From 63a48ca9fd6d6fdc716aa0715afc7cdd735b4db8 Mon Sep 17 00:00:00 2001
From: jbdurand <Jean-Baptiste.Durand@cirad.fr>
Date: Tue, 9 Jan 2024 13:53:03 +0100
Subject: [PATCH] Adding tutorial

---
 sequence_analysis/tutorials/Code/__init__.py  |    0
 sequence_analysis/tutorials/Code/amlseq2R.py  |  126 +
 .../tutorials/Code/matrix_plot.py             |   65 +
 .../tutorials/Code/python_dics2R.py           |  123 +
 sequence_analysis/tutorials/Utils/__init__.py |    2 +
 sequence_analysis/tutorials/Utils/dos2unix.py |   39 +
 sequence_analysis/tutorials/Utils/unix2dos.py |   40 +
 .../tutorials/seq1v_5s_LR_init.hsmc           |  119 +
 sequence_analysis/tutorials/sequences.ipynb   | 8217 +++++++++++++++++
 sequence_analysis/tutorials/sim_v_5s_LR.hsmc  |  172 +
 10 files changed, 8903 insertions(+)
 create mode 100755 sequence_analysis/tutorials/Code/__init__.py
 create mode 100755 sequence_analysis/tutorials/Code/amlseq2R.py
 create mode 100755 sequence_analysis/tutorials/Code/matrix_plot.py
 create mode 100755 sequence_analysis/tutorials/Code/python_dics2R.py
 create mode 100755 sequence_analysis/tutorials/Utils/__init__.py
 create mode 100755 sequence_analysis/tutorials/Utils/dos2unix.py
 create mode 100755 sequence_analysis/tutorials/Utils/unix2dos.py
 create mode 100755 sequence_analysis/tutorials/seq1v_5s_LR_init.hsmc
 create mode 100755 sequence_analysis/tutorials/sequences.ipynb
 create mode 100755 sequence_analysis/tutorials/sim_v_5s_LR.hsmc

diff --git a/sequence_analysis/tutorials/Code/__init__.py b/sequence_analysis/tutorials/Code/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/sequence_analysis/tutorials/Code/amlseq2R.py b/sequence_analysis/tutorials/Code/amlseq2R.py
new file mode 100755
index 00000000..86139ac8
--- /dev/null
+++ b/sequence_analysis/tutorials/Code/amlseq2R.py
@@ -0,0 +1,126 @@
+# Read .seq file and transform to an R file.
+import os
+
+def Setup(chdir=False, virtual=True):
+    """
+    Default setup and paths to data
+    """
+
+    print(os.getcwd())
+
+    if not(virtual):
+        home_dir = "D:\\"
+    else:
+        home_dir = "/media/sf_transfert"
+    base_path = os.path.join(home_dir, "devlp_shared", "AppleCultivarsTreatments")
+    data_path = base_path + os.sep + "Data"
+    
+    if chdir:
+        os.chdir(base_path)
+
+    output_file = base_path + os.sep + "Data" + os.sep + "multiseq_N_APPLE_R.csv"
+    
+    return base_path, data_path, output_file
+
+def ReadSequence(data_path="", data_file="multiseq_N_APPLE_unix.seq"):
+    """
+    Read sequence from file and return Sequences object + python list
+    """
+    if data_path == "":
+        base_path, data_path, output_file = Setup(chdir=False)
+    
+    # Python sequence 
+    from openalea.sequence_analysis import sequences
+    pyseq1 = sequences.Sequences(data_path + os.sep + data_file)
+    pylist = []
+    dim_seq = len(pyseq1[0][0])
+
+    for i in range(len(pyseq1)):
+        seq_i = []
+        for l in range(len(pyseq1[i])):
+            seq_i += [[pyseq1[i][l][d] for d in range(dim_seq)]]
+        pylist += [seq_i]
+        
+    return pyseq1, pylist
+
+def DefaultHeader():
+    """
+    Return default header for .csv
+    """
+    h = "######################################################################### \n"
+    h += "# \n"
+    h += "#  N-APPLE shoot \n"
+    h += "# \n"
+    h += "#  VARIABLE 1: treatment code (1: control, 2: treatment with 20 g N/tree dose, 3: treatment with 30 g N/h tree dose), \n"
+    h += "#  VARIABLE 2: cultivar (1: Rubinola, 2: Topaz, 3: Golden Delicious), \n"
+    h += "#  VARIABLE 3: axillary shoot type (0: no shoot, 1: short shoot, 2: medium shoot, 3: long shoot, 4: sylleptic shoot), \n"
+    h += "#  VARIABLE 4: lateral flowering (0: no lateral flowering, 1: LF in median zone, 2: LF in distal zone, 3: LF in median and distal zone = Long LF zone), \n"
+    h += "#  VARIABLE 5: terminal flowering (0: no flower cluster, 1: presence of a flower cluster). \n"
+    h += "# \n"
+    h += "#  Data : Martin Meszaros, Evelyne Costes, Jean-Baptiste Durand \n"
+    h += "# \n"
+    h += '#  read with read.csv(multiseq_N_APPLE_R.csv, header=TRUE, row.names=1, comment.char="#") \n'
+    h += "# \n"
+    h += "######################################################################### \n"
+    h += '"", treatment code, '
+    h += "cultivar, "
+    h += "axillary shoot type, " 
+    h += "lateral flowering, "
+    h += "terminal flowering, "
+    h += "sequence N. \n"    
+    return h
+    
+def RestoredStatesHeader():
+    """
+    Return header for restored states
+    """
+    h = "######################################################################### \n"
+    h += "# \n"
+    h += "#  N-APPLE shoot \n"
+    h += "# \n"
+    h += "#  VARIABLE 1: restored state. \n"
+    h += "#  VARIABLE 2: axillary shoot type (0: no shoot, 1: short shoot, 2: medium shoot, 3: long shoot, 4: sylleptic shoot), \n"
+    h += "#  VARIABLE 3: lateral flowering (0: no lateral flowering, 1: LF in median zone, 2: LF in distal zone, 3: LF in median and distal zone = Long LF zone), \n"
+    h += "#  VARIABLE 4: terminal flowering (0: no flower cluster, 1: presence of a flower cluster). \n"
+    h += "# \n"
+    h += "#  Data : Martin Meszaros, Evelyne Costes, Jean-Baptiste Durand \n"
+    h += "# \n"
+    h += '#  read with read.csv(multiseq_N_APPLE_R.csv, header=TRUE, row.names=1, comment.char="#") \n'
+    h += "# \n"
+    h += "######################################################################### \n"
+    h += '"", restored state, '
+    h += "axillary shoot type, " 
+    h += "lateral flowering, "
+    h += "terminal flowering, "
+    h += "sequence N. \n"    
+    return h
+    
+def WriteRSequence(pyseq1, output_file="", header=None):
+    """
+    Write sequence in a .txt file, one line per sequence index
+    """
+    if output_file == "":
+        base_path, data_path, output_file = Setup(chdir=False)
+
+    f = open(output_file, "w")
+    
+    if header is None:
+        h = DefaultHeader()
+    else:
+        h = str(header)
+    f.write(h)
+    dim_seq = len(pyseq1[0][0])
+    l = 1 # line
+    for i in range(len(pyseq1)):
+        # i: sequence
+        for p in range(len(pyseq1[i])):
+            # p: position
+            f.write('"' + str(l) + '", ')
+            for v in range(dim_seq):
+                # write variables
+                f.write(str(pyseq1[i][p][v]) + ", ")
+            # write sequence id
+            f.write(str(i) + "\n")
+            l += 1
+        
+    f.close()
diff --git a/sequence_analysis/tutorials/Code/matrix_plot.py b/sequence_analysis/tutorials/Code/matrix_plot.py
new file mode 100755
index 00000000..bc0c121e
--- /dev/null
+++ b/sequence_analysis/tutorials/Code/matrix_plot.py
@@ -0,0 +1,65 @@
+# Plot transition matrices
+# Use with conda environment bnp-mrf
+
+import matplotlib.pyplot as plt
+import networkx, os
+import numpy as np
+
+import os
+import numpy as np
+
+print(os.getcwd())
+
+virtual = True
+nb_states = 5
+
+if not(virtual):
+    home_dir = "D:\\"
+else:
+    home_dir = "/mnt/transfert"
+base_path = os.path.join(home_dir, "devlp_shared", "AppleCultivarsTreatments")
+data_path = base_path + os.sep + "Data"
+os.chdir(base_path)
+
+
+matrix_file = base_path + os.sep + "Models" + os.sep + "seq1v_" + \
+    str(nb_states) + "s_L.mat"
+
+TM = np.fromfile(matrix_file)
+TM = TM.reshape(nb_states, nb_states)
+
+G = networkx.from_numpy_matrix(TM, create_using=networkx.DiGraph)
+blues = cm = plt.get_cmap('Blues')
+
+f = plt.figure(1, figsize=(9, 9))
+ax = f.add_subplot(1,1,1)
+
+#networkx.draw_circular(G, node_size=150, node_color='#1f78b4',
+                       #with_labels=True, arrows=True, 
+                       #arrow_size=140,
+                       #width=4,
+                       #edge_cmap = plt.cm.Blues)
+
+# G = networkx.star_graph(81)
+initial_nodes = np.array(list(G.nodes()))
+final_nodes = initial_nodes + 1
+
+networkx.relabel_nodes(G, dict(zip(initial_nodes, final_nodes)), copy=False)
+
+colors = [G.get_edge_data(a, b)['weight'] for (a,b) in G.edges()]    
+
+# networkx.draw_circular(G, edge_color = range(81), edge_cmap = plt.cm.Blues)
+
+options = {
+    "node_color": "#A0CBE2",
+    "edge_color": colors,
+    "width": 3,
+    "edge_cmap": plt.cm.Blues,
+    "with_labels": True,
+    "node_size": 150, 
+    "with_labels": True,
+}
+
+                       
+networkx.draw_circular(G, **options) 
+plt.show() 
diff --git a/sequence_analysis/tutorials/Code/python_dics2R.py b/sequence_analysis/tutorials/Code/python_dics2R.py
new file mode 100755
index 00000000..dab48f11
--- /dev/null
+++ b/sequence_analysis/tutorials/Code/python_dics2R.py
@@ -0,0 +1,123 @@
+# Export python dictionaries of sufficients HMSC statistics for 
+# GLMMs to an R file.
+
+import os
+
+
+def ComputeGLMStatistics(pyseq1, seg_pyseq):
+    """
+    Compute statistics for GLM(M) estimation
+    """
+    dwell = {} # indexed by state, cultivar, tree
+    transition = {} # indexed by past_state, treatment, cultivar, tree
+    observation = {} # indexed by state, treatment, cultivar, tree
+    for i in range(len(pyseq1)):
+        d = 0
+        s_past = -1 # past state
+
+        for l in range(len(pyseq1[i])):
+            va = pyseq1[i][l]
+            t = va[0] # treatment
+            c = va[1] # cultivar
+            v = seg_pyseq[i][l]
+            s = v[0]
+            if s != s_past:
+                if s_past != -1:
+                    if dwell.has_key((s_past, t, c, i+1)):
+                        dwell[s_past, t, c, i+1] += [[d, 1]]
+                        transition[s_past, t, c, i+1] += [s]
+                    else:
+                        dwell[s_past, t, c, i+1] = [[d, 1]]
+                        transition[s_past, t, c, i+1] = [s]
+                d = 1 # dwell time 
+            else:
+                d += 1
+            s_past = s
+            if observation.has_key((s, t, c, i+1)):
+                observation[s, t, c, i+1] += [v[1:]]
+            else:
+                observation[s, t, c, i+1] = [v[1:]]
+        if dwell.has_key((s_past, t, c, i+1)):
+            dwell[s_past, t, c, i+1] += [[d, 0]]
+        else:
+            dwell[s_past, t, c, i+1] = [[d, 0]]
+
+    return dwell, transition, observation
+
+def GLMStatisticsHeader():
+
+    """
+    Return header for GLM statistics
+    """
+    h = "######################################################################### \n"
+    h += "# \n"
+    h += "#  Statistics for GLM(M) estimation issued from HSMC restoration\n"
+    h += "# \n"
+    h += '#  VARIABLE 1: type of statistics ("d"well time, "t"ransition, "o"bservation). \n'
+    h += '#  VARIABLE 2: restored state (current state for "d" and "o", previous state for "t"). \n'
+    h += "#  VARIABLE 3: treatment. \n"
+    h += "#  VARIABLE 4: cultivar. \n"
+    h += "#  VARIABLE 5: value of statistics. \n"
+    h += '#  VARIABLE 6: for "d", 0 if last dwell time (censored), 1 otherwise; for "t", NA \n'
+    h += '#              for "o", id of variable. \n'
+    h += "# \n"
+    h += '#  read with read.csv(file_name.csv, header=TRUE, row.names=1, comment.char="#") \n'
+    h += "# \n"
+    h += "######################################################################### \n"
+    h += '"", type, '
+    h += "restored state, " 
+    h += "treatment, "
+    h += "cultivar, "
+    h += "sequence N., "
+    h += "value, "
+    h += "info \n"
+    return h
+    
+    
+def WriteGLMStatistics(dwell, transition, observation, output_file):
+    """
+    Write statistics for GLM(M) estimation
+    """
+    f = open(output_file, "w")
+    f.write(GLMStatisticsHeader())
+    l = 1 # line 
+    for (k, v) in dwell.items():
+        f.write(str(l) + ", ")
+        f.write("d, ")
+        for o in k:
+            f.write(str(o) + ", ")
+        s = ""
+        for o in v:
+            for i in o:
+                s += str(i) + ", "
+        s = s[0:-2] + "\n"
+        l += 1
+        f.write(s)
+
+    for (k, v) in transition.items():
+        f.write(str(l) + ", ")
+        f.write("t, ")
+        for o in k:
+            f.write(str(o) + ", ")
+        s = ""
+        for o in v:
+            s += str(o) + ", "
+        s = s[0:-2] + ", NA \n"
+        l += 1
+        f.write(s)
+
+    for (k, v) in observation.items():
+        # common part of all elements in current 
+        # list of objects and associated string
+        s_base = "o, " 
+        for o in k:
+            s_base += str(o) + ", "
+        for o in v:
+            for i in range(len(o)):
+                # Variables
+                s = s_base + str(o[i]) + ", " + str(i+1) + "\n"
+                f.write(str(l) + ", " + s)
+                l += 1
+
+    f.close()
+
diff --git a/sequence_analysis/tutorials/Utils/__init__.py b/sequence_analysis/tutorials/Utils/__init__.py
new file mode 100755
index 00000000..f4504522
--- /dev/null
+++ b/sequence_analysis/tutorials/Utils/__init__.py
@@ -0,0 +1,2 @@
+from unix2dos import unix2dos
+from dos2unix import dos2unix
diff --git a/sequence_analysis/tutorials/Utils/dos2unix.py b/sequence_analysis/tutorials/Utils/dos2unix.py
new file mode 100755
index 00000000..732da430
--- /dev/null
+++ b/sequence_analysis/tutorials/Utils/dos2unix.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+"""\
+convert dos linefeeds (crlf) to unix (lf)
+usage: dos2unix.py <input> <output>
+"""
+
+"""
+__version__ = "1"  # version is needed for packaging
+
+import sys
+
+if len(sys.argv[1:]) != 2:
+  sys.exit(__doc__)
+
+content = ''
+outsize = 0
+with open(sys.argv[1], 'rb') as infile:
+  content = infile.read()
+with open(sys.argv[2], 'wb') as output:
+  for line in content.splitlines():
+    outsize += len(line) + 1
+    output.write(line + b'\n')
+
+print("Done. Stripped %s bytes." % (len(content)-outsize))
+"""
+
+def dos2unix(infile, output):
+    import sys
+
+    content = ''
+    outsize = 0
+    ifile = open(infile)
+    content = ifile.read()
+    ofile = open(output, 'wb')
+    for line in content.splitlines():
+        outsize += len(line) + 1
+        ofile.write(line + b'\n')
+
+    print("Done. Stripped %s bytes." % (len(content)-outsize))
\ No newline at end of file
diff --git a/sequence_analysis/tutorials/Utils/unix2dos.py b/sequence_analysis/tutorials/Utils/unix2dos.py
new file mode 100755
index 00000000..3da21705
--- /dev/null
+++ b/sequence_analysis/tutorials/Utils/unix2dos.py
@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+"""\
+convert unix (lf) to dos linefeeds (crlf)
+usage: unix2dos.py <input> <output>
+"""
+
+"""
+__version__ = "1"  # version is needed for packaging
+
+import sys
+
+if len(sys.argv[1:]) != 2:
+  sys.exit(__doc__)
+
+content = ''
+outsize = 0
+with open(sys.argv[1], 'rb') as infile:
+  content = infile.read()
+with open(sys.argv[2], 'wb') as output:
+  for line in content.splitlines():
+    outsize += len(line) - 1
+    if len(line) > 0 and line[-1:] == "\n"]:
+        output.write(line[0:-1])
+
+print("Done. Stripped %s bytes." % (len(content)-outsize))
+"""
+
+def unix2dos(infile, output):
+    import sys
+
+    content = ''
+    outsize = 0
+    ifile = open(infile)
+    content = ifile.read()
+    ofile = open(output, 'wb')
+    for line in content.splitlines():
+        outsize += len(line) - 1
+        if len(line) > 0 and line[-1:] == "\n":
+            ofile.write(line[0:-1])
+    print("Done. Stripped %s bytes." % (len(content)-outsize))    
\ No newline at end of file
diff --git a/sequence_analysis/tutorials/seq1v_5s_LR_init.hsmc b/sequence_analysis/tutorials/seq1v_5s_LR_init.hsmc
new file mode 100755
index 00000000..b06df421
--- /dev/null
+++ b/sequence_analysis/tutorials/seq1v_5s_LR_init.hsmc
@@ -0,0 +1,119 @@
+HIDDEN_SEMI-MARKOV_CHAIN
+
+5 STATES
+
+INITIAL_PROBABILITIES
+0.2        0.2        0.2        0.2        0.2        
+
+TRANSITION_PROBABILITIES
+0          0.25       0.25       0.25       0.25
+0          0          0.33       0.33       0.34
+0          0          0          0.5        0.5
+0          0          0          0          1          
+0          0          0          0          1          
+
+
+STATE 0 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.0   PROBABILITY : 0.005
+
+STATE 1 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.0   PROBABILITY : 0.005
+
+STATE 2 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.0   PROBABILITY : 0.005
+
+STATE 3 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.0   PROBABILITY : 0.005
+
+3 OUTPUT_PROCESSES
+
+OUTPUT_PROCESS 1 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.4
+OUTPUT 1 : 0.1
+OUTPUT 2 : 0.1
+OUTPUT 3 : 0.1
+OUTPUT 4 : 0.3
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.1
+OUTPUT 1 : 0.6
+OUTPUT 2 : 0.1
+OUTPUT 3 : 0.1
+OUTPUT 4 : 0.1
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.6
+OUTPUT 1 : 0.1
+OUTPUT 2 : 0.1
+OUTPUT 3 : 0.1
+OUTPUT 4 : 0.1
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.23
+OUTPUT 1 : 0.23
+OUTPUT 2 : 0.23
+OUTPUT 3 : 0.23
+OUTPUT 4 : 0.08
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.1
+OUTPUT 1 : 0.1
+OUTPUT 2 : 0.1
+OUTPUT 3 : 0.6
+OUTPUT 4 : 0.1
+
+OUTPUT_PROCESS 2 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.25
+OUTPUT 1 : 0.25
+OUTPUT 2 : 0.25
+OUTPUT 3 : 0.25
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.25
+OUTPUT 1 : 0.25
+OUTPUT 2 : 0.25
+OUTPUT 3 : 0.25
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.25
+OUTPUT 1 : 0.25
+OUTPUT 2 : 0.25
+OUTPUT 3 : 0.25
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.25
+OUTPUT 1 : 0.25
+OUTPUT 2 : 0.25
+OUTPUT 3 : 0.25
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.25
+OUTPUT 1 : 0.25
+OUTPUT 2 : 0.25
+OUTPUT 3 : 0.25
+
+OUTPUT_PROCESS 3 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.5
+OUTPUT 1 : 0.5
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.5
+OUTPUT 1 : 0.5
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.5
+OUTPUT 1 : 0.5
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.5
+OUTPUT 1 : 0.5
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.5
+OUTPUT 1 : 0.5
diff --git a/sequence_analysis/tutorials/sequences.ipynb b/sequence_analysis/tutorials/sequences.ipynb
new file mode 100755
index 00000000..f363529a
--- /dev/null
+++ b/sequence_analysis/tutorials/sequences.ipynb
@@ -0,0 +1,8217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!/usr/bin/python2.7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HSMC modelling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "### It is assumed that this notebook is run from singularity, mounting the directory containing tutorials as /scratch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seqint import Model\n",
+    "import numpy as np\n",
+    "from openalea.sequence_analysis import Estimate\n",
+    "from openalea.sequence_analysis import Plot\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "# import random\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Check pandas version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print pd.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.2.0'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xlrd\n",
+    "xlrd.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enabling R extensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#####  Place variables in markdown outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Markdown\n",
+    "from IPython.core.magic import register_cell_magic\n",
+    "\n",
+    "\n",
+    "@register_cell_magic\n",
+    "def markdown(line, cell):\n",
+    "    return Markdown(cell.format(**globals()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Determining if images have to be saved (not used for the moment)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading and preparing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append(\"/scratch\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from Utils import *\n",
+    "from Code.amlseq2R import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/scratch\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "home_dir = \"/scratch\"\n",
+    "base_path = home_dir\n",
+    "ressource_path = base_path\n",
+    "os.chdir(base_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['seq1v_5s_LR_init.hsmc',\n",
+       " 'tmp_dir',\n",
+       " 'sim_v_5s_LR.hsmc',\n",
+       " 'Code',\n",
+       " 'Results',\n",
+       " 'Utils',\n",
+       " 'sequences.ipynb',\n",
+       " '.ipynb_checkpoints']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir(ressource_path + os.sep )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Read an existing HSMC model**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openalea.sequence_analysis import HiddenSemiMarkov\n",
+    "\n",
+    "model_file = \"sim_v_5s_LR.hsmc\"\n",
+    "#model_file = \"init_v_5s_LR.hsmc\"\n",
+    "hmsc = HiddenSemiMarkov(ressource_path + os.sep + model_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Simulate sequences**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openalea.sequence_analysis import Simulate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb_seq = 10\n",
+    "seq_length = 100\n",
+    "seq = hmsc.simulation_nb_sequences(nb_seq, seq_length, True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[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], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 1], [1, 0, 0, 1], [1, 1, 0, 0], [1, 4, 0, 0], [1, 2, 3, 1], [1, 2, 0, 0], [1, 2, 0, 1], [1, 1, 0, 0], [1, 1, 0, 1], [1, 1, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 1], [1, 0, 0, 1], [1, 1, 0, 1], [1, 1, 0, 0], [1, 3, 0, 0], [1, 1, 0, 1], [1, 2, 0, 0], [1, 1, 0, 0], [1, 0, 0, 1], [2, 0, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 2, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 0, 0, 0], [2, 0, 0, 0], [2, 3, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 1], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0], [2, 0, 0, 1], [2, 1, 0, 0], [2, 0, 0, 0], [2, 1, 0, 0], [3, 2, 0, 0], [3, 1, 0, 0], [3, 2, 0, 1], [3, 2, 0, 0], [3, 2, 0, 0], [3, 1, 0, 0], [3, 0, 0, 0], [3, 2, 0, 0], [3, 2, 0, 1], [3, 1, 0, 0], [3, 0, 0, 0], [3, 1, 0, 0], [3, 2, 0, 0], [3, 2, 0, 0], [3, 0, 0, 1], [3, 2, 0, 0], [3, 2, 0, 0], [3, 2, 0, 0], [3, 0, 0, 0], [3, 3, 0, 0], [3, 0, 0, 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print first simulated sequence.\n",
+    "# First variable is the simulated state\n",
+    "print(seq[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Perform operations on sequences**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Discard variable 1 (state)\n",
