdef _MSC_VER
-    else state = (PyGILState_STATE)-1;
-#endif
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
-    if (full_traceback) {
-        Py_XINCREF(old_exc);
-        Py_XINCREF(old_val);
-        Py_XINCREF(old_tb);
-        __Pyx_ErrRestore(old_exc, old_val, old_tb);
-        PyErr_PrintEx(1);
-    }
-    #if PY_MAJOR_VERSION < 3
-    ctx = PyString_FromString(name);
-    #else
-    ctx = PyUnicode_FromString(name);
-    #endif
-    __Pyx_ErrRestore(old_exc, old_val, old_tb);
-    if (!ctx) {
-        PyErr_WriteUnraisable(Py_None);
+static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
+  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
+    const char* expected;
+    const char* quote;
+    if (ctx->head == NULL) {
+      expected = "end";
+      quote = "";
     } else {
-        PyErr_WriteUnraisable(ctx);
-        Py_DECREF(ctx);
+      expected = ctx->head->field->type->name;
+      quote = "'";
     }
-#ifdef WITH_THREAD
-    if (nogil)
-        PyGILState_Release(state);
-#endif
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected %s%s%s but got %s",
+                 quote, expected, quote,
+                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
+  } else {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_StructField* parent = (ctx->head - 1)->field;
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
+                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
+                 parent->type->name, field->name);
+  }
 }
-
-/* pyobject_as_double */
-      static double __Pyx__PyObject_AsDouble(PyObject* obj) {
-    PyObject* float_value;
-#if !CYTHON_USE_TYPE_SLOTS
-    float_value = PyNumber_Float(obj);  if (0) goto bad;
-#else
-    PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
-    if (likely(nb) && likely(nb->nb_float)) {
-        float_value = nb->nb_float(obj);
-        if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
-            PyErr_Format(PyExc_TypeError,
-                "__float__ returned non-float (type %.200s)",
-                Py_TYPE(float_value)->tp_name);
-            Py_DECREF(float_value);
-            goto bad;
+static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
+  char group;
+  size_t size, offset, arraysize = 1;
+  if (ctx->enc_type == 0) return 0;
+  if (ctx->head->field->type->arraysize[0]) {
+    int i, ndim = 0;
+    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
+        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
+        ndim = 1;
+        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
+            PyErr_Format(PyExc_ValueError,
+                         "Expected a dimension of size %zu, got %zu",
+                         ctx->head->field->type->arraysize[0], ctx->enc_count);
+            return -1;
         }
-    } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
-#if PY_MAJOR_VERSION >= 3
-        float_value = PyFloat_FromString(obj);
-#else
-        float_value = PyFloat_FromString(obj, 0);
-#endif
-    } else {
-        PyObject* args = PyTuple_New(1);
-        if (unlikely(!args)) goto bad;
-        PyTuple_SET_ITEM(args, 0, obj);
-        float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
-        PyTuple_SET_ITEM(args, 0, 0);
-        Py_DECREF(args);
     }
-#endif
-    if (likely(float_value)) {
-        double value = PyFloat_AS_DOUBLE(float_value);
-        Py_DECREF(float_value);
-        return value;
+    if (!ctx->is_valid_array) {
+      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
+                   ctx->head->field->type->ndim, ndim);
+      return -1;
+    }
+    for (i = 0; i < ctx->head->field->type->ndim; i++) {
+      arraysize *= ctx->head->field->type->arraysize[i];
+    }
+    ctx->is_valid_array = 0;
+    ctx->enc_count = 1;
+  }
+  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
+  do {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_TypeInfo* type = field->type;
+    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
+      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
+    } else {
+      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    }
+    if (ctx->enc_packmode == '@') {
+      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
+      size_t align_mod_offset;
+      if (align_at == 0) return -1;
+      align_mod_offset = ctx->fmt_offset % align_at;
+      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
+      if (ctx->struct_alignment == 0)
+          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
+                                                                 ctx->is_complex);
     }
-bad:
-    return (double)-1;
-}
-
-/* PyObjectCallNoArg */
-      #if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, NULL, 0);
+    if (type->size != size || type->typegroup != group) {
+      if (type->typegroup == 'C' && type->fields != NULL) {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        ++ctx->head;
+        ctx->head->field = type->fields;
+        ctx->head->parent_offset = parent_offset;
+        continue;
+      }
+      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
+      } else {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+      }
     }
-#endif
-#ifdef __Pyx_CyFunction_USED
-    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
-#else
-    if (likely(PyCFunction_Check(func))) {
-#endif
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-            return __Pyx_PyObject_CallMethO(func, NULL);
+    offset = ctx->head->parent_offset + field->offset;
+    if (ctx->fmt_offset != offset) {
+      PyErr_Format(PyExc_ValueError,
+                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
+                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
+      return -1;
+    }
+    ctx->fmt_offset += size;
+    if (arraysize)
+      ctx->fmt_offset += (arraysize - 1) * size;
+    --ctx->enc_count;
+    while (1) {
+      if (field == &ctx->root) {
+        ctx->head = NULL;
+        if (ctx->enc_count != 0) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
         }
+        break;
+      }
+      ctx->head->field = ++field;
+      if (field->type == NULL) {
+        --ctx->head;
+        field = ctx->head->field;
+        continue;
+      } else if (field->type->typegroup == 'S') {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        if (field->type->fields->type == NULL) continue;
+        field = field->type->fields;
+        ++ctx->head;
+        ctx->head->field = field;
+        ctx->head->parent_offset = parent_offset;
+        break;
+      } else {
+        break;
+      }
     }
-    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
-}
-#endif
-
-/* GetItemInt */
-        static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
+  } while (ctx->enc_count);
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  return 0;
 }
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
+static PyObject *
+__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
+{
+    const char *ts = *tsp;
+    int i = 0, number;
+    int ndim = ctx->head->field->type->ndim;
+;
+    ++ts;
+    if (ctx->new_count != 1) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Cannot handle repeated arrays in format string");
+        return NULL;
     }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    while (*ts && *ts != ')') {
+        switch (*ts) {
+            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
+            default:  break;
+        }
+        number = __Pyx_BufFmt_ExpectNumber(&ts);
+        if (number == -1) return NULL;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
+            return PyErr_Format(PyExc_ValueError,
+                        "Expected a dimension of size %zu, got %d",
+                        ctx->head->field->type->arraysize[i], number);
+        if (*ts != ',' && *ts != ')')
+            return PyErr_Format(PyExc_ValueError,
+                                "Expected a comma in format string, got '%c'", *ts);
+        if (*ts == ',') ts++;
+        i++;
     }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
+    if (i != ndim)
+        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+                            ctx->head->field->type->ndim, i);
+    if (!*ts) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Unexpected end of format string, expected ')'");
+        return NULL;
+    }
+    ctx->is_valid_array = 1;
+    ctx->new_count = 1;
+    *tsp = ++ts;
+    return Py_None;
 }
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
+  int got_Z = 0;
+  while (1) {
+    switch(*ts) {
+      case 0:
+        if (ctx->enc_type != 0 && ctx->head == NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        if (ctx->head != NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        return ts;
+      case ' ':
+      case '\r':
+      case '\n':
+        ++ts;
+        break;
+      case '<':
+        if (!__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '>':
+      case '!':
+        if (__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '=':
+      case '@':
+      case '^':
+        ctx->new_packmode = *ts++;
+        break;
+      case 'T':
+        {
+          const char* ts_after_sub;
+          size_t i, struct_count = ctx->new_count;
+          size_t struct_alignment = ctx->struct_alignment;
+          ctx->new_count = 1;
+          ++ts;
+          if (*ts != '{') {
+            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
+            return NULL;
+          }
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          ctx->enc_count = 0;
+          ctx->struct_alignment = 0;
+          ++ts;
+          ts_after_sub = ts;
+          for (i = 0; i != struct_count; ++i) {
+            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
+            if (!ts_after_sub) return NULL;
+          }
+          ts = ts_after_sub;
+          if (struct_alignment) ctx->struct_alignment = struct_alignment;
+        }
+        break;
+      case '}':
+        {
+          size_t alignment = ctx->struct_alignment;
+          ++ts;
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          if (alignment && ctx->fmt_offset % alignment) {
+            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
+          }
+        }
+        return ts;
+      case 'x':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->fmt_offset += ctx->new_count;
+        ctx->new_count = 1;
+        ctx->enc_count = 0;
+        ctx->enc_type = 0;
+        ctx->enc_packmode = ctx->new_packmode;
+        ++ts;
+        break;
+      case 'Z':
+        got_Z = 1;
+        ++ts;
+        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
+          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
+          return NULL;
         }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
+        CYTHON_FALLTHROUGH;
+      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
+      case 'l': case 'L': case 'q': case 'Q':
+      case 'f': case 'd': case 'g':
+      case 'O': case 'p':
+        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
+            ctx->enc_packmode == ctx->new_packmode) {
+          ctx->enc_count += ctx->new_count;
+          ctx->new_count = 1;
+          got_Z = 0;
+          ++ts;
+          break;
         }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
+        CYTHON_FALLTHROUGH;
+      case 's':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->enc_count = ctx->new_count;
+        ctx->enc_packmode = ctx->new_packmode;
+        ctx->enc_type = *ts;
+        ctx->is_complex = got_Z;
+        ++ts;
+        ctx->new_count = 1;
+        got_Z = 0;
+        break;
+      case ':':
+        ++ts;
+        while(*ts != ':') ++ts;
+        ++ts;
+        break;
+      case '(':
+        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        break;
+      default:
+        {
+          int number = __Pyx_BufFmt_ExpectNumber(&ts);
+          if (number == -1) return NULL;
+          ctx->new_count = (size_t)number;
         }
     }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+  }
 }
 
-/* ExtTypeTest */
-        static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(PyObject_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
+/* BufferGetAndValidate */
+        static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
+  if (unlikely(info->buf == NULL)) return;
+  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
+  __Pyx_ReleaseBuffer(info);
+}
+static void __Pyx_ZeroBuffer(Py_buffer* buf) {
+  buf->buf = NULL;
+  buf->obj = NULL;
+  buf->strides = __Pyx_zeros;
+  buf->shape = __Pyx_zeros;
+  buf->suboffsets = __Pyx_minusones;
+}
+static int __Pyx__GetBufferAndValidate(
+        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
+        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
+{
+  buf->buf = NULL;
+  if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {
+    __Pyx_ZeroBuffer(buf);
+    return -1;
+  }
+  if (unlikely(buf->ndim != nd)) {
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer has wrong number of dimensions (expected %d, got %d)",
+                 nd, buf->ndim);
+    goto fail;
+  }
+  if (!cast) {
+    __Pyx_BufFmt_Context ctx;
+    __Pyx_BufFmt_Init(&ctx, stack, dtype);
+    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
+  }
+  if (unlikely((unsigned)buf->itemsize != dtype->size)) {
+    PyErr_Format(PyExc_ValueError,
+      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
+      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
+      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
+    goto fail;
+  }
+  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
+  return 0;
+fail:;
+  __Pyx_SafeReleaseBuffer(buf);
+  return -1;
 }
 
 /* RaiseException */
@@ -23652,11 +25675,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
             "raise: exception class must be a subclass of BaseException");
         goto bad;
     }
-#if PY_VERSION_HEX >= 0x03030000
     if (cause) {
-#else
-    if (cause && cause != Py_None) {
-#endif
         PyObject *fixed_cause;
         if (cause == Py_None) {
             fixed_cause = NULL;
@@ -23684,7 +25703,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
         PyErr_Restore(tmp_type, tmp_value, tb);
         Py_XDECREF(tmp_tb);
 #else
-        PyThreadState *tstate = PyThreadState_GET();
+        PyThreadState *tstate = __Pyx_PyThreadState_Current;
         PyObject* tmp_tb = tstate->curexc_traceback;
         if (tb != tmp_tb) {
             Py_INCREF(tb);
@@ -23699,42 +25718,76 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
 }
 #endif
 
+/* DictGetItem */
+        #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
+static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
+    PyObject *value;
+    value = PyDict_GetItemWithError(d, key);
+    if (unlikely(!value)) {
+        if (!PyErr_Occurred()) {
+            PyObject* args = PyTuple_Pack(1, key);
+            if (likely(args))
+                PyErr_SetObject(PyExc_KeyError, args);
+            Py_XDECREF(args);
+        }
+        return NULL;
+    }
+    Py_INCREF(value);
+    return value;
+}
+#endif
+
 /* RaiseTooManyValuesToUnpack */
-          static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+        static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
     PyErr_Format(PyExc_ValueError,
                  "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
 }
 
 /* RaiseNeedMoreValuesToUnpack */
-          static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+        static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
     PyErr_Format(PyExc_ValueError,
                  "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
                  index, (index == 1) ? "" : "s");
 }
 
 /* RaiseNoneIterError */
-          static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
+        static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
     PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
 }
 
 /* SaveResetException */
-          #if CYTHON_FAST_THREAD_STATE
+        #if CYTHON_FAST_THREAD_STATE
 static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    #if PY_VERSION_HEX >= 0x030700A3
+    *type = tstate->exc_state.exc_type;
+    *value = tstate->exc_state.exc_value;
+    *tb = tstate->exc_state.exc_traceback;
+    #else
     *type = tstate->exc_type;
     *value = tstate->exc_value;
     *tb = tstate->exc_traceback;
+    #endif
     Py_XINCREF(*type);
     Py_XINCREF(*value);
     Py_XINCREF(*tb);
 }
 static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
     PyObject *tmp_type, *tmp_value, *tmp_tb;
+    #if PY_VERSION_HEX >= 0x030700A3
+    tmp_type = tstate->exc_state.exc_type;
+    tmp_value = tstate->exc_state.exc_value;
+    tmp_tb = tstate->exc_state.exc_traceback;
+    tstate->exc_state.exc_type = type;
+    tstate->exc_state.exc_value = value;
+    tstate->exc_state.exc_traceback = tb;
+    #else
     tmp_type = tstate->exc_type;
     tmp_value = tstate->exc_value;
     tmp_tb = tstate->exc_traceback;
     tstate->exc_type = type;
     tstate->exc_value = value;
     tstate->exc_traceback = tb;
+    #endif
     Py_XDECREF(tmp_type);
     Py_XDECREF(tmp_value);
     Py_XDECREF(tmp_tb);
@@ -23742,17 +25795,32 @@ static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject
 #endif
 
 /* PyErrExceptionMatches */
-          #if CYTHON_FAST_THREAD_STATE
+        #if CYTHON_FAST_THREAD_STATE
+static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
+    }
+    return 0;
+}
 static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
     PyObject *exc_type = tstate->curexc_type;
     if (exc_type == err) return 1;
     if (unlikely(!exc_type)) return 0;
-    return PyErr_GivenExceptionMatches(exc_type, err);
+    if (unlikely(PyTuple_Check(err)))
+        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
+    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
 }
 #endif
 
 /* GetException */
-          #if CYTHON_FAST_THREAD_STATE
+        #if CYTHON_FAST_THREAD_STATE
 static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
 #else
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
@@ -23789,12 +25857,21 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
     *value = local_value;
     *tb = local_tb;
 #if CYTHON_FAST_THREAD_STATE
+    #if PY_VERSION_HEX >= 0x030700A3
+    tmp_type = tstate->exc_state.exc_type;
+    tmp_value = tstate->exc_state.exc_value;
+    tmp_tb = tstate->exc_state.exc_traceback;
+    tstate->exc_state.exc_type = local_type;
+    tstate->exc_state.exc_value = local_value;
+    tstate->exc_state.exc_traceback = local_tb;
+    #else
     tmp_type = tstate->exc_type;
     tmp_value = tstate->exc_value;
     tmp_tb = tstate->exc_traceback;
     tstate->exc_type = local_type;
     tstate->exc_value = local_value;
     tstate->exc_traceback = local_tb;
+    #endif
     Py_XDECREF(tmp_type);
     Py_XDECREF(tmp_value);
     Py_XDECREF(tmp_tb);
@@ -23813,34 +25890,28 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
 }
 
 /* ArgTypeTest */
-            static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
-        name, type->tp_name, Py_TYPE(obj)->tp_name);
-}
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
-    const char *name, int exact)
+          static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
 {
     if (unlikely(!type)) {
         PyErr_SetString(PyExc_SystemError, "Missing type object");
         return 0;
     }
-    if (none_allowed && obj == Py_None) return 1;
     else if (exact) {
-        if (likely(Py_TYPE(obj) == type)) return 1;
         #if PY_MAJOR_VERSION == 2
-        else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
         #endif
     }
     else {
-        if (likely(PyObject_TypeCheck(obj, type))) return 1;
+        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
     }
-    __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
+    PyErr_Format(PyExc_TypeError,
+        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+        name, type->tp_name, Py_TYPE(obj)->tp_name);
     return 0;
 }
 
 /* BytesEquals */
-            static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+          static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
 #if CYTHON_COMPILING_IN_PYPY
     return PyObject_RichCompareBool(s1, s2, equals);
 #else
@@ -23858,7 +25929,16 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
         } else if (length == 1) {
             return (equals == Py_EQ);
         } else {
-            int result = memcmp(ps1, ps2, (size_t)length);
+            int result;
+#if CYTHON_USE_UNICODE_INTERNALS
+            Py_hash_t hash1, hash2;
+            hash1 = ((PyBytesObject*)s1)->ob_shash;
+            hash2 = ((PyBytesObject*)s2)->ob_shash;
+            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
+                return (equals == Py_NE);
+            }
+#endif
+            result = memcmp(ps1, ps2, (size_t)length);
             return (equals == Py_EQ) ? (result == 0) : (result != 0);
         }
     } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
@@ -23878,7 +25958,7 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
 }
 
 /* UnicodeEquals */
-            static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+          static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
 #if CYTHON_COMPILING_IN_PYPY
     return PyObject_RichCompareBool(s1, s2, equals);
 #else
@@ -23918,6 +25998,21 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
         if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
             goto return_ne;
         }
+#if CYTHON_USE_UNICODE_INTERNALS
+        {
+            Py_hash_t hash1, hash2;
+        #if CYTHON_PEP393_ENABLED
+            hash1 = ((PyASCIIObject*)s1)->hash;
+            hash2 = ((PyASCIIObject*)s2)->hash;
+        #else
+            hash1 = ((PyUnicodeObject*)s1)->hash;
+            hash2 = ((PyUnicodeObject*)s2)->hash;
+        #endif
+            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
+                goto return_ne;
+            }
+        }
+#endif
         kind = __Pyx_PyUnicode_KIND(s1);
         if (kind != __Pyx_PyUnicode_KIND(s2)) {
             goto return_ne;
@@ -23942,6 +26037,9 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
     } else {
         int result;
         PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        #if PY_MAJOR_VERSION < 3
+        Py_XDECREF(owned_ref);
+        #endif
         if (!py_result)
             return -1;
         result = __Pyx_PyObject_IsTrue(py_result);
@@ -23962,7 +26060,7 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
 }
 
 /* None */
-            static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
+          static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
     Py_ssize_t q = a / b;
     Py_ssize_t r = a - q*b;
     q -= ((r != 0) & ((r ^ b) < 0));
@@ -23970,8 +26068,8 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
 }
 
 /* GetAttr */
-            static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_COMPILING_IN_CPYTHON
+          static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
+#if CYTHON_USE_TYPE_SLOTS
 #if PY_MAJOR_VERSION >= 3
     if (likely(PyUnicode_Check(n)))
 #else
@@ -23982,8 +26080,37 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
     return PyObject_GetAttr(o, n);
 }
 
+/* ObjectGetItem */
+          #if CYTHON_USE_TYPE_SLOTS
+static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
+    PyObject *runerr;
+    Py_ssize_t key_value;
+    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
+    if (unlikely(!(m && m->sq_item))) {
+        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
+        return NULL;
+    }
+    key_value = __Pyx_PyIndex_AsSsize_t(index);
+    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
+        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
+    }
+    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
+        PyErr_Clear();
+        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
+    }
+    return NULL;
+}
+static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
+    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
+    if (likely(m && m->mp_subscript)) {
+        return m->mp_subscript(obj, key);
+    }
+    return __Pyx_PyObject_GetIndex(obj, key);
+}
+#endif
+
 /* decode_c_string */
-            static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+          static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
          const char* cstring, Py_ssize_t start, Py_ssize_t stop,
          const char* encoding, const char* errors,
          PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
@@ -24015,16 +26142,40 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
     }
 }
 
+/* GetAttr3 */
+          static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
+        return NULL;
+    __Pyx_PyErr_Clear();
+    Py_INCREF(d);
+    return d;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
+    PyObject *r = __Pyx_GetAttr(o, n);
+    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
+}
+
 /* SwapException */
-            #if CYTHON_FAST_THREAD_STATE
+          #if CYTHON_FAST_THREAD_STATE
 static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
     PyObject *tmp_type, *tmp_value, *tmp_tb;
+    #if PY_VERSION_HEX >= 0x030700A3
+    tmp_type = tstate->exc_state.exc_type;
+    tmp_value = tstate->exc_state.exc_value;
+    tmp_tb = tstate->exc_state.exc_traceback;
+    tstate->exc_state.exc_type = *type;
+    tstate->exc_state.exc_value = *value;
+    tstate->exc_state.exc_traceback = *tb;
+    #else
     tmp_type = tstate->exc_type;
     tmp_value = tstate->exc_value;
     tmp_tb = tstate->exc_traceback;
     tstate->exc_type = *type;
     tstate->exc_value = *value;
     tstate->exc_traceback = *tb;
+    #endif
     *type = tmp_type;
     *value = tmp_value;
     *tb = tmp_tb;
@@ -24041,13 +26192,13 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
 #endif
 
 /* Import */
-            static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
+          static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
     PyObject *empty_list = 0;
     PyObject *module = 0;
     PyObject *global_dict = 0;
     PyObject *empty_dict = 0;
     PyObject *list;
-    #if PY_VERSION_HEX < 0x03030000
+    #if PY_MAJOR_VERSION < 3
     PyObject *py_import;
     py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
     if (!py_import)
@@ -24071,17 +26222,8 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
         #if PY_MAJOR_VERSION >= 3
         if (level == -1) {
             if (strchr(__Pyx_MODULE_NAME, '.')) {
-                #if PY_VERSION_HEX < 0x03030000
-                PyObject *py_level = PyInt_FromLong(1);
-                if (!py_level)
-                    goto bad;
-                module = PyObject_CallFunctionObjArgs(py_import,
-                    name, global_dict, empty_dict, list, py_level, NULL);
-                Py_DECREF(py_level);
-                #else
                 module = PyImport_ImportModuleLevelObject(
                     name, global_dict, empty_dict, list, 1);
-                #endif
                 if (!module) {
                     if (!PyErr_ExceptionMatches(PyExc_ImportError))
                         goto bad;
@@ -24092,7 +26234,7 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
         }
         #endif
         if (!module) {
-            #if PY_VERSION_HEX < 0x03030000
+            #if PY_MAJOR_VERSION < 3
             PyObject *py_level = PyInt_FromLong(level);
             if (!py_level)
                 goto bad;
@@ -24106,7 +26248,7 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
         }
     }
 bad:
-    #if PY_VERSION_HEX < 0x03030000
+    #if PY_MAJOR_VERSION < 3
     Py_XDECREF(py_import);
     #endif
     Py_XDECREF(empty_list);
@@ -24114,8 +26256,108 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value,
     return module;
 }
 
+/* FastTypeChecks */
+          #if CYTHON_COMPILING_IN_CPYTHON
+static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
+    while (a) {
+        a = a->tp_base;
+        if (a == b)
+            return 1;
+    }
+    return b == &PyBaseObject_Type;
+}
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
+    PyObject *mro;
+    if (a == b) return 1;
+    mro = a->tp_mro;
+    if (likely(mro)) {
+        Py_ssize_t i, n;
+        n = PyTuple_GET_SIZE(mro);
+        for (i = 0; i < n; i++) {
+            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
+                return 1;
+        }
+        return 0;
+    }
+    return __Pyx_InBases(a, b);
+}
+#if PY_MAJOR_VERSION == 2
+static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
+    PyObject *exception, *value, *tb;
+    int res;
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrFetch(&exception, &value, &tb);
+    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
+    if (unlikely(res == -1)) {
+        PyErr_WriteUnraisable(err);
+        res = 0;
+    }
+    if (!res) {
+        res = PyObject_IsSubclass(err, exc_type2);
+        if (unlikely(res == -1)) {
+            PyErr_WriteUnraisable(err);
+            res = 0;
+        }
+    }
+    __Pyx_ErrRestore(exception, value, tb);
+    return res;
+}
+#else
+static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
+    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
+    if (!res) {
+        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
+    }
+    return res;
+}
+#endif
+static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    assert(PyExceptionClass_Check(exc_type));
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        PyObject *t = PyTuple_GET_ITEM(tuple, i);
+        #if PY_MAJOR_VERSION < 3
+        if (likely(exc_type == t)) return 1;
+        #endif
+        if (likely(PyExceptionClass_Check(t))) {
+            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
+        } else {
+        }
+    }
+    return 0;
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
+    if (likely(err == exc_type)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        if (likely(PyExceptionClass_Check(exc_type))) {
+            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
+        } else if (likely(PyTuple_Check(exc_type))) {
+            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
+        } else {
+        }
+    }
+    return PyErr_GivenExceptionMatches(err, exc_type);
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
+    assert(PyExceptionClass_Check(exc_type1));
+    assert(PyExceptionClass_Check(exc_type2));
+    if (likely(err == exc_type1 || err == exc_type2)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
+    }
+    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
+}
+#endif
+
 /* PyIntBinop */
-            #if !CYTHON_COMPILING_IN_PYPY
+          #if !CYTHON_COMPILING_IN_PYPY
 static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
     #if PY_MAJOR_VERSION < 3
     if (likely(PyInt_CheckExact(op1))) {
@@ -24153,6 +26395,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 case 2:
                     if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                         a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
@@ -24163,6 +26406,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 case -3:
                     if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                         a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
@@ -24173,6 +26417,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 case 3:
                     if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                         a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
@@ -24183,6 +26428,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 case -4:
                     if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                         a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
@@ -24193,6 +26439,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 case 4:
                     if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                         a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
@@ -24203,6 +26450,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
                         goto long_long;
 #endif
                     }
+                    CYTHON_FALLTHROUGH;
                 default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
             }
         }
@@ -24226,25 +26474,107 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
             PyFPE_END_PROTECT(result)
             return PyFloat_FromDouble(result);
     }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
+    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
+}
+#endif
+
+/* None */
+          static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
+    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
+}
+
+/* None */
+          static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
+    long q = a / b;
+    long r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+/* ImportFrom */
+          static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
+    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
+    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
+        PyErr_Format(PyExc_ImportError,
+        #if PY_MAJOR_VERSION < 3
+            "cannot import name %.230s", PyString_AS_STRING(name));
+        #else
+            "cannot import name %S", name);
+        #endif
+    }
+    return value;
+}
+
+/* HasAttr */
+          static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
+    PyObject *r;
+    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
+        PyErr_SetString(PyExc_TypeError,
+                        "hasattr(): attribute name must be string");
+        return -1;
+    }
+    r = __Pyx_GetAttr(o, n);
+    if (unlikely(!r)) {
+        PyErr_Clear();
+        return 0;
+    } else {
+        Py_DECREF(r);
+        return 1;
+    }
+}
+
+/* PyObject_GenericGetAttrNoDict */
+          #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
+    PyErr_Format(PyExc_AttributeError,
+#if PY_MAJOR_VERSION >= 3
+                 "'%.50s' object has no attribute '%U'",
+                 tp->tp_name, attr_name);
+#else
+                 "'%.50s' object has no attribute '%.400s'",
+                 tp->tp_name, PyString_AS_STRING(attr_name));
+#endif
+    return NULL;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
+    PyObject *descr;
+    PyTypeObject *tp = Py_TYPE(obj);
+    if (unlikely(!PyString_Check(attr_name))) {
+        return PyObject_GenericGetAttr(obj, attr_name);
+    }
+    assert(!tp->tp_dictoffset);
+    descr = _PyType_Lookup(tp, attr_name);
+    if (unlikely(!descr)) {
+        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
+    }
+    Py_INCREF(descr);
+    #if PY_MAJOR_VERSION < 3
+    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
+    #endif
+    {
+        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
+        if (unlikely(f)) {
+            PyObject *res = f(descr, obj, (PyObject *)tp);
+            Py_DECREF(descr);
+            return res;
+        }
+    }
+    return descr;
 }
 #endif
 
-/* None */
-            static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* None */
-            static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
+/* PyObject_GenericGetAttr */
+          #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
+    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
+        return PyObject_GenericGetAttr(obj, attr_name);
+    }
+    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
 }
+#endif
 
 /* SetVTable */
-            static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
+          static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
 #if PY_VERSION_HEX >= 0x02070000
     PyObject *ob = PyCapsule_New(vtable, 0, 0);
 #else
@@ -24261,8 +26591,84 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
     return -1;
 }
 
+/* SetupReduce */
+          static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
+  int ret;
+  PyObject *name_attr;
+  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
+  if (likely(name_attr)) {
+      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
+  } else {
+      ret = -1;
+  }
+  if (unlikely(ret < 0)) {
+      PyErr_Clear();
+      ret = 0;
+  }
+  Py_XDECREF(name_attr);
+  return ret;
+}
+static int __Pyx_setup_reduce(PyObject* type_obj) {
+    int ret = 0;
+    PyObject *object_reduce = NULL;
+    PyObject *object_reduce_ex = NULL;
+    PyObject *reduce = NULL;
+    PyObject *reduce_ex = NULL;
+    PyObject *reduce_cython = NULL;
+    PyObject *setstate = NULL;
+    PyObject *setstate_cython = NULL;
+#if CYTHON_USE_PYTYPE_LOOKUP
+    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD;
+#else
+    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD;
+#endif
+#if CYTHON_USE_PYTYPE_LOOKUP
+    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
+#else
+    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
+#endif
+    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD;
+    if (reduce_ex == object_reduce_ex) {
+#if CYTHON_USE_PYTYPE_LOOKUP
+        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
+#else
+        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
+#endif
+        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD;
+        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
+            reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD;
+            ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD;
+            ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD;
+            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
+            if (!setstate) PyErr_Clear();
+            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
+                setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD;
+                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD;
+                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD;
+            }
+            PyType_Modified((PyTypeObject*)type_obj);
+        }
+    }
+    goto GOOD;
+BAD:
+    if (!PyErr_Occurred())
+        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
+    ret = -1;
+GOOD:
+#if !CYTHON_USE_PYTYPE_LOOKUP
+    Py_XDECREF(object_reduce);
+    Py_XDECREF(object_reduce_ex);
+#endif
+    Py_XDECREF(reduce);
+    Py_XDECREF(reduce_ex);
+    Py_XDECREF(reduce_cython);
+    Py_XDECREF(setstate);
+    Py_XDECREF(setstate_cython);
+    return ret;
+}
+
 /* FetchCommonType */
-            static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
+          static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
     PyObject* fake_module;
     PyTypeObject* cached_type = NULL;
     fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
@@ -24301,7 +26707,8 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED
 }
 
 /* CythonFunction */
-            static PyObject *
+          #include <structmember.h>
+static PyObject *
 __Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
 {
     if (unlikely(op->func_doc == NULL)) {
@@ -24583,7 +26990,7 @@ static PyGetSetDef __pyx_CyFunction_getsets[] = {
     {0, 0, 0, 0, 0}
 };
 static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0},
+    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0},
     {0, 0, 0,  0, 0}
 };
 static PyObject *
@@ -24661,14 +27068,18 @@ __Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
     }
     return 0;
 }
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
+static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
 {
-    PyObject_GC_UnTrack(m);
     if (__Pyx_CyFunction_weakreflist(m) != NULL)
         PyObject_ClearWeakRefs((PyObject *) m);
     __Pyx_CyFunction_clear(m);
     PyObject_GC_Del(m);
 }
+static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
+{
+    PyObject_GC_UnTrack(m);
+    __Pyx__CyFunction_dealloc(m);
+}
 static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
 {
     Py_VISIT(m->func_closure);
@@ -24743,10 +27154,16 @@ static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, Py
         if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
             size = PyTuple_GET_SIZE(arg);
             if (likely(size == 1)) {
-                PyObject *result, *arg0 = PySequence_ITEM(arg, 0);
-                if (unlikely(!arg0)) return NULL;
+                PyObject *result, *arg0;
+                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+                arg0 = PyTuple_GET_ITEM(arg, 0);
+                #else
+                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
+                #endif
                 result = (*meth)(self, arg0);
+                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
                 Py_DECREF(arg0);
+                #endif
                 return result;
             }
             PyErr_Format(PyExc_TypeError,
@@ -24853,7 +27270,7 @@ static PyTypeObject __pyx_CyFunctionType_type = {
 };
 static int __pyx_CyFunction_init(void) {
     __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-    if (__pyx_CyFunctionType == NULL) {
+    if (unlikely(__pyx_CyFunctionType == NULL)) {
         return -1;
     }
     return 0;
@@ -24861,7 +27278,7 @@ static int __pyx_CyFunction_init(void) {
 static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
     __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
     m->defaults = PyObject_Malloc(size);
-    if (!m->defaults)
+    if (unlikely(!m->defaults))
         return PyErr_NoMemory();
     memset(m->defaults, 0, size);
     m->defaults_pyobjects = pyobjects;
@@ -24884,7 +27301,7 @@ static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, Py
 }
 
 /* CalculateMetaclass */
-                static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) {
+              static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) {
     Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases);
     for (i=0; i < nbases; i++) {
         PyTypeObject *tmptype;
@@ -24923,7 +27340,7 @@ static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, Py
 }
 
 /* Py3ClassCreate */
-                static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name,
+              static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name,
                                            PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) {
     PyObject *ns;
     if (metaclass) {
@@ -24989,8 +27406,48 @@ static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObj
     return result;
 }
 
+/* CLineInTraceback */
+              #ifndef CYTHON_CLINE_IN_TRACEBACK
+static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {
+    PyObject *use_cline;
+    PyObject *ptype, *pvalue, *ptraceback;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject **cython_runtime_dict;
+#endif
+    if (unlikely(!__pyx_cython_runtime)) {
+        return c_line;
+    }
+    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
+#if CYTHON_COMPILING_IN_CPYTHON
+    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
+    if (likely(cython_runtime_dict)) {
+      use_cline = __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);
+    } else
+#endif
+    {
+      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
+      if (use_cline_obj) {
+        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
+        Py_DECREF(use_cline_obj);
+      } else {
+        PyErr_Clear();
+        use_cline = NULL;
+      }
+    }
+    if (!use_cline) {
+        c_line = 0;
+        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
+    }
+    else if (PyObject_Not(use_cline) != 0) {
+        c_line = 0;
+    }
+    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
+    return c_line;
+}
+#endif
+
 /* CodeObjectCache */
-                static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+              static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
     int start = 0, mid = 0, end = count - 1;
     if (end >= 0 && code_line > entries[end].code_line) {
         return count;
@@ -25070,7 +27527,7 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
 }
 
 /* AddTraceback */
-                #include "compile.h"
+              #include "compile.h"
 #include "frameobject.h"
 #include "traceback.h"
 static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
@@ -25129,18 +27586,22 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line,
                                int py_line, const char *filename) {
     PyCodeObject *py_code = 0;
     PyFrameObject *py_frame = 0;
-    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
+    PyThreadState *tstate = __Pyx_PyThreadState_Current;
+    if (c_line) {
+        c_line = __Pyx_CLineForTraceback(tstate, c_line);
+    }
+    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
     if (!py_code) {
         py_code = __Pyx_CreateCodeObjectForTraceback(
             funcname, c_line, py_line, filename);
         if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
+        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
     }
     py_frame = PyFrame_New(
-        PyThreadState_GET(), /*PyThreadState *tstate,*/
-        py_code,             /*PyCodeObject *code,*/
-        __pyx_d,      /*PyObject *globals,*/
-        0                    /*PyObject *locals*/
+        tstate,            /*PyThreadState *tstate,*/
+        py_code,           /*PyCodeObject *code,*/
+        __pyx_d,    /*PyObject *globals,*/
+        0                  /*PyObject *locals*/
     );
     if (!py_frame) goto bad;
     __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
@@ -25153,9 +27614,9 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line,
 #if PY_MAJOR_VERSION < 3
 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
     if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
-        if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
+        if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
+        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
+        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
     PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
     return -1;
 }
@@ -25166,17 +27627,17 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) {
         PyBuffer_Release(view);
         return;
     }
-        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
-    Py_DECREF(obj);
+    if ((0)) {}
+        else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view);
     view->obj = NULL;
+    Py_DECREF(obj);
 }
 #endif
 
 
-                /* MemviewSliceIsContig */
-                static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs,
-                             char order, int ndim)
+              /* MemviewSliceIsContig */
+              static int
+__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
 {
     int i, index, step, start;
     Py_ssize_t itemsize = mvs.memview->view.itemsize;
@@ -25197,7 +27658,7 @@ __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs,
 }
 
 /* OverlappingSlices */
-                static void
+              static void
 __pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
                                void **out_start, void **out_end,
                                int ndim, size_t itemsize)
@@ -25233,7 +27694,7 @@ __pyx_slices_overlap(__Pyx_memviewslice *slice1,
 }
 
 /* Capsule */
-                static CYTHON_INLINE PyObject *
+              static CYTHON_INLINE PyObject *
 __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
 {
     PyObject *cobj;
@@ -25246,7 +27707,7 @@ __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
 }
 
 /* TypeInfoCompare */
-                static int
+              static int
 __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
 {
     int i;
@@ -25287,7 +27748,7 @@ __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
 }
 
 /* MemviewSliceValidateAndInit */
-                static int
+              static int
 __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
 {
     if (buf->shape[dim] <= 1)
@@ -25469,7 +27930,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* ObjectToMemviewSlice */
-                static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj) {
+              static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj, int writable_flag) {
     __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
     __Pyx_BufFmt_StackElem stack[1];
     int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
@@ -25479,7 +27940,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
         return result;
     }
     retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS, 2,
+                                                 PyBUF_RECORDS_RO | writable_flag, 2,
                                                  &__Pyx_TypeInfo_double, stack,
                                                  &result, obj);
     if (unlikely(retcode == -1))
@@ -25492,7 +27953,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* CIntFromPyVerify */
-                #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
+              #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
     __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
 #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
     __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
@@ -25514,7 +27975,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
     }
 
 /* ObjectToMemviewSlice */
-                static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj) {
+              static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) {
     __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
     __Pyx_BufFmt_StackElem stack[1];
     int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
@@ -25524,7 +27985,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
         return result;
     }
     retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS, 1,
+                                                 PyBUF_RECORDS_RO | writable_flag, 1,
                                                  &__Pyx_TypeInfo_double, stack,
                                                  &result, obj);
     if (unlikely(retcode == -1))
@@ -25537,7 +27998,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* ObjectToMemviewSlice */
-                static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_int(PyObject *obj) {
+              static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_int(PyObject *obj, int writable_flag) {
     __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
     __Pyx_BufFmt_StackElem stack[1];
     int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
@@ -25547,7 +28008,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
         return result;
     }
     retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS, 2,
+                                                 PyBUF_RECORDS_RO | writable_flag, 2,
                                                  &__Pyx_TypeInfo_int, stack,
                                                  &result, obj);
     if (unlikely(retcode == -1))
@@ -25560,7 +28021,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* ObjectToMemviewSlice */
-                static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *obj) {
+              static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *obj, int writable_flag) {
     __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
     __Pyx_BufFmt_StackElem stack[1];
     int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
@@ -25570,7 +28031,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
         return result;
     }
     retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS, 1,
+                                                 PyBUF_RECORDS_RO | writable_flag, 1,
                                                  &__Pyx_TypeInfo_int, stack,
                                                  &result, obj);
     if (unlikely(retcode == -1))
@@ -25583,7 +28044,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* CIntToPy */
-                static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
     const int neg_one = (int) -1, const_zero = (int) 0;
     const int is_unsigned = neg_one > const_zero;
     if (is_unsigned) {
@@ -25614,7 +28075,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* CIntToPy */
-                static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
     const long neg_one = (long) -1, const_zero = (long) 0;
     const int is_unsigned = neg_one > const_zero;
     if (is_unsigned) {
@@ -25645,7 +28106,7 @@ static int __Pyx_ValidateAndInit_memviewslice(
 }
 
 /* MemviewDtypeToObject */
-                static CYTHON_INLINE PyObject *__pyx_memview_get_int(const char *itemp) {
+              static CYTHON_INLINE PyObject *__pyx_memview_get_int(const char *itemp) {
     return (PyObject *) __Pyx_PyInt_From_int(*(int *) itemp);
 }
 static CYTHON_INLINE int __pyx_memview_set_int(const char *itemp, PyObject *obj) {
@@ -25657,7 +28118,7 @@ static CYTHON_INLINE int __pyx_memview_set_int(const char *itemp, PyObject *obj)
 }
 
 /* MemviewDtypeToObject */
-                static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) {
+              static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) {
     return (PyObject *) PyFloat_FromDouble(*(double *) itemp);
 }
 static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) {
@@ -25669,7 +28130,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 }
 
 /* CIntToPy */
-                static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) {
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) {
     const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
     const int is_unsigned = neg_one > const_zero;
     if (is_unsigned) {
@@ -25700,7 +28161,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 }
 
 /* Declarations */
-                #if CYTHON_CCOMPLEX
+              #if CYTHON_CCOMPLEX
   #ifdef __cplusplus
     static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
       return ::std::complex< float >(x, y);
@@ -25720,7 +28181,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 #endif
 
 /* Arithmetic */
-                #if CYTHON_CCOMPLEX
+              #if CYTHON_CCOMPLEX
 #else
     static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        return (a.real == b.real) && (a.imag == b.imag);
@@ -25855,7 +28316,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 #endif
 
 /* Declarations */
-                #if CYTHON_CCOMPLEX
+              #if CYTHON_CCOMPLEX
   #ifdef __cplusplus
     static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
       return ::std::complex< double >(x, y);
@@ -25875,7 +28336,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 #endif
 
 /* Arithmetic */
-                #if CYTHON_CCOMPLEX
+              #if CYTHON_CCOMPLEX
 #else
     static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        return (a.real == b.real) && (a.imag == b.imag);
@@ -26010,7 +28471,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 #endif
 
 /* CIntToPy */
-                static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {
     const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;
     const int is_unsigned = neg_one > const_zero;
     if (is_unsigned) {
@@ -26041,7 +28502,7 @@ static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *o
 }
 
 /* MemviewSliceCopyTemplate */
-                static __Pyx_memviewslice
+              static __Pyx_memviewslice
 __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
                                  const char *mode, int ndim,
                                  size_t sizeof_dtype, int contig_flag,
@@ -26108,7 +28569,7 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
 }
 
 /* CIntFromPy */
-                static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+              static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
     const int neg_one = (int) -1, const_zero = (int) 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
@@ -26297,7 +28758,7 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
 }
 
 /* CIntFromPy */
-                static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) {
+              static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) {
     const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
@@ -26424,208 +28885,19 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
                 case 4:
                     if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) {
                         if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
-                            return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(unsigned int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            unsigned int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (unsigned int) -1;
-        }
-    } else {
-        unsigned int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (unsigned int) -1;
-        val = __Pyx_PyInt_As_unsigned_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to unsigned int");
-    return (unsigned int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to unsigned int");
-    return (unsigned int) -1;
-}
-
-/* CIntFromPy */
-                static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-    const char neg_one = (char) -1, const_zero = (char) 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(char) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
-                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
-                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
-                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(char) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
+                            return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
                         }
                     }
                     break;
             }
 #endif
-            if (sizeof(char) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
+            if (sizeof(unsigned int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x))
 #ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
+            } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x))
 #endif
             }
         }
@@ -26634,7 +28906,7 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
             PyErr_SetString(PyExc_RuntimeError,
                             "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
 #else
-            char val;
+            unsigned int val;
             PyObject *v = __Pyx_PyNumber_IntOrLong(x);
  #if PY_MAJOR_VERSION < 3
             if (likely(v) && !PyLong_Check(v)) {
@@ -26654,28 +28926,28 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
                     return val;
             }
 #endif
-            return (char) -1;
+            return (unsigned int) -1;
         }
     } else {
-        char val;
+        unsigned int val;
         PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
+        if (!tmp) return (unsigned int) -1;
+        val = __Pyx_PyInt_As_unsigned_int(tmp);
         Py_DECREF(tmp);
         return val;
     }
 raise_overflow:
     PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
+        "value too large to convert to unsigned int");
+    return (unsigned int) -1;
 raise_neg_overflow:
     PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
+        "can't convert negative value to unsigned int");
+    return (unsigned int) -1;
 }
 
 /* CIntFromPy */
-                static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+              static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
     const long neg_one = (long) -1, const_zero = (long) 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
@@ -26863,8 +29135,197 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
     return (long) -1;
 }
 
+/* CIntFromPy */
+              static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
+    const char neg_one = (char) -1, const_zero = (char) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(char) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (char) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (char) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
+                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
+                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
+                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (char) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(char) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (char) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
+                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(char) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            char val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (char) -1;
+        }
+    } else {
+        char val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (char) -1;
+        val = __Pyx_PyInt_As_char(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to char");
+    return (char) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to char");
+    return (char) -1;
+}
+
 /* CheckBinaryVersion */
-                static int __Pyx_check_binary_version(void) {
+              static int __Pyx_check_binary_version(void) {
     char ctversion[4], rtversion[4];
     PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
     PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
@@ -26880,7 +29341,7 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
 }
 
 /* ModuleImport */
-                #ifndef __PYX_HAVE_RT_ImportModule
+              #ifndef __PYX_HAVE_RT_ImportModule
 #define __PYX_HAVE_RT_ImportModule
 static PyObject *__Pyx_ImportModule(const char *name) {
     PyObject *py_name = 0;
@@ -26898,7 +29359,7 @@ static PyObject *__Pyx_ImportModule(const char *name) {
 #endif
 
 /* TypeImport */
-                #ifndef __PYX_HAVE_RT_ImportType
+              #ifndef __PYX_HAVE_RT_ImportType
 #define __PYX_HAVE_RT_ImportType
 static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
     size_t size, int strict)
@@ -26963,7 +29424,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
 #endif
 