+    "obs = seq.select_variable([1], keep=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting marginal probabilities of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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C3/72N/zsZz+zdVMYhmEYK8G2d4ZhGIZhGAvBihXDMAzDMIyFYFcgwzAMwzCMhWCLFcMwDMMwjIVwslXF4vpvDMP0Hq5du9YqUWlPhMcvhul9mDt+2UyxCgoKQl5enq2qZxjGBkRGRtq6CRaBxy+G6X2YO36xK5BhGIZhGMZCsGLFMAzDMAxjIVixYhiGYRiGsRA2i7Fiei9qtRrXr19HfX29rZvCWAkXFxcMHToUzs7Otm5Kl8Fy3TvojbLNtA9WrJgu5/r16/D09ERQUJDJFeaZngkRoby8HNevX8fw4cNt3Zwug+Xa/umtss20D3YFMl1OfX09fHx8+OFjp8hkMvj4+PQ6yw3Ltf3TW2WbaR+sWDE2gR8+9k1vvb+9td+9Cb7HTFuwYsUwDMMwDGMhWLFimJ+4du0aQkNDbd2MVkyfPt0iySgtVQ4AvPbaaxg5ciSCg4Pxr3/9yyJlMtaB5dp8WK4ZS8CKFdPt2f/VfgRtDYLDOgcEbQ3C/q/227pJZqPRaGzdBItTWFiIjIwMFBQU4Pjx4/jNb36DpqYmWzerx8Fy3b1guWYsBStWTLdm/1f7sezoMhRXFoNAKK4sxrKjyzr9EEpLS0NoaChCQ0OxdetWab9Go0FiYiLkcjni4uJQW1sLAHjhhRegUCigVCrx3HPPAQDu3LmDefPmISoqClFRUfj8888BACkpKVi8eDGmTJmCxYsXY9KkSSgoKJDqEN+wa2pqsHTpUkyYMAFhYWE4cuQIAKCurg4JCQmQy+WYO3cu6urqWrX/+PHjmD9/vrR9+vRpxMTEAABWrFiByMhIhISEIDk52WD/PTw8pM+ZmZlISkoy2Sddjhw5goSEBPTt2xfDhw/HyJEjce7cuba/dEaC5ZrlmrFjyEZERETYqmrGxhQWFpp9buDrgYQUtPoLfD2ww/Xn5eVRaGgoqVQqqq6uJoVCQefPn6eioiICQDk5OUREtGTJEtq0aRPdvXuXRo8eTVqtloiIKioqiIhowYIFlJ2dTURExcXFNGbMGCIiSk5OpvDwcKqtrSUiorS0NFq7di0REd24cYNGjx5NREQvvvgi7du3Typz1KhRpFKpaMuWLbRkyRIiIsrPzydHR0fKzc3V64NaraaAgABSqVRERLR8+XKprPLyciIi0mg0FB0dTfn5+UREFB0dLZXj7u4ulXXw4EF64oknTPZJl5UrV0p1EREtXbqUDh482Oo8Q/fZXn73hvrBct075JqoffeasR/MHb84jxVjc2Tr2j/Lpriy2OR1lExGj+Xk5GDu3Llwd3cHADz22GPIzs5GbGwsAgICMGXKFADAokWLsG3bNjz77LNwcXHBk08+iZiYGOkN+uTJkygsLJTKraqqgkqlAgDExsbC1dUVABAfH4+ZM2di3bp1OHDgAOLi4gAAJ06cwIcffojNmzcDEKbrl5SU4LPPPsMzzzwDAFAqlVAqla364OTkhNmzZ+Po0aOIi4vDsWPHsHHjRgDAgQMHsHv3bmg0Gty8eROFhYUGyzCEsT7pWgIY82C5ZrlmeiesWDE2x9TDImhrEIori1vtD/QKxLVnr1m8LS2nUstkMjg5OeHcuXM4deoUMjMzsWPHDnzyySfQarU4e/YsXFxcWpUjPtwAwN/fHz4+Prh06RLee+897Nq1C4CQbPD9999HcHBwh9qakJCAHTt2YMCAAYiMjISnpyeKioqwefNm5ObmwtvbG0lJSQZz7uj2U/e4qT7p9qe0tFTavn79Ovz9/TvUB3uG5ZrlmumdcIwV063Z8PAGuDm76e1zc3bDhoc3dLjMadOm4fDhw6itrUVNTQ0OHTqEadOmAQBKSkpw5swZAEB6ejqmTp0KlUqFyspKzJkzB6+//jry8/MBADNnzsT27dulci9evGi0zscffxwbN25EZWWl9JY9a9YsbN++HUTCA/jChQsAgAcffBDp6ekAgMuXL+PSpUsGy4yOjsb58+exZ88eJCQkABDexN3d3eHl5YVbt24hKyvL4LWDBg3ClStXoNVqcejQIWm/OX2KjY1FRkYGGhoaUFRUhKtXr2LChAlG+860huWa5ZqxX1ixYro1iWMTsfuXuxHoFQgZZAj0CsTuX+5G4tjEDpcZHh6OpKQkTJgwARMnTsRTTz2FsLAwAEBwcDB27twJuVyOiooKrFixAtXV1YiJiYFSqcTUqVORlpYGANi2bRvy8vKgVCqhUCikN3ZDxMXFISMjA/Hx8dK+NWvWQK1WQ6lUIiQkBGvWrAEgBOmqVCrI5XKsXbsWERERBst0dHRETEwMsrKyJDfOuHHjEBYWhjFjxmDhwoWS+6clqampiImJweTJkzFkyBBpvzl9CgkJQXx8PBQKBWbPno2dO3fC0dHR1FfOtIDlmuWasV9kJL5WdDGRkZEWyz3C9CyuXLkCuVxu62YwVsbQfbaX372hfrBc9x74XvdOzB2/2GLFMAzDMAxjIdpUrJYuXYr77rvPaOZeIsIzzzyDkSNHQqlU4vz58xZvJMMwTEfhMYxhmK6kTcUqKSkJx48fN3o8KysLV69exdWrV7F7926sWLHCog1kGIbpDDyGMQzTlbSpWD344IMYMGCA0eNHjhzB//zP/0Amk2HSpEm4d+8ebt68adFGMgzDdBQewxiG6Uo6HWNVVlaGgIAAaXvo0KEoKyszeO7u3bsRGRmJyMhI3Llzp7NVMwzDdBpzxzAevxiGMYcuDV5ftmwZ8vLykJeXh4EDB3Zl1QzDMJ2Cxy+GYcyh04oVZ6tl7IVr164ZDXC2JeLitt2lnPLycsyYMQMeHh5YtWpVp8uzNfY+hrFcm4e9yTVjOzqtWMXGxuKdd94BEeHs2bPw8vLSS8zGMBbh5k0gOhr44Qdbt6RdaDQaWzfB4ri4uGD9+vXSWnA9HZuOYSzX3QZ7k2vGdrSpWC1YsAAPPPAAvvnmGwwdOhR///vfsWvXLilz7Zw5czBixAiMHDkSTz/9NN544w2rN5rphaxfD+TkCP8tQFpaGkJDQxEaGoqtW7dK+zUaDRITEyGXyxEXF4fa2loAwAsvvACFQgGlUonnnnsOAHDnzh3MmzcPUVFRiIqKwueffw4ASElJweLFizFlyhQsXrwYkyZNQkFBgVSH+IZdU1ODpUuXYsKECQgLC8ORI0cAAHV1dUhISIBcLsfcuXNRV1fXqv3Hjx/H/Pnzpe3Tp09LWapXrFiByMhIhISEIDk52WD/dRefzczMRFJSksk+6eLu7o6pU6eaXHetO9GtxzCWaz1Yrhm7gGxERESErapmbExhYWH7Lrhxg8jFhQggcnUlunmzU/Xn5eVRaGgoqVQqqq6uJoVCQefPn6eioiICQDk5OUREtGTJEtq0aRPdvXuXRo8eTVqtloiIKioqiIhowYIFlJ2dTURExcXFNGbMGCIiSk5OpvDwcKqtrSUiorS0NFq7du1PXblBo0ePJiKiF198kfbt2yeVOWrUKFKpVLRlyxZasmQJERHl5+eTo6Mj5ebm6vVBrVZTQEAAqVQqIiJavny5VFZ5eTkREWk0GoqOjqb8/HwiIoqOjpbKcXd3l8o6ePAgPfHEEyb7ZIi9e/fSypUrjR43dJ/t5XdvqB8s171Drok6cK8Zu8Dc8YszrzO2RyYz/efnB4gr1dfVAUOGtH2NCXJycjB37ly4u7vDw8MDjz32GLKzswEAAQEB0jpkixYtQk5ODry8vODi4oInn3wSH3zwAdzchMVzT548iVWrVmH8+PGIjY1FVVUVVCoVAMG95OrqCgCIj49HZmYmAODAgQOIi4sDAJw4cQKpqakYP348pk+fjvr6epSUlOCzzz7DokWLAABKpVJa3FYXJycnzJ49G0ePHoVGo8GxY8fw6KOPSnWEh4cjLCwMBQUFKCwsNPtWmOoT005YrlmumV6Jk60bwDAwtVzlzZvAiBHNDyAAcHUF/vtfYPBgizdF1uLhJZPJ4OTkhHPnzuHUqVPIzMzEjh078Mknn0Cr1eLs2bMGXQfu7u7SZ39/f/j4+ODSpUt47733JBcUEeH9999HcHBwh9qakJCAHTt2YMCAAYiMjISnpyeKioqwefNm5ObmwtvbG0lJSajX/e4M9FP3uKk+Me2E5bpDbWW5Zno6bLFiujfr1wNarf6+pqZOxaRMmzYNhw8fRm1tLWpqanDo0CFMmzYNAFBSUoIzZ84AANLT0zF16lSoVCpUVlZizpw5eP3115Gfnw8AmDlzJrZv3y6Ve/HiRaN1Pv7449i4cSMqKyulN/VZs2Zh+/btoJ8ewBcuXAAgJLRMT08HAFy+fBmXLl0yWGZ0dDTOnz+PPXv2ICEhAQBQVVUFd3d3eHl54datW8jKyjJ47aBBg3DlyhVotVocOnRI2t+ePjGdgOWa5ZqxW1ixYro3Z84AjY36+xobgS++6HCR4eHhSEpKwoQJEzBx4kQ89dRTCAsLAwAEBwdj586dkMvlqKiowIoVK1BdXY2YmBgolUpMnToVaWlpAIBt27YhLy8PSqUSCoVCemM3RFxcHDIyMhAfHy/tW7NmDdRqNZRKJUJCQrBmzRoAQpCuSqWCXC7H2rVrERERYbBMR0dHxMTEICsrSwrwHTduHMLCwjBmzBgsXLhQcv+0JDU1FTExMZg8ebLeDDhz+xQUFITf/e53ePvttzF06NB2uWUYsFyzXDN2jIzIlL3aekRGRlok9wjT87hy5Qrkcrmtm8FYGUP32V5+94b6wXLde+B73Tsxd/xiixXDMAzDMIyFYMWKYRiGYRjGQrBixTAMwzAMYyFYsWIYhmEYhrEQrFgxDMMwDMNYCFasGIZhGIZhLAQrVgzzE9euXUNoaKitm9EKcXHb7lLOv//9b0RERGDs2LGIiIjAJ5980ukyGevBcm0eLNeMpeAlbZhuTUnJRnh6RsHbe4a0r6LiU1RX52LYsOdt2DLz0Gg0cHKyr5+Zr68vjh49Cj8/P1y+fBmzZs1CWVmZrZvVo2C57n6wXDOWwmYWq//c+A+CtgZh/1f7bdUEpgfg6RmFwsJ4VFR8CkB4+BQWxsPTM6pT5aalpSE0NBShoaHYunWrtF+j0SAxMRFyuRxxcXGora0FALzwwgtQKBRQKpV47rnnAAB37tzBvHnzEBUVhaioKHz++ecAgJSUFCxevBhTpkzB4sWLMWnSJBQUFEh1iG/YNTU1WLp0KSZMmICwsDAcOXIEAFBXV4eEhATI5XLMnTsXdXV1rdp//PhxzJ8/X9o+ffq0lKV6xYoViIyMREhICJKTkw3238PDQ/qcmZmJpKQkk33SJSwsDH5+fgCAkJAQ1NXVoaGhoY1v3L7o7PjFcs1ybUlSUmzdAvum3d8v2QgMASEF5LbBjf556Z+2agZjAwoLC9t1/o8/fkI5Ob703/+uoZwcX/rxx086VX9eXh6FhoaSSqWi6upqUigUdP78eSoqKiIAlJOTQ0RES5YsoU2bNtHdu3dp9OjRpNVqiYiooqKCiIgWLFhA2dnZRERUXFxMY8aMISKi5ORkCg8Pp9raWiIiSktLo7Vr1xIR0Y0bN2j06NFERPTiiy/Svn37pDJHjRpFKpWKtmzZQkuWLCEiovz8fHJ0dKTc3Fy9PqjVagoICCCVSkVERMuXL5fKKi8vJyIijUZD0dHRlJ+fT0RE0dHRUjnu7u5SWQcPHqQnnnjCZJ+McfDgQXr44YcNHjN0nyMiIkyW11MwNH6xXPcOuSZq/722NrZ7kvcOxO/X3PHL5rbcWnUtVp9ajcSxibZuCmMjTp+WtX0SgOJiYYHa/PyH2jx3+nTjKzXl5ORg7ty5cHd3BwA89thjyM7ORmxsLAICAqR1yBYtWoRt27bh2WefhYuLC5588knExMRIb9AnT57UW0usqqoKKpUKABAbGwtXV1cAQHx8PGbOnIl169bhwIEDiIuLAwCcOHECH374ITZv3gwAqK+vR0lJCT777DM888wzAAClUiktbquLk5MTZs+ejaNHjyIuLg7Hjh3Dxo0bAQAHDhzA7t27odFocPPmTRQWFhoswxDG+qRrCRCUTovEAAAgAElEQVQpKCjAH//4R5w4ccKssu0RU+MXyzXLdVdgm0Xpeg/nzrX/GpsrVgBQUlli6yYwNsTUwwJodpP4+a3AjRtvQqE4oBebYklkMlmrbScnJ5w7dw6nTp1CZmYmduzYgU8++QRarRZnz56Fi4tLq3LEhxsA+Pv7w8fHB5cuXcJ7770nLQBLRHj//fcRHBzcobYmJCRgx44dGDBgACIjI+Hp6YmioiJs3rwZubm58Pb2RlJSEurr6032U/e4qT7pcv36dcydOxfvvPMO7r///g61314wNn6xXLNcW5OUFGDduuZtsevJyewatATGvl9z6BazAod5DbN1E5huivjwUSgOYPjwV6BQHNCLTekI06ZNw+HDh1FbW4uamhocOnQI06ZNAwCUlJTgzJkzAID09HRMnToVKpUKlZWVmDNnDl5//XXk5+cDAGbOnInt27dL5V68eNFonY8//jg2btyIyspK6S171qxZ2L59O+inV84LFy4AAB588EGkp6cDAC5fvoxLly4ZLDM6Ohrnz5/Hnj17kJCQAEB4E3d3d4eXlxdu3bqFrKwsg9cOGjQIV65cgVarxaFDh6T95vTp3r17eOSRR5CamipZQXozHRm/WK5ZrjtLSopgrfrmG2FbcFixUmUpUlIA3TBAIiAiwrxrba5YuTm7YcPDG2zdDKabUl2dq/cm7+09AwrFAVRX53a4zPDwcCQlJWHChAmYOHEinnrqKYSFhQEAgoODsXPnTsjlclRUVGDFihWorq5GTEwMlEolpk6dirS0NADAtm3bkJeXB6VSCYVCIb2xGyIuLg4ZGRmIj4+X9q1ZswZqtRpKpRIhISFYs2YNACFIV6VSQS6XY+3atYgw8mt2dHRETEwMsrKyJDfOuHHjEBYWhjFjxmDhwoVGHxCpqamIiYnB5MmTMWTIEGm/OX3asWMHvvvuO7zyyisYP348xo8fj9u3b5v6yu2Wjo5fLNcs15bip3kI7BK0Ah2duyAjss3tkPnJEPh8IDY8vIHjq3oZV65cgVwut3UzGCtj6D5HRkZaJOeQrZH5yTD0D0OR+rNUafxiue49dKd7/cUXwJQpgoL1U/gbYyFu3QIGDwaefx7485/NH79sFmPl4uyCa89es1X1DMMwHcbZ0RlfPvUl/Dz9bN0Uppcjuqvq6lixsjSixeqnTCRmYzNXYJO2yVZVMwzDdAoHmQNq1bW2bgbDSK7AWhZHiyMqVhpN+66znWJFrFgxDNMzYcWK6S6wYmU9epxipSUttKS1VfUMwzAdRiaToU7dOnM4w3Q1rFhZDzFjR49RrBzgAFWjylbVMwzDdBi2WDHdBd0YK8ayiBYrtbp919lMsXJ0cERVQ5WtqmcYhukwrFgx3QW2WFmPHucKdJQ5orK+0lbVM0wrrl27htDQUFs3oxXi4rbdpZxz585JeX7GjRunl4ixt9CTFCuWa/PoqXLNipX16HGuQLZYMe2lJ2YU1rT3F9kDCA0NRV5eHi5evIjjx4/j17/+tV320xQOMgfUaSzje2G57h70VLlmxcp69DyLlYMjKhvYYsWYj+66TZ0lLS0NoaGhCA0NxdatW6X9Go0GiYmJkMvliIuLQ+1Po9ULL7wAhUIBpVKJ535KanLnzh3MmzcPUVFRiIqKwueffw4ASElJweLFizFlyhQsXrwYkyZNQkFBgVSH+IZdU1ODpUuXYsKECQgLC8ORI0cAAHV1dUhISIBcLsfcuXNRZyB44vjx45g/f760ffr0aSlL9YoVKxAZGYmQkBAkJycb7L/u4rOZmZlISkoy2Sdd3Nzc4OQkpMCrr69vtQ5db8CSFiuW62ZYrtsPx1hZj44qViAb4T3CmzK+yrBV9YwNKSws7NB1lpLWvLw8Cg0NJZVKRdXV1aRQKOj8+fNUVFREACgnJ4eIiJYsWUKbNm2iu3fv0ujRo0mr1RIRUUVFBRERLViwgLKzs4mIqLi4mMaMGUNERMnJyRQeHk61tbVERJSWlkZr164lIqIbN27Q6NGjiYjoxRdfpH379klljho1ilQqFW3ZsoWWLFlCRET5+fnk6OhIubm5en1Qq9UUEBBAKpWKiIiWL18ulVVeXk5ERBqNhqKjoyk/P5+IiKKjo6Vy3N3dpbIOHjxITzzxhMk+teTs2bOkUCjI3d2dPvjgA4PnGLrPERERBs/tadw36j7a8sUWvX0s171Drok6fq+twVNPETk6Eu3caeuW2B979wq/z88/F7bNHb9sGmPFrkAGEFYNN+evvecaIycnB3PnzoW7uzs8PDzw2GOPITs7GwAQEBAgrUO2aNEi5OTkwMvLCy4uLnjyySfxwQcfwM3NDQBw8uRJrFq1CuPHj0dsbCyqqqqgUgkzXWNjY+H6Uxrk+Ph4ZGZmAgAOHDiAuLg4AMCJEyeQmpqK8ePHY/r06aivr0dJSQk+++wzLFq0CACgVCqlxW11cXJywuzZs3H06FFoNBocO3YMjz76qFRHeHg4wsLCUFBQgMLCQrPvhak+6TJx4kQUFBQgNzcXr732GurFYIRegjkWK5ZrluuuoLYW8PFhV6A16OisQJstacOuQEbE3NUqZTLrLzTa0vwvk8ng5OSEc+fO4dSpU8jMzMSOHTvwySefQKvV4uzZs3BxcWlVjru7u/TZ398fPj4+uHTpEt577z1pAVgiwvvvv4/g4OAOtTUhIQE7duzAgAEDEBkZCU9PTxQVFWHz5s3Izc2Ft7c3kpKSDD4cdPupe9xUnwwhl8vh4eGBy5cvIzIyskP96Ik4yBzazGPFcs1y3RXU1gK+vuwKtAY9LsbKQebAFivGJkybNg2HDx9GbW0tampqcOjQIUybNg0AUFJSgjNnzgAA0tPTMXXqVKhUKlRWVmLOnDl4/fXXkZ+fDwCYOXMmtm/fLpV78eJFo3U+/vjj2LhxIyorK6U39VmzZmH79u2gn56qFy5cAAA8+OCDSE9PBwBcvnwZly5dMlhmdHQ0zp8/jz179iAhIQEAUFVVBXd3d3h5eeHWrVvIysoyeO2gQYNw5coVaLVavdlP5vSpqKhICuotLi7G119/jaCgIKN9t0e646xAluveKdd1dYJixRYry9PjFCtHB063wLQPI/Gq7SY8PBxJSUmYMGECJk6ciKeeegphYWEAgODgYOzcuRNyuRwVFRVYsWIFqqurERMTA6VSialTpyItLQ0AsG3bNuTl5UGpVEKhUEhv7IaIi4tDRkYG4uPjpX1r1qyBWq2GUqlESEgI1qxZA0AI0lWpVJDL5Vi7di0iIiIMluno6IiYmBhkZWVJAb7jxo1DWFgYxowZg4ULF0run5akpqYiJiYGkydPxpAhQ6T95vQpJycH48aNw/jx4zF37ly88cYb8PX1NfWV2x2WVKxYrvVhuW4f7Aq0Hh1NtyAjsrYR2jBBiiBM/9N0vP2rt21RPWNDrly5ArlcbutmMFbG0H2OjIy0SM4hWzM8ZDimvjoV++buk/axXPceutO9jowEftKfsWePbdtib7z8MrBhA3D4MPDoo+aPX5zHimEYpp2YE2PFMF2B6ArkGCvLY1VX4PHjxxEcHIyRI0ciNTW11fGSkhLMmDEDYWFhUCqV+Pjjj9ss01HGwesMw1gfa4xf3THGSo/GRuDrr9s/nYnpcYjB67ZyBfbEBLfmIroCLb5WYFNTE1auXImsrCwUFhbi3XffbTXN9dVXX0V8fDwuXLiAjIwM/OY3v2mzYrZYMQxjbaw1fnV7xermTUClAm7csHVLGCtj6xgrSya47W5YzWJ17tw5jBw5EiNGjECfPn2QkJAgZdIVkclkqKoSlKTKykr4+fm1WTGvFcgwjLWx1vjlAMstaWNxGhuBu3eFz+XlbLWyc2ypWH36adfX2ZVYTbEqKytDQECAtD106FCUlZXpnZOSkoJ//vOfGDp0KObMmaM3rVWX3bt3IzIyEpGRkbhXcY9dgQzDWBVrjV+V9yq7r8Xq5s3mz0RstbJjiITYKh+fro2xSkkR8q899JCwLSawtTe3YEMD4O5uo3QL7777LpKSknD9+nV8/PHHWLx4MbRabavzli1bhry8POTl5eE+3/vYFcgwjM3pyPjlM8CneypWorVKnOxNxFYrO0atBhwcAC+vrrVYpaQIoiWGLAoLv9ifYlVfbyXFyt/fH6WlpdL29evX4e/vr3fO3//+dymPyQMPPID6+nrcFU3RxiqWOUCj1aCxqbF9LWYYK3Ht2jWEhobauhmtEBe37S7liJSUlMDDwwObN2+2WJmWxmrjl0M3jbHStVb9xLWyMpbrdtAT5FqkthZwdRX+bOEKrLRzp5NosbJ48HpUVBSuXr2KoqIiNDY2IiMjA7GxsXrnDBs2DKdOnQIg5Peor6/HwIED26y8X99+bLViTDJ48GDIZLJWf4MHD7Z108xC0+5l0XsOv/vd7/CLX/zC1s0wibXGr86mW7CaXNfUtF4fhwgwYIHrDCzX3YPaWsDNTfizhWJVVQXoeNrtjoYGwMPDChYrJycn7NixA7NmzYJcLkd8fDxCQkKwdu1afPjhhwCALVu2YM+ePRg3bhwWLFiAt99+u9XaVIbw6uvFAeyMSW7dutWu/eaSlpaG0NBQhIaGYuvWrdJ+jUaDxMREyOVyxMXFofan0eqFF16AQqGAUqnEc889BwC4c+cO5s2bh6ioKERFReHzzz8HIMTsLF68GFOmTMHixYsxadIkFBQUSHWIb9g1NTVYunQpJkyYgLCwMCmouq6uDgkJCZDL5Zg7dy7qDARPHD9+HPPnz5e2T58+LWWpXrFiBSIjIxESEoJkI2m9PTw8pM+ZmZlISkoy2aeWHD58GMOHD0dISIjpL9rGWGv86uysQKvJ9fHjCE1KQugTT2BrejowfjygVELj6MhybUdyLVJX16xY2SKPVWUl0EPecTtEfX3HFCuQjYiIiKBxb46j/9z4j62awNiIwsJCs88FYPSvo+Tl5VFoaCipVCqqrq4mhUJB58+fp6KiIgJAOTk5RES0ZMkS2rRpE929e5dGjx5NWq2WiIgqKiqIiGjBggWUnZ1NRETFxcU0ZswYIiJKTk6m8PBwqq2tJSKitLQ0Wrt2LRER3bhxg0aPHk1ERC+++CLt27dPKnPUqFGkUqloy5YttGTJEiIiys/PJ0dHR8rNzdXrg1qtpoCAAFKpVEREtHz5cqms8vJyIiLSaDQUHR1N+fn5REQUHR0tlePu7i6VdfDgQXriiSdM9kmX6upqmjRpElVXV1NycjJt2rTJ4Pds6D5HREQYPLenERERQY7rHKlR0yjt61Zyffs2KYYPp/NffslybaJPupgr10Ttu9fW5NIlopAQosZGIkdHop9uZZcRG0ukVHZtnV1JeDjRz35G9Kc/Cdvmjl82y7wOAF4uXuwKZAy6RMQ/a1yXk5ODuXPnwt3dHR4eHnjssceQnZ0NAAgICJDWIVu0aBFycnLg5eUFFxcXPPnkk/jggw/g5uYGADh58iRWrVqF8ePHIzY2FlVVVVCpVACA2NhYuLq6AgDi4+ORmZkJADhw4ADi4uIAACdOnEBqairGjx+P6dOno76+HiUlJfjss8+waNEiAIBSqZQWt9XFyckJs2fPxtGjR6HRaHDs2DE8+uijUh3h4eEICwtDQUFBq7xNpjDVJ5GUlBT87//+r551oDfi6uxqMuWCTeXa3R2PzZiB7JwcACzX9irXoivQ2VmYldfVcxQqK5tTEtgjHXUFOlmnOebRr28/dgUyIBPLVZp6mJi6rqO0rE8mk8HJyQnnzp3DqVOnkJmZiR07duCTTz6BVqvF2bNn4eLi0qocd3d36bO/vz98fHxw6dIlvPfee9ICsESE999/H8HBwR1qa0JCAnbs2IEBAwYgMjISnp6eKCoqwubNm5Gbmwtvb28kJSWhXkwfbKSfusdN9Unkyy+/RGZmJp5//nncu3cPDg4OcHFxwapVqzrUj56Km7Mb6tR16Ne3n8HjNpVr3VmBBupjuW5NT5RrUbECmuOs+vTpuvorK5uzk9sjVpsVaE28+rLFiul6pk2bhsOHD6O2thY1NTU4dOgQpk2bBkCYEXTmzBkAQHp6OqZOnQqVSoXKykrMmTMHr7/+OvLz8wEAM2fO1Mt5dPHiRaN1Pv7449i4cSMqKyulN/VZs2Zh+/bt0oP0woULAIAHH3wQ6enpAIDLly/j0qVLBsuMjo7G+fPnsWfPHiQkJAAAqqqq4O7uDi8vL9y6dQtZWVkGrx00aBCuXLkCrVaLQ4cOSfvN6VN2djauXbuGa9eu4dlnn8VLL73UrR8+1sLN2a1bzQzUk2uVCodOn8a0Bx4AwHJtr3ItxlgBtomzqqqyf4uVVWYFWhOvvl6cJJQxyaBBg9q13xzCw8ORlJSECRMmYOLEiXjqqacQ9tPy8MHBwdi5cyfkcjkqKiqwYsUKVFdXIyYmBkqlElOnTkVaWhoAYNu2bcjLy4NSqYRCoZDe2A0RFxeHjIwMaVo/AKxZswZqtRpKpRIhISFYs2YNACFIV6VSQS6XY+3atYiIiDBYpqOjI2JiYpCVlSUF+I4bNw5hYWEYM2YMFi5cKLl/WpKamoqYmBhMnjwZQ4YMkfa3p0+9nc4oVlaX6xkz8NSjjyJs3DgALNf2KtdiugXANikX2BVoGBlZw59iBpGRkfh56s/h0ccDqx9cbYsmMDbiypUrkMvltm4GY2UM3efIyEiL5hyyFZGRkcAyYFfMLkT6RQLoZnJdXQ188w0QHAx4etq6NXZHd7nX77wD/PvfwL59QGgo8O67wNixXVM3EdC3L+DkZLt1Cq2Nlxfw7LPCzyktzfzxi4PXGYZhOkB3cwXq0SLGirFPbOkKrK8XxMueLVY9MsaqX99+7ApkGKZH0iMUKwsnBmW6F4aC17uKqirA21uYjWiP+WKJhBWi3Nx6mGLFweu9Fxt5oJkuojfcX1dn11aKVbfpNytWVqPb3GPYNsaqslJwlfXta58zAxsbhRmWffr0NMXKhYPXeyMuLi4oLy/vVgMUYzmICOXl5SanttsDYroFkW4l1+wKtArdTbZtabGqrAT69QNcXOzTHVhf3xxD1t5ZgTbPY8UWq97H0KFDcf36ddy5c8fWTWGshIuLC4YOHWrrZlgVNyd9V2C3kuuaGuDuXUGxun3b1q2xK7qTbNfVAT4+wueujrGqqmq2WNmjYtXQ0KxY9agEobxWYO/E2dkZw4cPt3UzGKZTtIyx6lZy/e67wMKFwBtvACtW2Lo1jJXQtVjZwhXYr599K1YuLkJW+x7lCuTgdYZheiqGYqy6DeKTwB6DXxgJ3RgrWwSv23OMla4rsEcpVpxugWGYnoqbs5vJtQJtihgUYo+mBEbC1jFWXl72G2PVGVegTRWrY98ew736e5Ctk8HpFSfI1skQtDUI+7/ab8tmMQzDtImhdAv7v9qPoK1BcFjnAN+NvvDd6AuHdQ7mj2s3bwLR0cAPP3SuceKTwB6feL2clJTmz12Rx0q3Pl1s5Qo01h5LI7oCe5Ri9WPdj1h+bLm03URNAIDiymIsO7qMlSuGYbo1LRWr/V/tx7Kjy1BcWQwCobyuHOV15SCQ+ePa+vVATo7wvzOwK9BuWbeu+XNXpFvQrU8XW7kCjbXH0uharHrMWoFlVWVG4xNq1bVYfYqXuWEYpvvi6qQfY7X61GqTMVdtjms3bwJ79wq5p/bu7ZzVii1WdklLi5S1XYGmsnXYIt1CexWcztAjY6wamxpNHi+pLOmiljAMw7SfljFW5oxZJs9Zv745oWdTU+esVhqNMJ2JFSu7ICVFyHAuKlEymfBXVGQdxUqsz8FBvz5dN1xXplsQ29Onj/H2WBrRYtWjZgX2cexj8vgwr2Fd1BKGYZj209IVaM6YZfQc0VrV+NMLZ2Nj56xWGg3g4cGuQDshJUWwHh0/LmxrtcJ2v37WibES67t5U9hubBS2dRWZrsy8Lrbn7Flhm6h1eyxNj4yx8u/nDzdnN4PH3JzdsOHhDV3cIoZhGPNpmW5hw8MbjI5pQBvjmq61SqQzViu1Wlg9li1WdoVokaqsbN62ZoyVWJ6hcququj54vSvFuUe6Age4DsDuX+5GoFcgAMBR5ghAeKPb/cvdSBybaKumMQzDtEnLJW0SxyZi9y93o49jH8ggg4+rD5wcnCCDDIFegabHtTNnmq1VIo2NwBdfdKxxosWKFSu7QrRIlZYK/60dYyXWZ8gSZot0C6IVqavq6pFL2iSOTdQbaPq91g/5y/PR36W/DVvFMAzTNobSLSSOTcQLJ19AzpIcBPYPxC/2/wL/b8L/w5xRc0wXduGC8P+//wXuvx/49FNg+vSON45dgXaJqDiVlgJjx7ZWrCydbsGUxcoW6RYaGprjvrqirh7nCjTEQPeBuFPTDdbZYhiGaQNDipVGq8Et1S34efoBEGJJ1U3teN0VFaHKTq5IodGwK9AOERWckhLhFjc1NQd0W8Ni1ZYrsKvTLdTXd11dPTZBaEsGug3EnVpWrBiG6f60TLcAADeqb+A+9/vg7OgMQFCs2poBrYeoCFV1ckUKVqzsktpaYTZcaalgnXJ1FbaBro2xamoS1vn29Ox6V6BW235FpyOIMVY9alagIdhixTBMT8HQkjallaUI8AqQtjusWFnCYsUxVnZHXR0QGCgoVrpuQKBrY6xUKkFvd3CwTfB6V9THFiuGYZguxpArsLSqFAH9OqFYiX6OzlqsxFmBHGNlV9TWAsHBzRarlopVV8VYiYHrQNe7AnX/WxP7ibFyY4sVwzA9AxcnFzRoGqCl5jQJpZUtFCsHtlgxlqO2Fhg9Woixammx6kpXoBhfBXS9K1D3vzXRTbfQY5a0MYSvmy/u1t61dTMYhmHaRCaTwcXJRS/lQklliV4SUJu6AjnGyu6oqxMsVmVlgjtOzGEFCEqAWm3Z+CNRoWppCRNnBIr12qNiZT+uQHd2BTLWR8zWq5u1t6tWTGfsi5ZxVqVV+jFWzo7OUGvb8bpryeB1Trdgd9TWAj4+QtB4SYm+xUpc8saS7kCxrO7mCuwqxcp+XIGsWDFWRlwdXXeV9K5aMZ2xL1rGWVkkxsrLiy1WjEHETOsBAcA33+grVoDlFStTrkBbWqy6QpHrzKxAmyYIbQnPCmSszYsvCv/79tX/zzAdoeWyNhaZFXjffZxugTGIGFclKla6rkDA8nFWtbWCxcaUxcpeY6zsxxXIFivGSoiro6emCtu6a92Kn7tixXTGvtBd1qZOXYfKhkrc536fdLzDilVnLVZqNbsC7RBxJmBAAPD114YtVpZWrHx9W1vBdIPXu9Jixa7ADsAWK8ZapKQIPw5xICJq/t/UJHwuKrL+iumMfaHrCrxedR1D+w2Fg6x5WO2QK9BSFitXVyGboijgTI9HtFgNG2bcFWhJxaquTlCsDFmsdF2BXZkNHei6dAu6swLFZ4Y5dCvFyt3ZHQRqlRuGYSzBt98Cgwe33i+uPZWV1bXtYXo+uopVy/gqwIYWK41GCA7pSnMCY3V0Y6yqqromxsrHp3e6AsUYKwcHwZOh1bZ9jUi3UqxkMhl83XzZasVYhQsXgLAwIDlZ2Bb/A8Bjj7FixbQf3WVtWsZXAYCzg3P71gpsaAAGDgSqq9v3itwSjUZ41e7Kpx5jdXRdgUDXxFj5+Bh2BfaWdAtA+wPYu5ViBXCcFWM9Ll4UFCtD6RZ27wb+7//4GcS0D910CxaxWNXXC09OFxdhMbaOIipWXemnYayObvA60HUxVt0p3UJXhQ6KMVZA++Osup9ixXFWjJW4cAEYP97wMR8fQKEAcnK6tk1Mz0bPFVhpRLHSttMV6OIimAM64w7UVaz4bcFuEF2Bfn7Neat06aoYK1sFrzc0CPV2pSsQsJJidfz4cQQHB2PkyJFIFadVteDAgQNQKBQICQnBwoULzW9BC9hixVgDomZXoDF+8QvBHcjB65alpGQjKio+tVn91hq/9n+1HwcLD+LJD5+E70Zf7L24Fys/XomgrUHY/9V+ADoWq5s3geho4IcfTBcq+h+8vPQD2M29XkStZsWqB2PIqk4kPOxdXQXXlJ8f8GmLn5WbG/Dee5apDzAdY5WRIXxuy9tsaDwVxwTdsUF3u6Rko8GyGhqEd47OiLS547v4Uywp2QhHR7VlFaumpiasXLkSWVlZKCwsxLvvvovCwkK9c65evYrXXnsNn3/+OQoKCrB161bzW9CCgW4DeVkbxuKUlQGOjsCQIcbPERUrThZqWTw9o1BYGI8ff/yky+u21vj1Y92PWHZ0GVSNKgBAeV051Fo1CITiymIsO7oM+7/a36xYrV8vmEPXrzddsDiat7RYmXu9iG6MFbsCexyGkhjrBlMDgjuwZVyoqytw9Khl6gNMx1jt2SN8bssVaGg8FccEmcwJhYXxKC1N09v29IwyWJaYP7czipW547toPBbaUoE7dz43u442Fatz585h5MiRGDFiBPr06YOEhAQcOXJE75w9e/Zg5cqV8Pb2BgDcd999hooyC3YFMtZAdAPKZMbPiYgAfvyx69rUW/D2ngGF4gC++moWGhpudGnd1hq/yqrKTM5erlXXYvWp1ejj2Afud6uAt94SphXt3Wva6lRfL4zmutnXb94E/vpX864X4VmBPRa5XPjv1CJ9d8tFlw2FNbR0DZrD448brg8wnW5BxJSI/fa3hvd7e8+AXP4evv/+D1Cr7+L773+PPn2GoKTkNSgUB+DtPcPgdaIrsKPvCr/8pfnnioqst/cMuLh44fLlpWaPX20qVmVlZQgIaI4bGDp0KMrKyvTO+fbbb/Htt99iypQpmDRpEo4fP26wrN27dyMyMhKRkZG4c8ew8uTr5suuQMbitOUGTEkRLFriM4uThVoWb+8ZcHLyRmPjzS6t11rjlzkB6SWVJXB2dMb8979pziXV1GTa6mTIFbhuXfNc77auF+EYqx6HmMT466+FbVFkxNPeTBUAACAASURBVLHo1VcFi5R43q5d+sdlMmDjRv19psYvsZwDBwzXl5LSOnhdvEZ8fMtkwvHqasNlb9tmvD1eXpMACG+6Li4jUVPzFfz8VhhVqoCOuwLF9nz0kfH2GKpLjLHq06cv3NwmmD1+WSR4XaPR4OrVqzh9+jTeffddPP3007h3716r85YtW4a8vDzk5eVh4MCBBsviGCvGGrz7btuKFZEQmgIAhw8L6RgsrVgZi2WwdyoqPoVaXY4+fUz4Ym1ER8avPo592ix3mNcweJRXY2Z2WXPka2OjaatTS1fgzZvA2283H2/r+uZOsSuwh5GSAly9CgQGCtvZ2cJ/IuFv+XLBIiWOVbpJjsW/7dv197WlWGm1zVYuUY/XvbZljFVKiiBOffo0n2soeabYxpkzjbenvDwLgBaDBi1Gff33cHEZjhs33jQZj9lRV6DYFxFzvh/dWYEODrW4dy/f7PGrTcXK398fpaWl0vb169fh7++vd87QoUMRGxsLZ2dnDB8+HKNHj8bVq1fNakBL2BXIWIOvvzY+I1AX0Rz++99bJ9bKWCyDPVNR8SkKC+Ph5OSNvn39urRua41f/v384eZs3O/i5uyGDQ9vwIgd6ZBpWzx1TFmddF2BVVXCeS0zE5pjtWKLVY+ktFTIqg4054kSEXNYmaLlNW1RUSF4jAFhaUmg+R2gqUnQ47299WOsdLOuA4KYERmeNWdMp6+o+BTffvs0nJx88OOPWRg0aBEaGsowbNiLKCyMN6pcdcYVeP26+ecSCX3v00doa1NTGby8Yswev9pUrKKionD16lUUFRWhsbERGRkZiI2N1TvnV7/6FU6fPg0AuHv3Lr799luMGDHC/F7owBYrxtKIcVOjRpl3fnIyMGaM5duRni78f+UVy5fdnamuzoVCcQBAJxJedhBrjV8DXAdg9y93I9ArEDLI4OPqAx9XH8ggQ6BXIHb/cjcSxybC60Ih+jS16HdjI/DFF4YLbmmxOnOm2YxqzvUiPCuwR1JaKgSlJycLCoSuAtMyxgrQT3IMCNeMHt2x+hwcBHER3XriDER3d31rj5hqQbduY2LW0ABMnNh6f3V1LoYP3wAHh75QKA5g4MA4eHpGgEgDheIAqqtzDba3M7MCxfer2bPbPrexUfj5ODgIbXVzGwIHB/+2L/yJNhUrJycn7NixA7NmzYJcLkd8fDxCQkKwdu1afPjhhwCAWbNmwcfHBwqFAjNmzMCmTZvg4+NjdiN0GejOswIZyyD61UVRdHQ0L+5g3Trg2DFh2xKxVmI7EhOFbXFA6i1xXMOGPQ9v7xnQai241oaZWHP8ShybiGvPXoM2WYu7z9/F3efvQpusxbVnryFxrHCzr558D+PfHAekpQkX1dU15/4wRMsYqwsXgE2bgOHDgdjYZh+GsetFxOB1dgX2KERFJyWlOU+UiJjDSpeWY4eXl+Flu8ypDwAGDWoOTBfr69tXUDTEGCwxOahu3cZSLjQ0NAfj6zJs2PNwdR0FN7cx8PaeAQeHvnB09JTGimHDnjfYXksoVpMnt32ubnzVsGHPo29fD2g05q9pY2AeQGvmzJmDOXPm6O17Ree1WyaTIS0tDWni4NEJPr76Me7V34PDOgcM8xqGDQ9vkAYphmkPKSnCIPDOO4LVypwVQlJShL+iImDEiPavKiJe33LfhAlAaqoQN1FSIpj7O7NiSU+DiKDV2uYB35XjV0ukdAviK39lZXPghiEMpVsoKRGeBi3SRJiEXYE9ktJSYOxY4bOnJ6BSCZ5gBwfzXYHtWb9bVKx0r9dVrNzchJc/V1dBP3d3b+0KBIynXKivN57ntqmpEk5OQkEyWV+zxofOpFsoLRXaaU7eXd34KkBMEGq+YtWtMq/v/2o/fv3RrwGgVT4YhukIW7cCq1a1/zpxsGnvgqbGYqeysoQ8WUBzLq2WHh57RqttgEzmbOtmdDnODs5Qa9XNilVbTz1D6RZKSwXFSidWrE1YseqR6MZYOToKio1KSJVm0BXYEl2xMYeSEn3FSncyqm59uhnddbOui5hyBRoTeY2mCk5OQkEODn2h1ZqW06YmQcns6JI2JSWC9cwcxVM36zogrhVo/ptwt1KsVp9a3So3jJgPhmHay5kzwv/nn28di9AWTk6CC/G//7VMW0TFKjlZKNvTU0ha2lvQauvh4ODa9ol2hkGLlSkMpVsoLQXCw4XgF3PXK2HFqkfSUtExZEEyRXtXQtJV5Fper2sh01WsDFmsTLkCjbVHo6mEo6NQkIODC4hMy6n40+ioSJeWAqGh5lusdBUrJydAre6hilVJZUm79jOMIcSYJtGX7uEhWJLaG8s0aRLw3Xfm1ycmH20ZO3X1qjAojRvXvG/s2PYZIHo6Wm0dHB17sWIlmj7bel025AosLRXm4A8dav7UJjF4nWOsehQtXXO6FihDMVYtEfVxc8MMDNWna7ES63N11VesDFmsjLkCjYm84ArUtViZllPRmNvWEjrGKC0FQkLMs1gZcgWq1T3UFTjMa1i79jOMIcQcKiEhwrY5OUsMMWqUoBSZW59CIWx/841+fVlZwkwU3azvAQG9T7FycDARW2SntNti1TLdQn09cO+eEFXcHqFhi1WPo7paCBIfMKB5n66iY06MVZ8+wm03N4ShLUVO12Kl+27QHlegcYtV+1yBuharjrwrdNZiZfFFmLuKDQ9vaJUbRswH0y1o70KojE3prOIycqRpi5WuolZSAty+LXz+wx/0j2/b1hxfJWKvipUx5VVQrHqxxUp8/TfXFSharK5fF1bbdXBov2LFS9r0KES3nO4LWHtdgeI15lhltFohHGHoUMPXGouxaq8r0HiMlb4rsD2KVXtFurJSiNEKDDRPsWoZY9WjXYGJYxOx+5e7JQuVn6eflA+mW9DehVAZm1FVJfyQ1q7teBltWax0A9WzsoBZs4DVq4HLl4GTJ4XjdXXA998DP/+5/rX2qlgZCt4XMjxzjBWGDDHfFSiaDnRNCuYKDZEg/I6O7ArsQbS0HgHGXXOmMDeA/dYt4Vxdl5futcZirMy1WDU1NScZNTRRp6mp2WIlzAo0rS11xhUoKq2636cpDM8K7KGKFSAoV8XPFiNpfBJenPpi91Gqbt4UlpJoz0KojM0QB6nOZDg3ZbHKydHfFoPTX31VSDu0cqWwX5yR2L+//vkBAYKVy54oKDC8f906oKmpd1qsnB2doW5Sg+rqBMXK1BNPoxHMFU5OwpOssVGYPSFGF5srNKJSJZOxxaoH0TJwHei4xcocxapl4Lqh+ozFWJmTbkFUToy1R99i1XaMVWcsVuLzwNzvpqUrsEfPCtTlFyN/gazvsmzdjGbWr29Wu81dCJWxGYbe/trLsGHCW53ugCEGqk+bJmyLgeqixSolBZg3D/j2W+H4W2/pnye6yoYNsx+LlfidhIYK24YSn/bWGCsHmQMcZA5AbY2QudHU67Ku/0EmE54CBQXtt1iJ8VUAK1Y9CGMWK0MWJFOYa5Uxx0JmKMbKWPB6SzEzNMFVF41GN3i9fbMC22uEFfvq6QnU/H/23jxOjrrO/39W38dM99yZmWQmISSEBAJEiAvLoUFdQVdBV6NudlXc75fVH+4KfF1YOSQhsmA8CB6roCsqG4/segAqoGKAgCBXAkkGQkKSmU4yOWamp3tmuru6u6p+f3z6013VXX1NJuTinUceM13T9alPXZ961ev9+rzeE6VVoorDLhV4XACr0dQoD217CMcKB7NWz5qcl5XURL300qFpoyRbJa1nay2E+mYcsbB7+6s3XC6YNctqubB8uZC9SIHp734HH/+4qEPY1mZfIFX+NAva7Z6Rx6oDu9zn884Tn1MpYSuxYkVBL9La+i4WLfoje/cesW4esfA4PRgTOWBV6XW5+DU5HBZ5ZXkh14rG5YxAeDMVeAxFPUCnUtSaCqwG5OrxsbJLz9lNcDWHNRXowjA0DEMr218Jdg6FsZLeYLJsT7mwF68r5VcoiqMSWK3ZtIZrHrkGI/dv0kahUhO1bNmhaaMmWwj1zThiYUdzTybmzCnVWT38cEEzde21wtm9WJxeLdrbhfGf2ZboWC7MrGmwaZP4/cknC2BLHqf9+9eyefNSut/YGsxHRXicHpEKrAVYmYUdbzJWJ1TUk5qrFLWK1+2AlXndenys7FgkCYQqMVYyFagoStWZgfL2mKzGSu5rLcDT3m6h9u0dlcBqSoxCzZqoLVsOTRv19NOCpTJHLYVQ34wjFlORCgQhYC/WWUk91Re/WBgI7YCVNCW1MydVFDEb53hJB27fDh0dghh+KJfBP3AAHn1U/H6izgoEAaxIJOpLBYJ4Auzda30iGEb1p4KcEQhvAqtjKKoxVvWkAmthrOxY/XK+WX5//XYL1TRWZsYKqgvYDyUVKMuIQW2pUvtUYO3bOyqB1ZQYhS5fXnr0J8sybdgggFkgICiMZ5+trRDqm3HEYqqAVTFjlckIsLBpE9x6K/z+92L5ueeW6ork7+VSfD09cNttlc1Fj5XYsEGkQ7/85QKw+t//FU4BM2eeuOJ1EMBKqUW8bpcKhMITQVFqE7C/yVgdc2EY5Rmkw2W3UAtDdih2C8UTXM1hGBqalsDpDOaXVbNcmArxevE+VtuWjOMCWB2yUejgYEE1bI5D0UYNDgoL79NOO35ohuM4Dhdj9fTTojjzV79aqqWq14S0txeWLBHr3XGHWPbKK5MzMz3SsXEjLFoE55wjmKqBAfj5z4X+bNq0E1e8DjlglUhWt1soHs1DIfFEa24uLKslHWgGVm9qrI6JGB4Wp76hwbq8nOapUhyqxqqSj5WuCwlDY6N1vXpTgdlsHKezEUUpQJBqMwOl3YLLJcbIWoGOYQhdrJn4rcX1xJwKFLMCa9seHKXAys4oVEGhP9ZfXcg+OChencsdhcmyVtu2CfrieJwnf5xFube/yYRkrCTQueGG+vVU5cL8jJQsz0NH0UTYemLDBgGsnE74m7+B//ovQexedplIIeh66oQsaQPgwyXGnba2/BNvzaY1zFo9C8cKB22r2mhb1cZ531rExtFXC+ObfILs319orK0Nrr6aX6z7Np/5STNvuUuxjInR6DoGhr+dB1aP7H6MDf3P5CcB/eJP3yqd0FOr8fERMEiulQG+5pqniEbXWZZdf/1OBgZW1b2dSstqbafSugMDq7j++p2WZVu2PIfLNVHy3anysSo+jpmMOI2yILwMyeYYhrhvi+0WxschGBT3uTnKpQIVJYLH02/pTzS6jkhklSUNCAJYFc8MNPdbvndUcxEpvhaGhgA0hoZWWfbRLuT2VBX+8pfC8uOCsZJGoTPDM/PLDAQ1UFXIvnJlwQLbLiarjdq+XdAXx9M8+eM0hobEQBAMVv9utZCWC1JY/tRTpcCq3gLPMiSwisfhhRfgAx84NoGVzIqfdZb4fOmlgoFLpYR/lwBWJ24qsFF3oft9+Sfemk1ruPLBK+mP9WNgMJwcZjg5jEeDOOnC+Pbyy+LgmV8EX3sNY9s2hr7wOZ45OMoX50OzIsbE/3nxRvr6ltLoXAAuF2s2reHWp78Majo/CWj4C1djrF9vndBTq/HxETBINk/okL/bTfJYvfp8+vqW5h+o0eg6Vq06icbGxXVvp9KyWtuptG5j42JWrTrJ0tdnnrmLoaHSAatcUeRKYZcKLD6Oe/cK9kkSmzK8XgGaUil7uwW7NKBczw5YBQJBUqn7OHCgP7+vfX1L8flOxuWyNmSXCjT320zoVgJWxdfCli3PkUo589dCJUZPbi+VsnoVHhezAkGAq11X77KAKxllhezmFKDPJz7LHM0HPwhr105eG2VmrN4EVkd1TNWMQBA31MzcJXjddeLnX/2V9TuTTdtJ8vOPfxQFo3/4Q5FqnCh9cT2qY3BQ/JQz/t797sIMGr9fDFInMrAKaTlglTPRuekPN5RMzgHwZkF1ifFt9S+vF1YLUJAvDA7CSy+hAP/4gsZgBFa8AssXwL/NSeAduYMFC9bS7DwHXC5ufPRGoqTw5d60O8fgk89rKIZRmNBz991wzz3VJ/cMDgoa8g00SH79dfHzhhvEf/l7ccgH7IIFa9my5YO89tpn2bJlKQDNzUuqbueBB0qXPfZY/f398Y/L99Ecr74q+tTXt5TNmz9IX99SHA77EhFTkQqUnnrm41jpZVCub5cKtPOwAvuMcyoFwWALJ5+8lP7+P+X3dcGCtQQCp+J0ljJWZmBVzI/IVKDcnh2wkjOTFyxYy+bNl7Fjx008/fQ3gcK1UC4VKOen3XCDGJPNcdwAKxl1CdlXrizwdbpufbOqVvitWmzbJhirN4HVUR9TlQaUxpdyYPrKV8RPt3tqBObyUpKzDEMhoVFat676ukdTSLZKHpOOjoL2rLdX6BtWr15ywmqsGrNONL9X1PsLBoketB/TvBqkcgzCFb/ZU5jRIOULK1fmD6zDgJsfh42j8NQQvL0Dfr1HFw+P3KzAgdgAqksANoBbHgN3sTGirDti3o5dmMfWw2w1I++7OXPE59tvF//l71CY5KEohYdtS8sSLrwwyrx53+LCCw9avlcu1acoIl1d3OaSJVRdv7idT3zCvo/m9KCiiJcogAsuOMjChb/kggsOct11p9iuU652X6WQ68jtzZtX6Jfs249+VH7/5Pp2wCoer4+x8nqhq+sUNG0hQ0O/oqvr0zQ3L0HTYiWMlZgVmMr3e9o0ax9/+9vyjJVc54wzxOeWliVcdFGck0/+Ejfc8GNLOy+8YAWecl3Z9u23wzPPWLf95z9DNls7XDrqgVVFIbs551/NxLNa4bdqsX37m4zVUR5ycJhKYFWL2edkQ2aVf/7zQnrx0ksF0DoaxOt2+ha7v3/ta0JfJZeZj9nYmBiUr7rqFycsY9WgOdG8HvEhHGa+Z7rt93xZUJ2CWfrURqVA+6XTgom/9948uPFpcMVGWOKBi9pBM+AD0x0i/ZETr/eGe1GdArB1jsEnN0LFd+5yk3vM1jWVvjdFIa+hL31JfDZfTzLZIJcZBqxfL5aNjKzjscd8bNhwMQ8+eCog2N9y96rczvz5pW1efLF1WTVgZRgF0+AXXrBfV37vox8Vn9evb2XdOoX169u5+up+23X8fvFIU1Wr5qlSSMZJbu/Tn7b2R/5ebv/k+sUaK5kKtGOsKtktOJ0vMzQ0DMDevd8hGl1ncV2XIRkr2e//+i9rHy+80AqsiitiGAZ8+MPi8/79/8O6dQp9fR/n7//+m5Z2Lr/cyljJdX/zG/vjJK+H44qxshOyB9wBbnvHbdacfzUTz0NhrAyjAKy6uwVHWY9b2JvxhoTMj08VsDrcEQ6Lt6GxMThFvLDmgdXRYBZqp2+x+/u6dQVgVRxyQNa0E1e83ph1ovlywCoU4sYzP1sypkEhFbhivRO3UqQOTqdLvPTGzoAbFsJ9/eBUIN10ldCWqM+Ay8Vt77gNh9+PLyvYLUctFTns2KgjZJBsp4Oxe6jL7/X1LaWxcTHh8PnMnCkkIYOD66tuR/ozVdt2pTCMwsParo/miMcFmzZ7thBTz5v3X+ze/YjtdxVFtDc0JAjPYk2UXRSnAuvlASqlAu08rKC83QIMMjx8M5p2JgCzZ99OX99S4vHnbFKB1rI2xefA7C1VTmMl9/W1164EoLn5naRSIi0sNVflxOuVjpPbDZp2HAGrYiF7i6+Fe953D8vaLrYWRX7iicomnofCWA0OCiV0OCyu7GnTOCFrcxzFYZgeGlNRzqY4Kpl9HkrIfsqsz+mnl17GRzJSKfsBTB5v+dYohevmuOUWIYR1uUBVMycsYxXMKmR9Bcbqb6ddyD3vu4eQV6RCWv2tNLgb8GrgCgT5UGwGzkzRFCRdLwE36ikQvhNUpR2Ad53yDyxYsJax7BZwuVi2cBlfed838Gpw3m7wVKmPBthP7jlCBsmxGLznPYXPt9wiHopmRwr5PTCYP38tTmcQTUvgdot828GDm6tuJ5GASy4pbfPcc2vvayIhHr6yj74KWe+xsXEWL44RCIgcXSh0NoHA3/KOd9i/+IdC4hFUSxpQft/MyEQicOWVhc/VxrJKqcBK4nU7jZXDsZ8zzriJREKs5PF0smDBWpLJrTbidavdQixm3WezBUI5jVUkAj5fhunTP5dbJ8L+/dP42McGGRt7DigvXo9E4O1vL3w2H5/jTmMFBSH7Tz74E87vPZ9lC5eVFkV+29vEtKof/9jK6UruWJrzjY/X3wGpr5LxZjrwqAmZH3fkrmRFgZ/9rFR8OBXbMf+civYUBfr6xGeZy3c4YM8e67I3Mi0o+yWBnt9fGMzMGhR5vGWqYP788gapPh8kk9oJq7EKZhQyJmBFPM6yhct4z9z3cN8H7mPouiHu++B9vLV1IR95y8dpeXWXdQwz/49GIRwmk00z81R47o5vc8/f/icAqdQAzc1L6NWX5qmNj7zl4/g0hWtuf5sQj/zbv5W2+a//CqtXl46ZMjZsKOTMrryy/PemOOJxMXlRxvLl4qGezVpfpASIUAgGl6DrCXQ9kTezbG7+TNXtJBJw/vml2z7ttPr6Gg6LPobDpX00h66fxOLFYTRNoB9NS6Bp3Xzyk3Nsvx8Oi6xrrcCqoUHsk1TFDAwII2IZ1cYycyqwVvF6uVRgW9tZ9PQsJhZz5JaJa7ShYVHZVKCMeNz6LlE8K7AYyGUyIpmk624ymRYcDj+p1AADA/Af/9FFb+91+f2zE68PDBR0csXHR5S0OY40Vub4m5P/hsf7H0eN7LLXU/3xj2JKkl04HHDyyZNLB8o0oIw3gdVRE8uXi0HMk3tuZTJCu/RF+0k2R03Y6bfk/2JtyRsNrMzbNAxBWIBIWRoG/OEPwiRV/r1aX4UHjnHCMlYBzUHGk8vhmPIQkViEnpCgLMPeMNnkRCkdUxyNjTA2Rjw5CogZhDJ9oqq5MclsEOp249IMxpKj5XPktTpKyvzQGxR2D3G3Wxwi88xZ86w5AVIKwKpadw2jABiKt11POtDcV49HHH67FKPsUyIhauUBeSBYDjiFQgJY1aKvAvGoa2gQ9+v4uAAkra2174uZsSrWWE1GvB4KwdiYSG2nUuIaNdcJLPTbarcQiwnwJMnSaqnAvXvFxJmeHti1a5zGxreSSOxh3z6YbpI1VkoFlst0uFygaccpsHr49YdJZVP81wdPQs0UwdV0Ws7rLt+AXeG3WmLDBnj88YJY8xgHVkeDMHoqY2CgMIPknnvELLTp9vrgYyKqWUUMDKwqMUOMRtfVbIZoF8XXxMaNhd9lSmTVKgFYr7lGOM/XGmJQPnGBVTADGV8O6JhelyPxCD1hMZKHvCH0VLJyDglEbjUQYGxYeFwkM8l8+sQCrGStQEUh7XaSmoiVf3LUUuMDRL/LoYXDEOXSTsWpLnMtPV1PouvJfDerdTeTEY