 /* InitStrings */
-                static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+              static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
     while (t->p) {
         #if PY_MAJOR_VERSION < 3
         if (t->is_unicode) {
@@ -26988,6 +29449,8 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
         #endif
         if (!*t->p)
             return -1;
+        if (PyObject_Hash(*t->p) == -1)
+            return -1;
         ++t;
     }
     return 0;
@@ -26996,50 +29459,57 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
 static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
     return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
 }
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
     Py_ssize_t ignore;
     return __Pyx_PyObject_AsStringAndSize(o, &ignore);
 }
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-#if PY_VERSION_HEX < 0x03030000
-        char* defenc_c;
-        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-        if (!defenc) return NULL;
-        defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+#if !CYTHON_PEP393_ENABLED
+static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    char* defenc_c;
+    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+    if (!defenc) return NULL;
+    defenc_c = PyBytes_AS_STRING(defenc);
 #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        {
-            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-            char* c;
-            for (c = defenc_c; c < end; c++) {
-                if ((unsigned char) (*c) >= 128) {
-                    PyUnicode_AsASCIIString(o);
-                    return NULL;
-                }
+    {
+        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+        char* c;
+        for (c = defenc_c; c < end; c++) {
+            if ((unsigned char) (*c) >= 128) {
+                PyUnicode_AsASCIIString(o);
+                return NULL;
             }
         }
+    }
 #endif
-        *length = PyBytes_GET_SIZE(defenc);
-        return defenc_c;
+    *length = PyBytes_GET_SIZE(defenc);
+    return defenc_c;
+}
 #else
-        if (__Pyx_PyUnicode_READY(o) == -1) return NULL;
+static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
 #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        if (PyUnicode_IS_ASCII(o)) {
-            *length = PyUnicode_GET_LENGTH(o);
-            return PyUnicode_AsUTF8(o);
-        } else {
-            PyUnicode_AsASCIIString(o);
-            return NULL;
-        }
+    if (likely(PyUnicode_IS_ASCII(o))) {
+        *length = PyUnicode_GET_LENGTH(o);
+        return PyUnicode_AsUTF8(o);
+    } else {
+        PyUnicode_AsASCIIString(o);
+        return NULL;
+    }
 #else
-        return PyUnicode_AsUTF8AndSize(o, length);
+    return PyUnicode_AsUTF8AndSize(o, length);
+#endif
+}
+#endif
 #endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
 #endif
+            PyUnicode_Check(o)) {
+        return __Pyx_PyUnicode_AsStringAndSize(o, length);
     } else
 #endif
 #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
@@ -27063,6 +29533,26 @@ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
    if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
    else return PyObject_IsTrue(x);
 }
+static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
+#if PY_MAJOR_VERSION >= 3
+    if (PyLong_Check(result)) {
+        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
+                "__int__ returned non-int (type %.200s).  "
+                "The ability to return an instance of a strict subclass of int "
+                "is deprecated, and may be removed in a future version of Python.",
+                Py_TYPE(result)->tp_name)) {
+            Py_DECREF(result);
+            return NULL;
+        }
+        return result;
+    }
+#endif
+    PyErr_Format(PyExc_TypeError,
+                 "__%.4s__ returned non-%.4s (type %.200s)",
+                 type_name, type_name, Py_TYPE(result)->tp_name);
+    Py_DECREF(result);
+    return NULL;
+}
 static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
 #if CYTHON_USE_TYPE_SLOTS
   PyNumberMethods *m;
@@ -27070,9 +29560,9 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
   const char *name = NULL;
   PyObject *res = NULL;
 #if PY_MAJOR_VERSION < 3
-  if (PyInt_Check(x) || PyLong_Check(x))
+  if (likely(PyInt_Check(x) || PyLong_Check(x)))
 #else
-  if (PyLong_Check(x))
+  if (likely(PyLong_Check(x)))
 #endif
     return __Pyx_NewRef(x);
 #if CYTHON_USE_TYPE_SLOTS
@@ -27080,32 +29570,30 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
   #if PY_MAJOR_VERSION < 3
   if (m && m->nb_int) {
     name = "int";
-    res = PyNumber_Int(x);
+    res = m->nb_int(x);
   }
   else if (m && m->nb_long) {
     name = "long";
-    res = PyNumber_Long(x);
+    res = m->nb_long(x);
   }
   #else
-  if (m && m->nb_int) {
+  if (likely(m && m->nb_int)) {
     name = "int";
-    res = PyNumber_Long(x);
+    res = m->nb_int(x);
   }
   #endif
 #else
-  res = PyNumber_Int(x);
+  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
+    res = PyNumber_Int(x);
+  }
 #endif
-  if (res) {
+  if (likely(res)) {
 #if PY_MAJOR_VERSION < 3
-    if (!PyInt_Check(res) && !PyLong_Check(res)) {
+    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
 #else
-    if (!PyLong_Check(res)) {
+    if (unlikely(!PyLong_CheckExact(res))) {
 #endif
-      PyErr_Format(PyExc_TypeError,
-                   "__%.4s__ returned non-%.4s (type %.200s)",
-                   name, name, Py_TYPE(res)->tp_name);
-      Py_DECREF(res);
-      return NULL;
+        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
     }
   }
   else if (!PyErr_Occurred()) {
@@ -27176,6 +29664,9 @@ static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
   Py_DECREF(x);
   return ival;
 }
+static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
+  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
+}
 static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
     return PyInt_FromSize_t(ival);
 }
diff --git a/tigramite/tigramite_cython_code.pyx b/tigramite/tigramite_cython_code.pyx
index 715a30c9..a2c24da5 100644
--- a/tigramite/tigramite_cython_code.pyx
+++ b/tigramite/tigramite_cython_code.pyx
@@ -150,8 +150,9 @@ def _get_restricted_permutation_cython(
     cdef int i, index, count, use
 
     # Shuffle neighbor indices for each sample index
-    map(numpy.random.shuffle, neighbors)
-    
+    for i in range(T):
+        numpy.random.shuffle(neighbors[i])
+
     for i in range(T):
 