8Mc3uqKv7XUhbG3AdzXyuVlUkmxX8JrDQtWVGYHg7XlwqU68Ryp3zGjAIDXeu6o6NW8fpk7RbMwMowCteomBVobUjOCpQhj5/8WS0VKG12enqgvz9DOHw+e/akaGsr3A5y/+zOzQkJrKSpXlpLc95uMdPFEpom7pJKgsriwm+1xu9+J57esu3e3mPaff1oEEZPZUjv1iuvhC98QQyU1V78j6Yo1jr09FQGV42Ni0vMEKVwd7JRfE1s2ACf/Wzh89VXw7e/LW6BtjYxs6ZW3Zl82z1RxeuBDKS9VsZK0zUGxwaZ3ijeAMK+sABWtVy4oRATOWCVyCTQdRWXq8mesQIyboXUeAVgVUuND00T9McbyFiVE0oXE2xmxkrX62OszPXvzNstXlYtigFHJRJQMlYyFViNsao3FSjXiccnN5EnHBYpNY+n4LBeTbxeibFyu8Hj0clkpuev0Ww2XjUVKI+f/FnNIFTua08P7N7tIBw+n0hEqVhoWoauCwnGjBn2x8TlMo4vuwUZNz56Y95U7y2fhtbrYNQLs77eCz/9aeGLlaYBT4axGhyEXbsEZyzbPoYZq6nWHh0NIb1b7767MN32WIpitqinp6Bhsovm5iU5M8QP8corn8ib7tVihmgXW7daP4+MiNpld91VWHbnnfAv/1L4vdifp1IIk1DjhNVYBTKQ9pgYq1iMfeP7aA204nWJJ0XIG7LmOipFOExiyAqs/P65+TRLMbBKuxyoiTjGoTBWY2Pi5xucCizHWJUDVppm1VhNBljJ3+thrIr7WumQFqcCJRCcqlSgefuTAVZye+b+yJej0dH6UoGSYWpszKDrZ5iAlX0q0G5WYDlgVcyQmYHVnj0+gsEz2L+/ixkzrDP47c7NwYMifVruHDidOprmtv+jTRwzwKrYEHTOCGxrhYF4RMwIlANJpWnAk2GsbrqpICSRbR+DwEqKkt/1LvH5SAijD1ds3w47doj9kd4nx/L+zZgh9AJaMStriqamt+Nyhdm//8d0d39mUqBKXhOnCsuf/DG75ho488wCuJPfk6zWokX1HVuRJlBO2FSgL22Q9uRe/XNUQiRe0FeB0FihpjGkWLBShMOoI2LKfgFYzSnLWKXdDkITmhAmtbfbtlcVRZjRyxsUlRgru1SgZKx0PZnvZrVUoPx7cXvt7fUxVsV9rXRISxmrZFXGarKpwIGB+qtQyO2ZgZzDIRisAwfqTwUCNDaqZDLzUdU9GIZmmwq0E6+3txeOYzXndTkbfPr0LPv2teD1djEycjrd3dYT6fORS0ta1610nFwuDV2v4d6U+1LzN49wFBuFzh2G7S1wNt0W47yK5nVz58Krr9ZeSHRwEO67r/BZtu31Qn9/aTHTozDMrILZIeJICKMPV2zbBlddZS8GPxb3z+sVNfbMtXfBqq3q77+dVGoXTmeISOTrJZqrSmG+Ji6/HM4+W3weGRHH7MwzrfYJ5YT2tR5b8bbrOHGBVUYn5ckNtbnX5YHYQF5fBeB1efFpkJEArFKEQqjRgygoJLNCY+XznUQmcxBdTwtJNI9QhwAAIABJREFUhIWxUjg5Ctr0LnuxTS3i9VhMrPsGaax0XZBkduyIXSpQUcQECU1LommJfDdrYawUpbS9np76U4HmvpY7pLK4cUFjpaDriYoaKzsGqVpIjddUMVYgPu/bV794HaChIU0y2Y7L1Uw6vZ9sNm7DWJXaLZjPQ62pwK6uIYaGTkZRnAwNzWXatIOW7ylKKWtV7Tg5nVk07TgEVsVGoXNGoL/NxZq+U2s3r+vuFk+PWguJrlxZShtoGnznO4ITffJJazHTozDM2pmHH4aFC49cXw5XFE/aPB7Crta31FYNDKxi164v0tGxDE2LM2vWCovmqlrIa2JiAn79a/imMCbmD38QPzdsKG/4OZk44YFV2iBVzFjFrIwVQKPuIVmLi2c4jBYdpiPYkZ8V6HQG8Ximoap7reJ1IO0SL6Lprg779mpJBcbjYsrVG8RYjY+L68ZpgzPtxOsdHTAxkQW0ulOBHR2l7XV3S2Pb2vpbrLEqd0hVtTATMZuN4XZ3VE0FHqp4fTIaK7vtBQJieb3O6wChUIqJiRBebw+qGrF1XhfidStj1dNjFa/Xkgrs6NjNgQPC9/LgwV46OvbY7mOx11dlYKUdn8Cq2Cj0tJibi975T5zy2nDt5nX79xeM9mopyfDnP5eCtnRapB4NQ/xNFjN9gwqT1hPFPioPPSQsa1yu+p2Fj9bQNNi5UzhpyJhqE88jEXbZZqmt2rnzJtzuaUSjD+Nw+Onq+pQwhswZ4FUKeU2Mjxfqhp13XsHxHcSMwHLAajLH1u83UFXHCaux8mV0VHeOKco98YpTgQANhouEI2vTQlGEQmRHo3Q2dOZTgQ6HN//QKk4Fqk6FuSOQ6Cwz577WVGBn5xsGrMqlAcGesershPFx8RyoV7ze2VnaXlOTcLaoVWdVayrQ3C9Ni+PxdNYkXk+l6tNYHap43W57sqB6rc7r5lRgMJhkYqIRn6+HVCqCpsVtDEILdgvZrAC23d2Fc1PNbkHOCmxv38H+/Z0YBuzf30l7+w7bfayfsToONVZQMAr99nu+zTmJJv56yccL5nXF/+3M68ysUi0lGe69V1AhxW1fdFEppf4GlHioNexMMxUFHnwQ3vtekfp5+eUj2sUpi0hE5OHNg8CxmP4rjnIyvqamt6MoHjKZvXR3fwaf7yRUNSKMIXMGeHZRfE00NhZMAxVFgKr/+R8x4G/fXt4ccTLH1uvVSacDOBw11OM4DsOraiQ9ufEiR2VE4pESeUPAcDHhqIEiCYcxYmZglUJRvHi9vbbAKuWG+aNuxjqa7NurlbF6A4FVOeE62IvXRdfSKIonb7fg8dSmsZo2TbxoyHdoue1KlgnV+lsuFWjuVzYbw+PpRNOSFs8ou/2F+lOBhyJet9ue/Fyr87qZYWpsTDA+3oDX20MyuQ1w4HBYJ2qYU4HxuBijzAC1kt1CMilSx+3t4PHsQFEcxGIwONhCa+srtvtYD7ByuY5jYCVjbstcWvdErW7o1UIWEpWv7LUUEn3ooUJ13OJ27Jiso4S1Wr5ckHPyIkwkRKHSU08VBvRnnXXYTZPfsCg2xT9eohywOnBgLbqeYObMm9m79zs4ncGCaLlCLF8uKrZ3d4vPdpqp7m5Ys0bULZxKuwqfTyOdLvOUPAHCm9YKGitzKjBsHcmDmpOJWhircBglPkZnQ2dOY6XicPjw+coxVjB3xCDa0WjfXrFVt11I9PIGaayqMVbyYStr9HV2ilSg292WZ6za2mpjrORsMFmUQ267Vt9Uu/6WA2XmfmWzgrFS1RSKYvVaKt5fqD8VuHOnaLMcQK20rt32AgHRnp3VWrVUYENDgomJBrzeXiYmNpWkAUEAKzkr0O4cVNJYRSLCu9DhgHQ6Qnf3BNu3QzzupaFhk+0+ms9PNfG605lF149zYHWK0iYGgba22leqt5Do4CB8+cvwV39VvZ1a2nuD45FHCpjwa18T/k7y86JFVgPIYzmOR30V2FulRaPreO21K2lsPIeTTrqVBQvWMjGxiZGR8h4akmG65RbhRWUubVEcl1wiTEDrMROsJXy+LJnMiQusPGqWhByTzeL1olSgX3cwptRQKDIUwhEfo6uhqyQVmEoN2AArg65olqHWMpSIw5F3dC8bsZigA3T9DSlAX86IEqwP21RKXK8tLQJYuVzNGEaaiQmd1tbagFUgYG1TbrseYFUrY5VIiL6mUpDJxPF6u5iYyFYETZNlrDZvnlzN1IYGcUztgFUoZD8+uFzi0sia3gvMQCgYnGB8PIjP18PExOaSNCBYU4F256A4FWhmyMyMk6oOMGNGhmeega6uLJlMqedk/eJ17fhnrGbsT7KtBZLZVPUvy6i3kOhNNwlI+8QT1duppb03OCTZ9q//KnyHnnyyUGh00aI3GaujPewYq7Gx52hr+wDNze8EhOaqvf2jTEy8VLYdKVS/9VZxeX7841adlPn3Sy8VQPWl8s1NKny+DOl0w9Q2egyFO60VgFU4jBGPM5IcobOh0/I9v6YwptQAWsJh3OMJuhq78uJ1i8aqaFZg0mXgMOBAcwUaslo6UFIIgcAbwlpVSwWaXblltxKJLE5nAIfDTyKh0dpaWypQAitzm5NJBdYiXk8kIBgEr9cgkVDxeLqYmNAqgibZbr0aq61bJwesJM6201iVA7uKUpqeMwOhhoZxxsb8eL09JBJbcTrtGStzKrD4HFRKBZqBUSoVobfXyVNPQU+PE1XdjVEkODaf72xW2EhINt8unM4s2exxKF43h3PHTvZ1NrAjWipKKxtSizUwIPJhlbRYZpuF++6zpvfsNF3Dw+JKTCaPCsSiafD734sH5V13FSqbn3ee+Hn66eKmK4cPj6U4XhkrO2DV23sdmhajoaGgLG9qehter71d8G9+I35ee634uXq1GDTtCiWDqOxeraLKZMLjSZPJnLjAyqNmSbhyA3vORGeWvwunwzrlzZtViFHDy2I4jHs8adFYiVSgSWNlyiulnGLbe5oqDPfVBOwS6YjCj9X7eIhRq3jd2i0dhyOA0xkgkdBrTgX6/VYgNBnGqlbxugRygQCoagCXq4lEQq8ImiabCkynJwes5Pp2jFW5cwKl6TkrYzXGxEQAr7cHw0jbpgLNswLtzkG1VGCBsYowc6afp56C3l4nDoePTGa4ZP9ku3v3CjK2XCoWwOnMoGm1a0SPSWDFtm2M9XayfWQSdf+mTxdWCTKhbhdmm4Va0nstLcLHoJjdOkLx3HMCO37/++JN4o47xHKPR3xetQpOOgn6+o5sP6cijlfGqqsLhoZKwe/Y2AYaGgomU2KWjZXqlkL1971PfL7zTvFTzrmwE6AvXy4GTkmvT6XBqs+XJp0OHnpDx2i4Uxkm3IU35kyDn3nuzpLvebMGMaMGYBUK4Z1QhcYqkyxKBZZqrJIOnVTAw0F3hTepY4yxKgZBgQBMTOg5xqo+YGWXCjxc4nW5vUBAJ5udlutrZcaqMSeNqzcVCJMHVqFQ+VRgubADVvJFLRiMMzbmw+PpAhxlUoFWYGXHWFVKBfb2Fpz3Z84M5MFWnskt2j95fmoR+AuN1RQDq4cffph58+YxZ84c7pBPaZv4xS9+gaIoPP/88zV3oO4YHITvfAfPtG62jQgX9TWb1jBr9SwcKxzMWj2LNZvWlF/f4YDZs+H1161tSpPPYnF6raL0Sy+F//3fo8I09PrrRXcqGTsuWmRNA031TLqpaq9SO5omgNXs2VOzraMpnE4hyDWbumYyI2SzI/j9BW+J/EwwU8jzLute1WLsaXet9Pev4nOfO/Riz16vekSB1ZEev1xqmokcY7Vm0xoGjTg3rXqWS/6tm/3nzM+PE56sUTNj5UukLXYLBw/+kvs3rSaZHmb5uhv4xgvf4X9evJGBgVUYmo5DMzAGByu2aUERg4Msn2ka93IIZtSZ4V3fPZ+P/UDhku938os/fSs/1kWj67j/2Y+VjMW1js/m6zIeh7BjzHYcLU7bFVKBotC3SAUatLbC6wf2Wrb92z9dzvVnfiffpjAI3cFY9hk+/rPP4ljh4E+vPsdfhn5vAUdyH5S3r2DW6lnc/+zHLL5x8Tjo+hP5e0MCguKC6YkEOJ0HcLlGyGTac+yaURE0uVwifSgZ6FpCMksvvFD7OsXrP1fk3iJ9rMqFzwef/Pyr+eO9bf8AD++6H4BgMMZLL/mIxdbj9U7PpwLN44nQWBVSgeEwJBL3MjIyjqaJ8d7thq1b/5mRkbstIO7hhw+gKN9EVSN4vTOYOVMIwZ5+elOOyRUvn1u3/jNbt/4z4TD86U9i3Wee+T5DQ6aBltJxzuWaYsZK0zSuuuoqHnroIfr6+vjpT39Knw3VMTY2xl133cVfFYu9pzpWroQDBzhz6yjbR7bnizP3x/oxMOiP9XPlg1dWBldz51pL26xcWTD5LGcKWo21uuQSWLv2qDANfeKJ0smMxXHWWfDAA4XPU12Yearaq9ROJCJOTT1vcsdSFAvYx8dfoqHhTBSlcNt6vTNyZSKsEyoSCVH/6lBiqoo9C2B1ZE7S0TB+udQMEy49P1YFUgaL98JXfjRI24uvsvXqfwDAndWJGjWwQaEQwWSWVn8rDsWBpid5JZbCM3I7oxkINII2PYZn+D/4y1CMhYMabjXDu37yl/JtFlMsK1eyYuCKwhgWj/PHoWfZkRxkNDrIq2Pw6d79JL73OYz164l+99O8+PJl3PHsryxj8RW/voJP3f+pmsZn870ei0H46Ydtx9HyqUADp1OmAmFX+gW27N1h2fbI9x5k1cufIfrdTwMQjUYYGf0e2xN9DI2kMTBQJ3x89cWb2ZXcSDyO5RnD47fQH+vnjmd/xYsvX0Y0uo5sFlIpnf7+v8vfG7KPxffQwYOvkE6vIxBQyGbbc4yVUnUMC4WEHUqtIZmlX/+69nWK11+/3rrM74fXXiu/jkqc3//k1Pzxzmac3Pj451mzaQ2BQIzdu6fR17cUpzOEyxUqGU/MswIlE9nVdSrDw1H27XscrxdGR9dx4MDPSCQeY3x8CBDjUiTSgdf7M4aGHsTr7SEUegaAJ55YCDhIpSJEo2LdAwd+jtO5hRdfFOtu3z7A6693VxznpjwV+OyzzzJnzhxmz56Nx+Phox/9KPfff3/J926++Wauv/56fIdDpCFDsknArKdfYWjHZktxZhmJTIIbH72xfDtz5hSKMZsZqnvvFaikeNZLLaL0ri5xNRxh01D5MD3/fOvyYmNHaQAZix27ZqH11tM+1qJYZzU+bk0DAjidflyuRjIZK4rasUOke+V5r8fYU35XGpL29S1l584vTrrYs8eTQlWPDLA6GsYvVyrNuEvnxkdvJDSSoCUlBt7TD4LTgN5f/gn27cOd1oga1fVLRihEY1In5A3hd/vRtCRf3/AHlvdBkxtmXQinfQiW98GqB3/E/IOgAEvW7Sw/HplzI6ZxNj+GxWKsfuVexl06/gxsHIVvvgAnfURn5ycN+hbdz/c2enlm2DrnPqNnSGvWFKTd+FxsZhzblyD07B9tx9Fy4vVkUsHhEKnAZBIe3vPf6GrhfHaOwYf+V7yA9C26n52brmXv3kd5PKZzgCioOTSihkg59vPHPb8iFqPwjDH18Zlhla9sC9DXt5RNm27H749z2mmFe0MeTvM9tGPHzezc+QO6ui4gGHSRybTgdPpJJo2qwvRK2qap+H4t61cDf8PpIofzrJeUEeXGR28kGBwFYMGCtSST2xkf31AynphTgVK83tNzHqlUJy+//E+43Un6+pZy+um/Zu7cf2d4eFN+XAJ429vuoL9/BZnMMPH40nw3otE/cuDAT/Lrnn76rxgdFdOjn332n0il/hmg4jjncKSnFljt2bOHHlMCcsaMGezZYz2AL774IpFIhPe+970V27rnnns455xzOOecczg4mddpE5ukGAbvX/tSSXFmGeWWA1bGauXKgpBF0wT1fNFF8NvfVjccNcdttxXcF2W8gfYLUlfTkata4fVaNTLmn4oC7xQTy2hqEv/h0HU1sm05HXey7VVr53guKG0Oe2BVaome19aYQor6i89/LWH+bnPzEhyOBvr7V0662LPXmyKdPjLlbI6G8cuZVBlzaQzEBrj5ccvzGQBFN2DlSpyZLMNaBe1nLhIBNyEV3E43AXcATU8RiQ+ycRQGEtBxFgy8KMDPFb8p7Kvcjm3k8mvLl4PS3YWSEsyZkkygdHWyfOcneC2zj4QbArn3zr/7JTS8CgMfh+7fOLjwv4dqPiZyfC5nZvyX38cJ61GxsGgcDQaF3iaTKTBWBWDlzzFWCsPKVsjk0MC6W9j3NYNAVhz9Cy43mH3G19m69RJemdgP3hioOTSRCoMvRtTYRSwG/b++ApYbsCJ35pYbsNzgkf/+DKHQBWzdejfhsGG5NxoaCiVxmpuX0NHxUQYGvoTX+w6am6fj86XJZFqqMlby+Lz6qvX4VLqfly+3TkKpZ3yU2/v5z63rKgp86Uv27cl1MoPzLccHNQRPX0v/Nbv44Ae/B0BLyxLe/vYUd931jpLxxM5uIRSCsTE3mtaNyxXPr9PWdiaRyKnMnn0rF1wg7sXZsy/koovG+L//9y7e+c7C8/9tb5vg7LP/zNq1a7nrriW0tCzhuut+AsAll+zghz+cDsAFFxxk9uxbWbu29OXR6cyQzb6Bdgu6rnPttdfyta99rep3r7zySp5//nmef/552u2qrFcK+RaVY5Mc6Qwf+ss4i+iy/Xqxq7ElJGNlp6f6wQ/g+efFFKl6+3YETUOlRub668XncnoaOy3NL35ReZ16+/Czn4nPzz8/ufZkO9/9rvj8+uvWduTf/8//mZp+H61RCqw2lgVWxTqrqRL1Hzz4IKq6i4aGRezd+526ij0X+pdEVY/OOoGHffzSdRzpDOOOLGfTzRUbQU4QlHZAPg24916caoYhozqwiikqLh1IpwWw0lK0Bbs5qwlm+CH2GvScDRd74FMbQermPZpRfjzKUSzL/3kQw+dnCUKAYqBg+AMsd9xKY/uMPLDqHIMPGhA/C/z9sPe9On8HTKtghWUOOT7Le/nv/14sNwww9g7SHX+FkARWReOoohTKzZjF64mEI2+3kEw66Gx354FV5zkrSLgU/AhG8InfKjxzn0LntGc5c5obfDEBqHQF0o3gGaO9xUs8DjMvvxeWK3D6T0V/liuwXOHd//AdotE/oGnn4PPttdwb0rIgHheppX37fgw4OXBgA4qyK5ceb8HpDJBMOioCq3oLoB9K0fRy61ZqT67jm/uk5fiAAm9fzsw7Z/HssyKFMjj4GE8+2c4tt+gl44mYFSg0VhIwu1zCYPjgwTg+nyO/Tjr9As3NA2zY8C5+9KOLABgZWcfjjwf44Q+/xJNPtjMyItp+4okmnnqqh6VLl/K5z61jZGQdTzwh2Enz99avb+GxxzwsXVpad9XhyKJpNRRIl9+v9oXp06cTMY3uu3fvZvr06fnPY2NjbN68mbe//e3MmjWLZ555hve///1TL2D/938v8cx3GPCp3+y1/fp4ery8zkoyVnZ6qkxGLKt1OggcVaahk3F7ONSCu8U3rKw5V2tfyt3w1do5CpwtDmv09MBDD40Qja5D05Ikk68TDC4gGl3HNdc8lf+eAFZWhrZeG4pikS1AJPJ1+vo+jMczg0zmIPPn/7yuYs8yPJ4E6fQUWrnXEUd8/Eql0L0e0nqGNX2nohTTVTI0DUdKrYmxiqfHGPc7IR4n4A6gGyr/vviDLF8Ajx+E5E74y/3whYUwflaNLLpUhOfGsgQB6zrRKJ9/z0oyXieBDNy+G7bfBC3PgisBC1bA9hth1V7r9twONx6n1f8n4A5w2zusTrUWa5GVK4kbjYQxaRSK+i27K1OBotC302S34OTf3vUpyIr9uPlxyOoukrn96noQfPsU3Hu7+fCsUWa3JgXDkgmCK0nA5+X//vVSYjG47R23EXAHIFZgPs9t9fJvcxO0tV1GNnsq7e3TS+6NUAgikT/T17eUrq7/A2gEAh9kbGwNbvdB0ulwLm3pqsuj6miNhWbrcl0B3Ynf5+a2d9yGoggG9IknPs+CBWvzBsfmY1acCgyHBSj1+w+SSJyJ3+9hwYK1bN58OZHINWhaE+Hwefh8oor85s0foKnpYjo6lua/BzB37n+iKI78ss2bP0B7u0gVmr83a9YKDCPD/Pk/LTmXTucUpwIXL17Mtm3b2LlzJ+l0mp/97Ge8//3vz/89HA4zNDTErl272LVrF+eeey4PPPAA55xzTs2dqCl++9uSRT4N/nq3/deHk8PlRezScuGpp0r1VJomeOZ6wNBRYhoqM5bXXFPb96WWZtYskTo8cGBy2zWLTnUdHn4YFi+u3d3dTqCeTsO6dXDOOfbtZDLCLuILX5hcn4+F6O2FnTtb6Otbyr59P8TvP4VYTAzUq1cXRHQ+X29JKrBexspOqL5z580Eg2fkahA68Xg6ai72bA6vdwJVPTLA6oiPX4kEut9HWktzymvDgp2yi9z4MZKp/kIXS8VI+EUldb/Lj6GrvKWthXTrDfQnHbi8kB5u4ZR7ppGYY8Oi241HUm2dG8sSBLiYRwvrGAYfO/uTnH3yhbQrQU4NwWkrwJkEtQOaN4rPb20L4XcJlDAzPJN7L7+XH1z2A1r9ogB0d2M397zvHpYtXGbZfCRiKqTx9NPEjJAVWBX1W3bXnApMpZw4nQEUJUAq5eRTf30ZPqOZRk8j5+2GtC5Yig833YdvH+gug8ygwenzb+DDC0/FnW2HVBhnYJx73ncPf7foncTjokbtPe+7ByXeC+2bmBmeyb+/9QO85Yz7cbmaicedtLQ0ldwb4TAMDm5lwYK1OJ1iVmwm08TMmUtxuw+SyYTz7FotE3AmUwD9UArS261bqb3Te3tZ9LbdtPhbQPOiuNN87/3iXOu6SldXFL//e/k0m9SfyWPmcPhKxOtjY8/R0tLI6Og03O5MPq3a2Xkx6bSHbDZGNHom8+cP0NHxEZzOIE5nKP+9q656kI6OD5FO76Op6SI6Oj5KR8dHcDjcfPKTt1u+J2dbNzaeU3IuXa701DJWLpeLb33rW7z73e9m/vz5LF26lNNOO40vfvGLPGCeVnY4Y3CwIKz0+1m8YgbKclCWw1s+XX61siJ2abnw4x/DkiVw//0ClXz/+4Xv1JPCszMNvf9+ePe7a9NnTVEMDorN1ZDVAApMkaLAuedOrsxNceHNjRvFQPflL9e22zINWRxPPilqG37hC/btvPqqAB7/8R/19/lYCSkNOvXUtezYIQosb9mylBdesE7XsUsF1stYyUFu8+bLeP3163JCzwdJp3fT0nIpra2XMjLyUNViz3bhdieOGLA64uNXIoHh95PRMvlxovXLLQxNHBQ369/9ncidj49DIEBcrQ6s4mqcZMANsZhgUow0PT2f58NvuQ2Pq5mulja+9De30/vAPpTvDtDz9RnouoZzhQNNy9rfUFJtvWEDbN5MEj/ndOcGlH37hIMiMGfGGVw6/W2cNx0Sj+8mO6eTdDPomkrzBoNTvxFlQfsCOhs62XX1LpYtXMayhcu46aKbAPjx5T8uAVW6Dnv2iLHEMIANG4h1nELoQ+8Wg4CNzlUK2K3idRcOh59sthG3W6ehAdIpN/94xse56kvnEVsn1v/QvyxCv+sr6Bf+FYkFi5k2bRHvPPtaZvoWghpiTtc0li1cZtHzf3TBMoyxblwte9l19S4ue+tPaW5egqbFiMfdhMOU3BuiX1fQ3Lwkf3+OjydpbZ1LQ0MjqtqI0xkglXLVBKwmI3U4FHlEOVuWchEKwT9eNoPrz7+eJlcXoYAvf651PcUFF7gYHbWmR8zHzM55vbf3Opqbg4yMNOLxCNA1b97dzJ+/nHTaRTYbIxKBj3ykl3nz7iabjeXNR+fNu5tvfet9OBwe3O4W0ul9zJt3N/Pm3U0qFeETn7gRw9Dz38tmxb2nabGSc+lwpMlmpxBYAbznPe/htdde4/XXX+fGGwVQufXWWy1vfjIee+yxqWerigw7P/lgGZrKJsqK2OfMETfqc8/BxReLZc8/XzDWO9QUnqwbUzzd5TDGhg1is5Op9VZvmRspWJQUthQ0fv7zwurhrLPg5ZfL13WV63/oQ9b15Y0rS/KU69eGDWIbx2MUT0Roa1uSE2Wu5sILD/K5z50KFI7ZnXeebwFWyaRgHysVFbWLxsaz0bRxIpGv0N39GdzuFpzOIIHAHFpaBLCaTHg8E6RStZeDmOo4ouNXIoER8OdnxhmGQVyNE/KaHBwjkbz74URmAk2vUAwZiKkxUg2+fCrQMNI4HEKtPKKqGK5CSZu0lsbj9OBQHDR4GhhLlxFBmc2hIhESzhDxWG7sMrtfBgJMxIRIXdVUNIeY4qiqBZF8JB4hlrJONZaAMRIvLRi+fz80N4trWQKZeBzCqf1lJRl2jFUy6cbpDJBOh/H5Mrhcwg8umcoSU2PE94t0VDyqYRgqhqFaStpMjDlBDdPQmLVsA3L+TbqLbCJoAb/ZbJzxcbftLDrz7MVUKoLfP5fx8RSBAHi946TTjSiKm1QqgN9f+ZwfC5E/J6kYyZRuKeSu6yq9vYZtYXkZiuLGMDQMQ7eUCAqHYXi4KQ+sQGRY0mkXmha3GHxqWtzW1b14go8YLw00U+pd08TJlgDLHAJYHU/O61IYLgFKOs2nXlLqFkmWRFeXKKR31lliCsfgIPzwh4UqkocqPJ8xQ6AK8/pmI9LDEBs3Th5s2BVmrvTGIgWLkh1buFAcuu3bBSBqbhbUvtmHtbid9ethfm4iyV/+Imhm2f6PfiTacThWMT6esaQpo9F1PPHEc4esDTtao1hA2tWl8rvfNXHuuTFmzNjB3LkTQMHE85prnmJioi+fxtuw4S/MmDFMPF6fmefu3asRkmoPe/Z8iz17vkVLy6Vs3frPHDz4K8bGniObFTdePUahHs8YqnrkgNURjWQScqlAgGRhJbQ/AAAgAElEQVQ2icvhKuiOenqEWVkqheL1EnQHy4OfXMRSMTJBP8RiBN1+wEBRXBiGwVAqgeHJlgArgJA3VJ4RK7KiTjgbiCXc4qY212vx+0nFRHkQNauSdak4VPIav1Q2xWhqlIyeESydqc9+l9/2RXdgQBwGiTHTaZHq948fLOsFI7srH8B+P6iqG4cjQDodwu8XxzsQgERSbD+2TwCr2KiBrqvoumpxXk+MeyAVxhfM5LcRj4v7LBIBxZ1ESTcRiRUe0NlsjHjcY+tIbi3HMkAo9NdMTGRywCqOqgZRFIV0OoTPd/gLWx/uyJ8TNYaqGvh8BVLBMFR6epQqwEpBUTzoumrB8qEQjIw04XYX0iMCWIlUoLx+QJwPp7P0ZBTrUFV1IMduFq4v+bt5mQyhsaodLh39wGrlyhIdlAcnK5600nK1iiTz0dcnqrlL0bmdAP1QWCtFESjHTLeYjUgPQ0jGajJR3FWw1z4VL5PrtLTAV78qBqC3va18m+Z46CG4XOgGufrqQtuRiPDjOuccCIUWM3v2s6xfL1CfNG/bunXOcctYFcfChQ/w059ez913X8b8+WHe+94v5f/W2LiY7dv/FU0bo69vKZHI13n88W8za9ZoXWaeAijdTmvr3+JwuPF4utm//0doWpIDB37G0NCv8PnmEo0+WrdRqABWtb/tHVeRe3JLYBVLxQh7TW/Uvb0FxsrnI+wLV00HxtU4WmMQ4nGCbg8GLhRFIZVNEc/oGB4tX/jMDKzC3nAJk5QPMwoYGCCpeYl7WgsyDFPBOnV8FKfiFIyVJ0tgwJFnA3bHd9Pd2F0C4uJqnAXtCyygRIZkHCTGlJtTxuIwMWFLexeL1wVj5ckxViF8vgKwmpgQLGF8SCyLj4nUlK6nLLUC1QkPqCH8DYIZ8XqFaiSVEn10dLyKO9NqAYeaFmd83FuRsTIMA1XdTTh8Xr58jccTz3u7pdMN+HxqaQPHWOTPiRqHrA+PpwCsdD1Fb6+zIrACobPStJQFy4fDMDrajNtdMM/1eLKk0948YyXZ+XKMVb6OJpDNjqPrKj7fTDTNyj7KNopDAKspTgUe0Xj66ZIby5nJ8uHRGcwMz0RBsYgkZ4ZnAhBwBWxFkoAYLJ4Rzqy88IJgkOwE6IcqPDfnsYqNSA8Da3Uo6bH588WgVqmEol1Wc+NGUeT5zjshl2XJpwerpRdluu+LXyxotYaH4Ze/FL87nSIHf+65vfzxjw+wY8fN9PUtZf78tWze3HzcMlbmuPrqp7jtNrj/fiEm/MY3WnnXuz5MZ6dgNaQ2CqCx8Vxef/3zjIy8m9bWP9Rl5hmPP4vT2cDs2V/m9NN/QSLxCoZhcODAT/Kmesnka/T3r6zbKNTtHiOVOpGBVTAPrCxpQChJBVYEP7mIqTH0xgaIxWh0CWAll09ogFezMFZuhwBZFUGbKRWo9e9G1VzE3O2ib1LwAhAIkB0f46Tmk0hlU2R9WYKv66jJfgAisQg9oR5C3hAxtbAfMTXG6R2n26YC5YNRYsz8Q9WcF7TprjkV6HaDYTjQtACq2oDXK4CK3y8Yq7H0GNEDOWF03GHLWKUTflDDeAIpy3ZEaRoDvf1ljFTIsg/ZbIzxcX9FxiqTGcbh8BEILCCREGagbnecVEoMlKraeNwAK8lYkfXi9hSICpEKdFkqSdiFw+FlYkLF5RK1bWW70WirBVi5XHEyGS+ZTIzdu62MlV0dQnMqUJS96cHpDFvYqUIqsPT+UxS1Lo3V0T/aPfccTJsmBDumadItwC6bry9buIx94/uY/+35fOS0j9i3aWaMjJxp3uEQmC9aBL/6lfj9+usL6EEyYd/+9pRtKhYTWoVTTpnc+m43LFgAn/ucsPKSYafXkstuuEGk+i67DN7yltK/f/SjEI3ab2/fPti5E/7wB+vpyM8MMrVz+eU9JJPnMzDwTmbOvJl4fAmBQEGDdDxHOHw+i03EkEidvgW3W1xGEnwGAvMYGfkN4OTVV8dZvHh6XWaera3vZXDwuwQC8wgGT6WjYykHDvyMnp7/l2+ns/MT7N37n/T0XFdX2yc6sFL8JsZKjRH2md6oi4BVyBusibEyck/8oNuLLoFVKsZEFgyvDoZ9KtAMdiwh8ziGQbJf5N1jjmaIbBXpzBx9kHQp+NIaXQ1dqFkV/AbB3U5S4zsAoaHqCfcIhsi0HzE1xuLuxTy3t3RGqWSskknxez4NNBgXtJGwMC/prpmxUhRhRKuqDahqAz6fGGsDAUglxUAyPKziJUVsrACspMbK4wFDd+BMduFqSVi2E4vBjl1ZXNO2kX3Zz8CoFViNjQVsGSsJNOSD3OvtIZGI5bYXJZUSujgzEDyWQ56TjBoHzYvbqwEuDEPHMLL09rprYKy8xGIZC1BtbDSIRtvo7t5p+mYcXW/iwAEPwaA4h4ahoWkTOJ2NJe16vT3EYsKiRp4PRXFa9FTZbBxF8doCKzEr8HhKBT77rNArmUBVtehs6OSkppN4OvJ06R8lcyTZqcNp4ilzYYOD8JOfFJYfhm2+9JLQOTlrB9UlcdZZcPbZ0N8v3I3BOtHxtlxW9WBuQtPllwsg96Uv2RvIrVpVXr//8MPC/f3WW+3XNf/+//7fs/T1iYrCe/Z8k/XrN50wacDly2H79uvYuVOk/+QxmTmzULcrGl1HJnOQadP+EdDZs2cujY0/rstvamTkIVpaLkVRFKLRdUSjf2TmzJvzhnzR6DoOHlxLMHgGe/Z8s662HQ5BgxY7m5wQkUyiBINkdLHzcTVuTQVOmybePmIxwVj5wuXBTy5iqRiOcFNOY+VGyw3jcTVeAFY2GquKbJjMe6kqiYjQUMWNxiIKCYaVBK0E8Lq8pCdGQAH/eBNqYhcgGKveUK8AcaZtxdU4p3eczkBsAKNoQDCnAksYq56esoxVNCoyhY2556jPlyKTCZJOB4uAlTg+0WiGHiLEE04MQyWTyZLNClClKOD0jRNMzcPpH7dsJx6HXQNZgh37cXs0dh4QTt+GYaBpcSYm7IGVBBoFYDWdVMqF36/h8YzkZ8qm0wF8vhpqRB7lYRavk/XhcotMk66rKIqHtjYFVa2cFXE4fIyOZi3HMxzWGR3twOWayC/T9RgeT5r+/k6TcH0cp7PBUktVhnnmtKpG8Pl6cLlCeZYKBEj2+XpsU4GKkkbXjxdgNTgIH/sYXHhh3av2hHr425/+LcoKBdetLpQVCrNWz+K1q/9RTDk2hZbNHB7d07x5sHcvnH765Ao71xEbNhz6g0um7t77XvjsZ8WypOl+l+L23/++sM1KAEfq9z//+cIyKU7/yleqF4oGcuaYl3HgQC8/+tG9uFyt/OlPjxCNjub/Xo9A+1iM8fGNNDZa854Ss0u9U2/vF/jKV07n5JO/ytatZ3P22RdWNfM0m4JKYBWJfJ1Nm95nMfGTpnoLFqyltfV9GAZs2fIhi+dVpXOg60l8Pr3EmuOEiEQCR7DBorGypAKdTujuFtSvz1cCSOwipsZwhJuF3YLLhWY488uTGuA1MFxiaM/omdrE6yCQwOgoyb1RGhoMYtlAEYUE+/Uxmg0vPpcPbXQProSCT20ilRYztSVjVQwQY6kYveFeFJQS4FgsXo/FINSgicFj2jRbAXsoBLt3ixdAWQ7H602QSvlR1QA+n2CdZKkbgOioQY9zL7GEG11PkUop+P2m0ln+OO6x2eCLW7YTi4l+hTpiNIR0du0XNLywBnAwMRGmocH6TIEC0EilBvD5enA43KTTDbhcB3C7h/MzZVOp4HEBrCSQjKkxfEoTTo84Jrqu4nB4UZTSahLF4XB4GR3NWhirhoYMExNh3O7CpI5sNobHk2HPnlnMmKHnl9mlAQF8vh5SqdwEi9QAXm+vbSrQ6+2xZawgg8tV+8zNoxtYrVwp7rrdtdsrgKhG/sjrj+QHEc0QB6Q/1k/iiUdxZqw3gTOTZeTRUgPSQw6nU7xOjYyU/m2KjUM3bhRysUOJRYuE/dbmzXDTTWJM27Sp8PcNG+Ctby04om/cWCqWNxvIKYr4+9e/Xli2YoWYaNTXB5dcUn5d+fvY2HMsXPjfzJr1Cj/84SdJpXbR3/8Onn66qW4R9bEYhmHkawSaj48EwWNjz7FgwVoMI8u9915HW9u1JBJh2tq2VTXzlKagQ0O/ZWzsORTFyc6dN3PSSbdaTPykqV5z8xKam9+Boih4vTMYG3uupnMggVXy2H921B+JBI6cxsowjNJUIAhx0bZteY1VLalAd0ursFtwudGMAmPldnpBVdDcYowrYawqsWHhMOzYQcLbzLRpCmOqB6PfpCYH9ulxwpobr9OLMXYAp+rEq7WiaoNADliFekr2Q+53T7inRMBu1ljlxev+jNim2QaiqKuRiLVYsNebQFUDpFJ+PB5xsfn9oCadeJ1exsYVegPDxJPuXBrQQyBgmrnmiaHHpou6gabtxGKwd4+T5mnjNIVh94GCyNnlCpNIhGlsLH1rkF0XjJVQV6tqA4oygNs9SjLpyi3z4/VWL759tIc8VnE1TrO7E6dLXIOGoebtQOQEhXKhKF5iMd1yXkMh8VLichWormw2jseTZc+e05gxQ80vczrtq097PJ1ks1F0Xc0ziC5XuCQV6PX22jJWhpHB6SxTXcUmjl5gZa6w/vvf15U2u/HRG1E1+5z1ok+TNxc1/69kNDrpGBwUamwQd/jgoBAjPfbYlBuHTkVTZ5wBQ7k6qg0NglGS7cbjhYzmI48IDb7dLMRiiwbJaF11lfgP8IlPiJ/d3eXXlb/39l5HMHgqc+e+AsAPfvAczz8vJijUK6I+FkP4Ayl4PF2W4yPtMYSB3hIeeUSY2V15pfh7S8viqmaeUvj+yit/j9vdzquvfoKFC39DT8+1lu9JUz25zmmn/YKJic0kEq/UdA50PYXfb5yYwCqZRAkEcDlcZPWsEK97it6qe3qET4kUr1dLBaoxPM1tOed1FxlDUC6xVIyOYAdKCrIuMf7VbLcA4sm4eTOJjlmEQuD1GEz0D1nE63uzURo0J16XF8YP4kq7cbtb0Q0VTZtgIDZAT7jHNhUY8oboCfVYxN/ptBhzuroEw71njyiKEfalCsDKhrGSwMrMbEjGKpXy4/WKtFEgAOmUk45gB7EJNz1NcWIpUTolnfZbgJXuiZIcbkP3RC3bOXgQxkZdtHVotDS52DecxDCMPEMyPt5EMFh6cUu2Sz7IgZxgvQ+/XyeRUPLLPJ7jBVgZYuarcxpOj0ihSMYKamGsfCXAqrFRAqsEei6lrmkxPJ4se/eewvTpifyycoyVojjxeLpQ1T1VU4F2jJWuZ3C5jgdgtXJlwVOqzrRZWVPQKV6naqxcWeCp5T5cemmB8pmCkGaSL70kPtdTyby4ncbGgr5JUYSt1/dEUXJefllkNE8+WQjMn31WsFlnnlm5X1/9qvj8n/8p/kNBblZ7pfbp/OY3YnbnffctYmysBRDVyFtalhx3xZfNIQsvK0WzCGQq8JZbxDGUgOq//1v87Or6eE3XQVPTRTgcflKpnSXV5stFa+slNDUtYd++H9a0jmCsTlBglZt25nF6SGtp8dApZqwksMqlAmthrHyt0yAex+90kpU+nmqM9mA7SlIh6xQMSlpL43YWZgVWTDOGQrBlC8n23vxMuVgkbmGsItlhAhnwOr2QHMaV8aCEwni1FlKpSH5WoJmx0nSNRCZBg6dBACsTY7V3L3R2CnLf7xdj0OuvQ8idEv0x+2sVdTUaLWasxlFVH6rqw+cT7EYgAGrKSXuwnbGkl54OlZjqy6UCA/j94uBltAy6N0ZqLEDWPWLZTl8fNLUnafI30tzkxJNtYygxlPNMCjMxEaKxcYLiMIvXfT4BrFTVi66/TCDgyN8PqurD661eI/Joj2BQzM9SdDd+pQmHSwKrVB3ASjBW1lSguJZ9PgNNE+lAwS5p7Nkzm66ueG5ZzNZqQYbPJ3RWqZR5VmDhXtO0eNlUoGEcD8BKslWTNOssawpaZZ01m9Ywa/UsHCsczFo9q3wR51pC7oMUPsl9eOtb4YEHqhqF1tqX5cvhxRfhtNPE53oqmRe3Uywcf/zxghG9WU916aXwjW+ImYR2os1y7Zl/r7Wvy5fDnj3f55VXrgAKFcyhUJn8eANWZu2TTAMW65i6usS5icfhggsKAFge2y1bPlb22Jrb37Hj38lmh+no+Acika/XJEqPRtcxPv4iTme4pEK9XWhaEr+/tPzRCRHFwEot8rEC8bSRqcBq4AfBTAXauiAWw+t0kMmN93E1TkewA0cCNBOwMqcC4+kaGKvWHuHt1OQQ7usHD+Zv9P7MEL6Mgc/lw5Eaxan7IRzGq4YZHd9KRs/Q4m+xzEAcS4/R4GnAoThEKtDEWJlds+Wh2LwZwq6JqqlA809dz+L1TpBMukmlfHg8BWCVSbnoCHYwrvro6daJZ/y5lFAAv1/PHztPQDAfGfdQ8SGhqWOMkDdEKAStjtlE4hE0LY7TGWJiooFgsBRYSc2RfJAXHgUvEwy6SORIqlTKi9dbo+P1URyKAo0hg0am4zKCKK4CY6UotQMrs4cVkE+zer1WE0+v1yASmUlX12huWflUoFhf6KxUdSCXCgyVGIR6vfbidQGsaq+icnQCq0M068xXI68xAu4A75n7Hq588Er6Y/0YGPTH+ssXca4lyu3DQw+JV7L168vuz5pNa+rqy+Eq73LWWYKV0jRr2u/SS+HnPxeU/RsRyeR2/H5R+K6vbyknnSQKBBZXRz9ewlwQeXx8A4rittUxLVoEq1cLn9srrrC2kUxur9r+rl3/QSTydaZP/xei0Yc56aRbqx5PqalasOB/cbkamT37K1XX0XUBrE50xiqjZ0p9rEA8bcbHc3YLFSwRchFTYwRbBbDyOR2ouWEmlooxLTgNJQFZhzjYaS2Nx2GyW6iBsUo0deUYK4VY28mCsslRCDtSg3jULF6nFyUTx6ULd01vIsi+2Mv0hHpQFMUCEM2mqL3hXkt2wOyaLQ/Fli0QcoxXZazMPwUrmiaZVEilPHg8Aqj4/ZBRXUwLTmMsE6Cz10NGd5LJZFFVf76UjABWIn2ach2wbGfLFmjoiBL2hgXWUwTrls3GyGbbcbk0XK7SVJ5MjaXTe/F6p5NIgM+nMzGxxQZYHfuMFUCwQaPB6MapB1Dc4niKVKDQWEmvsnIhGCuliLES17LP58gDISE0N0gkAnR2DuWXlUsFggBW4+Mv4XB4cbkacbnC+VSgpqUAA4+nowJjdawDq0M065TVyKVZqFMp70GgoHDP++7hd9t+RyJjvTnKFnGuJcrtw/r1AqkYRlkW7sZHb6yrLxL0HEolc3PIdkIhwYy89ppVqH7hhQUT0HraM/9eT1+TyW34/XO5+uqnWLBgLdOnX8UVV9xBIDC/qkD7WAypfRKA5U/s3fttWx2TBNN33ilSKfKY3nRTgkRiW8m09uL2+/tvwec7if3772PBgrX09Fxb9XhKsXxLy8W0tFxKNjtSdR2hsXKcmMAqmQS/38pY2YnXQTivVxGvZ/UsalYVjFU8jtfhIK2L8xxTY7QH2nGYgFVGK8wKrGrlEA7D/v0kGjvzqcB422xhkBcOYxgGO9V9uFJpvC4vjuw4TqUBwmF8cR/Rsa30hAVKMrNj5n0u1liZXbPlodi/H8JKvE7GKoHPlyGREA7skgEKBCCjumkPtDOWbSTcEyLknCAet9boi6kx/A1ivE459xcfEgKtw4S8IcJhCGrdROIRstk4yWQnDQ0T6HopsJKY0OVqxeHw5jC2QSazn2DQYwJWHtzu6sW3j4UINGbwa504tSA4JbCypgIridcdDh/xuGJhrBoaxIHy+Rx5NkmkAkFRdNrb9+eXVU4F9hKP/zmvdzOnAuVEhOL0oAzDyOJ0HuvASswjFwpqCULqFHsvW7iMXVfvwrjFIPvFLAr2lYkNDJYtXFZWYzVp7ZU0cCr+f9FFhfm9ZVi4evsiQc9UpcTM7Zx1ltBTvfqq0FgJzZPwj4HadVLFv9fTV8lY3Xnn+TQ3L8HhcHPttc8zMvJwVYH2sRrNzUvo7PwUmhaju/v/s4AqqV+74w7x+eKLrUaut97qR1EUMpnhsu2HwxdiGAqp1OsWnVS14ynF8kC+MHO1dQRjpZyYwCrHWLkdbtJaujxjBTX5WMX/f/bOPMyOqlz3vxr2rl177N1zd9KdhCRCQoIJAuKAEEEFRxAENCqOIOq54lG8asQkYJADDqh4RERUjhFEGRQUECSAzEMSMkHmdO9Oz917Hmu6f6w9du/OQIJX5HzPkyfd1TWsWlW16q33e9f75RMEtABSMAjJJG4Z8lYlndXqa0VJO6I4Mi9DvA5k/S14vUVg0FBEPcEgY9kxbN2DlMmIVKCVRpUDIhUYdZPO7aEr2FU+Vomxqj7nibMC66UCAUJObJ/idZerUooGRLpZMFaiZqDbLbbxesHMayIVaPsJzQwTUlIkk+5isWYhN4nn4viKxZfTcv/ELsEdHiTkCREMgsdqozfei2XFyWZb8PmyWNZkYKVpFF/Ggm0XZqTilev3a2Sz4hVgGEqNlcCrOTy+ArrVjmzpoIp7UMwKrE0FTvHNhyRpJBJKDbDyeLIoionHo9akAj0eiebmJLIcKy+rVyewFJrWRTL5fBlYVacCS3q5aharOoR4/dUOrECghWOOqYi/DzGm0l1JSNiOPeXfX45ea8o4QO3YwbTFtoVw/ZUyzFy8WIjNZ80Sg9RU2qlXSufkOE5NKrAUTU3vZnz88E0C+FeLaHQNAwM34HK1MjDw85pU2/6ugSRJ6PpcstntU+5/ePj3gFVjAnqwEQ6fSiLxFKa57zSGAFbK/2qsSuL1iRqrxkaBEoqpwH2Bn7LBqKqCx4NmFshZFcaqpLEyJfGiP2CDUCijlIzehK4XMY1fGPMSChGJR2hp6oJCAU1yIUs5wRAEg2ijMlZhoDxGVZfPqT7n6cHp9CX6ymzqVMAqaEX3mQosNbeasdJ1wVhlMiouVwLHcfB4bOyChyZPEwmCBGc1EZSSJBIahtGErpvlfvUHLBTFIWnXpgIBlIb+cirQbTYVGas42WwzgUAO267/1RAIFDAMUQ5D3AoKkuTC5/MU2wq6btRlvF6N4fbm0MwWZFvHUSanAgMBYchaz4EIRCowmVQmlAjK4/Nl8HjUMmMlhOYSnZ2JqnTevsXrmtaF4xSqgFWoZn+qGiyDrYls/79HKhAOraJwnainu/K6vIT1MLuiu1h16qpJKcOpiji/bJF7Pd2VaQoLhipwNZVGrCfeM+l4O3eKag+NjQfWhIONxYtF2ZlqR/fS+QP7PP+D7adqUXUpRkb+CEg1ufPe3qtRlADR6APYthgY/52MQks6pmnT/oNQ6K0vS0um63Om1FlFo2vYvv0L+HyvL5uAHuz+e3uvJpl8nkDgOGKxqY1CbdvEcWy83tcoYxWNwre/TUdKmjoVKEnQ0UHq5hv5zA3vZe3AWpqvbqb56mbklXL5Z2mlxOwfzy6PAxmPSvMvfoMnkYeTT0YdHhWMVQqsOsDqwV0PcoJ/F8f+SKp5HrduvYh7nnw7Vz4qtIvfe+wGBmK3YlkPkpBEW29/7mb+809LmK9tJKvC9r4NyORR3A30tvydwdx20tndrHxkJTOvnUn/nq/yroZNANz0w25CnhDR6BoeWPdpLmjK88gZMsdf3sU/Ho8RCgk/tA0b3k1Dw1oAbtgm2KpoaBe9C8V+qseT5qubiRrbuefKezn+8i7+su12PB67mAqU0PUC9mkn4S5EkS0fvqwPNwVcTUH89jh7x7LsjRk8MXgftz90HScsvZQ2tYDLBYlClY+VIfQ7PmlHORU4MJrlyWfuYM/PlrP6sXsZHdexrEzN+FX62edL8IOfnMLMa2dy5Z0nEMuvxZAa0LQAigI9PU+gadkaYNa76VtEL3g9DA6W91P9bO1vrKtuR6nPjv2RxKdv9pXvqanG43pj8NatF7F160UTbutKG0rb9PZejcczgGo0oRk5GtRtRKNrGB6+tcxYrd64moz3JZrfdX25DdVZjO3RPazr6eH8Vb8tG3t/5I8fQvGMcdOdzXzjb/+H1RtXFxkrhbG4xE+fuhp5pczvN/6KZwc2T9knuZyoZ1maoZlKbSKd7WXmtTM55abjeHZgC7ds/iO2A6/7ce17y3EMXK7/BVaTolp3VSrcfMP7buCk7pNYP7iejyz4CH63n1avKEDXHequW8T5YIXlNVFPd2UYgsmqSgmW2upRPZN2MfF49Uw6D2csWiTYkJJRaPX5c/KKKc//5fRTtWgbxMO7detn8XhmTlpv+/bPo6qNJJNP/9sZhZZ0TLLsQdfnlDVR9XRMU2nV9sVYJZPP0t5+AcHgCQD73P9UUbpWuv46xsfvnfIaCH2FjsfzGgVW27bBpk1cfN/o1KlAIJ2K4h0Y47N/EXqRsewYY9kxHJzyzwC2Iz7MeuI95NNxlMG9vHlzFh57jKV37aLV14qadjARufqS3cLqjav56t++yotJh2/Pg7Aknsc/rF1G38D/QHoNx1sxHGDRUAFNfgmHpxkc2o0DpH95CV86Is5LSUirDvdtvANLyqBqjWwfNxk5NUKrJl48YakHv/EsiwIxotE13HH9QuboKdZu+ABXPXMnb3oE1M/B57f0ERtvYDx+Dps2nUk4fBqplDC5W/2PDxBtH2CLdDmBbfKk8WQsO0Z7LsYzuTP4xN19fO/xVThqgmy2KGuTM9jPP4H3b3ehmH7c4x4CUoLbe+7Fa42RSwdIZr0Y0hhj37iElrVbOXXtDnI5SdRiLDIWwdU/A+DsR9cQ8oR4IfoomyK9fO2hAu02TNusMjQwjScja2rGr0DgeDZtOgtN6+XuOz5GWOphoe4l7E0RSYyyZWwPum7w3HPfRNftmlRi4A8b2XLWBqLXf668n02bziQQOP6AxrpSO/6wdhkX3n0hYamHb8+DtdFM+V/JAJ8AACAASURBVJ6aajyuNwYPD9/K8PDva5ZVt6G0jSSphLW1tJtpAoUdoOTZsuVcNK0bSdLK19Dw7YLnP1duw8qV4tgrV8J9Ox8il/bCxo+Wjb3zZhK3nmDHsx8ib4xz4d0XMpLaQ9Qcom/PdEwrgYMDdpqfPv/ruu+YQOB4tm37LJLkQtO6iEbXsPHFT2DZeXriPXgVGM/n+eRdnyReMBhL99b0U29s17+BxgoEYjjM+a2S7spebrPnkj0sXbiURe2LWDewjheGXqDF18LQpUPMbZzLXefdNQlUwcELy2tiou7qsccqf5uQEvzIgo8QcAeYFphcI7H6eK/UjMBSdHTUFjuuOf8lKye1p+56ddpdL0ov+E2bzmTbts+zZcu5dHZ+lkDguLrrFQr97Nz5tX87o9CSjqkk2oeptU9TpWD3xViJ/Uh4vXPLyw5Wq1a6BiMjf2Bo6JYpr4FtZ1EU/bU5K3BgQCifHYf3PzEOA4P1U4EDA2gjUWTgk+uh7QDkNu1JCOXAdkFj3AHb5qynYrSlHNQUmI5Iz5YYq2V/X0bGzLA+BitfhJXz4cuzM2jjV3HNjiA/fBb0i2DzSvDN9fL0SIYN8Sx7pqXYeCUcca7Nj5+H9THIuMCVN7HlPKrezA/WPoTvWnDLcNERsHweXLYZvr0Zvv/9WwB4q/53rtnuZc+ePB/+Ixy9ErrOEjNgAoFebDvH0NBqGht3Isvihbpl+k3Mb/4J4bXOpPGkPQmzM4JZ+uR6aEjnSdgDIr0WzaHbcSyXQ/Dxv6NnfSgxDZ+cYMXTVxN24uQyIVJZL345yyees5AdeOMLgu3wqB7SRhoGBgj96WYAznlyM01xg1t33ICa8vOZtWB5YXqPuJb3bruz/Exs3nwOO3deim1n8PlEG5fPgxu36/QaGaZ5HXqG/oLLNUZDw7X4fEolFTgwQPi79zJ/JWx+w5/Y+dKXsO0Mtp1jbOwvBzTWldoRiH6X86ZlWD5PXPP1E2Zx1xuPK2PwWWzbdjFbtpzLggV3sWDBnWze/EFefPETk9pQ2mbPnuV4fGkaciPkzQY6PTuZP/82dH0OsqxVrmGoqBW++3oyd4iyHBcVCbEHbryO5NDratrkksCtFzVzqmh3LNPPSzHBZPqKlkACHBXqvmNKbXQcm2j0QbZsOZdrtnuRivv3qZA2RQmolCmOU91PW0e3lK2HDiT+NYFVLicM8xYseMUPtbh9MesG13Hv9ns5Y44oXnfGnDO4d0d9/c5hFbmvXj2lkL0/2V/z/1THO8zEXk2sWCEkbsNFyYEkQc+X98CayTTJxPN/uf0UDi9Bktz09/+Mjo7PAUoNAKher739kyQST9DW9vF/G1BVHfW0ZQca+9NYHcq+SxEOL6Gz8wtYVmzKa2DbWWRZf236WF1xRVkIJ9sO7df+gqyZxef2TVrPKg4DsgOXPbL/XV/2CJgyOC6QiyS4ZEPLD67HlQbDFuisVCuw+rlbH4NtSTitDe7aa/O3vcOcfQeEnofRt4G81cuxm7McvW4Aa1cD42+Ctrvh7DvE9hkX6AaoGii+VraagzSthZgB53fBnwdg/Z3LWXuJzapVNwBw+tsz3P/ZQY793XJWWctpXO/wjvMFmHjXaQ6nnJLnM5/5PqedlsG2he7graclaFzwUVYMXFQ7bqxZzuD3HdZwGgA+0+GBrztseeGtAli9FEEni+0B3Ukzc8iNE3PhVeJsy0QI23Hy2SDZnM6ieIZV9nIkHE41HxbntyxNQPMjdXbQnRfPkNe0Oe4N72H46VPoGA1xpb2cBZc7XPriTQD84qPjSBL86Eei7NN1172XU04psG7dqQCc9S6HXTfcS3JkJn8dgIXBHD6fm3x+EbpuV1KBV1wBhkF4PYQ2SaTyGwmH34Gi+Ojr+/4Bm/g2NLwNj+Lw8RnF6zGFNU698TgcXoLb3UF///Xl44XDS9D1Ixka+k3dNohJRV4afIMkMq3Y+QKjUoBweEmRtfbQc9cnYYUDzxfLnDx/kfgH3CBuE7Y9fBGFdFHXssKBFQ4PfN4h8tLbALjxArHsvNMNxp46H4DvnCeWrbltOWlz6neMeGd8nKGh/6Gz82L+tneEtCVAlE+FVLEUYNoC3wQjAcPK/hsAq4cfFm/0aHS/qx5K9PZezej4g9y/836++dA3+f2m33PPk2/nDe6HufyRy8u5/JN/FuLDNwldQqNeX8x00CL3gQHMm26k9zyH6CKgUGBP/HqiO28nGl3Dpu3fYnHHYt45rZXzuyZsu2Z5+Xj/+McrC6wmiqRn/HBmmamqjlJ7Sjl9h/q06f76aWjoFkxzFEUJs3fvj4jHn0DX50zSFUSjaxgZuQ23u4u9e3/C1q0XTSoK/GrXXWUy2+uCyv1Fb+/VFAqDNZYLE/uimg17uSEE9tejaTMYGPhlTb+XwrazOI5NofD4a4uxqi7JBbgth/Y/3MfsvA9ZkietpxWllx5r/6xVe1Ks47bBdoNSqGzrvvl3uFNQcIQot8RYVT93ixpgYQj6MnDmNJlzG5r4oAOxN4Iag2iHlzdJGU5R4owGg2DA8PvgbEe0K+sCrwGyDqqvlWDLdOQjwa/C34fg/R2w6KyVLPqhVHZAf2CNn/OubeGPIyu5nJWML5L4wRUCGP31bzKPPtrAnXc+xKOPhlizRvTPY2saGB/+GyuMZXQHK4Ng+3EryagSTnGmt4NEzzsl3vDGP5MZy5AZTOAlg62Bz0zQENOwenN4lAQdjd0E5QSpVAgj42VeOsNKVuJQ2d9bVs1h28ZHcTx6zTEsj85bFt2MOxNkBSt5/jqJT4VFqnDZr4M4DnzpS2sYH7+PL3zhDh59NMTZ514DwJ33S3Qv/Rjhmc9zSgv8aTCEqg4SiWxF1xGpwKp7JroIooscAlsl4rF/YJpJWls/csATTUZHRe3b2/uK16Oh/nr1xuNodA253A78/jeUj1fy1PN4ZtdtQzS6BsMYo+BRyac1CoaXbm+EaHRNeVbgjDN/BSsk8Q9qfi69Y869UeLim2v/fuYNEpf8Viz75q1i2QMPSRxxrbBT+vFdYtl5H1tJ2pr6HRONrmFs7O7yhJ13TmslbQoQ5VUgU5xTljbFvVwdPpf2bwCsrrpKzOc/iDI2LyeeHo3TnL6OY0JiVOt0D0N6DY3OBuYF8jg4dGljfHl2gpeSQteQyCdQ5doenkrkvq/YdsnHME2DwEuwZbl4kPQXbTbsOF+kwlISp3Y0cuncDHsyWu3Gj6xg1amrGBwU3dQ1EXi9grHq1FXoaq2JVen8a/RXdWJ//RSNrmHbtgvx+RYDNqCQSDxBIvF0TU6/YlJ5G3PnXofjmAwO3symTWcRifygnO9/NeuuTDOJZSVxuzsOetuSnsBxTExzfJImwrZNcrkePJ4jXnb7qq9BS8sHaW09t6bfSwNvLPYPCoVBgsH21xawqjdRxbb41iPOftfbH2t12SMgFXdju0EyKn+TLAt/DMxi6Y8SsCpNiFnUINJSW1MQN2XyjV/n4qOibF8B7X8VIM2zVid2bob4BQnsXUFwwbwrYMe34Lt9grFqsF1IHlCDHSx7x0fo+zo8Ow4b4iLtdMXR8PUZDeRyfgDG/P/B518XJ3GMGOu2LIfCg2Lg8toK4KCqDVBlizM/8D22bFtK9FiJq966ojyhp/r8S+FocHRBJvv8i2Tx4CWDpYGXDFnHi/HA87jUGKtOuxLdHSeZDuKPevG4Js/Gu+SBJIGrr510XSTb5hcPpUgg0n+mD/bmxDl8IBYuPxNtbReg67MBiY52UZx65YvwvmadE9oy/Nc2jeOO/ikNDdPZuvUO3O6ESAUW74VS/7Q8DN4IkBOz67zeIw9ooonQpgrH4L+Pelj5orjmE8FVvfG4dA6KEkTTOsrSjE2bzkLXZ6Mo+qQ2lLaRZR9tgQix3EKMgspuI8iWLeeSTm9BlrUpJ5DV9LHkwjUBlbhkKBSvt0+FsKajSC4uf7uYbFFil3wq2JJe9x1TPV6VJuxcOjeDU9zOpwqmyiW7yNoS3irGyuvyMjs881UOrAYGKtqjgyhj83Li0kdXs2KLuOk+ObOiDbhss/j5U8Vl1flpwzZwy25csqi/VRLB19Nj7SuMxx/FY0F4PcxfKR6kxIngWCbgkE09wXHqXzj2mD/xxZN/WTY7LWmuTmlcykMPiX1NKCP3ikRJJL104VL+71v+b3mmUbPeXD7/erqqUrT52vbbT8nks/h8x9Dd/RUWLLizqDuw6O//eU1OvyTuDoeX0NLyfjo7v4jjmGjadHbu/Crh8Dvo7f3uq1p3JVJ1s5Gkg39EK3qCPDt3XjpJE5HP9+J2t6EokydHHGhUXwO/fzGWlSwe0ywPvLt3f5tduy5F1+fS2Dj7tQWs6kxUUQ2L43vM/a7nseBtexWa9CYkJJr0Jpr0JkCYHb+pT6wDQmMlV29eKOBOgWXXAqvShJjXN2isfBGCLjevC3fyoWNX0f58iNY14I4KsGDGvLRvy9D1UpxURoCIwHahi5oXBFv38JE5Z+F4QWmYxvEdTcy7UmZHDNo8EHVmgG8Jz/QcCUDHjJfQ/G9k4U3d5F8HyaPEmDcY62bxrEc55sYZLFhwF9HogyxYcCcezyy++JYrCTedJiZVHOPm/K4zuOF9N9CgNdSc/3JWiH5xQUsuQWYkRcbRBWPlKQIrvCQjSVRXgnPmnUPQWyCVCiGnvXhdtTflclawaFcG73MvlK9L6RhSoUD3YA8JxOQD0wf9hS6WzrieUCpdfiZK7t0LFtzJiSd8ko6ufnZmAmwe8zJOgS+e/EuWLlxKMBhElj+K2x0XjFXxXij1jysJhs9hwS+6aGg4mUzmxQOaaJJMPssRR/wXACvfdinb0z5WvgjHhr3lcXuq91Yy+Szz5q3GNKPkcr2Ew0tobT2f1tbzMM1EeVl1G0rnDQZb7AZyhddhGDqoGebPv41cbjey7Cnfg7qqw8krym2oNo0+b8HHBLA6eUV5ln6DW8dBpe1dK2jSNH56xtW4XWGWLlzK2Rc9hd8lXoB+Fa5510/rvmOqxysQY+Sxx/yJFm8TPhX8CnhcjfzqzF+xuPMthLXa93uLN4zLNWWXT4qDwGD/pKj+givpjn7601fkUL3xXnqAhAkfnwE391QAVH8OPjZhWSkyZoYrllzBT575CXsu2fOyjr3wsyYOQhcRuwoeysBJHxJ0pNfKcYJvK/5mYQy5NAzbb1/KypWwt7j99OmVfZWA1fLlr5yfVPV+ZzfO5ux5Z3NS90k82fdk+Ubel37qG2/9xn7B5/TpX6KnZxXh8Ltwu5vp7LyIvXt/QlfXV2oA0kSR9Zw532ds7G4ymc2AyvDwLcyYcdmrFlTBoWugwuEleL3zGRz81aS+OBz6qupr4PcvoqdnFfPnLylqKgqYZpyenitoa/sY+XwEXYdYrJ9C4ZX7UPqXinXr4NZb4Y474LbbOO+P59HqbWXd4Doem7henVgIjNb9C/Dtyo/2S59h2TN38ouH8iz5djePf2ULNEs1wKr0Ebh04VLGMmP4x7YxN3QbctEj8MhvihmHl93Rwam+QdKnvo85X/gA3d2QfEbIMfbufJI33XwWA18ZgPe/n4aGoxnzgNrQSXfz17B7voeVT/LVN5/NDfNFJfAf3n4hup7E7xPlYMJ/2EF40ybsN53IoJojcuppnH+CQ/hrYpJF6R51uVr4jnElhIr6nkc6IZFg6cKlbBzayP1/aGDxW7+Orbk54q3Xwd8drJ6r0B9pIjN4MpnNoL/+SKw1u4l+/Ax23BcmceJ7cD+3iXg+TmPAIJ1uYMircOIH3gYv/IoXXhckdv+fWTHzZM65bRPnHX0eHzr6Q/C737Fi6VK++R4PV96TxetAQbWJuzSMkEVf8Bh+d88WLOW95WdifPyvNDaeTji8hHnzoLUR3njEqfz14TCnTXsbSxceAwjrslSqi3A4JDRW67bB5s10L1gAv/0tw8c9THPgBMLf+CxDL32GYPCN5X7a19jW3f01YjFxl719xomcPud0zl9wPufMPwf3PRezsG0hnz/+81Num83uRlF85PPCyPXII3+ObRsMDt6EJLkwzURNG0rn7Tg2ESeHktFRbQ+bcy7C4SVEow+WawUuXbiU/3nhf+AzT3HfR/dUbnbEO6Z375tYu+fXOA+vgCKg3b372zzW+zjTvtrDsaGjOHruO9i48ccA3PKTI1nzmAuXVMCluFl6zITaXlXnNTHC4SXo3vn4lH9wfMfRnDJ/GW1tH2b79qdZ0DxCY8uJ/PrMXwPw3HM/RFUPnMH412KsSjnmUsL1IIsvH2x0h7o5dzpM0yFnwVmdgi59ZxvMCwiQUy8/7ZJdnD7ndNKF9H4r0e/r2ACODOtPgRNb4blxcCQJSVLoyUjkYreV6dYVK2DZ37/FWd+6TWz3TzTpnBjbx7Yzp3EOZ8w9g/t33l+eAj5VbrtRb2Td4P5d8+Pxx/B6j8Ltbi5O873lgAws4/HHsO0UbW0fAyxk2f+yTS//VeJQNVBCJ7EbXT9qUl8IYHVo+qrq8HqPIp+PlI1C9+4V6VmA4eHbsKw0tr2FoaF/oCi+fe3q3yuq3C/dipuRzEhdq4VDiZJ+Je/z0G6JFL2WAtuunRVYiq5gF3vjuzGM0Uk10Uq/J1NG2Xk9kRCgrD+xveyqjteLnowh50FSxb7tgB8npZVfxgBKYhrzFmwmOtpZOe9IBI47nuakRX+/zowZk2tjKbIX28xU3Dmr3NfLsypzOeSCgZQQAFLMwJPLpps+uYCtgX+0D7vgIREHly7G66A/TzoVQLbCOLawt2gey5X9xUJalft98bhHpEQ/SBIEXVlemDOTjKGRy0FLS9WsvmI/lowqS07jkXiETs8cclLFGdPrhbEx8PmUit1CvHLc6v0oindKE9J6UTLNNM14TeHvAynync9H8PmOwbazWFbRtqPQj8vVhsczo+Yal8JxbBynQFoZJpd2YxheZEUcRxiEVuQsWTM7ZXUB05HwKLXKcdvO41YDRPMFLKu2X1Q1iEoBvwqSfPBjS8aS8KkgOZmqfYZQyJI1K/0tDEIPfL//WsDqEIsvH0hUi2uvOenDXHgE3D8IgzkYLwhtwKWvg7v6Rc71hXhtftqresGBha0LmdM4hx3jUxe7nWiQ+adnPlw+9qpTV+GSXZwzDYz/BCsHfx8WTuNpI0eT22HVizJrN3ygLAo+vtHFs480A5QFhaWI7vwjvd884rCC0ElmcQMDRC94PY3jtzO3cS6PRx5nPDuOerlK89XNjGYmf2N7XV4ueeMlrB9cv99jjI3dS2PjGUQiP2DjxvfV5MOn0hWUcufd3d9gfPxeZs26EttO0dX19Vd1geZDYZVKfXLUUb/GMAaZN+93NX0hQNuhMVbVIcsufL75pNMbiUbXsHv3t2hr+yizZl2J4+RJJp9nePhqNG0JihI4bMf9l4+qCsNu2c1oZnSyOeghRmnGVUZXaTd1sCyRGnRsbDtfnhVYiu5QN6nsbjRtOpaVxCl+FOXNPCpi2mY6Y5RrBSaTYtvhxK5yHUC8XtyJEeSq97zp9yGnPDUvXSvWzusWRoiNd+BThdaKSAR59mzGfDID/SFmzZqsqpZxYwXdFVfiKvf1eD4uQFqxfqA/a5IqpLCsDF6vWvax8kp5rM5GQiMR7IKHeELC7c0Tz8XR/RkymRCOEQRnCLq6aI4VCLpEG2vc7xMJCtM66E5U2IqQnGTbkbMZ6etm+nQJRfHW+FCZZqJcWqW5WQC93tEx2rVZpJ2R8nrVwKoMzCYAq9J+ZNlbt2zOVFFdqiWei5eB7YEU+c7nI3g83WhaF7mcuJ65XASPp6tmWXWUZv9m5CHSSRemoYMs+lCA/4rsIGNkpgR3BdupC6w0l4/xQg7TTNT0ryQpGI5MqybDywBWScPGr0rITra8T0UJoji5GlmLMAh9tTJWh1h8+UCi2gDtKPdWbLmVt7bI3DcA073gcTUjyR62Z1sJqmA4Gj/cGeSogPja+/pJX2de6zw0VWNO4xy2j9Wf0l7PIPOqZ+4sA6WlC5fy3u5pfGoWvLgB1AI0uIW+a9lmKNjQkxjmWxsLPLr9BgKB4wnFf8zw+gVceGGkbBr3ta/tFi/S7R8jcP+ewwpCJ5nFXf85tpy1gRl397EzupML774Q0zbLhn1pI12zfZPexA3vu4GvvvmrbBvbRt7M7/MY4+P34na3snv3Zcyadfkkn5R6uoJS7ryk7Zkx4+vIchDLir2qCzQfCqtU6pOWlg8WPWTcNX2Rze54WbMN9xV+/yJSqXUkk8/idrfR2XkxM2Z8g8bG9wA2DQ3TsazW/e7n3yqqKgyXGKtJHlaHGLadR1Y8JD0SLZYGpomlSCD7Mc3EZMYq1EUhvxeP5whk2VNmJPYm99KoiZdfOuPg9RYLGBsKpqkymo7UMFauxChUASsj4EVLecjn95bBWma8jcbOOP7AOOkRq9InXV2MNHoYGm5n9uzJ94RiqtjhKlFzVSHmRD4hwGkRgLRYHiLxCLadxedTyrjEY9nYMztoGI5g5TXiKQV3ME88H0fzJcmkG7AMP9gj0NpKXJdoiInxqYbVicdJvm4G0+IVtXzIjtE/YzZjkS66u0tsUuUlXF1aRZJgepdNbChAg9JJ0qkUePZ6YXRUlLkps1GJCqArFQYGUBT9oMreVBcXLvcZ7LfINwgQpWkCRJWAcj4/eVl1WFYGWdZJSn2kkjIFw4uqlhirXA1jlTEyU7ahYIOm1MIS287hUYOMZrNYVmJS6Zq0CQub2nCkydVK9hexgkGXvxGF3ATGKj8JWL16U4Hr1sFTT8Fxx9UaaR5E8eX9RcXE7UNEo39DVwqc9IYHufUzDrNnrUJ1Rmlv+QDL5tkcNedKzp57Ao9cHGfc/U6uPf1a2n3tLGoXjpxzG+dOyVjVE3I/NZbnmu1eNm8+mxc2ns0FnXu4dlcLO1TwPw+3RoSea30M/jYEJzSKbb70xJNCu2LdS0tonE995oNYVgbbznLBBcvYsukc5l9mEF7rHNbUacUs7v3sfOELbFn8Z+avhHetjvPnR26YUqheCr/bz9KFS9FdOkeEj2DzyORyA9WmerncbvbsWc7ChffQ1fWfk9arlycvmWlWFwf2eo+gqekDr+oCzYfCKk0slDw2VlsoOZM5vIwVgN+/mFRqHa2tH8Y0EwSDJxCNriGZfJoZMy7DcV4kmXxl7VP+5aIqFehSXIxmRg97KtC286iyTkxzaDZcAljJEo6sY5rxScCqxdtCQMmgujtQ1VCZ2eiN99LgFukuUb9OgAKfL0s6HSSW6asAK11Hyozj5CsvGsPnoTHnRlVDFAoCPMRH2tFaUoSaIgz3FT+qiixe/7RuJMmhqWky0JRNBTtUNbGiHmNV/L2poNIb78W2M3i9LkZHRdvlrI3V1UJwcABshfGUGy1ok8gncPmS5DJBLMOHyihOKERv0CE4LPZZw+rE44zPmUb7ePGD33EIGmNE27oZ2zudrk4LWdZr0nQTiwG3tOdoNF6PywoSMwfKy3W9BKxUbDsvAOmkVODLY6wmpgIPjrHqrQJRvcVl+wZWwgjYS0YeJJmQMA0PqlpK0+YnAaup2pC3HDSlFsA4Th6PK0g0n0SSXOTzA+X+zRgZUqbNnFAQSzr4yTijuQzT/CFc5Mt9rShB3JJR834TRZhfrcAKJlflfAWiJLyz7TTTpv1H+SU0Y8Y3aGu7gJGR39PZeTHTp3+JVGodhhETpqHb72X94HoWtwvjqDmNc9g+Xp+xmkrI/be9w8KXaewOtmSm8UD/KN3d0PUYeKqmTT8zDm9srN3XE0+cwFFvWEM2/RxNTe8iEDie4eFb6FzbRfi54hfhYU6dBgLHY1kZItH/pvNuifB6UGz49D0D+922ug8WdyyeMh0YDi+hoWEJtp09YAO8fYWmddd9+F8tYZopTDOOpnUe8r4aG8+oKVbtOBa53B48ntmHvO/qEMBqPePj99HY+E5isUdrpjcfeeQyotHtWNYB2Ir/u8QEjdVoZvQVYKxyqIrOmMuk0VDBNLEVGUfyYlmTgZUkScwOBsk5wZoitJF4hIBLJmW5yedkvMWPf78/TaEwm2R+qKKh9HqRMmNYVYaveb9OgyHXvHijI+2YwRF8TRH6erOVPunuZm/bkbQ299U9J8VQsIJVFjMTGSut+HtbG6GCRCQRwbIy+HwuUinBBClZG7vBi+1WUbU8g+kAetghnouj+KJkMiHMvI4ijWIFfPSHZNR+MabVsDqJBKPTGwmlDJE9iccJSQkswgyOzKArFK+bCqxmVIKtcUK5Bcimn3Gzcs5eL6RS4PNJFXBWPC8SpZTXy9NYmWYCl6utwlhVaaz2x1iVUoEeTyXtV7ts8rvNsjIgefDrblRVIpkM4lJFaSDbzpfF6wBZIyvSt7Y1aT8F20GTawGMbefxukW7FSVIPh8p929fog/D0Why21gcPLAayqRo1QO4JKPc16oawi0ZZI1ajdWrNxUI/xRgJYzC7iEcfkeNsDcWe5jx8b+UBdM7dnwZXT+KaPRBTNvkV+t/xRM7/pvNOy5j9cbVzG2amrGaSsh9UmuAeOJZYgU4LrCXU9v9HBWCsV3wyJPwmVb4bCv84H6YocMnZsA7p7USja7hRz8Z4p1vvpPHR2Fg+B4Gxp/EsGHvzBeEySiIh/9nP4MNGyoHHRiAk0/eJ5NVr/hmNLqGzZvPBmyanpTpf58wM3Xb8Kn18MSN+zYyrO4D27a55L5LkFZKTL9U4ZGZEsdf3sXqjauLacC/Egq97bCIzqu/tP7ZUW1MerCGpaV1qq0WDsXktLf3av62488Mxjfxmd9InPyzEGf9MkSikGf2T47iD2uXHRYD1d7eqzGMKOn0FsbG7qax8QyGh2+lufmDZZDc1nYskjSvnHr6t49cTrwki/Wg3IqbglV4ZRgr1cuImqchL5dTgTaecirQpdTOwOPSIAAAIABJREFUE+/2eUhYHhQlWGasIokIXsUhY/vIZdUysPL5UhjGXLL50RqNlZOP1ag2cl434XwFWFmWzdhoByPuPXjC/fT2Flcuju/D/tm0BvdSL+SChB2sgMEaxioXr6QCu7rwZ61yKtDvd5eah5wysT2QagvjVvMM5kLozaIOIN4omayffE7HLUUp+HSGmzyCTaOYCqxirKJeiUSDDv39EIkQ9Fk4eZ3BsW66tBFkeepUIIDWOIKWmYNZcIG7oi8q9bHXC7IsCjmXzot4vGY/E4+xvxBFirsoGFHyZr7sFxXS9i9er5cKzOVKLFb9j1bbzuBIGiFPiFAIotEQQd0gb+XLOsBSZIwMsiSTLEx+eeQsa5KPVQlYxXOiP6qBVSQewZG9NKgFTMc9aX/7i/50nEbNhUsyUVWh/1TVEB7ZmpAKNP+XsdpXVAzQ/MyZ8+OyMLpkLFktmB4evpVMZhPPb7+C5Q8vZ2HIYvk8eG4sxYV3X8iGoQ1TMlarTl2FptQaex4XVvjqnARJA/5rKygSXDonSX8OnCPA+A948yPin3ohOH2gKSpfm5vg4X98mNHhNpSZj/L6EFgIobvZB513VExGAZE+/chHKge+4grhDbYPJqte8c1Nm84kFnsY2VAJbpXKflvRReAx4cS+qY0Mq83nVm9cze0v3l5+kL75sM1be+ETd/dx3SOfZu2GD+DzLaSr6z8PyABvf1H9pfXPjuqCpNX31YEYlpa2HR29C12fe8jFpZ8ejeMev4rtSRtVgkvnJrhoVpqBnCiU6x67kqdH9z3IHkgEAsezdesFqGoTY2N/RVF8jI7eQWvr+eV1PB4oFAK43e2HfLxXRfT1QWenqCABZdbocIvXHSePS/YyKGdpyAOGga3ImJKnbioQoM0jMZJnEmPllkxyTohC3oVenKzn98fJ52dhGNEajZVlxslXAaus10WoIOHxdJPLRejrG8XnS9CX70VrGiYSkcS41NcHXV2Myd20ees/o3Lewaq2vq6eFVgtXu/qQk8XyoyV1yvGW10HOW1geSQSrSE8Sp5Bowlfi5t4Pk5Oy+J2FYjHvHiUGFmfm/Emr3j3MFm8HtMcEq0Nxel9EUIhByvrYXC8iy6pb5/idQAp2IcU7yaTkWgNBYgkIuV2FruzwkgVgZWTiGFZmfIs2jLwOsCwrLgQmhfGCGpBpKInz4GK1yeCqAPRWNmoBLUgwSBYlkKDbpM1snVTga2+1roAL2fauCfgF9vO4XOHhTm3KhirUv9GEhEUJUBQyVFwDsJoCjBtk/50HL+Sx3BkpKJvlqIE0BW7jnj9wOHSvx6wqppJ80pEMvkss2b9F4rix+s9sqzxiUYfnGQgtmDBXTQ1vR+tsGFSMcuMkeGaJ66Z0nJh6cKlLJm5pEzBtvvbOaZB44FheGIMnhqH2yLgViAoSyS/LEzhPvxHykVKA21wzvQmVEnluWdPBmBOOM+aEfjmJrhsCwSGodAqtk0eVdWALVsEQ1WysLDtfeqvSv1QMnbcsuVcWlrOQ1XDtD/VQK7RKpuZJo8S/sgS8KkXJNqTlE0MJaRJ5nPL/r6MvCU0FqVyHIoj/l+gCN1ZPh/B7190QAZ4+4upHv5/RpTa39NzJaHQSQdlWFraNhK5hkJh8JCLS5cMcI8KwFuaxfWSAQcx03XFFrHOoUap3YYxjMvVyrZtn5vU7tdcEebe3rJwHaqA1SsgXnepPsbdFoGcU04FWmhYVgLDMiYBqwa1wN6MUaOxiiR6UciRNTtQXWYJD6LrMXL5LiQnS7u/vbQQy22SKVQE3Rmvi2DWKbPFu3aN0tI6SCQeQWscZ+9erSQoAq+XsVw7nVJ9VlnJOti+qpdkMRXoOA7JfLKisersRC2Y7I32YNsZVNWLrheBStLAdjvEWoPoco68oxHs8BLPxUkqefzeJPm8SkjPM6LmibUGy8CqhtWJxxlTDTLtjeKa9vYSalQopD0MxbroNnbWaKwcx8ayUmX2A6AQ2EEh2k4mA+3hIJG4OE6JsdL1Kg1VIgHd3ZiFGIoSKBsETxTI7y9MM4GmdVMwYjVgfn+pQGGNksHlaq5J+5WAlcfTRT7fVy6VVQrbzmKhEtIEYwUQ9grWp2QJAmBYBpIkTG/rtSNnW6jyxH3n8WvhYiowRC5Xy1i51DA+OUPePjhbzoHkAC61AdkaJWtVZiLaki5K3EwSr7+agVXVTJpXIrq7v4ZpjtHYeEYZxYfDSzjmmL/WLSw5f/6tZCzqFrOMxCPMbpw9ZTpwT3wPD378QT56zEf5zpLv8KvdWabp8HTRyuQXu2FPGlq9DtPukQmvF47CWtGRveM+md/+5nOcfHKCK7/zewDOfKfDD5Y6rL9zOetjcFU/DJ8IjWfCPTugbLbhcgmG6oorwCy6Pe9Hf+XxzMI0E/T0XEFn58V0dX0ZWXbTePFN5N/QBd//Pl+87CNkrLeXXUl1ycVA9vOMfm2U0a+NYi+32XPJnhoz0Gqt1WWPgKuYWpcdWHQTPDs8hG3n0LTucr8fiuj8/yewAvB4urGsFKOjdwKK0MEdoHZMFDudQyLx5CHrzXrjvayPwd0D0CVcQjAcODJQuZdfVvHwKdrd2Hg6hjFQt92vuSLME5j3Erg5/KnAHG7VT0IDX9YUwEpVMBz3lIyVLiXZlUjVpAIHEj1IkophtuLWKhfK5xtnPBmm2aOjyMWXj9eL5YOMJb76AdIehUDOKT97u3cnaGmP0pfow9OUor8/WPPRPBxrYVZ+d91zkjM2ll71aiqmAtNGGk3VREmxRAIaGrADfqJDPcVZad7ybEY5nsNy20SbffiK0xdDnX4S+QQpJYdfFynpkGYSceKk2sI1jFWZ1UkkGHEVyHW0lhmrYLNGPuVmJDaNrsTmGmG5ZSVRFG+Z/QBIeraQGmkgk4HOxobyM1edCiwDpyJjZRmxspgaSsDrYDRWIhVomrGaey6oBfeZChRpwOlIklSV1s1imgnc7lYUxYcsezCMsZrtbDuD6VQYK4Cw1yRjZIqMlUgFZowMXpe3Nt1aFRnTQJ1Qr8hxxMeDW3GD7CWf7yv3TW+8F93dhC6lyDnKpP3tK3rjvYT0DhxzmKxVockKtgufSo2PlW2/2hmrf4LGanxc+CUdSMRiD6PJ8I+RyWah3aFu5jbOrWu5sCe2h/HsOMd2HMvi9sWsG1zH7IbpLAzCc8XJUcc0QKMK034L/e+1K6k8RLpt+O02y92ruPq/zqZz2jZAFPNcdK1ULoR8/FrIACcAn1ovVUBUoQA33ST+VS/bB2v10kufBGzc7un09/+MvXt/QmPjGWjadPLWIJxxBuM7NzP3z48dlIlrSWvVnoRPrgO1uGmp4OwZgWb8/sVloHuo4fH8/xWvv/jiJ5AkpWxYCu4D1o5Fo2tIpzfT3v7pQ9abdYe6WdQA72oTFQRKdbFv7qncywddPHwf7U4kHp/S0PU1x1hNGMdK7ueH38cqj6b6iXvAWwRWjiJTmAJYmWYCWYIdicGaVOB4RqRXbKsFl1a5UF7vGMNxH41alazB68X0Q9aWyxYqSV3Gn7PKafje3jztHWksx0JvNxgcbK75aB4aCTMnHSGXmlDWAlAyFrZeNRYUU4HVImzicQgGkUIhEiN9ZR+lErBS4nls1WKsyYvfTiFjEe4MC1sYtUBQT6PrFroLdjlj5DpaKoxVNasTjzOs5rGmdVRSgZ1ehgbCSLJBYGgHsqzhOAUcx6oRnJdizLWesUEvmQx0NzeXU4F1NVbFFKdp1QrgD5axsqwEmtaFZSVrWNKSMH8i41QKwUyJa6SqQSRJJZ3eWARbAi7U07BaVoaCLZU1VqIfS8CqYrdQBlZT2D5kTBNVqvWyLG0f8oSwJR2wyn0cSUQI6O3IkkPWOjg4E0lEaPR1AxbpKh19zpbRFSiYubL59aubsSoUBF3ccfCFZ/cXJWGwaSZJJp8lHH77foXBJY1L3nMywwW1ppil1+Xl3XPfzQO7HuCuZ87n9BvbWb1xddkU9KzfzOL05ii3bLqFxfpW4rGH+e5bz+e6G1eQtuCcaXDlAoj/WuaIm1Xmr4SvHLm8WNVc/Dx/Jfz3ny/juutWcfHF3wDgfx6DH86CJW4BVD48HfricFkbpBeJwSi6CHrPh+i8HL1n1XpH9Z6VZ/DDczj+8i4+fJPE6Te284e1y9iw4f3E4o+wPqYykurjL9tz9Pf+jB39e7nix6eQaTOY9sujed9tL2CaE7zGcjn4xjcqv1eL5QcGePq3OrNyOt99sFLjqxSyA++LjHLrtmdZvXH/aamJpqv1tnG7OykUhrBts84eDk9Ui9NLP0eja9iw4X0kEk/Q2vpRRkbuYPbs7wEWLS3n12jH6k0WEMao70WSFObOve6Q9WbXvG0pK+aL9PW6GCIX6IifV74IK+aLdQ416hU4ndhuj+e1DaxeOcaqCKw08KTzRWClUHBULGuyj1U+H0F1dRCJ95UZq1QhhUoet6sBx2rFrZXYlxxeb4KRmErQXcUG6DqmF9KOSs4U7FbCI+PLWmWWIxKBadPFw+5vVkgkGsju7in3yeBgIw1OP0MvPT/pnORUAdtTBayEBXxFuA4CWIVCyKEGWgw3ppVCUUQqUNdBjmWwFJORZp1APklQShLSQ7w4+iKKVyPgSeHx2LhkiW3GIHZ7G4yMgGFUUoGOA/E4Q0oWp7urwlhNC7Jzezv+cB9EIkiSVGaUql3BS9FfeAlVlRgYgO6mlkkaK12foLGaPh3TSdUAtIPVWIlZxV3YdrIGzLsUF27FPaVVTinlVwpN6yIef7xmWb0PV9vOCmBVTAVqmkXAJZE1szWzAkvAairmLG0YKFgT9i0Yr5AWwnTEfsqpwESEBq+on5uZPMlwnxGJR2jxixq8SaMCNLNmjpwtE9Y8ZI1s0ZfNfhXPCuzvh/b2iuPuYYySMLiv71qCwRNJJJ7ZrzC4ZLT4piMv5T0zjyLqzODyF+HElgYueP0F/OaF35DIJ3gpCZ/rHuLaNRfwqT99irDUw/J5sCFucOHdF9KX93NOy2aO1LZz7++Xc3yji0/NhDsHw7xnXReKYRJeD7/6/QqSRwkN069+v4Lwevje0LeZlRzg83mTLx29glW/UFm4Ar4QU/j+w26aN8MRnRAOK7y0zCZyjhCYSyZs+TYEXprQD1tsdn4pzee39PFSEr44cwh9/Er6Rx9gzbDEbG8OtwJLmlLIJhjP/YV3L0yABN96yuFNfSJVWROOA3ffXfm9Wix/xRW0rdvGvbvfzFlbJSbemh4Lpofh+bEkF9594T7BVT3T1XrbyLILl6uFQmH/lhAvN6rF/oHA8WXD1kTiCdraljI8/Ftmzbqcrq7/pKXlHDKZLTXasXqTBXbvvozOzovwel+HongOWW/2xuYQmYZLeSEmcVQArtke5Jodwux21J5OoembvLH50BmUegVOJ7bb7a4Qp6+JmAJYvRJ2Cx5XgIQGWjongJWqkLfVMmNVYstApHq8+kwiiQiqGsQ0E0TiEWYGW1GUILLVgttdAlYJgsE8w1EJf/WMqFIqUFLL2smE5qBnDNzuTgxjhL4+lRnd4pwbvA00NQ2ze7dwObcsm+HhNrzuKOPbXph0TkrSwNKqGJUiY1Xtx0QiIQBXMMgcpQXTTNekApXxDLZsMBzWCOXiBOUUIU+IbWPbcPt0/O4UXq+NKju8ZAzg9zYIm4O9e/G6vBSsAkY6CarKuJVC7p5RYaxmNNDb04ba0ItTZLmEgWe2aOpZAc/JfBLDNujukshkYFZr2ySNlWCsqlKBzc1YQQVVqjiJH7zdQlywTHaaoFZb7WBfAnZhq1ANogSwql5Wz33dsjLkbMqpQLfbxqs6ValAAYiyZhZd1WvLBlVFxjCQ6wArSdIIasGyQL3Ux5F4hEavaFvqIMeXSCJCe3B2cdsKS5Y1s+RthWaPh6yZxXEMJEk9KGD1r1WE+RVMA5YG+40b300odNIBCYNLOp98vh+/M8DuL42U01Uzr51ZRv3riwzAd462eHDI4uSWisgdMlz25O0c5W1j5u3C7XrVQjfHvf5+zggvgSJh0NcHdMFl7sqAcoF0Mzjwk/glNK7axLX2n8UfBuHsFxzABBsWfEdhy3csPJ4j2PmFXQSDb6b3i1uYf0mM8HrEJ9GuXZBO0zBnDvNXgrMcvghosiAyokaOYxuEIL4xA99+A7jGoHkuzF8O278M5w7A0UthqPicHsc0nl01DIYhXAUHBwXIKonlr79erGjbHPmHNeVUFLrO+Z8I8N1bhjniS3Dz62HHZvE1s+zvy6Ys1lzPdHWqbUp0dfWAcDijYmx6blFTlUGSHNzuDsbH72XhwnvK91Zn5+fYtevSmsKl1ear7e2fZnh4NQsX3kMutwvDGK05zsvVWXV3f42Nub/wthnPcMsnHi4vP+lXJ/GbM1fy9llvf/kdMOE4E2NiuyWp8oX+moh/EmMlzBNDxD2gprJgGDiKStaSp2SsfPosJJ6j4LixrDiRRIQZgSbBAlitaJrQH5lmnEDAYDQi4ZGrXnZeL6YPUgl3ORUY10DPFJBlFbe7jb17NebOdsNzAky2t4+zezDL/DceTX//GD6fhN3sJb1rwpcfICfy2K6qdFBRvD4pFRgKQSjEDDmHbYvZeV4veDULOedgk2OoQcVHmpArIxgP28Tj9RJ0J9F1B0lxGHfbYr/Fwn7SzJkEtSDJ4T4ag4JZ0WbOgZ4eyGQIHdGEaarIjXthTx6SyTIwEuagFfAcSQjH+q4uic2bYU5rZ91UoG1XiddDIcxmHdWuPDAHY7fgOA6WJTRRDhJhzV/z91KqszMw2Scvl4sQDJ5Q/l3Tuhkbu5v29k9ULZusYbXtDDnLIaSFMELg8TjCvsPI4KmyW6hmrOqlAtNmHlm1cRy7nHqsTgXmbAUVUBTBKtqOTVAXGa6UWT+9OVVEEhFOnnEy8qiPhJHGcRwkSSJjCCF8WNPIGBnCmo4kuV7FtQInzKQ53BEOL8HlaiEafeCghMFudweSpJDPV8zdJop++zLgluED0yaL3Hvu+iT3XzTIz38u0o7vXJKisXEJK1aIwsmSVBmDb75Z/AO42fkYAMc4LyDZFitYXtmpbZd1To1PQ2vPXHK5XXg8s0gknqBj02zCm4tfqiXR+ve/jyUJYXzTwzC7QRiRPj0OLVql3efcLiwc8h3Q8WcIrQfPMBgttfYKn7xnb1nEjmFUxPKGMamNNTUgLYuTnh3GZcHCODRr0Jup36/VMdXf6i3/Z1guCMH2aaRSa2lqOoPGxneTz0cm3Vuh0FvIZLZTKAzXbO92t2JZafbu/VF5m1RqPX7/oomHetlx7457OWNOrZ5wcfti1g0cvmoGBxqvKWBVZ1agpmhoqraPjQ4uROFbC2+RsVKTKcFYuVSytlxXYyU+NrrpCnURzecxzTi98V6m+RtQ1RCS1TwBWFmMx2VUqtL/RWCVUbVyKjDqsvCkxc+a1sXQUAdHze1AkRSCWpBp05LsGlegu5sdO0Zobx/BmNaBuWfXpPOS4zkstQrIFcXr1TXvqhmrboKAgSx7BLBS8iiuAJaVIS2buDWToCtX3lbzeQm4Uni9Dsg2Ca0IeLu7awTs6bEBCAlmxds5Q3w8BgIEWwVI8DQNlbVXJcuFiYxVb7yX7lA33d1iqJzdOl14LznOJI1VORUYDGI2eVDMyr1yMKlA284BMrKsYaHV6uPYt4BduK5X7ltN66JQGJi0bCKwsqwMGdMsM1aaBh7ZnjQrsEa8XqcNGSOLjXCir5yPSAUGtSBZW0AWVQ2W+7aUFkwaB5cLrGwfJGcr5Xs5Y2QwHBdhzV1k3IxXBljdd999HHnkkcyZM4errrpq0t9/8IMfMH/+fI455hhOPfVUenp6DrwF1fFP8LDK5/cybdoXD0oYLElSsRba+rI25qK5obKQfVEDfP/1gvnJWUIYfOKG5eW/h11NNDXt5YQTRbrsTw8o3Pb8sjKwchx4e5E8cBxwig7ATjFx5iDhILGClbUNK4KW5AKLaMN22oLnkMvtQUanv/t5okcXAU6hQObn/03uFz9DdYQGa/Q0cA/CoiC8vqEiaP6gBedZMHw6zLgZBt8PiUWgjYDdJMTmbUmh7/rUeqlS29E04Ze/FCyVZdW0sff8osdWleD9lFNg/f+FC8ZA74GjB2DNk/CVLjEoVWupmq9upvnqZhzqf5HUE2AfLvf11RtXc/Hvwhz7I6ms6Vq9cTWn39jOV34n0Tf4e3LqfGKxh4nFHqkr3u7r+yE+30K2b/8/5eXj4w/x5HMnYToOKQM27lzFH9YuI5lch9+/+IC0ZNVtnHntTKSVEurlKtLK/8fdecfJVdb7/32ml52yvWU3IQ3SSAKEIqJGvEJQBC5IMSKCCioIeL2ilNxkCREBC9wrCpGiXqI0DaHcULNCqClkSdn0ZHvfnZ2ZnXJm5pzn98czc3ZmS3Y3FH/6zSuv3T3znKfNOc/5nO/383y+ijFn92++n/veuy/n/IVlC6nrHFkF/+OyNTvW0J9qZ+vetk+0XfgE168Mt/Ctt6SkdjxOWVkZiqJw6bxLUZepKIqCoiiUlUnpgr17r2Hv3mtyqhnvsb6+lxEo/ODF6wnaIdDVxKuttxCdlSSmSWCV1JNEw2+zffs5BAK1xOPNbOtu5VDgEL944z/Z0byO69dfz5rffZZn9r3MjrZ27GmO1UsH/kZ7sp7m3VUIPcy69y6V1284zAMvLUdJWhkIvckPf/gWvfYUXWcP8JP5v8MU9tPbW0Zd01punqyx6OWbGNDf438blhCw7mTv3h0MRKy8HqhjUv1r/PgRP3/d8Bv47Gf5a+39NO7fTvjQDhbdXiUT16c2QyTCpy+/laqoVXJjFx2kyfZXdpbuZU/DOyQ0QfE9JTQEnudvtVtpV8OEY40UmpqhIkkrLja+9xQL/BB07CeVbKClI4UwaQTt8J+v/Cf/2/wC+1+/mnW1N3C6r5kfr/sCWyL78dPAPS+eRNsZdrbf1Af9MpNBqOkkEoV+Ard9iXCknaUPfJb6H1/K89v/z1gn/uMPl3Hrsld4/sA9mEw6z+xdS1JPYrrdxIKHjgXgksen8ugHf+b6Z79FIqXyVP1Kek+IY0lKQLxmxxrm/u4EIqpMeJ/hxmbf04FALes2XcaUe6dQcpeLYCLJXzf8BmeXSkU0kSNQfLxXI958mL33lLN32+Xs3XsNz7/zeabcO4Xdba9w92sXs37D6ey9pxyHKtfjdzoOcPZDZfz8rwrLXr2e5r6tOW3va32Cd9vquPGlG7lz808JhHTsphS0taOHA5i6pachmozitDpHJK83Nd2NRz8AwoJ+zhfggw8IXDEfLRWkre1BZrqiRFIACk/Ur+Px109mSf5uLnz6GwAEE9q4BJUz6+VMy/vc+sI5RDSFhLASTUbZu/caUl13k8KOzyaP9ffXktTi/Pq9u8e9fo0JrDRN49prr2X9+vXU19fzl7/8hfr6+pwyCxcuZMuWLWzfvp2LLrqIm246yq3yHyOwyhBsQWHatF9MmBicyYWW4cacNf0cVsyWJPRVc6DKCXFdAqvfHoR3/7aClXPg1EI7gVevY8HCDVx82R0ALN+lY+v9GU+9fysA4TBs2pTV2ARS0gQWSE5V1eMm+rqfl2RpLYF3p5IjGmrTwKINlq94FhwtaTJ5FqH5ByfDrpVSr+qYRzFEQYUJ1FJJNl/2OtRsNGNVhnDhVHUQPGWZZ0+ugGlgAfR+FtwLYXExlO2CP+wFy3fh8p1Th3GpemO99MZ6h9ULuUKk2fZRSC5k+vFudz//NUuKal75zJXc9/dvct3kThaXQDApeHDPfpJ6EhD4/YuHXVsezyIikTqi0X2GaGjd9iWY9ABxDcwmuGuPvCb6Q5tY37RvXFyy7D42BiUY0IQEtdlz1j7QnnP+grIFn6jHKtNHzTwAAx8+Tc9E7BNdvzLcwiuukB7aO+6gs7NzxKKdnZ0EArV0dT1OV9cTOVy78R6r2/lV4ppOd7SbkB08MZ0ntrxO65Vd2EUXKS3EXG+KfXu+Rn7+F+S11/13frHpTxyXF2dpNSR1nVgqxsEXVtAZjfDE9ldwOiKs2bGGBzf9koSlF/pmktLhl1vTSeTX/hcPr13Bl9r6ibTexL33nk7AnMS3Q+Pu7d+j65UmhDATf+RGPlcC5kVwUvIQmw+eyfbg9ezY+R4tzdM52Ryj8yaY/XKQ3ptvRN/4Bj0330Bpl4bDLAWEf75pLe/vuIDAiSbK6g5wwaY0N7Ze44NgkvZ/20ZZSZy4DlX2XsrzBmg+cAY90zWiiQhfzNtI4sQeGsLT0f/4E5bPgkgC3EqAvi4Pull6rACmNA9QuCmCO/LfJDSdy0+H4Llyw1JvLMjB60L4N+m093wLgJ66swkVtlD/jQaSnUHOe7ebYxIwbV+EK5+5kqvWXcV1L/Xz6Sb4/KGdaJqJq9ZdZew009O59G44rhO7ouIV0LYIbH13koyBRbUY986B/masJtCFZnB6f/P6t4zMFe9vP4+fb1pLY7ARlwVCSZ3em2/EEUhxyoubcoSLLyrehXfDcrpmddDZ+zgt7X+ASC2L8hopdUDLQBib+jZdszpIrpcyP49t+2++W93J1gCcWxogGt2X0/bWzn2EE/JFvk8/RCTkwILK7Af/im7WMP38V4BMZ2OQ14dwrDyeRZzufA2zmkRsfYfAivOpv2A7IpXA6z2Zz7lfJ5VoQVec/OaN73BafpzFJWDT5I70VHzfmLzp7PVyTxi+P6WH9nA70ZRCb+A1uroexxLfiNUEPquFSOgtttd/g4Fkgv5k97jXrzGB1aZNm5g+fTpTp07FZrNx6aWXsm7dupwyixcvxpX2a5566qm0tIycA2pM+xiBVTi8mRkzfovVWojJZJ8wMdjjkcAqc15R4iVsrhP5fjrlmqrDsl1S/NObjsCt3XkMl1tlvPryq27mf7WlpVyFAAAgAElEQVQtYIlQ11DFinrYsOe3AGzYAKecAsszkb533mE5KwCMn6OO6zgJfkBn9kOVVFX9B9UvFROtFjmioRakzEGmvDkuRaG9v5C8quM80NEMZbVQUivDhYAhCqo5QC2WQOxz7VYuCk7CnBwfWzBTR/1yOHyl/DlvGUz/TSHhL0KiGGI3SFHU4x/Yw71rfzpmgmeASk9ljhBptkkhuw8HrDKcrgyHbvks+EZ1kluOTVHbDfelVTYcpiR37/cyd+4z6R2nuddWfv5iZs58mEikDp/v0xw8+J+k9IRxzdT1S67bbw5ALJXilto7R+WSjdbHsSz7/DklczgUOJSTC+vjNKOPlk9+W+Antn5lC/EePCiPPfroEU+pr7+YuXOfYe7ctdTXX8yhQ7eNcOyWYccOHryZ+vqL+e0hB7G0czhpgZQJ2g4n4XcKC2zvEo83snw2zJ79pJHVwKI1ssivsnyWvN5QgISM0UY0GIhacDvktWIhAfag8dmuYILf19mpP/UlAMInFdPVLb3oyT1OAnXHAPCukIvO1It15t4Gc5dB4WnyXmxvqaKhQX7u+bZcFy59Cr6xVcMk4BubNXxh0O3SO364QeX3dXZ23arR8E2B6YuHmF32APnvJbh1y7N88FeY92WwKPL+3BWT90LsO+AwA7qGI+2Bm3qxzn9vhVACvCbpLRE2GEjvsD65DQrqYO4KuLwa2tvAdIl86by8CmbVQPXTcOLdg4Dg4He6mF0DJS2wpAl0J8xvgIJgkoL+hCGGfFmTHH9CywqpWmW/fn4wxqcK4eRCaPqpFO4NDEQxx0zGvSOAhC7pJpn16CfHqmzZ9X3q6y/mnv0u3u2V4TO3RQYSLn9fwxKBqvd3kK/OMoSL+6JFBE/bh38bkEiRTCV5PwDfmwYpAZdUwpxl8ntrnPY2AFdOTlKzG55uhZX14LeS03Z/QkfNsD3scm6tisaxL7yNsILy8B+ho+OIOlb5+YvZ2DIfYdNovExQf0UDs2pAKDr5ieOp5yJKtdeIp2L8ZKbKsnq5fn4/nU9+aUXzmLzp7PUyM4/lDsFsT4yOQ99l7txn6HL9gDJrHyd6e0l1LuPBzH1mGj87fsyoYWtrK1VZYGfSpEm89957o5Z/+OGHWbJkZI2o1atXs3r1agC6u7uHF/gYgVV19U0Eg+/mbBudCDE4L28Bhw79FACv9zSE0HCmtqKY7LgUlf9tRIp2vr7COOeRHx7ikfTv3/xaerEu2Q4HllDnX80H/UF+B6xfD0uWwI9+lC68bVsaTq0wfo46LuDsx87m2kXXcu6x5wIw6Wf7aH6ngkkXDsgtqENOf+Nt2Pxp+Dvw3GeAfnmR/ellOHZHOqRphocXwnVflucsyofV550Jd73KnJE68v3vw+rVMgxos8G3vw3332987BcC/U0/jd8IMXnyMvK33Y73B9+hIfUQfadC9Z/SYM6m8c3nWtjy5VGHbFjN52pGJbqPtHNlopbN3arrhx1B+Ppk+HOTFHf990rY3AePN4NC1zByeva1VVp6MQ0Ny+jpeQZFsWMzqfypUdY7xQUnF0iu27Z+MSEu2UQEPjNlbWYbxxYdy86unSyqPLp0OROxxmeuhNeXj13wY7BPbP26/fbhCqjZIfERLJuL53BMo6lplbw30sfy8k6iqenOnGN+/2Kam3/O5MnLeHvjSi7OUqYJ2qEgBupenXp1KgtNe3ix08r5xrmfQxdwfqUM/b/x9HJe3rDCOP/Rb0pvc8PMzTSv+yaNfx/87MKz5GdPpP8DXPLdQX7UszVrSG+tYfnPngbgjAuyvNfpF7XLvj14Tubz/2KFQXNw6NLxrTkGveMKPSg/hcZvQNVjCvkvvwYDA9THQzR0wNQP4Nm9y/njHwf7+/mvDPecZ9rLjlGccb48dkL5ChQh++CvA1svTJ8GIgmLS6D7Fck1XcFyajYPtvPpdJ3fzV/B7dYaUnlgaYdlO+U6emdqOStZARnQsWJ4vz74D50lwBVXrGCep4a6Ioi5UliiSs79reryBUzV5brRq8J5ZSEqKpbx8qt3GOXcZijtkWnHLBHQnAJWriT//vsBQWVeG4oKvWeAOQzWhOAzxaBqYDdD/xtQ9L58Ea943kTTZTrr2gZ5w1v6ZXQmu+1Fs2D3s8vh/wbn5szPy7Fe8fAKPqfdCStXEr3yeFyW0cnrp/2xHcv10HqhpKL4dwACTHfcifj6AhojO5lu2cbTLYP9ea5Nrsvr2uC8MZ7nQ9fLun7YFYLTCsHqv4j8/MUE2INFL2KGs5O482JeXDOLp17LjOukI9afsY+UvP7YY4+xZcsWfvzjH4/4+dVXX82WLVvYsmULxcXFwwt8zOlsMhm6j8aczhkkkz0kkwH2778OXY9SWno5QiR4q8/NueWw4IIaXDUmjj3uXQCu+lMBtbUyXGYIe376LjhwNiC5QUIMAqujtQVlC9jWMRjasVg8eDwn88WKEeYYqCuDSRboypK4KgvDZenFAAbFOzNJls3WitE9QJm39cyDZATR0K6uJ9C0EFZrseQgHXyafXmPoFvlDdT+lXSoMJHgqg+UIyZ3BplCZ/2B9aN+/lEkYs7mbp2cL9PCdKsSUC3wD4KhoWVHMqmh1pdzzWREOt/rk3XNyIPupG/UukY6PhGBz+yyQ6+Zj9Mmn/8orFDk//+P7ajXr/Z2+MMfhp+QSAw/lmUZLl4gUMvAwLacJOSBQC3B4Bs4nTNzjvX2/h9Wawltbb9jcXkhiaw9ISE7FEbBNMfMcfZDgJVzSlNG+LCn5xnMyiCf8vjzanjxVQU88qXvSw8o8MUfMWvuGxz376u5/BGFKx+S7vffrjVz+kqFjhMV1vx2JgBvrlXoWSQ/j1lNBif0zbWDP/sWyP+ZY288p/DGc7mf11BjrDsKYFFBt6XXoG1woYBkPhRvgI4vCwJbHwKHg0n5k3FNA/8C+OIlNYNrLPDqcwrXPajw+nMKr68fbK/9RIV7H1Go+4o8VvuyQtmPFJ7ursGWnsvYPHAUQ/d+wAK6gPJFMDAfVlBDz0kKtRtyx/CD02vQ7ZBygWNA9vvKbXA7NQZHFqD0R8rgvZC+Hxbcq/D8KwrnnFdD4t/kmmDyWDAP6Dn3rKqlvXCkxX1d8EHQQVvb7/hiZYlRrjIJkzrkXJqjoNtlOrO+g0+RSvVRtAGEFUpfkhNu0+GNZrCa4I0WOdbwfLkety/RqXwMzi8jh1NsM8Fr3XlG23YTVCypyRnXW08ohKcqfOuSmpxnQoZjNYy83t7OKaEWUm4oewHavgJ9J4IpATz6KFUD+5lk2s26Dp+xdi7ww5fL09d0BWNSe4aulwv88sV2TZMJNfAUgUAtJnU7flMvbckirNH1nHv5b/jjCxNbv8YEVpWVlTQ3Dz5QW1paqKysHFbu1VdfZdWqVTz77LPY7Uex8+XgQQgExnzL+zA2VPxsvNbUdDf9/a/jdh9Pc/Mv6eh4hKKiiwwByFMLTTzVauVnc8D/zFNEU/Lq/3pV0EhEe9c+O8tnwdwTXoLDi7H8/U5WnbmK3buhvx9mzTr6cUUSEX7+5s8NovO6TZfhdM7g27PmGVnNF/jh0vTQt5VBvhP6EhZsZhtlYdjxW+myzrbMG6PL6uIHp9+OqjaPqNjb9OS/E5iX6yYNzE3S9OS/y98Dtezbdw02WyU2WzklJV9nx8Gv0flFnUmPgzlGToJnRRejJncG2Z8vz/wyf9v9txHJ3U1NdxOJ1JNM9tHb+5LRh2wC55EsQ248zdto3Lw1c2BbQL7hmICa2TDfB1sDkkd336dOG7W+DL+vuvpm+vrWG9fMk60WfjYHTi+UGixfKIG9YZ0rjp3P5ZOHJxRtDDYOG+uqM1fhsDiGlR1pzrK5aJqu8aOXfzQucvxoNl6C/aozV8k0JP8