         index = order[i];
diff --git a/tigramite_overview.pdf b/tigramite_overview.pdf
deleted file mode 100644
index 580d312c..00000000
Binary files a/tigramite_overview.pdf and /dev/null differ
diff --git a/tutorials/tigramite_tutorial_assumptions.ipynb b/tutorials/tigramite_tutorial_assumptions.ipynb
new file mode 100644
index 00000000..f0c49fe4
--- /dev/null
+++ b/tutorials/tigramite_tutorial_assumptions.ipynb
@@ -0,0 +1,1224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results.\n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "This tutorial explains the causal assumptions and gives walk-through examples. See the following paper for theoretical background:\n",
+    "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
+    "from tigramite.models import LinearMediation, Prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Causal assumptions\n",
+    "\n",
+    "Having introduced the basic functionality, we now turn to a discussion of the assumptions underlying a causal interpretation:\n",
+    "\n",
+    "  - **Faithfulness / Stableness:** *Independencies in data arise not from coincidence, but rather from causal structure* or, expressed differently, *If two variables are independent given some other subset of variables, then they are not connected by a causal link in the graph*.\n",
+    "  \n",
+    "  - **Causal Sufficiency:** *Measured variables include all of the common causes.*\n",
+    "  \n",
+    "  - **Causal Markov Condition:** *All the relevant probabilistic information that can be obtained from the system is contained in its direct causes.* or, expressed differently, *If two variables are not connected in the causal graph given some set of conditions (see Runge Chaos 2018 for further definitions), then they are conditionally independent*.\n",
+    "  \n",
+    "  - **No contemporaneous effects:** *There are no causal effects at lag zero.*\n",
+    "  \n",
+    "  - **Stationarity**\n",
+    "  \n",
+    "  - **Parametric assumptions of independence tests** (these where already discussed in basic tutorial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Faithfulness\n",
+    "\n",
+    "Faithfulness, as stated above, is an expression of the assumption that the independencies we measure come from the causal structure, i.e., the time series graph, and cannot occur due to some fine tuning of the parameters. Another unfaithful case are processes containing *purely* deterministic dependencies, i.e., $Y=f(X)$, without any noise. We illustrate these cases in the following.\n",
+    "\n",
+    "#### Fine tuning\n",
+    "\n",
+    "Suppose in our model we have two ways in which $X^0$ causes $X^2$, a direct one, and an indirect effect $X^0\\to X^1 \\to X^2$ as realized in the following model:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "    X^0_t &= \\eta^0_t\\\\\n",
+    "    X^1_t &= 0.6 X^0_{t-1} + \\eta^1_t\\\\\n",
+    "    X^2_t &= 0.6 X^1_{t-1} - 0.36 X^0_{t-2} + \\eta^2_t\\\\\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(500, 3)\n",
+    "for t in range(1, 500):\n",
+    "#     data[t, 0] += 0.6*data[t-1, 1]\n",
+    "    data[t, 1] += 0.6*data[t-1, 0]\n",
+    "    data[t, 2] += 0.6*data[t-1, 1] - 0.36*data[t-2, 0]\n",
+    "    \n",
+    "var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "# tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since here $X^2_t = 0.6 X^1_{t-1} - 0.36 X^0_{t-2} + \\eta^2_t = 0.6 (0.6 X^0_{t-2} + \\eta^1_{t-1}) - 0.36 X^0_{t-2} + \\eta^2_t = 0.36 X^0_{t-2} - 0.36 X^0_{t-2} + ...$, there is no unconditional dependency $X^0_{t-2} \\to X^2_t$ and the link is not detected in the condition-selection step: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = par_corr\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.2]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Variable $X^0$\n",
+      "\n",
+      "## Variable $X^1$\n",
+      "\n",
+      "## Variable $X^2$\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable $X^0$ has 0 parent(s):\n",
+      "\n",
+      "    Variable $X^1$ has 1 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.534\n",
+      "\n",
+      "    Variable $X^2$ has 1 parent(s):\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.434\n"
+     ]
+    }
+   ],
+   "source": [
+    "parcorr = ParCorr()\n",
+    "pcmci_parcorr = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=1)\n",
+    "all_parents = pcmci_parcorr.run_pc_stable(tau_max=2, pc_alpha=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, since the other parent of $X^2$, namely $X^1_{t-1}$ *is* detected, the MCI step conditions on $X^1_{t-1}$ and can reveal the true underlying graph (in this particular case):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = par_corr\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.2]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Variable $X^0$\n",
+      "\n",
+      "## Variable $X^1$\n",
+      "\n",
+      "## Variable $X^2$\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable $X^0$ has 0 parent(s):\n",
+      "\n",
+      "    Variable $X^1$ has 1 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.534\n",
+      "\n",
+      "    Variable $X^2$ has 1 parent(s):\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.434\n",
+      "\n",
+      "##\n",
+      "## Running Tigramite MCI algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "\n",
+      "independence test = par_corr\n",
+      "tau_min = 0\n",
+      "tau_max = 2\n",
+      "max_conds_py = None\n",
+      "max_conds_px = None\n",
+      "\n",
+      "## Significant links at alpha = 0.05:\n",
+      "\n",
+      "    Variable $X^0$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^1$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.536 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^2$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.543 | conf = (0.000, 0.000)\n",
+      "        ($X^0$ -2): pval = 0.00000 | val = -0.365 | conf = (0.000, 0.000)\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^1$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.536\n",
+      "\n",
+      "    Variable $X^2$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.543\n",
+      "        ($X^0$ -2): pval = 0.00000 | val = -0.365\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, however, that this is not always the case and such cancellation, even though a pathological case, can present a problem especially for smaller sample sizes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Deterministic dependencies\n",
+    "\n",
+    "Another violation of faithfulness can happen due to *purely* deterministic dependencies as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7e14dca630>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e146bfef0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e146f50b8>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(500, 3)\n",
+    "for t in range(1, 500):\n",
+    "    data[t, 0] = 0.4*data[t-1, 1]\n",
+    "    data[t, 2] += 0.3*data[t-2, 1] + 0.7*data[t-1, 0]\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rung_ja/anaconda/envs/py36/lib/python3.6/site-packages/tigramite-4.0.0b0-py3.6-linux-x86_64.egg/tigramite/independence_tests.py:1113: RuntimeWarning: divide by zero encountered in double_scalars\n",
+      "  trafo_val = value * np.sqrt(deg_f/(1. - value*value))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 3 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 1.000\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.708\n",
+      "        ($X^0$ -2): pval = 0.00000 | val = 0.489\n",
+      "\n",
+      "    Variable $X^1$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.501\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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pZtbkdZPm7rXvAT/4ZR+kLk8u/ik5kVYSEJGPA4cZY65wnn8NWGCMuUxELgN2GGMeEJFfGWPeO55AgsGgF7ge8ONcyu3ds5sju7dG59eJVFRXjFjzx/qj7N7U0bN7S8ffdr2545ZYb/T3i1au0EXRi0BrY9Nk4B3AucDZ9/9rY11Hdz+Tqso4YFYt+8yooaJssHjERD7y2NsXH0xcWSrr62NW24aeeeHw1prdXXPS+Nhd2MnMBpp5NmX771Lua21sqvRUeM8sqyr/ZH9X38mPr9pcuXZbF1XlHvabWcv+M2upqRy8aKitn7LqP/N8R6zZe9aQusnRCYSAawOBQMG2LKTbW+N24CURuQ57+Xw2tucH2H69zzs/Z/IPcT1wZfyOt3RvZ78ZlSkv44wxdG/r7OrauuvhXZEd3+nt6P5rMfbRL3WLVq7YCdwF3NXa2FTpEdqAvTq6+3n6jXaeW7eThTNqOGh2LZOryxFjfjRjwwbZPHv2nveo29nBgf9ZXYntn57KJuD/gPux4xZ0Dd0i4/yf3gfc19rY5C2rKlsH1Hf3xXgh0sGqSAfz9q7moPpaZkyqpLOt/S0LqjaG1+w9K9n4hVoG66yr3Pobsi3t5iARuQH7R58FLDbGrHH2XwZsN8Y8KCJ3GWMuHmsQzmXWahJu5tT1dnNJzY5hE0r17e6NdW3Z9Uz3tl3f2Ll222/10rw4+MXnwU6aNhu79u3shJ9nAbM9wmGxFP/jc/eq4qD6OqZMr+O5446lc5IzMNUYTmz9GzW7hy3V0IPthnoHtpNA0Q3OKkV+8Ql2Goth5Sf+sbzMc3Rfirm+ptdVcHB9HfNm1rDhiENYPTPlkJBO4KBAILA+y3+GK8bSb/sn2Ir63QMJwHEv8AMRORt76TweF5Hkbv6uiiq2bu9h+rRqYtEYHVt3t0d37Pp2Z1v7Dcf/4ZFRF15R+ckvvknYOXUSR4vuTxrz6qRKAADrt3ezfns35fM87HVC+eDdQBHaGhrY9xXbBbyrquq16u7uZcA9OgakcPnFV4UtN4ll6UDS6CGUKgEAbNnVS+sr2/Bu6WOvw2tG6s9ci63Dbh5L7PliLEngS8DmxN8xxnQCH8owjpmpXnhWJlG/qYvnaqbROXV2S+DbgWCGn6Vc4JyJNZB8aoDxjLJNW9WhC5l0+jF4a6uG7G9rmIMYw4Y59XTX1NwTCAR+nMs4VPb4xTedoXNZDWwLyeEsp95pk9dNPf+UeeWzRu2IOCtXMeRaWklARK4EqrDZLog9+8+mlDfdXq/bm7huPRuz/LkqQ37xVTD0TGzgS3ogmU2eNh6tU849+ZmaI/f/VLIXe6uqCO+3Z1kILUt5xi8+L7ZST3bi4PbaCi8B18xYcv7+4pEb0zi+YMvTqElARN6GPdNvNMZ0iMhkETnSGPOvLMZxNza5jHT51ukcpyaQc/l9IoODchrJ7upW7UAb9ku1IcnjBuxI3vgrwtexN+h+W3Pk/nOBj6BlKe/5xVeG7c9/mrMtIv2xGunoZLDMJCtHGwEfttPBgO3AF4FbQibc59yvLOq6acQbwyIyHzuh19nGmNXOvsuB04wxl2czkGAweCMJvYMS3BQIBAr2Dnyh8ouvmuHTPWRa6Uex00DEr062GngpZMKjts/7xXcp8AugC/gGcGPIhPcM2NKylJ+cSv9oBkeJLyI7CyCFSb7i3aaQCY/YacQvvrdgZwM2wC3AF0ImvCX+mGIvTyNeCRhj1gL7JOy7DbgtB7Fc6zym7Iubg89UCfziq8FW+qdhv6gnMP5FUHaSZK1i4LWQCWeyAMg27JnX1SETTjbTqJalPOAX38AcPwMnEGOa4ydBN7aJJrGifzlkwpnM5rsNO2/YJ0Mm/GyKY4q6POXdtBHBYHBgpa9Z2Mu1uwu161Uh8IuvFjvmY+CLejxjm9gN4E3s2VTiF3TDaGdi4+EXnydkwqOOB9Gy5C6n0j+WoRO7pTWHT5zt2HFHiScPa9P5Px8rp1uySaecFmt5yrskoHLL6bVzGHbytdOxU/SOdYrntdhmwsewM2a+novKXuU/v/jmYyvG/8LO9zSmKZ6BLTjLj2LL06pcVPYqtbF++VWB8otvP+Bi4BLswi9j8TpxK4CFTDic1eBUQfGLbxb2JOISBmcOSNdmBk8eHgNWa6U/sTQJFDG/+BqA92K/rMeO4VfXMLTSH3GFL1X8/OKbCpyPPZE4nfQXkN/I0Er/Rb1qzC+aBIqMM6jmQuyX9RTSG0jzCkMr/YJv51SZc+4XvRN7EnEW6XUQaGNopf+yVvr5TZNAEfCLbzLwbuyXdTGj/79GsTNk/gr4U8iE38xthKpQOIP/3o4tS+eQ3o3dldiy9BDwqlb6hUWTQIFy+u+/A/tlPRs7onskBvgrdmDMPYl9oVXpckbqnoYtSxdgJ14bzb+BO4G79B5RYdMkUECcnj1NwEexi62kM9DmKeyX9W5t5lHx/OIbWHPhvaQ3982rDFb8/8llbMo9mgQKgHOmdh52zvIT0viV1Qx+WV/JZWyqsDgnEqdgy9I70/iVCLap507gaW3qKT6aBPKYc2PuQ8BnSBi5nUQY29RzJ/C8fllVPGfKhguwlf9oPcW2YhfXuQto1S6cxU2TQB7yi2828AngCmCkOWw3YqdPuBN4Qit+lcgvvjrgw9gTiQUjHLoL+A22LD0SMuE+F8JTeUCTQB5x2mivxC6APlJ3vIeA7wKPhky4YNc2VbnjF98c4JPYNv+RbvQ+hV0M5bchE9aFmkqQJoEJNoY22l7g58DNIRNe5UZsqvA4s2JeBbyPkeeAegC4Edvco1eQJUyTwAQZQxvtduB/gR+ETLjNjdhUYXFOJN6KLUtnjXBoD3Yt5W+HTHi1G7Gp/KdJwGXO+rr/zehttGHsZfpPQya8y4XQVIFxZu18D7byP2qEQ7cBy4HlIRMu2BWwVG5oEnCJX3yVwKeApYzcRvsk9jL93pAJ97sRmyoszvTHlwJfB+aPcOga7InE7SET7nQjNlV4NAnkmHOpfgFwA3b91FS0jVaNyi++k4BvY9d9SOUJ4FvAfdpxQI1Gk0AO+cV3DPYLuyjFIQNttDeHTPhF1wJTBccvvgXAN7ETAyZjgN9il9r8u2uBqYKnSSAHnO55Xwc+SPJZPHcA30fbaNUonHtInwM+S/L5oXqBn2JPJF52MzZVHDQJZJEzqduV2C9tstkXo8APgWDIhLe6GZsqLE67/weBbwCzUxx2L3BNyITXuBaYKjqaBLLAafe/GLgemJfisN8DV2rXPDUav/hOxTYjpurx8yzwmZAJP+5eVKpYaRLIkF98J2K/sCemOOQ/2Mr/D+5FpQqRX3z7YjsQnJ/ikA3A57G9ffSGr8oKTQLj5BffPOyNuktTHLIV+BJwi3b1VCPxi28KtnL/FMmnC+kBbgK+GTLhDjdjU8VPk8AYORNyXYMdoFOd5JB+4HvA10ImvN3N2FRhcUaNfxj4KjAjxWG/Aq4NmfAbrgWmSoomgTHwi+8CbAU/J8Uh9wFX6xz+ajR+8Z0A/Ag4LMUhT2Lb/Ve4F5UqRZoE0uBcrn8fO7tnMv/GfmH/7F5UqhA5Uz18Adv8401ySATbu+yXOo+/coMmgVH4xfdW4DaSD8/fhP1C/0Rv1KnR+MV3EPAzkk8Y2IW9KfwtneJBuUmTQAp+8VVh+2h/JsnLfdg5Wb4RMuGdrgamCo7T538JtpJPNuDrF8DndA1oNRE0CSThF99R2DO2tyR5+QXg/SETfs7dqFQh8ouvATuid3GSlzcAHw6Z8EPuRqXUIE0CcZwF3a8BggxfkMNgu+l9MWTC3W7HpgqPX3yXYEeIJ5s19teAP2TCW9yNSqmhNAk4nIE6dwAnJXl5LfABHaGp0uEX397Y+fuTTfa2E7t+9M91tliVD0o+CThTPnwEO+o32Xw/twOfCplwu6uBqYLkF99ibEeCZN2IHwMu1z7/Kp+UdBLwi28W8GOSr+27FfhYyITvdTcqVYj84qvBjiD/ZJKXe7GLCX1Hu32qfFOyScAvvvOAW4DpSV5+CHvDboO7UalC5BffcdiOBAcmefk5bEeCF9yNSqn0lFwS8ItvMvBd4PIkL+/Gdgn9kbbXqtE40z5ch50jKnHgl8HOKvvlkAn3uB2bUukqqSTgF99h2Kkdki3zuBJ78/dVd6NShchpSvw10JTk5dexZelv7kal1Nh5JjoAt/jFdw7wd4YngH7sEP5TNAGodPjFdyR2bp9kCeDHwBGaAFShKPorAaf3zzXAMoYv9bga2177jOuBqYLkF9/52Pb/moSXNgEfDZnw/e5HpdT4FXUScKZ+uIXkE7/dAnw6ZMJd7kalCpFzMnEd8LUkLz8CvC9kwpvcjUqpzBVtEvCLbzbwG4av+BXF9vtf7n5UqhA5a0ffClyS5OXvA5/VhYNUoSrKJODM/XMfw9f73QG8J2TCj7gflSpEfvHNAX4LHJfwUj/wiZAJt7gflVLZU3RJYIQ225eAc0Im/LL7UalC5BffsdiTicTRv9uAC0Im/JjrQSmVZUWTBJw2289jl+pL9DDw3pAJ73A3KlWo/OK7CDv9Q+ISoquBd4VMeI3rQSmVA0WRBJw225+QfMKu7wFXaputSocz9/+XgECSlx8CLtV5pFQxKfgkMEqb7ZKQCd/iflSqEDnz/9wOXJjk5ZuwC77rCnKqqBR0EtA2W5UtfvHNxZaloxNe6gOaQyb8U/ejUir3CjYJ+MX3XmybbeJyff/Bttm+5npQqiD5xXcC9mpydsJLW4DzdPSvKmYFlwScG8BfxK7+legh4BJd91elyzmZuB2oTHjpeWxvsrDrQSnlooJKAs5Nu++QfM72G7GLdRdkm20k2OzBXtVUY7u3xj9WY+d5iiVs0ST7Rtvfj13dqrMh0FLSc9v7xdcM/C/DpxO5HzudSIf7USnlLjGmMGZMdqbtvRX4QMJLfdjFX25zPSggEmyuBmak2PZieIWerIKvYXizVq4ZoAObEHYC7XE/p7O1AxsbAi29LsedFX7xXYOd6jnRN4HP6+IvqlQURBLwi68SuBM4L+Gl7dhL9qy12UaCzZOBmdhKfDqpK/iBLXFQWqnZDLyZsEUSnm9qCLTkxRWa05z4dexKX/H6gf8OmfDP3I9KqYmT90nAL75a7BxAixNe2gAsHuuKTZFg8xTAN8I2ddzBqlSi2P+vVMniVeCNhkBLTsdyOM2J3weuSHipG7gwZMK/y+XnK5WPMr4nICL7YEfqTjHGJOtfPSbBYHAecBEws397x07KvOfRHz0m4bAwcEayUZvOmbwvbltI4VTyPdjVzbqcLf7nKPa+gAe7ipUnyZZsf+K+KmAS7jY/eYEGZ0ulNxJsXgO8jJ3iI/5xc0OgZcxnK/FlyfT1b5HK8hNNT9/5CYftAt4ZMuHHx/r+qrTElyfs1OF3BwKBdRMbVebSuhIQkY8DhxljrnCefw1YYIy5LO6YezJJAsFg0Itto/UDtdFdXWz72cP0b9yWeOiLbzl09nnnX3jEZOAgZzuQwcp+r/HGME592K6Em5NsW7EV+UBlnvg45Gc3b9RGgs0V2GQweZzb3tjmsMSbqrmwg+GJ4SXg1YZAy+7EgxPLkunrZ/s9j9Pz0trEQ7cBZ4ZM+MlcBq8KW2J5inupEwgB1wYCgbxo7hyPdK8EbgdeEpHrgJOBs4GTshzL9cCVA0+6nnmZ2JbtLNxnb6ZNr2P69Frq6yd3zGmYMtXjkdVZ/ux4Pdimi4GKPFUFP7DtHM9Z6kRzbuhudbZxiQSby7F96+fEbQ0Jz+eQeWKeCpzgbIkxrMUmhReBfwFPean/YBT5zMAxPa+1JUsAbdjmxFUZxqaK35C6KU5t3P6r3Asnu9K+JyAiN2D/6LOAxcaYNQmvj/tKwLnMWk1cljXGsPv3T/CpdywYz1uOpAd4A9ukNLC9HvfzplLvOpltTg+qxMQwkDAWAAdgb8JnRRTYSjlb9mwVrP/nK7Q/9A8AvFPrTOX+c0+++Z9/+Hu2PlMVp2R1UxKdwEGBQGC9O1Fl11juCfwE+4/x7vgEICLTsL0tjhKRpcaYZeOI4yIS/pFFhJozT2RH51qm1paP5b16GVrJx1fwYWy3Rq3kXdQQaOkC1jhbUpFiMLKUAAAQLUlEQVRg897YZHAAtnlv4HF/xnj/wgvMpI+Z9O3Z13/8VB6LHsKqZ9bxlstOFc/k2gsiweYnGwItfanfSanhdVMStc5xN+c+nOwbSxL4Erb5Y8jvGGO2YtvKMjEz2U7xCO01dUylJ/GlfmyPkhfjtlewlfwGreQLT0OgZRvwhLPt4Qyim8fw5HAA9ioirXsSZcAZjfM47dg5lJX3ATs+C3wiEmx+Dng6bltVqGMfVE4krZuSmJXTKHIorSQgIldiz8Yuwk7XcG+W40i5Nut6qaIHD+2U4cX89Gh23QCs0TO40uAk9Dec7U/xrznNTPtik8LhwDF9yCnlmEmp3q+s3Bv/tAI7+2z8DLRdkWDzCuAxZ3tSk0JJS3fd6I05jSKHRr0nICJvw87J32iM6RCRZ4EPGWP+la0gSqHdTbnja8Evz6si9uI0+mqm08t0+phOHzWM++KwC9CkUKJKoW4aMQmIyHxswT/bGLPa2Xc5cJox5vJsBhIMBm8k+R34ATcFAoGCvQOv3JOsLNUQdRJCLwvofm0a/TUMnzU0HZoUSkyx100jNgcZY9YC+yTsuw07hXO2Xes8puyLm4PPVMVpWFnajZe1eDvXUhV6hsnXBgKBaCTYPAc4Jm47ntHbgKuBM5wNtPmoFBR13ZR300YEg8G52HsPs7DtbHcX6mWWmlhjLUuRYLNgbzifFreN9Wph4ErhD8ADDYGWl8cat8pPxVo35V0SUCpfZCkpvAw84Gwrcj0/klJjpUlAqTRlISlsB36PTQh/aAi07MhuhEqNnSYBpcYpw6TQD/wV5yqhIdCSciCdUrmkSUCpLHGSwoHAmcC7gFNIf0DmagabjVbmy/oLqvhpElAqRyLB5qnA27EJ4R2kP5HeVux62b8Bft8QaOnOTYRKaRJQyhWRYHMZdubddznbgWn+ajtwD/BL4HG9QlDZpklAqQkQCTYfwGBCOBk7791o3sQus/pL4NlCnMJc5R9NAkpNMGcG1YH7CGcBU9L4tRexyeCXelNZZUKTgFJ5xFmo52TgQuzApHTWWfgH8Avg7oZAS8FOZKYmhiYBpfKUkxDOAN4HnMvo89pHgUewVwi/aQi0dOQ2QlUMNAkoVQAiweZa4BzgUmzT0WhdT7uB+4GfYXsY6Q1llZQmAaUKTCTYPB3bXPQ+bNPRaNYBPwJubQi0vJnL2FTh0SSgVAGLBJsXAJdgE8KhoxweBX6Lnfnyz7oCnwJNAkoVjUiw+TBsMrgEmD/K4a8CLcBtDYGWLbmOTeUvTQJKFRlnXeYm4EPAxdg1EFLpAf4Pe3Xwdx17UHo0CShVxJypKz6AXRDl4FEOfwGbDH7eEGhpz3VsKj9oElCqBDiT2y3CJoMLgfIRDu/EdjMNNQRannEhPDWBNAkoVWIiweaZ2KaiZmDhKIc/CfwQuLMh0NKT69iU+zQJKFWinHsHi7FXB+cAnhEOfxP4DnCLNhUVF00CSikiwea5wEeAjwJzRjh0J/a+wXd1zEFx0CSglNrDmfL6ncDHgf8a4dBe7GjkGxsCLS+6EZvKDU0CSqmkIsHmfYH/wV4h1Ixw6H3ADQ2Blr+7EpjKKk0CSqkRRYLN04ArsAlhpFlNVwDXA7/T0ciFQ5OAUiotkWBzDXA5cBUj9ypaDXwLu9aB9ijKc5oElFJj4tw3OB+4Fjh6hEO1R1EB0CSglBoXZwDaW7HJYKSbyDuBH2BvIm93IzaVPk0CSqmMRYLNRwJXA+8l9XrJ7cBNwHd0wZv8oUlAKZU1kWCzD/gMI/co2gp8E1jeEGjpcik0lYImAaVU1qXZo6gN+DrwY72BPHE0CSilcsZZFvMT2PsGe6U47A3gK8AdDYGWfrdiU5YmAaVUzkWCzVOwzUSfBSalOOwVIAD8SscZuEeTgFLKNc76yFcDnyT1YjcvAF8E7tNFbnJPk4BSynWRYHM9sBQ7nXVFisOewiaDP2oyyB1NAkqpCRMJNi8AvoBd3yBV19K/AV9oCLQ87lpgJUSTgFJqwkWCzfsDXwYuASTFYQ8AVzYEWl5xK65SoElAKZU3IsHmQ4EgdlqKZPqA7wFf1akoskOTgFIq70SCzccAXwXOSnHIZmwz0q0NgZaoa4EVIU0CSqm8FQk2nwzcADSmOOQ54NMNgZbHXAuqyGgSUErlNWeiuouxyWBuisPuBa5uCLS85lpgRUKTgFKqIDjrGVyNHX2cbIxBL3aCumU6QV36NAkopQpKJNg8DzsB3aUpDtmAHYNwh448Hp0mAaVUQYoEmxuB7wLHpTjkKez9ghXuRVV4NAkopQpWJNjsAd6PvTKoT3HYXcC1DYGWta4FVkA0CSilCl4k2FwHfA67/nFlkkO6gOuB6xsCLd1uxpbvNAkopYqGs6jNDcB7UhzyCnBFQ6DlEdeCynOaBJRSRScSbD4Fu8j9USkO+SV2CooNSX5XSmnCOs9EB6CUUtnWEGj5K/aG8UeATUkOuRR4MRJs/rhzXwGASLD5YOzMpSVDrwSUUkUtEmyeil3G8uMkn5zuH4Af+DfwOHA8cFhDoOVl14KcQJoElFIlIRJsPgEIAUcmeTkGPAosdp7/ETirFJqFtDlIKVUSGgIt/8A2EX0G2JXwsofBBADwduBcl0KbUHoloJQqOZFg81zsQLNUU1YDrAUObgi07HYnqomhVwJKqZLTEGhZ3xBouQB4F7A+xWHzsfMUFbWMrwRE5FzgbGAmsNwY83Am7xcMBucBFznvtwm4OxAIrMsoSFWStCyp0USCzZOAVcC8FId0Y68GwsVantJKAiLyceAwY8wVzvOvAQuMMZfFHbMXcKMx5sPjCSQYDHqxI/r8QG3cS53YmznXBgIBXTxCjUrLkkpXJNj8HeBTIx1j4Ne3MidMkZansjSPux14SUSuA07GnvmflHDMF4DlGcRyPXBlkv21cfuvyuD9VenQsqRGFQk2l2PP6P8XaADmONts4prKBS6op4e24bNRFEV5Srs5SERuwP7RZwGLjTFrnP2CnbzpT8aYcQ3Fdi6zVjM0yybqBA4KBAKp2u+U0rKkMhYJNpdhJ6Obv42yI9dQfXMvnorVqYtUQZendK8EAH6C/XK9eyABOD4JnAFMEZH9jDGhccRxESN/aXFevwi4eRzvr0qHliWVkYZASz+wDlj342DwRKBilF8p6PI0liTwJezizkN+xxjzPeB7GcYxM83jZmX4Oar4aVlS2VT05SmtLqIiciVQhc12I95EGadkc3skszEHn62Ki5YllU1FX55GTQIi8jbgQ8AHjTGPAZNFJNmw60zcjW1XG0mnc5xSI9GypLKp6MvTiElAROYDPwbeY4wZWLj5u8CnsxmE09d2tHsJoUK98aLco2VJZVMplKcR7wkYY9YC+yTsuw24LQexDIzMS9kXNwefqYqTliWVTUVdnvJu7qBgMDgXe+9hFrad7e5CzrJq4mhZUtlUrOUp75KAUkop9+gEckopVcI0CSilVAnTJKCUUiVsLCOGlSpZVcf5jXi8eMor8Hi8iMeLp6zceaxAvPbRM/A8Yb94vHjLPIiIffQIXq/zWCbJ93s9iAc8HsHj9eDxCGVlHrweocJ5rNzz3GsfvYn7kz+Wezx4Bcq9HjwilHsFj8jQfR5xjpU9x3iHHCuIgNcDHgSvxy7g6/UIHudRBLwieAS8gv07BTzO70osipgYxKJgYkisH0wMov1J90usH2J2v329H2JRTH+ffezr2/PcxKLQ34eJRoces+fYXojFMP29mFiMWG//nsdYNEqsrx8TjRF1Hoc/77PP9/xelFg0hokZor1RTNQ+xhKeD3m9L0osapzfNUT7Y0SNoTdmiBqcR0NvjKT7Y8QfM/hayISTraWclF4JKKVUCdMkoJRSJUyTgFJKlTBNAkopVcI0CSilVAnTJKCUUiVMk4BSSpUwTQJKKVXCNAkopVQJ0ySglFIlTJOAUkqVME0CSilVwnRRGaVcIiIfM8bconEM0lgmPg69ElDKPR+b6AAc+RIHaCzJuBqHJgGllCphmgSUUqqEaRJQyj0T3t7syJc4QGNJxtU49MawUkqVML0SUEqpEqZJQKkcEZG9ReRPIvKK87hXiuNuEJFVIrJaRL4nImmvD5vNOJxjJ4tIRER+kOUYzhSRl0TkVRH5XJLXK0XkV87r/xARXzY/P9044o67UESMiBybizjSiUVE5ovIX0TkWRH5t4i8IxdxaBJQKnc+BzxqjNkfeNR5PoSInAQ0AYcDhwLHAae6HUecrwKPZ/PDRcQLLAfOAg4BLhGRQxIO+zCw3RizH/Bt4PpsxjCGOBCRScD/AP/IdgxjjOULwN3GmKOAi4Ef5iIWTQJK5c67gdudn28Hzk1yjAGqgAqgEigHNk5AHIjIMcAs4OEsf/7xwKvGmNeMMb3AXU5MqWK8Bzg921dEacYBNhHeAHRn+fPHGosBJjs/TwHezEUgmgSUyp1Zxpg2AOdxZuIBxpiVwF+ANmf7ozFmtdtxiIgHuAm4OsufDdAArIt7vt7Zl/QYY0w/0A5MczsOETkKmGeMeTDLnz3mWIAvA+8XkfXAQ8AncxFIWS7eVKlSISKPALOTvPT5NH9/P+BgYK6z608icoox5q9uxgFcATxkjFmX/RNwkr1hYrfEdI7JaRxOIvw2cHmWP3fMsTguAW4zxtwkIo3Az0TkUGNMLJuBaBJQKgPGmDNSvSYiG0Wk3hjTJiL1wKYkh50HPGGM2eX8zu+BE4ExJYEsxNEILBKRK4A6oEJEdhljRrp/kK71wLy453MZ3rQxcMx6ESnDNn9sy8JnjyWOSdj7Mo85iXA2cL+InGOMecrlWMDeJzkT7BWjiFQB00n+/zdu2hykVO7cD3zQ+fmDwH1JjlkLnCoiZSJSjr0pnO3moFHjMMa8zxgz3xjjA64C7shSAgB4EthfRBaKSAX2Juf9I8R4IfBnk/1BTCPGYYxpN8ZMN8b4nH+HJ4BcJIBRY3GsBU4HEJGDsfeONmc7EE0CSuXON4HFIvIKsNh5jogcKyI/do65B1gDPA88BzxnjHlgAuLIGaeN/xPAH7EJ7m5jzCoR+YqInOMcdiswTUReBT7LyD2YchmHK9KM5UrgoyLyHHAncHkOEqOOGFZKqVKmVwJKKVXCNAkopVQJ0ySglMoZZ+qFn8U9LxORzSLyYNy+s0TkKWfajBdF5EZn/5dF5KqJiLuUaBJQSuVSJ3CoiFQ7zxcDkYEXReRQ4AfA+40xB2O7aL7mepQlTJOAUirXfg+c7fx8Cbany4BrgK8bY14E22vGGJOTOXJUcpoElFK5dhdwsTPY6XCGTsx2KPD0hESlAE0CSqkcM8b8G/BhrwIemthoVCJNAkopN9wP3MjQpiCAVcAx7oejBmgSUEq54SfAV4wxzyfs/xZwnYgcAHYSNxH5rOvRlTCdQE4plXPGmPXAd5Ps/7eIfBq4U0RqsDNp/s7t+EqZThuhlFIlTJuDlFKqhGkSUEqpEqZJQCmlSpgmAaWUKmGaBJRSqoRpElBKqRKmSUAppUqYJgGllCph/w+yO8SysjJ11AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "parcorr = ParCorr()\n",
+    "pcmci_parcorr = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)\n",
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the partial correlation $X^1_{t-1} \\to X^0_t$ is exactly 1. Since these now represent the same variable, the true link $X^0_{t-1} \\to X^0_t$ cannot be detected anymore since we condition on $X^0_{t-2}$. Deterministic copies of other variables should be excluded from the analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Causal sufficiency\n",
+    "\n",
+    "Causal sufficiency demands that the set of variables contains all common causes of any two variables. This assumption is mostly violated when analyzing open complex systems outside a confined experimental setting. Any link estimated from a causal discovery algorithm could become non-significant if more variables are included in the analysis. \n",
+    "Observational causal inference assuming causal sufficiency should generally be seen more as one step towards a physical process understanding. There exist, however, algorithms that take into account and can expclicitely represent confounded links (e.g., the FCI algorithm). Causal discovery can greatly help in an explorative model building analysis to get an idea of potential drivers. In particular, the absence of a link allows for a more robust conclusion: If there is no evidence for a statistical dependency, then a physical mechanism is less likely (assuming that the other assumptions hold). \n",
+    "\n",
+    "See Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.\n",
+    "for alternative approaches that do not necessitate Causal Sufficiency.\n",
+    "\n",
+    "\n",
+    "#### Unobserved driver / latent variable\n",
+    "\n",
+    "For the common driver process, consider that the common driver was not measured:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(10000, 5)\n",
+    "a = 0.8\n",
+    "for t in range(5, 10000):\n",
+    "    data[t, 0] += a*data[t-1, 0]\n",
+    "    data[t, 1] += a*data[t-1, 1] + 0.5*data[t-1, 0]\n",
+    "    data[t, 2] += a*data[t-1, 2] + 0.5*data[t-1, 1] + 0.5*data[t-1, 4]\n",
+    "    data[t, 3] += a*data[t-1, 3] + 0.5*data[t-2, 4]\n",
+    "    data[t, 4] += a*data[t-1, 4]\n",
+    "\n",
+    "# tp.plot_timeseries(dataframe)\n",
+    "obsdata = data[:,[0, 1, 2, 3]]\n",
+    "var_names_lat = ['W', 'Y', 'X', 'Z', 'U']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable 0 has 2 link(s):\n",
+      "        (0 -1): pval = 0.00000 | val = 0.632\n",
+      "        (2 0): pval = 0.00493 | val = -0.028\n",
+      "\n",
+      "    Variable 1 has 2 link(s):\n",
+      "        (1 -1): pval = 0.00000 | val = 0.624\n",
+      "        (0 -1): pval = 0.00000 | val = 0.442\n",
+      "\n",
+      "    Variable 2 has 4 link(s):\n",
+      "        (2 -1): pval = 0.00000 | val = 0.626\n",
+      "        (4 -1): pval = 0.00000 | val = 0.461\n",
+      "        (1 -1): pval = 0.00000 | val = 0.450\n",
+      "        (0 0): pval = 0.00493 | val = -0.028\n",
+      "\n",
+      "    Variable 3 has 3 link(s):\n",
+      "        (3 -1): pval = 0.00000 | val = 0.625\n",
+      "        (4 -2): pval = 0.00000 | val = 0.454\n",
+      "        (1 -3): pval = 0.00828 | val = -0.026\n",
+      "\n",
+      "    Variable 4 has 3 link(s):\n",
+      "        (4 -1): pval = 0.00000 | val = 0.628\n",
+      "        (4 -5): pval = 0.00562 | val = 0.028\n",
+      "        (2 -2): pval = 0.00702 | val = -0.027\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable 0 has 2 link(s):\n",
+      "        (0 -1): pval = 0.00000 | val = 0.632\n",
+      "        (2 0): pval = 0.00661 | val = -0.027\n",
+      "\n",
+      "    Variable 1 has 2 link(s):\n",
+      "        (1 -1): pval = 0.00000 | val = 0.624\n",
+      "        (0 -1): pval = 0.00000 | val = 0.442\n",
+      "\n",
+      "    Variable 2 has 10 link(s):\n",
+      "        (2 -1): pval = 0.00000 | val = 0.715\n",
+      "        (1 -1): pval = 0.00000 | val = 0.385\n",
+      "        (3 0): pval = 0.00000 | val = 0.174\n",
+      "        (3 -1): pval = 0.00000 | val = 0.105\n",
+      "        (1 -2): pval = 0.00000 | val = -0.073\n",
+      "        (2 -2): pval = 0.00000 | val = -0.050\n",
+      "        (2 -3): pval = 0.00002 | val = -0.043\n",
+      "        (1 -3): pval = 0.00009 | val = -0.039\n",
+      "        (3 -5): pval = 0.00034 | val = -0.036\n",
+      "        (0 0): pval = 0.00661 | val = -0.027\n",
+      "\n",
+      "    Variable 3 has 12 link(s):\n",
+      "        (3 -1): pval = 0.00000 | val = 0.717\n",
+      "        (2 -1): pval = 0.00000 | val = 0.278\n",
+      "        (2 0): pval = 0.00000 | val = 0.174\n",
+      "        (1 -3): pval = 0.00000 | val = -0.125\n",
+      "        (1 -2): pval = 0.00000 | val = -0.113\n",
+      "        (0 -5): pval = 0.00000 | val = -0.086\n",
+      "        (1 -4): pval = 0.00000 | val = -0.068\n",
+      "        (0 -4): pval = 0.00000 | val = -0.057\n",
+      "        (1 -5): pval = 0.00000 | val = -0.055\n",
+      "        (3 -2): pval = 0.00000 | val = -0.048\n",
+      "        (0 -3): pval = 0.00060 | val = -0.034\n",
+      "        (2 -2): pval = 0.00241 | val = -0.030\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for data_here in [data, obsdata]:\n",
+    "    dataframe = pp.DataFrame(data_here)\n",
+    "    parcorr = ParCorr()\n",
+    "    pcmci_parcorr = PCMCI(\n",
+    "        dataframe=dataframe, \n",
+    "        cond_ind_test=parcorr,\n",
+    "        verbosity=0)\n",
+    "    results = pcmci_parcorr.run_pcmci(tau_max=5, pc_alpha=0.1)\n",
+    "    pcmci_parcorr.print_significant_links(\n",
+    "            p_matrix = results['p_matrix'], \n",
+    "            val_matrix = results['val_matrix'],\n",
+    "            alpha_level = 0.01)\n",
+    "    link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                            val_matrix=results['val_matrix'], alpha_level=0.001)['link_matrix']\n",
+    "    tp.plot_graph(\n",
+    "        val_matrix=results['val_matrix'],\n",
+    "        link_matrix=link_matrix,\n",
+    "        var_names=var_names_lat,\n",
+    "        link_colorbar_label='cross-MCI',\n",
+    "        node_colorbar_label='auto-MCI',\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The upper plot shows the true causal graph if all variables are observed. The lower graph shows the case where variable $U$ is hidden. Then several spurious links appear: (1) $X\\to Z$ and (2) links from $Y$ and $W$ to $Z$, which is counterintuitive because there is no possible indirect pathway (see upper graph). What's the reason? The cuplprit is the *collider* $X$: MCI (or FullCI and any other causal measure conditioning on the entire past) between $Y$ and $Z$ is conditioned on the parents of $Z$, which includes $X$ here in the lower latent graph. But then conditioning on a collider opens up the paths from $Y$ and $W$ to $Z$ and makes them dependent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Solar forcing\n",
+    "\n",
+    "In a geoscientific context, the solar forcing typically is a strong common driver of many processes. To remove this trivial effect, time series are typically anomalized, that is, the average seasonal cycle is subtracted. But one could also include the solar forcing explicitely as shown here via a sine wave for an artificial example. We've also made the time series more realistic by adding an auto-dependency on their past values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 4 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7e1467eb70>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e09d558d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e09d2be10>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e07b9b390>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(1000, 4)\n",
+    "# Simple sun\n",
+    "data[:,3] = np.sin(np.arange(1000)*20/np.pi)\n",
+    "var_names[3] = 'Sun'\n",
+    "c = 0.8\n",
+    "for t in range(1, 1000):\n",
+    "    data[t, 0] += 0.4*data[t-1, 0] + 0.4*data[t-1, 1] + c*data[t-1,3]\n",
+    "    data[t, 1] += 0.5*data[t-1, 1] + c*data[t-1,3]\n",
+    "    data[t, 2] += 0.6*data[t-1, 2] + 0.3*data[t-2, 1] + c*data[t-1,3]\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we do not account for the common solar forcing, there will be many spurious links:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 4 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.420\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.403\n",
+      "        ($X^2$ -1): pval = 0.00027 | val = 0.116\n",
+      "        ($X^2$ 0): pval = 0.00488 | val = 0.089\n",
+      "\n",
+      "    Variable $X^1$ has 3 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.478\n",
+      "        ($X^0$ -1): pval = 0.00002 | val = 0.135\n",
+      "        ($X^2$ 0): pval = 0.00330 | val = 0.093\n",
+      "\n",
+      "    Variable $X^2$ has 5 link(s):\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.559\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.197\n",
+      "        ($X^1$ -1): pval = 0.00274 | val = 0.095\n",
+      "        ($X^1$ 0): pval = 0.00330 | val = 0.093\n",
+      "        ($X^0$ 0): pval = 0.00488 | val = 0.089\n"
+     ]
+    }
+   ],
+   "source": [
+    "parcorr = ParCorr()\n",
+    "dataframe_nosun = pp.DataFrame(data[:,[0,1,2]], var_names=var_names)\n",
+    "pcmci_parcorr = PCMCI(\n",
+    "    selected_variables = [0,1,2],\n",
+    "    dataframe=dataframe_nosun, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, if we explicitely include the solar forcing variable (which we assume is known in this case), we can identify the correct causal graph. Since we are not interested in the drivers of the solar forcing variable, we don't attempt to reconstruct its parents. This can be achieved by ``selected_variables = [0,1,2]``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 3 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.378\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.356\n",
+      "        (Sun -1): pval = 0.00707 | val = 0.085\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.429\n",
+      "        (Sun -1): pval = 0.00000 | val = 0.258\n",
+      "\n",
+      "    Variable $X^2$ has 5 link(s):\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.560\n",
+      "        (Sun -1): pval = 0.00000 | val = 0.309\n",
+      "        (Sun 0): pval = 0.00000 | val = 0.307\n",
+      "        (Sun -2): pval = 0.00000 | val = 0.302\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.205\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "parcorr = ParCorr()\n",
+    "pcmci_parcorr = PCMCI(\n",
+    "    selected_variables = [0,1,2],\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)\n",
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Time sub-sampling\n",
+    "\n",
+    "Sometimes a time series might be sub-sampled, that is the measurements are less frequent than the true underlying time-dependency. Consider the following process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7e079dacf8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e079fb550>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e079826d8>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(1000, 3)\n",
+    "for t in range(1, 1000):\n",
+    "    data[t, 0] += 0.*data[t-1, 0] + 0.6*data[t-1,2]\n",
+    "    data[t, 1] += 0.*data[t-1, 1] + 0.6*data[t-1,0]\n",
+    "    data[t, 2] += 0.*data[t-1, 2] + 0.6*data[t-1,1]\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the original time sampling we obtain the correct causal graph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.529\n",
+      "\n",
+      "    Variable $X^1$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.515\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.532\n"
+     ]
+    }
+   ],
+   "source": [
+    "pcmci_parcorr = PCMCI(dataframe=dataframe, cond_ind_test=ParCorr())\n",
+    "results = pcmci_parcorr.run_pcmci(tau_min=0,tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we sub-sample the data, very counter-intuitive links can appear. The true causal loop gets detected in the wrong direction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sampled_data = data[::2]\n",
+    "pcmci_parcorr = PCMCI(dataframe=pp.DataFrame(sampled_data, var_names=var_names), \n",
+    "                      cond_ind_test=ParCorr(), verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_min=0, tau_max=2, pc_alpha=0.2)\n",
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If causal lags are smaller than the time sampling, such problems occur. Causal inference for sub-sampled data is still an active area of research."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Causal Markov condition\n",
+    "\n",
+    "The Markov condition can be rephrased as assuming that the noises driving each variable are independent of each other *and* independent in time (*iid*). This is violated in the following example where each variable is driven by *1/f* noise which refers to the scaling of the power spectrum. *1/f* noise can be generated by averaging AR(1) processes (http://www.scholarpedia.org/article/1/f_noise) which means that the noise is not independent in time anymore (even though the noise terms of each individual variable are still independent). Note that this constitutes a violation of the Markov Condition of the *observed process* only. So one might call this rather a violation of Causal Sufficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7e0775ad30>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e07784fd0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e0782b710>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Z48lpboCw07/rO1l+pZvkhhQrJvpi7D/B+DmpIl2fsHBuxfc7SGdT6s2sp9wa9xcSFnnNjSwOI2ea2T/NbJBLYp1CMOfQlFt6JUiHfKJ9kFoupfMVZJreo3wptZz9CQs5d5PTCm5vKrj9e5zpj6du6eQdb8y4lzqKs/oG/U9SY8zsttypw/h1L+pGBOV3a8Hv6k7yiKxLsDVUUdeDWqJFGlu1yWN1Mzu1E6EaMO9+xqGUVO53NIg/N8a9ysyOBc5pE/4+gl2bd5vZ8xmxQDxe/j7B+CmlsnJitdLwgDmXYOivKYNumVfcN854xwI7m9lvCn51jb/XEgxGNmmAP9rG/3Qz+y3RKnYDmiiH0vPL6zcIo17jYalxxOlJA3FCa/GZ2b+6JUgPaTK77O0E43wAX+0kcTP7U1R+FybO7437dBLJbjVJ5IrtRuDg5DxfBPYsYJn443LFulmYh2mBmd2ROT23VfiGGKOHDdMKv64iqLidrLVnZq8EfhGPxciilicCS5jZH8zsd2ZWVVyPJdEv61D2XMamLd4vJ8epCfU/EL7XzpPHzPZqkU5eKR3OSOVYVUY/SPzf1lC+Ep288z8gVPBNuCc5Pinzu6uDPCHYokp5hHIFDMECdMVXMr9DzOxdZpZP4MHMZGb5bzZVeTvDzC5m5N6nZetW4m8tkao3/P8IK+/n3B/TPBz4WnSru5Yqr0sI1ql3ZOxi1X+gsBCumT2SnFZl+cOEuqijjkQy8vR2QgO8xE86SXPcDNpERjc3yuY2lgiXaTB6efj1Ere1GTGxPSMLZ0n8h0vu0e/biXtlb+jHWfwvFtL+U01690W3txCNA8ZtuySMAa8vXNtqhAozdfsa8FCWhwFnF+Kn2+Mt/NJtaaIZi5ieCC+ZxeN76+LG8LOBPbNn9zHgrkze99c8+12TNK/K8riZzGxKYbs5OV65wfV+hRErx3n5emV2/y8ryLsB4XuuAYfF/fWF51MZFaws6/4h+lV2uH6VyfXWFjL/kDDZ6W+Z+xVxX5mpXyTuD6Fscr60nZTdv+sYscl1cMM0jGAleVHCN8zKbdXs/n4sOU4ND24b99dW8hfu+7znVSj3+2d+P02epwGnRPetkjiVfbQZhHfVCBOwnhGPpxfyfzPwr5rr34VQdirr3aMMK9bI/fHoVtmWeyILu0EhnxsYa9stLcu/AhZJ8voaibmV6J7aoHtBL+r0ybCa+WOMaPsPJu5Vi+/ZZnYTcDqwtZmlraXvM/rj+2HJ8eVZPkckx1UL5qY0gJkdzEhvpeptVC2wzzO6tVT1wh4xs2WqOGaWrwT+p+x8hpndYWZPEiqcTxGGwT5MeeJC7WQGCz2XaqhuI8JHfiiPdxvBpPzRMa4Rhzjj8a/r8olhbjazEzPnwwkVahPSe31F5vddgoIsUX2LazcZZd64v4XW9/5mdgNjV2Y/28zOTtL7KaF1PQozu9bMqh/Lq/JiWbDDCOXik8DPY77ViuLVc3tl3FdDLKcQGiJjMLM3mtmdhLJe4pMxXDpRpUkdsWkiD2b2DzNbn2BnalHqVxnYtuD2eTP7L/Cfgt/Ncf+HJK+05zsHOMHMxrscWX6tlwGPxue5PcFaNAXZnm9mj5nZ9wkjKd8hKCAolytL8ro587vTzJ6M7291fXXT40+KYQ6J5/+N+/y+lmR4ykIvsWJPkh6omW2VlYPTCGbsP5SE+Q1BGUNo9HSfXmi9QW3UmHzPwhjwxwZhYr3Kd4AHcj+i1c/EfY3EbzbBcu+UNK0k7BKMmE6/Ou4/WCPHq5LjXZvI3CLMRsBLkvPrCcorvS4j2BBaLro/ml2nET4M5y2vJQv5fTy5jzsTlPJL4n35QhOZC9dY7EEl/kcQKsS8Rbhf4vajuD82eUZ/TvyrHlTae1iGYOb9sizPFYENkvzPjMeVDEu0uaa/EWxoGbE33fBeXJI9l9uAf8bjVQjDVWmvPi+DhxJmTBphdq4B707ulxF6ULMZ6ZGk26aM7jG/Pbr/pyDrpwrx8zJVbStGt49GGY0wpA3w6Xi+aPZsS9dnwJcLsjyQ5f1DQuPlXqIV6Yb3f82Yx/Nr/KtyNbUg12tIRjOy669Lr3TPNgRenLidCZxbiLtukn7VW742fWfi8ctK1x/Lh+LxKzM5VgJmN32HO916kuigNporqN0bhHkkHk8FFk78jqOsoARs1a5gFfxOj/ula/xfHY9XaSDzayZw7+oqsn9n1/mzWCiN0SbuVUhzCqFHV8rvi6X8GsjYTkF9vXQ9wEey85WifJUiuT6JUymoFxGHVzuQ74TsfLGGcX9AUPybNAyfl7/lgOVrZLqtpuLZC7g6Cbd1cn8MODR5jhaffXoPL06OV49+Nxfy2Te5t+8kDCFOT/ynR79bC3Gn5m6JfFXFOqoRlfiXFNRfs/s2jZGhrGnAjh2UxxVb+FXlSpn7evF+vgF4e15Wgee2uN5fJOefIxs+bCFLqqBeHffXJOl+Ih6v3a6skymoXm9NFotdELHGAUMXOx1G+AbhQ2k1Tl+FM8K4bSe8gNCDWsxGf+RMuTWm32SJp8bX1QFHESo/ohy7AsR/oO8nWmytaqlRwpjNZfQH5pTfAAeNQ552s6bqhqQq900BzGzeR31JPyH09r5K+AYwN4vTlFmMDO1UNPqPzcJqLZ1wCsnMNms9YelzjJ4ZWsU5ATghHgtA0jJ5OEK5OpfREyEq9yqtv7f4L/54wvO+gWAdYdS/bmb2uKS7GD3ppPKrm2DzLEaGlTqZtr8FsHCS/lPE4dF4fG7ThMysbti4eh/GyGUjPwuns2i3YORTwt9qknw+ydR368zs0d8Ipt13N7O7Jd1IaFxAGPm5O6Z5U0nmjOuIEz/6gSuoeooPKhawqpB9rmF+P65J68p4WFRO1vn/MhP5qfc2QmHNZagzEvluwizFzxCme3eEmZ0v6Xw6MwC5DmPH7HNSpfJ7Rsr4lJhv/u0QM9sNQNLvgF3M7J+xsq1anY2wsT9+v47OpnQ3xsyOBI5sGLyxkUgzG/P9Kla2O0lalFCBn1kTfXsK08HNbK6kyr34e4OZNf3WWIXPZ/oVgxXiDZ3BTDO7JJa3C+oaqmY2nl9WqriPkcxMNbN1kuO/dZjWbcAK45WlUyarguo3/ZqM0m46dyvWI0zgaPLPEmZ2DICkVRiHgop0pIDN7MYGwSpFpHRxY5qV9XQ68haEiTB3kLS4O8HM6iYj9Jv76NzQ5v1kq3OY2X8lpf94/SPzzyeMpFSNp14o7I0oP98bepBXrziWsKSbkzBZFVSTLuog//YfD++g/B9GIyzMnLqcsbMT2/EDyn+2N+FDjP2Ha6LM+28sDtlUHMVIz7eO7zHyL8wlAJK2Yv5/T8zM2g2N5hGKrWQzu17S2vF0X8JkhkZJxv1EGlHlhMsNl0Xov4HVcWNmTVZPmXTM7y/eeFhlAN38nis7Mzu+13nU5GuMc9klC9O0u93KrRuafYj6oakqzFyyFSXM7MHuiTYwmq5v2Agz+0vcP0r7b4JjondTltpMRv/I7synTIb/oEbRgXLqplKZdPd5gPRqcd35mU5XcugF4aNeYTKN49QxGXtQTdiQ7g0PXEj9j5FOd3kQ+N2ghRgmxjHRplcMixzOfIQrqAJmdlUX0yr9Le/0ADNbdtAyOLX4KILTMV5oHMfpB/9tH8RxRuM9KMdxek6cUOHDfE5HeA/KcRzHGUpcQTmO4zhDiSsox3EcZyhxBeU4juMMJa6gHMdxnKFkaBSUpP0lmaTl47kkHSnpFknXSNpo0DI6juM4/WMoFJSk1QhmitN10HYgWPOcTViU8ugBiOY4juMMiKFQUMDXgAMYvZDkrsBJFrgMWFrSygORznEcx+k7A1dQknYB7ioY5FqVYIun4s7olsffV9IcSXMIpqMdx3GcBYC+rCQh6UJgpYLXwQSz39uVohXcShYyjwOOm5CAjuM4ztDRFwVlZi8vuUvagGBm/Opo8ngmcKWkTQg9ptWS4DOBu3ssquM4jjMkDHSIz8yuNbMVzWyWmc0iKKWNzOwe4CzgrXE236bAwwMwNOg4juMMiGFeLPZcYEfgFuAx4G2DFcdxHMfpJ3IDl47jOM4wMvBZfI7jOI5TwhWU4ziOM5S4gnIcx3GGEldQjuM4zlDiCspxHMcZSlxBOY7jOEOJKyjHcRxnKHEF5TiO4wwlrqAcx3GcocQVlOM4jjOUuIJyHMdxhhJXUI7jOM5QMjQKStJUSX+SdE48X0PS5ZJulnSqpIUHLaPjOI7TP4ZGQQEfBG5Izg8FvmZms4EHgX0GIpXjOI4zEIZCQUmaCewEHB/PBWwDnB6DnAi8ajDSOY7jOINgKBQU8HXgAGBuPF8OeMjMnorndwKrliJK2lfSnLhd13tRHcdxnH4wcAUlaWfgXjP7Y+pcCFq0rGhmx5nZxma2MfB4L2R0HMdx+s8wmHzfAthF0o7AdGBJQo9qaUnTYi9qJnD3AGV0HMdx+szAe1BmdqCZzTSzWcDuwEVmtgdwMbBbDLYn8LMBieg4juMMgIErqBZ8DNhP0i2Eb1LfGbA8juM4Th+RWfHTznyJpDnxW5TjOI4znzPMPSjHcRxnEuMKynEcxxlKXEE5juM4Q4krKMdxHGcocQXlOI7jDCWuoBzHcZyhxBWU4ziOM5S4gnIcx3GGEldQ8xmS1o7mSOr8n91PeRzHcXrFpFFQktaXtNWg5egCNxJsZ9Vxs6RndTPDaO14pW6mOdmQtJykhcYZV60aJQ3TOETSphNJo19IWn/QMgwaSStLeu6g5Rg0k0ZBAWcQFqCdb5C0jqTzCl4z2kRdpMuivA/4R5fTLCJpEUndln8YuB/4SqeRJE0BrgV+NcH8Pwa8a4Jp9ItrJS08aCEAJE2T9LYepv+ApL8WvE4Fru5VvvMLk0lB9YRYgfSK7YBX9DB9ILTu2wR5Rq9lSLgIeFzSknUBJK3WR3mQtIakLbuQ1AckbdIgv1UkrR5PPw2sB7xE0tITzL/RwpuStpK01HgyiEPQp4wnboxf9RQn1GPsIs8FvjuRBGKja58a72WANQruC8e4L5K0RYM8VpD02gmIWUrz+DrlLGkzST/oZn4lJpOCmj3eiJKWlXRrwX1X4OkJSdWa8a7k2+nLfb+k0ktSK0cc9pvaYT5N2DDui8OUklYG/t6DfFvxA+C3JQ9Je5bug6TdJZ1YiDLGMnQsX2mv+AqgKm8bJO4vbS5ykb0kPb9BuIuBj48zj12AN8YhzfHUL9W9HHjdJGk34I81fkt0kNRmwPEdZv+iuP898LsG4T8CnF7nKWmWpAtr/Bat9pK+lXjtQ9LrlrSSpG/E0zcCb2og14QYeCEYViTtLWmxeLomsKakF2TB1uuxGHUKqp3iOlrS5pIu6yCvwyUt3cG3jl9QGHaKLauXSroznj9f0kclNa3wnmrjv2lMd5qkl7WrBCXNkHR+w7xL8TcB5rYIcgKwTcH97cBbG2bzL+BHyfkqwLT4zejViftCUaY31j3bBs/wIw1lalkOJB0l6baCV/U87icMDbfOJFScHy3E70XjpzHxHv64xm854JEOkpuIpe9u9SRfArysxu8xSWsRGkDvLQohzSIM83+gS/I0YoFXULEl20iRSHpubBHPJNif2rnyivumFc6EiT2aI2u8iwoqaYW/OG4vkrRYw57OcsCDwN4Fvxkx/WmSqry3BkYNe0VlcQmhoFc9hT8BhwFfbiADjFzbDjHNXSStGI/XJ3xLBHgSuBAoDplJeo2kzxB6YtvVZSZpQ0nPi8cLSzooHq8gaV3gcsK9aUXppa56glWvr+L1ifuU5NmsIck0eoLLAbm4cb8L4dlull3LuwnPcNcWshataMce8asSJ5P0iuR552wJzIqNhLQSTeuUlWPa8yZnSDoiXufUeH9vAw6rngGweRW0xTWkck+XtJCkxZuEL8Rfvsar1WzY3zRM+y3x/rVq4ORxlpSUfitsWke3u1/z0pG0buG57g6cl4RZM/PPe+/vbyjXxDCzBWYD5hTcjDA8Y+FyW8a/IYZ7NO5fHx/EpvH8giTs5k3S7ED26cnxQlXaVfrA8oRWtgG71aRhyXZAcnw4YeLExsAza+I9EfefKvifFf2mJ/KMunbgecC3o/vn8nBcWUvSAAAgAElEQVRxuzIJvwhwHDAtcVu/cN0GHAGcmKVVbScQhkNe2OJeVGmtDvyiJtwywPOTsHcTenNpOjNq7t1P4/FKwJfy+0PoTaTprBDdvwfcHt2uK1zbA9n5GwkVxamJ29SY1rGJ23salI3to/vrCeVti+yefwn4YPqMs/T+lKS3TuL+icT9kCS91TI53lq43hWT4+WTNNcAHkzOpwIbVTID/y+XE/g+8Fngu4nb1sTyHeNPifEfbXO/8rQfj+7rp36E70ZfJXw3PI9Qbg14YYv7WOWxeTwv3ZcxcQkNr/TdOTSTZRNgkeT8bdnztdQ9zys5vzyeX1zyb1K3TWTraeL93qhXUH9rckMJU7jTh/W6uN8xcdsyhv1cnibwa2DDLM2NG+S7RpZOXkDXzc4PIb7wSZx1sjAfy86vbFHY03CfBlarkWd63E9J3Na35AWJ2/WFgp7fq52j27sIFeSMFi/KRYV7Utqe1+IezgL2SNJduEU6e9a4fzS7b6dF93OBpeO1GEkDo0aWyv2h/J612U6O+0cStxmFPN7X5hkb8DBhiNYIwz8vzuQ9DPhQ+syy9K7Oy0B0/1TNM5yVyfHu0vPLzrcjvLvHpnIQWvtG6EnmeW1GUORpOrsThjZzmV6VukX3JQhDe3Xl9t7EvaofloyyPyOLd0QpjbrnEs/fXPPsnwv8JIZZPnFfitC4mZvJacD707oxu3YjfFv9S01+6bZ9zTMd9ex7sQ1cqXT1YuoV1D2lQhLdtk7O78kezDvjfufE7Z68YBEqpNnx+ITEfZPkeNsY73xCC9CAxYH/AF+L588kfM/IC8icUsGJ6a0IrFDw/0yLArcy8JwY/1+ZX6poDiZMAqnOF437RRK318V0vlgjX8lto8RtP0JPIu+tVHIaQfG3e4kM2Ln04ifX8pbked3VMM10uzG518sU/Kvew9Gt7kGN+587kOOx7PykwrNfFZjS4n6k21bAG+LxsxP3fyWyPjte+0rx/OYkXPU8pzG23FVlfY1MjiYKKr/eHwDfTNx3zMJ9uMn9S+TYK3Nbi9jbqYmzT+b+hux8lTZ5fyHuHwUWr1FQb6qJWzUwv5K5b5idr8XI6MmNhG+hL0/8/5sc/7vJ/QIuyM6nZOdLuIKamIJ6MCsESwCXJoVmd2DbFg9oq+T4rsIL//WGD7oqKO+L+0/G/aVt4l1R475LvLZbGuafp1eqEE5Ljn+Z+S0d908mbh+I9+MLeVoNKkYDDmwQ5jcdXFteEVbbnYxUSMt3kF6+PaeF39kFtzVrwt43ARny7fYa9xOBfYFT2sT/aCv/wv38dE3Y1xTc/hD3p2TpHFQIO6aR02bLFVTTreo9pwqnGj0pKihGGkvpliuT1dvkm79Peb2x6zivZ7zbow3DXdjGfylXUBNTUPOGRKLbTh0+yLQH9XfKPZZebn+occ97P93emg6tPQP4fO7eUEGd1jCPpttHWvgdHvd5y7eTrZWCKj2nf/b4GbXbxtNTHM9zbLKlLe+8JzCebbwKqlKk7yj4jSnHcTtjwM+xF1veEx/vtmSv6vTijJ4FkOnVgaTNgXMSv/8QhtpacXZyPJcwzNBP6mbhLdvjfFdvHwQIQ6Pj5XUTiFui1WoN+8f9j1qEacd1LfxeWHBbcQJ5dYNVBpx/yr+SY+tCevlvH02pZryVfnz+RE2cV9e4z890axb3w5I2NrPiP2MTYYFWUMn013QNtN9nwTqdnvrMuPWTRfucX8WE1vSTdGC3BBkiBvp/ziCQtGeXkkoVwv61oZrzuXHG+37cd+u65le6uaTYLtT81DwROtKgGudilxMh/otxk6RbOvjZs2JB+c9rnUELME6+NGgBnK5wwqAF6DJVg6/XP9pPJp7XPkjntK3AJa0Vt7UJH0f7RvyJ8SjCT5vrEpZQWbeDJBboHqLjOM6Q0JO6tkkP41DCj6qbUl7UsJdsAtxiZn81sycI3w5a/SWf82RvxHIcx3ESejL03UTrfcrMrgWQdHEvhGjBqsAdyfmdjCyiCICkfQlTaSFMH56Hmc1tvrSc4ziOM0568vmnbQ/KzK6V9LJ43O9VpEvaZdTsHzM7zsw2NrONCUvKOI7jOP3lkl4k2nQSwd6SDpU0r8claUVJH+uFUAl3EpbdqZhJWCPNcRzHGR7O6kWijRSUme1BWCvsAkmvl3Qqwb7JX3ohVMIVwGwFg3ELE1Z86MmNcOYrrh+0AH3m7PZBHGeg/KkXiTZSUJJWIvReZhCWprnezHYxs5/2QqgKM3uKsCzQ+YSVxk8zs15WTl21SBk5t8vpVf9wnNnldOcn2tmMqhjvzKLdxhmvFZ/tIOxhcT/HzERvjWI6k5NuTCBL7Vw1NinSCU2H+I4BriGYmNgYWFTSyeO1wdIJZnauma1lZs8ysy92MelzsvN3mNkZjB5SbEK7XuRfO0yvHZVxt5W6nO4wMcbqbEap3L4jdzCztGLvxDx1q9UixsstHYQ91sxkZtXKFJ2sutDEoNzJBbfvF9z6RS++XxyTnffj08B3aW4QctD8exxxDkuOnyKYgwGYtx5Wt2k6xPcqMzvHzOaa2dNmdiBhBeqf90KoPvGv7LwyhNfJjf4jiQG6hEOS424Pg1YTQdpO64ytbwhKbesuy9GU3QkmCkrUuT9Y414N74pgn6ZiO9q3CI9r439Fctyt1mBlfO8EOpiGa2Z5o6aTMtkk7KMFt9KqCr/sIN/x8jsyw5fAtV1I9+Hs/AcEcyud8CbCupsl/k5YRipd5eVWMzuiwzygxootZaXarbLZca/czNI5B8+ivLRXVxn3Sgtm9ks6+ydp2Bi1LIeZVa2BunnpswtuLzSzq+PxQ4lC+HUSps7c81MEi7l1Q5Yl97sYKaCdFNRLzexXyXmdFdGfExZ+ncjaeqMws1MJi3qmnBD3vy1EOcbM/luTXNUYmErSMzWzX1D/3K7J9hV54yodQ286/JFWIKXncQVhYdp9CeWtyX0tVc55bx+gaPI9oW5NOShYhK1pAaeKrJMeaE5unfXBmKfM7MUx7/SeH08wZwLjL4v583jazE7pMI2nzaxuZvClZvaPLJ+mDYlRFp7N7NuFMLcRPm3kdGrJ9v/VuDcZJv8nYxvyQJjRbWY975VOaCkgMysKP59Q9/3sAcLq56M++plZOkTzjehWVyBTpXQVIz2FKxP3d5jZB4Gf1aSxO2PXwrs8yTNfU7COJc0sD5srgGpB0S3N7F6CGQBo3/urrrPupan+Wate4mph2CmE1tebkrDHxv0jNWldZGb/Ixij25fQ6nwi8a9TUNXCsFUFuHvcvyLuq+G8LxPMcQD8j2CELuXPhO+gKWn+pyXHJ8X902Z2mpk9aWbXmVll+v1PBMurc7L0ziIYDhyFmX0P+Ek8rZbnuTHu838Tq0r4WwQjjRVpRfoPxvZaSuySHN/UIHzFY+mJmd2W+V/B2AZD2hO53cweisft6qjbCI0qCD2+6jOAEawQVPyjTTppY6nqNd1RCFfd96rHmfZE0nt8QnKc9ub+SjBfUWqc5ezD2G/YlybHVaP6swQryzC28VE1xNJRnXcx+t6cwtgG8dVmtpKZVY3Z6tqm0c8l5Hq1TPogNurNbRhhUdh0ifiZ6Xkprdw/Hm9KqAz3SMJuCbwgCbNZFu+uLA0D3hrPN8jkqraVs/B/I1rIjOdvKcQZZRKgcE1LAStm6Y6SKx5XRuZSO1OVxdf3J26VFdwPZHJMJSivKq8pRGOC0f+kwvOprJseGt2XiPvKEOROheu5O5E5Ncr278R9XUJlUdkAWizuvxX316b3Kqa5aH6PCCa8K2N+NxKG716b+H8pOd62dP+TsnBU4foNOL5F2X4e0VIuoXG1b4wzK+4PTq75kXi9in6nEobkq3xeGsPNs0lVkMUYbURyj4J/afszI/alLgJ2KaS9CIk58qSMGLB6dm/uI5Tr3Ghetf0wCXsYoZdtwBei+6bxfIWaa1yTESOMde/N25PwlxIaJlML9cwByfFeSZzfJsdn5s8+Hp8Zz4+N+79G931IrPgWZNsUUE2dZ4RV2I1oJTtNgzinIB5Xtq0+D+xXSG9uTbksytWtbeBKpasXU1ZQaWWVV6IvIrS4vliI96c0fBJ/dhsZjGDl8n9JvJK55T2yONX2c+D0VgUguqUK6hdxvyiJpdc2cl7E6Gv7YVowM5mMaO498XtXcvxhgsK/Cbi3wf05ueD2asJQ4HKZ3zOj/46FtO7MXrhK1gcL96yqAKtKuzIWeW0eNomzLiPGGc8H1o7HC0X/5ZI8Fyb0yoxo8r0mzSkkFQqhJ1WlsV8pTk06uybPYxVqzLPHdKfE40pxvCSez7MnFs9/kshyLmGyyqjnXyh7RujV7p+Uhf1qymxtuUyey9KJ28eBfZPzcwrl8uToN53wTlfWnj/fovxV2wWZ32dr7uFaSZzTatI9CVg7c7ufkXe6ip8rqG8n5eK5hB6KERVUlv8TDctGeo2VgprXIK+J88YWfq3ita1rJrL1JNFBbZQV1O+Ap7Ib+tYGab2SMHyQK6i12sQ7kqAo3g78Nrr9KkvDgNeXClSTAhDTmxX9vkG5Nde20LQpeIeRWByNbs+M+0WyNPaPx8uQKZhCulsTW6yJ2wXAqm3kLCmo21P5GWnxj1FQ0X/JJL1KQV3T6l4xoiAvJAyv5c/imkyGpevSqkl/CqFSnUbWEm4Tb5ss3w/WXPMXGa0QjTCUC2GIyIi9kKwsbp2XkdIx8Jd4/uHEr05BXQhc0+Y5z2jhn/bUd4v7E2vS+WxNGorvTOlevavF+2AEhbloB8+o6vksT2jsGPCzLM3X1uSVKqhqFKFR+UjukTHS659njr4mzu5trr1jv25sk2G17x0Za/ek7iP8PMzsbEmrEVZTXybxarm4n5lV03yPjxuUx2zz71TfIPRqci4jdOPTPLYCiOsM/sVGT6fulNI4O2Z2QMzj+MTt9rj/Xxb88eheN/suTffigtt2pbAZpUkI72b0lPTq2VhN3tX3rTmEynIHwjfAQ0rhY5zbJR1EeA4LF4IcTLCyW4V/qBCmFjObS+htd8rFjJ58UHfNB5ec4/5SYKaZvTHxW5XwTaLdVPuNgfWBm+P5MYx8Yy3F3ZDwXaMoZ+QRWtwLMztS0jfiaTXDsG6y0J9r0jDVL9BZ+27byASoTrAY937g/phtms5awK2FeHdS+C2hzb1LWYKgFD9iZrdk+Z5RE6fVpKtWE3L+Aazcwn9CLPAKKqmUKtZj5ENn0zSqSudLhF5Vp6QzkZYkmB7/VZL+huNIE8IQzR/GGbeinVL5DDWVX+S5dHg/x8H9FCocM8s/ID/FyBBJLTbyf9HmAJIObxP+yzHcGFMvZnY2A1jpIVZWaVk8h+bTfv8W97uQVcoWZmZtVRNvEcL3NSxYT503E9bCzMtfx+MLCule1U4oM1uqifCRaobhFQW/qVHx11FSCtCm8dkl5uVhZjfXhNmI0TMo76D871oRM/sPwVJ4OnmpZeON0FgpNZABXtIiu68Qhg97gpor5eFH0hwLi8Z2K733ED5oT6jgSlqMMLzUbiZRKe5GhO9ep7YNPBJnYcKzbdkyjz3Ex83svk7lGkYkrRUPLwWWnehzq8ljFevD9NphQVLVCxgKswBRng3MbNw/U0uaAkw3s8cy9/cC3+rWtUo6DXiOmT03nu8C/NlGzwjuOfGerU74d+sUC0vXzRcs8D2oCfK3biRiZo9S/jmySdwrGT09vUmcJ9qHAjMrDu/Nr5jZX2CkUu1RHpNGOUV2pf5XiL7TDeURe1ePFby+R5fe+cgbGN1jGuQ6ov8j/Nd04QBl6BjvQbVPc5F2PRFnuJB0P2GyxlC0+h1nkEh6hpn9c9ByjAfvQbXBldN8yVuApQcthOMMA/OrcgJXUM4CiJmdN2gZHMeZOP1bssJxHMdxOsAVlOM4jjOUuIJyHMdxhhJXUI7jOM5Q4grKcRzHGUpcQTmO4zhDiSsox3EcZyhxBeU4juMMJa6gHMdxnKFkaBSUpP0lmaTl47kkHSnpFknXxFW9HcdxnEnCUCioaPZhW8Jy8BU7ECxJzgb2BY4egGiO4zjOgBgKBQV8DTiA0ca0dgVOssBlwNKSema50XEcxxkuBq6gohGvu8zs6sxrVUabI7+T0ea9q/j7SpojaQ4wvXeSOo7jOP2kL6uZS7oQWKngdTBwELBdKVrBbYzxKjM7DjhuQgI6juM4Q0dfFJSZvbzkLmkDYA3gakkQbNtfKWkTQo9ptST4TGCyWTN1HMeZtAx0iM/MrjWzFc1slpnNIiiljczsHuAs4K1xNt+mwMNm9o9Byus4juP0j2E2WHgusCNwC/AY8LbBiuM4juP0E5mN+azjOI7jOANn4LP4HMdxHKeEKyjHcRxnKHEF5TiO4wwlrqAcx3GcocQVlOM4jjOUuIJyHMdxhhJXUI7jOM5Q4grKcRzHGUpcQTmO4zhDiSsox3EcZyhxBeU4juMMJa6gHMdxnKFkaBSUpKmS/iTpnHi+hqTLJd0s6VRJCw9aRsdxHKd/DI2CAj4I3JCcHwp8zcxmAw8C+wxEKsdxHGcgDIWCkjQT2Ak4Pp4L2AY4PQY5EXjVYKRzHMdxBsFQKCjg68ABwNx4vhzwkJk9Fc/vBFYtRZS0r6Q5cbuu96I6juM4/WDgCkrSzsC9ZvbH1LkQtGhZ0cyOM7ONzWxj4PFeyOg4juP0n2Ew+b4FsIukHYHpwJKEHtXSkqbFXtRM4O4Byug4juP0mYH3oMzsQDObaWazgN2Bi8xsD+BiYLcYbE/gZwMS0XEcxxkAA1dQLfgYsJ+kWwjfpL4zYHkcx3GcPiKz4qed+RJJc+K3KMdxHGc+Z5h7UI7jOM4kxhWU4ziOM5S4gnIcx3GGEldQjuM4zlDiCspxHMcZSlxBDTGSVhu0DAsSkpaUtNYE05CkZ3RLJsdx6nEFNaRIWhP4+6DlGASSpvco6W8DN00wje2Be7ogizOfIGmzgttSg5ClFZK2kTR10HJ0E1dQw8vigxZggPxX0go9SHfJ6kDS8pKeNY40luuiPLVIWkbSu/qRl9OWSyQtn7k9FJdnGyZ+Cbxi0EJ0k2FYi88ps0C1hMbB4sB9PUz/p8CWlBcmHgbeABwdW+pPmdlXBy1Qv5E0C7jd4moCks4A7jezfQcgzrz3UVLVSFl5AHK0Y1jL87jwHtTwMtkbD7140dJlU5asDVVA0vMkfaVBuBdKOq1jyQpJxf0hwGFdSK95xtLZkjbvZ5413Aa8Ojl/NUFxD4K0PN5fcGsdWfqdpBcV3J8naZWJCpfQk6WBJG0gyZLzLSX9pBd5pbiCGl7yIYX5DkkbR+OT44reVWEmzt7ARxqEey3wunaBJO0haa+Geff7Pd2Z0YphHtG4aD/JGxIdNSy6SKk8dlJGtwBeIekzkg5M3K9ixDDruJG0ejxsq6AkLSJp6Xi8d0lxJmE/J+kDQD65aDfgNeOVtymuoGpIHvigWDLKUWeo8SxJO/RSAEmrS3pxg3DTJJXK0hXARlnYt0japkn2hXwWkTRF0qIDGP+vvle1q5Q+1jC97wLfa+HfsYKW9It23+46+LhfN8R8xzDOLo3fFNfsYRa1daWkpqMd7wM+DXwpc1+4kOaHJG0Uj1eXtISkFRP/9ST9KIny87hv0oP6DvBgctxqZOCTUea8PPZlEVdXUAViq/92STMkPbvgf4mkU3osxkJxv06N/yuB1/dYhu8Bv5F0lKQ1WoS7HvhRjV/+8p4EHCtpjTbDSNMgzKBKhhYeJ7zcbwb+T9Jikk5texUjKKY5jc5fsJ3GJCYtNIFZU3PbB+mYlwOfkLRQizAPSVp3gvksMp5IkhaW1KvvNmcCt0rarW3I0TJJ0gGSHhlHnoq/LTxZ47m8pFdKuis61Y2KvKDg9jXgoHh8O3AZ8M/E/9XAG2LjcFtG6okm5XqP7HzLguznSVosSbN6dyRpYWC1eN7THu2kVVCSXlFSPpHqvjwK3Fzw34xQGfSSqpLZS9Iqkp5TeUjaIh6apE0kHd4jGaqeznsIQ1ejkPQsSe8jdP/HFPLIMwtuBvwY+L2kT9YMA1bfGn6fua+fHM+mMyX9yrh/kpEX7uOS9s8DxiG4teJxXYV8OzW9IEnrStq7hSztekilHqQkbZKcr13ouX6AUFF/rRC/qrzGVCqSviXpZYlTNTHBJO3eRtYqjUUlvVPSLjVBPktmeDSm/2pJi9bEGVPhSnpbIVzVM9wrC7tMzOP4mvS3AQ4Flqjxn5dUwW1N4IiYz7MlbRR7O1MkLUvonZwFtP3GpJqh06RHnDcQq4bRssAFiXv13LaWdFa7fFvwCqISIlFQcX8HI/XB0hPIoy2TVkEB5wFHw7yXZM14vDHw61IESZ9PWsxzo9vvq6E2SR+RtE48Lk4Tj3m9TtLSNRVzRfXg5wL/B9wQ4+8FvD/6GfBuYF4FG1upy8fexXsT92/XtXbiSzwzc8vDTlX4MJr2HD8EfDMeT4nxjtHoYcFSDyct8J8DSpVTNZxRKZK0oqribhb9KkWyRby/TVp1M+L+y4SJCDknEypUgCNr0li5kqHAOSQ2zDQy5r+LpBmMXNdKcS9JqbItDTFvBlyenN9IWUGvBry9Ri4IDZvNJC0laZ1YDt8L7JuFqRpCG2bxX1yjJDYCjgF+Vl1XxgoAij3n6p4AZwAflvQpSb/J4rwkhk3z+24h7arhslMMb7FMV3nskwaW9B+FnuaYf+6ie9WIrcrdFEnXZcr3Y4z0rF8K/JHQaLkT+Fd1vSU0tpd7h6Q5eTDg3izep+Lh1CRMSiXvbsArY7map3xj76cpacOkymcqI+8mhHJygZLhx65iZgvMBszpIKzFbb+43xrYNnFPt38ReglGKJAG/CNJ55vJ8THJ8QmEltm20e09WbpHAUu0ke8MQkVkmXu+XQSsR2jRGWEIwAiFqQrz0pq8flulH89nFNI/N8qbhjs6DZPI951U1prrujI5XizxV3T7fc31/h+hIrVC3nvH8w3a3NN8exrYhKAUZgBfje6nxnjnJGHfHPfLxf1f2+SxGbBBPP553L8NeCIJ83lCr8aA5xCU9p8LZeWr2f03wo/H55fybnH9L4r7b8T9Jtn1GnB4Ev7bxHJac9+nEb6jvDTxmxn9XkwowysTZr+l1/N06Xnk8sbjC7Nw3yMMKz+rRiYDfgFslfjtn6X9ekKvuvJfl6DQqjS+lPhtF/dHlmQGvlhwu6wmrAGLtnpmhfCPZtdXys8IIw9T4/0tpXtV4T4ZoZ7aJsv/0ri/l6CsDPhBlt/MuN+hJ3V6LxId1Mb4FNRFcb8VoyuifPtPdn53ks7Xk+OjCMNzBjwEfCopDKcX0v1qItNsYFahgN4b91NbyGfA2cnxBwr+W7W6F8n58jXpfyu5lmUL/tW3neMJLd9R6WbXdXtyvBiwTPR/Z80LVG3/V3NtRqisDVi/cI1H1cQxQi/VgGsILXHLZLgicasU1HvbXF+r7U2MVlAGvKVh3GIlVso7Cbdk5ldVyvk9OY3Q+zLCh/M87zENl5j+mYRhnzsLfnUV+i0dXN/GwPlZuCfjftcamarzYzP/t2X+h9fJwIgCT7frmz6nNmHXbPPM8vBp/fNRQq+/Lu2D8mdbk+4mhbhnkjQSa/IvXeOuvajTF/ghPkk/lXRJ5rZBcpp2lVsNuS2WnVtynH7wfopQ+UB4eO9O/MZ8xyGYtZ8p6e/AXwj/fsDosfpqqKA0tJGyc3Jc25WXNCsOVyySuZ8VhzRaLjUk6TxCrzKn+lhsJMOkklaTtKGkK5Kw6RCWgAdi3sck8erWzSsNH8HYZ1QNea5I6L22YwPg2iz+qYQKckzSSZg1JT2/QfoVTzC2rLWa2DA6Y+kcSbMbhKu+W+Qf56tvE/nwpBhZXqtUfh4t5LElQUnMBIozTmvoZBWPKxj7OaK6f/8uyJTO8JyReX9XzWe/fqDg1skEk1Zhb5V0QO4o6c0N0j2M1v9JrkZo7Lbj8oLbrozUiWkdN+bdilTX2MnQYXMG3evp5kahB8XYFkTVu6m2P8T9VoRhrKatI2NkmOcwRlqpR9SEvbJFOttn56/qUI7Slg+JGOVW68ot5Lkgc/9mw7yP6VDWMzsM/8M2/uslz/vDXbiX6fa+uP9s4vb/4n7KBNIdM2zZZvtIdr5nKVy8B2s0TPPHXb5XbxpnvBMJFV7qlrfgq15v455nzfbTGndNMN1xbzU9qMc6eFYnFdzW6qCH/+y4/33D8Aa8vid1+qCVSr8UFOEj6c9pXfn/ssPCdHHcH0L4/jPeQvmJ7Dwf/hnUdnF23lRB5S9Tv7djCQ2GxSgP0wzj9u4Owx/aMNxMwrTiQV9fp9vRA87/vQPMu91QfrvtgRr353SYzuMdhN3dFdTEFNQv4v79Ay748/P27SGQoen2EOFnxO8MgSy++dbJVjcBYpi3N/WiTp9M671V/y3VTRl22tP4O8kQUP0X0+pfJMcZRg5qH2To6Ml8hraJSvq6pCMVfggtfTTsKfFfhJsk3SLp4/3O3xnFZGrQOI7TnMEoKOAeM/sAsDlhWmLfiD/FHgXsQJgt8kZNfJkWZ/zsNWgBHMcZSp5oH6RzmiioKwDM7OtA/pd3r9kEuMXM/mpmTxB+zNu1zzI4juM4rbFeJNpWQZnZLxXX6DKz43ohRAtWJfwAWHEn2b8WkvaVNCcuE7J85jdsJhscx3EWRHpS1zYdN9xb0qFKlpWXtKKkpqYFxkvpokdpajM7zsw2NrONGTEkVuHfTBzHcXpPUzMuHdFIQZnZHoRVES6Q9Pr4d/3xhJUPesmdjKyoC+GfjrtrwpboSbezj9wyaAEcx+mI2wYtwIDoifHCRgoqrkw8k7BsyIHA9Wa2i5n9tBdCJVwBzFawHbQwYcHCxkvIm9lTPZOsP7ysfRBnCOnWUPgXu5SO0z9a2U1bkOlJXdt0iO8YwkKamxPWJVtU0lGvaC4AABGSSURBVMl1JiW6RVQw7yMsFHkDcJqZXd+l5L/fpXTGy/kNwjzdcym6z2ldSOPhLqQxMMzsnS28P99BUhdNVJYhZXUzk5l1+t2iZJst5Tlt/MfDNMKyUvMj1wKdrBE5Eb7ek1QnsGrDy4DfDXr1iA5Wksi38Sxlcvw44tRtTdY96+d6YF/rUjo/T46vKPif2iCNfJ25bm5p2n+vCfPlieTRotxd3KZM5ttywPP69PxLK+13e6tWUZ/Z4P0sbbkJkqb3fSKbCHbPcvcv1IS/ocY9X86s29sHGbv24Yo9uifF+9SLOn3cP1eZ2S+Zv6d8H0OoTKHcU6nm9adG3FLDdpUxulIL5d6CW0XVc2syg9KS0zErHyc0Wam7bXbJ8UcnkE56XRfH/QcYuSe5uekS+arZdUYB68zM17GomX01Oa+erWXhzusw3ab8MztvVU4AMLOr4+F148gvnzTUilJvpqvWUs3s7HhY+mcmN4o4FMR3sFQevlIT5aYa9zPbZPW7xkIVMLNvMLqHua+Z1ZWvzSeSV8xP2Xn+DnWFCf39a2YlkwvDRnEmn5k9TVirDcLPwCnfI1jHhNEvbvUQbiAY+asqkB9naT+jRpZjGFGGhxJsIuXcFfd5gc4rkKowHmxmRxNe8OkEI2utOKnG/Srg1nj8kzZplKiW6P9f4nYCgJl9k2jRtOF3wdR8CWZ2GWEF8z8mzj8zszcSVvFuhJk9njlV5gZWycJ143+/fALR+xn7bF5Jax5Ljh9pmG9qefZPmd8JhfDV8xrzXMzsYcJq9yVuI6x2XzJF8r+C23bJ8ZPJ8cdiXlfV5JP+OnJ4TZiJsBNtGnhmVimdPxMWhZ5qZg8lQQ5Mjkt16kWE1dhbcWkb/ybcmRz/vS6QmV1K+bm141vZeWUt+8ZxpNWMQQ/L9XqIr6aL+6vofgrloYETgWWAZ5AY0APWTMLvlRwvnsZP0tuC0cb/PkOw6WSZbDcnYSqrsEtH/8qS5gGZjNUK1a9ocL3GyHDWJ0v+MV5l4mDNmjTy7b4s/izCbJ7UbXbcb524VUbxLiqkeTfw3Fy2GO97ifuU6LZMPF+8kNaYa4xxVk3u784E5b9WdY8a3MPStkThXlZmRx7Knk9lWfaFDeXdrV3YZKssP3+FoKxSvx0Yuwp6ZWF1xcz9t6WynWyLAAvFMJtlfqnhwl9VcsWwX6ieXXJ90+P+zVk6udXZl9Q8m2sLz21uFua2uP913F8C3N/mncnf00szuTcj2HhbLImTGz09qF0ecTus4fOtntk0YNMqzySPpaPbRlm+ryhc01M16T/EyOLao+5FdlwNKc7oWZ3eD8XRr416BXVJdrMV3b9TKNgGnJLETRXUGkn4vQsFOH+Qa8fjjeL5/pQVVDq2LuKLGf0rMx6fzmSsxvO3L1zvZ4FtkrCbECoUI7RYqxe1sve0RiZPaka7ZFeqWGDj+bq5W3QXsGqWz2sK6U7L7uffkjiV3aXnJG5VBbpo3N8Y91tk6T6jQfl5bU15MMLkjzlt7sUOwLZJepVdrQOyfCr7VxvXpPeLwv2bSujZ/oXQ0LmKke8d/1dzz7dM0tyF0PJPr20xQi+rkr8yN/NsYPkkneWSOFcBxxTuXeX/RxLTM+N4hy2Pm7i9tObZ/Dp7bu8kmJlPw5xE+G3j8/F8SnU/sjT/A/y3IMN2ablrIfds4NWMmLNfPAtzF8FYZxX+vwSbZc+subZ8W7aQ7ymZmwphKpP16TX9ryaPB4H94nFle+3zMc5Jyb1+3XiecUfloZeJ93ujXkGNsmOUuC8NPD8rGHcC70/CvCsp4LOSh7N7ltbv0vTj8TqJ//MIrervZPGMZHn9guxVhf9gIuN9jBjHG6Og8pcmO696Yi8jtqqyONsQlMnHCC33WtPSMfyqxI+x8XztumspyFayzVVVoqcB2zK6ElkW2DxLp1JQVe/vukS2u5vIkqS1Qfb80u3HjPROqq0yTvlQizTnkLRmo1v17F4Q9/dn6YqGrdIYfj2SHn3it3np+qPbufH4yuSaV6BQCTNijHM9Ys+zEKZqjS+e59HhO1zdg8syt78ytvFSbXsl1zAt3r9KQVUm578V/fcplYckrekkRk07lHvz5Lyy6TYjC/Pm5PjbLeQobavXhP9cA9lenl8TI/aeclt2DzAymWhDEgWVpTsV2KTTZ9xReehl4v3eqFdQo2ao1YTZCti04L5bUvgXBf6QPJz1S4UrOV6vkN5M4FV5ASO0TE8uhJ9BGDb4dwx7DtE4GPAO4vBIi4J5cnb+dsJwxypEZdLmnq4V5TPCx+J5irJF+CYK6nOEv88N+FvyfDqaDcRIz7Cq9K9OnsHhhN8jWsrS4t6l26HxmX8h3vfXJeEeGGf6M+P+CsIEio4qxSy9PfO4jAwB5e5G7AURejztnlU1hLVEizA7xTDVsNwM4hBgh9exa0znw1mZWjSTv9o+ThxqytL5SfYufjYei6RXk4Sflr5LE3kWMX5V+S+UuB0CrJCkf2Qh3trJteXWfse8GwTlMq1BWdsivybCyMoW8fggwndOA/5FGO1J798XxnsvJrL1PcOeXky9gqqGKH4BXNthmiLpITQoCOlD3aBhnJYtoBjukU5fGIJV0vQFWZ2kR9JhWhYrwV/Q4uUlDHEY8L0O0v0S8VvSOGVLW+0/zl7CxYjTu8dxvZsQeohL1lUCMdx945E7iX8loSc4Zmivg3TeWqik64ZblwUWicdNFNR0st5ATbgxDbxxXsvLW+WVVdoHUlZQpyfv4mrAwuN4LuN6FjF+NVw6pUX6R9T4fYbws/d0QmNyR5Ie5ThkWY+RX1Y+2SDscxiroL7YjWfb6TYp1qqzMNtQkhaiw0UN4xNqOx04ciDhH5qKJrMkP0QYjmjHbwit1MaY2buz89qZPZ0k28b/8ZjX29qEq5gF3MME/kQ3s3SG1EdIjBSa2aOECRqdsp6Z/blh2Lntg9Tya8Kknf9IOpbx/6Q85idWM/tzXC/5R5n7A8npPQ3SrmaetnxGFmZbThgzu7BBsKsJw+YQZtSe2yK9O+r8esj2wAvNrFXZeLLkaGafSU6rlSlqr68dFhc3iGWh5fJpSdidM69eL2tXZFIoqAozKxaILpL/39H25bfw/0ITTqNDBdVlbgX+QFDWLyFM9hiDmd0hac2miZrZvKn2cUmtifKUmf17ool0oJxgAgrKzLZKjk8ntPzHk86l1De+jmgR9Y2EGYitaKSg+owIk0R+HRVQ/m78iDAkPxAs/E7Rbup4r+ujnE0JQ95NSMvSwgzo2U8qBdVPrPNlXNom2eX0Osvc7Nnx8AbC0EOrsLeNM4/8R9bx0G8TKz9nuBf1fRVhCLGImT1Cm3+szGyupNXa9Ab6yQWEafBfqAswEWXfJw4mzJDrG2Z2eftQ85j3T1UfGva1VNOtFwgkzbFgdmNQ+X+YMK7c9UpS0gsJEzTcxlUNkgxYKla6jjNuYln6vZltOWhZBkG0pbfEoN8l70HNJ5jZFfS/dzBf4crb6TJNv6MucMRv7wNv6LmC6i7H0ctlPxzH6RffoMVyQU5/8CE+x3EcZyiZ0GKxjuM4jtMrXEE5juM4Q4krKMdxHGcocQXlOI7jDCWuoBzHcZyhxBWU4ziOM5S4gnIcx3GGEldQjuM4zlAyNApK0v6STNLy8VySjpR0i6RrJG00aBkdx3Gc/jEUCkrSagTz3unSIjsQjN/NBvYlGN9zHMdxJglDoaAIJtkPYLRJiV2BkyxwGbC0pJUHIp3jOI7TdwauoCTtAtxlZldnXqsCqSXMO6NbHn9fSXMkzaGNnSLHcRxn/qEvq5lLuhAoWUs9GDgI2K4UreA2ZmVbMzuOsIq44ziOswDRFwVlZi8vuUvaAFgDuDrYx2ImcKWkTQg9ptWS4DOBu3ssquM4jjMkDHSIz8yuNbMVzWyWmc0iKKWNzOwe4CzgrXE236bAw2b2j0HK6ziO4/SPYTZYeC6wI3AL8BiT2Lql4zjOZGSBMlhYQtJ1wOODlmMIWR64f9BCDBl+T8bi92Qsfk/GMt3M1u92osPcg+oWj7uV3bG49eGx+D0Zi9+Tsfg9GUucRd11Bj7N3HEcx3FKuIJyHMdxhpLJoKD8H6kyfl/G4vdkLH5PxuL3ZCw9uScL/CQJx3EcZ/5kMvSgHMdxnPkQV1CO4zjOULJAKyhJr5B0U7Qp9fFBy9NLJK0m6WJJN0i6XtIHo/uykn4h6ea4Xya619rbkrRnDH+zpD0HdU3dQtJUSX+SdE48X0PS5fH6TpW0cHRfJJ7fEv1nJWkcGN1vkrT9YK6kO0haWtLpkm6M5WWzyV5OJH04vjfXSfqhpOmTrZxI+q6ke+O/o5Vb18qFpBdIujbGOVJxfbuWmNkCuQFTgVuBNYGFgauBdQctVw+vd2XCMlEASwB/AdYFDgM+Ht0/Dhwaj3cEziMsyrspcHl0Xxb4a9wvE4+XGfT1TfDe7AecApwTz08Ddo/HxwDvjsfvAY6Jx7sDp8bjdWP5WYSwduStwNRBX9cE7seJwNvj8cLA0pO5nBCsJNwGLJqUj70mWzkBXgJsBFyXuHWtXAB/ADaLcc4Ddmgr06BvSg9v9mbA+cn5gcCBg5arj9f/M4IRyJuAlaPbysBN8fhY4I1J+Jui/xuBYxP3UeHmt42wyPAvgW2Ac+LLcT8wLS8nwPnAZvF4WgynvOyk4ea3DVgyVsbK3CdtOWHEtM+y8bmfA2w/GcsJMCtTUF0pF9HvxsR9VLi6bUEe4mtkT2pBJA45bAhcDjzD4iK7cb9iDFZ3fxa0+/Z1gjHMufF8OeAhM3sqnqfXN+/ao//DMfyCdE/WBO4DvheHPY+XtBiTuJyY2V3AVwgWvf9BeO5/ZHKXk4pulYtV43Hu3pIFWUE1sie1oCFpceAnwIfM7JFWQQtu1sJ9vkPSzsC9ZvbH1LkQ1Nr4LTD3hNDi3wg42sw2BB4lDN3UscDfk/hdZVfCsNwqwGLADoWgk6mctKPTezCue7MgK6hJZ09K0kIE5fQDMzsjOv9T0srRf2Xg3uhed38WpPu2BbCLpL8BPyIM830dWFpStQ5len3zrj36LwU8wIJ1T+4E7jSzy+P56QSFNZnLycuB28zsPjN7EjgD2JzJXU4qulUu7ozHuXtLFmQFdQUwO87EWZjwMfOsAcvUM+KMmO8AN5jZEYnXWUA1k2ZPwrepyr1kb+t8YDtJy8SW5XbRbb7DzA40s5kWbI3tDlxkZnsAFwO7xWD5Panu1W4xvEX33ePsrTWA2YQPvvMdFmyt3SFp7ej0MuDPTOJyQhja21TSjPgeVfdk0paThK6Ui+j3b0mbxnv81iStegb9Ua7HH/x2JMxmuxU4eNDy9PhatyR0ma8BrorbjoSx8V8CN8f9sjG8gKPivbkW2DhJa2+CHa5bgLcN+tq6dH+2YmQW35qEiuMW4MfAItF9ejy/JfqvmcQ/ON6rm2gw+2iYN+D5wJxYVs4kzLaa1OUE+CxwI3Ad8H3CTLxJVU6AHxK+wT1J6PHs081yAWwc7++twLfIJuqUNl/qyHEcxxlKFuQhPsdxHGc+xhWU4ziOM5S4gnIcx3GGEldQjuM4zlDiCspxHMcZSlxBOU4fkTRL0jaSVpJ08KDlcZxhxhWU4/SXWcA2ZnaPmX1x0MI4zjDjCspx+su+wFsk/VLSyQCSLpX0TUlXS9pd0hnRxs7zov/Okn4j6RJJrxio9I7TR/xHXcfpI5K2Iqz9djzwBTN7s6S/AC8m2DC7ktDL2oiwPNOHgItinCnAeWb2sv5L7jj9Z1r7II7j9Jj7zOyfAJJuNbPHJd1NWIJoeWAd4MIYdkVJMm9ZOpMAV1CO01+eJPSUUqzmuDKueC2wvZk9LWkhV07OZMEVlOP0l+uALxMWIn2yXWAzmyvpCOCXkoywyvZ7eyui4wwH/g3KcRzHGUp8Fp/jOI4zlLiCchzHcYYSV1CO4zjOUOIKynEcxxlKXEE5juM4Q4krKMdxHGcocQXlOI7jDCX/H/OVmuZEcG83AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "T = 10000\n",
+    "# Generate 1/f noise by averaging AR1-process with wide range of coeffs \n",
+    "# (http://www.scholarpedia.org/article/1/f_noise)\n",
+    "def one_over_f_noise(T, n_ar=20):\n",
+    "    whitenoise = np.random.randn(T, n_ar)\n",
+    "    ar_coeffs = np.linspace(0.1, 0.9, n_ar)\n",
+    "    for t in range(T):\n",
+    "        whitenoise[t] += ar_coeffs*whitenoise[t-1]       \n",
+    "    return whitenoise.sum(axis=1)\n",
+    "\n",
+    "data = np.random.randn(T, 3)\n",
+    "data[:,0] += one_over_f_noise(T)\n",
+    "data[:,1] += one_over_f_noise(T)\n",
+    "data[:,2] += one_over_f_noise(T)\n",
+    "\n",
+    "for t in range(1, T):\n",
+    "    data[t, 0] +=  0.4*data[t-1, 1] \n",
+    "    data[t, 2] +=  0.3*data[t-2, 1] \n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)\n",
+    "# plt.psd(data[:,0],return_line=True)[2]\n",
+    "# plt.psd(data[:,1],return_line=True)[2]\n",
+    "# plt.psd(data[:,2],return_line=True)[2]\n",
+    "# plt.gca().set_xscale(\"log\", nonposx='clip')\n",
+    "# plt.gca().set_yscale(\"log\", nonposy='clip')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here PCMCI will detect many spurious links, especially auto-dependencies, since the process has *long memory* and the present state is *not* independent of the further past given some set of parents. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 7 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.470\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.388\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = -0.183\n",
+      "        ($X^0$ -2): pval = 0.00000 | val = 0.100\n",
+      "        ($X^1$ -3): pval = 0.00000 | val = -0.096\n",
+      "        ($X^1$ -4): pval = 0.00193 | val = -0.031\n",
+      "        ($X^0$ -3): pval = 0.00202 | val = 0.031\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.468\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.065\n",
+      "\n",
+      "    Variable $X^2$ has 8 link(s):\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.468\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.288\n",
+      "        ($X^1$ -3): pval = 0.00000 | val = -0.145\n",
+      "        ($X^2$ -2): pval = 0.00000 | val = 0.082\n",
+      "        ($X^1$ -4): pval = 0.00000 | val = -0.076\n",
+      "        ($X^2$ -3): pval = 0.00018 | val = 0.037\n",
+      "        ($X^0$ -1): pval = 0.00696 | val = 0.027\n",
+      "        ($X^0$ -5): pval = 0.00831 | val = 0.026\n"
+     ]
+    }
+   ],
+   "source": [
+    "parcorr = ParCorr()\n",
+    "pcmci_parcorr = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=5, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Time aggregation\n",
+    "\n",
+    "An important choice is how to aggregate measured time series. For example, climate time series might have been measured daily, but one might be interested in a less noisy time-scale and analyze monthly aggregates. Consider the following process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7e07a28ef0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e15104a58>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7e07b83588>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(1000, 3)\n",
+    "for t in range(1, 1000):\n",
+    "    data[t, 0] += 0.7*data[t-1, 0] \n",
+    "    data[t, 1] += 0.6*data[t-1, 1] + 0.6*data[t-1,0]\n",
+    "    data[t, 2] += 0.5*data[t-1, 2] + 0.6*data[t-1,1]\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the original time aggregation we obtain the correct causal graph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.574\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.514\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.512\n",
+      "\n",
+      "    Variable $X^2$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.534\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.440\n"
+     ]
+    }
+   ],
+   "source": [
+    "pcmci_parcorr = PCMCI(dataframe=dataframe, cond_ind_test=ParCorr())\n",
+    "results = pcmci_parcorr.run_pcmci(tau_min=0,tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we aggregate the data, we also detect a contemporaneous dependency for which no causal direction can be assessed in this framework and we obtain also several lagged spurious links. Essentially, we now have direct causal effects that appear contemporaneous on the aggregated time scale. Also causal inference for time-aggregated data is still an active area of research. Note again that this constitutes a violation of the Markov Condition of the *observed process* only. So one might call this rather a violation of Causal Sufficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aggregated_data = pp.time_bin_with_mask(data, time_bin_length=4)\n",
+    "pcmci_parcorr = PCMCI(dataframe=pp.DataFrame(aggregated_data[0], var_names=var_names), cond_ind_test=ParCorr(), \n",
+    "                      verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_min=0, tau_max=2, pc_alpha=0.2)\n",
+    "# pcmci_parcorr._print_significant_links(\n",
+    "#         p_matrix = results['p_matrix'], \n",
+    "#         val_matrix = results['val_matrix'],\n",
+    "#         alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "link_matrix = pcmci_parcorr.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tigramite_tutorial_basics.ipynb b/tutorials/tigramite_tutorial_basics.ipynb
new file mode 100644
index 00000000..3c434153
--- /dev/null
+++ b/tutorials/tigramite_tutorial_basics.ipynb
@@ -0,0 +1,1498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results.\n",
+    "This tutorial explains the main features in walk-through examples. It covers:\n",
+    "\n",
+    "1. Basic usage\n",
+    "2. Plotting\n",
+    "3. Nonlinear conditional independence tests\n",
+    "4. Symbolic time series\n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "See the following paper for theoretical background:\n",
+    "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider time series coming from a data generating process\n",
+    "\n",
+    "\\begin{align*}\n",
+    "X^0_t &= 0.7 X^0_{t-1} - 0.8 X^1_{t-1} + \\eta^0_t\\\\\n",
+    "X^1_t &= 0.8 X^1_{t-1} + 0.8 X^3_{t-1} + \\eta^1_t\\\\\n",
+    "X^2_t &= 0.5 X^2_{t-1} + 0.5 X^1_{t-2} + 0.6 X^3_{t-3} + \\eta^2_t\\\\\n",
+    "X^3_t &= 0.7 X^3_{t-1} + \\eta^3_t\\\\\n",
+    "\\end{align*}\n",
+    "\n",
+    "where $\\eta$ are independent zero-mean unit variance random variables. Our goal is to reconstruct the drivers of each variable. In Tigramite such a process can be generated with the function ``pp.var_process``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)     # Fix random seed\n",
+    "links_coeffs = {0: [((0, -1), 0.7), ((1, -1), -0.8)],\n",
+    "                1: [((1, -1), 0.8), ((3, -1), 0.8)],\n",
+    "                2: [((2, -1), 0.5), ((1, -2), 0.5), ((3, -3), 0.6)],\n",
+    "                3: [((3, -1), 0.4)],\n",
+    "                }\n",
+    "T = 1000     # time series length\n",
+    "data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)\n",
+    "T, N = data.shape\n",
+    "\n",
+    "# Initialize dataframe object, specify time axis and variable names\n",
+    "var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']\n",
+    "dataframe = pp.DataFrame(data, \n",
+    "                         datatime = np.arange(len(data)), \n",
+    "                         var_names=var_names)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we plot the time series. This can be done with the function ``tp.plot_timeseries``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 4 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb9d5362b00>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9d4e76358>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9d4e9f898>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9d4e46dd8>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's stationary and doesn't contain missing values (covered in other tutorial). Next, we choose a conditional independence test, here we start with ``ParCorr`` implementing linear partial correlation. With ``significance='analytic'`` the null distribution is assumed to be Student's $t$. Then we initialize the ``PCMCI`` method with  ``dataframe``, and ``cond_ind_test``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parcorr = ParCorr(significance='analytic')\n",
+    "pcmci = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before running the causal algorithm, it's a good idea to plot the lagged unconditional dependencies, e.g., the lagged correlations. This can help to identify which maximal time lag ``tau_max`` to choose in the causal algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Estimating lagged dependencies\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0NLBnZaOdWbi2zeL6Nodr2w6tzv6SdDvwZ3HRY/raUuB8M/twDJ8HYGYXjNVIx3EcpzBd7v66BzhQ0gGSdgJOJ/gMcxzHcTpKW1OKT5G0CVgK3ChpdTz/Fkk3AZjZK8A5wGpgA3CNma1vw17HcRynGFO3+NFxHMdpj6lx05KHpHXASzUktQfwzBSmA7CLmb2z7E0d1LbOtOpKp5K20El9u5aOa9tcOlBB39YqFUmXAycBT2UZrbCI5W+AE4AXgBVm9pOKH/dSHS4a6nL10LV0emlVvLVT2taZVp3pjHB7p/TtYjoj3O7aFkir7D1tDtRfARyXc/144MB4rAQuHoNNjuM4zgi0VqmY2Y+A3+REWQ78gwV+DCyIWw87juM4HaXLU4oLr6hPLnKS9L8yolxak03Tms7AtCZQ2zrTajydqvqm9lcZyYaSTEw6E5h3u5ZOpbTaXvy4P/D9AWMqNwIXmNm/xfBtwJ+b2b1D0nQX1w3h2jZLUX0zthN2J5ND8Lw7PrrcUtkE7JsI7wM80ZItTgEqPD071Ujvunmk6+50hS5XKquAP1Dg94DnzGxzmQTq3I9F0qOS1kq6r8yMCEmXS3oqTl/sndtN0i2xaX6LpIUV0zlf0i+jTfdJOqFAOvtK+oGkDQp72nx2BJuS+n6R8PR8M/CzMgVc29rmpFVK3wa1PVeDtxN+FriSAbpX1TZHk4nPu14uZKZTW97FzFo5gO8Cm4HfElolZwJnA2fH6yLsp/II4U/0roLpromvO8R7FwM7AfcTugmq2vsosEeF+z4AHA6sS5z7KnBufH8u8JWK6ZxP8J1Wxp5FwOHx/a7Azwn71Qy1qaftAH0fImwkZcDzwNJJ0bYufUfRdkjeXRv/K88DjxFaKPMJHimOjuczda+q7bTm3QxtvVyoIe/2pVVVzK4eicyzFFidOH8ecN64M0+8d//Uj/4gsCjxYz5YMZ3SmScjzeuBY4rYRH+lktb3vxOemrcXfJOkbRP6ltF2SN69GPjfZFQchMrlsUG6j6LtNOZdLxeaybvJo8vdX6WQtEzSpcAb46m692Mx4GZJ90paOUI6AHtZ7MqLr28aIa1zJD0Qm8GFunp6KEyUOAy4K8+mDG1htr4bgauJuxPG+4r283dVW6iob1FtY9zMvNvr8iLslPoyGdsJWxigP4jErpCprrI6tWXYdylJG3nXy4UhlMm7WUxNpWJmN5jZSuC5eKrwfiwFea+ZHU5YlPkZSR8YIa26uBh4K3AooXvka0VvjAXOPwF/Ymb/nhc3Q1vI1vdlM7szvi8zvtJFbaGivmW0hYF5d0dmNPwSiQrbUjO9LLGZl2Zmht0cX4+ZJm1h5Lzr5UIOZfNuFq1WKsMGzCStUJhj3htwOqtE8rXOHjOzJ+LrU8A/E7Y7rsqTigs54+tTFW160sxeNbPXgG8VtUnS6wgZ5ztmdl1Fm/L0Tc9OWjLke3RO22hPaX1r1PY/MqPhfOC3VmwXyLT2e8bvUoe2MPl518uFAdSUd9urVCTtQBiIP57QfP+4wp7caa42s0PjcVmJj6htPxZJ8yTt2ntPeGJcl39XLquAM+L7Mwj9l1XsSnoYOKWITZIEfBvYYGZfH8GmPH2Ts5O2AGs1YLpxV7WN9pTSt2Zt9yKMk2wlPF1/u6DZ6Zlhv4ja/2eCW6RRtIXJz7teLmTfU1febXX219ABM2AF8I2S6SYHk08gzGJ4BPjCCLYuJswSuR9YXyYtsme57Q7cRpgxdRuwW8V0riIUIg/EH39RgXTeR2juPwDcF48TitiU1HaYvszMTuo9aWcOJndB27r0HUXbAXn3oWjLX5XMrz3t3xntfxl4lVDJlJlAMZV5Ny/ferlQLe8mj9ZW1Ev6KHCcmZ0Vw58E3m1m5yTirAAuAJ4mZII/NbPHM5JLpusrZxuiqrZxsPlmQmG3lfBEt5bQVbPWfDU40EzezdB+OfAic0x3LxfGR5tjKkUGzG4A9jezg4FbCYu8ZieU8PED7FevmXObmrRNd4dtpOJCyWljDHm38ELJacPLhXZos6WyFDjfzD4cw+cBmNkFA+LvAPzGzN6YdT0Rz59IGmIUbWMBtoSZFsqcf3pO01TeTWj/ekKf+JxrMXq5MD7a3Plx+4AZ8EvCgNknkhEkLbIZ1ywnE/aqdyaQWGDdCSCp9/QMM0/PC4Atktw5Ys30tI+VS0/3ZItxIa69UxOtVSpm9oqkc4DVBNcJl5vZeklfIgyqrQL+WNLJwCuEvVdWtGWvUx8W1lMcxOynZ4AlsdKZ+qfncZPSvadxb/oxuPZODbTq+r4JvJnbHA0NJKfduB9BaMXOObfu4867c0l7LxfGx9SsqHcmE0u5GSFM0+xbODlojYszGgW0d5f6TmnaHFOpFUnLgGX0+6dyaqBpbXPGW7L6/Y8gFH5T0z3TZt4tM9YVz09U19ggbZMTRyblu0wKI3d/SVoMfAF4o5l9tBarRrPHm7kNMS5th8wU2wbMI3bPxFumonDoQt7NmSm2HLiCRNdYvGUitE9qm9HtNzXdfF2gUPeXpE9L+mYi/GVJVwGY2UYzO7PKh2u476+dJV0dr9+l4D3TmXIs4SCR/nUWvQple/cMqfUuya4y7zYrT0974G761xZBqmuMfu33Smrdce1L+aZzylG0++tK4EFJf0lYzn8i8J5RPlgzvr+OIbgXuEfSKjP7WSLamcAWM/tdSacDXwF+f5TPdSaL1IyljYSBZJhd0EEo6K6I556N50buvkl3leSF058xqd0sGTPFoL9bEvq1v5/YgoxdlNsH/DvYdZbuYl07qb9TJynhq+arhEpgI/DWjOvXFk0rxi/i+2s1cUMiQgX4DLHLLifdNWXsmJaDhK+tdDjvWla4y9pmfK+kT7HkLojb6N+J8uhU3PkZaWVpslfqvrzw4/EYFLfz+lbU/lf07z75qVQ4rf1eVfLfsHxdRtsh+WigfWX+S6PEHfF3qd2+MvaUGai/nLD4cLmZPdI7KWl34K+BwySdZwNWxGeQtVnOuwfFsbCu5TmCg7NnSthN9EYK/W5glDqXDheJk3Q1MyxO3enOZ8ZhIARHcL0nw6WEwdesJ/b3AHck4qbDI/Uvj0Hrl4E1iXOHAO8geGIV/QPNAK/FczvQ/2S9lOCVdZhGyS43CF5fFw4I97qT3zAg7hLioHhROpZ3/zdwb+LcwYTv9AuCXhC0/p/A/5MI78jgVk0yr/bCBxAcNG4jaPlOZn7f+xnQEi2Tby20xHrf5cgc+5J5If2ZWbZX+d+l4x4cw0vi994a7emFoT/PD9KkLvtK6VumUvkiwbFj3z1m9mvC3vJlKeL7q9CGOgo7rn2eINB0LbzpZx4hM/2CRGYhdBMm/xTLGFzQnZSKmw73FXwToO02wg51MLuSgZk/ojG4+2aYRtsS990A/OWAcLoi+34q7vYdG3tMgL55JLU/mFD49yqDQZV9upJO5lUIlVZvIkZfwQacxeDfbFaFXULbdTn2JfNC+jPTtlf936XjHsHMA88WwgNQZmVAviZ12QdlHogKNq8+B1wHHAX8sKamtHd/lW/WDupuyOpeKNolU7iLZtK1zdGziEa1dCnMlbybo31e/ivTjZb+zUbqWixoX5n/0ihxk924aR3yunWbsm+ovn1aFsgMRxNq8l1j+KfAoTVksh0J4zMHADsRmnDvSMX5DHBJfH86cM20/zFzMnc6o/0qcS23MMu7lhWeVm1ztB6pMqjRnqnSt4juqfDQgi3vN6tL27r+S1Xjkj3GM7AyHYd9ZX7f3HUqkvYDbgdONLMN8dwK4CgzWzHwxoJIOgH4H8z4/vrrpO8vSbsQNpw5jOD763Qz2zgkzdbn+pchY+ZQb/58shmetU5g7IsAJ03bScP1HT7TboR0J0rbMjMM27JxEO77q0UyFmGtoH/BWacW+k2StpOI69scru34mBo3LZNCxmrx5GAY9A8mZ7VGSs0echzHGSdTU6l02fdXoiLpLd5LdmElK5G7CS2SZGvkybEbnKLL2k4Drm9zuLbjx7u/GibVxZUeJ5monfe6pu204fo2h2s7PlpxfS9pN0m3KOwffYukhQPivSrpvnisGredVUn5PUp2cc0jVCw9f0prrd/PleM4zkTT1n4q5wK3mdmBwG0xnMWLZnZoPE4en3nVSbRMbo6vG+l3zHcIcf8Kr0gcx5k22hpTWU5YSAnBWeXtwF+0ZMvIDBl8X0wHx0kcx3GaoK1KZS8z2wxgZpslvWlAvF0krSHsUX+hmf3L2CwsSMa04PTge68i8VlbjuNMPY1VKpJuBd6ccekLJZLZz8yeiBuB/auktZZwZpn4rLH7T0ptZjSsZTKxTLhvqs7j+jaHa9sOrcz+kvQgYVX+ZkmLgNvN7G1D7rkC+L6ZXTskXuOzPFKtk7SDt6kdK/EZNM3i+jaHazs+2hqoXwWcEd+fQVhF3oekhZJ2ju/3AN5LKMhLUdcOdDkzuhbE7+CD747jzHnaGlO5ELhG0pkEp2inAUh6F3C2mZ0FvB34e0mvESq/C61/V8ihpMc7qu4VUmDc5G6vTBzHcVqqVCzswfLBjPNrCPsDYGZ3MPre0emZWKU3SRqQzlSNmziO49TFtLtpmbUXdYn0ktOEZ6Uzl2Z0uauLZnF9m8O1HT9T76aljCvtHB9drXsI7gI+2Nksrm9zuLbjY2paKoNItigyxli2ewGO0bN8dAEsMbM7mSMtE8dxnKq05fvrNEnrJb0WB+cHxTtO0oOSHpY0yJVLGZJjIwsJu0323KkcSY6Prho+23EcZ+ppa0rxOuBU4EeDIkjaAbgIOJ7Q/fRxSQcNil+Q3thIcgOsXgUD7qPLcRxnJNqa/dXbmjgv2pHAw73tgyV9j+AzrPRalcTnbo0VU3LcBDq8l4njOM4k0VZLpQh7A48nwpviuVlIWhnd6D8N7JmXaMLV/JOESmR7a8Td0M+mjLZOeVzf5nBt26Gx2V95vr/M7PoY53bgz+L6lPT9pwEfjgshkfRJ4Egz+6NGDHYcx3FGprHuLzP70IhJbAL2TYT3AZ4YMU3HcRynQbrc/XUPcKCkAyTtBJxO8BnmOI7jdJS2phSfImkTsBS4UdLqeP4tkm4CMLNXgHOA1cAG4BozW9+GvY7jOE4xpm5FfRaS1gEv1ZDUHsAzU5gOwC5m9s6yN3VQ2zrTqiudStpCJ/XtWjqubXPpQAV9W1tRL+ly4CTgqSyjFeYb/w1wAvACsMLMflLx416qw0VDXa4eupZOL62Kt3ZK2zrTqjOdEW7vlL5dTGeE213bAmmVvafNMZUrgONyrh8PHBiPlcDFY7DJcRzHGYHWKhUz+xHwm5woy4F/sMCPgQVxl0jHcRyno3R59lfhxY8FuHR0c6Y6nVHSmqbv0sV0umDDtKbTBRu6nE6ltFodqJe0P2Hf+awxlRuBC8zs32L4NuDPzezejLgrgc8TtvZ9wcz+Q5N2zyVc22ZxfZvDtW2HLrdUCi9+NLNLzexAM9sTeHocxs0VXNtmKaqvpPmSlsbtG5wCeN5thy5XKquAP1Dg94DnzGxz20Y5zrhJ7AN0M/Azr1icLtNapSLpu4RNr94maZOkMyWdLensGOUmgifhh4FvAX9Y4TNq249F0qOS1kq6r8w0O0mXS3oqzonvndtN0i3R2d0tkhbmpZGTzvmSfhltuk/SCQXS2VfSDyRtiHvafHYEm2rRt21tc9IqpW9T2gJfpX8foCVFvlNMp5K28d6pzLteLmSmU1vexcym6gDWxNcdgEcIOzvuRNiQ66AR0n0U2KPCfR8ADgfWJc59FTg3vj8X+ErFdM4nOOQsY88i4PD4flfg5wRvzUNt6mlbt75ta1uXvqNoOyTvrgU2A88DjwHzm9Z2WvNunfm2C3m3C9qmjy53f43K9v1YzOxloLcfy1ix7KnTy4Er4/srgY9UTKeKPZstLiI1s+cJLnD2rmBT6/rWpW1OWmXtaUrbfwQuIbFNg8YwxjKlebf1fAtTqy3Q7TGVUkhaJulS4I3xVJ1TkgEMuFnSvQqzSkZhL4vjQ/H1TSOkdY6kB2IzuFBXTw+F2XeHAXfl2ZShLQQtNycKtlH07aq2UFHfotrGuEXy7p4W9/pRuTGWOrVl2HcpSRt518tbt/4xAAAcyUlEQVSFIZTJu1lMTaViZjeY2UrguXgqa1vJUeZPv9fMDies9P+MpA+MkFZdXAy8FTiU0D3ytaI3xoLon4A/MbN/z4uboS3A64GPEgs2YGeq69tFbaGivmW0hUp5dwnFx1imSlsYOe96uZBD2bybRauVyrABM0krJD2dGHA6q0Tyte7HYmZPxNengH8mNKOr8qSid4D4+lRFm540s1fN7DXCZIZCNkl6HSHjfMfMrqto0y7x6BVs/4mK+nZR22hPaX1r0nZY3l1L2AJ7a3zdOKgrrGZtYfLzrpcLA6gp77Y6+2sH4CJCDX8Q8HGF/ePTXG1mh8bjshIfUdt+LJLmSdq1957Qt70u/65cVgFnxPdnANdXtCvptuaUIjZJEvBtYIOZfX0Em64hPOG9QCjY3gfcUrafv6vaRntK6Vujtrl518J2172tsI+I8Wd1hTWgbZXvkkmLedfLhex76sq77c3+IuylsjoRPg84LxVnBfCNkukmZyidQJjF8AhhG+Oqti4mzBK5H1hfJi3gu4Qm6G8JT0lnArsDtwEPxdfdKqZzFeGp9YH44y8qkM77CJXBA8B98TihiE1JbWP4VMJspI3AX8X3pWYodUHbuvQdRduqeTf+j56Pn/s8sLQObac57xbVdhLybhe0TR+tuWmR9FHgOOvfg/7dZnZOIs4K4ALCatifA39qZo9nJJdMtza3z04/edpKWkp4Wp5P6JZZDrwIrLXwZO0MoUreTQzaLyS0GHut/SW49tvxcmF8tDmmUmTA7AZgfzM7GLiVmalt/QlJKxUW5zwN7FevmXObEtom+/mfJfxWvgJ8CKPmXevvCutVKL76Hi8X2qLNlspS4Hwz+3AMnwdgZhcMiL8D8Bsze2PW9UQ8fyJpiGHaxgJsCWFm2PXMtFqOJVQ6/vScQx15N6PF6Nrj5cI4aW3nRxIDZsAvCQNmn0hGkLTIZvx9nUxYkON0lFhg3Rkrly3x9BbCmMv2LhpJB83Vwm0M9FqM4No7LdBapWJmr0g6B1hNcJ1wuZmtl/QlwqDaKuCPJZ0MvEJYNbqiLXud4lhYoHcQ8emY/nUVAEskzfmn5yYooP2Rknysy2mMVvdTaQJv5jZHVW0zBpN702CT4cXM8YKuibyb0v7ZeHoBc2xQ38uF8dFm95czRyjw9Hw/MA/vnqmdlPbJsS4Ii+KuwLvGnBqZmkpF0jJgGf3+qZwaqEPb3nhLTC/Z77+NUKHM2a6xpvNuzlgXTHm3pJcL42fk7i9JHwFOJDgau8jMbq7DsBHs8WZuQ9SpbWKm2EZmd4Ulw31dNMn301DoJRlH3k3o3tOycLdk8t5J097LhfFRaJ2KpE9L+mYi/GVJVwGY2b+Y2acIg+i/X+bDNdz3186Sro7X71LwnulMAWa21YLX3SfpX2exmH5niUcys+5iQzy2r8FQB7bZTdrQBXvySOi+NWONS1r7+5nRei9S61+6/l2dlijoUuANBHfRC4CTgJ8Cr0/F+Rpxk5eCaQ7dLIew2+Ml8f3pBD9gw9JdU9QGP8od49CWUKAl3b0czYwbkm3x6LkkOToVd348lqbfJ9LODI94rWfD4/HItKcL+pbQ/lf0u3/5VCqc1n6vqlpn2DByOsO0LWPDiHqOnM6kHWUE+irBAeRG4K2J8wK+AnyopOBFfH+tJvoyIoz/PEPssiuaeeK5neLxusSRPlclzk4l4tSd7kLg/YSK/nXx9f3xfDq8gODbZ0FMY2FGeGjmH6O2SdsXMrjQPpb+gu6YnLh7018I7pMTN+9aOp1jGFzpHUsJf2hZ+raQj3t5JalBWpP0905WQmW17v3G78u4Ly88qwIvqi2zH1wW5HzvpH29eIP+Z8nwsHSG/U55n5P1/+2FRykXZuWJKpVKmYH6ywldD8vN7JHE+T8CPgS8UdLvmtklBdPL2izn3YPiWFjX8hzBwdkzyUgKm+N8niDONM2Rnge8g+Bl9IVE+BeEQe9e3/fSnHB6Gul7gDsScfvC6RlALWm7jbBBUI+DgXcSnPbBjCZi8MBzr2v3DfF1WeIahBb3oLh519LpJD+7p/VrGfZAGI+4M/lFO5h3e9oLOIT+/JcMw+DJFmW0PgK4LF4fls6ynHTKaJuefXgEwUNvlg1J+9L/pbz/XV46WwhaQr+eS+L7nmPHsv/nMval46bteQGYJ+k/UXYMrUTL4h8JvvRPraWJBKcBlyXCnwT+LhVnPbBPIvwIsPsoT3tdP5hpMu/F7O6Fol0TyXD66Tkvbp+X20nQltndIYOeZPP0zIs7LJ2BXW7MfiIeqaXStaNgXh2mZ7J7M93iKZPOKC2VPBvyul+H/ZcGpZPuPhymSdH/cxn7hnUlz8rnhfNFwczzOeA64CjghzVlyLF1f3X5YHAhNKziqPrnG1YADMw8k6Bthp6NjqmUsadA3M7rW/R7FtWT2QV8LWMzw7QtakPqWtn/0qB0yo4V1vUAVKZyL/ywOUvrAj/G0YTm0K4x/FPg0Boy4Y6E8ZkDmBmof0cqzmfoH6i/ZtL/mEMyc9mntpELzKzwpGo76cdc1bdo/mtS2zwb6vov5fz3h07wKGpDGfuGlEXNtFQI7qI3Am9PnFsBXFHTDz1rsxzgS8DJ8f0uwP8HPAzcDSweNfO0/KdJ/1B5TeKhFUcL36Nz2k7T4frOLW3zKoO27ckKFz3c91czNswne4FZcvCut5HVFUzIBktd0HaacX2bw7UdH+6mpX475tO/SnkF/TNNtsXXLYTW13afWIlKpG8WS9t0RdtpxfVtDtd2/HhLpZ7PTLZMljB7W90rmAKPvP601yyub3O4tuNjaloqbTHArXty7URWa+TJFkx1HMdpnFYqFUm7AVcD+wOPAh8zsy0Z8V5lZlziMTM7eVw2DiO1dW6ye2sxE9Cl5TiO0wRttVTOBW4zswujI8lzgb/IiPeimR06XtOGk7HxUW916hZmKhKvRBzHmXO0VaksJyykBLgSuJ3sSqUzZIybJFsnywHfotVxnDnP7wyP0gh7mdlmgPj6pgHxdpG0RtKP474tmUhaKekhSU8T1tbUSqJlcnN83UholWyNr3dbdCde92e3TdPaznVc3+Zwbduhsdlfkm4F3pxx6QvAlWa2IBF3i5ktzEjjLWb2hKTFwL8CH7R+Z5ZZn1vLLI8hM7qOTZyfM60Tn0HTLK5vc7i246Ox7i8z+9Cga5KelLTIzDZLWkRwVJmVxhPxdaOk24HDCKvvG6XAjC4fN3Ecx8mgre6vVcAZ8f0ZwPXpCJIWSto5vt8DeC+hoG+M3k52hN0Gkzvg9WZ0HUvYSGxOtEwcx3HK0tZA/YXANZLOJPi4Og1A0ruAs83sLODtwN9Leo1Q+V1oZo1VKj6jy3EcZ3RaqVTM7NfABzPOrwHOiu/vIIxZNIbP6HIcx6mXqVlRX9THT6Ii2QjcQ85KeK9MAu4/qVlc3+ZwbcfP1Pv+KuExeE7O6CqDz6BpFte3OVzb8TE1LZUsSnoM9nETx3GcEWll9pek0yStl/RaHJwfFO84SQ9Keji6cylLcpyktw4muWjxEHxGl+M4Tm201VJZB5wK/P2gCJJ2AC4CjgE2AfdIWlVyBtha3GOw4zjO2Ghr9tcGAEl50Y4EHjazjTHu9wgzsgpXKma2VZJ7DHYcxxkTbS1+LMLewOOJ8KZ4bhYpHz97Jq+Z2dZp9cs1DvK0dUbH9W0O17YdWvH9ZWbXxzi3A38W16ek7z8N+HBcCImkTwJHmtkfNWKw4ziOMzKt+P4qyCZg30R4H+CJEdN0HMdxGqTL3V/3AAdKOkDSTsDpBJ9hjuM4Tkdpa0rxKZI2AUuBGyWtjuffIukmADN7BTgHWA1sAK4xs/Vt2Os4juMUY+pW1GchaR3wUg1J7QE8M4XpAOxiZu8se1MHta0zrbrSqaQtdFLfrqXj2jaXDlTQt7UV9ZIuB04CnsoyWmG+8d8AJwAvACvM7CcVP+6lmjbuqmsDsE6l00ur4q2d0rbOtOpMZ4TbO6VvF9MZ4XbXtkBaZe9pc0zlCuC4nOvHAwfGYyVw8RhschzHcUagtUrFzH4E/CYnynLgHyzwY2BB3CXScRzH6Shdnv1VePFjAS4d3ZypTmeUtKbpu3QxnS7YMK3pdMGGLqdTKa1WB+ol7Q98f8CYyo3ABWb2bzF8G/DnZnZvRtyVwOeBBcALZvYfmrR7LuHaNovr2xyubTt0uaVSePGjmV1qZgea2Z7A0+Mwbq7g2jaL69scrm07dLlSWQX8gQK/BzxnZpvbNspxHMcZTGuViqTvErwFv03SJklnSjpb0tkxyk2ELX8fBr4F/GGFzxh1P5ZkWo9KWivpvjLT7CRdLumpOCe+d243SbdEZ3e3SFqYl0ZOOudL+mW06T5JJxRIZ19JP5C0Ie5p89kRbKpF37a1zUmrlL7TpG28dyrzrpcLmenUlncxs6k6gDXxdQfgEWAxsBNwP2EzrqrpPgrsUeG+DwCHA+sS574KnBvfnwt8pWI65xMccpaxZxFweHy/K/Bzwh4zQ23qaVu3vm1rW5e+o2jbVN6tqu205t06te1C3u2Ctumjy91fpZC0TNKlwBvjqe37sZjZy0BvP5axYtlTp5cDV8b3VwIfqZhOFXs2W1xEambPE1zg7J1nU4a20AF969I2J62y9pTWFjzvlrCnjrzr2manUynvZjE1lYqZ3WBmK4Hn4qk6pyQDGHCzpHsVZpWMwl4Wx4fi65tGSOscSQ/EZnChrp4eCrPvDgPuyrMpQ1uoV9+uagsV9S2qbTzXZN6tU1uY/Lzr5cIQyuTdLKamUskga1vJUeZPv9fMDies9P+MpA+MkFZdXAy8FTgU2Ax8reiNkuYD/wT8iZn9e4XPrlPfLmoLFfV1bQvRVt71ciGHGvJuu5XKsAEzSSskPZ0YcDqrRPK17sdiZk/E16eAfyY0o6vypKJ3gPj6VEWbnjSzV83sNcJkhkI2SXodIeN8x8yuq2hTbfp2UdtoT2l9u6CtpPmSlkqaX7O2MPl518uFAdSUd1ud/bUDcBGhhj8I+LjCfvJprjazQ+NxWYmPqG0/FknzJO3aew8cC6zLvyuXVcAZ8f0ZwPUV7Uq6rTmliE2SBHwb2GBmXx/Bplx9kwXbEHs6qW20p5S+49I243O3ax31/hlwM7BB0ptjnDq0hcnPu14uZN9TV95tb/YXYS+V1YnwecB5qTgrgG+UTDc5Q+kEwiyGRwjbGFe1dTFhlsj9wPoyaQHfJTRBf0t4SjoT2B24DXgovu5WMZ2rgLXAA/HHX1QgnfcRmvsPAPfF44QiNiW1zdIXmB9/172Ax4Dn4+v8xLX5XdO2Ln1H0bZq3o26JrU+Or43YFv8zLWE6fl/VTLfT2XeLaqtlwvF827yaM1Ni6SPAsdZ/x707zazcxJxVgAXEFbD/hz4UzN7PCOtpDsGM7NRB2edSFFtE0/ICwmF2TxCgbeVMIPkinhtC2EK59bGjZ8ARs27kpYSWiWDtD6C8HSeDC8G1k77b+DlQju0OaZSZMDsBmB/MzsYuJWZqW39N/W7Y3isXjPnNiW0XUIouOYTKpRthEJuS7zeu7YQWFK0e2zaqSHvriVo3NP6bkJ38rHxdTH92t9PqIR+Nu3al9HW82N9tLZJFwUGzMzs14ngt4CvjMEupxq9wg1ST8SJc73Xjcy0arZImjNPz3VjZlvjWOQS+vW7E0BS8ndJtiAhVO5rM+6dU6Ra2VskeUt6BNqsVLYPmAG/JAyYfSIZQdIim/H3dTJhQY7TQQYUbk/2riev0d+qgfD0PA//Q1ci6nXnoGsJ7TcS/neQXbnPVe3T+XEJA/R0htNapWJmr0g6B1hNcJ1wuZmtl/QlwqDaKuCPJZ0MvEJYNbqiLXud4Qwr3Kj49ByfJOf00/QopLTPq9z7tO+dY/p1T7ey1+bEdYbQ6n4qTaAa92d2+qlT20RF0Xt6zhtYnhMDzePOu+luH/q1fjZGWxCv9ab7T2QlM0xbf3Cpjza7v2pF0jJgGf3+qZwaaELbEk/PJzPlXWVV9K2jEEx3WdKvfW8Szxvi65EkZpUl1pR1uiAuqm0qP3oFMwLeUnEKk9a2iT/fkKfn9FTlY5kpDCe+q6xo3s3QqJbKNZVuuqWygrDwLW/qcmdbkG1rO5cYuaUi6e3AZ4E9gNvM7OKRrXI6T1MzZrIG/EsMNPd1laVnlU1yhZOikYHljJYLqfdbUq8DW5DJeydMex+0H5WCqz8/DXwzEf4ycFUqzu8A3y6SXuKe44AHCRtxnZtxfWfg6nj9LsKalWFpriljgx+lfq/kquSlzKzcfh5YOiYbtq/Mz7DhU6nwr5hZaV54lX8ZG7LCdeg75HunV9GP9LkVtU/a0NO5p/vRKfvS2u/VhH5Vtc34jrXkk7l6FP1B3kBwF70AOAn4KfD6xPWTgTuAT5T4kYdulkPY7fGS+P50gh+wUpnHM0SNmaW/UmmlYMv4bQcVXumCLl3hZBV8mQXdkAK9tgIzJ+/WViE2oH+WfUlXMWUr+1z9Bv0uZbUt+L0G/d6V7Bslbg2/UZX8WO2hq4RxXyU4gNwIvHVAnBtLpFfE99dq4hMwoavuGeI4UJHMk5EhFsTjfYQm7k7A6+KxUyqcdW5QuEiccae7AHh//J6977wgXl+YER6aecgu9HqfUeb71alB8ru8LobfT9gjI/nbp8PHMrig2ycR9/F49K4dk7rv7ILpDK14GZx30xXk+8eob9F0k/+rhanvndQh/V3K6Jf3u5TKuwPiZH3f9+fYl5fH0vaV+S7pdHvlVvr/3CvDkv+BXv7v3ZP3H9g7lc6CVDqVHhrLjKlcTlh8uNzMHumdlHQUcCqhq+qmEullbZbz7kFxLKxreY7g4OyZZKS0j5/EpXT/6BEET5y9QbhDCIOO08A8wvddx4xjuKwB1/cQWpULs8LpsZEcbbHQV35Xg9+pCNsIXaPpsAEHM6PJ1lQYBq+VOYnBs6CS920huBL6ywLpQEb/fIm8uy3xmaN6Gm6Cnu4990uHAO8g2PpCIvwLZjQoq1/e71JG2zKsY3A+WZayLxlO21fmu6TTTZZbef/n9LUzc+wDuDd+n6xy4SyG5N2BlGhZ/CPBl/6pZZpCOemdBlyWCH8S+LtUnPXAPonwI8DuI7RU0s3ysYwDNHGQ30RPe6rdxuCuiHR4oCZM2XhVQsOsLpiBT8RJ7UukM0pLZVa3yyQfI+iX+7sU1bahfDLMvlHiFv0/p6/ljW2V7R4unO+Kivo54DrgKOCHNWWsxru/0hk444/a6T9oiT9furJMZogymTtXk7S203TkaF22736UPuw5Px44TL8Rfpda8u4o9lWNmyq38v7PtT4AVc1/RUQ8mtAE3DWGfwocWsOPsyNhfOYAZgbq35GK8xn6B+qvGTXzVM2ULfyp8n70YbNtsgaXC2Xucfwx/XB9XdvS9pepgBp/AMo7chc/StoPuB040cw2xHMrgKPMbMXAGwsi6QTgfzDj++uvk76/JO1C2HDmMILvr9PNbOOQNCdqkVNy/n48NWhPkv8L+Hoi3Ls+NhcavrC0WVzf5nBtx8ecXVGfsbnRsWbW+CKnnEoka9VysuJo3QeW/zGbxfVtDtd2fMxl319rSXkmbWrVb47zxBX0z7Do2dJ7Ta8IH+havkncr1qzuL7N4dqOnznbUolx81oNhbvD0pVRiS6tWdvsxviddGfhT3vN4vo2h2s7PqampVIF6/dMupT+VsORkl5kgO+iQa2PtP8pZrdGkusNelu/Zu7a5ziOM2m0UqlI2o3g02t/4FHgY2a2JSPeq8w87T9mZic3aFayO+xZ4EriIqIBjgqzPOfCbFftkNOlFc97JeI4zlTwO8OjNMK5BI/GBwK3xXAWL5rZofFoskLptVoOIrjvOINQocwnVBDJiiIdnkeoWLYSKo1V8bUXvjuR7kFm9qSZ3dm1ri3HcZw6aKtSWU5oCRBfP9KSHX2Y2dY4A+xu+iuGdEWRDh9CotKgvxLZ2kvXKxLHcaadVgbqJT1rZgsS4S1mtjAj3ivAfYQ96i80s38ZkF6fjx8ze1MNNg4cfM8Kj/p5XaUJbZ0ZXN/mcG3bobFKRdKtwJszLn0BuLJgpfIWM3tC0mLgX4EPWsKZ5YDP9VkeDeHaNovr2xyu7fhobKDezD406JqkJyUtMrPNkhYRHFVmpfFEfN0o6XbCyvrcSsVxHMdpj7bGVFYRBsOJr9enI0haKGnn+H4P4L2E9R6O4zhOR2mrUrkQOEbSQ4RNjy4EkPQuSZfFOG8H1ki6H/gBYUzFKxXHcZwO08o6FTP7NfDBjPNrCJvDYGZ3EAbCHcdxnAlhalbUu4+f5nBtm8X1bQ7XdvzMad9fTjlc22ZxfZvDtR0frYypSDpN0npJr0ka+ENLOk7Sg5IeljRo1b3jOI7TEdoaqF8HnAr8aFAESTsAFwHHE1aofzy6fnccx3E6SlsD9b1dJPOiHQk83NvpUdL3CO5dfAaY4zhOR2mrpVKEvYHHE+FN8ZzjOI7TURqrVCTdKmldxrG8aBIZ5zJnFUhaKekhSU8De1Y22pmFa9ssrm9zuLbt0Orsr+h65c/i+pT0taXA+Wb24Rg+D8DMLhirkY7jOE5hutz9dQ9woKQDJO0EnE5w7+I4juN0lLamFJ8iaROwFLhR0up4/i2SbgIws1eAc4DVwAbgGjNb34a9juM4TjGmbvGj4ziO0x5T46YlD0nrgJdqSGoP4JkpTAdgFzN7Z9mbOqhtnWnVlU4lbaGT+nYtHde2uXSggr5zolIBXqrDRUNdrh66lk4vrYq3dkrbOtOqM50Rbu+Uvl1MZ4TbXdsCaZW9p8sD9Y7jOM6E4ZWK4ziOUxtzpVK51NNpLK1p+i5dTKcLNkxrOl2wocvpVErLZ385juM4tTFXWiqO4zjOGJjqSqXO/VgkPSppraT7ysyIkHS5pKfi9MXeud0k3RL9Et0iaWHFdM6X9Mto032STiiQzr6SfiBpQ9zT5rMj2FSLvm1rm5NWKX2nSdt471TmXS8XMtOpLe9iZlN5ADsAjwCLgZ2A+4GDRkjvUWCPCvd9ADgcWJc491Xg3Pj+XOArFdM5n+A7rYw9i4DD4/tdgZ8T9qspZVOd+ratbV36TpO205p3vVxoNu+a2VS3VLbvx2JmLwO9/VjGipn9CPhN6vRy4Mr4/krgIxXTqWLPZjP7SXz/PMEFzt4VbGpd37q0zUmrrD1Toy1Mbd51bbPTqSvvTnWlUvd+LAbcLOleSStHsgz2MrPNEH5M4E0jpHWOpAdiM7hQV08PSfsDhwF3VbCpTn27qi1U1HdKtYXJz7teLgxhxLw71ZVK4f1YCvJeMzucsL3xZyR9YIS06uJi4K3AocBm4GtFb5Q0H/gn4E/M7N8rfHad+nZRW6ior2tbiLbyrpcLOdSQd6e6UtkE7JsI7wM8UTUxM3sivj4F/DOhGV2VJyUtAoivT1W06Ukze9XMXgO+VdQmSa8jZJzvmNl1FW2qTd8uahvtKa3vlGsLk593vVwYQE15d6orldr2Y5E0T9KuvffAscC6/LtyWQWcEd+fAVxf0a5FieApRWySJODbwAYz+/oINtWib1e1jfaU0ncOaAuTn3e9XMi+p668O72zvyzMVjiBMIvhEeALI6SzmDBL5H5gfZm0gO8SmqC/JTwlnQnsDtwGPBRfd6uYzlXAWuCB+OMvKpDO+wjN/QeA++JxQkWbRta3C9rWpe80aTvNebcObbuSd7umrZn5inrHcRynPqa5+8txHMcZM16pOI7jOLXhlYrjOI5TG16pOI7jOLXhlYrjOI5TG16pOI7jOLXhlYrjOI5TG16pOI7jOLXx/wMqhPLDA+btuQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlations = pcmci.get_lagged_dependencies(tau_max=20)\n",
+    "lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations, setup_args={'var_names':var_names, \n",
+    "                                    'x_base':5, 'y_base':.5})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the dependencies decay beyond a maximum lag of around 8, we choose ``tau_max=8`` for PCMCI. The other main parameter is ``pc_alpha`` which sets the significance level in the condition-selection step. Here we let PCMCI choose the optimal value by setting it to ``pc_alpha=None``. Then PCMCI will optimize this parameter in the ParCorr case by the Akaike Information criterion among a reasonable default list of values (e.g., ``pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]``)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = par_corr\n",
+      "tau_min = 1\n",
+      "tau_max = 8\n",
+      "pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Variable $X^0$\n",
+      "\n",
+      "## Variable $X^1$\n",
+      "\n",
+      "## Variable $X^2$\n",
+      "\n",
+      "## Variable $X^3$\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable $X^0$ has 7 parent(s):\n",
+      "    [pc_alpha = 0.3]\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.796\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.748\n",
+      "        ($X^3$ -2): max_pval = 0.13715, min_val = 0.048\n",
+      "        ($X^3$ -1): max_pval = 0.19326, min_val = 0.042\n",
+      "        ($X^2$ -1): max_pval = 0.20477, min_val = 0.041\n",
+      "        ($X^3$ -7): max_pval = 0.25822, min_val = 0.036\n",
+      "        ($X^1$ -4): max_pval = 0.26548, min_val = 0.036\n",
+      "\n",
+      "    Variable $X^1$ has 4 parent(s):\n",
+      "    [pc_alpha = 0.3]\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.695\n",
+      "        ($X^3$ -1): max_pval = 0.00000, min_val = 0.484\n",
+      "        ($X^3$ -7): max_pval = 0.14660, min_val = 0.046\n",
+      "        ($X^2$ -1): max_pval = 0.22144, min_val = 0.039\n",
+      "\n",
+      "    Variable $X^2$ has 4 parent(s):\n",
+      "    [pc_alpha = 0.2]\n",
+      "        ($X^2$ -1): max_pval = 0.00000, min_val = 0.450\n",
+      "        ($X^1$ -2): max_pval = 0.00000, min_val = 0.447\n",
+      "        ($X^3$ -3): max_pval = 0.00000, min_val = 0.183\n",
+      "        ($X^3$ -1): max_pval = 0.12516, min_val = 0.049\n",
+      "\n",
+      "    Variable $X^3$ has 1 parent(s):\n",
+      "    [pc_alpha = 0.05]\n",
+      "        ($X^3$ -1): max_pval = 0.00000, min_val = 0.372\n",
+      "\n",
+      "##\n",
+      "## Running Tigramite MCI algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "\n",
+      "independence test = par_corr\n",
+      "tau_min = 0\n",
+      "tau_max = 8\n",
+      "max_conds_py = None\n",
+      "max_conds_px = None\n",
+      "\n",
+      "## Significant links at alpha = 0.05:\n",
+      "\n",
+      "    Variable $X^0$ has 7 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = -0.653 | conf = (0.000, 0.000)\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.566 | conf = (0.000, 0.000)\n",
+      "        ($X^1$ -5): pval = 0.00496 | val = -0.090 | conf = (0.000, 0.000)\n",
+      "        ($X^3$ -2): pval = 0.02086 | val = 0.074 | conf = (0.000, 0.000)\n",
+      "        ($X^3$ -1): pval = 0.03017 | val = -0.069 | conf = (0.000, 0.000)\n",
+      "        ($X^0$ -5): pval = 0.03358 | val = 0.068 | conf = (0.000, 0.000)\n",
+      "        ($X^1$ -4): pval = 0.04107 | val = 0.065 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^1$ has 3 link(s):\n",
+      "        ($X^3$ -1): pval = 0.00000 | val = 0.663 | conf = (0.000, 0.000)\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.622 | conf = (0.000, 0.000)\n",
+      "        ($X^3$ -7): pval = 0.01579 | val = 0.077 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^2$ has 4 link(s):\n",
+      "        ($X^3$ -3): pval = 0.00000 | val = 0.451 | conf = (0.000, 0.000)\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.446 | conf = (0.000, 0.000)\n",
+      "        ($X^2$ -1): pval = 0.00000 | val = 0.425 | conf = (0.000, 0.000)\n",
+      "        ($X^0$ -3): pval = 0.01086 | val = 0.082 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^3$ has 1 link(s):\n",
+      "        ($X^3$ -1): pval = 0.00000 | val = 0.372 | conf = (0.000, 0.000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pcmci.verbosity = 1\n",
+    "results = pcmci.run_pcmci(tau_max=8, pc_alpha=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see from the output, PCMCI selected different ``pc_alpha`` for each variable. The result of ``run_pcmci`` is a dictionary containing the matrix of p-values, the matrix of test statistic values (here MCI partial correlations), and optionally its confidence bounds (can be specified upon initializing ``ParCorr``). ``p_matrix`` and ``val_matrix`` are of shape ``(N, N, tau_max+1)`` with entry ``(i, j, \\tau)`` denoting the test for the link $X^i_{t-\\tau} \\to X^j_t$. Per default, the MCI values for $\\tau=0$ are not evaluated (can be changed with ``tau_min`` argument)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-values\n",
+      "[[[1.    0.    0.262 0.894 0.886 0.034 0.417 0.255 0.877]\n",
+      "  [0.067 0.296 0.095 0.765 0.502 0.242 0.234 0.899 0.951]\n",
+      "  [0.832 0.657 0.453 0.011 0.359 0.48  0.901 0.243 0.375]\n",
+      "  [0.827 0.656 0.495 0.592 0.643 0.976 0.775 0.931 0.759]]\n",
+      "\n",
+      " [[0.067 0.    0.15  0.51  0.041 0.005 0.559 0.533 0.392]\n",
+      "  [1.    0.    0.911 0.07  0.576 0.556 0.175 0.732 0.741]\n",
+      "  [0.651 0.764 0.    0.231 0.495 0.43  0.423 0.944 0.238]\n",
+      "  [0.153 0.171 0.088 0.989 0.965 0.859 0.915 0.43  0.671]]\n",
+      "\n",
+      " [[0.832 0.1   0.182 0.443 0.37  0.265 0.998 0.89  0.65 ]\n",
+      "  [0.651 0.072 0.594 0.387 0.883 0.765 0.696 0.44  0.69 ]\n",
+      "  [1.    0.    0.074 0.942 0.956 0.626 0.818 0.526 0.942]\n",
+      "  [0.788 0.575 0.332 0.658 0.603 0.65  0.816 0.055 0.851]]\n",
+      "\n",
+      " [[0.827 0.03  0.021 0.597 0.635 0.2   0.392 0.081 0.85 ]\n",
+      "  [0.153 0.    0.558 0.534 0.72  0.284 0.798 0.016 0.162]\n",
+      "  [0.788 0.114 0.781 0.    0.975 0.098 0.265 0.161 0.642]\n",
+      "  [1.    0.    0.937 0.85  0.503 0.657 0.394 0.795 0.567]]]\n",
+      "MCI partial correlations\n",
+      "[[[ 0.    0.57  0.04  0.   -0.    0.07  0.03 -0.04 -0.  ]\n",
+      "  [-0.06 -0.03  0.05  0.01  0.02 -0.04  0.04  0.   -0.  ]\n",
+      "  [ 0.01 -0.01  0.02  0.08 -0.03 -0.02 -0.   -0.04  0.03]\n",
+      "  [ 0.01  0.01  0.02  0.02  0.01 -0.   -0.01 -0.   -0.01]]\n",
+      "\n",
+      " [[-0.06 -0.65  0.05  0.02  0.07 -0.09  0.02  0.02  0.03]\n",
+      "  [ 0.    0.62  0.   -0.06 -0.02 -0.02  0.04 -0.01  0.01]\n",
+      "  [ 0.01 -0.01  0.45  0.04  0.02  0.03 -0.03 -0.   -0.04]\n",
+      "  [-0.05 -0.04 -0.05  0.   -0.   -0.01 -0.   -0.03  0.01]]\n",
+      "\n",
+      " [[ 0.01 -0.05 -0.04  0.02  0.03  0.04 -0.    0.   -0.01]\n",
+      "  [ 0.01  0.06  0.02 -0.03 -0.   -0.01 -0.01 -0.02 -0.01]\n",
+      "  [ 0.    0.43 -0.06  0.   -0.    0.02  0.01  0.02  0.  ]\n",
+      "  [-0.01 -0.02  0.03 -0.01 -0.02 -0.01  0.01  0.06  0.01]]\n",
+      "\n",
+      " [[ 0.01 -0.07  0.07 -0.02  0.02  0.04 -0.03  0.06 -0.01]\n",
+      "  [-0.05  0.66  0.02 -0.02 -0.01  0.03  0.01  0.08 -0.04]\n",
+      "  [-0.01  0.05 -0.01  0.45 -0.   -0.05  0.04  0.04 -0.01]\n",
+      "  [ 0.    0.37 -0.   -0.01 -0.02 -0.01  0.03 -0.01  0.02]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"p-values\")\n",
+    "print (results['p_matrix'].round(3))\n",
+    "print(\"MCI partial correlations\")\n",
+    "print (results['val_matrix'].round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to control for the $N^2 \\tau_\\max$ tests conducted here, we can further correct the p-values, e.g., by False Discovery Rate (FDR) control yielding the ``q_matrix``. At a chosen significance level the detected parents of each variable can then be printed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 2 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | qval = 0.00000 | val = -0.653\n",
+      "        ($X^0$ -1): pval = 0.00000 | qval = 0.00000 | val = 0.566\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^3$ -1): pval = 0.00000 | qval = 0.00000 | val = 0.663\n",
+      "        ($X^1$ -1): pval = 0.00000 | qval = 0.00000 | val = 0.622\n",
+      "\n",
+      "    Variable $X^2$ has 3 link(s):\n",
+      "        ($X^3$ -3): pval = 0.00000 | qval = 0.00000 | val = 0.451\n",
+      "        ($X^1$ -2): pval = 0.00000 | qval = 0.00000 | val = 0.446\n",
+      "        ($X^2$ -1): pval = 0.00000 | qval = 0.00000 | val = 0.425\n",
+      "\n",
+      "    Variable $X^3$ has 1 link(s):\n",
+      "        ($X^3$ -1): pval = 0.00000 | qval = 0.00000 | val = 0.372\n"
+     ]
+    }
+   ],
+   "source": [
+    "q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'], fdr_method='fdr_bh')\n",
+    "pcmci.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        q_matrix = q_matrix,\n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tigramite currently offers three plotting options: The lag function matrix (as shown above), the time series graph, and the process graph which aggregates the information in the time series graph. Both take as arguments the boolean ``link_matrix`` which denotes significant links by ``1``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "link_matrix = pcmci.return_significant_parents(pq_matrix=q_matrix,\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the process graph, the node color denotes the auto-MCI value and the link colors the cross-MCI value. If links occur at multiple lags between two variables, the link color denotes the strongest one and the label lists all significant lags in order of their strength."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x7fb9cef5fac8>)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp.plot_graph(\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='cross-MCI',\n",
+    "    node_colorbar_label='auto-MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='MCI',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While the process graph is nicer to look at, the time series graph better represents the spatio-temporal dependency structure from which causal pathways can be read off."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Nonlinear conditional independence tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If nonlinear dependencies are present, it is advisable to use a nonparametric test. Consider the following model:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "    X^0_t &= 0.2 (X^1_{t-1})^2 + \\eta^0_t\\\\\n",
+    "    X^1_t &= \\eta^1_t \\\\\n",
+    "    X^2_t &= 0.3 (X^1_{t-2})^2 + \\eta^2_t\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c85e5be0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c85299e8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c84f2ef0>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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v8r+jsbH1nmybsAP7iMhUrP2fjI3/Udm1Tvb/b8UcSrAbdKd6PV7r/19LiqDC9h7v13YsNh99AWZz7gAm+/WM9zLOrjh3LWlHoC4Ve1Dge7D7WrqVTZgBisreD7iJRg/m1dJ4h/yxWMc6GGuYs2g01EcAM9xZfnR2nFHufeWDYArweuDfMCGfT2M4W7dIYj7WwcI7WuPHl6wcdXNQTwRe6emOCf73LMzjuY70xISZfryYj7iTJIh3Yp3sMMwwzq+cI09VgN2rtR2LVJ5AEoMQiz2BL/tnD2CrD/FzHUJKHfwvZrCO8PNHSm4y5onFw0NPxgzCPTQL1AysrecCf8IG4DJgbx/AZwMXYQNwKSlCqaaapmFG6qbss/0xkfg98Fj/bDbNi072wdJjp2DL1MOgTME8zX2wAbnMPzscM3IH+fePJYugsInwj2IDPgx/zCdUo/NIyf2Xv56J9asQ0aMwAz4H658TwOYEROSpJIHK5+d+Dxzu9TQPTy1hTwuA5ghqI2Y035xFwiF2f4v1xylYf2slUBFBnYH155dkKbRTMScBUv0fhkVJYP13NinqOD7bFhqzG08BFmH99EhsPmUJjfdixTgdj9mC3Ck4CIuG1mBzMGdgArcRS6ueiPXJNSQHdCpW95uwdnw58HxVfal/fozYysELsJRy/MzKFOBjWJ/Cj7XEP/8y8GwRydOTk/yapmGCuAxbyfzYbJtegcIE43TMcdkbE4yZYlMc4WCsVNWbMRv6kJdhgwcCj8b67xysz0e/f5zX3aOBf8AcillYO+3rr5OB5ZgdaWt5er8CparXAL9S1Xeo6h/bOWiH2OivB4rIM7FBdD2NUcQzgcdmYnMM1rEiNbaAxgUMR/j+j8Ce6BAD7V3Af5C8pkjxzcUaBsxDz1esVBdJrMeMeojqdlJjTydFTQ0pPveYT/N9IoIYjxmEd2H3fQQf9gF/KGmARQR1KzaYDwF+Sh8RlIfrH/Nt3gK8Ofs+UnxgA/8CL8M2/2wBaQI5tt/bPw+DGkblQFJbnAr8FhvM1RRfXPf+wCpV3YDV0zxsUGzBBt4U0oCYQL1AzfW6iIjvIEywl5EWadQJ1AzMWL4au4cqH3C/wup8ItYH7iNNLj/By/8EkkDtjRnRz5EG72LSvEg1gprh5T0OcwYejYnseGzu4BBsvm8uVn/h5Z+DiWC0XZ42uhoTtpgHi2g+6iAXqAMwR2gK5lQc59/lC0lCVO/B0mlTxG5eHefedD4HNR8bY18EFnjq9cjs+qeQBOoazGDfSRLYqV6GB4E57rhNwdKWcX8epHmYe2mMuPNFEpO9XJOzaOUIrH7Xehli0cBW4INYxPwx/z6PoNZjtuFI4Heq+nM/3s8xp+J4rN7/BhO97V6mp5PmMnujcKw9v0Hj8wtj7L0Pc1wXeV08UkTiu1gwcgTWjvtij7cKB+7FmEMStmqVvy71etmkqg8DuAYsJwnUOqy/HG9f62+9Ph5FiqDmY2N4ltfJMtp8wn67q/jOF5EP5HMEIjJLGpdddpoQqP2weaP9sEE0LQsnZ2KG/P88hXQ0KYICM2CTPIc7A2vAsVgHexD4otgzuF6OGYEJpE44Fav06dhkIphxGiMiF2BGYSMpgooUYHAXlg54IWbMVvjn92L3GoySdCf/wViHnY4NpolYOuhHmAAGZ2PG5EgsZbOBFEHdhg36/bFBv1hEPp3t+yjMCMR1bQROUdXFwC2Y2N+LdboQ0K2q+llVXU6KoB5FMv5BRK5h9PP02UFuGOZiOf011EdQeNnX+PswOPtjxj0M00qS0Yj+uxG7Kz4iqGXZ9gf6PitIQjid5hRfeOi3kCaXl/t3v/ZzPAYTy3WY4G7DHJflWNuEQL0AuFxV15ME6kYvx3aaI6jwqvfHIsgppPnLDap6h19PVaBOw/rDcV6GEKj7sLFypNfHclX9OiYAC8RWX55D8qbn+/azvF7m+3F6MMHUrKyLsT55JxYRXYAtbc4jqP2xlDRe1g/TfOP0BMwI3ordBJ4L1BRsTPzJy38EsFhVH8LqPoj2jwgq9h+LjbF5/vkGrN1CcI8G/uLlnZ4dYyvWl1+Gpb1+RXJAp/i513p5om+AjbdHkqLt20hO1cGY/ThWbIHXRKxvT6bxcVSzRWS9n0uBazzCWYzZ9RuAL7ntm471j4iG7wMWeT9ZhT35ZRqp3VYCuCiFfctZgbXZGKzPzAXOw27pwMf/NiwLcnu233yv2+UMpUCp6guwAXO5iDxXRL6BeXu3tbP/TmIjyViBVeCtmLBENDID6+STsJVZW7GBcTRpovwMrHK/hXlFK7GK/qR/dinWoY8BVngjriN54wA/8NeIoD7j/0cENRWbq4nll2CdcxIWxl+KDbb1fk0P+uuzfNv9/BgTvXyzoLdjhEC91o85E+tEN3odRQSVC9RdmOF8DPQuDz0Dm+Ce4uf7q6rGHeS/xEL6q0lzfZCipngf9Rv7bfDXs7GBEQI1F7gfM4BbsM4bE7MryDqzD7iZfi25QC0jCdRdJPFfQRKo0ViEe4KXIReodb5tLlA5430iOpgJrPb2/z0WES3GIrptmJP0Qq+DEKjLMXH4GiYUczABOhwzgJAEaRtm8K6nOYKahBnSUVg/gTSPFnW80Y+/CktDHYa12YNY37seE6jNXj8LSffxhDFdhhn1d2AGdQnmaIUXvABr3+gDPVjf/Zz/f7dvt9Kv4UzM6TuA5Nzt6cf8qu8zHRtbdU+HOAVzCF6HLZSYifWbqZjx+z22wOUnmIBBs3EFa9ulWEp4Demm3Rf49eyDCXs4QkeRBCq4lzS3+VtVPVtVv0FyQKf69YVA5fPLf/ZrnOrfL8DGyGL//vNY29/i9XMvySENXkxKsf2YNMYWYUJzOhbtneH7XYX1+/uBuyIi8uu8Dmv/KOPK7Dx1AnUv7uSo6jJVXamqX1fVz2TbXI3Z1Tuzz/bB+ufQRlBiSxIjp/5W4EZVPUdVL2ln/53ERho7wQHYAFlLiqJiueVfMLW/HGug2diSZEjR1AOYiK3BwuHbMZFaAPyPbxcDeZ2f7wHMyF6RHSNfHbQVMwgRkeQ/ALaadN/GK7CJ5oPcqx6NdcQY+HOwzj0BM0CjfH9IAnUl1vFOwlZh/tnP/xkva6T4wqCvIK30e4rvv9iv80yvy+C7wNexOZA8NZgL1FasI4M98gXMiN7t1/8jGiOoK72Mt2BGcjrWdtcBJ/g84MuwdMJKbEDlArXEyzIfG4APel2EQJ2EpSB+pKq3+3GnY+K4HEtdXkkSqHyQgg36M0QkjFmkd8EWcuyJpbOinq7BxktEUHOxdNAFwL9jRiyixLkkYYkIahu2ivPTWP99kdhNmXuQ5rbABEqxqAKSMG/A0jMrMQfjSsxbv8zLtsk//yMWvT3sdXJOHFtV1bc9Dzgfm9c70ut+Pek5i+eKyFuxPvpNbB4WrD1DoMDE8SQ/b6zI24a19z/5vqdgYyaivrxPPQzc7PMj8dMPyzDxm48JFKT5Vvw6r6aRSPHt4/tGWvk8fx2DteV+bjciglrr3z9EiqCgMTqK+5YO9HOs8uvLbdONfs7ZwDqv+5v9nIuAS7AxOM7raD1pMdLnsX4dz++8T1Wfqqpx/MWYcGzBxvr5WD//DdZHv43dyB98EltUdDdmQzbSnkDNrVxTlR/76xLSQopRDIdAYYPkBswLPAGbdP5KxaPsNJE+O4GUK77L30/DBn2E+BdijXAF1ih3YZ0JzAv/JvAkN3JrsEFzh6rehnWM8BSiY67HvOAlwGdJS9Ynkq0C9AG/ieRJ/DuWb74f6wQbMa/8M6r6c1WNAQFmiJ5BetrBFFKaRGkWqC3Y4HgT5j39AEutfNG/vx0TpBCotVjefTQmaN/263qal/Ou7Dp+71F1taNVI6gQqPhBuv/28oAZykki8g5MWH6jqsdjxv5p2KBagw24UVi7HIHV/Sletv2z677V6/SCrKzrSSm+i0lppPhuCmYgtqrqd3zbg7ze1tD4pO2F2Mqmt/mijkjxgc3rfB2bk4qoeLG/RgR1P/AHT4Fu8s8egRmGuMMe0iKJ7Z6yWY0Z3IsxAzwrO+aDmOFcRjKeeQQFZpjeAbzWJ+ivxrzzTVgEdYe3JX78c2g0uD/x8kTa8iiSQD3C6+Bw7Ibj5/k2K7Bx8E0/Vxi8a7E2zAVqT5KQrscm9yMyeZDGSPZqT9uB9a+on6WYAY/U+p3+F2mqU7G2zFNYS0jPzYt2PgjLDoD1oROwXyHY5P+HM/QX338rsNnbM9iMrQ6+RVWXYGNvNFmdqup2rH+cSEotfw/4iaoeqKpr3Vbc68dbj42R9cArvb5igVZ+bjDbc6W//6Vfwxyvm41Y6rP3lhpV/ZOq3uLXsxGr/3uy47VK8UF7ArWSxqmMoRcoVX2Ge54Pq+pDqvpWLMTuplV9G7HOci1WKXHPTgjUTNLNjYswI/ctrNNFLvYBrE5WVwbCHrjoeNS4HOvs0UDrsEG6XFVfqaobMSH4BBaB3KWqYRxXYt7hRlXdoKo/IBmcDTSnloJFqvp9UucbhXlgm33/VgI1E/ilt9tyUjSzwutlArDGB/IazOg8DhO0fIK57hdol1T+r0ZQtwLzfRFD3EfxI/8+Hqz6bmwFWwzgr2KGbqaXSzGjfzLmlV7u3uFmzGteA72G6AJMeH/nx1rv19l0M6i373oaV/BtwsRgpX+/imS8rsLq5XDSPUNr/VgPqup5qvq/qhpL5xdn9bAOuM0dnmA1FnFExBWDOE/x4efY2/+/FlsRtgnrK6uxCOdckjcfx7nfXy9X1U/5nBKq+j5Vfa/X3740tvHv/dxfyz67lBSxbiXd3xMRzJ/9NR4VtFGNC1T1K95nV/q+t2BiPMGvaQuWblzl7bwBE8AQ+ZtJ4nY36T6ccPbW+nF/4d/fjKW+/gGLQvJtN2Hj/hZVvR9r9zCS+X1AL/E6uAv4ABaxfM6PEQJ1vn++lWYjvQlzOr/t/0c2pbrdXZgTvd7L+DNV/XZlm+WkCOpAbDw8gI3FWH2aG39U9SZVjQcs3+r7xWKFm0mOeJV7sLa7zq81WEezQG3C+mOrY6Gq9wInuJO9ERtHkcL/LnYTcL8M+lFHqvozrCG6hY0k47wCE4XoxCdgxjzmme5R1ZhEXY0ZtStJefA8cjkeeiscf/+g75en+PYl8zxU9aWq+hP/rndf0oCrpv4igqqmloIwBHnZ9sEafAvNArU5O1b+S5i/Ah6T5aCvyjrkCiy6/ELWscDq5UdUqBhcaBaoraoa0cxxnq/egi22WIqtPDrC/77qx7yd9GSHuNYfYvMDB2bXspn0ZIsozx9U9UlqK0/jeu6h9bPw1tIoUFF3t2T7h2G5CnMKVuArp2quvxdVjUE9BmvbmyubrMacnBjk1RRfpHtvxZyGO7Do8kkkgVrljscfSQIVxxEvRz5vkrOJdG9c8GPgnKz+UNUbgUO9j8Q5FrojeAkWJe2PrUyDisF0Vvp1xniJe5giUoqJ9A1Y1iF+CPBDpJtyX4ctnshZ42X6KXCnOwpfVtVfqOrdlW03AUtUdYFf1wosXV8VhdVqafXot6/CIn9I/fE2Vd1KvUBtxqLCuKbbsNToXZXtYg6xbo4syAXqIFJf34Q5XTfTHEH14hH4QuBa76ufIkWIVe6m0VkJmiKoLLrrK4LC+whYn1hEcl7uUdWb+ti1lx367ZE+On8nyAVqZfb+fswTehDzZqbR2FkWYdHJH0TkIWz1UC4C55AGUs4KGgUK6p9GsYhMoFRVfVFhfg9KHkHV1emhJI98bfa6t5djKyndVI2gogy95ydFYdNp7HwrsSghfsJ+KXCvqr6wpkzBA6Tl23WLJOK8d2fvr/bXf6WeW7G0RC6Q78M827iWOPYttOYZWD20+gmWqkCFUMecxd34jZ6kxQhnYvn3n/Vx3pxZWHql+tPmq0n3zUD9HBSqulZEfoxd72+x1NlSzHCszo4XfTTq7H8wZ6MVcd68b27BnmjSgEfAkAmUfx7zIIhIGMq6sRLL9mO8/A6LBOsECmwuDKyOY2HAQlXNV6hCiqC+Sf8P+s3tA17+W8R+IiX6+4OkPhw24lseceXXEfXQKoICH68+3v6+pjzhmNSJQhCrZNdjfTAiy41Y5HkQgBEpAAAgAElEQVQP6WbmVtxISnd+qY/tvkpN25Pm0arkzlt//CvmWA34h1e78cexBkusdAGrvHiMycuw1M8izJDOzzocaunKIDrX2uz76gRrcDfJWIaR/1PNdnH/UU48jSHII6imFJ9HFUGUbTEmcpF6ySOo+1T1IRFZhQl0bUfSxjkusAG4yKObWBXY3wNel5Pm2ZoiqH72bcWtwFER2anqZhH5EvD6zFge4N/d0+IYhEET++mJUdg9Szk/weZWgn18v7iO52I5/6MwkblQbYL+Zhrns1qxDUspXVTz3Wps4FdTc1tJ98UFb8fSwtswg3UL1t/za2+IoDw7UCcWQfT1Vv27jgaBylHVdSLyexqzBcGfsKhzOZbmeT62yjTKEIY32vYu4A1+rFgsUReZrcWi9IdpdCrr2ER9fTTMj2TZhJuBX6hqrxOgqltEZL9sm2+TIsf8PHENfRHp8b4Eajlp1WR+7E1Yf15OHxGU895+zgGA2jxa3bHe1WKXz5KlXPs59mViv9nVbzmq7EoCdREpZflj3BvOjNRsYK9cnGpoEqg+eBZpdUoMrLoI6maaDfXHsGW7wQbSZGKrOahgLebpLcVy2FtoFKhY2gomordk6bz+WEGKrtrlI9iNhk+j8Tp3RKBuobkN/pXGm5DHUW+0mtC0/PWTlc/fXtn0ndjAi++3eWSw2VM6L2/nfNn+rZ5SD+a0rCKJevSh1Zjx6RUoT7Phj6kBc0IuwVKfQTXF1x9xE+etfW7VyFaszy+v+1JVT27x+VXAVWIPR17tRv/fAPy+wqqxXq6qH/Xvo37qjGeMhXZoiqCyz8Ecud66cyet+gOQIfzxfgXN43UzsLYm2qvSrkBtwaL8L5Ci9k2Yfbuc/tNsdTapbVrZjhZOV1+sp/2+2csuI1BekXG386U13z9I/505BkFfeeE4Xu7hLgU+5LnrKu+h8cbFOsP4LMwwjaH1HFSwliRC+1ERKFVdJSLH+PtbxX7AsF0uHsC2+Dk+CnxURLbTGEF9nMELVKxMy8+zjcY0Tt0DSHcIVa1bXh5zfEPNav+rCks4Gtua9rB230a6sz/PBDwkIh+kTdHGU1UDcF6ijHcMcJ+cG6mkHStzRVEHuQD2JVBraH46SCv6i6Aixb6jxIq//lgKbNP0HNE6fgDc4NucXznHA2qrBKsLlbqVZdRH132yywjUELEFE5N2IqhePCqr/VExtVU3/e0fOd5ftHG6RVgu9+nYIoEt2Eq43vRidryqkPZXjh3xti4n8wbVluQPll+R7kurZSfOf26m3vPeUa7EvO9twP2ZoQqBamo3n79c1qo8qtr2k11U9cukZye2yw3URBUDOOc60j1SdWzA0my5OG/GRLEu83Et7T8ZO26SrzLUAnUTtkqtP+4k3fJRSwuHCazMg3X+OsWl2GKWAVEEKsMNwDoaJ5+7Cs9/XyXpp5q3qK2o7CiqOuAJ0D6OpQxP1DIYltPHctrBoqp/AP4gImfTaBjDuaiLoPCy9DfvMCx4u/QX4e8IC7F7yvJz3ke6kbZanm8M4Ngb2QkRlKr+hcYb8Fttt4XGp/kPhI60/47gUXdf0WItRaCaOc49l24nwuVuMeS7JGpLuNv9ldjBsI1Gw9gygnKWMjwRXcfxifp3DdPhv0C96A91BLUz2ET7c28jmiJQFTTdt9PtxORsEaiRzVLSsmroew4K7H64umW/hT5Q1etafJUL1IBXmXWIKygCVehySgS1C6CqC0m/gQVmJB+iRQSlqh/YGeXajdiMLa5aywiJoOoWge2qFIEauZQIahdEVR/2J2y3iqAKQ4jX9ymYOO0WUclIogjUyGUd9hSHIlC7HjdR0ng7DV+wUuhCikCNUNzz+3f6v7G3MMJQ1TM7XYZCoRsQbXhw7chARK5R1RP637JQKBQKI5VBP828UCgUCoXhpAhUoVAoFLqSIlCFQqFQ6EqKQBUKhUKhKykCVSgUCoWupAhUoVAoFLqSIlCFQqFQ6Eo6LlAi8i4RWSoi1/vfkztdpkKhUCh0nm55ksRHVPVDnS5EoVAoFLqHjkdQhUKhUCjU0S0C9WoRuUFELhKRqXUbiMgFInKNiFyD/dR5oVAoFHZhdsqz+ETkp8Ccmq/eBlyN/UibAu8B5qrq+cNeqEKhUCh0NV31sFgRmQ/8SFWP7HBRCoVCodBhOp7iE5G52b/nAn/tVFkKhUKh0D10wyq+D4rIsViKbzHw8s4Wp1AoFArdQFel+AqFQqFQCDqe4isUCoVCoY4iUIVCoVDoSopAFQqFQqErKQJVKBQKha6kCFShUCgUupIiUIVCoVDoSopAFQqFQqErKQJVKBQKha6kCFShUCgUupIiUIVCoVDoSopAFQqFQqErKQJVKBQKha6kG55mDoCILAY2AQ8BD6rqCZ0tUaFQKBQ6SdcIlHOmqq7udCEKhUKh0HlKiq9QKBQKXUk3CZQCl4vItSJyQfVLEblARK7xv/Kru4VCobCL0zU/WCgi81R1mYjMAq4AXqOqv26x7TVljqpQKBR2bbomglLVZf66ErgEOLGzJSoUCoVCJ+kKgRKRCSIyKd4DZwMljVcoFAq7Md2yim82cImIgJXpa6p6WWeLVCgUCoVO0hUCpaoLgWM6XY7Crk3Mc3a6HIVCoT26IsVXKAw3IrI3cFWny1EoFNqnCFRhd2EyMKHThSgUCu1TBKqwuzARGN3pQhQKhfYpAlXYXZgI7NXpQhQKhfYpAlXYXZhAEahCYURRBKqwuzAR2ENERnW6IIVCoT2KQBV2Fyb6a5mHKhRGCEWgCrsLsYKvCFShMEIoArUTEZH5IjK10+XYTYkIqsxDjXBE5MkiMr/T5SgMP0Wgdi5vA57f6ULspux2AiUijxKR/TtdjmHgH4DHdLoQuwsiMk5EvtOJcxeB2rmMJRnKws5ld0zxvQp4WqcLMQyMByYNx4FFZJ6I/M1wHHsEMwt4pojsKSJjduaJi0DtXMYwhE8z8KfAHzdUx2vznHuJyK/En+zbxvYzReSo4S5XG+x2ERQwjl3o6Rkisr/3u3EMn6N3FvC6YTr2SCV0Yg7wFxGZvbNPXNg5DKlAAX8Grh3C47VDD5Zeafc6/g+4YfiK0za74yq+4TTkneDnwALsuoYlgvLj9tu3ReRsEemKh20PBSLyFBH5zxZfR9S0LxZNTd/Bcx0lIme1s+1uJ1Ai0tPB0w+1QB0ErBnC47VDGLxZACJyhIg8Nd9ARPYVkef5v4+qfPcmETmk3ZOJyL79fV89fwui3nsjKDGe3W5ZOo2ITBvgLmPpUAQlIk8QkRuH8HgCzANmYCm+4RLeiVTqTESmi8gLK9t9BjhsmMqQn/tWEXnxcJ8HmA8c3OK7GDP7MDROz98AT29nw91OoIA7RWRy9cOd5A3txQAMhoi8R0RqPf66G05FZIqIDLfHHOWf6a+PpXnhx4eAr/uPTwLckn33HODwdk4kImcCd/ez2T8BH2njcHUpvnHAt3bk5l0ReXy76c4dQUSeAKwRkbED2K2TEdTzaLOd26QHE9xpZNclIs9wAfmH3FGJtLI7IV8QkXZt3SSa6+yRwBsqnw17+lRETgUOBQ4czvM4EzHhryMiqP2x8bOj0etUYG07G+5yAiUiH24lNiKyF+aBzap8fjjwwE4oXtsRlBu9NwNzW2wyEXiIxk71TuAVgymYiBwsIv/cxqYxeEOgJtB8TREh7edlzCdWZ9N6IFR5rZetdt7IheU8YK6InNePEZoI3E9jii+W/I8RkX8eaITi5/sJ9qT0dvd5kogc7+8/JCKPa3PXf/LXOQMo4k6ZgxKRMTV1t/cOHvMoEbki+yjmPabTmOL7Z+Ak4CnA8dn2vxSRuVjb/B0wv84xraEuxddDcxsPWXTqt59cWvNVzN221b9E5AARGTfIYkzA6rWOGH8xrmsFys8/1d+f30f2Yxqwrp1C7VIC5QbrDSTDUyUaeka2z+HYxOjOYCApvrGYMZ3R4vseYCUwVkRGuWe9NzBFRB4xiLJ9HPivNrZrKVAiMk1EZmFeH5gxvRmYJiI9LroDEahIOUxp8f2+mOj8CPgacER8ISJTRSTPlU8FVtEYQfUKFGboGurNV3T1NWc1GZAon4i8XkSWtNrYU5vPBs70jx4JHNnH8XOiDhocln4mrHfWIolnA/9T+WzeQA8iIm/OMgDzaBTjeD+NxhTfeKxuDsXb0/tZj/9FP/0w8Jo2itGU4suOlTPoFbki8l+VtPTe1KcLZwD3kMS5Pz4CvKjNMlwoIo/MPpoIjBeRWSLy/srm4WAe4K+TPDL9bmV8/DvwfBE5Dfg8cG6L009jN42gosO0MmjxeW703wy8aaAnco99oINwDJlxFpFDROQGETkxS4cFTWJaYRKwAdiK5YaXYyH4IcBv+yj3niLyTzVfPaG9S2hK8U0gXdMrME9/AjY3Nhu4zct6B2ZE9ortReQCEXlpH+eaCDxMa4fjQOBO4JXAn7COH7wKa9twXOYBC2mMoKZkr7Np9lQvAl4vIp9scf44XxznRbSIHMTmPv+KGbqowxm0b8h7sLqsRtR/FJH9/BwvqaQb2zKiA0wbxj5HiUg4NFF/OYOJoN5E8tKrKadcoPIIahwWVR1M6id7Yb8WPpE0fo6olskjv+q4q4ugJpEJlNdxv86mp9wPrHz2SMwZenL2cQ/17TQduInmjM8+LWxPDxZNtsOpWHo+mIjV5XyaU/bh1EVdTsLa/Fwa++8MP86p/n8rYd1tBSo6bXhS1ZsU6wRqf8zAxxLqducTXkGbncEHwqdo7tTHY2H8B4HNlXO3I1CbgC1YR5kCHINFFVNE5DCpf2rFXOA/41xuaJ7o3y1sUf7TPTKC5ghqIjBBRB7lZY6oZSPWQZdjQjoTixgg1cH7gYs8FVPHBMyDbOVwHAgsVNV1vl0uUBNJRm02llLYRH0EFRFf9TzTscH2+PxDEfmoiOQGMfZrNckMdv17eZlygTpURJ4tInuIyDv62H8ScCuWznyRpMU+04F93dB+kUYxb4qgRGSOiMzJ/j8U2CIib+7j3HUsAJ7h73MhQEReC9zr79ua2/XtppGM8SQaBWo2cJ+/jqYxgjrWP5uS7Rvlin56IM3ifgHwvspnk7D+nNvGHiwNHJFECHp/4v984N2Vz2LR0P3Vc9bsP4MagQIWYSsaq0wATu6rQCIyWkSO8WPmEVQ4mmNoXqU3BrMzUZeTSMK0X7bddD9OD+aQHi8if1tTjKnsbik+sZUukWaY4p7K4orRrxOo+dn7rwLPFZETW5xjpkc9X8U6Z5PhFJGbxH5ePGdv4HyaBeoA4IfAGf5//l0YoHYE6jn+2Rg/1x7YQoXqyiO8zHuR8s0vJEWQrW7CezvJ45uIzddVU3w/x4xAiMQ4zJisIHlLkVMPwyPAZcA5Lc47EVhC3xFUiOpaGgVqHGlg74sttngAFygROYzkQbYSqCl+jv1c8Pd34/46LLqKcsVrD7BeRFREjvbz7Okp12jHvbFUivhnTwe+hdXVu/uYQ8gjqHdhg38UVpfzcCeL1C5RB1Uj+s/YXGVwBrCd9j3vYCpwgKd4JgLTReQMEVHMML8Sc0zanVCP+gmvuy6Cuo0UBcVxxwMn+jVMzfaNbeK4e9AsUHPwBQhij096XLZv3g4xFuOcIVBNouJp7shGTKexT4KtvL2Nxr7Wg/WJqj0OgZonIqdkn+8JPOxpttwBmAAsEEunHy31c7dPBb7tx66m+Mb5tU2Uxhtyx2DjKxeoqMv9RGSSiPzRr3eiX88NWFbmizUO6G4ZQR2EeVJgjR/KnXei6BSPEVsefQSNIeqBmMH+bvXgHkGsBL6AedUR5sb3e4rIJMyzPLqy+2xSausASY8NmY9NssdS8bwz90ZQfuxPVY6ZC9RxWEeG1HEOJOWMc2IQ/6NHPUdh9baE1pOks0j1NBGLVmLgT/CyTvRtpmHGYixJoMJbinoZ74ZtEvC/wFlu3PIl4KP8GMuoESgReQ3wVsybhGaBGksSqP28zPcDF4jICcBLSSuzIv9fTfFNwepwL+Bq4EbMoP/ZP++NoEQkjOkyfz3bX08Gvkmqr32wOqv+gOJ8f23wXj2yOgLrO7dj7TsbM9T5fE0rgYr5weluzOYDjxeRn4rIY7G+fAltzHOILekPh28qMMqPFxHUC7Ly3IMJVLu3dUS58whqQna+2VgfD4HKheQw/64ugsodvGpabCrmvIA5YOdk++Z2Iz6La2mIoETke5KeDXgucJnXbYNAiS0kOQS7dzEXqFxsc2Zgc7jTgd+JSL4AagnmCDzgYhRR2HIsSrsIqFuA8zgs0t8IPCKr33AI4tryfrgXNr7GZNuGndnf/04g9e0ebIyAjf+/r5RheARK+p4w7jQzSZ1tKhBpq7yip2Cryp4KXInNCYwmGZXZmDe2d6i+D+yJwDN9m5NIS1En+zazsLmgb/k2C/zz070DREplKtbIz/QU0QFYBPAmLCWSlzVP8e0LvEIa5wpCoDZjneoP/nm00XxMDM8UkWdm+8XAeBnwaEygpgJ30VqgZpI65ASsvnqy/8O4zCV1vroI6mhsTml8tt2dmID8El8u7vX9JKxO1wJPFZF1lcj0XExw4kblviKo/UgR1BOBL3n9xOA8BKvHM0Xk77wMsfghjIf4/s8ELvXrzuegwmDGIH66iJyEteMBWR2N9n1nYO0XhDNxnKR0KpggXg5sA5ZixmUC8GrgYt9mLhWB8vLnc1BfwAzw/pgzdwTwFqy//wCYJSL/6RmCVsua78a8b2hMj8YKsBhzMSe6EegRkeNF5DQR2SqNC1dy6gRqFKk/z8FEaB/M4ZkjIq/D+tJozHmoi6BmYg4TwOxKlJIL1AysT9QJVE/ltTeC8np+OhDzlCGCT8D6xzToTWFeg/Xba2iOoKrnjDLd6+8vAZ4mvgIUc2rjGD8DfuX7/wxzimZRSTmLLdJ5GqCYwN0HvFIsxR9tGNeWC3tEUEFEUJuxsbV3tl1EULdg7fQvpFRw9MuhS/GJyKH+dxiNqYEhRUSeKHZT2h0i8pZBHGIGFvpCmjS93d/HkuCZmCGGxhtcH49V6CzSAImO8B0sj7wf8Bs/Rw8eQYnIy4H/58eL0H6Bd8grsU4f3ml+v81TMKO0WFUvInlKQQ9mlGZgBgUaPfxJmAHY4v//gcal8uP9+K/ClmIHMYgPwuooxPMubEXgaBF5V2zsHaoaQS2ncSCHoY8Iah1pvmUFNsj+gtXhTZgXdzWwGvO0Y/VdrPo5C/gUNgDWYdHwFOB0L9NozEucrapxj1WtQHn552PGNfL+h9MYXc7y78/GhGUOJpZ5+uSPmJg+GWvX8TSm+MZh4htG5nTgQtIE+KHZsUKgbvN9yMrzYdzjFJHnAy/H6nUjsJjUL08ipWjmkQxtGPoQypkicjFm2Pfzuvg+8B6svuZgC0z2xSLSj2LpOUTkVGl+TNXZbuimYg7EYSRBmIFFtJtUdZuX+TDgd5ihGkfrRSGzKq/5HBPYGLoRGwOr/bOPkvreX2kWqIigbifNQb5SRGLeaSowVUS+7Mc/AOvXa6kXqBh/4chNzM4VizvmY87WQXgE5dHV80hi2yqCmigiB4rI6/3/6X6tJ2LOxEmYuEUZpmLTGkf5eSPVfhJWj703xHuE/37Mnl2LZYM2Yk7L6TSm+MBStqeLyP+QIqi8vHOxMdE7h+9ERmWdf/4dzFE+VUSe5vtuU9W2butpJ4L6AKbIJ1OfMtphPJ3zCcxrPhw4T2z590DIFf9YzDDeQzL6fw+8lxQtjQLWA89S1ZswQx8daA3wahH5ANb4z8AG8GX+feTdp2Di9ELMC12OCcXLSN7t8TTfu/JnLB0yBzM6cc5qim+hlz8EqtqpI8V3HzaX9cHKeQ7BxDc3jjGIR2Ed+SZM2O711yOBd0paYNKDddC+BCoY49ewFRODuX5drybdTPtLTBjnY4NvuR/jr9igGIP1s3mYQK33/T4OnCQib8fm8xaranwH9Sm+mDh/JFbn+cR0vqx3KqlfxOqkfEnyUszILvLrv4Z0C8Ay32esl2EqVo//gfW/MG4nZMcb7/uuwsT4Pjzqxto6ruN/McMG1taLaKzv8FwjxbcBE6RZmHOz0b9/DtbXDvbynwtcgRmxyVgfDFtwJqltvwfcIA7meX8K+Ee/zl9j/TuM9I2Y0EddbsRSzXth0fpWTBAOi5SoiLxF7H6ZcB7zCCrqCi//9f7+Ppq5FXewaE7x3YoZ5LuwfhQOcIyFF2L9fr7ve292TWBtuJWaCApr++0k+zMf+CmpHadi7fZFbMw/2csxX0R+kR0/jvci4I2e7h4PbFTVcI7GY87RW7x807C+udqPMQ6zP4/CxuLBACJyDmYnZmDOySLMPm7ABGY2qV/l8/RfxsbBGJJDfz9JoK7B+l6e2YgIaqOqbnUh+gW2svgHvt9q2qQdgfo3Vb1YVb+EeVjDwYnAHaq6UFXvB75Om4/CyAjP8UFMTG/BKjUEKgzAt7ABuh9wvarGfFNEIgBvxAxSeOp7kLz+laRIZQpmWMEioJ9gXvPPSYPkRFIEpf76VWzF3c/c0wQzbtUU301Yh28VQYVArVLVJZjIB0qatzgkS23kInc4Fuov9+vajs1nQfo5g5l+vXNF5GVYRNZKoMAGyTb/m4V11E3YQHoY66jh9a72DnwvNgCXYo7AfKzOt5CMyCWYoJ6DRRV/qZy3LoICGxDHYlFCtNsarC5v9/+n+jXhnz+ZxrHxemy11yKsrleSlvcvwup0HClPvwlzZqaRjM+jSNFSpEVXq+ovMUNxFCm6j+uIgfwwFpVs8bqKz/fwa5mDGYnrMcNyRrYfmEGdC5wC3K2q6vvtC2xQ1Qex9o96myEi12KGHUyExnj9/TfwYkzgf4L177hp/C+k/gRJoOKa/uR1dQvwJk/lvhubS5mJCVxVoCKNNgvrH/gxQriDpV6PL8Vv8CbNE9+OGeS/w8bUH/37fG4zlkiP87qozkEtAX4gtgIun4OajLXbJLHHe52Bpdkigop+9EpV/bWqXoo5XZOBx4otisnH0tOwdjkMWONthao+hNmYH2COwUQv/1pvv63Y+F2E9YeHSCm+sNuRWlvs15gLVAhy9L0F2DiMla8bsf60yst7pJcj0surMZsTc1DhHIHZ0uDxWDqyLfoVKFX9i/iD/VS1v8fODJa9MW8zWELzPQsXiMg1InIN9Svb4rMlWAPfSqNARfQ3D+tQo2jMg27N3t+iqu9W1Teq6kLMA5jjZTyY9PidKaQc8c3A+ar6eeCz2XlPIQlUeH4LMe/0i9k587KCNfK1ft5YXFDNW8ccVBiXzdn3G/yaT8YGRNTnVBoN1zKSQG3DBGod8GgROdv3uwnriJF6WIbNLTyT5lVK+HG2YymyDf7ZnVj0tDLbLgztEqxN7sKi1bjezZhxWYkZ3yOxAfVIkrgEVYGPlNujgRUebUUEdaG/fgEzij0kr38yVmcb/DoAlqlqDP54vtwWbGAuxLzeJ5CivS2kiDgEagqpj0eKL65/IxblhuhGne6FzetcRxrwd2IG9kH/P5bhhyGeSbrxvLpA4ViS6GzAHIUoQ7Q/mGE6DhOmX2F9eCywXVWXYgb4KMzwzCYtQgmBqkZQYO2ziCQK272cW7E54X2wFPo8sWXq+QKQqVhaKOaSZmBzYUqax1uKtflnMJF4mOTNX4KNzRuAZ2Gp+WP8uEdjUQWYs7nZ/54qIvF5D2nsnkhjBBXprLWYYz0Kay+wDMZW4CpV/RyJfO5xP1I7HYfZrt9h7X4vjbwDuyF9MymCitTbemCLO7wrsLaYJiIHkFJ9MX/3Wa+njaS54on+XbTPS7xexvnffVi/XonZlX2xudGpWH+5Gmv33ggqK3cI1DLMwfwBbdLuIonzReQDki1pFLvjeKD3TrSi7t4jbfhH9UJVPUFVT6ASIrqHNQOrlDCA38GMxMdF5MPYQHk7FuKHMFUF6mE/Ri6WYGE1wBKPBqLyYxL9w8Cvw9vBOtAsbLAegw3m7VgjX+7He76qfi87R12Kby3mbT4OM9BTROSdIhKPa+qNoHyfiAI3Yp7VYve8bgMOE5GPYN5l/rSDpdgTx6/HDNQjsQ50KDYYXooJx31+nDjPaGxieDrND6zd6sd6wPdDVe9U1TBIQS5Qi/31v0hPWtisqp9X1dmqutaPFx72bTSyBNg/Sw2P9Wt7DCnKDYF6N3Cwqr6PJDjh9c/A2vV6Uvo1+sl3gX/N6mBfLCoASzuGMG8hRXQ9mBCCGa6Yp8oFapOXN1ZiThO7z2lPrL/cSzJqC71sh2FjZCnpSQcLvX7iNok9MEcoosc98MU03i/WkdpuJcmzjfTuOEwITvLyhUD83l/XeP0dhC29/wKW3vqxf58L1ELMiIajuNnL+SlMJJ6K9buTsB/2jGXVl2ELU8JY3weMVdUYq8uw/rnSj/Gs7HriBtvVqnpnVuZpmLMU6b9FWL88lfS09NeTbn/oIRn5I0hZgoig1pPa8lRVvQ9roz0whyLODfTWfdiQ/Uk33b/B6/CPmMOzorLfj1V1mdddLNJpECh/vxgbD5/F5v56sH4yFVinqrf7tEY4j/NI80xTsSXi+2HZpvX+/X1+3uv89Tee7VqFpa8/iT1Joi6C+hPWt1ZiT2v5E23SlkCp6guwjni5iDxXRL4BfI5mIzFYIuoJ9iF5Ye0wGetg92K59U+o6q9Jg+88bBB80lNhYXDyib+tmBE4SlWX08gvMXEK47rR/6ZgjfGNLFUH1lFmYZ32Ssw4/Aq4X1WfoKorMzELqhHUVKxz3OB/sfLn5Via5VGYJ9srUD5ot2ACm1/bjVj0EUvvF5JEapmqvkdVr8MG3VF+3AVenvOwzr2JNB+wFOukERnmUREkQ72h5jpjEH0Xi0zB5jnj3oxgJY1pV7D+ti1734un3T6A3ecDZkTuwhyE6EsPAPep6vbMYIVnfLXRE2sAACAASURBVI9fYw/WPxZREShVXaGqV2XXMQ9Lc7wBG0vbsH60heSNTgauVFUhDdKYg8ojKMWckYewet8HuMfrbwVpwH8Z+KFH9uv82mIu5FYs4u5diKCq52KReIh0pLfA+kiU4YPYkzc+S2qHtVgbnYwJVNR9TNSvI6Ul/6yq61X1p6r6df9sCSZQf8Y87HWk+60mYoZ5Odb224CbVPUeLAXe4+cfi43dMNa5c7oB62PzVfUhVf059tgrsPEUApIby2iXKV4/92Ntv1pVN7l9uA9bqblFRD6O1e/jMAN8uJdpNSmC2uD/b8j6RzxbL9LXVdZ7Gef7tS7HhP772E2uJ1ERqIwt1EdQkUFZjPWzH2DivgKzEQ/SnGUB62vRb6di7bE/Nge6AhOr+/379ViW412+b6T7o+4nY2Ovd+x6Hb+SdNtDdVy3pC2B8lVN+2AD663Ajap6jqpe0u6J+uGP2DzJAT45+HwGEAaSBvsa4DZVfbV/Hgb0owD+xAEwQ/QwzRHUpro0phuDfFJ9Izb4IoLaVNlli3++BWvIF/k+dZO7wWoa72E5CBOSb2HzH+uxzjMTE5r/9LKuovGJ35v8//zabsAGeaR31mApSWh0BMIrvBYT2GWYZ/QOUlT4AlW9msZBv77yf8xB5Z8FIfIXquoVAKr6ezdMr8OMYaTwNlf2vRXrK/dS7xz9FVtKPIYkUI8ged/315QpT7se4eVbidXh3dggrlsSuwVL56z1cs7IrnuLz61tw1Kkcc4NXpa6CGo1JorXYYZnX5ITscK3QVWvyIzgWtLjruZiArcfJnB3ZGW9B4vOltMoUGv8D1X9uafAPlDZ72bSLRwRQcXS/g0kgaq2FVgbCvAVVX0N1k/iSQchUJv9nG/KnJmr/TXabQHJWEe/jfPnjmEYQ0hP3Y5UeHz/IM3j9W4ahe9pqvpkzBH7e+AMVf0tFt0cQRKoEMAN2Di8KzvGlzCxidRnlcswJy0iqLjWiOgm0lqgWqb4/P2dfrw7MBu4DGvndRWHcUP2PuaxpmJO3DJ3eO/1MkYEtU1Vb/aFG2B96maPGmMcbPJ9c7ZhY3I8AxCodn9i4tNYxPR+rMP9h4h8BXiFqtZ1zAGhqg+KyKuxSddRwEWqOpDfkrkb83DeT0rVgKUPrsc6Sm/nUdWHRWQ9NQLVRxnz1FSkFg7DGrRq9KIBtkZDikistmvFvfhqP09ZHgLcrqrX+meHYt5WtNlP/PUTNKZIN2PGNp+vugFbWbgXls57J2YkT6JZoPDPVmAe7Rv8/BExRT3kdbUFM6YLvCwxYZsPgCD2b1rJo6q9oiMia6gXKICz3NBUWYmJ6CLS5PUoGgWqWqZok62qeo+IbPDjXIwZgX/xwVcl2nidv4/VXNuy79ZibbYx+38pFtXNIqVmN2L1/SssvbmadO8WWFag7onWa7F22IhFTbHSK4QnJsm/6+d7iaeX8v2r7RBlXYRFfg+LyB9IT5xAVTeJyDhVfUhEYlzVGZ2YJ4xMxjqsnlaSlmhvVtU7aBTUSCGuxCKWQ0mR0bNJfXsjjSnjnLgP7oFMtIKon7iZ9Lek+5jIjOsSP3eIYqzKnIONsbMwByj61OjsGPcCz/Cx3ISqvlxEXoAt2JqJO1yqulZEFvtmrQRqGzaWZ9IoUDFv90FsHG7AxtByrO2qjlZkgnqwyPkEbIxvz7a5F3MqIoLaVjnG8uy4dbYhL/NEbLHN9prva2lLoFT1GZWP3uoLJy7D703ZUVT1x6Tc9UD3vR9YJCLnkc1dqWpMvINNYObExGawhT4EqkLMGazGGrQqUGFY80G7lb4FajnpZth5mBdSjVL+BjNwF3vEQY2h/h52b0R+LX/FBvp24GxVXQEgIp+mcbHBdizFtAEzjrlHGGnLfJ5rK2YQt2KDdw7m1UUkUXevQ3Ti6rxVlTU0G71PAxNaiBOYwT+EZMzDwIdAPUBrgYqBtwFbqVeXlsnZgq+uy8rZG0H5/2uxlGm046VYBHMHFpHkKb4V7t1uFntc0NG+HaoaqdAqa/z8G4BpqnqfiITB6O077uzVOXy9EVRGb1k96gGLaB5LZlg0LVi4C+szdX17EVZHcY5YRPJb0s3udWPubsyRCsO3HymNvYHUhhuoN3av8e8+Tn0Uvxbz+l/sx1yOPX27ylIszbrVt3tYRG7GFjOsAL6CRf1vw/pWVQipSXHn/BFz/JfR+BSJxf5aK1Cqqt5HRpP6be8CB68jAETkDsy2PEzz0xsiO3A81lbf92NUBWoCWQRVOcYvSPPMD7keV+fw8f1isUs1umrJoB91pKo/Y+BLwYcVVX24nw6Rs44BRFAVNvq2kYKpGtLeCKryWVMHzliBLe99G7byrLpKbT3mzS1S1X+t7hyo6r+o6j2a3SOktrDjLsxwr8o+f7N/F2wjLW2NFFewGRsQcW2bsCiuB1uQcgXW4SO1NeAIqkLcSJhf23q1VWR97ZNHGov9ta0Iyl/X0zynVscWLGXyMMkhyeegwAzCnvh1qOr9apPcWzGjm6f4cmO0Botu86iijpUkMYq6ilWZ3yCtJmvFd7D5xl48WryfxrFxK5baqhonsH61uW7cueO4mMYICkyg8hRfdT9V1afS2FarqtuR0pvV/T+O3RdYnawP1lBvRKssIUXtwY2YQd+GiUuU43M0/+RIf9yO9ZWryZa1q91OsJLWERRYZmBVVu95ii/nTqxPrKY5gtoArFfVOzyy7k3xZdvkY6cpglLVr6nqdyrH/QnNbMOiurbTe7CDz+JT1f684G7mjTT+LMVABWozvnigxiPYUnmN47eMoDwqWIvd4PkxmvPWq7F5wHaMZx2/w7z0vryXbSSj+SVSWgWao8JNwEqfWL4YuIrUgUOgmoyDz828jP476gexemgbH9j5AKqLoFrNQYWhixRff8RCiHgPzSm+eMpF9Zz5vW/xmqdal1K/lL7KG7GJ7FyglgL3qq1+PK7lnoCqfi+bS8jZSKO3vYHGRwbl3IYtHmjF35IewxXG9FZSBNXXFMFWUrvUtUnTHFTlu63UC1Q8waQ/rsMWweTchKX2t2Opc7AnmixS1f7aqwEXl99hY+dc0r2aYAtF+pvmuD5730qgPo9Fo2uoT/Gtr/yfr9aExtWT99C89L3KdZjtaMDHfcxztc3O+JnzrkRVr6x8tJn6zlzHN7HB9bIW+2zHBmPeGP2l+MCM1P3YwojqY6UiL99uGav8Fpv76ItegVLVH1a+C0OSr2TMjcafsNVsXyEtM68tq9q9Yn2itqx8MKwkTeqGB7oi++6uyvbRJjEoY8K7P3KByiOoXKDegz11oXoti4F9slTlJ2h0Fr+PLcHuM80YdSQi1QhqQF5qDRtpNGYbMI+9SaA8SvpI9fPs+99n//4WOI30JJZWKb5gFdavTqd1BFX7W1aeBltKfR98M/XRffUY36r5OKLS7X6Ox9G4cGOgnI9FoA11q6pvbGPfXKAuId0Okx/nUgARWU1afBL8jsbbeaKO6wTqflV9Pf3Qj1OUj4222G0FqoZvUh+aNhGektivpzYNMO+4W2hsjP4WSYClZm6r65yqut7zuwc17dUel5DuxG/FNlrPDVUjqD+TpQB9gP1URPIIql8jMAyswtJn2zAj+8IY/GqrTqsrT+/DFkjEQH0v/XuJUB9BNQiUz/XVTZI/HXtiAL5dtQ99HTivMgfZF7lAfZx0E+9gqRMoGMDkdh2eRvqdiBxHHym+jA9jEczV1AvUrfT9o4G1AtVPmrg/QgQm+bF+1se2/aKqbT/2p8IzSLdpoKqLSSntuvNUU/Yx9/bd7KMoS12Krz/b1Q6RAm+bIlCOG4OBRietPDSwgVeNoPqag4rjLerj+3+h/7RPLT4ndXk/m22n9dxQg0Cp6kdbbBdGulV6ZbhZidXRJBedr/az/X1k7aS21Lod4mZcSHWzncY5qFo88mlKg2TfLyT9dlY79PZdzVZC7gA30dgPox1bpdMGymbaSPH5pHsIU5NAqeoXqp9V2BEhalWm+9xRrD6lY6eiqt/vf6sB01cENVQCVSKoncj1NIfNQTWCWk1jvreOt9OHEVDVDw2odAMnUnN1VFN8rQgj/d99HGs4+XdsqfbL29y+QaAGQDzoFhojqLqVccPNYJyrlqjdmJ8zJBFUxmZsMctY+q/7tVgaajB1upT2fzBxIExhaAx2t1EnUGuxiLw/57odikDtTNzbfl2Lr7fQOPj+j34iGE8JdZLPUnnEVMYW7J6S/h6Tvw1bSlr78/HDjar+wW8sb1ccBytQF5F+PiXmHLdjv+31UKudhon1DG86NVKQQyVQm7BFF5vbWHK8AXicNt6/1S7/xzBEOvky7l2MEKhe8fWl9SsoKb5djoYIygdiV3tdqtpXenEz7Xk/F5FWN3WKZbQfUQxKoHyBw4P+Xv1G5m3afFPozuAiWiwWGApU9QERiZuvh4K6Va6tzq3YY3QGjNrjzgrtUxdBgc3bLmHHKRFUF1GdgxrptCVQ7azQ2wlci/1kRjsMNoKqUneX/U5BG38Xa7joa0n3gHCvHNr4mfnCTqUpggLQdMP2jjJggdqh+6AKffJuBvC7JyOAESO4fqNnWz8pjUVbQ7GwYDNDF2F0IxsZ2usr0U2XoelxbqP63HDwlBRft6Cqv+l0GYaYdlN8IwpVvQb/ifUdpGMR1E6i1WOFBst76XuJeKEzPJt+7r/bAUoEVRg2/krjL/YWGrmI9OSIXZEhFShVvVztt90KXYSqfqeNhSuDpURQheHBH2v1uX433E1R1YE+h22ksZ4RkuItdC2rGOCj2qTmGY9dj4hcU7yvQmHnISL7Yz/qt8uleQs7BxHZA58ibnefEkEVCoV+UfvpmkJh0AwmdVjmoAqFQqHQlRSBKhQKhUJXUgSqUCgUCl1JEahCoVAodCVFoAqFQqHQlRSBKhQKhUJXUgSqUCgUCl1JxwVKRN4lIktF5Hr/a/cp1IVCoVDYhemWG3U/shN+LbZQKBQKI4iOR1CFQqFQKNTRLQL1ahG5QUQuEpGpdRuIyAUico2IXMMw/npooVAoFLqDnfKwWBH5KTCn5qu3AVcDqwEF3gPMVdXzh71QhUKhUOhquupp5iIyH/iRqh7Z4aIUCoVCocN0PMUnInOzf8/FfhivUCgUCrs53bCK74MiciyW4lsMvLyzxSkUCoVCN9BVKb5CoVAoFIKOp/gKhUKhUKijCFShUCgUupIiUIVCoVDoSopAFQqFQqErKQJVKBQKha6kCFShUCgUupIiUIVCoVDoSopAFQqFQqErKQJVKBQKha6kCFShUCgUupIiUIVCoVDoSopAFQqFQqErKQJVKBQKha6kCFShUCgUupJu+D0oAERkMbAJeAh4UFVP6GyJCoVCodBJukagnDNVdXV/G4nIZar6xJ1RoEKhUCh0hpGa4pvR6QIURiYi8lgR2bfT5SgUCv3TTQKlwOUicq2IXFD9UkQuEJFrROQaikAVBs8/Amd2uhCFQqF/uinFd5qqLhORWcAVInKLqv46vlTVC4ELAVykCoXBMAYY2+lCFAqF/umaCEpVl/nrSuAS4MTOlqiwi1IEqlAYIXSFQInIBBGZFO+Bs4G/drZUhV2UIlCFwgihW1J8s4FLRASsTF9T1cs6W6RCHSIyGrsNQDtdlkFSBKpQGCF0hUCp6kLgmE6Xo9AWPwTeBlzb6YIMkiJQhcIIoStSfIURxTT/G6mM8b9CoVBBRM4TkVGdLkdQBKowUMYAe3W6EDtAiaAKhdZcCMztdCGCIlCFgTLSBWosRaAKhSY8cpoITOh0WYIiUIWBMtJTZCWCKhTqmeSvEztaiowiULshIjJWROYMcveRHkEVgSoU6pnsryWCKnSUpwP/Pch9i0AVCiMAEdlTRKYPYJciUIWuYDLQM8h992KEpvjEbrTbiyJQhQ4gInuIyM9FZGfZ3ScCnxvA9mETSoqv2xCRqZ0uw05kAoP3kkZyBBXlLgJV6ATjsAcVT9lJ55tKmlf6/+2debhdZXX/P4vMN/NIRhImawABARErTkBFrUWt1uJIHQq2WLW1rVh/tQ61trZ1qFqtU8Uiap2q4IAIOCKTGGaFJISQALm5IfNkIOv3x1or73v23eeec29u7rncvN/nuc+5Z+999n6H9a7vmvbe7aDfHpSIzBaRA3bbSSEoQEQW8Ni98XQgGBBBueU3hmFIUCLyChFpRTzh+TUcJyLvEpH1IjK15jf706aniEhHn7wvIl0i8sZOtmEwICJnicgJIvJ4ETm70+0ZIGLNzRnC6/XHGBtIiO+twBvyDe4pDsq9VIWgDNP974BDRC7ttNJi4B5UEFO/Q3wicp6IHMgnl7wfeHyLY2oJCrvvYxZw0iC36e+BTivTo4C/rm70QpnFHWjPQPHHwFnAmcAr8h0iskhEXlXZds7+KEkRedYBCMV1+edwJajaEJ+ITBGRa5v8Znr1eOA8bD3uNwpBGSaShOdA42XAGUN0rWYYKEGFgh+IB/Vx4IgB/K5dTAJaJYSbEVRs79eLDD0JfYeITGhyyDSSVTpkEMNz/eskLLRUxTnAR4euVb0hItP6ER6KNVonuycBr69s+xb790aES4HH7cfv6xA6ZvYgn7cZJlI/980QsvpUEXlqtn0G8OQmhD+V3utp0J42c9ARlIg8TkS+VNk8CRh7gC38HL3u1BaRI0TklSLyRhG58ABff0gJyhX4RGD+AK7ZzvkFi7W3Q1C/pfeC6gLuBRb289KPA46heb+m0wGCwrzBb/r/zQjqUDrTthzvBjZ4iL0VJmHzNAmYKCLzROR9vm8C9Xmd/ZG3qQx+VKUhxOeGxIHUwQMJ8W0E/gDIPdIujCvqxngKveVrLE10hIhMF5HPt9ugg46ggMPp7cGE4PTH2gjF2PYxGQHWWeqnYaGLtwEfE5FPiMiz4xwisi/OKyJTReTw/rTVf/dCETmHPghKRP5HRJY2OcVAQ3xBHPsUkYgcIyJHVq49RkR+KCJn9fP8Y7EHH7ciqPHAZuoJ6m766UEBJ/pnM0U4jaFLiOeYCIwTkXE0J6jZ9C+B3oB+li83PY1/HtPGsbkH1YV54+f6vmYENaB7/fyJ/RMY/GdOVkN8rwEePYAkNZAQ3wP+fx6GDF1Rl5qYSj1BjYN9+ai8f4voR9j7oCEoETlKRE7HBnSOv3cqEDHUtsN8TjZ7ReS4Po55EvCdbFMohN+pOTwmehzwCPBykgKcDnwim+hrgN+029YMX8JCHxMxj3FMzTGvxGLIdRhoiC8EO1fkb8AWKGDzgxWqnEn/X8ke89eOB1VHUBMwgtrnQbWpgFsRVKc8qJDtqZjMja6JDjQlKBH5PRGZ4v9PEJF3V/aPBnoqa2ggiHlrx1OphvjGkZToeDKCykJR/fWIAzEug+1BVUN80f8/EHsnXlvGjIj8RW6w9oGBeFBBUHkYMto9y68/XkSi7a08qItozIHWHd8UBw1BYTH315MUxpJsXwx2fxbcof75T30cszA7DlISsi62PRUThAnAr/zYSGKHspzgyuGJJEHqD4LUJlY+q2gWchkoQUX7F2eVdjMq15kPhOfW3c/zh0LZRyrujY117/MMEZlDIqiqB9iFjc0i/+1S4Ko2rrsUWE0NQXk/x1Fj2YvIYe143/uBUChTSbJdVVR9eVDvJxkJi4C/rrQ3zllnaLWEz8ml2Nhspj0iyEN8XVh/Jnn4eAIwJSOmmN+LROTNTdpwoog0e6dZ6IgD4UEpiVhj/OcDnwc2VNp4mohcVHOe44BTReQDLa43kaQz2kFOULkH1UBQwPlYeDZ+UyWc/HFoR9K4Pg5ugnLhr7NaZ2ICEQojD5HtszhFRKW9e6JCuR7axzHVCpfJwH1AnYKahk3cBGCZb1uStR1MUI7y/x9qo41VrPTPWHgDJag+Q3wiMkpEcgtsFuYVXgj8wJXTLBot3Jmk95Nt9/NMEJHvtuHN1HlQf4m9t+q5GNlc4O2Oc+eLtgtYTur3dNq7kXkKcAf1HlTIWZ0sXk17YS1E5LY25TFHzOs00thUlUJfBDWLJHuTScSAW87xu3b7sFREXpdtmoYVCx0G3E+TMKhHPeI9cXUeVPQj+hZzlsvny5s0qy+ZivPUjruI/J2IvLiP3yMiR9es8YnAWpLyD9mYBOwFDhGR52THH4vdbFvFbOAZwF+1MHTCg7pRaio2ReRkEckLZfIQX75+94X4xCqQZ2Rtb+VBzaNxHKdg0Zu2KixHHEFhlTuX1WyfgQlCDOzhIvJ8EfkCaRE/0z/nglUZ+ed0EflF5XwLMIXfl9c1vbJ/MkYs2+ldyRNPd1DMg7qZeoI6DBOi6d623xORl/r/x3uorBlCKeeLPqzJXCE3C42060E9F/hC9n0WVoQAZnW/DDiaRiLMrdW4zlI/17+0uF4ozFeJyL/5/3Mw6/8IYA9mkIwDdgO7gPFi99SMwxbYGmC6L/h2q58mY6HBeQAicraIXOL7YlHmoafj3CKeSyU/4obVGyvbRmFKqr9lydUQH9R7UF1NFMVskuyFXMwVkWcAW0nr5di6i4vdvJmf93XAJ0UkIgcLs8/7ae5BvQdY5jJdzUFFf+Zk/8dYj8O88POxopg67PG2TvE8SS5/oSOatWspyVBshiuwvHKOLsxAjfmcgumDScAW3/Y9EYk5m0i9sTgHk+tR9J2WmIjl+ZZQ7w0eDjwl+x4e1AZgcpYCiGv8ObAeiwB1ecqhFUHNB6a50TqVNLZthR5HIkFNp96iDQ8qJmERNuBnkBb0i/xzjiuu1W5pzwOOr5xvARYWqn0siIi8HlvA+f4p2AJfjRFNjqnexp3Y40l+H1jiCjMIahIW9ruVtHheBrzTj7vAvzfDRCykgrcj+v0BGhOXC0XkGdI7v1ZLUCJyriuvwBIaleos4HLgP0lhgsNpJMJ8AYXALwU20Xus8ms/DngLsMM3neyfUzBFuwD4GYmgdmFjPBH4Lyzn1YWNyw6Sx9CUoETk077YJmMyEEpkMXCMk/2l2BjnHtQTsPt5JtLbQJkDfNRDVpGoX4IpmH3naGYxi8gXxHKs0EhQfXlQYGGyfeMrIl1Y/08Xkc9g9x6BRQriHDFvTxIrta9a+RcDZ7vy7wJ+D/M0Q8aiGGUObhjU9Qmb952YUdNQxUdScLkHlRPULsxTjbDtCRlBkv1mAeaN5EZtkHKzEN9k+vCwfY4W0Pu+ui5gFWnsQxdN8mu9G5PDfL3PF5FxIvIZEQlvMF9bU8XuA3uJiLyzcr3ck64jhK7quYBbgK8BPaS1GueJ/iwmzYXQpEjC536eX/8c4LOkcWsrzDcSCaqL+mqTGaQQ3z2Y8D+TxlzPaaQYcRDaDGwBdVXCQvsIyq3fYwFE5Iki8ofApzGymJQplcmYpdRAUGLVbOFt7VDVPar6EGblTScJ7M3YC8VuIVn7J2PW3BNodL3rMAm43f/vJgneTJKS2I0Jz5uAT0fbReQIkoI5WURu8X7iff2RpKT5AhoXd3hQ7yLJnPjYhMDOxBQ6pMV0DPAL+q6E+13gpZglDhbzhuYEtRtYh3kwM72dXZgi3OjfGzwosSdNnJ9d86WYkp6MedEhbzMwhXgsVkCxo9L2yaQ8W9UrCqUdc30u8HX/f6q34zxghVSqLF3xvgp4kVhoNSze2hCfiIz1feuwfOYySQU40ZdT/Jwxx3Ozcz0DuB4b6w8D3xXL8QWmYeP+fuDnmJK6jKSYg+CEJh6Uy90TgB/7mEwgeVDjsz7WeVDjsXleA8zzdftXwKuzS8TvF5IMi1inU7AoRzPinEzfFZAzMCVdJaiJeEhTRL6K5ZJygvoJJk85QXVhXs7rsPLv6HPkz6YA12EPgD7OoyjRjzx6U0cIE4FDK/3+taq+AfOU5ojICzFjOXAdNrdh1EH9fYVjsTGcg83LYhpD5wc1QU31RYiIvF9EXkajB7Uc84jWYfmeuCltDHAbtpBCSGaTBD/3hnIP6nSS4r8G+DMspjwec8PD45hMxYMSkZO8PWGF7syuscnbG0ojzrMcI69ZWKjsEsxKbUpQIvIEb+ttvqlKUNHH0ZhwnoyNUQjnCuDt2MJf6PvCYv8NlmN6sn9fSGOMfz421j2V/t1L8j5mYArtkySBXwpcS4WgRORrbp1BUno3YoQW3u9UEkFdhy2Qt2JKcY1vn04iqB3AwySCyuPk/wD8mVh5/7HYIgvv5L6srzOwOQkPvnqv0WSSF7pYRPL74YKgYq4XkLz2Ka5EPohVOlZvSn0BZrS8BQst1XlQuRL5KFbNuQnzTqaTnsIxm0T2nyeF+g4lzcMZmIycj0UhtgDPy84/CSO004BHsTzQAyRSzsv5m4X4TsDWygoSoQVBkf2mmQe1W1V3Y2tmByabR4jIB/2Y3IOa77+NeZyKeTrNPKhJ9J2jnI/1u86D2oKN+4vpTVAPY+G1nKAg5fomul7rIuWT5/r1jsfG9xYskgJ9EJRYgcg52PiEfE0lhRq7sTn/QyzM/lFM12z269VFPAJjK9umYUbKRAaboETkwyLyHyJyqoi8qZ2Tdhj7Kk489PAUbPHlOajlWBz1QWxCF2EhATCimUNSFLNJiyG3mhZjntho4PmwrzR5KuaZXZ0de6orzSCo+0ge1HsxMgvkCnwzFof/u0ofV2PW/p9g+aor/JozMOtsooicFKEbETkVCwseSiLSHioeVOYhrvL+/QPw/6TxPobwctaQvJVZmMILol+AxbBnisjt2AK7U1XV+74ZWwh5wngGNi8rSMr0SOAmGvM4E7HFHYs/fr9VVe/CFN7hNHpQ9/kxkzGvZC3Jy5uFLahdmIKYTpKhcSISVvft2H1q/5W1dwI2FzN9gceijTDjJZjn/aHs+oE3k26mhd4e1CzSfUKRnxwDfI7e4eYlwBex8Xs8SXanZdc8JvNwz8QKSLaScimRi5gF/Bp4Oua9xly83fu1C/MQt6nqlX7c39KboBZjcn+6nQAAHHBJREFUBsbTVPWHuEWe9fUR/78ZQX0fyz1uJBHURJLSnoGtmznYPOyhQlDZucZgRPws4C+zMCbAf+PrF1PAYGO9EosU1D3HcDK2zs4Ck0lJxRxgCnwZvZ+cEoZQN2luqwRVF1o7BiO1GZhM92Bh+WUkWXg8qTr4XSLyR/77jb4t96BjvEMHRfj2USf1aNd8TLYOAR5U1eXY2h1DYwSgjqBywywIKif2QfOgHlLVN2GhlP15dMgBh4h8HHi2fz0G+B4WwohwTh7iG4UlKG/x46O0+XZMMUeyPfegJvt1BBOMWzE39gW+P4RlNI0E9RMsLj+FFOJbLPbQyycDeex4R/b/FtLNiPm2OzFB+QCmHH4MPM37eBS2uH4I3CdW7vxW/+1MTInFecIim0SKU+/GFDiYgj0GW1hB4EGgN5CU22zg/2gkKDAL8Vhsod7t264EvkvvxTjTt+0iCe9cTFlOy8IQUTUZCejwoLb55wqM2KZg83WE92cRcLyqfsm/H+n9XQDsdPLMQ3xgivNi//8QLOQUnsYCLBy7A7OWu0gEdQrwb6r6Ksybfr3PQ5DFTkz+nuwh4amkwpXcMApMJSmwW4ETKrmow7DxfTxwF7ZWN5M8qF0Ysf3MjY2FmAwGQe3GjCjBlFyPqv6UVPbc4/19LbZ2psR4q+rPcAMpa9MkLBz8G1UNeenGvNs/xMjsh749ilNeL1a0dK5Y/m028DHv80K/XnhQ4emGVzYeX7ceLs8J6gVYCBpSYcqRmIzFbRdP8fOHgp+Kyfd7SFGCHJOxdftN7/ONpMpbMMV+B1biPR72FbxMJRFUYC19e1C7MKK/14+ZA3Sr6qewdR5yMxYjgTuBvwGCoGIOc0KIvGLotTnYnG7OjllLIihIeik8rEMxOX4DFqrP73UKgtqGGTWTsDkcfA8KG3xU9cOYoh3OOJHkDocimYIt4HFYf2eTlPRDJMHqxip+Vvj3sNBn0duDOgxTauuxSTgKU1JPxKy6vaSxCu/oj2kskjgSs6BfSQq7PUpvDyosoBCMM1V1DS44qvpzVe3GFuRi739Ylff4dZf6ucFyBx/BhPuJpMUwnZRcXuPXXYcp0rnZ9UOwbsSs8hf5MT8ATvOFuBBTHuFh3aOqv/X2vol6gpqBLaadWIXdKN+2xtuekxZ+rZeQ7sUJz24dNsexEMYAm1V1jarGGKzFyAZva4x5HuIDI7WHsBDVNMwAmZn9bmv2u8jjrcYIqtv7+0lMWT2FJD+/9s8rsBDKNZjy3455/s8jeQ2Pkh67s9HHdRSNtzccBqx2kr0JyxGtx/KIT/f/wdbHxzBvc6e3/2jMC18MPAn4D1IoOZTbB7Dw4liSoREGAap6n3+Pqr5QSDdkbYyQ0T/6eP4CU3obsDH/IyxP9SXckMuMhkWkkHSX92cG5n1FiO/v/bcXkRGUqn4bKzr6VtaWo/03l5Ee6XMVyfOdga29673NiMjcLL8zmZSTOx0jtn3jgSn2tVmfwZ4Qc573eT1JLzyArYEx2Pzna2ISFs04CiOomd72Vb5/M+lm8cA6bG0+2cch9Md4z5XfQzKoD80+8/BetGsJydjcnl0TTBaWYwbcJOBfJRVVRWXsJlW9GZOz3+FAEJSqXiUiZ/r/n2rnpB3ETJICC4J6BFPaD2MDNQ8T7D1YiO8OTFjWYwKVhwbAbsSNJx7E4J5IIrZtmMK4x7f/CFvEESOO+5U2YoIcBHU8sFZVv++/xdtQJajZWHL3K9k2sAm+Nzv2Xmw+Q7nsxe7g/lNMWfzSt21W1bdgIaGXk4Q0PKhd2OJa6wriAWxRxAKMEMtq//wGsN5Jcj2WyN2EhdXCw7qZRtyJKcV8Mc7GlNUub8dsYKOqPuLnC2tvLja+TwD+GfOklmft20hjMvZq70eOtZh3B+4J+f8R4ssJqptUaZZXNeYEFVbvDGz+p9NoJX8PU/BHYIRzh1/z81jyfj02jt/GjIsvYiHbTSTFNBd42PtyJ6nYAoxcYj7uwhTEFcD/ZO0DI64/y44ND+om7+tCTEn9a+V3G/yakGQ1+h74PFYok+eEbsz2d5MMuaswOd7m595LY0n2VJKcR4ivB5OLR7yNQVDhQe3AHkj8QpKhBYCq3oAZAo+QDMoIt4Vsfg54roic6MdeRfL65mCh+OswbyzPRb8Rq3obJenpCvOxdbOOtL7CE9uJ6YSb/PuD2NjH3G4AXuPhw4kYGS0i5cSe4u3Ax+EEEmHh577bj72DpE8mYGvtKOAlmP4ahenBIKiqB/Xk7HvVgwKTgVxfRrQndFBce5OPSYT4NjLIRRKvFZF/yXIUiMgcEXlbm78fKuTVaI/HFNeHMQvzXsxKARugHix8uRPzZO7BBPJqLD8A6SbCuKk3LOAnkkKD27CJeggjqO+QLOhuP8fDWDjhWRhxrcMEIyzpFVgJ5jZ6E1S0o2rBvBqrJguspBF3YJb5KZgw/BrYHspaVX+DLYZnYZ7jdBJBrfE/sIWWW4iRk3jYz70KG0uwiq13YEp6A7YYLqWS1FfVm1X19f67WR7imoItiiCoQ32cos/TPBx5pF9nMSn88DZSPmcTiaA+hBFAFVGksgdTfrH4IsQX+YlF3sYoJthNWpARdoJGgrrCt+UE9T4/9zOBL2MW/VlYdWEXcLGqvhhTPKdhMncIFvr9KqYEP06yhlfgMuljNxqbD0hGy02q+mrMao+8482+PwhqA6bMb/L+zAG+rKrXZ/vB5Dtu4OzlQQGo6nswGTyJ5B3kHtQm//yWy+AmzJNTb9d47DYESE+YwPs8i0SIj2LzVfWgdmFrcLp/bzA0VXWv77+G5EHtxNbllVjOayIWcbhcVe/E5G+Bj9e5WBj91ZhiD7wUix5EXhMqBOVeV6RHxmFe5Nu8H2FQxfxtwOTuvZhCvxfLV633Pp1FIqjN3paI1qzDdNpeTJ+8gUTUE0gG4zjgp/7//Zjs1oX4jsf0CNQT1LaK8RceaJWggozGe1/XMZgEpaqv8Ib+QEReKiJfwRbZ3X3/cujgsfXIAXRj1u6PsHwSWEhhEinfsB73blT1Jdji6lbVrVjFyjbSogIjiCCok0kvONzm19uEWbWrVHW7h5MWYgLXjSmVKcAtLkD34wSlqr91hb2D3jkoMOHdnm9T1f9R1ZuyY+8l5Y5+DnzN+xIeQDcVpYKR7DO877kH9U3gL/yYKkEFNqjqL7EwSISQLvGx+ZG3+Wgsn9HsZskezJq7HLjLx2UnJrw5QUU1479j4ab7SKEugCtUdZX/Hxb3LlX9K1UNQyLHXdmxU2kM8c2l0YNajy3cGX7dbqzEt5kHdRWmRKPtuBcYec/PqOqPVfUXHqr9T8xzivF4NknBX0yy8JfQSEJHiMhCzBBbkymKMFS2+7W/gMnMbt92LYmg/tc/o21H00isG72vW0gEtdLbV5WHaNdxfo5PkbyuIAgwbxKSBwVGkMtV9UJv43yS7AcpR7u2+TEzSR7UBGxdh3c1l96RELCx/jo2r/GbR1X12S6jd2FhzjC4NpAqLzdjc7EWv8mXZHxegleGish8Gglqrp93JybT/+vRhl9i857nTsm+L8EM3jA4Nvs5jiN5XzFG3yHN0ToAVX2Lqv6cRpI40s/zQHa91T6WdSE+MBl8hN4GMjR60StI0asgqNBlF2DkvhMb+/tp87mnbRGUiMzFFmQXVs1zh6qeo6rf7PuXQwOvTjqb1J/bMGHo8ST2VpLV8aB//nu2DWwhrAXwxb4Si1tf6ftX05yg1mNKdDSZu62qe7L9KzHBj+uvJnlQgZ3Ue1APY5P9W1XdRT3uxYRqD7YI4jldd2IC0U0SssBtGEGtIOWgdjvBRp4uJ6j7snOEsvyV9w+v1no+pvg2+O/iuDr0YHJ1OslSq/OgIsQXhLQDU6rXAtepat6vuLE3X2wN8HmBZNDEQroSK9ePJHLuQUEaxzU0EtQWzDPq8v3/RAqFBWLBN4TGVPVCVQ2ltAwbt//D5nErjQohlPVKLFz4ASzPtD47JhRabujswtaCYvnHCP1di0UM7va+nUwjsT7qfc89qEjk1xHUKiz0ulVVL3DCyDHFlSbYOoh2X40V+oCN5SIaZR9SDum7mAxGzhLMw4p1swEjiF4Eparvx4yy+aQQX45ujPA3ZP3vweTibB+/+0lh+/eo6kwnt7VY2fZ9GBEEWfwuZhg/T1W7Iw+qqltV9WWkcQwyv8zbdj0m76t8+2bv59ZMXqJS7qeYLuuh9yPQQl8sxrzDz2GEEesjPKinkgwXsvNc7OfNPahd2PjmMnAnvQlqp/f1/7yYZhumY9YyyCG+T2LVQ7+Lh4xE5JIs5tppzMSs8MCv/DMsoW+SFsADYJalFzkEvkQK7YF5Xh9R1WdjCuFerHR6HjbIq/y43IOCxngw2GLqxsKNyzJL9+2YIsrRiqA20xy/wMJLm2gU0jsxhRoFHTluwxT/V7BQyafJYveOnKBOIlUNhXL4NMlLRVW/q6qbMaXXRWuCwvuWE9Q0LBwW/QiCihDzcj//zaqaP6oFTIkvpu+xApvPmP9N3vZV2IKMGyEP8zZuw7yG1dhcrsKUTpDNbr/ec9TwTvdec9QSVA4PK83G8obfd1nJbw6PsQyCmod57T3ZOWKO85L2nXGMqt4Ynre39UxV7cFk5BQygnI8hM31Bow0N/kY1PUjPKiqIRRty39zA17erapXquqf+/YqQYWcfcf7/SFsjkdj496NEU7IbVQc1nlQYPMwjxTiy7Hef5s/tLUbuFVVQz5Xe9u20zhWa7FnTcZrXx7y/edgRtRt1CPauRz2zclOUnRqlX/GeFyffrpPbh9U1bMx46P6kOO4feJPsPG+TVUvJ83fakyGXoOFovFz7gEOcdmI9Ee0Yxu2ZvP5vBsL148i3etXHd/tpFqAtgiqrafcquoLK5ve7oUT36e+DHOocT+Nj5WJAoZYlOcBiMgOzALrBZ+QPdn3nDxOx6qspuPeU0Y020g3320lWblk+3dhuYRrsvNfT2/spNGq24wJ8E5scpsqXV9Afy8i59JIUF/2do+iN0Fdi5XTX4I9O++kvI2OBzFF/WNVfVhEQhFEqPFh6kkoLMJ2COo5JG9yF5bjW4QtKki5oTnAi1X1myJyJ/UID+pXTfYHTsFykntpJOVQDFHevF5V94rIFkzOxmE5gaeRxvNNwIWVeHwVLQkK9nnvD2OKDcy7eDZWRJF7UIdjinQJvYtQ1tEYft9Jo5dVh1swz7G7sv3prqQQkdd4P1Y3Od8qzIO6oWZfA7yfdSSyBRv3zX7cLhE5xI8/0tsRBLLL27GY3h5Us9fR9GB6Yhq9FWj0vUpQeaRjNWYcbKVxrP4Ly/W9CDhdVX8rItdgxTF30ASqql6dv7qyK+YvD/H9Po1y/WFS3g5VzV/tEwginYHlMeNZkbkHdQJm7DXMfcizqj4927wF6/tYGmV5PSafs+kd4gtsI+mzwSOoOqhV9y1rfeSBh0/ybVgicixmzawnsywdk0mPCOnP+R8UkW1YcvNFWNVOYDspX7WqRkk9DOxxq+h++kbVg9qCV/c4ubbyCsAqsPblXdQqmOL1Dw2v6HBy+RvfPw8jozoPCtJ9LztE5PAsp9AM7RDUWuCf1O65CUT/L8sWXFT7zSaVbzdTQBsxGaiG2Brgfa9DnLcHK4wIGdqEzd9HsDDQBbgHXBPKqkNbBFXTzt3AlSJyH2ksHyI9bmcCFTlX1eqL+nZWj6nB97H75Ro8qCAn//+LYC++JDPmMtyDjX1d+K9dbMUIap8BV7OmgkB2Y8r/FBo9qCdhEZ9ecGNjHUYyVQUapJsT1HtIOUtIxvClZEaAqq4EVoo9NfwI37ZMRM6o/L4O/40ZizmCmMJz3exhsoa+0Hu9VvE+TKY/gj3GKOYtJ6jRtNZNgXswY/blpHm+HAvBvgIL81WLJALbsYKsA09QAKq6ofVRQ4bIOx2NCd43qFhRbSjVvhDCHC/WC3zb9x1F7/Ae2N3w7ZJiXYgvlFJbBKVNbgVQy131ZVDkVmmOBoLyc61qox09ItJDHwTlyvcdlc1x/ZxM4wnKc2j9rqgItVbze+0ilE6MR3ij67BE/iMicgewVFX7c40HMDmoKsV28VW8Gs8NlntJ9/y1Wod30jzkFQjl1/I1Ltq86CWUan9eklfFFixM+IM+jgnS3IUR63m0kYPK8ADmpdeF+OIcAKjqjyvH/AwYraofbXLuG0lhcFS1GpHoBVV9bc3m3/g+FZHl9P8daXHuB0Qk8t65kRKGUhBT1YNrdr6N2JMqXhjnUNU/APDrBEHtoPf4biMVe8ykDewXQQ0zfB+rNjkau8O/nTdO9gefwaym1WQhDLXCANyD+3n1R5rupG8H1Un9JZarArOwmimG/Yaq7hGRTfRe2CHcA7GK30ATS7YPBEGtzbb1YPnPObQOVUUYbKAEFYs4D6eB3SAdlXE6gPOvB/50oEaSqr67smkliaD69I7U7rVrdf5dIjKmTW+w2Tn2erhqQC8ydEQOqmmRC43GVBQxPZLtm0ZrgjqV+iKJ/Py94LmkZvkkgB+o6hV97G8Lqrome8TYsS3Cx60QOiWXkxjftZjh1BZBZajmoPBzXIDlZyNvnmM76V7Pg+thsWp3jP+zfx2oldrX+dUV1GGq2msy1apyVtT8tD/4GOkJ1qhV013m/69rEmMeTKyn4kF5YnsrAyAoVf16Pwka0mKqelAL8bvTW/x+O1bm3SqsUgtXBNNJlmUU1dQm/vtx3r2q+tn9OUcFK2lDofYH+0NOGSIPMVBsIZV1N8O+EJ9HcUZnCjyUcF8EFQTT0oPqL/aTSGrPNQjnzL3LwNbsM8LX/cEOeuuEi7DS+NF+3joPaj3mad9LGxgxBOUIq2DQCSqwv4qqxblvVdX+Cspgoof6mPYD7F9eoT9oFuJbSiqVbgrffxEtclAtzrEJL5nV9Hik4YaVpBtwh1OoPa/0HAhCcS7v45iGcHRljvJim2aI+86qCvRB7PmBdfm1xzLqPKitwGe9r+tJD1RuF5fSmOqIlE8Qz3Z6j28PcL+qXqaqH6QNjKQQH9ig7E+c/2BHLw/KMZQElT9NOdBDel9OS6jqv7U+qiXmtT6ko/gulvM8ldYFEEOGdvKTLRBPgrmpj2PyIokqflmzrYqoemyouFXVLaRHpI0k9PKgnNTjCS/n0M+HLqjq55rsyj3Yqh5+G+mZoG1hRBGUJxT/mH5WShXsQzOC+gTtLfz9hs/hT2isJguhryvNP1BYReN7i4YVVPUe4B6xV8iv6nBzBhMbsSe69KXINtH79gDAIhyeB2tKNJ4HrH0z8QjFTtKT93uhj4rYgSBIMKr18uu0KtTphRFFUACq+tVOt+ExjIeoX/RDOqaq+ozK911e5j+UBPUqGp+5Niyhqh/vdBsGGe/AnkzeFKr6qIh8keb5yH+k7yrAgw09DJGBSaN329/8cy+MOIIq2C/8KwO4T2yIcDmpFPqAQys3bhcMDTyH2DIMpPYg3Gb7+iS4gw1+L9vTWx44OMg9qP1OtRSCKtgHtUcUDUuoPbesoKBgeCPC8ctos1KvLxSCKigoKCgYLPwUe0Hp21se2QYKQRUUFBQUDAq8ivNxg3W+kXYfVEFBQUHBCEEhqIKCgoKCYYlCUAUFBQUFwxKFoAoKCgoKhiUKQRUUFBQUDEsUgiooKCgoGJYoBFVQUFBQMCxRCKqgoKCgYFiiEFRBQUFBwbBEIaiCgoKCgmGJjhOUiLxLRNaKyDL/e16n21RQUFBQ0HkMl2fxfWiQ3oJaUFBQUDBC0HEPqqCgoKCgoA7DhaDeKCK3isjnRGR63QEicr6I3CQiNwHjh7h9BQUFBQVDDLEXWB7gi4j8EJhbs+sdwHXYS64UeC8wT1Vfe8AbVVBQUFAwrDEkBNUuRGQJcLmqHtfhphQUFBQUdBgdD/GJyLzs64uA2zvVloKCgoKC4YPhUMX3ARE5EQvxrQIu6GxzCgoKCgqGA4ZViG9/ICK3A7s63Y5hjllYvq+gOcoYtUYZo9YoY9Q3elT1Oa0OGg4e1GBhl6qe0ulGDGeIyE1ljPpGGaPWKGPUGmWMBgcdz0EVFBQUFBTUoRBUQUFBQcGwxEgiqE91ugGPAZQxao0yRq1Rxqg1yhgNAkZMkURBQUFBwcjCSPKgCgoKCgpGEApBFRQUFBQMS4wIghKR54jIb0RkuYhc1On2dAr+sN1uvycsts0QkStF5B7/nO7bRUT+w8fsVhE5qXMtHxqIyCIRuUZE7hKRO0Tkzb69jJFDRMaLyA0icouP0bt9++Eicr2P0VdEZKxvH+ffl/v+JZ1s/1BCREaJyK9E5HL/XsZokPGYJygRGQV8HHgucAzwMhE5prOt6hg+D1RvfrsIuEpVjwau8u9g43W0/50PfGKI2thJPAK8VVWXAqcBF7qslDFK2A2coaonACcCzxGR04B/wd7bdjSwEXidH/86YKOqHgV8yI87WPBm4K7sexmjQcZjnqCAU4HlqrpSVX8LfBl4QYfb1BGo6k+AhyubXwBc7P9fDLww2/4FNVwHTKs8F3HEQVUfVNWb/f+tmHJZQBmjffC+bvOvY/xPgTOAr/n26hjF2H0NOFNEZIia2zGIyELg94HP+HehjNGgYyQQ1ALg/uz7Gt9WYDhUVR8EU9DAHN9+UI+bh1meCFxPGaMGeOhqGdANXAmsADap6iN+SD4O+8bI928GZg5tizuCDwN/C+z17zMpYzToGAkEVWeJlNr51jhox01EJgFfB96iqlv6OrRm24gfI1V9VFVPBBZiEYqldYf550E3RiLyfKBbVX+Zb6459KAdo8HCSCCoNcCi7PtC4IEOtWU4Yl2Epfyz27cflOMmImMwcvqiqn7DN5cxqoGqbgJ+hOXrpolIPLszH4d9Y+T7p9I7zDzS8FTgHBFZhaUUzsA8qjJGg4yRQFA3Akd7Bc1Y4Fzg2x1u03DCt4Hz/P/zgG9l21/tlWqnAZsjzDVS4XH/zwJ3qeoHs11ljBwiMltEpvn/E4CzsFzdNcBL/LDqGMXYvQS4Wkf43f+q+nZVXaiqSzB9c7WqvoIyRoMPVX3M/wHPA+7GYuXv6HR7OjgOXwIeBPZgVtvrsFj3VcA9/jnDjxWs+nEFcBtwSqfbPwTjczoWWrkVWOZ/zytj1DBGxwO/8jG6HXinbz8CuAFYDnwVGOfbx/v35b7/iE73YYjH65nYW8DLGB2Av/Koo4KCgoKCYYmREOIrKCgoKBiBKARVUFBQUDAsUQiqoKCgoGBYohBUQUFBQcGwRCGogoKCgoJhiUJQBQUdgogsEZEzRGSuiLyj0+0pKBhuKARVUNA5LMGeHP6Qqr6v040pKBhuKARVUNA5nA+8SkSuEpFLAETkFyLyUX8f07ki8g1/F9UJvv/5IvITEblWRKqvVikoGFEoN+oWFHQIIvJM7FFCnwH+UVVfKSJ3A08DRgE3Y17WSdgjdd4CXO2/OQT4nqqeOfQtLygYGoxufUhBQcEQYr2qrgMQkRWquktEHgCmA7OwJ4v/0I+dIyKixcosGKEoBFVQ0DnswTylHNrkfwF6sGcCnq2qj4rImEJOBSMZhaAKCjqH24H3Yw8Z3dPqYFXdKyIfBK4SEQXuBC48sE0sKOgcSg6qoKCgoGBYolTxFRQUFBQMSxSCKigoKCgYligEVVBQUFAwLFEIqqCgoKBgWKIQVEFBQUHBsEQhqIKCgoKCYYlCUAUFBQUFwxL/HycJLe8xfWO+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(500, 3)\n",
+    "for t in range(1, 500):\n",
+    "    data[t, 0] += 0.4*data[t-1, 1]**2\n",
+    "    data[t, 2] += 0.3*data[t-2, 1]**2\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^1$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.234\n"
+     ]
+    }
+   ],
+   "source": [
+    "pcmci_parcorr = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=parcorr,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``ParCorr`` here fails in two ways: (1) It cannot detect the two nonlinear links, (2) it wrongly detects a link $X^0_{t-1} \\to X^2_t$ because it also cannot *condition out* a nonlinear dependency."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GPDC\n",
+    "\n",
+    "Tigramite covers nonlinear additive dependencies with a test based on *Gaussian process* regression and a *distance correlation* (``GPDC``) on the residuals. For GPDC no analytical null distribution is available. One can either use a computationally expensive shuffle test throughout or the null distribution can be pre-computed for different anticipated sample sizes with ``generate_and_save_nulldists`` and stored to disk (could be run overnight:). Then ``significance='analytic'`` loads this file. If no file is given, the distribution is generated and stored in cache. GP regression is performed with ``sklearn`` default parameters, except for the *kernel* which here defaults to the radial basis function + a white kernel (both hyperparameters are internally optimized) and the assumed noise level ``alpha`` which is set to zero since we added a white kernel. These and other parameters can be set via the ``gp_params`` dictionary. See the documentation in ``sklearn`` for further discussion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpdc = GPDC(significance='analytic', gp_params=None)\n",
+    "# gpdc.generate_and_save_nulldists(sample_sizes=range(495, 501),\n",
+    "#     null_dist_filename='dc_nulldists.npz')\n",
+    "gpdc.null_dist_filename ='dc_nulldists.npz'\n",
+    "pcmci_gpdc = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=gpdc,\n",
+    "    verbosity=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In contrast to ParCorr, the nonlinear links are correctly detected with GPDC:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.030\n",
+      "\n",
+      "    Variable $X^1$ has 0 link(s):\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.028\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = pcmci_gpdc.run_pcmci(tau_max=2, pc_alpha=0.1)\n",
+    "pcmci_gpdc.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a short excursion, we can see how GPDC works looking at the scatter plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "array, dymmy, dummy = gpdc._get_array(X=[(0, -1)], Y=[(2, 0)], Z=[(1, -2)], tau_max=2)\n",
+    "x, meanx = gpdc._get_single_residuals(array, target_var=0, return_means=True)\n",
+    "y, meany = gpdc._get_single_residuals(array, target_var=1, return_means=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(8,3))\n",
+    "axes[0].scatter(array[2], array[0], color='grey')\n",
+    "axes[0].scatter(array[2], meanx, color='black')\n",
+    "axes[0].set_title(\"GP of %s on %s\" % (var_names[0], var_names[1]) )\n",
+    "axes[0].set_xlabel(var_names[1]); axes[0].set_ylabel(var_names[0])\n",
+    "axes[1].scatter(array[2], array[1], color='grey')\n",
+    "axes[1].scatter(array[2], meany, color='black')\n",
+    "axes[1].set_title(\"GP of %s on %s\" % (var_names[2], var_names[1]) )\n",
+    "axes[1].set_xlabel(var_names[1]); axes[1].set_ylabel(var_names[2])\n",
+    "axes[2].scatter(x, y, color='red')\n",
+    "axes[2].set_title(\"DC of residuals:\" \"\\n val=%.3f / p-val=%.3f\" % (gpdc.run_test(\n",
+    "            X=[(0, -1)], Y=[(2, 0)], Z=[(1, -2)], tau_max=2)) )\n",
+    "axes[2].set_xlabel(\"resid. \"+var_names[0]); axes[2].set_ylabel(\"resid. \"+var_names[2])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at some even more nonlinear dependencies in a model with multiplicative noise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c7a7d400>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c7a41cc0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c79dde48>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(42)\n",
+    "data = np.random.randn(500, 3)\n",
+    "for t in range(1, 500):\n",
+    "    data[t, 0] *= 0.2*data[t-1, 1]\n",
+    "    data[t, 2] *= 0.3*data[t-2, 1]\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since multiplicative noise violates the assumption of additive dependencies underlying GPDC, the spurious link  $X^0_{t-1} \\to X^2_t$ is wrongly detected because it cannot be *conditioned out*. In contrast to ParCorr, however, the two true links *are* detected because DC detects any kind of dependency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable 0 has 1 link(s):\n",
+      "        (1 -1): pval = 0.00000 | val = 0.034\n",
+      "\n",
+      "    Variable 1 has 1 link(s):\n",
+      "        (2 0): pval = 0.01000 | val = 0.017\n",
+      "\n",
+      "    Variable 2 has 3 link(s):\n",
+      "        (1 -2): pval = 0.00000 | val = 0.025\n",
+      "        (0 -1): pval = 0.00400 | val = 0.018\n",
+      "        (1 0): pval = 0.01000 | val = 0.017\n"
+     ]
+    }
+   ],
+   "source": [
+    "pcmci_gpdc = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=gpdc)\n",
+    "results = pcmci_gpdc.run_pcmci(tau_max=2, pc_alpha=0.1)\n",
+    "pcmci_gpdc.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can see in the scatter plot, that the Gaussian Process cannot fit the dependencies and the residuals are, thus, not independent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "array, dymmy, dummy = gpdc._get_array(X=[(0, -1)], Y=[(2, 0)], Z=[(1, -2)], tau_max=2)\n",
+    "x, meanx = gpdc._get_single_residuals(array, target_var=0, return_means=True)\n",
+    "y, meany = gpdc._get_single_residuals(array, target_var=1, return_means=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(8,3))\n",
+    "axes[0].scatter(array[2], array[0], color='grey')\n",
+    "axes[0].scatter(array[2], meanx, color='black')\n",
+    "axes[0].set_title(\"GP of %s on %s\" % (var_names[0], var_names[1]) )\n",
+    "axes[0].set_xlabel(var_names[1]); axes[0].set_ylabel(var_names[0])\n",
+    "axes[1].scatter(array[2], array[1], color='grey')\n",
+    "axes[1].scatter(array[2], meany, color='black')\n",
+    "axes[1].set_title(\"GP of %s on %s\" % (var_names[2], var_names[1]) )\n",
+    "axes[1].set_xlabel(var_names[1]); axes[1].set_ylabel(var_names[2])\n",
+    "axes[2].scatter(x, y, color='red', alpha=0.3)\n",
+    "axes[2].set_title(\"DC of residuals:\" \"\\n val=%.3f / p-val=%.3f\" % (gpdc.run_test(\n",
+    "            X=[(0, -1)], Y=[(2, 0)], Z=[(1, -2)], tau_max=2)) )\n",
+    "axes[2].set_xlabel(\"resid. \"+var_names[0]); axes[2].set_ylabel(\"resid. \"+var_names[2])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CMIknn\n",
+    "\n",
+    "The most general conditional independence test implemented in Tigramite is CMIknn based on conditional mutual information estimated with a k-nearest neighbor estimator. This test is described in the paper \n",
+    "\n",
+    "Runge, Jakob. 2018. “Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. \n",
+    "\n",
+    "CMIknn involves no assumptions about the dependencies. The parameter ``knn`` determines the size of hypercubes, ie., the (data-adaptive) local length-scale. Now we cannot even pre-compute the null distribution because CMIknn is not residual-based like GPDC and the nulldistribution depends on many more factors. We, therefore, use ``significance='shuffle_test'`` to generate it in each individual test. The shuffle test for testing $I(X;Y|Z)=0$ shuffles $X$ values *locally*: Each sample point $i$’s $x$-value is mapped randomly\n",
+    "to one of its nearest neigbors (``shuffle_neighbors`` parameter) in subspace $Z$. The following cell may take some minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = cmi_knn\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.05]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Variable 0\n",
+      "\n",
+      "Iterating through pc_alpha = [0.05]:\n",
+      "\n",
+      "# pc_alpha = 0.05 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link (0 -1) --> 0 (1/6):\n",
+      "    Combination 0:  --> pval = 0.61300 / val = -0.002\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (0 -2) --> 0 (2/6):\n",
+      "    Combination 0:  --> pval = 0.56500 / val = -0.002\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (1 -1) --> 0 (3/6):\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.186\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link (1 -2) --> 0 (4/6):\n",
+      "    Combination 0:  --> pval = 0.64700 / val = -0.003\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (2 -1) --> 0 (5/6):\n",
+      "    Combination 0:  --> pval = 0.69800 / val = -0.003\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (2 -2) --> 0 (6/6):\n",
+      "    Combination 0:  --> pval = 0.60300 / val = -0.002\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable 0 has 1 parent(s):\n",
+      "        (1 -1): max_pval = 0.00000, min_val = 0.186\n",
+      "\n",
+      "Algorithm converged for variable 0\n",
+      "\n",
+      "## Variable 1\n",
+      "\n",
+      "Iterating through pc_alpha = [0.05]:\n",
+      "\n",
+      "# pc_alpha = 0.05 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link (0 -1) --> 1 (1/6):\n",
+      "    Combination 0:  --> pval = 0.54800 / val = -0.001\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (0 -2) --> 1 (2/6):\n",
+      "    Combination 0:  --> pval = 0.03500 / val = 0.014\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link (1 -1) --> 1 (3/6):\n",
+      "    Combination 0:  --> pval = 0.79600 / val = -0.006\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (1 -2) --> 1 (4/6):\n",
+      "    Combination 0:  --> pval = 0.36600 / val = 0.001\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (2 -1) --> 1 (5/6):\n",
+      "    Combination 0:  --> pval = 0.82600 / val = -0.006\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (2 -2) --> 1 (6/6):\n",
+      "    Combination 0:  --> pval = 0.08400 / val = 0.008\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable 1 has 1 parent(s):\n",
+      "        (0 -2): max_pval = 0.03500, min_val = 0.014\n",
+      "\n",
+      "Algorithm converged for variable 1\n",
+      "\n",
+      "## Variable 2\n",
+      "\n",
+      "Iterating through pc_alpha = [0.05]:\n",
+      "\n",
+      "# pc_alpha = 0.05 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link (0 -1) --> 2 (1/6):\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.103\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link (0 -2) --> 2 (2/6):\n",
+      "    Combination 0:  --> pval = 0.32100 / val = 0.001\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (1 -1) --> 2 (3/6):\n",
+      "    Combination 0:  --> pval = 0.49700 / val = -0.001\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (1 -2) --> 2 (4/6):\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.151\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link (2 -1) --> 2 (5/6):\n",
+      "    Combination 0:  --> pval = 0.05900 / val = 0.009\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link (2 -2) --> 2 (6/6):\n",
+      "    Combination 0:  --> pval = 0.32100 / val = 0.003\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable 2 has 2 parent(s):\n",
+      "        (1 -2): max_pval = 0.00000, min_val = 0.151\n",
+      "        (0 -1): max_pval = 0.00000, min_val = 0.103\n",
+      "\n",
+      "Testing condition sets of dimension 1:\n",
+      "\n",
+      "    Link (1 -2) --> 2 (1/2):\n",
+      "    Combination 0: (0 -1)  --> pval = 0.00000 / val = 0.040\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link (0 -1) --> 2 (2/2):\n",
+      "    Combination 0: (1 -2)  --> pval = 0.73900 / val = -0.010\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable 2 has 1 parent(s):\n",
+      "        (1 -2): max_pval = 0.00000, min_val = 0.040\n",
+      "\n",
+      "Algorithm converged for variable 2\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable 0 has 1 parent(s):\n",
+      "        (1 -1): max_pval = 0.00000, min_val = 0.186\n",
+      "\n",
+      "    Variable 1 has 1 parent(s):\n",
+      "        (0 -2): max_pval = 0.03500, min_val = 0.014\n",
+      "\n",
+      "    Variable 2 has 1 parent(s):\n",
+      "        (1 -2): max_pval = 0.00000, min_val = 0.040\n",
+      "\n",
+      "##\n",
+      "## Running Tigramite MCI algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "\n",
+      "independence test = cmi_knn\n",
+      "tau_min = 0\n",
+      "tau_max = 2\n",
+      "max_conds_py = None\n",
+      "max_conds_px = None\n",
+      "\n",
+      "        link (0 -1) --> 0 (1/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (1 -2) ]\n",
+      "        pval = 0.52000 | val = 0.002\n",
+      "\n",
+      "        link (0 -2) --> 0 (2/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.76800 | val = -0.002\n",
+      "\n",
+      "        link (1 0) --> 0 (3/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (0 -2) ]\n",
+      "        pval = 0.55000 | val = -0.001\n",
+      "\n",
+      "        link (1 -1) --> 0 (4/8):\n",
+      "        with conds_y = [ ]\n",
+      "        with conds_x = [ (0 -3) ]\n",
+      "        pval = 0.00000 | val = 0.108\n",
+      "\n",
+      "        link (1 -2) --> 0 (5/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (0 -4) ]\n",
+      "        pval = 0.16200 | val = 0.004\n",
+      "\n",
+      "        link (2 0) --> 0 (6/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (1 -2) ]\n",
+      "        pval = 0.92300 | val = -0.003\n",
+      "\n",
+      "        link (2 -1) --> 0 (7/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.52600 | val = -0.002\n",
+      "\n",
+      "        link (2 -2) --> 0 (8/8):\n",
+      "        with conds_y = [ (1 -1) ]\n",