A+0TWr5Urj2rdknIb57Nz5wXY7ZPweE7MOVZQcBZms3vIsS8iRJLZs5/k21NiZEvrBB0ws9yE5WrBut4F2BzH8OeWPIPXt3fvVaTMJTzR6qJmN9x8HFhMQEISlKMaWDUfZnuc751wBT6bhZgi7+v27nJuCCrsvw2Ux+VCMrsGdt8iP1d0YXAoZ9cM/ty5Uv7PHMsZf82Q5PFpM8VBT1/S0Xlw4DZQktJ7MbsG6m9OEjhe557PLGXxOZDcCB+kwzorZ8vzmtbAybUmCWfEYHsHboOZGhhZe6yw7A3IZFHJcFBNcSgug0MvSoBS/qQ83nwR7L4NzAO5Y0i6ZZ81t/QS2TTIVozI2IqNwx+5y2fBw4chlYCBh6Rwr6fUhSWss+rMVcYaHk97rBb45TntcYUp5ecxe/aT/HhGlFML5XV7yUGwpNkBlghoeXI9rj9wOaakmf4TYNrvoOd0EGbQzfKl8XcH4VN+qPhz7vc29U8W+v9oYvksySlePgva4grzpv3YaLvErgyGAhlsO+kHU3oPFZrGiQ+vN0KBQz1WgQe+y75bBe5DULJBtr3nFkCT/S9NPcz64CJOmnM/d+2zs3I2rJwjv/fHW5z8cr97TN509nxm5vGufXbeDh+HVnQbO3eez7TUo7SJBQT1Yjpd3+VbU2LYJuiCGrP4okWL2L9/P4cPHyaRSPD444/zla98JafMtm3buOaaa3j22WcpKSkZpaYx7LbbJPH5jjvGLnuUdrTAKkP8s1qLaWn5JT7fZ+ntfcYQgDzh+HVcPOdCnmyspG3bhXzhipu5/IqfYbdVEgi8zE03Hea6zz7MA02lzC3vxVbaQOrvP2XpvKWsXy93Dh9tNpc1O9bw0LaHpJBZmuj8801rae14DL++kzs//zNO8CvyZlC9FDoL+aAMbD5Y8YX7eeS8R7j7nTwKY4Peqoxl+FSrz13N147/FiaTfViOKADPm93U35LKIafX35LCs1HKC4TDmykqugC//zOoahMdHX/AGgJHq9x16NlDToJnhwafyqK5uK3unATPV8y/gqfqn0Kk/w0ld3s8i9i9+1LMZi+7d19Gc/OvDOLmRMmNy2fBl8ok9+D5DjitAFDkW2xfAk4u9nPHPBufmXH1qHVmBDSFSBl8lxOOX8fZ0/6NRxvgqmPgcARKHKCmwpxgeZZjSs/GNMLtOXSsS+ctZem8pbitUkzQnN5QcKSk2Gt2rOHJXU8ykBgYkxw/nnkaq47L5l6Gx+ahPK8c8j7ZXYGfyPr1zjuDEiNDrHSUUwoLJc+zpORSSkouIZUKkEoFc46ZzV5UtTnnGEAqFcDrPZljp/+CIlc+k32TUVBQ3Q5OmTmV6nWz2B/Lw2wtJ6LbjSTzkyb9iLL8k1l97mpD7FhNmkD1MvnYt3DbSzh7yoVY7Conl8/h7KlnoOEATwt9vTOYUehkTg109lUxs/gD8uskL/LGebdj1+S9e1OpFDZejvxZksXZvHHOCuamuTs3zlmRc89nW0YySzdD9DiYtRKEHeLFWevEtCSnFPmwBr6GWzXTpUKzWsjGXgsX/NsKIj1eFhV7mbtMesS/df4Ko79uJzhVha+fV4uSVPhMt8XICBE+DmbdIblSVXfC3joFJQKKQ7YbOBGm/wZswcExzq6RgEqzQyoNrDKc1mxbzgouDlYx2SfVvs2KmTlfWcGvD3r5IJSHzw1qu5tE4S1Y3GDpT7J03lJWnyvDz6oGLrOJ4zxwf0Mh5Z5y5hVNJT9/MSccv46fnnwBDouDaXGwpYGfZUB60cLTUhzzbBGWiMKcGqh6Wn4vpa9C2atQtNnNyz1efL8Gizn3ezMnU3z5/Sru2mfnxHx4oKmUYs8svjDlU0bbkz1+ksJsrDc3Fd6LJQKJbGCVSFCxo2FU8np4YDNzasDZIcFgfh3M/LV8NoanpZj2YDG7BxwsnbeU6z7zMLXdUNsFATGZX5/1a+oH7GPypjPzmWfN4ziPHMt1n32YY/KPIWqeSUnJpbTrs1DNk3GadPpENcfN+BVFTj951rzxr19iHPbCCy+IGTNmiKlTp4o77rhDCCHEsmXLxLp164QQQpx55pmipKREzJ8/X8yfP1+ce+65Y9Z54oknDv7R1iaEzSYECOF0CtHePp5uTdh27LhQdHY+flTn9vVtEG+8kSdef90lNm4sFH19G4aVWblSDmHjRr+orUVs3Ogfsdzy5bLc0qVCTJkifz9am/zryYIVDPt/1u9LRG2tSazd+Bnxf6/Zc/qRHOgUb7yA0CMROfdW67jmftOmeSIUen/Ez+T8eMXu3d8Sb75ZNGzc27efJzo7nxB9fRvE3//uEK+/7hYbNxaKs35favTZ9xNE0IZw3Jo7lsm/njyuMWeXk+1YxZtvlovaWsSmTceP2K+x5nPBvYiXXkPcuxax9iX598n3IV7bgLjvGbN4cYNzzDpHs7W71wrnHU6x4F7Ec68g1r862EbpPaVi/u/mj2us175wrfjV278ad7vjqfOjrGNj40Yx97dzhRBD7vtPyD729UsIIe6/X4hrrhm1fE1NjTCbzULXdbFxY77YsuVk47NkMixqaxE7dlyYc86OHeeL2lpEKhU1jm3ZcpKorUUMDOwWgcAbYuvWTw2ecMEFQlx8sej60mJx+sOni00fXCKu+t8i4+Pm5v8Re/d+L6eNl1+eJ0CIs856SfT3vy2uv16Iy675iXit/hdi585LxO0vfFaccOZB8ctf3i/a2h4WQgjxwx9uEN/7Xq248pkrxUNbHxJCCFH2izLRFmobc96G2lXPXCVWb1lt/H3fu/cJViBe2WATyWRQCCFEItEramsRr/7KJt5tfleIt94S4tRT5Qlbt4r6u/JFW5vsx5rta8Sz5x4rxF13CSGEmHbfNLGz6QmxZcspRhvbti4WvadahXj+efHGixaRSAQGO/S5z4noq4+Jt9+uEuLb3xZi9Wrx7rvTRSSyxygSDG4WmzefIH7x1i/ED1/8oRBCiPXvnS/uWYmoXWMTweAmIYQQu1/6s1xXzzhjzHl4o6FWvPYqQrvuu0IIId55uVhEbrhICCGEmlKFucYstm49TfT3vyk+8+hnxIZDG8Rbb5WLvXu/n1PPaQ+dJr7zJ7s49OcvCAHij7daxI7dskxX19Ni+/bzxBM7nxB/Pe9Y2bdt24xzv/63rwtWINbuXivEcccJMXlyTt1nP3a2MNWYRFJLih07LhBdXU8bn214c4b42p9Pyin/8ivHiuYLEe88aTOO/fDFH4pfvv1LEU/GhfV2q9B1Peec73+zWGz/nwrR1vaIEIGAiE53i3femSLnpPkdcfLv5X3TOdApCu4qMM6LJqLCcYdjzHnO2JLHlojn9j5n/P3VJ78qHt8hscEN628Qj7x5pfjLa9PFXW/eJYLBd8WWLYvEV5/86rjXr3E5uM455xz27dvHwYMHufVWubPo9ttvN978Xn31VTo7O6mrq6Ouro5nn332SNUNt2xXekbM8mOw7ASTE7X8/MVUVPwAXY9SWfn9HGJyRuRz2TL59xlnBFi8WPDUU38bsVxN2i2+Zg00NMjfFUX+X7FiYv0ajbz8cms3eXkL8KfeIOE6O6cfcdGFPWhH2bVLdjrztj3G3B9JwiA/fzFms4eOjodHFF+VwpcLyc9fzKRJ16PrESorv8/LrYOimUEnfFAGn2048hjHQ+7Oz19MWdk3SSbbcTqPJRLZjt9/5pibFYbWvSv9UjXfPyiguikgiZLH+zRe63YftTL6/t79xFIx6vphbavkT2Ta6Ix0smT6knGNdVvHNhaWLxx3uxMhx3+YOjKhwjMePYPmYPNRhRs/CvvY1y8Yc+NNKBRC0zSi0SipVCjnPsr8nkmInLFUKpTzOUA83oTLNQtVbTaEEw3z+aCvD5PFKsUOzcUUZ6WGkeKPuX2Mx+X28fb2IiwWL7EYWGwaMbUHTQvSFgkyudpEV1eF0Z+WFjPV1SYcFoeR0iajTzRRq/JV5VwzTcEmZhXNIiFMhiBmpl2rOyXz3mVU19NjVvNixrh8dh/mgagUDwXiqTgOa0HO3OokMEWTEAhg0sxpQc1Mh6pQu3bI+tJinWazz+gDkBYB9eWEs4KJONZCLyZLyhCsDO7fQVNFnuQOj2F+RyGpqIlEifQ4a+Y4lvQuv5AawufwpfMRRqnyyjlLpULDrpmQGsKMiugeQMyahasvhRlZTzwu09X47D4sA5H0hA/2rTnYzKyiWTLlTigEra05Ie6QGkIXOu3hdiyW3DlJagMUunOlCKxxCz1VNkyxwTpiSZnSxm6xoyiKcf1krLgnhtmeTyoVBI8HXYsaeQazeVnNQalonzGHxYGaUg0pi7Eso4ifMZfVRSwVM/posfixK6kcgdDR0gCNZP945fVxEJ8/KhtpYRmvBQK1dHT8fkQByBUrZLaDoiL595tvFnPo0H9x8cUXDysnXxPk3yP9PlFgNRp5+YwSD13BbbTFQAk/Z2hmQXoekn645JJc0u0oc5/hJmUDq6Fcpe7uZ0gkWnG5Zg2bn2Syj1SqF6dzWnoeHzHmMZt0CbB+OlxYD39/dJA4P3SM4yF3BwK19PSspbT0cmKxfTgc0+jufoq+vpdzygzlWw2t+/wKeZNkCL8ZztXiYniq1cGn/D054qETsQN9B8h35LPAD+eW57YB8GjdoxQ4C444Vl3obO/czvzS+eNudyLk+InWIRAU3V1E3s/y+Prfvm7oawXVIFc/dzV9sb5xt/FPZWNkjQgG5aIcCHSgKBaSyR50XZLbVbUZszlPPkyyTKZHyTPuOV1XSaUCeDwnoapNOYrWgAQTvb2YrDZUTUU3F1NoG3zQjLR5JxYrIy8vRWdnKWazj2gUbA6NWKJHqrKHe5kxxUFnZymaJvvX2upiyhQHdvOg8npGn2iiVu2rNlK8gHzgfarqU6gaRgoXTQtiVtyY/LpUSM+orqfHrPoTxrrutXuxDUQN4KVqKg5bYc7cGvPW2YlJWBAi6+FeXY0aPCDnKZ1eRuZUHDxffi/enHBWXzyCt6QMbDpmcxrUNeynZfYkmQN3DA6e1+4l1Q/xAg0hBCklhqVHjj+jOJ/JF1jtq6Y52ICuR4ZdM0E1iN9mQbT3kzr1ZAoCCromF9KMqrrX7sU6EIO8PENpHiSo/VTVpyTQDQZl5vSsZ0EwHiTPlkdTsMlI4J0xXY9Skpd7bdmiCoEKMyZVl6r1QDQVNa6TkQjsJX0qVleJvNbMZnSPAxNWY44yQDajmp4xRVFwWBzjlpAZer7L6jJkGKKpKFaLD5uSTCdhlsBqpF2Mo9k/HlitXJmb7gQ+Fq+VridIJnux28vHLjzEMuTj2bOf5Jhjbh9RXHT9evjc5+RFeKRyH7Vlk/EydlK+mR9OD3F7vcBvhVW7cwVJVbUZRxdw+PDwG36Euc9wzIRIEo83GfOR4SoFArXs2fMNrNZiTCb7sHEPDNThds+nv//1YfOYTboEWD8DvloPn24aJM4PFQAdaczZ5UYiiyeT3YDCjh3nGTusRuJbrTpzFU6LfPNe4Jf8p9e6FNY02wxy7Mo5sGqPmYcO69TsxhAPnShPaX/ffn560lmsmC0JmI82DOplLfBLr1VIDeWkJBk61gN9ByhyFZHvzB93u2PN39HWkbHeWC+RZGTY8WgySmuoddxt/FPZODxWAIFAG1ZrPjZbGaoq50JVm3G5Zg/zPmhaCJdrtiEboqot2O2VOByTicczHqssYOXzSWBlkzn8dHMh+dbBTSXx+HCOaTxewvTpYXp7i1GUNLByCtRkgFQqxOFQN3NmeGlvHwQn7e1+jjnGj91iR02pJLUkutBzkj2P16q8VbnAKiiBVVTTcjxWLtcstHzwmp05Hivh9aIW6Njtk+QUOHzYo6oBvNSUistenOux0lVMJie0t2PCmpM+haoq4rGmHI+VxeLLOd/wWGUBg574AIXF5QgXmNLJk0VTE4mp1VBQAKMIxWbM5/Ch94HqT0gPmmLCFJBEqUyOxExamypvFR2hw8bcZFtIDVHu8mJq62dg0XyKexXje8twjH0OH/aICrNnG8BKFzqt4VZOm3QaLYFGiMXg2GNzgFdIDTG7eHY6gbfPANqyApWyIcDKEYJ4vsCk2I16MjpWmTFne4GEEJQFkli95ca4dH8eJt0yrPxQjxPkep2OZCE1hKZr+B1+45jT4hwEVskodmshFkVNp+RJYjJZR00DNJL944HVO+8M35acSMDbb3+kzahqKzZbGcpQtfBxWIZ8PDSBbjZJbv16OP30Ldx00+EjlsuYIQY65PeJWoaMl0Hf5XnlHO+3U1Mvt/HvCUtl72xB0njPbuz1PSNXOMLcZ8bR3f0Uvb0vDEtgHQ5vxus9hUmT/oNY7AB+/+dyxj0wUIfHs3DEecyQLjME3EShH58qdyhe9YHCn067e5hWVWbMGRJosas4h5w9Ell87txnKCq6ACFUGhqWj5qEe+m8pXxt7tdwWV0c54GwZmbhzBU8ct4jBMRkarthc7+bw3E/CS1hiMwd5xldxHM0O9B3gC9Omkyi8BYCQo4luz6ApJ7EY/MY32+VtypnrNvat7GwbPxhwOz5K3ZJyYCh5Pbx1nHXF+4akWB/JMsRSPxXsjGAVcZj1dfXjtnszfH+xuPNuN1zR/RYud1zc8rZ7VVGuiY9K7ktMAisrHZUTSWl5JNvHZzvkTbvRKNF5OdH8Hp76epyE42C3amQSvWTTAVI6FaOneqivT3feNh1dpYwdWopdrNsJ5aK4bQ6jQT1E7EqX5UMPWWmMe2xiiRTaNpgzkKLrRjTAFi6DxmeJICkEsaUBEtCrus+uw9nJGF8Hk/FcdoK0bQoIp2VQNfjmCxO6OjAhG14KFDvkPOUbkeGAnM9VhaLNycU2BUN4XPZQIG+JqnVZW3rxDp5qpHc+UjmsXlQenTinogEbopHAjukF8rn8KU9VlGqfFX0RBoAcsCNLnQGEgMUOhzY24IEFh5HaZdugMLsUKAzmoC5c41+dUW68Nl9zCycSaCrETyenNyJmX7MLZ5Lc7A57bEaBHUmklT6puWMyTMFy+UAACAASURBVN6vkfLomMyOHGCVCRkP1bJK6kmqQmDLrzbmW893YdLkGuO2ulFTKik9JUOBvuHAajxiyZlzs6/XHI9VMorDVoCZQWD1z+ex2rYN7rkHrr12MB4mhDz+EdrR7ggEKS469AGcn7+Y6mqZ+iKRgNpaWLr0y9x11zGjlsu27JDfRMN/Q23pvKU03tjItYuu5YZTbuDRwzFDPG1TH5ySLx/YD+5Pv7m8vx57Rt/QZpPinmPMfWZnUjS6i7Ky7+TMR1XVfzIw8AGlpZehKHYSic6ccQ8MbCMvb8Go83jeyX+h4cYG9OU6uwKXoZjkZelUrFz4dG76kewxN9zYwC2fvoVrF12bAwoy7WS3l5+/mLlznyIvbwHB4MYjJuHui/fxwJce4NGvNVDhzuerC28z2vvl1wQ1Fw3khLTq+qVIKIyfpxRLxuiKdDF3xh189YRVNNzYgJLel5ldH0BfrI/GGxuZXzqftZeszRlrXUcdC8omnrR56byl1H23jiJXEYdvODwhUJUxl9XFRXMuMvo9HhvqffuXME2T4Z5Jk0YtEgwGcTqdBAIdWCy+HGClqs243XPGBFaZNSxzrq6rBv8EkF6aSASTTYboUkoBPkvCSNYsk+nmSk1Eo0W43UFKStpoaTERi4HTZSGVCpFMhShwT6K6GtraPGhakN7eIEIoFBZ6DV5LthdiopbxWAkhSOkpOgc6mZY/jZQw0xttS09vEF1xIgKQaKszPElyTpqw91kMEOK1e3HFUuD1ogudpJ7EbnGkQ60yJKbrKkoaWCmKfZjHSrUGhnisRgoF+nJCgR3RAHaiEIfevVINNa8zgHvqceMCVmaTGXu3QsTenwZuWcAqHQrM5lgFoq2YTM4ccBNWw7itbjxmBUdXnO5yHyJhIqX2pedKXj9yjpIwZ47RrwzYqPJVEepqlsA0q98Z0JbtscrMiRACi6IxyZ8LrBw9Kopbx2R1GfVkh4yHhgJjyRhVIQVLUbUBBoXXiUmToFlRFDx2DyE1NKLHyml1jg9YjebtSg5yrJy2IswiRjSV4VhZ/sk4VgCvvy6Tl36M9mHEQceyN9+UXtORNE8/SVsyfQnrD6zPiR1nxCchzYtpb0ftP4CjbWKctgxnyek8ltbW+3LCm+Hw+1itRTgck3G5ZhCLHcg5N5NYeEzLTgsyzr5NROgyEKglFjuEzTZp1CTcCS3BhsMbOGv6WfT1vUh+/lkoyvDb5MPylA4FDjHFPyVH32msOqcXTOdAX+7cbuuYuMcqY+V55ZgUE63howvPrT+wniXTl4x7zC6ri0rvcA2pf3rr7AS/H46gfxUKhaiurqa/vwez2YvDUZ0V4pOEdF2Po+sydCcf9hou10zi8SajnN1eZaRrGjEUCJitMhSYEAoJ3UIy2Y2qtqfTeeUC22g0H6ezj7KyTpqaJBXG7bYjtDBCj1KcN5nSUgiFbESjMQ4c6KS0tAuTScFukQDuaPlVAG6bG6fFSU+0h7ZwG8XuYqxmK4rJSWdYjjuVCqEpdpJhM/Hu+pxQoKo2Yw/apHcJCazy4jq610NCS2Az21AUJSd0pesqJotLhgLNw4FVPC+Kwz7JaOdIocCQGiKlp+iKBFH0PlDNhA7uAqCwJ0LBsfMlQBkHgd3VbSZq6SOVCmG25htjyoQCMxyrKl8VoVgbdnt1DuALqSEJmkSSqMVBKBUhaHOgJfrR9STJZDc2WwV5tjw8cYE2e5bRr6ZgE1XeKio9lah93Qhv2mOV/nwgMYDL6mKKfwpNwaacOdH1OCkB1b4pOeOxd0awOTQU2yCBPxuED/VYRQcCFEQF5sIsj5XXiSk1+OKWAWPNoZE9VuPhWGXGmm3ZoCyajOKyyzBhIjmAEElQLOMCbRn7xwMrTZPI5Iwzjur0DLE620bKBB8I1KKqH4+Gzm23fTjl9I/KuiJdvNH4hkEYvrQK/DYwKzDT62DVmasIPPBdBqbq2LuyThyD05bNMSsq+gpCwI4dX6K5WSbWbG29H6u1iECgFl1XicX2G9+LpsWIxw/ids8ZW5zzKPh2C8sX5gKr9nYJ0js6Bn9PZ0mv33kRs2c/QTzZwz174fXNnzeyxDc13c1T79/KtX8p5BhnkJN/fzI7Dv4KGDlj+oflKR3oO8D0gukTqnNGwQz298kEhWt2rGHyvZN56eBLfO+F7x3VjjtFUVhYtnBcCZmzxUCL7i6i6O4inq5/mmUblnHOjHPGfLAWOgtZfe7qUcn4/9TW3HxE4jpIj1VVVRX9/T3DPFYyRFONxeI1HlapVAizeeRyMl1TUzoUOMRjBZhtcrdeQksQ1p3E482j7oiORHy4XJ2UlfXS3CyBVV6eAxtBdKxM8lZjMkFZWYK2NjuHDwepqAgAGKHAD+OxgkECe3Ow2QDpFoubnkhLei6CJIWdWNyKGjqQQ16Px5txDLgM747ZZMYXhwGnGTWlGuK52WRrIVRMVrcMBZocueR1nw+1SMce92YmYxhROxMK9Nq9BONB2sJtOGw+kolOhG5FPXwALZmgOKRROvOEYSG10czboaCa5G5Mi80vUySlUgTVjMfKia5HyXfk4zBpWGxlaFrQEG3OhAytqHRbpYcl5HKT0kNpKkwpJpMFRVHwxxVC06vkGqlpNIfk3FvNVqrwkchz5nisgvEgPrtPhm5DzTlzEoh1kNAlByrbbG1BbBaByekZF8cq0XSYLq8Fi61gEFh57ChZEnEZMDaUfA4TCwUe6VyjjyY3mhZGiBSagDxb3ph1Z+wfD6w2bJDJoY7SMsTqsbPDr8HtPv4j6fJQe+edfzywWrNjDdetvw6RkRkG9qYFLttUB3edfiHnTKqgftELpJwMhgJhTE5bNjeqoGAJiiKw2SZz+PB/0dz8K7q6HsPlmk19/cW43fOJxQ4Y30tHxx9wOmcSDL49pjjn0fDtpuZPJRALDIbmVq6UQH3lysHfly4lnNzB7NfO4MW2Hrb2JQjGe6jZDfmmTq5+7moe3vUWtt6f0R8fYPksOCWvEVtqH/tDyRH7PZTn5bV7J8RT2t+3nxkFM0atcyRhz4zHKiPOmQk7toZbJ0ycz9jCsoXUddQdscxQMdDeWC+9MSkU2xJu4Y8f/JEr5l9h9HuoOOlj//4YPTf1HFW48Z/CxpHjNBQKUVVVRSgUyAFWQggjRJO9rT/jFRkaMrTbq7BYvCiKhUSiYzjHCrBYJalc1VQiuhtVbR6VChGNenE42ikv76e5WXKWfd48nEqYpLAZXoFJkzTa2tw0NsaorJQPoOxQYGbDx9FYhmeVHaKxWbz0RTvScxEkISxENJeci6Eeq7jX8O6g6+QlIGQTxFNx7GYJPLPlAXQ9jsnmTktTuHI4VpoeQ3coWPd0yF1zJtMwj1UG9ObZ8oin4jT0N+B3lpFK9QEuRHMTXQe30+8yYXd7xxUKBPC1Q0rpSQM3n+Q5hcNZ5HUZClQUhSpPAQnhAgblIjLgB2K0mHWC8SBhbz4acVS1cfD7FwKPKgh6rJCfD52dOfIFU00FRF3WXGCVBm1VXvldZYcCW/oPkBJDMiuEQljSu7pNTn8uxyp9rQwNBWqNDXQW2HJeMHSPXaa0ycyRw0dfrI/OgU4qPbne72wC+pFsNG9XNJULrBRTHroWRogkSZ303I7P/jF5JrKtpkbezStX5iTtHa9liNU7dnyZysrr6Oh4hLlznwFgx45zKS//Nl1da8jLW0B+/pkfde9pSSuEL/r4c9ke0bKzdmdsW79Ulr312BgW7U3q619i5ilPsnfv1Zjjo5DXR7Bsjpic7yfYufM87PYqDh78EWChp+evzJ79JIlEOz09zzB16qp0Ko6v4HBMHZUsntvhLM/JqlXQ3Q333nvEvpkUE/PL5lPXUcfnHbPgkUek1+uBB2QBXYddu6jeBThf5l7TViaV65xcAHfvzSTyjLJq8wvM88GdcyGQgO9Og2ASTNpaZp/46oj9zqier9uzjge3Pjgh4HCg7wDzSuaNWudINqNwBo/UPcLfG/4+7LvOEOcnCl4WlC3giV1PHLHMSNfW0Lb/b///0XBjw4Ta/pexMYCVpmlEIhEqKyvp79+M2VyKwyG9TqlUAEWxYLF40w8U+aDJeEUsFj8gSKWCOeDIbq8iFjuA252VCysNNhSrFbPJTCQRIZoGVkIkcTiG93FgwENeXgsVFT7efls6afxeH/Z4jJiWR1W+PKeqSqG93U9vr25QyewWO3Et/qE9VhmeVSwZMx7uTqufjlhnei5CxDQTIbOXuOiCUH4OsHIn8w2PFQMDxG0mgskBPOlwJWCEAoUQMhRokzv3TFZXTihQVZuxDzhRDu4aBKqWXPK6pgXT4FZyfnZ17aLAXQ7Uo1h92Fo76d1Xh1LolMr74wRW/i4dnTiJRAdms0965YJBgvEg+c58zGYXiYQM21e4fEQ0BWe6b2azMw3APAhTkn2WFIoaJFpWhDfVQCRSPwisYjF0k0JQxI2+NYeajYTsk035hOwK+Vn9zoQZS/NKCapBNOwG+GkLHUZXhnAnm5uxeEqBRkxuPzRLrmwsFRs1FCiam+gpdORsFtDdNkzq4Nrjs/vY07OHIlcRVnPuLtRxe6zG4lilpNaWyZwHegQhkqR0McwjdyT7x3qs2tullwI+lHaV3V6Frkdpbr47i5Sso+sRWlvvo6Lie2habMSF5WgtI/aZWU8tlqMT+PyobHSh0C4qKq5FVRspKDgbh2Pyh56HoqJzqai4BlVtwuk8DkgZ8+50DnKs8vMXY7dXE4lsPyJZfERbskRutRyHGeGs228f9Hhli4RlTNP45nMtbOqTSZWzKdea0Ci0QSQF5U5oisow6jOt+pj9PpqExvv79jOjcMbYBbMs47H6KAQ+MzYslHqU9R5N2/8yNgawCofDuN1u/H4/wWAwxxMVjw9q62U/wDMEaUVRjNBfPN5k3LsORzWx2P7h5HUAqxWHxUE4ESYmPMa5I3msBgZcOJ0dVFbGjFBgoS8fp1ljIDWYBLi62kx7eyEtLVaqqiSZ2G6WnrHsh+XRWEbwsinYZHgS3PYCwvFuYy6imkLYVYxq6x9CXm/GoRcNAqtgkIjTTEgNjRAKDCFECkUxozglsFKs7hxgFY83Y0/4YNeuwdDqiKFA+ZD12r3s6t5FiVv22+oqIq+rn9D+XYRL0g/i8QArXccd1dBN+UQi9VgsXgnsgsGsUKALTZMP/xJnHqFEKse7E1SDFDldmJNWOv12DgUOES8vwaJaiER2DX7/oRARV5qMnQWsMmCjUs8jYNOhvBx6e0FVDW+YSTFR4amgKzaoodUZbkJRhuQrbW7G7E97lPLSHishckOB9txQoNLSQm+hO2dMwmXFFB+UBMrM91CPE4xfbmGkHYVD5RZcVhdmsxcTkrye0NMaauO0fyywWrFikFPzIbSrWlruQw7FSmvr/fT1vcrevd9BUSyAhdbW/yEWO4TdXj2uJLzj7boQcN998u+jFfj8qGw0AvEXK0vo6Pg9LtdcOjv/TG/v8wbX4mjnIhCopbv76bT45l5KSy83yOBO53Risf0IIejre5VodDeVlTeOShYf1RYsgL4+OOWU0QF3mkNl6+7jqT/fhv7AAyOrr2YskeDsmXCsLhPOfn+a1Iv6vA1qe+AHU8FtBg5DlQte6oDzK00EDj5tcLUM/laWVfuqUVMqXQfk53+tvZ8p905BqVGw3G5BqZECon/d8Bs6T5rFotur2HB4A1euu3IwfDcSN2xIO+V55UQSESZ5R959NhGBz4xtat1EY39jDndqaFLl8dR7NG3/y1hT05gaVj6fD5/PRzg8gMXixWotRtejxGJ7jU012VvYZShQLuR2exXRaD1CJLBYCoxjsdiBEUOBWCzYzXbCahgV3xFDgQMDTtzuEJWVCYO8XlIgMxz2J1LGA2jyZCudnWW0tbmYPFmCkozy+of2WKV5O9kP9zxHIWG115iLgZQgnF+F6onlyC3E403YTWWDocBQiJjLRlANjhAKDKLrcQlGXbK/JnteTihQVZuwKyWwc2eOx2qkUCBIcLCzayclHvkdOr1VFPVGSTQcQC1Pix+Xl0NPz3CaQ7ZFIiTsVlTFTySyUwI3nw9CoSzyutMQTS10OgioiRzvTkgNUWBzYImbSFSUsrNrJ9qkcswRiER2Dr5QB4PEnGn5gDRBPRvUlmpOei0JMJtl31tbDY8VyHu9LRI05qR7oAWzecj339SEtUjSJDSbS3od+vuHcayyPVbmljb6i/Mwm/MMeQzdbcUUH+TdZuZ7qMcJxuexEkKM6rEaCqwsFj+KkAKhCU2bUCjwHweskslxqX6PZYFALe3tD1JauhSTyYrdXsXOnecSjzeiKDZMJiuK4kLXw3R0/Glsns8E7cCBsct8EjYS8fnUQjs/nhFl9uwnmT79PkChoeF2QBlVIHMsG0l8s69vPdXVN1NffzEDA3Uoip2enr9RX/9VHI6pzJjx64mLpZpMUFgImzePDrhXrkRs3Mj0+x/noSfi49r0799n4pZ5Mumx0yzFPm9ZAP5J4LWAVQNzMXQ+B6cWgu69lPr9lxMY2AhLlw7yt7JMURQWli8kvvxWxMaN9Nx8g7GBQEtr5zQGG+n+6fUUvb+Hbz4n48dt4bZBbtRI3LAR2plWMI1rTrxmGKdlogKfILlT1zx/jZHMOsOdGppU+UhioEfb9r+UjUFeDwaD+Hw+vF4vodBAlidqEsHgOzkeq9xQoFzI7faqdLlqQ3sn46XPIa+73fIBZrFgt9gJJ8IkFL8BrEbaFR0O23G7g5SVQSCQvu28UkQ5oKoGiK+uNtHdPYX2dh/Tpkkx2oxA6NGms8mYVBJvNgjUAD5HKfFEvzEX4aROvPgYknk6en83eL1pCYkO7NbyHI+V6rYTjAdRNTUrFCjDrMZOygywsuUNCwU67NU5wGqoxyoTCgTpQdnZtZNyzxQAPIVTKRzQURoaEZlrIgugjGrBIKrbRlTkEYnslOrtmVCgmiu3AOC3WemOR3OkIILxIAUOG+YBgaiuYmfXTsSkSVjCGpHIzsHNC1lzRFUVWmMj3ZFuKjwyJU1xykaHJQ020x6toBo0gIUM3XYb4Kcn2orN4skdT3Mz5tKpcr4wQXU1WmMDSS1pSK4MTcRsa+sgWOpDUUyYzR5SqTC6w4wpNihym5nvkV7kxsOx6on24LK6cKdDwRnLAKuklkQIgdVsxWb1YxZxCax07Z/EY9XeDqlU7rGj8FoFg2+jKBamT/9v5sx5mkhkB7qeQFHMzJv3PHPnPkcq1YXJlEdDw/KxeT4TtP374bLLPrLqjtpGIj7/9OQLOOH4dWnS+eeZOfN+QCcS2Tk+ztMINpL4Zvbf4fBmnM7p9PW9SkHBlygp+SpwZLHUEa29HQ4dkl6nkQB3WppBEYJvbtGY3cO4gFXhFp3pDxZxQr6UoTALsAL9J4A5DuWvwNxlcP5vFLBczwmuY5i9XCM8U8jwgK6P2J8zLNMpf/olFCG4fKtmpOPJWFkYrtgmMAu4sm4wXU80GeXev/1kUGbioYfg4YdHbWdGwQymF0znkjmX4La6RyS5j9fGw53K8LZWn7sas2IekZx+NG3/S9k4VNe9Xm/aYxXLAUyh0NtDQoHZuwLlQu5wVBMKvZ0Tws/8ngOsTCb5MLZYZChQDZNU8tMhx5E9VuGwDbc7iN3upaICnE7wOyWwSmIzQmlVVdDVNZmurgqmTpUerUxKm1gyhsvy4TlW2SEav7OUlBYhpadIpYIEEym8rkJsITOquR98PhKJDqzWAkzewhyPVcLtHCEU6EuHAtP5FZ1OsNsxWV05uwLj8Wbs3mkyBJYOBQ7Ni5dKhYzv0Ofw0RvrpdInQYTNUURvnominYewT8nSdRorHBgKkchzMaA5SaV6h3ussgRCATwWE52xcI43LagG8VutWIIpbJOn0RvrxVI9BUsgQSrVmxMKTOTJOaKqinjDfkrzSg3pl3zVRKsSzul3MB40OEZV3iqaw61p8BOiP9qB3ToEdDQ3Y540laQOKSE5M4mGg5IUnn45GEpet7d3EyktSM95Ggg7zSjRwW2Bmfk+Wo/VSN6qzLmxVCwnrG23FmBBlRlHtNQ/CXl9YGD41vqjUFz3eE7E4zkRq9VPYeESiou/Snf3E1RX32qAhtLSK+joeJjJk5d9pKAKpMfquec+0iqP2o5EfAaoqLiGzs4/Ewy+cdRzkSGyZ5+bn784R4gzEtmB13sKra3/Q0XF1SOWG9NWrpRv3yAB+NDNDStXGq51e9ZlFDfDwwvhui8Pr7L+NzBr/WZet+yiu/VOTnLvJTUAphToTpj0Fzjm0TRAs1k57+kUqB2wKUn+pqyKMi8AWf257JkDmJPyRcEkZDqe7D6seg0c6feIoZ9f+XwrpNK3Yna4YIR2ZhTM4EDfAXpjvfz+3N9z2byjR/UT4U59acaXcFgcBH8axGyaePaCf1lLJGSYp3z0VFkZj5UEVvGcEF9X15+prLwWyPWMSK/IIAAbGNhGaenlRp2Zh2QOsAL5ME6HAkOJEC53EYlEJ6Bgs5UO61s4bMHtliCuqkpu0LaYbURTGMAO5PO1sXEaeXkhPJ5C4KMLBVZ6K2kPt2NSTJS4ZfjMavHgtztoC7ehaSECiQTH+HzYB1yoFVGcDgfxUBos+nywZ09mskl53COGAqPRjkGJCpcLfD5MJseQUGAz9qKLBueSXE+iEDqaNoDZLLfeG14c33T2IQFcT6GL4xoG2Dt99uAgxwJWwSApj4tA0sYUm2zT8FiR5jeZFHRdcoicZp2WcP+wUOBMu44lIiiYMwP2g8dbhDlpA9ScUGAqz53mWC1Ab2rIARseFZroz+l3qCJqeGyqfFV80PEBliLJhQrGu3DZTswdT/plI66bSeoSWCUbD+dcJ0PJ666OXqJlRcacp1JBdLsJSyQLWBnzPQo4GkPHaiR+FQzqWGVfy3ZrPjYlia4nUDXtn4S8XlEBixfnKn4fheJ6X996Cgqk1kEgUEt//2s5iZIDgVp6e9eNmDz5w1oyKekVxxwzdtn/HywQqCUarf9Y5iLbnM7pBINvEo8fwus9beIVZIRCMyAjmcz13rS3D+7+QwKhjLfKoeV6hLLtrTkeWL+eDxr/wMK8Vl5p9GADhAUm/wnavwL9GRHzREK2kR2uJuuzIf2Z8cxGTGLkPpSF4RsfjNzHsjBcVcf/Y+/O46Oq7v6Bf2bLziSBrCQTA7IGQgKJIAUXoGgtFBeQwgOtojx5XPqrS63SPipQW6W0rq3VJy2irRSsuKCg1LKogKCGRQlRZAvZNzLZM/v5/XEzk5nMTDYOJBM+79drXpnMcu659869851zvvcc79ZbX8uBksCeX52Pjws/xuzLZ3e5KTvTk9ypIxVHMCF+AoOqjsrKgIQEpbvHj/r6euj1euj1ejQ2ml35OcHBBghh61ZXoPvrnI8B8MyxAlwtVsFaJcdKqw6BTheLoKBEn9N51derER6uLMtgcPWQweRQI1jXPubY4MGAVmtHfHz7OC2yugKDNEEYEjYEQwcNhbptMF61OhTRwWEori+GzVaPc2YT9MF6BFujYE4JBVSq9ryxtgCkbYXgGBSutFh5dQU2eHYF6vVQq4O9rwocmtm+LQGPnB+7vREaTbhrW+qD9dCpdUgYlAi1OgRarR5NcVHQOYAho9xmROhGYOWIiMA5i7ptmb6S10NdXYFBKiuKmmqg1Q7yaLEa3NIMjQiDISrFVT+tehBU0EGnaxvBuqEBdn2EqytQU1ruEWyEtphRoWmF2WbupCtQGXLBYjWi2VyL8KAO49O1BVZmhxbWthYrR1Ghx+fEYxyrpiZozFY4Bke1rb/SSugIUkHd1L5/XMHdBWix6hhY6XTRiNCpYbWb0WqzBkhX4NmzPRr8yX0gUOd9o3E3Kis3YPDgG1Bc/AyOHv2RxwS/+fk3IT//5gs2KfLZs0p82MmAy/1GdyaSPl/O/RIaOhJVVf9EVNQs1Nfv7XmCvK+BQq1WYNIkJchYsaLTsc+cLUIdbRluxZefrsI81cfYuE+Du9ZZXMFO1GEgbTVQsBIwOs+HJpP/WemdrUnl5UBWFlRWq8fTagdw8P+A9HLgyEtKl6O7EBvw1A7gDzs1CPYRU7mYTMCvfuX6t7CuEG/kv4FGSyOyc7O9k9/dE+x9Peamq9wpQBlxecPRDThScaTXI7wPRF8dPgiVSgXVsGFQFRcr9zvcYmMHwWjcjcLCN10tVs3NNlRXvwmjcbcr58l5UU1DwxdeXYFFRWtdl9g7g6nY2DCEh4/CjBlAfPwij+UhNBSrVgokNqkwPjgfcZpShISkuJblfsFKUdFaNDQIRETYoNVGIiUFqKtrwNb9M9FgdeBg5bdIfS4VW/fPxHff/Q8SE6thNke4yrEZX4fJdv7DLWw4ugG1rbU4U3fGddHE/tKDaLEYMX39dJgstbDVvYtndv8Ep8oqUP69ZhhPvYW6ul1Ka5zdDmPldhQdewwoKcHIL0/DUVYGs83sarFytuwogVWIcj6pqICq2YK6uo9hNO6GEEK58jJqNIxXD0JR09/apr1Ru6bEce8G3HB0Azbmb8T8JCt+uG4obEKLPSWHcMxRgdpM4Jl9M9uPT70eeOEFz2PQ/SKVs2dhHnsCoY3KEBNabSSgUsG4+xnMC69ElLEVmjvugqPFCFxzDYz1hdAarSh96c9Yv+1RFDwUh6EN3yC6pgbaWgtKvzuIB0cCb346FWWNRugqHVBVVsFo3I3PzH/CgYYCrNm3BnGvZ0B7rg73/uJfuOI3Bmw4ugFV5SdRF+RA6O9C8dBnq2D+59+hqqhEfIMDuOYanPlmP/K/+g+aD+dj2dqJeGi/De9/9S7e2vVnZX2OHEFR1glsLXwBRosFv9/3DFYfehatX76C/4pun5x915ldOF5zHOrVatz6qxFwOOyIaxJt6690BYogQN1kcW3vX3z0CwDAmlIwbwAAIABJREFU/H/Nb19e2/ltSIMV2spqj/Of82Ih58U5D//nYbyU95LHxTkoL0fMDfMxvKgR8T9cCEOLzrUPInVaWOurkfnBV0hs6v7UXX0XWNlswDffdPvl7gOBDhp0BfLzb8bRo/MAOGC1GnHmzGMYNuw3Hl1ScXGLEBf3425NitwbJ04AI3t2xXyf6c5E0ufLuY9stjo4HK0IDR3Wu4sFfA0UarMpJ6InngDefbfTt4fYgatLNQjXeSYofpRkwpBYB9JXAvc9Vw9HkhnjH1NyqhrHANFHlOCqcUw36ujstn7iCYjycq/8rhAHMLQJ2PA2ENfinf+lBvCjE8Ctp0M6zw0TwtXXvOHoBjy9/2nXILDuCebug6H6GiDVVzJ8x7y8IaFDvLbZudZzyHk/B28VvNWrOQkHKpuj69fU1DShoGAhLJZYV/J6U5Mdev0UFBQshNWqDGrb2noKBQULERY21qsrcNCgK3Dy5EMAlFwrZWop390dNTVNQEUFVtf+P+R8UIlTzVqk4U2oVMEIDjZ4XbCi002BEFaEhiotJEOGfIfaWj3QvBsCytAj0aqzQPNulFb8A/HxlTh7NtlVTljERNckzL0NrJyDzzon5z5bfxbL3l2G5z5/GVqVHcFqwC6Aow0OPD4W0Jus0FgEvj65COXlryAiYgKMB15CwYNNGPTmUeCDDxBRXY8Zr++FyWZy5Vi1t1i1XRW4YwfQ1AT1zj3QamNQULAQNTXvQ6XSoLHxoFLe53WuY8b5Ra8Mg6F31bvB3IBvG4G7UirRYG7CU/uex6goG75ZCaT9p7H9+Ny3r/385eR+kcrGjYg6bMHUSGW8J41GD2PZNhTcXY2sXWbo174A9WeHYK8ugdizB47aEty+DxhWDsz+rgnxu6rx/SF5iKg4DI3RgsS3XsCMWGBGHBCktSO0zI6v1/4Qh76+EXlffo0qrfLDtNpcC5MWmF4E3P5+CZa9uwzlJcdRG+SAgMDkr2oRVN+Maa/twuT1/4bYswfBT63Fit02RBuBX38BDGsBRp5oxrlf3Q+xZw+waBEivgVCbC8BELA4gCybEacfNCFzR7Nrv6/YsQJ2YYeAwE/+XYkgqwOT/ratbXu3tVjpBNQNra7tXd2itJiWNpa2L6/t/Pa9V3di1ob9rm363f0/cV0s5Lw4xzkcQ8dzp+azA8jd1Iqwzw/hZ/82uvaBXqeGav9exJQ1Ytpru7r9uVYJ0fGa9IsjW6VCXmiokqCckNCt9xiNu3Hs2K0IDk5Gc/MxAALh4eNhsZRKT0rvjhdeAI4f79W4pgOWcx8pCZiDMW7c5vPfL84JboVQEnSdrVldfH5Sn0t1XZ0HKN1uJc8orUcCSrBj1gDBJeWeZZSXK03gdrv3MurqlCvAKiuV+8OHAyYTTBog9X6gchDwo2+BLZs8g6kWLTD8PuWxomcAnQDsADQqlbJeHZdTXg6kpioBXNtzqZuu9Fgfp2wk4cvfVnp2J7ZNZO3R8teN463jNnPSqXX47M7PkD002+97uyM7Oxt5eXnnVUZ/4EzA7Upt7S48/fROBAcH49FHH4VOp0Zzcx1aWg7h2LH5cDjM0GjCkJb2L9jtjSgv/xvS09/D11/PwdChdyMmZi6Mxt346qtZGDr0LlRXv4np0/0P7rtJtQiLxEaYdBpct3oE/uuqmRjreA0RERPR2vqdx3myqgoYN86CN98MQ2LiHXj33WasWLEBmc+psDYd2FsDTIwCVn8DxIfHQ/POGnzwwe3YuzcWaWn/Qqt2NLJzs3HTmJswPm487rninh5vR3+ft0lRwJIU4HffAn/NAubvV4ZFWTVBOXYdwQC0KsRE/BD1JduQthqI/lrtSikxB6nx7rZn8EHTIbx202tobDyI48f/G5df/gwKv/sVJl51ELBaUbJQh5ZfLUXsZT/BsWPzAWihEnak3W9E9KH24/KLou8jLW0j7PZGnDr1EG75tMyj3plRwB8nALuLgB9EAuNWA8H5yjFvGJSEL5+qUVrZncegEK5zB9Tt9a7IBr79A5AUtRxVhX9D2mpg0BFAq1bDEunAl+uAabcAe98Fsm4HjNOBhtHAmKeBmkyg4Clg0DdA8zDg8W+U883KTCC0DDAPAf58NgZXvFGD8gjg6Wlt58SnAQ3az1GfrQNm/xRo0QGnnwdCbYANgEatgsohYIOS/nr8f4HB+wFTImAPAlLXe7bMV2QBR38PHKoEpoW1b5Ow4nKPc1lCI3D2WSDIAbRqgdDichyvX4mIiImor92NIQ+/jSnXJ3mdy53vcXK0HZJqAUCthl04oBHt61XZ4cJFoO3c+bsqwGp1fSeYdGqEFJXinO4QNn8yHzN3mxFSKTD0vSBMGT0GeV991eXnum/HserhVYDR0TMQFjYWzc1fISbmRsTE3ITm5q96PvikJCdPAiNGdP26S0l09AwkJSkn2KSke+Xsl9/+FtC1jbIrRHtSexefn47J2Y99Atg6fB8KwLuMJ55oz5npuIyoKCAjQ5k4/IknlC4Ft/IB4AcnAWuHI8vZPfnYJ4Dd11HXcTnu9y0W4Ikn/Cab/+/GUu8cLV/jeFmtXR5v/pZhdVgxPm58p+8lb9HRM1zJ60LYERoKNDfbER09A0OH3guHo8V1/nJPXnfPsYqOnoHExByUlb2EoUPv7nR5i8RGAECI1Y5Pf30c7756OxIT70JDw2de50llZpggvPXWuxgzJhcrVihdI0fuF7hulsDZD1bivXLgyDsr8e//rsAHH9wOAJg+vRqDB8/Ac2uiXF2BvZ3Sxt/nzeQAgjXKuHLNbR/t+W8D8VsARzgweB8QWzwMNU3bMHSbGtFH4PF5VzkExv3fO25dgcq2FcIM9ZkS1zlEbXZAHDrQduX09bDZqjH062GIPtp2MUnbcekc1sDZYtWx3kfqgK1lwPcvA+LfV1q/ncf8sq2l7XVzHufu6Q5u9Y4+DCSeSkNp3d9c66Vqe43aBNhDlHOWPRzQNis3e9sUdtFHgNASoH6iUof5byu3xPeA5suVx656vQaRJqChLX3FPWXCWV+9WXn+sU8AVVvVlDp43te0APYwwBECqM3tzztFHQEa9gNThnpuk47nssc+gSs/VdX2vDPf0KGyQWV2oOKcZ/D92CeApkOrsUq41UEIV5n+UkMA54VDnudOtRBKK5ZGj+GNVgi1gMoOZX+Vl/suqIO+Daw6JOcmJCT4zFWIjx/c1vz8X2ho2IeYmPk4d24bjMYdFzwRuzOB1BV4sRiNu1FW9pK8/dIxkd09YOhi7DP35OyERiVh3HkFoXsiuVdivPvyfC3jhhuAv/9dmTanLQfLmZCeXq78Depw0IfYgWWHlVtIW9qWxrk+HZfTsQ52O7BuHfLW61wJ7x+vV5b12V+Becd9rLyvwMpmU4ZymDrVb65HFobi4/W+k//H/HlMr+YjvJQZjbtdgZXd3oDwcBUaGhraxt972eM4cR953X2sJKX77y3XazszDvkAAAEVWrQq3L7wFVRV/cPn8VhfD0RENGLRomU4ffpx7N2rJDdnPqfCO/9WQT1jNeYlApk3r8b1f01wPb93byxqa3fj8ZWO874q0N8FFGY7EKIGwrVKYJXQCNwigOofKhea1GcBtdGncdk/NSib41DyIt0+80E2gdHvf4bYBuVgcw5L4DhXDnVhmevYUpvscJz5DsZTb+Hcua2ICB6PsuSDMI5r+8HUdlxq7SGurkCtNtKr3plRwIwYIOl1oOpHSp6m85i/4wg8zyevvOL3nNaSDtTEFHisl/M8oTYrLXWOYAAOQGcFNM2ArW3TN2QCLZcBcTuUOsx3ALc4gKrrlW1W9SNgPoC4ZqA+pP2c6LykwXkOizQBYRblvvu5StVWR03bTdusBHj2YEBrai/HqTUdiB8PJP3Dc5tg/XpkqZRR2Z110Lpd+IP166FtVStdgQ5lwuxxQe2DInest5MKbgGNEF1e0KRcOKRybXvn64NsyhA/2tMViHTYIbSAygblqu+a7k0F1/eTMLv9Uq+srPT5kqoqI44enYvq6s2Ijp4No/E/bVdlCERFzbggidjdwRYrTxckQd5XIru7Tlqt3JOz3X99dVqGr+V1XMYNNwCbN3sFLmqh5FT5W06QHdB1lpvj69esk9mMiWctWL1Xg8c+UXIiNrwNXFnavfG73MvBgQN+cz02FIzB9CLfv/A88hKoWwoKFuLcudPQ65VR1SMitCgp2eHzOGluPua6wss5urevY6ozRWj/wm+YACTY1/s9HktLD0Ot/sqr7CfGKd1/6wuVv0+MAx4e2eB63lmOqenAeU9p4+sCCp1aBzt0CG4LrFrswFMlwMlHlRzIKLcLx6MOOrwvOmmjcjjww00HAbhdvv/mP6G2tB+gagtginGg4ORSDBkyDzEHQ5H2O61neXY7NN8Uw2ZrcE2O7V7vzChlsvu6VwHDq54XwQTZgSBHhyPUYvE5CrsxU3lf2m9UGLZOeK2X2qF8wVujAG2T8pgzuHG+V1cLXPa6UofvVgMnViv3h61X/p78X2BMrHeLlGt7COV88si+Ts6XbbRNgC1CabHSdLiWyFmfMW7Ldq2L3Y7XC0YjTBfm+7xst0Pzn32ukfLVQeF4fNL93TuX++Gr1Wr1Hg10Pq6UddZBe9+vYQ+DK7Dq0fJ69vILoJtjVwlhhUqlRWPjIcTFLUJ6+vsYP/5dNDZ+eUESsbsSaEMtXAwXJEHeVyK7u04+P+7J2VNL2n99dVqGr+V1XEZ8vEcXoFOIHbjc6H85WrT/Muu0Hn7WWQVg+SFg+WEll2F8tedQEz3SsXWsbXDSUe/ugUYAd3yl8tlq5Rw09FKn7cZGj4mJQFrav1BbW97WYlWPiAgdKioO+jxOWltPeHUF+jqmYmJ8d7sNCVahEXoMhXIVoXkUMOylaL/HY2VlIRITR3s8f++97wPhM2AUygUNRnEZED4DSQk/QXT0DKxc2V5OS9MhCAg0mBt6PdyCr4GN19+0Hk9+/48I02oQrgGsIghpehXGrVa6kxrHoP2ik1HC70UnWqsdw75RWmaV8b5UsBV/C5XZrcvQAlgjBdL+lgSVSo2QrysR/aXNszyLBdpSo0dXoHu9xwxSJrufe/gyhNg9L4LRAlA7Ohz0DofPH4uNY5T3RR8SgMPhc73UFsAyGNC2jSqgaQZs4cprxq4GbHoguEqpQ/xuIG63ch9Q/o5bDeiGaRA6JN7nOTHEDqjValxdpvV/vmyjaVFayxxBbV2BPtYlpq01ymNdLBaMPn4OuT/KxbRStfdyLBZoC4pgt7cN6BoUgRsTru3eudwP5wVN7gMbL6hPdo096MVigeabItjCAaEB1M7ldTclXfQRrVbt7BHt1m3v3jixezfE6dOP9VWVPZw4IURqal/XgvrE3XcLodEoDfhBQULcc8+FX55W23HEt/Zbd+vgq9533y2EWu2zzD9nQ2CV9021StXrVcnKyur1e/uTrKwsUVFRLYAQERRkF83NQvzlL38ROTk5QgghDhwYJZqaCoQQQkyaNEl8+eWXwmj8VEyfHi22bt3qs0y73Sw+/lgrHA672L1bLex2q9/lv//++yIqKkpMmTLF9djRo0JERQmRkCDEPVvvEVgF8Ub+G37LePVVIX76096sfbvQ34aK9L+ki31F+86voA4slnNiz55oUVa2Tnzzze09em+zpVmE/DZE/O/O/xW/+fg3rsf37o0VhYW/Fd98c6frsZqaD8SRI9cLIYQ4dOgaUVu702eZJ08+JM6e/b04ffpRcebMb3y+pku33SbEunXt///850I884zHS4b8foiobq52/f+v/H+J+W/Md/2/b1+iqKp6R3z5ZZY4XXtaZP55qNi3L0lUNlWK1KcHi08/jfS9bINBiNOnlftjxwqRn9/+3MGDQmRkKPe//VaIkSNFTXONiHxKKctkNQndb3TC4XB4FFle/pooKFgqjh69RVRVbfa52BPnTojU55QvyiMLrhJv5lzl8fwf9v1BPPDh/UKEhYkbXpoudp5Wtn9V1Tvi66/niby8K0T9f00UYtcuz4KPHxdi+HCPh45VHRNj/jym/YHcXCHuuEMk/jFRFNcX+94un38uRNs56YrcK8RH148Qb951jRBCCIfDJnbthPj6Nxqx/i6VMFlN3T5/9VmLla071yu7cTjMfZpP1RHzqy5RzhYe5/hWvZzjssfL8zWAqFN36uCr3q+80j59jo8y/bVaXdITLrvZv/8ItNoEGAxqlJQABoMBxW2DQAYHG2A2K/edOVY2Wz0GDQpBfX29z/LU6iCoVFpYrTVQq0OhVvufGKO4uBhTp051LU95DMjOVuYu1wll2AznvGy+1Ne7xsDstRBtCOpMdec1jpUvznnx3Kf26a5QbSisdisazA2u4RYAJYHdYqnyGK3efeR1fxNVO9/rbLFy5r71WMdBQn1MhaQP1ntM8+IcHLS9vmGwWCqg1UYiSZ+EwoZq2O31aDA34LKIUI9pj/wuu+OOd3+uoQHQ6zE4dDAsdgsazY2uCZg7XgnrnDRcmbPS9/5P1iejtKEUdocdtTERiKs1eTxv0BtQW3YK0OlQq7W6Pkfuk2argwe1DwDbybbzGiDUYICj6CxqWmqQGOFnZgS3csJ0YSjWA7FtdVSpNFBZVWhJCMHgJrgGm+2Ovu8K7Karr67HlVf+uc/yqTo6eVKZtJQuMd3JwbrQy/Olqzr4ydvqrJs1xCawdpfnKaLTCZfdBzx0vz9AHT58AqGhMa7vJffAKiTEO7Cy2+sxaFC438AKUAayNJmKuvzyLi4uxuTJk1FdXQ1rW7d0cTFw2WXKaBqWOiXRvKvAKrL7s3T4FKwNhtFkvACBVTCEsMBmM7qujuwulUqFyJBIVLdUe3wZarWRsFo7BlbBEMIMIRwwm0sRHJzsq0jXF73SFdjLjWYwKPkjTkVFXsGBx2jkUCZWdp+jTqNxBlZ6BGmCEBo0GHZ7C+paa5EcFtw+0XJHKSmewZP7jo+JAVpagOZm14dCpVIpk2M3tI267mM6F+c2sdtboNH43v8h2hBEh0ajsrkSNUNCMbjGc2T0lMgU2M6eAQwGj4sgXBcbOMxQhUX2OrCyFRUiISLB/4wRbuWE6kJxOsKKIW51VLeo0DJYhbgeDA4KBFBgBShJ7H2RT+XLiRPAABiOh3qqOzlYF3p5vnRVB1/ldJEvoBLAwtNhHvkvnU647D7gofv9Aerbb88iKirG9X2ZkpKCorYvzuBgA0wm5b5zEmabrQF6fTgaGhr8lqnV6mE2F3cZTBQVFSE1NRXx8fEoLS1te0z5jjAYgNaargOrtsaJ8xKsCUaTpanXwy34o1KpoFaHwmKp6HGLFaDMKVfVXOUabgFQvqy9W6yUKW0slipotYOg0fheD+cXvTN5vVfcgxtAuZ/iGQhFBkd6zJ/nnIC5vb6hsFjKXcGdIfIyQB2KRlMlEkPVflvcXNG/3a4EURER7c+pVO3Pu30oDJEGFNcXu1qsOnINieBohVrtf/8b9Eo5VYODEF3t2QRuiDRAU1zqCqycnyNnC6HDYYY6LKp9ku1Otl2oNtRzrkCDUrZB7ztYBuA8cAEogdnx8FZE1bTXUdcEWMLtiPF/yPoUUIGVU3T0DNdkwH3l2LE+XTz1lcOHfWc69XCOy/NeXk/r0LGcd95pfy40VGlhcj5XVuYaxyvEYkfhogNwrHSg8P5C/0FVeXl7t+JLLwH/93/K/QvZTdrHiooqERcX4/q+jIqKgt1uR0NDg6sr0GRSuhVCQkJgsylzBnbWYqXVRsJsLu4ymCguLkZKSopHK5nzx3dKCtB6Tpko+UK3WDm72mS3WAHurTM9r6Q+WI+q5iqvrkClxar9MWdXYGfdgM73Su0KNJuVAYbjPSfG7qor0L3FClCCFjtC0GCqQGwwuu4KbGxUgiq12vfzbh8K55yAHVvN2uuib7tS0n9XINAWoDUUoyJah8gqzwglMSIR+up62JOH+mmxMkEd3r0WK+dEysL5g1Gvh12rxhi19+TjvsoJ04WhILgRkVVty3I4ENTogDrIAn2zw+cFS/4EZGDVl1atUgL8XW2j26tUym3Vqr6sFVEPffRR+6CrvgYndQ6QarN1r9Xpscc8W8Sc3Y4Xspu0j1VUVCMlJcH1naRSqVyBTkhICszmYldrFQDY7Q2IjIzstMVKo4nsVotVcXExDAaDz8DKYACaapRJcXVqnd8yOvYI9Yazq+1CBFbu+UQ9FRkSiepm765Ai6VamdKmjUqltFgpgZX/3EHnF71zGIxecX5QhABKSpSJZjsEOJEhPlqsgt1brDy3iUFvgEVo0WyqxBCdrfMWq6Ii/9G0n8CqqL6oy65Ah8N/V6CznOL6YlTo1Qg1Nnrki2rUGoxpCUdDXJTHsB3OAVmFMEMdPti7xcpHN6pWrYVOo4PZ3n6JYl3sIIw3d7K/3AMrbRhOhLYirKZBOW9VVkJl0kCntqM5SKf84OymPgustNreLdrXAKLOW0I3p8Y5H0IAkycDr73W/r8QDKwogDgT2a2egyD6HJzUalWS3KdO9T/B85VXAq++2l6+exfjhU7u70NG4zmMGpXs0RDhDHSUrsBiV34VgLYWq6guWqz0MJk6D6wcDgdKS0uRnJyMlJQUj8AqJUX5nmioVt5/oZPXnV1t7i1DspxPV6A+WK/kWHl0BephtVb77Ao0m4v9t/agvcXKfeDWnldKr/xgqavz2eICAPogvWeOlVfyuuc2MUQa0GJXo9V8DnqtueuuQH/9v/66Ahs66wrUt3WPtnTdFdhQjEZhgnlwpFeAMqIlGFWDg5WuwLZhO5R9pAwSqo6I7laLFeCdZ1UdHYwRzZ0knXfIsbJqAUvUIOX8VlwMIZTjpzEi2LMbtwt9FlhlZEzEokVpUsv0N8CoTL/5jfJjfOnSC74oogujswR8f0nuBw74n+D588/brzb0ZYC2WrW0VCMjY4TfwMpsLkZdXZ1HYBUVNeS8uwKrqqqg1+sRGhrqWp6zEcTZYtVQpbz/YiSvh2pDuz13Yk+cT1dgZHAkHMLRoSswEoCjQ1egkrxuMnXeFeicFNhmO48cK6C95chHiwvQlrxu6pC87tZa1HGbGPQGNFkdMFnPIULdgpCQLpLXe9BilRKZguJ6/12B7cGPsdMWq5TIFBTVF6HF2gJTYqxXgJLSoEKh3gGr3eqVEwc4oNIP6XZg1THPqjgSMDT4+WxarUB1NZCoXDHobC1z1bGoCEKlBHrN+ojACKwA4Pjxkr5cfI81KxNz47nnlBbclSv7tj5EvdJZAn5nSe7HjrkGEnXlU3Un2dBiUeZWHGCs1gpceWW667tSiPYEdq12EFQqHYzGEo+uwOjomC66ArtOXi8qKoKh7UvFYDCgqKgI1dVAWJhyMxgAY9uMsxc6eT1EG3JBugEBpdtLCEuvWoicgUDHrkClXO/hFszmoi4DK+eUNr3uCgTaAxwfydfOenfVFahsk/bgx2ixwmI9h2BVk9+rGl1X/pWX+97p7oGXvj1/y3lVoK8WK0DZLkJYupVj1WJtgWVovOeVkQDiz1lwLLgeYbowjwBd2e8aqCM7dAXW1ysHW1SU17I6tlidjLAgvtbs9ToAQGmpcgmtVut6LwBYkxJc+8ihUY6j1kh94ARWJ0/6GCTHjejiqqWLxZlX5byQYvr09nmAiQJOZwn4HZ+7+27vRFdfU3IEBQH33OP53pYWICREOViuuebird9FYDJZAKgxbFgKIiOVVayv9x5yoabmjEeLVWRkXDdarEo7bbFyJq4D7ctz/wGfkgLUVnRvHKvzbrHSBF+wwMrZCtKbQMYZCHTsCgQ6BlbuOVaddwVarbVQWry6P56RF2fLkL+uwODOuwLbt0l7d905Uyu09go4VGH+66ZSAcnJQH6+/xaroiKPxDvnVYH+WqwAZZuqVFqoO8nlc+ZYtdpaYU9K9AxQHA5E1jYhT13h9TnSaCKV9dHrPVusnNvOx5dwx8CqIKTR60pEr3Lc3gsAtqShrn0kQpTgzTRkcGAEVmZziys/tr9btUrZpkOUC22YV0WXBvfpbrriK5eqrk5JVBViwOVZtbS0QKdTrjZyv1q94yChtbVFrsBKabFK6GK4BaX7o7MWK2fiOuA7sBoyBLBYNIAlrMsWKyldgb2czqYrzryd3iavA54tVs4AzTN5PQhCWGEyne0ixyoEKpUWWm3k+XV7dhFY+Uxe7zDcAtC+TeLD41FrNiNUVMChjul62ceOdbsrMCIoAsHaYJypO+MzeR1wBj+d7//EQYmoaalBnakODkOyZ4BSWQnboHDkN5z0+hxptW2BVWSkZ4uVn20HtF8ZCCjTb50KtyC4vNp3xTqU4xzqQSQntQdW4cqXvi02zqulrTN9FliVlrairu7aC7qMhISETpPdu3NzJsT/8IfKj3eiS0Z3Byd18nV1ofNLaIDlWbW0mBEeHuv633dglYLa2lJXV6DNVo/o6IQuBghVXttZ95d7YBUbG4umpiacOtXi+o5QqYDYBBNQb4BO47slweEAmpo8hzPqjQvZFai0zqig0YT3+L3OVh73HKv2Fqv2x5TzfDAslioEBQ3ttEytVt+rRHoPPW2xMnXeYqVRa6DRDEKEqgZqXSfDCjiXnZ/vuyvQmVh/9qzH8wa9AflV+Z10Beo77QYElKv14iPicdp4GiofY3nZkpNwovaE1+dIKTvEu8XKT34aoLQ6tdqUHKvi+mLYkhOh8tfS5KfFSpWSoiyjuBiIVL7/HXEJgdFiFRJiRGZmOEJD/X0YlMfj47v4sLi/o8NrZSSzV1ZW4vPPgaNHgUceYV4VXUK6Ozipk/sgpf6uPOzBWDD9mdlsRXT0ENf/HQMrIQRCQgwwGis9ugIHD05GfX293zQHZ0tEZ91f7oGVWq1GcnIyvvmmxCNlJ36oFWgw+G2xamwEwsNx3r0GF7IrUK0Og0ajh0rV86+7q2xWAAAgAElEQVQpV46VVzI0vLrL1OpgBAUldNqdBSj75LwS14Guk9eD25PXrXYrLHYLwnXtgaUziHGvR3DQEMQGWaAL6mQgTEDpIz550n8zpcHg9XxKZApO1p7spCswstPEdfdyTDYTtJcN8wqstCmpMNlM/rsCIyN9dwX64N4VWNxQDK0hVcml8vUD0U9g5apjURHUMcpBpU5KCYzAqrXVhIqKa7F9ewUAgR/+UJlvec4cgRtvFACUboOKigoIIfze3n77bcyePRtCCFRcoK6GH/9Y+RsRwe4/uoT0ZHDSjoOU+rvysLz84q/HBWA2W5GQ0N5i5fyRGx4ejrCwMNTU1CA42ID6+hro9XoIIWC3NyAsLAZardY1cGhH7S1WnSevp7hFUQaDASdPFnl81yQmW4H6FL+BlYzEdeDC51j1dmiDzroCfQVWnXUDOmm1EgKrlBTgm2+UHxiDB3s97d4V6GuOPmcQ416P8OA4aFTwf0Wgk8GgHIP+dnxKitfzBr0BdmH322KlBD9d73+DXtm+usuGe03rE5R6uc+WTyV/qy3Hqqmp/XziJ/Ef8AysiuqLkBCbqiS5+2pkcRt13fleANClXg6cPg3U1EAbm6o8Fpes/Brppj4MrGywWidh507l/w8+UP5u2wZs2aLc787gm7NmzcL+/fvR7Lxk7wI4e7b79SEi+L/ysKmpb+ojmc1mQ2pq+7h5/oZcqKszIjIyEg5Ha1uSb1Cno687vzC72xXoXF5JSbFHYJWU5ADq/bdYyUhcB5SuNtnT2Tip1aG9DmS62xXo/L+zxHX39593V2BysnKJv5/ka/euQF9X4yn5TCpoNO19uPpQZbiAiNBhnS/b+QHprMWqw/OGSOUxfzlWyjbpev87A6uQoSlKVN/aNiRCcTFUKSlI1id7fY6UHKsQpVk1NLT93NFZjpW2PcequL5YWW7Hya+dOuZYteV4hSRdptQxPh4hIXEAgEGhQ4CkpC7X00V0w4cffihGjRolLr/8cvHUU095PW8ymcTChQvF5ZdfLiZPnizOnDnTZZkAXLeYmIi2x5TnrFbl/pYtQqxc2f4e5333x8LD4z3K4o033vr37WK7GOevHTuE0Ol4LuKNt4F+644uX2Wz2cTw4cPFqVOnhNlsFhMmTBDHjh3zeM2LL74o/ud//kcIIcTGjRvFwoULe3RiclbWvc6AECNGeD/m/bq+39C88cZb928X08U6fx0/znMRb7xdCrfu6LIr8IsvvsCIESMwfPhwBAUFYdGiRdji7Ktrs2XLFtx2220AgAULFmDnzp29GoPKPTF85Upg1ChnHZSb837Hx4iIfLlY5y8/PRNEdAnSdvWC0tJSj/785ORkfP75535fo9VqERkZiXPnziEmxnNcjdzcXOTm5vpcTnZ2NgBg61ZlKiH3HNcpU7zvuz/WM1ke/yUmKnNhOh08eLC3BRNRP3Oxzl9hYdmSa05EgarLwEqmnJwc5OTkAFACqby8vIu5+AtmIK0LMLDWh+tCsgzU8xcwsNaH69I/DaR16UqXXYFJSUmuAe8AoKSkBEkdsuPdX2Oz2VBfX48hQ4aAiKgv8fxFRBdbl4HVFVdcgRMnTuDMmTOwWCzYtGkT5s2b5/GaefPm4bXXXgMAbN68GTNnzrwgs50TEfUEz19EdLFpVq3qfFQmtVqNkSNHYunSpfjTn/6EpUuXYv78+Xj88cfR2NiI0aNHY8KECdiwYQN+/etf48iRI3j55ZcRHR3d5cKzsrK6fE2gGEjrAgys9eG6XLp4/uq+gbQ+XJf+aSCtS2dUojeX7xERERGRlz4beZ2IiIhooGFgRURERCRJnwRW27dvx+jRozFixAisWbOmL6rQa8XFxZgxYwbS0tIwbtw4PP/88wCA2tpazJ49GyNHjsTs2bNhNBr7uKbdZ7fbMXHiRMydOxcAcObMGUyZMgUjRozAj3/8Y1g6zvnWT9XV1WHBggUYM2YMxo4di/379wfsfnn22Wcxbtw4jB8/HosXL4bJZArY/TLQ8PzVvwyU8xfAc9hAcdEDK7vdjnvvvRcffvghCgoKsHHjRhQUFFzsavSaVqvF008/jYKCAhw4cAAvvvgiCgoKsGbNGsyaNQsnTpzArFmzAuqE+/zzz2Ps2LGu/x955BE88MADOHnyJKKjo7Fu3bo+rF333XffffjBD36Ab7/9Fl999RXGjh0bkPultLQUL7zwAvLy8pCfnw+73Y5NmzYF7H4ZSHj+6n8GyvkL4DlswOjZzFvn77PPPhPXXXed6/8nn3xSPPnkkxe7GtLMmzdPfPTRR2LUqFGirKxMCCFEWVmZGDVqVB/XrHuKi4vFzJkzxc6dO8WcOXOEw+EQQ4YMEVarVQjhvb/6q7q6OpGamiocDofH44G4X0pKSkRycrI4d+6csFqtYs6cOWL79u0BuV8GGp6/+peBcv4SguewgeSit1j5mmKitLT0YldDisLCQhw+fBhTpkxBZWUlEhMTAQAJCQmorKzs49p1z/3334+1a9dCrVY+CufOnUNUVBS0WmVQ/kDZP2fOnEFsbCyWLVuGiRMnYvny5Whubg7I/ZKUlISHHnoIKSkpSExMRGRkJLKysgJyvww0PH/1LwPl/AXwHDaQMHm9l5qamjB//nw899xz0Ov1Hs+pVKqAGGBw69atiIuLGxBji9hsNhw6dAh33303Dh8+jPDwcK8m80DZL0ajEVu2bMGZM2dQVlaG5uZmbN++va+rRQMIz1/9D89hA8dFD6y6M8VEf2e1WjF//nwsWbIEt9xyCwAgPj4e5W0zR5eXlyMuLq4vq9gt+/btw3vvvYfU1FQsWrQIu3btwn333Ye6ujrYbDYAgbN/kpOTkZycjClts3MvWLAAhw4dCsj9smPHDgwbNgyxsbHQ6XS45ZZbsG/fvoDcLwMNz1/9x0A6fwE8hw0kFz2w6s4UE/2ZEAJ33nknxo4diwcffND1uPu0GK+99hpuvPHGvqpitz311FMoKSlBYWEhNm3ahJkzZ2LDhg2YMWMGNm/eDCBw1iUhIQEGgwHHjx8HAOzcuRNpaWkBuV9SUlJw4MABtLS0QAjhWpdA3C8DDc9f/cdAOn8BPIcNKH2R2LVt2zYxcuRIMXz4cPHb3/62L6rQa3v27BEARHp6usjIyBAZGRli27ZtoqamRsycOVOMGDFCzJo1S5w7d66vq9oju3fvFnPmzBFCCHHq1ClxxRVXiMsvv1wsWLBAmEymPq5d9xw+fFhkZWWJ9PR0ceONN4ra2tqA3S+PP/64GD16tBg3bpxYunSpMJlMAbtfBhqev/qfgXD+EoLnsIGCU9oQERERScLkdSIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkm0fbXgmJgYpKam9tXiiagPFBYWoqampq+rQUR0wfRZYJWamoq8vLy+WjwR9YHs7Oy+rgIR0QXFrkAiIiIiSRhYEREREUnCwIqIiIhIkj7LsTofQgjYbDYIIfq6KkTkg0qlglarhUql6uuqEBFdVAEZWNlsNqjVaqjVap64ifoZIQQcDgdsNht0Ol1fV4eI6KIKyK5AIQSDKqJ+SqVSQa1Ws0WZiC5JARlYAWBQRdSP8fgkoktVwAZWRERERP0NAyvC7bffjs2bN0sv98knn3TdLywsxPjx47t8T3V1NaZMmYKJEydiz5490uvUH7z66qv42c9+BgB4+eWX8fe//93vaz/++GN89tlnfp9/7733sGbNGgC924/u+wgAvve97/Xo/URE5ImBFV0wHb+0u2Pnzp1IT0/H4cOHcdVVV3k8Z7fbZVWt37jrrrvw05/+1O/znQVWNpsN8+bNw4oVK3q9/I77qLMgjoiIusbAqheam5sxZ84cZGRkYPz48XjjjTcAAAcPHsQ111yDrKwsXH/99SgvL3c9npGRgYyMDPzyl790tdy4t1wAwNy5c/Hxxx8DAD766CNMnToVkyZNwq233oqmpiYAylRAK1euxKRJk5Ceno5vv/0WANDU1IRly5YhPT0dEyZMwFtvvdVpOf74W4drr70WjzzyCCZPnoxRo0a5WpNaWlqwcOFCpKWl4eabb8aUKVOQl5eHFStWoLW1FZmZmViyZAkAJTD67//+b4wbNw7XXXcdWltbPZZ95MgRPPzww9iyZQsyMzPR2tqKiIgI/OIXv0BGRgb279/f77bxK6+8gvvvv99V/l//+lc88MADXtt1/fr1GDVqFCZPnox9+/a5Hl+1ahX++Mc/AgBeeOEFpKWlYcKECVi0aBEKCwvx8ssv49lnn0VmZib27NmD22+/HXfddRemTJmChx9+2Gv9duzYgezsbIwaNQpbt27tdBv42kcREREAlAtEnNsxPT3d9Rn/+OOPce2112LBggUYM2YMlixZwiR1IiJ3oo9kZWX1+r1ms9nj/5W7VwqsgrTbyt0rO13+5s2bxfLly13/19XVCYvFIqZOnSqqqqqEEEJs2rRJLFu2TAghRHp6uvjkk0+EEEI89NBDYty4cUIIIdavXy/uvfdeVzlz5swRu3fvFtXV1eKqq64STU1NQggh1qxZI1avXi2EEOKyyy4TL7zwghBCiBdffFHceeedQgghHn74YXHfffe5yqqtre20HHe33XabePPNNztdh2uuuUY8+OCDQgghtm3bJmbNmiWEEOIPf/iDyMnJEUIIcfToUaHRaMSXX34phBAiPDzctYwzZ84IjUYjDh8+LIQQ4tZbbxX/+Mc/vOrScZsAEG+88YYQQvTLbdzY2CiGDx8uLBaLEEKIqVOniq+//tpjncrKyoTBYBBVVVXCbDaL733ve646rVy5UvzhD38QQgiRmJgoTCaTEEIIo9Ho9bxzX82ZM0fYbDav9bvtttvE9ddfL+x2u/juu+9EUlKSaG1t9bsNOu4j9/83b94svv/97wubzSYqKiqEwWAQZWVlYvfu3UKv14vi4mJht9vFlVdeKfbs2eO1H4XwPk6FOL/jnogoEATkOFYdrbp2FVZdu+qiLS89PR2/+MUv8Mgjj2Du3Lm46qqrkJ+fj/z8fMyePRuA0jqTmJiIuro61NXV4eqrrwYA/OQnP8GHH37YafkHDhxAQUEBpk2bBgCwWCyYOnWq6/lbbrkFAJCVlYW3334bgNJSsWnTJtdroqOjsXXr1k7L6ej48eM+18HXcgsLCwEAe/fuxX333QcAGD9+PCZMmOC3/GHDhiEzM9OrjM5oNBrMnz+/0/r15TYGgJkzZ2Lr1q0YO3YsrFYr0tPTPZb1+eef49prr0VsbCwA4Mc//jG+++47rzpNmDABS5YswU033YSbbrrJb91vvfVWaDQan88tXLgQarUaI0eOxPDhw12tbT21d+9eLF68GBqNBvHx8bjmmmvw5ZdfQq/XY/LkyUhOTgYAZGZmorCwENOnT+/VcoiIBpoBEVhdbKNGjcKhQ4fwwQcf4NFHH8WsWbNw8803Y9y4cdi/f7/Ha+vq6vyWo9Vq4XA4XP+bTCYASjfM7NmzsXHjRp/vCw4OBqAEHTabzW/5XZXj6/W+1qGny/XH+X5nGR27An0JCQlxBRH+6teX2xgAli9fjieffBJjxozBsmXLulwnf7Zt24ZPP/0U77//Pn73u9/h6NGjPl8XHh7ut4yOwxw4R0D3tQ16q+N+7M1ngYhooGKOVS+UlZUhLCwMS5cuxS9/+UscOnQIo0ePRnV1tetL32q14tixY4iKikJUVBT27t0LANiwYYOrnNTUVBw5cgQOhwPFxcX44osvAABXXnkl9u3bh5MnTwJQcrp8tXC4mz17Nl588UXX/0ajscfl+FuHzkybNg3/+te/AAAFBQUewYBOp4PVau30/T3RH7cxAEyZMgXFxcX45z//icWLF3u9b8qUKfjkk09w7tw5WK1WvPnmm16vcdZvxowZ+P3vf4/6+no0NTVh0KBBaGxs7PY2evPNN+FwOHDq1CmcPn0ao0eP9rsNAP/76KqrrsIbb7wBu92O6upqfPrpp5g8eXK360FEdKliYNULR48exeTJk5GZmYnVq1fj0UcfRVBQEDZv3oxHHnkEGRkZyMzMdF1htX79etx7773IzMz0SPSdNm0ahg0bhrS0NPz85z/HpEmTAACxsbF49dVXsXjxYkyYMAFTp07tskvn0UcfhdFoxPjx45GRkYHdu3f3uJzO1sGfe+65B9XV1UhLS8Ojjz6KcePGITIyEgCQk5Pj6t6SoT9uY6eFCxdi2rRpru5Bd4mJiVi1ahWmTp2KadOmYezYsV6vsdvtWLp0KdLT0zFx4kT8/Oc/R1RUFH70ox/hnXfecSWvdyUlJQWTJ0/GDTfcgJdffhkhISF+twHgfx/dfPPNmDBhAjIyMjBz5kysXbsWCQkJXS6fiOhSpxKiby7pyc7ORl5eXq/ea7FYEBQUJLlGF0dhYSHmzp2L/Pz8vq6KFHa7HVarFSEhITh16hS+//3v4/jx4326f/piG8+dOxcPPPAAZs2addGW2d/5Ok7P57gnIgoEPW6xuuOOOxAXF+dzsMenn34aKpUKNTU1UipH/V9LSwumT5+OjIwM3HzzzfjLX/4SsEFvb9TV1WHUqFEIDQ1lUEVERD1vsfr0008RERGBn/70px4tAsXFxVi+fDm+/fZbHDx4EDExMZ2Wc6m2WBFdKthiRUSXoh63WF199dUYPHiw1+MPPPAA1q5de9EmX+2jHkwi6gYen0R0qZKSvL5lyxYkJSUhIyOj09fl5uYiOzsb2dnZqK6u7vXyVCoVHA4HT95E/ZAQAg6H46L9yCIi6k/OexyrlpYWPPnkk/joo4+6fG1OTg5ycnIAKF0CvaXVamGz2Qbk3HFEA4Fz/CwiokvNeZ/5Tp06hTNnzrhaq0pKSjBp0iR88cUXF+zybJVKBZ1Od0HKJiIiIuqt8w6s0tPTUVVV5fo/NTUVeXl5XSavExEREQ00Pc6xWrx4MaZOnYrjx48jOTkZ69atuxD1IiIiIgo4PW6x6mreue5MrEtEREQ0EHFKGyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgk6VFgdccddyAuLg7jx493PfbLX/4SY8aMwYQJE3DzzTejrq5OeiWJiIiIAkGPAqvbb78d27dv93hs9uzZyM/Px9dff41Ro0bhqaeeklpBIiIiokDRo8Dq6quvxuDBgz0eu+6666DVagEAV155JUpKSuTVjoiIiCiASM2xeuWVV3DDDTf4fT43NxfZ2dnIzs5GdXW1zEUTERER9TlpgdXvfvc7aLVaLFmyxO9rcnJykJeXh7y8PMTGxspaNBEREVG/oJVRyKuvvoqtW7di586dUKlUMookIiIiCjjnHVht374da9euxSeffIKwsDAZdSIiIiIKSD3qCly8eDGmTp2K48ePIzk5GevWrcPPfvYzNDY2Yvbs2cjMzMRdd911oepKRERE1K/1qMVq48aNXo/deeed0ipDREREFMg48joRERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkvQ4sLrjjjsQFxeH8ePHux6rra3F7NmzMXLkSMyePRtGo1FqJYmIiIgCQY8Dq9tvvx3bt2/3eGzNmjWYNWsWTpw4gVmzZmHNmjXSKkhEREQUKHocWF199dUYPHiwx2NbtmzBbbfdBgC47bbb8O6778qpHREREVEA0coopLKyEomJiQCAhIQEVFZW+nxdbm4ucnNzAQDV1dUyFk1ERETUb0hPXlepVFCpVD6fy8nJQV5eHvLy8hAbGyt70URERER9SkpgFR8fj/LycgBAeXk54uLiZBRLREREFFCkBFbz5s3Da6+9BgB47bXXcOONN8ooloiIiCig9DiwWrx4MaZOnYrjx48jOTkZ69atw4oVK/Cf//wHI0eOxI4dO7BixYoLUVciIiKifq3HyesbN270+fjOnTvPuzJEREREgYwjrxMRERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkibTA6tlnn8W4ceMwfvx4LF68GCaTSVbRRERERAFBSmBVWlqKF154AXl5ecjPz4fdbsemTZtkFE1EREQUMKS1WNlsNrS2tsJms6GlpQVDhw6VVTQRERFRQJASWCUlJeGhhx5CSkoKEhMTERkZieuuu87rdbm5ucjOzkZ2djaqq6tlLJqIiIio35ASWBmNRmzZsgVnzpxBWVkZmpub8frrr3u9LicnB3l5ecjLy0NsbKyMRRMRERH1G1ICqx07dmDYsGGIjY2FTqfDLbfcgs8++0xG0UREREQBQ0pglZKSggMHDqClpQVCCOzcuRNjx46VUTQRERFRwJASWE2ZMgULFizApEmTkJ6eDofDgZycHBlFExEREQUMlRBC9MWCs7OzkZeX1xeLJqI+wuOeiAY6jrxOREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkkRZY1dXVYcGCBRgzZgzGjh2L/fv3yyqaiIiIKCBoZRV033334Qc/+AE2b94Mi8WClpYWWUUTERERBQQpgVV9fT0+/fRTvPrqqwCAoKAgBAUFySiaiIiIKGBI6Qo8c+YMYmNjsWzZMkycOBHLly9Hc3Oz1+tyc3ORnZ2N7OxsVFdXy1g0ERERUb8hJbCy2Ww4dOgQ7r77bhw+fBjh4eFYs2aN1+tycnKQl5eHvLw8xMbGylg0ERERUb8hJbBKTk5GcnIypkyZAgBYsGABDh06JKNoIiIiooAhJbBKSEiAwWDA8ePHAQA7d+5EWlqajKKJiIiIAoa0qwL/9Kc/YcmSJbBYLBg+fDjWr18vq2giIiKigCAtsMrMzEReXp6s4oiIiIgCDkdeJyIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSqYGV3W7HxIkTMXfuXJnFEhEREQUEqYHV888/j7Fjx8oskoiIiChgSAusSkpKsG3bNixfvlxWkUREREQBRVpgdf/992Pt2rVQq/0XmZubi+zsbGRnZ6O6ulrWoomIiIj6BSmB1datWxEXF4esrKxOX5eTk4O8vDzk5eUhNjZWxqKJiIiI+g0pgdW+ffvw3nvvITU1FYsWLcKuXbuwdOlSGUUTERERBQwpgdVTTz2FkpISFBYWYtOmTZg5cyZef/11GUUTERERBQyOY0VEREQkiVZ2gddeey2uvfZa2cUSERER9XtssSIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkCQMrIiIiIkkYWBERERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEimBVXFxMWbMmIG0tDSMGzcOzz//vIxiiYiIiAKKVkohWi2efvppTJo0CY2NjcjKysLs2bORlpYmo3giIiKigCClxSoxMRGTJk0CAAwaNAhjx45FaWmpjKKJiIiIAoaUFit3hYWFOHz4MKZMmeL1XG5uLnJzcwEA1dXVshdNRERE1KekJq83NTVh/vz5eO6556DX672ez8nJQV5eHvLy8hAbGytz0URERER9TlpgZbVaMX/+fCxZsgS33HKLrGKJiIiIAoaUwEoIgTvvvBNjx47Fgw8+KKNIIiIiooAjJbDat28f/vGPf2DXrl3IzMxEZmYmPvjgAxlFExEREQUMKcnr06dPhxBCRlFERHOQcf4AAAbNSURBVEREAYsjrxMRERFJwsCKiIiISBIGVkRERESSMLAiIiIikoSBFREREZEkDKyIiIiIJGFgRURERCQJAysiIiIiSRhYEREREUnCwIqIiIhIEgZWRERERJIwsCIiIiKShIEVERERkSQMrIiIiIgkYWBFREREJAkDKyIiIiJJGFgRERERScLAioiIiEgSBlZEREREkjCwIiIiIpKEgRURERGRJAysiIiIiCRhYEVEREQkibTAavv27Rg9ejRGjBiBNWvWyCqWiIiIKGBICazsdjvuvfdefPjhhygoKMDGjRtRUFAgo2giIiKigCElsPriiy8wYsQIDB8+HEFBQVi0aBG2bNkio2giIiKigPH/27u/kKb6MA7gX33XVZBFbE02xWyz5mYiU3bthhEsJrhBRrvoHwO7yIqgu+giLIIoi25GXRgUu+imSJLIughrkWwEIsaKCfsjUtNdZAyne96LaNBb8u6t83o64/u5+x3nOc/DV8aD53B+OiVOkslk0NDQUF6bzWa8fv36h8+Fw2GEw2EAwNTUFDo7O5W4vOo+fvwIvV6vdhmKqJZeqqUPoLp6mZmZUbsEIqL/lSKDVaVCoRBCoRAAoLOzE5OTk+t5+f8Ne/nzVEsfQPX1QkRUzRS5FWgymZBKpcrrdDoNk8mkxKmJiIiINEORwaqrqwuJRALJZBLLy8uIRCLw+XxKnJqIiIhIM/46f/78+d89SW1tLaxWK4LBIG7cuIFgMAi/3/+vv+d0On/30n8M9vLnqZY+APZCRKQVNSIiahdBREREVA345nUiIiIihXCwIiIiIlKIKoOVVre/SaVS6O7uRmtrK+x2O4aHhwEACwsL6OnpgdVqRU9PDxYXF1WutHKrq6vo6OjAvn37AADJZBIulwsWiwX79+/H8vKyyhVWJp/PIxAIYNeuXbDZbHj16pUmc7l69SrsdjscDgcOHDiAQqGgmUyOHDkCg8EAh8NRPrZWBiKCEydOwGKxYPfu3YjFYmqVTUSkqHUfrLS8/Y1Op8OVK1cwPT2NaDSKmzdvYnp6GpcuXYLH40EikYDH49HUsDg8PAybzVZenz17FqdOncL79++xZcsW3L59W8XqKjc4OIi9e/diZmYGb9++hc1m01wumUwG169fx+TkJKamprC6uopIJKKZTA4dOoSxsbHvjq2VwePHj5FIJJBIJBAOhzEwMKBGyUREypN19vLlS9mzZ095PTQ0JENDQ+tdhiJ8Pp88efJEWlpaJJvNiohINpuVlpYWlSurTCqVErfbLePj4+L1eqVUKsnWrVulWCyKyI9Z/any+bw0NTVJqVT67rjWckmn02I2myWXy0mxWBSv1ytjY2OayiSZTIrdbi+v18ogFArJvXv3fvo5IiItW/f/WP1s+5tMJrPeZfy22dlZxONxuFwuzM/Po76+HgBgNBoxPz+vcnWVOXnyJC5fvoza2q9/BrlcDps3b4ZO9/WF/FrJJplMQq/X4/Dhw+jo6MCxY8ewtLSkuVxMJhPOnDmDxsZG1NfXo66uDk6nU5OZfLNWBtXyPUBE9E98eP0XfP78GX6/H9euXcOmTZu++1lNTQ1qampUqqxyjx49gsFgqIp3Cq2srCAWi2FgYADxeBwbN2784bafFnJZXFzEgwcPkEwmkc1msbS09MOtNS3TQgZERL9r3QcrrW9/UywW4ff7cfDgQfT19QEAtm3bhrm5OQDA3NwcDAaDmiVWZGJiAg8fPkRTUxP6+/vx7NkzDA4OIp/PY2VlBYB2sjGbzTCbzXC5XACAQCCAWCymuVyePn2K7du3Q6/XY8OGDejr68PExIQmM/lmrQy0/j1ARLSWdR+stLz9jYjg6NGjsNlsOH36dPm4z+fDyMgIAGBkZAS9vb1qlVixixcvIp1OY3Z2FpFIBG63G3fv3kV3dzfu378PQDu9GI1GNDQ04N27dwCA8fFxtLa2ai6XxsZGRKNRfPnyBSJS7kOLmXyzVgY+nw937tyBiCAajaKurq58y5CISNPUeLBrdHRUrFarNDc3y4ULF9Qo4Ze8ePFCAEhbW5u0t7dLe3u7jI6OyqdPn8TtdovFYhGPxyO5XE7tUv+T58+fi9frFRGRDx8+SFdXl+zYsUMCgYAUCgWVq6tMPB4Xp9MpbW1t0tvbKwsLC5rM5dy5c7Jz506x2+0SDAalUChoJpP+/n4xGo2i0+nEZDLJrVu31sygVCrJ8ePHpbm5WRwOh7x580bl6omIlMEtbYiIiIgUwofXiYiIiBTCwYqIiIhIIRysiIiIiBTCwYqIiIhIIRysiIiIiBTCwYqIiIhIIRysiIiIiBTyN5B42w4uFEMHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82d50ef850>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Variable 1: lateral shoot type\n",
+    "obs.plot(\"Intensity\", 0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c5811a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Variable 2: lateral flowering\n",
+    "obs.plot(\"Intensity\", 1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82ca10e8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Variable 3: terminal flowering\n",
+    "obs.plot(\"Intensity\", 2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HSCM re-estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Discard state variable\n",
+    "seq1v = seq.select_variable([2, 3, 4], True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb_states = 5\n",
+    "init_file = base_path + os.sep + \"seq1v_\" + str(nb_states) + \"s_LR_init.hsmc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openalea.sequence_analysis import Estimate\n",
+    "from openalea.sequence_analysis import HiddenSemiMarkov"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conversion dos2unix file if needed\n",
+    "try:\n",
+    "    hmsc_init = HiddenSemiMarkov(init_file)\n",
+    "except:\n",
+    "     dos2unix(init_file, init_file)\n",
+    "hmsc_init = HiddenSemiMarkov(init_file)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimate HSCM with default initialization\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Left-right model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "0.900252   0.0997281  1e-05      1e-05      0          \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0          0.33353    1e-05      0.66645    1e-05      \n",
+      "0          0          0.246779   0.500003   0.253218   \n",
+      "0          0          0          1e-05      0.99999    \n",
+      "0          0          0          0          1          \n",
+      "0          0          0          0          1          \n",
+      "\n",
+      "transient class: state 0\n",
+      "transient class: state 1\n",
+      "transient class: state 2\n",
+      "transient class: state 3\n",
+      "recurrent class: state 4 (absorbing state)\n",
+      "\n",
+      "probability of no-occurrence of state 0: 0.0997481\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of no-occurrence of state 1: 0.600004\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 21.185   variance: 414.804   standard deviation: 20.3667\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 4\n",
+      "mean: 7   variance: 24.6667   standard deviation: 4.96655\n",
+      "\n",
+      "probability of no-occurrence of state 2: 0.90126\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 42.7108   variance: 414.291   standard deviation: 20.3541\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 1\n",
+      "mean: 30   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of no-occurrence of state 3: 0.200012\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 32.1872   variance: 438.525   standard deviation: 20.941\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 179.184   variance: 7074.62   standard deviation: 84.1108\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 2\n",
+      "mean: 29   variance: 32   standard deviation: 5.65685\n",
+      "\n",
+      "probability of leaving state 0: 0.0349796\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 249\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 1: 0.0451669\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 85\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 2: 0.333333\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 3: 0.00553306\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 500\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 140\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 6   PARAMETER : 1.38965   PROBABILITY : 0.0579558\n",
+      "mean: 28.5881   variance: 389.747   standard deviation: 19.742\n",
+      "coefficient of skewness: 1.69735   coefficient of kurtosis: 4.3202\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 9\n",
+      "mean: 28.6667   variance: 439.25   standard deviation: 20.9583\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 11   PARAMETER : 3.47785   PROBABILITY : 0.237916\n",
+      "mean: 22.1401   variance: 46.8238   standard deviation: 6.84279\n",
+      "coefficient of skewness: 1.08235   coefficient of kurtosis: 1.74656\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 22.25   variance: 62.25   standard deviation: 7.88987\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 3   SUP_BOUND : 4   PROBABILITY : 0\n",
+      "mean: 3   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 3   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 64   PARAMETER : 5.24353   PROBABILITY : 0.0429885\n",
+      "mean: 180.732   variance: 2715.42   standard deviation: 52.1097\n",
+      "coefficient of skewness: 0.873621   coefficient of kurtosis: 1.14464\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 63.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "absorption probability of state 4: 1\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 71   variance: 32   standard deviation: 5.65685\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.900253   variance: 0.0897979   standard deviation: 0.299663\n",
+      "coefficient of skewness: -2.67135   coefficient of kurtosis: 5.13612\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.9   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: -3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.397442   variance: 0.239482   standard deviation: 0.489369\n",
+      "coefficient of skewness: 0.419144   coefficient of kurtosis: -1.82432\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.4   variance: 0.266667   standard deviation: 0.516398\n",
+      "coefficient of skewness: 0.484123   coefficient of kurtosis: -1.95\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.0963897   variance: 0.0870987   standard deviation: 0.295125\n",
+      "coefficient of skewness: 2.73518   coefficient of kurtosis: 5.48123\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 0.790159   variance: 0.165808   standard deviation: 0.407195\n",
+      "coefficient of skewness: -1.42516   coefficient of kurtosis: 0.0310716\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 0.196045   variance: 0.157611   standard deviation: 0.397003\n",
+      "coefficient of skewness: 1.53125   coefficient of kurtosis: 0.344731\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.2   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: 1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 25.6055   variance: 400.175   standard deviation: 20.0044\n",
+      "coefficient of skewness: 1.22439   coefficient of kurtosis: 1.65642\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 25.8   variance: 472.622   standard deviation: 21.7399\n",
+      "coefficient of skewness: 0.719323   coefficient of kurtosis: -1.27249\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 8.73412   variance: 134.435   standard deviation: 11.5946\n",
+      "coefficient of skewness: 0.895227   coefficient of kurtosis: -0.433344\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 8.9   variance: 152.767   standard deviation: 12.3599\n",
+      "coefficient of skewness: 0.996808   coefficient of kurtosis: -0.987263\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 0.288765   variance: 0.782368   standard deviation: 0.884516\n",
+      "coefficient of skewness: 2.73779   coefficient of kurtosis: 5.49728\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.9   standard deviation: 0.948683\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 54.2895   variance: 1073.79   standard deviation: 32.7687\n",
+      "coefficient of skewness: -0.689373   coefficient of kurtosis: -1.02092\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.8   variance: 929.289   standard deviation: 30.4842\n",
+      "coefficient of skewness: -0.913914   coefficient of kurtosis: -1.00596\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 11.0821   variance: 576.344   standard deviation: 24.0072\n",
+      "coefficient of skewness: 1.89686   coefficient of kurtosis: 1.97136\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.2   variance: 899.733   standard deviation: 29.9956\n",
+      "coefficient of skewness: 1.79631   coefficient of kurtosis: -0.0129807\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.373\n",
+      "OUTPUT 1 : 0.3209\n",
+      "OUTPUT 2 : 0.2047\n",
+      "OUTPUT 3 : 0.0856\n",
+      "OUTPUT 4 : 0.0158\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2624\n",
+      "OUTPUT 1 : 0.512\n",
+      "OUTPUT 2 : 0.2143\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0113\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1602\n",
+      "OUTPUT 1 : 0.0315\n",
+      "OUTPUT 2 : 0.2343\n",
+      "OUTPUT 3 : 0.5738\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2118\n",
+      "OUTPUT 1 : 0.4285\n",
+      "OUTPUT 2 : 0.2467\n",
+      "OUTPUT 3 : 0.1128\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          4          \n",
+      "0  0.37307    0.320988   0.204747   0.0856092  0.0155848  \n",
+      "1  0.262485   0.512029   0.214399   1e-05      0.0110767  \n",
+      "2  0.99996    1e-05      1e-05      1e-05      1e-05      \n",
+      "3  0.160279   0.0315353  0.234306   0.573869   1e-05      \n",
+      "4  0.211869   0.428512   0.246796   0.112812   1e-05      \n",
+      "\n",
+      "theoretical weights: 0.256054 0.0873431 0.00288765 0.542894 0.110821\n",
+      "\n",
+      "log-likelihood: -1394.05   (normalized: -1.39405)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 1.69704\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 1.70699   critical probability: 0.789447\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.258 0.089 0.003 0.508 0.142\n",
+      "\n",
+      "log-likelihood: -1393.2   (normalized: -1.3932)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.000822734\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.000822714   critical probability: 1\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 1.78547   variance: 5.11887   standard deviation: 2.26249\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 2.04538   variance: 8.57475   standard deviation: 2.92827\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 3.82346   variance: 17.6613   standard deviation: 4.20253\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 12.0826   variance: 147.8   standard deviation: 12.1573\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 26.0706   variance: 5519.76   standard deviation: 74.2951\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 5.05809   variance: 24.2594   standard deviation: 4.92538\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 8.39139   variance: 257.032   standard deviation: 16.0322\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.33411   variance: 14.3995   standard deviation: 3.79466\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.13606   variance: 8.63834   standard deviation: 2.9391\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 29.8874   variance: 8523.38   standard deviation: 92.3221\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.29483   variance: 0.434466   standard deviation: 0.65914\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.46325   variance: 0.853172   standard deviation: 0.923673\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.2966   variance: 0.374029   standard deviation: 0.611579\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.2241   variance: 2.86203   standard deviation: 1.69175\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01402   variance: 0.0138247   standard deviation: 0.117578\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.991   variance: 12.1005   standard deviation: 3.47858\n",
+      "coefficient of skewness: 0.173224   coefficient of kurtosis: -0.045584\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 12.289   variance: 51.532   standard deviation: 7.17858\n",
+      "coefficient of skewness: 0.733401   coefficient of kurtosis: -0.655465\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 17.4949   variance: 8.48847   standard deviation: 2.9135\n",
+      "coefficient of skewness: 0.0268775   coefficient of kurtosis: -0.0323519\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 16.6298   variance: 39.2819   standard deviation: 6.26753\n",
+      "coefficient of skewness: -0.467843   coefficient of kurtosis: -0.795518\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.489383   variance: 0.563337   standard deviation: 0.750558\n",
+      "coefficient of skewness: 1.7372   coefficient of kurtosis: 3.62197\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.1834   variance: 36.2786   standard deviation: 6.02318\n",
+      "coefficient of skewness: 0.5122   coefficient of kurtosis: 0.276166\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 19.152   variance: 178.251   standard deviation: 13.3511\n",
+      "coefficient of skewness: 0.897434   coefficient of kurtosis: -0.415304\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.5706   variance: 17.8842   standard deviation: 4.22898\n",
+      "coefficient of skewness: 0.132517   coefficient of kurtosis: -0.00361517\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 34.5974   variance: 277.854   standard deviation: 16.6689\n",
+      "coefficient of skewness: -0.54201   coefficient of kurtosis: -1.00324\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.496457   variance: 0.586887   standard deviation: 0.766085\n",
+      "coefficient of skewness: 1.7775   coefficient of kurtosis: 3.84913\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         0.200693  _         0.517819  _         \n",
+      "_         _         0.737475  0.593766  0.145199  \n",
+      "_         _         _         _         0.788091  \n",
+      "_         _         _         _         0.461057  \n",
+      "_         _         _         _         _         \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9922\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0077\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9774\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0112\n",
+      "OUTPUT 3 : 0.0114\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.253\n",
+      "OUTPUT 1 : 0.1474\n",
+      "OUTPUT 2 : 0.2811\n",
+      "OUTPUT 3 : 0.3185\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9858\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0141\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           \n",
+      "0  0.99222     1e-05       0.00775961  1e-05       \n",
+      "1  0.97746     1e-05       0.0112647   0.0112651   \n",
+      "2  0.99997     1e-05       1e-05       1e-05       \n",
+      "3  0.253013    0.147432    0.281103    0.318452    \n",
+      "4  0.985878    1e-05       0.0141021   1e-05       \n",
+      "\n",
+      "theoretical weights: 0.256054 0.0873431 0.00288765 0.542894 0.110821\n",
+      "\n",
+      "log-likelihood: -1073.5   (normalized: -1.0735)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 2.61361\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 2.59627   critical probability: 0.458144\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.258 0.089 0.003 0.508 0.142\n",
+      "\n",
+      "log-likelihood: -1072.2   (normalized: -1.0722)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.00110573\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.00110591   critical probability: 0.99999\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.00914921   variance: 0.00906551   standard deviation: 0.095213\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 39.255   variance: 1227.05   standard deviation: 35.0293\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 39.2914   variance: 1663.74   standard deviation: 40.789\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 34.424   variance: 1006.11   standard deviation: 31.7192\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.2965   variance: 6.69682   standard deviation: 2.58782\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.74852   variance: 162.583   standard deviation: 12.7508\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 5.0793   variance: 166.113   standard deviation: 12.8885\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.20649   variance: 67.7003   standard deviation: 8.22802\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 3.55873   variance: 168.953   standard deviation: 12.9982\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.16995   variance: 0.193917   standard deviation: 0.44036\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.37961   variance: 0.515853   standard deviation: 0.718229\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.45603   variance: 0.640529   standard deviation: 0.80033\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 11.6377   variance: 37.0881   standard deviation: 6.09\n",
+      "coefficient of skewness: -0.358174   coefficient of kurtosis: -0.998784\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.84159   variance: 21.3091   standard deviation: 4.61618\n",
+      "coefficient of skewness: -0.155882   coefficient of kurtosis: -0.982288\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 11.4802   variance: 44.0422   standard deviation: 6.63643\n",
+      "coefficient of skewness: -0.42045   coefficient of kurtosis: -1.00445\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 11.9602   variance: 54.2515   standard deviation: 7.36556\n",
+      "coefficient of skewness: -0.484581   coefficient of kurtosis: -1.03118\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 58.8938   variance: 587.115   standard deviation: 24.2305\n",
+      "coefficient of skewness: 0.636788   coefficient of kurtosis: -1.03049\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 8.00446   variance: 30.1612   standard deviation: 5.49192\n",
+      "coefficient of skewness: -0.0797906   coefficient of kurtosis: -0.928734\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 15.7143   variance: 89.2172   standard deviation: 9.44548\n",
+      "coefficient of skewness: -0.348204   coefficient of kurtosis: -1.01049\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 17.3874   variance: 119.125   standard deviation: 10.9144\n",
+      "coefficient of skewness: -0.417377   coefficient of kurtosis: -1.03107\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          0.0147602  _          0.739208   _          \n",
+      "_          _          0.0225098  0.724448   0.0112551  \n",
+      "_          _          _          _          0.0140921  \n",
+      "_          _          _          _          0.732865   \n",
+      "_          _          _          _          _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8897\n",
+      "OUTPUT 1 : 0.1103\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3457\n",
+      "OUTPUT 1 : 0.6543\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2424\n",
+      "OUTPUT 1 : 0.7576\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8652\n",
+      "OUTPUT 1 : 0.1348\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.889777  0.110223  \n",
+      "1  0.345704  0.654296  \n",
+      "2  0.99999   1e-05     \n",
+      "3  0.242406  0.757594  \n",
+      "4  0.865235  0.134765  \n",
+      "\n",
+      "theoretical weights: 0.256054 0.0873431 0.00288765 0.542894 0.110821\n",
+      "\n",
+      "log-likelihood: -693.834   (normalized: -0.693834)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 1.69765\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 1.69825   critical probability: 0.192516\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.258 0.089 0.003 0.508 0.142\n",
+      "\n",
+      "log-likelihood: -692.985   (normalized: -0.692985)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.000451244\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.000451248   critical probability: 0.983052\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.288469   variance: 0.799016   standard deviation: 0.893877\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 6.45382   variance: 44.1849   standard deviation: 6.64717\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.65422   variance: 8.02699   standard deviation: 2.83319\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.58158   variance: 3.37319   standard deviation: 1.83662\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.21154   variance: 10.6718   standard deviation: 3.26677\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.62073   variance: 10.8924   standard deviation: 3.30036\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.4164   variance: 11.1767   standard deviation: 3.34316\n",
+      "coefficient of skewness: -0.012518   coefficient of kurtosis: -0.0959775\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 16.2104   variance: 11.8336   standard deviation: 3.44001\n",
+      "coefficient of skewness: -0.02167   coefficient of kurtosis: -0.108697\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 48.8401   variance: 329.018   standard deviation: 18.1388\n",
+      "coefficient of skewness: 0.56199   coefficient of kurtosis: -0.933792\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 51.1599   variance: 329.018   standard deviation: 18.1388\n",
+      "coefficient of skewness: -0.56199   coefficient of kurtosis: -0.933792\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         0.544073  _         0.647372  _         \n",
+      "_         _         0.654286  0.103298  0.519531  \n",
+      "_         _         _         _         0.134755  \n",
+      "_         _         _         _         0.62283   \n",
+      "_         _         _         _         _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2425.07   (normalized: -2.42507)\n",
+      "\n",
+      "state sequence entropy: 7.01163   (normalized: 0.00701163)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2422.07   (normalized: -2.42207)\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (AIC): -4922.13\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (AICc): -4925.38\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (BIC): -5113.53\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (BICc): -5028.9\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (ICL): -5127.56\n",
+      "\n",
+      "39 free parameters   2 * penalyzed log-likelihood (ICLc): -5042.93\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "hsmc1 = Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"LeftRight\", Nbiteration=300)   \n",
+    "print(hsmc1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Irreducible model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot model and data characteristics.    \n",
+    "Syntax: hsmc1.extract(int type, int variable, int value)    \n",
+    "types correspond to SELF_TRANSITION , OBSERVATION , INTENSITY , FIRST_OCCURRENCE , RECURRENCE_TIME , SOJOURN_TIME ,   INITIAL_RUN , FINAL_RUN , NB_RUN , NB_OCCURRENCE ,  COUNTING , LENGTH , SEQUENCE_CUMUL , SEQUENCE_MEAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82cb165ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Type 1: emission distribution,  Variable 1: lateral shoot type, State 4\n",
+    "hsmc1.extract(1, 1, 4).plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hsmc1.extract(2, 1, 4).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0d61c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0c83050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0b9ebd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0b170d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0c8e9d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# All emission distributions for variable 1\n",
+    "plt.figure(1)\n",
+    "for i in range(5):\n",
+    "    plt.subplot(3,3, i+1)\n",
+    "    hsmc1.extract(1, 1, i).plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TODO: states 3 and 4 are swapped?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0d06790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82ca0f6ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0dab290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82c0d9abd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f82ca07f710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# All emission distributions for variable 2\n",
+    "plt.figure(1)\n",
+    "for i in range(5):\n",
+    "    plt.subplot(3,3, i+1)\n",
+    "    hsmc1.extract(1, 2, i).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "1e-05     0.099997  1e-05     1e-05     0.899973  \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0         1e-05     1e-05     0.330827  0.669153  \n",
+      "1e-05     0         1e-05     0.99997   1e-05     \n",
+      "1e-05     1e-05     0         0.99997   1e-05     \n",
+      "1e-05     1e-05     0.99997   0         1e-05     \n",
+      "0.334078  1e-05     1e-05     0.665902  0         \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 42.6452   variance: 1548.84   standard deviation: 39.3553\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 3\n",
+      "mean: 9.33333   variance: 4.33333   standard deviation: 2.08167\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 1.04302   variance: 694.887   standard deviation: 26.3607\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 1\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 133.99   variance: 4450.56   standard deviation: 66.7125\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 1\n",
+      "mean: 54   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 59.2976   variance: 2783.15   standard deviation: 52.7556\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 0.0216902   variance: 14.3768   standard deviation: 3.79168\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1.52855   variance: 42.7353   standard deviation: 6.53722\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 54\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1.00363   variance: 2.41484   standard deviation: 1.55398\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 32\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 7.70238   variance: 605.89   standard deviation: 24.6148\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 15\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.14876   variance: 1.97416   standard deviation: 1.40505\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 484\n",
+      "mean: 1.03306   variance: 0.528926   standard deviation: 0.727273\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1.10531   variance: 4.36154   standard deviation: 2.08843\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 393\n",
+      "mean: 1.09924   variance: 1.99268   standard deviation: 1.41162\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 15   PARAMETER : 4.24286   PROBABILITY : 0.521053\n",
+      "mean: 18.9   variance: 7.48485   standard deviation: 2.73585\n",
+      "coefficient of skewness: 1.03748   coefficient of kurtosis: 1.54774\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 19   variance: 13   standard deviation: 3.60555\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 10.148   variance: 33.7613   standard deviation: 5.81045\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 33   SUP_BOUND : 34   PROBABILITY : 0\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 17   variance: 90.6667   standard deviation: 9.5219\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 5   PARAMETER : 1.87866   PROBABILITY : 0.234221\n",
+      "mean: 11.1422   variance: 26.2239   standard deviation: 5.12093\n",
+      "coefficient of skewness: 1.47218   coefficient of kurtosis: 3.23191\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 16   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 7.24768   variance: 27.8932   standard deviation: 5.2814\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 16   PARAMETER : 2.12158   PROBABILITY : 0.0348733\n",
+      "mean: 74.7152   variance: 1683.67   standard deviation: 41.0326\n",
+      "coefficient of skewness: 1.37331   coefficient of kurtosis: 2.82868\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 27   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 49.1232   variance: 1602.6   standard deviation: 40.0325\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 58.125   variance: 387.554   standard deviation: 19.6864\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.31441   PROBABILITY : 0.0308293\n",
+      "mean: 42.3207   variance: 1340.3   standard deviation: 36.6102\n",
+      "coefficient of skewness: 1.74469   coefficient of kurtosis: 4.56552\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 9\n",
+      "mean: 28.6667   variance: 439.25   standard deviation: 20.9583\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 37.4924   variance: 1241.69   standard deviation: 35.2376\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 72   variance: 8   standard deviation: 2.82843\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.31639   variance: 0.298485   standard deviation: 0.546337\n",
+      "coefficient of skewness: 1.59711   coefficient of kurtosis: 2.03051\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: 1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.100016   variance: 0.0900147   standard deviation: 0.300024\n",
+      "coefficient of skewness: 2.66647   coefficient of kurtosis: 5.1105\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.373379   variance: 0.272749   standard deviation: 0.522253\n",
+      "coefficient of skewness: 0.929715   coefficient of kurtosis: -0.296133\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 1.10624   variance: 0.460447   standard deviation: 0.678563\n",
+      "coefficient of skewness: 0.0292603   coefficient of kurtosis: -0.460447\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.9   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: -0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09229   variance: 0.320825   standard deviation: 0.566414\n",
+      "coefficient of skewness: 0.642155   coefficient of kurtosis: 1.95209\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.1   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: 0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 5.72978   variance: 100.296   standard deviation: 10.0148\n",
+      "coefficient of skewness: 1.61552   coefficient of kurtosis: 1.99289\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.7   variance: 87.1222   standard deviation: 9.33393\n",
+      "coefficient of skewness: 1.16737   coefficient of kurtosis: -1.08077\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 3.30041   variance: 98.0207   standard deviation: 9.90054\n",
+      "coefficient of skewness: 2.66648   coefficient of kurtosis: 5.1103\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 3.3   variance: 108.9   standard deviation: 10.4355\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 3.57456   variance: 33.1059   standard deviation: 5.75378\n",
+      "coefficient of skewness: 1.66346   coefficient of kurtosis: 2.53544\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.6   variance: 25.6   standard deviation: 5.05964\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 46.2552   variance: 871.729   standard deviation: 29.5251\n",
+      "coefficient of skewness: -0.349981   coefficient of kurtosis: -1.15396\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.2   variance: 875.956   standard deviation: 29.5965\n",
+      "coefficient of skewness: -0.85303   coefficient of kurtosis: -0.955818\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 41.1401   variance: 999.377   standard deviation: 31.6129\n",
+      "coefficient of skewness: 0.398737   coefficient of kurtosis: -1.076\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 40.2   variance: 842.844   standard deviation: 29.0318\n",
+      "coefficient of skewness: 0.213423   coefficient of kurtosis: -1.49886\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3662\n",
+      "OUTPUT 1 : 0.4624\n",
+      "OUTPUT 2 : 0.1527\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0187\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1212\n",
+      "OUTPUT 1 : 0.5757\n",
+      "OUTPUT 2 : 0.303\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1601\n",
+      "OUTPUT 1 : 0.0931\n",
+      "OUTPUT 2 : 0.2622\n",
+      "OUTPUT 3 : 0.4843\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1603\n",
+      "OUTPUT 1 : 0.0271\n",
+      "OUTPUT 2 : 0.2322\n",
+      "OUTPUT 3 : 0.5802\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3178\n",