+      "        with conds_x = [ (1 -4) ]\n",
+      "        pval = 0.20800 | val = 0.000\n",
+      "\n",
+      "        link (0 0) --> 1 (1/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (1 -1) ]\n",
+      "        pval = 0.71100 | val = -0.001\n",
+      "\n",
+      "        link (0 -1) --> 1 (2/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (1 -2) ]\n",
+      "        pval = 0.59800 | val = 0.001\n",
+      "\n",
+      "        link (0 -2) --> 1 (3/8):\n",
+      "        with conds_y = [ ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.31900 | val = 0.002\n",
+      "\n",
+      "        link (1 -1) --> 1 (4/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (0 -3) ]\n",
+      "        pval = 0.91400 | val = -0.006\n",
+      "\n",
+      "        link (1 -2) --> 1 (5/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (0 -4) ]\n",
+      "        pval = 0.64300 | val = -0.001\n",
+      "\n",
+      "        link (2 0) --> 1 (6/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (1 -2) ]\n",
+      "        pval = 0.12800 | val = 0.006\n",
+      "\n",
+      "        link (2 -1) --> 1 (7/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.23600 | val = 0.003\n",
+      "\n",
+      "        link (2 -2) --> 1 (8/8):\n",
+      "        with conds_y = [ (0 -2) ]\n",
+      "        with conds_x = [ (1 -4) ]\n",
+      "        pval = 0.39000 | val = -0.000\n",
+      "\n",
+      "        link (0 0) --> 2 (1/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (1 -1) ]\n",
+      "        pval = 0.94400 | val = -0.003\n",
+      "\n",
+      "        link (0 -1) --> 2 (2/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (1 -2) ]\n",
+      "        pval = 0.73200 | val = -0.010\n",
+      "\n",
+      "        link (0 -2) --> 2 (3/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.65800 | val = -0.001\n",
+      "\n",
+      "        link (1 0) --> 2 (4/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (0 -2) ]\n",
+      "        pval = 0.14600 | val = 0.006\n",
+      "\n",
+      "        link (1 -1) --> 2 (5/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (0 -3) ]\n",
+      "        pval = 0.73500 | val = -0.005\n",
+      "\n",
+      "        link (1 -2) --> 2 (6/8):\n",
+      "        with conds_y = [ ]\n",
+      "        with conds_x = [ (0 -4) ]\n",
+      "        pval = 0.00000 | val = 0.080\n",
+      "\n",
+      "        link (2 -1) --> 2 (7/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (1 -3) ]\n",
+      "        pval = 0.10900 | val = 0.003\n",
+      "\n",
+      "        link (2 -2) --> 2 (8/8):\n",
+      "        with conds_y = [ (1 -2) ]\n",
+      "        with conds_x = [ (1 -4) ]\n",
+      "        pval = 0.62800 | val = -0.001\n",
+      "\n",
+      "## Significant links at alpha = 0.05:\n",
+      "\n",
+      "    Variable 0 has 1 link(s):\n",
+      "        (1 -1): pval = 0.00000 | val = 0.108 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable 1 has 0 link(s):\n",
+      "\n",
+      "    Variable 2 has 1 link(s):\n",
+      "        (1 -2): pval = 0.00000 | val = 0.080 | conf = (0.000, 0.000)\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable 0 has 1 link(s):\n",
+      "        (1 -1): pval = 0.00000 | val = 0.108\n",
+      "\n",
+      "    Variable 1 has 0 link(s):\n",
+      "\n",
+      "    Variable 2 has 1 link(s):\n",
+      "        (1 -2): pval = 0.00000 | val = 0.080\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmi_knn = CMIknn(significance='shuffle_test', knn=0.1, shuffle_neighbors=5)\n",
+    "pcmci_cmi_knn = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=cmi_knn,\n",
+    "    verbosity=2)\n",
+    "results = pcmci_cmi_knn.run_pcmci(tau_max=2, pc_alpha=0.05)\n",
+    "pcmci_cmi_knn.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Significant links at alpha = 0.01:\n",
+    "\n",
+    "#     Variable $X^0$ has 1 link(s):\n",
+    "#         ($X^1$ -1): pval = 0.00000 | val = 0.108\n",
+    "\n",
+    "#     Variable $X^1$ has 0 link(s):\n",
+    "\n",
+    "#     Variable $X^2$ has 1 link(s):\n",
+    "#         ($X^1$ -2): pval = 0.00000 | val = 0.080"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x7fb9c7e2f8d0>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "link_matrix = pcmci_cmi_knn.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "                        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "tp.plot_graph(\n",
+    "    val_matrix=results['val_matrix'],\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=var_names,\n",
+    "    link_colorbar_label='cross-MCI',\n",
+    "    node_colorbar_label='auto-MCI',\n",
+    "    vmin_edges=0.,\n",
+    "    vmax_edges = 0.3,\n",
+    "    edge_ticks=0.05,\n",
+    "    cmap_edges='OrRd',\n",
+    "    vmin_nodes=0,\n",
+    "    vmax_nodes=.5,\n",
+    "    node_ticks=.1,\n",
+    "    cmap_nodes='OrRd',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here CMIknn correctly detects the true links and also unveils the spurious link. While CMIknn may now seem as the best independence test choice, we have to note that the generality comes at the cost of much lower power for the case that the dependencies actually follow some parametric form. Then ParCorr or GPDC are much more powerful measures. Of course, ParCorr also detects linear links better than GPDC. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RCOT\n",
+    "\n",
+    "Another option as a fully non-parametric test is based on Kernel measures (https://arxiv.org/abs/1702.03877). We here implement a test wrapped around the r-package ``rcit``, so you need to set that up as described in the documentation of the RCOT class. Tigramite provides a bash script ``install_r_packages.sh`` that will install RCOT (this might not work in Python3.6, just Python2.7). The test does not require a shuffle test and is, hence, much faster than CMIknn. However, it should only be used for sample sizes larger than 1000 or so, because the analytical approximation of the null distribution may not be valid for smaller sizes, see the results in the AISTATS paper referenced above. Another drawback vs CMIknn is that it may not work for nonlinearities on very fine scales. The method is very new and extended numerical comparisons are still missing. The interpretation of the RCOT value is not straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable 0 has 1 link(s):\n",
+      "        (1 -1): pval = 0.00003 | val = 374.270\n",
+      "\n",
+      "    Variable 1 has 1 link(s):\n",
+      "        (2 0): pval = 0.00796 | val = 86.039\n",
+      "\n",
+      "    Variable 2 has 2 link(s):\n",
+      "        (1 -2): pval = 0.00016 | val = 290.334\n",
+      "        (1 0): pval = 0.00796 | val = 86.039\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tigramite.independence_tests import RCOT\n",
+    "rcot = RCOT(significance='analytic')\n",
+    "pcmci_rcot = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=rcot,\n",
+    "    verbosity=0)\n",
+    "results = pcmci_rcot.run_pcmci(tau_max=2, pc_alpha=0.05)\n",
+    "pcmci_rcot.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Symbolic time series\n",
+    "\n",
+    "Symbolic (or discrete) data may arise naturally or continuously-valued time series can be converted to symbolic data. To accommodate such time series, Tigramite includes the ``CMIsymb`` conditional independence test based on conditional mutual information estimated directly from the histogram of discrete values. Usually a (quantile-)binning  applied to continuous data in order to use a discrete CMI estimator is not recommended (rather use ``CMIknn``), but here we do it anyway to get some symbolic data. We again consider the nonlinear time series example and convert to a symbolic series with 4 bins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x288 with 3 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c7d7cac8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c7d8a978>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb9c7cf0b00>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(2000, 3)\n",
+    "for t in range(1, 2000):\n",
+    "    data[t, 0] += 0.4*data[t-1, 1]**2\n",
+    "    data[t, 2] += 0.3*data[t-2, 1]**2\n",
+    "data = pp.quantile_bin_array(data, bins=4)\n",
+    "dataframe = pp.DataFrame(data, var_names=var_names)\n",
+    "tp.plot_timeseries(dataframe, figsize=(10,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CMIsymb is initialized with ``n_symbs=None`` implying that the number of symbols is determined as ``n_symbs=data.max()+1``. Again, we have to use a shuffle test. Symbolic CMI works not very well here, only for 2000 samples was the correct graph reliably detected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.040\n",
+      "\n",
+      "    Variable $X^1$ has 1 link(s):\n",
+      "        ($X^1$ -2): pval = 0.01000 | val = 0.005\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -2): pval = 0.00000 | val = 0.036\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmi_symb = CMIsymb(significance='shuffle_test', n_symbs=None)\n",
+    "pcmci_cmi_symb = PCMCI(\n",
+    "    dataframe=dataframe, \n",
+    "    cond_ind_test=cmi_symb)\n",
+    "results = pcmci_cmi_symb.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_cmi_symb.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Significant parents at alpha = 0.01:\n",
+    "\n",
+    "#     Variable $X^0$ has 1 parent(s):\n",
+    "#         ($X^1$ -1): pval = 0.00000 | val = 0.040\n",
+    "\n",
+    "#     Variable $X^1$ has 0 parent(s):\n",
+    "\n",
+    "#     Variable $X^2$ has 1 parent(s):\n",
+    "#         ($X^1$ -2): pval = 0.00000 | val = 0.036"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tigramite_tutorial_causal_effects_mediation.ipynb b/tutorials/tigramite_tutorial_causal_effects_mediation.ipynb
new file mode 100644
index 00000000..be760f2b
--- /dev/null
+++ b/tutorials/tigramite_tutorial_causal_effects_mediation.ipynb
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results. \n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "This tutorial explains the Mediation class. See the following paper for theoretical background:\n",
+    "Runge, Jakob, Vladimir Petoukhov, Jonathan F. Donges, Jaroslav Hlinka, Nikola Jajcay, Martin Vejmelka, David Hartman, Norbert Marwan, Milan Paluš, and Jürgen Kurths. 2015. “Identifying Causal Gateways and Mediators in Complex Spatio-Temporal Systems.” Nature Communications 6: 8502. https://doi.org/10.1038/ncomms9502."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
+    "from tigramite.models import LinearMediation, Prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal effects and mediation\n",
+    "\n",
+    "The preceding sections were concerned with estimating causal links. In this section we discuss how the estimated time series graph can be used to evaluate causal effects and causal mediation in a linear framework as discussed in more detail in Runge et al, Nature Communications 2015. Consider the following model of a simple causal chain:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "              X_t &= \\eta^X_t \\\\\n",
+    "              Y_t &= 0.5 X_{t-1} +  \\eta^Y_t \\\\\n",
+    "              Z_t &= 0.5 Y_{t-1} +  \\eta^Z_t\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "# links_coeffs = {0: [],\n",
+    "#                 1: [((0, -1), 0.5)],\n",
+    "#                 2: [((1, -1), 0.5)],\n",
+    "#                 }\n",
+    "links_coeffs = {0: [((0,-1), 0.8)],\n",
+    "                1: [((1,-1), 0.8), ((0, -1), 0.5)],\n",
+    "                2: [((2,-1), 0.8), ((1, -1), 0.5)],\n",
+    "                }\n",
+    "var_names = [r\"$X$\", r\"$Y$\", r\"$Z$\"]\n",
+    "    \n",
+    "data, true_parents = pp.var_process(links_coeffs, T=1000)\n",
+    "\n",
+    "# Initialize dataframe object, specify time axis and variable names\n",
+    "dataframe = pp.DataFrame(data, \n",
+    "                         var_names=var_names)\n",
+    "med = LinearMediation(dataframe=dataframe)\n",
+    "med.fit_model(all_parents=true_parents, tau_max=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We fit the linear mediation model based on the true parents here, in practice these are estimated with PCMCI. If the assumption of a linear model is justified *and* causal sufficiency is fulfilled, the *link coefficient* of $X_{t-\\tau}\\to Y_t$ estimated from standardized time series (default in ``LinearMediation`` class) corresponds to the change in the expected value of $Y_t$ (in units of its standard deviation) caused by a perturbation of one standard deviation in $X_{t-\\tau}$. Let's check the link coefficient of $X_{t-2}\\to Z_t$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Link coefficient (0, -2) --> 2:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Link coefficient (0, -2) --> 2: \", med.get_coeff(i=0, tau=-2, j=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The link coefficient is non-zero only for direct links. The *causal effect* evaluates also *indirect* effects and is now simply computed by summing over the products of link coefficients along all possible paths between the two variables. For example, here the causal effect of $X_{t-2}\\to Z_t$ is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Causal effect (0, -2) --> 2:  0.06386220811405569\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Causal effect (0, -2) --> 2: \", med.get_ce(i=0, tau=-2, j=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *mediated causal effect* quantifies the contribution of an intermediate variable to the causal effect. For example, let's look at the contribution of $Y$ on the causal effect of $X_{t-2}\\to Z_t$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mediated Causal effect (0, -2) --> 2 through 1:  0.06386220811405569\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Mediated Causal effect (0, -2) --> 2 through 1: \", med.get_mce(i=0, tau=-2, j=2, k=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since $Y$ mediates *all* of the causal effect, MCE is the same as CE here. Mediation analysis is a powerful tool to quantify not only direct causal links, but also indirect pathways. This can answer questions such as \"How important is one process for the causal mechanism between two others?\". In the ``tigramite.plotting`` module are functions to visualize causal pathways both in the aggregated network and in the time series graph. Here we look at all causal pathways between $X_{t-4}$ and $Z_t$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i=0; tau=4; j=2\n",
+    "graph_data = med.get_mediation_graph_data(i=i, tau=tau, j=j)\n",
+    "tp.plot_mediation_time_series_graph(\n",
+    "    var_names=var_names,\n",
+    "    path_node_array=graph_data['path_node_array'],\n",
+    "    tsg_path_val_matrix=graph_data['tsg_path_val_matrix']\n",
+    "    )\n",
+    "tp.plot_mediation_graph(\n",
+    "                    var_names=var_names,\n",
+    "                    path_val_matrix=graph_data['path_val_matrix'], \n",
+    "                    path_node_array=graph_data['path_node_array'],\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In both plots, the node color depicts the mediation of a variable and the link colors depict the link coefficients. The graph plot in the bottom panel is easier to visualize for more complex pathways, but it's harder to see the pathways across variables *and* in time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Causal effect and mediation analysis can also be used for more aggregate measures. The Average Causal Effect (ACE) quantifies how strong the effect of a single variable on the whole system is and the Average Causal Susceptibility (ACS) quantifies how strong a single variable is effected by perturbations entering elsewhere in the system. Last, the Average Mediated Causal Effect (AMCE) quantifies how much a single variable mediates causal effects between any two other processes. In Runge et al, Nature Communications (2015), these measures are compared with conventional complex network measures to show that causal effect measures are better interpretable alternatives. For example, *betweenness centrality* gives the average number of shortest paths going through a particular node. However, causal effects do not necessarily take shortest paths and betweenness also does not properly take into account the causal effect weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Causal Effect X=0.39, Y=0.24, Z=0.00 \n",
+      "Average Causal Susceptibility X=0.00, Y=0.27, Z=0.36 \n",
+      "Average Mediated Causal Effect X=0.00, Y=0.12, Z=0.00 \n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Average Causal Effect X=%.2f, Y=%.2f, Z=%.2f \" % tuple(med.get_all_ace()))\n",
+    "print (\"Average Causal Susceptibility X=%.2f, Y=%.2f, Z=%.2f \" % tuple(med.get_all_acs()))\n",
+    "print (\"Average Mediated Causal Effect X=%.2f, Y=%.2f, Z=%.2f \" % tuple(med.get_all_amce()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that per default self-loops are excluded in these measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tigramite_tutorial_missing_masking.ipynb b/tutorials/tigramite_tutorial_missing_masking.ipynb
new file mode 100644
index 00000000..18b9ac9d
--- /dev/null
+++ b/tutorials/tigramite_tutorial_missing_masking.ipynb
@@ -0,0 +1,1173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results.\n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "This tutorial explains the missing values and masking and gives walk-through examples. See the following paper for theoretical background:\n",
+    "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
+    "from tigramite.models import LinearMediation, Prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing values and masking\n",
+    "\n",
+    "### Missing values\n",
+    "\n",
+    "Tigramite consistently handles missing values. For example, missing values denoted as ``999.`` in the data can be flagged with ``ParCorr.set_dataframe(data, missing_flag=999.)``. Then all time slices of samples where missing values occur in any variable are dismissed while consistently handling time lags. To avoid biases also subsequent samples for all lags up to ``2*tau_max`` are dismissed. Missing values and masking will be descriped in more detail in a future paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "# Initialize conditional independence test\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = par_corr\n",
+      "significance = analytic\n",
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "independence test = par_corr\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.2]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Variable $X^0$\n",
+      "\n",
+      "Iterating through pc_alpha = [0.2]:\n",
+      "\n",
+      "# pc_alpha = 0.2 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^0$ (1/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.682\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^0$ (2/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00002 / val = 0.532\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^0$ (3/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00419 / val = 0.371\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^0$ (4/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.19048 / val = 0.174\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^0$ (5/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.99720 / val = 0.000\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^0$ (6/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.75189 / val = -0.042\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^0$ has 4 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.682\n",
+      "        ($X^0$ -2): max_pval = 0.00002, min_val = 0.532\n",
+      "        ($X^1$ -1): max_pval = 0.00419, min_val = 0.371\n",
+      "        ($X^1$ -2): max_pval = 0.19048, min_val = 0.174\n",
+      "\n",
+      "Testing condition sets of dimension 1:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^0$ (1/4):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -2)  --> pval = 0.00006 / val = 0.507\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^0$ (2/4):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.61423 / val = 0.068\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^0$ (3/4):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.78960 / val = -0.036\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^0$ (4/4):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.23381 / val = -0.160\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^0$ has 1 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00006, min_val = 0.507\n",
+      "\n",
+      "Algorithm converged for variable $X^0$\n",
+      "\n",
+      "## Variable $X^1$\n",
+      "\n",
+      "Iterating through pc_alpha = [0.2]:\n",
+      "\n",
+      "# pc_alpha = 0.2 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^1$ (1/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.800\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^1$ (2/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.720\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^1$ (3/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.734\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^1$ (4/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00003 / val = 0.521\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^1$ (5/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.09525 / val = 0.221\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^1$ (6/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.92663 / val = 0.012\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^1$ has 5 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.800\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.734\n",
+      "        ($X^0$ -2): max_pval = 0.00000, min_val = 0.720\n",
+      "        ($X^1$ -2): max_pval = 0.00003, min_val = 0.521\n",
+      "        ($X^2$ -1): max_pval = 0.09525, min_val = 0.221\n",
+      "\n",
+      "Testing condition sets of dimension 1:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^1$ (1/5):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1)  --> pval = 0.00000 / val = 0.680\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^1$ (2/5):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.00001 / val = 0.557\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^1$ (3/5):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.01177 / val = 0.332\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^1$ (4/5):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.00762 / val = 0.350\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^1$ (5/5):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1)  --> pval = 0.53826 / val = 0.083\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^1$ has 4 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.680\n",
+      "        ($X^1$ -1): max_pval = 0.00001, min_val = 0.557\n",
+      "        ($X^1$ -2): max_pval = 0.00762, min_val = 0.350\n",
+      "        ($X^0$ -2): max_pval = 0.01177, min_val = 0.332\n",
+      "\n",
+      "Testing condition sets of dimension 2:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^1$ (1/4):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(1, -1), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1) ($X^1$ -2)  --> pval = 0.00000 / val = 0.680\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^1$ (2/4):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1) ($X^1$ -2)  --> pval = 0.00032 / val = 0.463\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^1$ (3/4):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1) ($X^1$ -1)  --> pval = 0.93261 / val = 0.012\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^1$ (4/4):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^0$ -1) ($X^1$ -1)  --> pval = 0.97020 / val = 0.005\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^1$ has 2 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.680\n",
+      "        ($X^1$ -1): max_pval = 0.00032, min_val = 0.463\n",
+      "\n",
+      "Algorithm converged for variable $X^1$\n",
+      "\n",
+      "## Variable $X^2$\n",
+      "\n",
+      "Iterating through pc_alpha = [0.2]:\n",
+      "\n",
+      "# pc_alpha = 0.2 (1/1):\n",
+      "\n",
+      "Testing condition sets of dimension 0:\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^2$ (1/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00293 / val = 0.384\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^2$ (2/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.607\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^2$ (3/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.771\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^2$ (4/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.773\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^2$ (5/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.737\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^2$ (6/6):\n",
+      "            Constructed array of shape (2, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = []\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0:  --> pval = 0.00000 / val = 0.607\n",
+      "    No conditions of dimension 0 left.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^2$ has 6 parent(s):\n",
+      "        ($X^1$ -2): max_pval = 0.00000, min_val = 0.773\n",
+      "        ($X^1$ -1): max_pval = 0.00000, min_val = 0.771\n",
+      "        ($X^2$ -1): max_pval = 0.00000, min_val = 0.737\n",
+      "        ($X^2$ -2): max_pval = 0.00000, min_val = 0.607\n",
+      "        ($X^0$ -2): max_pval = 0.00000, min_val = 0.607\n",
+      "        ($X^0$ -1): max_pval = 0.00293, min_val = 0.384\n",
+      "\n",
+      "Testing condition sets of dimension 1:\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^2$ (1/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1)  --> pval = 0.00004 / val = 0.518\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^2$ (2/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2)  --> pval = 0.00005 / val = 0.513\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^2$ (3/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2)  --> pval = 0.00836 / val = 0.346\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^2$ (4/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2)  --> pval = 0.00065 / val = 0.438\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^2$ (5/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2)  --> pval = 0.00660 / val = 0.356\n",
+      "    No conditions of dimension 1 left.\n",
+      "\n",
+      "    Link ($X^0$ -1) --> $X^2$ (6/6):\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2)  --> pval = 0.40395 / val = 0.113\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^2$ has 5 parent(s):\n",
+      "        ($X^1$ -2): max_pval = 0.00004, min_val = 0.518\n",
+      "        ($X^1$ -1): max_pval = 0.00005, min_val = 0.513\n",
+      "        ($X^2$ -2): max_pval = 0.00065, min_val = 0.438\n",
+      "        ($X^0$ -2): max_pval = 0.00660, min_val = 0.356\n",
+      "        ($X^2$ -1): max_pval = 0.00836, min_val = 0.346\n",
+      "\n",
+      "Testing condition sets of dimension 2:\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^2$ (1/5):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1) ($X^2$ -2)  --> pval = 0.03298 / val = 0.285\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^2$ (2/5):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2), (2, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2) ($X^2$ -2)  --> pval = 0.00000 / val = 0.687\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^2$ (3/5):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2) ($X^1$ -1)  --> pval = 0.00000 / val = 0.649\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^0$ -2) --> $X^2$ (4/5):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2) ($X^1$ -1)  --> pval = 0.67377 / val = 0.058\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^2$ (5/5):\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -2), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -2) ($X^1$ -1)  --> pval = 0.00002 / val = 0.531\n",
+      "    Still conditions of dimension 2 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^2$ has 4 parent(s):\n",
+      "        ($X^1$ -1): max_pval = 0.00005, min_val = 0.513\n",
+      "        ($X^2$ -2): max_pval = 0.00065, min_val = 0.438\n",
+      "        ($X^2$ -1): max_pval = 0.00836, min_val = 0.346\n",
+      "        ($X^1$ -2): max_pval = 0.03298, min_val = 0.285\n",
+      "\n",
+      "Testing condition sets of dimension 3:\n",
+      "\n",
+      "    Link ($X^1$ -1) --> $X^2$ (1/4):\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(2, -2), (2, -1), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^2$ -2) ($X^2$ -1) ($X^1$ -2)  --> pval = 0.00000 / val = 0.695\n",
+      "    Still conditions of dimension 3 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^2$ -2) --> $X^2$ (2/4):\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -1), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1) ($X^2$ -1) ($X^1$ -2)  --> pval = 0.00039 / val = 0.461\n",
+      "    Still conditions of dimension 3 left, but q_max = 1 reached.\n",
+      "\n",
+      "    Link ($X^2$ -1) --> $X^2$ (3/4):\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1) ($X^2$ -2) ($X^1$ -2)  --> pval = 0.25270 / val = 0.157\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Link ($X^1$ -2) --> $X^2$ (4/4):\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (2, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "    Combination 0: ($X^1$ -1) ($X^2$ -2) ($X^2$ -1)  --> pval = 0.33333 / val = 0.133\n",
+      "    Non-significance detected.\n",
+      "\n",
+      "    Sorting parents in decreasing order with \n",
+      "    weight(i-tau->j) = min_{iterations} |I_{ij}(tau)| \n",
+      "\n",
+      "Updating parents:\n",
+      "\n",
+      "    Variable $X^2$ has 2 parent(s):\n",
+      "        ($X^1$ -1): max_pval = 0.00005, min_val = 0.513\n",
+      "        ($X^2$ -2): max_pval = 0.00065, min_val = 0.438\n",
+      "\n",
+      "Algorithm converged for variable $X^2$\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable $X^0$ has 1 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00006, min_val = 0.507\n",
+      "\n",
+      "    Variable $X^1$ has 2 parent(s):\n",
+      "        ($X^0$ -1): max_pval = 0.00000, min_val = 0.680\n",
+      "        ($X^1$ -1): max_pval = 0.00032, min_val = 0.463\n",
+      "\n",
+      "    Variable $X^2$ has 2 parent(s):\n",
+      "        ($X^1$ -1): max_pval = 0.00005, min_val = 0.513\n",
+      "        ($X^2$ -2): max_pval = 0.00065, min_val = 0.438\n",
+      "\n",
+      "##\n",
+      "## Running Tigramite MCI algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "\n",
+      "independence test = par_corr\n",
+      "tau_min = 0\n",
+      "tau_max = 2\n",
+      "max_conds_py = None\n",
+      "max_conds_px = None\n",
+      "\n",
+      "        link ($X^0$ -1) --> $X^0$ (1/8):\n",
+      "        with conds_y = [ ]\n",
+      "        with conds_x = [ ($X^0$ -2) ]\n",
+      "            Constructed array of shape (3, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.00006 | val = 0.507\n",
+      "\n",
+      "        link ($X^0$ -2) --> $X^0$ (2/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ]\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (0, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.55892 | val = 0.080\n",
+      "\n",
+      "        link ($X^1$ 0) --> $X^0$ (3/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(1, 0)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.60886 | val = 0.070\n",
+      "\n",
+      "        link ($X^1$ -1) --> $X^0$ (4/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -2) ($X^1$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (0, -2), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.92609 | val = 0.013\n",
+      "\n",
+      "        link ($X^1$ -2) --> $X^0$ (5/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ($X^1$ -3) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (0, -3), (1, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.40860 | val = -0.114\n",
+      "\n",
+      "        link ($X^2$ 0) --> $X^0$ (6/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, 0)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (2, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.44605 | val = -0.105\n",
+      "\n",
+      "        link ($X^2$ -1) --> $X^0$ (7/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -2) ($X^2$ -3) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (1, -2), (2, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.34628 | val = -0.129\n",
+      "\n",
+      "        link ($X^2$ -2) --> $X^0$ (8/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -3) ($X^2$ -4) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(0, 0)]\n",
+      "            Z = [(0, -1), (1, -3), (2, -4)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.86467 | val = -0.024\n",
+      "\n",
+      "        link ($X^0$ 0) --> $X^1$ (1/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -1) ]\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, 0)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.60886 | val = 0.070\n",
+      "\n",
+      "        link ($X^0$ -1) --> $X^1$ (2/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -2) ]\n",
+      "            Constructed array of shape (4, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(1, -1), (0, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.00000 | val = 0.610\n",
+      "\n",
+      "        link ($X^0$ -2) --> $X^1$ (3/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (0, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.65340 | val = 0.062\n",
+      "\n",
+      "        link ($X^1$ -1) --> $X^1$ (4/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -2) ($X^1$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (0, -2), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.00167 | val = 0.414\n",
+      "\n",
+      "        link ($X^1$ -2) --> $X^1$ (5/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ($X^1$ -3) ]\n",
+      "            Constructed array of shape (6, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (0, -3), (1, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.05226 | val = 0.266\n",
+      "\n",
+      "        link ($X^2$ 0) --> $X^1$ (6/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, 0)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (2, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.21886 | val = 0.168\n",
+      "\n",
+      "        link ($X^2$ -1) --> $X^1$ (7/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -2) ($X^2$ -3) ]\n",
+      "            Constructed array of shape (6, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (1, -2), (2, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.11950 | val = -0.214\n",
+      "\n",
+      "        link ($X^2$ -2) --> $X^1$ (8/8):\n",
+      "        with conds_y = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -3) ($X^2$ -4) ]\n",
+      "            Constructed array of shape (6, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(1, 0)]\n",
+      "            Z = [(0, -1), (1, -1), (1, -3), (2, -4)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.97732 | val = 0.004\n",
+      "\n",
+      "        link ($X^0$ 0) --> $X^2$ (1/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -1) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(0, 0)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.44605 | val = -0.105\n",
+      "\n",
+      "        link ($X^0$ -1) --> $X^2$ (2/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(0, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (0, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.33813 | val = -0.132\n",
+      "\n",
+      "        link ($X^0$ -2) --> $X^2$ (3/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(0, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (0, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.12526 | val = -0.209\n",
+      "\n",
+      "        link ($X^1$ 0) --> $X^2$ (4/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -1) ($X^1$ -1) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, 0)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (0, -1)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.21886 | val = 0.168\n",
+      "\n",
+      "        link ($X^1$ -1) --> $X^2$ (5/8):\n",
+      "        with conds_y = [ ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -2) ($X^1$ -2) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(1, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(2, -2), (0, -2), (1, -2)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.00000 | val = 0.606\n",
+      "\n",
+      "        link ($X^1$ -2) --> $X^2$ (6/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^0$ -3) ($X^1$ -3) ]\n",
+      "            Constructed array of shape (6, 58) from\n",
+      "            X = [(1, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (0, -3), (1, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.32855 | val = 0.136\n",
+      "\n",
+      "        link ($X^2$ -1) --> $X^2$ (7/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ($X^2$ -2) ]\n",
+      "        with conds_x = [ ($X^1$ -2) ($X^2$ -3) ]\n",
+      "            Constructed array of shape (6, 58) from\n",
+      "            X = [(2, -1)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (2, -2), (1, -2), (2, -3)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.54650 | val = 0.084\n",
+      "\n",
+      "        link ($X^2$ -2) --> $X^2$ (8/8):\n",
+      "        with conds_y = [ ($X^1$ -1) ]\n",
+      "        with conds_x = [ ($X^1$ -3) ($X^2$ -4) ]\n",
+      "            Constructed array of shape (5, 58) from\n",
+      "            X = [(2, -2)]\n",
+      "            Y = [(2, 0)]\n",
+      "            Z = [(1, -1), (1, -3), (2, -4)]\n",
+      "            with missing values = 999.0 removed\n",
+      "        pval = 0.07281 | val = 0.244\n",
+      "\n",
+      "## Significant links at alpha = 0.05:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00006 | val = 0.507 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.610 | conf = (0.000, 0.000)\n",
+      "        ($X^1$ -1): pval = 0.00167 | val = 0.414 | conf = (0.000, 0.000)\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.606 | conf = (0.000, 0.000)\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X^0$ has 1 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00006 | val = 0.507\n",
+      "\n",
+      "    Variable $X^1$ has 2 link(s):\n",
+      "        ($X^0$ -1): pval = 0.00000 | val = 0.610\n",
+      "        ($X^1$ -1): pval = 0.00167 | val = 0.414\n",
+      "\n",
+      "    Variable $X^2$ has 1 link(s):\n",
+      "        ($X^1$ -1): pval = 0.00000 | val = 0.606\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "data = np.random.randn(100, 3)\n",
+    "for t in range(1, 100):\n",
+    "    data[t, 0] += 0.7*data[t-1, 0] \n",
+    "    data[t, 1] += 0.6*data[t-1, 1] + 0.6*data[t-1,0]\n",
+    "    data[t, 2] += 0.5*data[t-1, 2] + 0.6*data[t-1,1]\n",
+    "# Randomly mark 10% of values as missing values in variable 2\n",
+    "data[np.random.permutation(100)[:10], 2] = 999.\n",
+    "\n",
+    "# Initialize dataframe object, specify time axis and variable names\n",
+    "var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']\n",
+    "dataframe = pp.DataFrame(data, \n",
+    "                         datatime = np.arange(len(data)), \n",
+    "                         var_names=var_names,\n",
+    "                         missing_flag=999.)\n",
+    "\n",
+    "tp.plot_timeseries(dataframe)\n",
+    "pcmci_parcorr = PCMCI(dataframe=dataframe, cond_ind_test=ParCorr(verbosity=3), verbosity=4)\n",
+    "results = pcmci_parcorr.run_pcmci(tau_max=2, pc_alpha=0.2)\n",
+    "pcmci_parcorr.print_significant_links(\n",
+    "        p_matrix = results['p_matrix'], \n",
+    "        val_matrix = results['val_matrix'],\n",
+    "        alpha_level = 0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Masking\n",
+    "\n",
+    "Different from missing values, masking can be used to include or exclude samples depending on the situation: For example, in climate research we frequently are interested to detect the drivers of a target variable *only in winter months*. Suppose we are given dataseries ($X^1, X^2, X^3$) and want to estimate the causal parents affecting the variables in winter months. This can be achieved with ``mask_type='y'`` in initializing ``ParCorr`` and marking all winter month data in ($X^1, X^2, X^3$) in ``mask`` with a ``1`` (or ``True``). During a PCMCI analysis many independent tests $X_{t-\\tau} \\perp Y_t | Z$ are carried out, where $X,Y,Z$ denote any of the $X^1,X^2,X^3$ in different iterations of the PCMCI analysis. For example, to estimate the parents of $X^2$, the argument $X_{t-\\tau}$ as well as $Z$ will iterate through ($X^1, X^2, X^3$) including their lags.  Then Tigramite considers samples of $Y=X^2$ from the winter only using the mask of $X^2$, while lagged samples of $X$ or $Z$ can also come, e.g., from the previous summer. If we want *all* samples, also in $X$ and $Z$ to be restricted to the respective mask, we need to mark them in ``mask`` as well and set  ``mask_type='yxz'``. Correspondingly, also  ``mask_type='z'`` or any combination is possible. Missing values and masking will be descriped in more detail in a future paper.\n",
+    "\n",
+    "In the following example, we generate data with a different underlying causality for winter and summer months. In particular, assume a causal effect is of opposite sign in both seasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 576x216 with 2 Axes>,\n",
+       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe4d3fd2e10>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe4d3f93400>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Masking demo: We consider time series where the first half is generated by a different\n",
+    "# causal process than the second half. \n",
+    "np.random.seed(42)\n",
+    "T = 1000\n",
+    "data = np.random.randn(T, 2)\n",
+    "data_mask = np.zeros(data.shape)\n",
+    "for t in range(1, T):\n",
+    "#     print t % 365\n",
+    "    if (t % 365) < 3*30 or (t % 365) > 8*30: \n",
+    "        # Winter half year\n",
+    "        data[t, 0] +=  0.4*data[t-1, 0]\n",
+    "        data[t, 1] +=  0.3*data[t-1, 1] + 0.9*data[t-1, 0]\n",
+    "    else:\n",
+    "        # Summer half year\n",
+    "        data_mask[[t, t-1]] = True\n",
+    "        data[t, 0] +=  0.4*data[t-1, 0]\n",
+    "        data[t, 1] +=  0.3*data[t-1, 1] - 0.9*data[t-1, 0]\n",
+    "\n",
+    "T, N = data.shape\n",
+    "# print data_mask[:100, 0]\n",
+    "dataframe = pp.DataFrame(data, mask=data_mask)\n",
+    "tp.plot_timeseries(dataframe, figsize=(8,3), use_mask=True, grey_masked_samples='data')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup analysis\n",
+    "def run_and_plot(cond_ind_test, fig_ax):\n",
+    "    pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)\n",
+    "    results = pcmci.run_pcmci(tau_max=2,pc_alpha=0.2, )\n",
+    "    link_matrix = pcmci.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "            val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "    tp.plot_graph(fig_ax = fig_ax,  val_matrix=results['val_matrix'],\n",
+    "                  link_matrix=link_matrix, var_names=var_names,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x144 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x144 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x144 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Causal graph of whole year yields no link because effects average out\n",
+    "fig  = plt.figure(figsize=(3,2)); ax=fig.add_subplot(111)\n",
+    "run_and_plot(ParCorr(mask_type=None), (fig, ax))\n",
+    "\n",
+    "# # Causal graph of winter half only gives positive link\n",
+    "fig  = plt.figure(figsize=(3,2)); ax=fig.add_subplot(111)\n",
+    "run_and_plot(ParCorr(mask_type='y'), (fig, ax))\n",
+    "\n",
+    "# Causal graph of summer half only gives negative link\n",
+    "fig  = plt.figure(figsize=(3,2)); ax=fig.add_subplot(111)\n",
+    "dataframe.mask = (dataframe.mask == False)\n",
+    "run_and_plot(ParCorr(mask_type='y'),  (fig, ax))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, however, that the failure to detect the link on the whole sample occurs only for partial correlatiol because the positive and negative dependencies cancel out. Using CMIknn recovers the link (but gets a false positive for this realization):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 216x144 with 3 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x7fe4c3873128>)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x144 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pcmci = PCMCI(dataframe=dataframe, cond_ind_test=CMIknn(mask_type=None))\n",
+    "results = pcmci.run_pcmci(tau_max=2,pc_alpha=0.2)\n",
+    "link_matrix = pcmci.return_significant_parents(pq_matrix=results['p_matrix'],\n",
+    "        val_matrix=results['val_matrix'], alpha_level=0.01)['link_matrix']\n",
+    "fig  = plt.figure(figsize=(3,2)); ax=fig.add_subplot(111)\n",
+    "tp.plot_graph(fig_ax = (fig, ax),  val_matrix=results['val_matrix'],\n",
+    "              link_matrix=link_matrix, var_names=var_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tigramite_tutorial_pcmci_fullci.ipynb b/tutorials/tigramite_tutorial_pcmci_fullci.ipynb
new file mode 100644
index 00000000..3d78daf2
--- /dev/null
+++ b/tutorials/tigramite_tutorial_pcmci_fullci.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results.\n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "This tutorial compares PCMCI with FullCI. See the following paper for theoretical background:\n",
+    "Runge, Jakob. 2018. “Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.” Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
+    "from tigramite.models import LinearMediation, Prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# PCMCI vs FullCI\n",
+    "\n",
+    "An alternative way to estimate the time series graph is to directly test links by their defining equation (FullCI)\n",
+    "\n",
+    "\\begin{align*}\n",
+    "X^i_{t-\\tau} \\perp X^j_t ~|~ \\mathbf{X}^-_t\n",
+    "\\end{align*}\n",
+    "\n",
+    "where $\\mathbf{X}^-_t=(\\mathbf{X}_{t-1}, \\mathbf{X}_{t-2}, \\ldots)$ is the past of the whole process. This is the approach of a *lag-specific* version of Granger causality or Transfer entropy. This approach gives lower detection power for two reasons:\n",
+    "\n",
+    "1. Smaller *effect size* of FullCI compared to MCI\n",
+    "2. Higher dimensionality "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Smaller *effect size* of FullCI compared to MCI\n",
+    "\n",
+    "Consider the following system:\n",
+    "\n",
+    "\\begin{align*} \n",
+    "Z_t &= a Z_{t-1} + c_{XZ} X_{t-1} + \\eta^Z_t  \\\\\n",
+    "X_t &= a_{X} X_{t-1} + c_{WX} W_{t-1} + \\eta^X_t  \\\\\n",
+    "Y_t &= a Y_{t-1} + c_{XY} X_{t-2} + c_{WY} W_{t-3} + \\eta^Y_t \\\\\n",
+    "W_t &= a W_{t-1} + \\eta^W_t\n",
+    "\\end{align*}\n",
+    "\n",
+    "with independent Gaussian white noise processes $\\eta^{\\cdot}_t$ with variances $\\sigma^2_{\\cdot}$. Considering the link $X_{t-2}\\to Y_t$, here it can be shown (see description paper) that the test statistic $I^{FullCI}(X_{t-2}\\to Y_t) \\leq I^{MCI}(X_{t-2}\\to Y_t)$ generally. In the paper this is proven for arbitrary models. Measuring a smaller effect size implies a lower power to detect this particular link:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup analysis\n",
+    "np.random.seed(42)     # Fix random seed\n",
+    "links_coeffs = {0: [((0, -1), 0.9), ((1, -1), -0.25)],\n",
+    "                1: [((1, -1), 0.95), ((3, -1), 0.3)],\n",
+    "                2: [((2, -1), 0.85), ((1, -2), 0.3), ((3, -3), 0.3)],\n",
+    "                3: [((3, -1), 0.8)],\n",
+    "                }\n",
+    "T = 100     # time series length\n",
+    "N = len(links_coeffs)\n",
+    "tau_max = 5\n",
+    "realizations = 100\n",
+    "alpha_level = 0.05\n",
+    "\n",
+    "var_names = [r'$Z$', r'$X$', r'$Y$', r'$W$']\n",
+    "# Define whole past\n",
+    "whole_past = {}\n",
+    "for j in range(N):\n",
+    "    whole_past[j] = [(var, -lag)\n",
+    "                         for var in range(N)\n",
+    "                         for lag in range(1, tau_max + 1)\n",
+    "                    ]\n",
+    "def get_sig_links():\n",
+    "    p_matrices = {'PCMCI':np.ones((realizations, N, N, tau_max+1)),\n",
+    "                  'FullCI':np.ones((realizations, N, N, tau_max+1))}\n",
+    "    val_matrices = {'PCMCI':np.zeros((realizations, N, N, tau_max+1)),\n",
+    "                  'FullCI':np.zeros((realizations, N, N, tau_max+1))}  \n",
+    "    for i in range(realizations):\n",
+    "        data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)\n",
+    "        dataframe = pp.DataFrame(data)\n",
+    "        \n",
+    "        # PCMCI\n",
+    "        pcmci = PCMCI(dataframe=dataframe, cond_ind_test=ParCorr())\n",
+    "        results = pcmci.run_pcmci(tau_max=tau_max, pc_alpha=0.2)\n",
+    "        p_matrices['PCMCI'][i] = results['p_matrix']\n",
+    "        val_matrices['PCMCI'][i] = results['val_matrix']\n",
+    "\n",
+    "        # Condition on whole past\n",
+    "        results = pcmci.run_mci(tau_max=tau_max,parents=whole_past)\n",
+    "        p_matrices['FullCI'][i] = results['p_matrix']\n",
+    "        val_matrices['FullCI'][i] = results['val_matrix']\n",
+    "\n",
+    "    # Get true positive rate (=power) and false positive rate \n",
+    "    sig_links = {'PCMCI':(p_matrices['PCMCI'] <= alpha_level).mean(axis=0),\n",
+    "                  'FullCI':(p_matrices['FullCI'] <= alpha_level).mean(axis=0),}\n",
+    "    ave_val_matrices = {'PCMCI':val_matrices['PCMCI'].mean(axis=0),\n",
+    "                  'FullCI':val_matrices['FullCI'].mean(axis=0),}\n",
+    "    return sig_links, ave_val_matrices\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_links, ave_val_matrices = get_sig_links()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We estimate how often a link was detected at the given ``alpha_level`` and plot this fraction as the width of the links while the average effect size for each link is given as the color:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x7f9082d00710>)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Showing detection power as width of links\n",
+    "min_sig = 0.2\n",
+    "vminmax = 0.4\n",
+    "link_matrix = (sig_links['PCMCI'] > min_sig)\n",
+    "tp.plot_graph(val_matrix=ave_val_matrices['PCMCI'],\n",
+    "              link_matrix=link_matrix, var_names=var_names,\n",
+    "              link_width=sig_links['PCMCI'],\n",
+    "             arrow_linewidth=70.,\n",
+    "              vmin_edges=-vminmax,\n",
+    "              vmax_edges=vminmax,\n",
+    "\n",
+    ")\n",
+    "link_matrix = (sig_links['FullCI'] > min_sig)\n",
+    "tp.plot_graph(val_matrix=ave_val_matrices['FullCI'],\n",
+    "              link_matrix=link_matrix, var_names=var_names,\n",
+    "              link_width=sig_links['FullCI'], \n",
+    "              link_colorbar_label='FullCI',\n",
+    "              node_colorbar_label='auto-FullCI',\n",
+    "             arrow_linewidth=70.,\n",
+    "              vmin_edges=-vminmax,\n",
+    "              vmax_edges=vminmax,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evidently, the power of FullCI is lower mainly due to FullCI's test statistic effect size being smaller. MCI has been constructed to estimate a certain notion of *causal strength* as discussed in the paper. MCI, thus, alleviates the effect of other dependencies on a particular link. In large-scale studies, this feature allows to rank links by their causal strength."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Higher dimensionality \n",
+    "\n",
+    "An obvious drawback of FullCI is that it always includes the whole past as a condition leading to very high estimation dimension which results in lower power. In the following we consider a model with many irrelevant independent variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup analysis\n",
+    "np.random.seed(42)     # Fix random seed\n",
+    "links_coeffs = {0: [((0, -1), 0.9), ((1, -1), -0.25)],\n",
+    "                1: [((1, -1), 0.95), ((3, -1), 0.3)],\n",
+    "                2: [((2, -1), 0.85), ((1, -2), 0.3), ((3, -3), 0.3)],\n",
+    "                3: [((3, -1), 0.8)],\n",
+    "                }\n",
+    "T = 80     # time series length\n",
+    "tau_max = 5\n",
+    "realizations = 100\n",
+    "alpha_level = 0.05\n",
+    "n_variables = 9\n",
+    "\n",
+    "# Add independent autocorrelated variables\n",
+    "for d in range(4, n_variables):\n",
+    "    links_coeffs[d] = [((d, -1), 0.2 + np.random.rand()*0.7)]\n",
+    "    \n",
+    "var_names = [r'$Z$', r'$X$', r'$Y$', r'$W$'] + list(range(4, n_variables))\n",
+    "\n",
+    "N = len(links_coeffs)\n",
+    "# Define whole past\n",
+    "whole_past = {}\n",
+    "for j in range(N):\n",
+    "    whole_past[j] = [(var, -lag)\n",
+    "                         for var in range(N)\n",
+    "                         for lag in range(1, tau_max + 1)\n",
+    "                    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rung_ja/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:44: RuntimeWarning: invalid value encountered in less_equal\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This cell may take some minutes\n",
+    "sig_links, ave_val_matrices = get_sig_links()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 3 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x7f908d55ffd0>)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Showing detection power as width of links\n",
+    "min_sig = 0.05\n",
+    "vminmax = 0.5\n",
+    "link_matrix = (sig_links['PCMCI'] > min_sig)\n",
+    "tp.plot_graph(val_matrix=ave_val_matrices['PCMCI'],\n",
+    "              link_matrix=link_matrix, \n",
+    "              link_width=sig_links['PCMCI'],\n",
+    "             arrow_linewidth=70.,\n",
+    "              vmin_edges=-vminmax,\n",
+    "              vmax_edges=vminmax,\n",
+    "              var_names = var_names,\n",
+    "\n",
+    ")\n",
+    "link_matrix = (sig_links['FullCI'] > min_sig)\n",
+    "tp.plot_graph(val_matrix=ave_val_matrices['FullCI'],\n",
+    "              link_matrix=link_matrix, \n",
+    "              link_width=sig_links['FullCI'], \n",
+    "              link_colorbar_label='FullCI',\n",
+    "              node_colorbar_label='auto-FullCI',\n",
+    "             arrow_linewidth=70.,\n",
+    "              vmin_edges=-vminmax,\n",
+    "              vmax_edges=vminmax,\n",
+    "             var_names = var_names,\n",
+    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here adding just 5 independent variables makes it already impossible to detect the true links with FullCI, while PCMCI still detects them with almost the same power as before while still well controlling false positives at the expected 5% level (note that only 100 realizations were used to estimate the false positive rates making them not very reliable while the true positive rates are more reliable):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean true  positives PCMCI  0.781\n",
+      "Mean false positives PCMCI  0.054\n",
+      "Mean true  positives FullCI     0.252\n",
+      "Mean false positives FullCI     0.023\n"
+     ]
+    }
+   ],
+   "source": [
+    "true_links = np.zeros((N, N, tau_max+1))\n",
+    "for var in list(links_coeffs):\n",
+    "    for par in links_coeffs[var]:\n",
+    "        true_links[par[0][0], var, abs(par[0][1])] = True\n",
+    "# print true_links\n",
+    "print (\"Mean true  positives PCMCI \", np.mean(sig_links['PCMCI'][:,:,1:]\n",
+    "                                                [true_links[:,:,1:]==True]).round(3))\n",
+    "print (\"Mean false positives PCMCI \", np.mean(sig_links['PCMCI'][:,:,1:]\n",
+    "                                                [true_links[:,:,1:]==False]).round(3))\n",
+    "print (\"Mean true  positives FullCI    \", np.mean(sig_links['FullCI'][:,:,1:]\n",
+    "                                                [true_links[:,:,1:]==True]).round(3))\n",
+    "print (\"Mean false positives FullCI    \", np.mean(sig_links['FullCI'][:,:,1:]\n",
+    "                                                [true_links[:,:,1:]==False]).round(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tigramite_tutorial_prediction.ipynb b/tutorials/tigramite_tutorial_prediction.ipynb
new file mode 100644
index 00000000..f50f9e76
--- /dev/null
+++ b/tutorials/tigramite_tutorial_prediction.ipynb
@@ -0,0 +1,668 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal discovery with `TIGRAMITE`\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on the PCMCI method and create high-quality plots of the results.\n",
+    "\n",
+    "PCMCI is described here: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2\n",
+    "\n",
+    "This tutorial explains how to use PCMCI to obtain optimal predictors. See the following paper for theoretical background:\n",
+    "Runge, Jakob, Reik V. Donner, and Jürgen Kurths. 2015. “Optimal Model-Free Prediction from Multivariate Time Series.” Phys. Rev. E 91 (5): 052909. https://doi.org/10.1103/PhysRevE.91.052909."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline     \n",
+    "## use `%matplotlib notebook` for interactive figures\n",
+    "# plt.style.use('ggplot')\n",
+    "import sklearn\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite import data_processing as pp\n",
+    "from tigramite import plotting as tp\n",
+    "from tigramite.pcmci import PCMCI\n",
+    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
+    "from tigramite.models import LinearMediation, Prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prediction\n",
+    "\n",
+    "Tigramite also contains a class ``tigramite.models.Prediction`` to perform predictions based on the sklearn models. The ``Prediction`` class includes a wrapper around ``run_pc_stable`` from the ``PCMCI`` class to perform predictor selection. Consider the following data generation process: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "T = 150\n",
+    "links_coeffs = {0: [((0, -1), 0.6)],\n",
+    "                1: [((1, -1), 0.6), ((0, -1), 0.8)],\n",
+    "                2: [((2, -1), 0.5), ((1, -1), 0.7)],  # ((0, -1), c)],\n",
+    "                }\n",
+    "N = len(links_coeffs)\n",
+    "data, true_parents = pp.var_process(links_coeffs, T=T)\n",
+    "dataframe = pp.DataFrame(data, var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We initialize the ``Prediction`` class with ``cond_ind_model=ParCorr()``. Secondly, we choose ``sklearn.linear_model.LinearRegression()`` here for prediction. Last, we scale the data via ``data_transform``. The class takes care of rescaling the data for prediction. The parameters ``train_indices`` and ``test_indices`` are used to divide the data up into a training set and test set. The test set is optional since new data can be supplied later. The training set is used to select predictors and fit the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = Prediction(dataframe=dataframe,\n",
+    "        cond_ind_test=ParCorr(),   #CMIknn ParCorr\n",
+    "        prediction_model = sklearn.linear_model.LinearRegression(),\n",
+    "#         prediction_model = sklearn.gaussian_process.GaussianProcessRegressor(),\n",
+    "        # prediction_model = sklearn.neighbors.KNeighborsRegressor(),\n",
+    "    data_transform=sklearn.preprocessing.StandardScaler(),\n",
+    "    train_indices= range(int(0.8*T)),\n",
+    "    test_indices= range(int(0.8*T), T),\n",
+    "    verbosity=1\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we estimate causal predictors using ``get_predictors`` for the target variable 2 taking into account a maximum past lag of ``tau_max``. We use ``pc_alpha=None`` which optimizes the parameter based on the Akaike score. Note that the predictors are different for each prediction horizon. For example, at a prediction horizon of ``steps_ahead=1`` we get the causal parents from the model plus some others:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "selected_variables = [2]\n",
+      "independence test = par_corr\n",
+      "tau_min = 1\n",
+      "tau_max = 5\n",
+      "pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "Warning: Link specified in selected links that is outside the scope of the selected variables\n",
+      "\n",
+      "## Variable 2\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable 2 has 4 parent(s):\n",
+      "    [pc_alpha = 0.2]\n",
+      "        (2 -1): max_pval = 0.00000, min_val = 0.558\n",
+      "        (1 -1): max_pval = 0.00000, min_val = 0.467\n",
+      "        (0 -2): max_pval = 0.09687, min_val = 0.161\n",
+      "        (1 -2): max_pval = 0.11617, min_val = 0.152\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target = 2\n",
+    "tau_max = 5\n",
+    "predictors = pred.get_predictors(\n",
+    "                  selected_targets=[target],\n",
+    "                  steps_ahead=1,\n",
+    "                  tau_max=tau_max,\n",
+    "                  pc_alpha=None\n",
+    "                  )\n",
+    "link_matrix = np.zeros((N, N, tau_max+1), dtype='bool')\n",
+    "for j in [target]:\n",
+    "    for p in predictors[j]:\n",
+    "        link_matrix[p[0], j, abs(p[1])] = 1\n",
+    "\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=np.ones(link_matrix.shape),\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=None,\n",
+    "    link_colorbar_label='',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, that ``get_predictors`` is based only on the first step of PCMCI and skips the MCI step since correct false positive rates are not that relevant for prediction and the first step alone is faster. Now, we set ``steps_ahead=2`` and get different predictors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "selected_variables = [2]\n",
+      "independence test = par_corr\n",
+      "tau_min = 2\n",
+      "tau_max = 30\n",
+      "pc_alpha = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "Warning: Link specified in selected links that is outside the scope of the selected variables\n",
+      "\n",
+      "## Variable 2\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable 2 has 9 parent(s):\n",
+      "    [pc_alpha = 0.5]\n",
+      "        (1 -2): max_pval = 0.00000, min_val = 0.598\n",
+      "        (0 -2): max_pval = 0.00057, min_val = 0.432\n",
+      "        (2 -2): max_pval = 0.03584, min_val = 0.277\n",
+      "        (0 -12): max_pval = 0.33745, min_val = 0.133\n",
+      "        (1 -6): max_pval = 0.40552, min_val = 0.113\n",
+      "        (1 -12): max_pval = 0.39462, min_val = 0.112\n",
+      "        (1 -21): max_pval = 0.42749, min_val = 0.104\n",
+      "        (2 -21): max_pval = 0.48092, min_val = 0.097\n",
+      "        (0 -17): max_pval = 0.49660, min_val = 0.089\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tau_max = 30\n",
+    "steps_ahead = 2\n",
+    "target = 2\n",
+    "\n",
+    "all_predictors = pred.get_predictors(\n",
+    "                  selected_targets=[target],\n",
+    "                  steps_ahead=steps_ahead,\n",
+    "                  tau_max=tau_max,\n",
+    "                  pc_alpha=None\n",
+    "                  )\n",
+    "link_matrix = np.zeros((N, N, tau_max + 1), dtype='bool')\n",
+    "for j in [target]:\n",
+    "    for p in all_predictors[j]:\n",
+    "        link_matrix[p[0], j, abs(p[1])] = 1\n",
+    "\n",
+    "# Plot time series graph\n",
+    "tp.plot_time_series_graph(\n",
+    "    figsize=(6, 3),\n",
+    "    val_matrix=np.ones(link_matrix.shape),\n",
+    "    link_matrix=link_matrix,\n",
+    "    var_names=None,\n",
+    "    link_colorbar_label='',\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These predictors now efficiently avoid overfitting in the following model fit. Here one can specify whether multiple target variables should be fit at once (assuming that for all of these predictors have been estimated beforehand)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tigramite.models.Prediction at 0x7f184a2f30b8>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred.fit(target_predictors=all_predictors, \n",
+    "                selected_targets=[target],\n",
+    "                    tau_max=tau_max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to predict the target variable at the test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Predicting target 2\n",
+      "##\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted test data')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predicted = pred.predict(target)\n",
+    "true_data = pred.get_test_array()[0]\n",
+    "\n",
+    "plt.scatter(true_data, predicted)\n",
+    "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
+    "plt.plot(true_data, true_data, 'k-')\n",
+    "plt.xlabel('True test data')\n",
+    "plt.ylabel('Predicted test data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also predict other new data by supplying a new dataframe to ``new_data``. Because we have more samples here, the estimate of NRMSE is more reliable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Predicting target 2\n",
+      "##\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted test data')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_data = pp.DataFrame(pp.var_process(links_coeffs, T=200)[0])\n",
+    "predicted = pred.predict(target, new_data=new_data)\n",
+    "true_data = pred.get_test_array()[0]\n",
+    "\n",
+    "plt.scatter(true_data, predicted)\n",
+    "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
+    "plt.plot(true_data, true_data, 'k-')\n",
+    "plt.xlabel('True test data')\n",
+    "plt.ylabel('Predicted test data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This prediction is much better than using *all* past variables which leads to overfitting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Predicting target 2\n",
+      "##\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted test data')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "whole_predictors = {2:[(i, -tau) for i in range(3) for tau in range(1, tau_max+1)]}\n",
+    "pred.fit(target_predictors=whole_predictors, \n",
+    "                selected_targets=[target],\n",
+    "                    tau_max=tau_max)\n",
+    "\n",
+    "# new_data = pp.DataFrame(pp.var_process(links_coeffs, T=100)[0])\n",
+    "predicted = pred.predict(target, new_data=new_data)\n",
+    "# predicted = pred.predict(target)\n",
+    "true_data = pred.get_test_array()[0]\n",
+    "\n",
+    "plt.scatter(true_data, predicted)\n",
+    "plt.plot(true_data, true_data, 'k-')\n",
+    "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
+    "plt.xlabel('True test data')\n",
+    "plt.ylabel('Predicted test data')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before, we rescaled the data before fitting which requires us to also rescale the test data. We can also leave the data unscaled:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Predicting target 2\n",
+      "##\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted test data')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pred = Prediction(dataframe=dataframe,\n",
+    "        cond_ind_test=ParCorr(),\n",
+    "        prediction_model = sklearn.linear_model.LinearRegression(),\n",
+    "#     data_transform=sklearn.preprocessing.StandardScaler(),\n",
+    "    train_indices= range(int(0.8*T)),\n",
+    "    test_indices= range(int(0.8*T), T),\n",
+    "    verbosity=1\n",
+    "    )\n",
+    "pred.fit(target_predictors=all_predictors, \n",
+    "                selected_targets=[target],\n",
+    "                    tau_max=tau_max)\n",
+    "predicted = pred.predict(target)\n",
+    "# predicted = pred.predict(target)\n",
+    "true_data = pred.get_test_array()[0]\n",
+    "\n",
+    "plt.scatter(true_data, predicted)\n",
+    "plt.plot(true_data, true_data, 'k-')\n",
+    "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
+    "plt.xlabel('True test data')\n",
+    "plt.ylabel('Predicted test data')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the different scales on the x- and y-axes.\n",
+    "\n",
+    "Last, let's try out a Gaussian process regressor in conjunction with a GPDC predictor selection. Here we supply ``cond_ind_params`` and ``prediction_model_params`` because the sklearn defaults don't work well here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## Running Tigramite PC algorithm\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "selected_variables = [2]\n",
+      "independence test = gp_dc\n",
+      "tau_min = 2\n",
+      "tau_max = 10\n",
+      "pc_alpha = [0.2]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "Warning: Link specified in selected links that is outside the scope of the selected variables\n",
+      "\n",
+      "## Variable 2\n",
+      "Null distribution for GPDC not available for deg. of freed. = 380.\n",
+      "\n",
+      "## Resulting condition sets:\n",
+      "\n",
+      "    Variable 2 has 4 parent(s):\n",
+      "        (1 -2): max_pval = 0.00000, min_val = 0.266\n",
+      "        (0 -2): max_pval = 0.00000, min_val = 0.112\n",
+      "        (2 -2): max_pval = 0.00000, min_val = 0.050\n",
+      "        (0 -4): max_pval = 0.13600, min_val = 0.010\n",
+      "\n",
+      "##\n",
+      "## Predicting target 2\n",
+      "##\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted test data')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tau_max = 10\n",
+    "steps_ahead = 2\n",
+    "target = 2\n",
+    "T = 500\n",
+    "dataframe = pp.DataFrame(pp.var_process(links_coeffs, T=T)[0])\n",
+    "pred = Prediction(dataframe=dataframe,\n",
+    "        cond_ind_test=GPDC(),   #CMIknn ParCorr\n",
+    "        prediction_model = sklearn.gaussian_process.GaussianProcessRegressor(alpha=0., kernel=sklearn.gaussian_process.kernels.RBF() +\n",
+    "                                        sklearn.gaussian_process.kernels.WhiteKernel()),\n",
+    "        # prediction_model = sklearn.neighbors.KNeighborsRegressor(),\n",
+    "    data_transform=sklearn.preprocessing.StandardScaler(),\n",
+    "    train_indices= range(int(0.8*T)),\n",
+    "    test_indices= range(int(0.8*T), T),\n",
+    "    verbosity=1\n",
+    "    )\n",
+    "\n",
+    "all_predictors = pred.get_predictors(\n",
+    "                  selected_targets=[target],\n",
+    "                  steps_ahead=steps_ahead,\n",
+    "                  tau_max=tau_max,\n",
+    "                  pc_alpha=0.2\n",
+    "                  )\n",
+    "pred.fit(target_predictors=all_predictors, \n",
+    "                selected_targets=[target],\n",
+    "                    tau_max=tau_max)\n",
+    "predicted = pred.predict(target)\n",
+    "# predicted = pred.predict(target)\n",
+    "true_data = pred.get_test_array()[0]\n",
+    "\n",
+    "plt.scatter(true_data, predicted)\n",
+    "plt.plot(true_data, true_data, 'k-')\n",
+    "plt.title(r\"NRMSE = %.2f\" % (np.abs(true_data - predicted).mean()/true_data.std()))\n",
+    "plt.xlabel('True test data')\n",
+    "plt.ylabel('Predicted test data')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}