+      "OUTPUT 1 : 0.3579\n",
+      "OUTPUT 2 : 0.2195\n",
+      "OUTPUT 3 : 0.0948\n",
+      "OUTPUT 4 : 0.01\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           4           \n",
+      "0  0.366203    0.462481    0.152752    1e-05       0.0185537   \n",
+      "1  0.12121     0.575746    0.303024    1e-05       1e-05       \n",
+      "2  0.160177    0.0931718   0.262248    0.484393    1e-05       \n",
+      "3  0.160347    0.0271556   0.232285    0.580202    1e-05       \n",
+      "4  0.317895    0.357923    0.219547    0.0948046   0.00983027  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1393.29   (normalized: -1.39329)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.164562\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.164611   critical probability: 0.996793\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1393.21   (normalized: -1.39321)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0107418\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0107417   critical probability: 0.999986\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 2.66891   variance: 13.7052   standard deviation: 3.70205\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 2.01069   variance: 20.5595   standard deviation: 4.53426\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 3.42567   variance: 15.196   standard deviation: 3.8982\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 11.823   variance: 146.537   standard deviation: 12.1052\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 43.598   variance: 6027.8   standard deviation: 77.6389\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.5987   variance: 20.8907   standard deviation: 4.57064\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.53965   variance: 199.37   standard deviation: 14.1198\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.38623   variance: 15.0431   standard deviation: 3.87855\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.40231   variance: 13.4673   standard deviation: 3.66978\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 42.0107   variance: 5812.08   standard deviation: 76.237\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.32812   variance: 0.475583   standard deviation: 0.689626\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.52273   variance: 0.902345   standard deviation: 0.949918\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.29426   variance: 0.371534   standard deviation: 0.609536\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.15129   variance: 2.79827   standard deviation: 1.6728\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01118   variance: 0.0110572   standard deviation: 0.105153\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 17.3872   variance: 17.5437   standard deviation: 4.18852\n",
+      "coefficient of skewness: 0.0584217   coefficient of kurtosis: -0.423724\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 13.3592   variance: 46.0381   standard deviation: 6.78514\n",
+      "coefficient of skewness: 0.388686   coefficient of kurtosis: -0.886706\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 17.4831   variance: 8.95742   standard deviation: 2.9929\n",
+      "coefficient of skewness: 0.0273391   coefficient of kurtosis: -0.0387641\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.941   variance: 36.0444   standard deviation: 6.0037\n",
+      "coefficient of skewness: -0.204394   coefficient of kurtosis: -0.771918\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.505432   variance: 0.640116   standard deviation: 0.800073\n",
+      "coefficient of skewness: 1.78689   coefficient of kurtosis: 3.54399\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.566   variance: 52.9183   standard deviation: 7.2745\n",
+      "coefficient of skewness: 0.276865   coefficient of kurtosis: -0.513004\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.8642   variance: 128.391   standard deviation: 11.331\n",
+      "coefficient of skewness: 0.333177   coefficient of kurtosis: -0.838437\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.5893   variance: 19.4883   standard deviation: 4.41456\n",
+      "coefficient of skewness: 0.163523   coefficient of kurtosis: 0.0138123\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.4692   variance: 258.561   standard deviation: 16.0798\n",
+      "coefficient of skewness: -0.248885   coefficient of kurtosis: -1.06867\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.511258   variance: 0.660977   standard deviation: 0.813005\n",
+      "coefficient of skewness: 1.81735   coefficient of kurtosis: 3.72311\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.659725   0.161589   \n",
+      "_          _          _          0.61933    _          \n",
+      "_          _          _          0.0959795  _          \n",
+      "_          _          0.0959795  _          _          \n",
+      "0.161589   _          _          0.498136   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9823\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0177\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9696\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0303\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3215\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.4654\n",
+      "OUTPUT 3 : 0.2131\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.248\n",
+      "OUTPUT 1 : 0.1578\n",
+      "OUTPUT 2 : 0.2681\n",
+      "OUTPUT 3 : 0.3261\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.99\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0099\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           \n",
+      "0  0.982333    1e-05       1e-05       0.0176466   \n",
+      "1  0.969678    1e-05       0.0303024   1e-05       \n",
+      "2  0.321589    1e-05       0.465421    0.212979    \n",
+      "3  0.248095    0.157832    0.268155    0.325918    \n",
+      "4  0.990022    1e-05       0.00995797  1e-05       \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1072.33   (normalized: -1.07233)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.268359\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.269214   critical probability: 0.965711\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1072.3   (normalized: -1.0723)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.202107\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.20173   critical probability: 0.977309\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.0117915   variance: 0.0116525   standard deviation: 0.107947\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 64.6206   variance: 2815.95   standard deviation: 53.0655\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 40.6833   variance: 1353.38   standard deviation: 36.7883\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 55.8141   variance: 2173.79   standard deviation: 46.6239\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.86551   variance: 4.83638   standard deviation: 2.19918\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 7.23316   variance: 53.2919   standard deviation: 7.30013\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.14467   variance: 38.9366   standard deviation: 6.23992\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.20339   variance: 8.45628   standard deviation: 2.90797\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 4.25799   variance: 164.722   standard deviation: 12.8344\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.18208   variance: 0.209024   standard deviation: 0.457191\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.3834   variance: 0.541493   standard deviation: 0.735862\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.46769   variance: 0.677099   standard deviation: 0.822861\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.839   variance: 37.1365   standard deviation: 6.09398\n",
+      "coefficient of skewness: -0.110561   coefficient of kurtosis: -1.06927\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.17639   variance: 19.2377   standard deviation: 4.38608\n",
+      "coefficient of skewness: 0.0897507   coefficient of kurtosis: -0.889554\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.546   variance: 43.4575   standard deviation: 6.59223\n",
+      "coefficient of skewness: -0.153054   coefficient of kurtosis: -1.09724\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.9496   variance: 50.6788   standard deviation: 7.11891\n",
+      "coefficient of skewness: -0.200948   coefficient of kurtosis: -1.10551\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 62.1837   variance: 564.066   standard deviation: 23.7501\n",
+      "coefficient of skewness: 0.331579   coefficient of kurtosis: -1.19244\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.3011   variance: 27.8618   standard deviation: 5.27843\n",
+      "coefficient of skewness: 0.160849   coefficient of kurtosis: -0.805722\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.577   variance: 91.0403   standard deviation: 9.5415\n",
+      "coefficient of skewness: -0.0523614   coefficient of kurtosis: -1.0567\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.9383   variance: 111.63   standard deviation: 10.5655\n",
+      "coefficient of skewness: -0.143707   coefficient of kurtosis: -1.07632\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.734239   0.0176366  \n",
+      "_          _          _          0.721583   _          \n",
+      "_          _          _          0.270761   _          \n",
+      "_          _          0.270761   _          _          \n",
+      "0.0176366  _          _          0.741927   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3724\n",
+      "OUTPUT 1 : 0.6276\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3333\n",
+      "OUTPUT 1 : 0.6667\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4903\n",
+      "OUTPUT 1 : 0.5097\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2248\n",
+      "OUTPUT 1 : 0.7752\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8803\n",
+      "OUTPUT 1 : 0.1197\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.372465  0.627535  \n",
+      "1  0.333333  0.666667  \n",
+      "2  0.490302  0.509698  \n",
+      "3  0.224869  0.775131  \n",
+      "4  0.880379  0.119621  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -693.085   (normalized: -0.693085)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.200141\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.200195   critical probability: 0.654564\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -693.023   (normalized: -0.693023)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0766384\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0766333   critical probability: 0.781913\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.314463   variance: 0.922357   standard deviation: 0.960394\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 5.92123   variance: 42.726   standard deviation: 6.53652\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.19914   variance: 6.32696   standard deviation: 2.51535\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.74674   variance: 5.16151   standard deviation: 2.2719\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.78457   variance: 16.3586   standard deviation: 4.04457\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.40088   variance: 11.059   standard deviation: 3.32551\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.029   variance: 13.5779   standard deviation: 3.68482\n",
+      "coefficient of skewness: -0.0308354   coefficient of kurtosis: -0.221071\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.8544   variance: 14.5911   standard deviation: 3.81983\n",
+      "coefficient of skewness: -0.0472219   coefficient of kurtosis: -0.215015\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.6071   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: 0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.3929   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: -0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.147595  0.507914  \n",
+      "_         _         _         0.108464  _         \n",
+      "_         _         _         0.265433  _         \n",
+      "_         _         0.265433  _         _         \n",
+      "0.507914  _         _         0.655509  _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2434.09   (normalized: -2.43409)\n",
+      "\n",
+      "state sequence entropy: 25.5422   (normalized: 0.0255422)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2421.31   (normalized: -2.42131)\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AIC): -4930.63\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AICc): -4934.78\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BIC): -5146.57\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BICc): -5044.53\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICL): -5197.65\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICLc): -5095.61\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300))  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Discard state sequence computations (entropy, Viterbi?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "1e-05     0.099997  1e-05     1e-05     0.899973  \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0         1e-05     1e-05     0.330827  0.669153  \n",
+      "1e-05     0         1e-05     0.99997   1e-05     \n",
+      "1e-05     1e-05     0         0.99997   1e-05     \n",
+      "1e-05     1e-05     0.99997   0         1e-05     \n",
+      "0.334078  1e-05     1e-05     0.665902  0         \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 42.6452   variance: 1548.84   standard deviation: 39.3553\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 1.04302   variance: 694.887   standard deviation: 26.3607\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 133.99   variance: 4450.56   standard deviation: 66.7125\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 59.2976   variance: 2783.15   standard deviation: 52.7556\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 0.0216902   variance: 14.3768   standard deviation: 3.79168\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1.52855   variance: 42.7353   standard deviation: 6.53722\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1.00363   variance: 2.41484   standard deviation: 1.55398\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 7.70238   variance: 605.89   standard deviation: 24.6148\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.14876   variance: 1.97416   standard deviation: 1.40505\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1.10531   variance: 4.36154   standard deviation: 2.08843\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 15   PARAMETER : 4.24286   PROBABILITY : 0.521053\n",
+      "mean: 18.9   variance: 7.48485   standard deviation: 2.73585\n",
+      "coefficient of skewness: 1.03748   coefficient of kurtosis: 1.54774\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 10.148   variance: 33.7613   standard deviation: 5.81045\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 33   SUP_BOUND : 34   PROBABILITY : 0\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 17   variance: 90.6667   standard deviation: 9.5219\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 5   PARAMETER : 1.87866   PROBABILITY : 0.234221\n",
+      "mean: 11.1422   variance: 26.2239   standard deviation: 5.12093\n",
+      "coefficient of skewness: 1.47218   coefficient of kurtosis: 3.23191\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 7.24768   variance: 27.8932   standard deviation: 5.2814\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 16   PARAMETER : 2.12158   PROBABILITY : 0.0348733\n",
+      "mean: 74.7152   variance: 1683.67   standard deviation: 41.0326\n",
+      "coefficient of skewness: 1.37331   coefficient of kurtosis: 2.82868\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 49.1232   variance: 1602.6   standard deviation: 40.0325\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.31441   PROBABILITY : 0.0308293\n",
+      "mean: 42.3207   variance: 1340.3   standard deviation: 36.6102\n",
+      "coefficient of skewness: 1.74469   coefficient of kurtosis: 4.56552\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 37.4924   variance: 1241.69   standard deviation: 35.2376\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.31639   variance: 0.298485   standard deviation: 0.546337\n",
+      "coefficient of skewness: 1.59711   coefficient of kurtosis: 2.03051\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.100016   variance: 0.0900147   standard deviation: 0.300024\n",
+      "coefficient of skewness: 2.66647   coefficient of kurtosis: 5.1105\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.373379   variance: 0.272749   standard deviation: 0.522253\n",
+      "coefficient of skewness: 0.929715   coefficient of kurtosis: -0.296133\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 1.10624   variance: 0.460447   standard deviation: 0.678563\n",
+      "coefficient of skewness: 0.0292603   coefficient of kurtosis: -0.460447\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09229   variance: 0.320825   standard deviation: 0.566414\n",
+      "coefficient of skewness: 0.642155   coefficient of kurtosis: 1.95209\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 5.72978   variance: 100.296   standard deviation: 10.0148\n",
+      "coefficient of skewness: 1.61552   coefficient of kurtosis: 1.99289\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 3.30041   variance: 98.0207   standard deviation: 9.90054\n",
+      "coefficient of skewness: 2.66648   coefficient of kurtosis: 5.1103\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 3.57456   variance: 33.1059   standard deviation: 5.75378\n",
+      "coefficient of skewness: 1.66346   coefficient of kurtosis: 2.53544\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 46.2552   variance: 871.729   standard deviation: 29.5251\n",
+      "coefficient of skewness: -0.349981   coefficient of kurtosis: -1.15396\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 41.1401   variance: 999.377   standard deviation: 31.6129\n",
+      "coefficient of skewness: 0.398737   coefficient of kurtosis: -1.076\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3662\n",
+      "OUTPUT 1 : 0.4624\n",
+      "OUTPUT 2 : 0.1527\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0187\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1212\n",
+      "OUTPUT 1 : 0.5757\n",
+      "OUTPUT 2 : 0.303\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1601\n",
+      "OUTPUT 1 : 0.0931\n",
+      "OUTPUT 2 : 0.2622\n",
+      "OUTPUT 3 : 0.4843\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1603\n",
+      "OUTPUT 1 : 0.0271\n",
+      "OUTPUT 2 : 0.2322\n",
+      "OUTPUT 3 : 0.5802\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3178\n",
+      "OUTPUT 1 : 0.3579\n",
+      "OUTPUT 2 : 0.2195\n",
+      "OUTPUT 3 : 0.0948\n",
+      "OUTPUT 4 : 0.01\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           4           \n",
+      "0  0.366203    0.462481    0.152752    1e-05       0.0185537   \n",
+      "1  0.12121     0.575746    0.303024    1e-05       1e-05       \n",
+      "2  0.160177    0.0931718   0.262248    0.484393    1e-05       \n",
+      "3  0.160347    0.0271556   0.232285    0.580202    1e-05       \n",
+      "4  0.317895    0.357923    0.219547    0.0948046   0.00983027  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1393.29   (normalized: -1.39329)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.164562\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.164611   critical probability: 0.996793\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 2.66891   variance: 13.7052   standard deviation: 3.70205\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 2.01069   variance: 20.5595   standard deviation: 4.53426\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 3.42567   variance: 15.196   standard deviation: 3.8982\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 11.823   variance: 146.537   standard deviation: 12.1052\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 43.598   variance: 6027.8   standard deviation: 77.6389\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.5987   variance: 20.8907   standard deviation: 4.57064\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.53965   variance: 199.37   standard deviation: 14.1198\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.38623   variance: 15.0431   standard deviation: 3.87855\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.40231   variance: 13.4673   standard deviation: 3.66978\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 42.0107   variance: 5812.08   standard deviation: 76.237\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.32812   variance: 0.475583   standard deviation: 0.689626\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.52273   variance: 0.902345   standard deviation: 0.949918\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.29426   variance: 0.371534   standard deviation: 0.609536\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.15129   variance: 2.79827   standard deviation: 1.6728\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01118   variance: 0.0110572   standard deviation: 0.105153\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 17.3872   variance: 17.5437   standard deviation: 4.18852\n",
+      "coefficient of skewness: 0.0584217   coefficient of kurtosis: -0.423724\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 13.3592   variance: 46.0381   standard deviation: 6.78514\n",
+      "coefficient of skewness: 0.388686   coefficient of kurtosis: -0.886706\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 17.4831   variance: 8.95742   standard deviation: 2.9929\n",
+      "coefficient of skewness: 0.0273391   coefficient of kurtosis: -0.0387641\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.941   variance: 36.0444   standard deviation: 6.0037\n",
+      "coefficient of skewness: -0.204394   coefficient of kurtosis: -0.771918\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.505432   variance: 0.640116   standard deviation: 0.800073\n",
+      "coefficient of skewness: 1.78689   coefficient of kurtosis: 3.54399\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.566   variance: 52.9183   standard deviation: 7.2745\n",
+      "coefficient of skewness: 0.276865   coefficient of kurtosis: -0.513004\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.8642   variance: 128.391   standard deviation: 11.331\n",
+      "coefficient of skewness: 0.333177   coefficient of kurtosis: -0.838437\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.5893   variance: 19.4883   standard deviation: 4.41456\n",
+      "coefficient of skewness: 0.163523   coefficient of kurtosis: 0.0138123\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.4692   variance: 258.561   standard deviation: 16.0798\n",
+      "coefficient of skewness: -0.248885   coefficient of kurtosis: -1.06867\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.511258   variance: 0.660977   standard deviation: 0.813005\n",
+      "coefficient of skewness: 1.81735   coefficient of kurtosis: 3.72311\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.659725   0.161589   \n",
+      "_          _          _          0.61933    _          \n",
+      "_          _          _          0.0959795  _          \n",
+      "_          _          0.0959795  _          _          \n",
+      "0.161589   _          _          0.498136   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9823\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0177\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9696\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0303\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3215\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.4654\n",
+      "OUTPUT 3 : 0.2131\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.248\n",
+      "OUTPUT 1 : 0.1578\n",
+      "OUTPUT 2 : 0.2681\n",
+      "OUTPUT 3 : 0.3261\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.99\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0099\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           \n",
+      "0  0.982333    1e-05       1e-05       0.0176466   \n",
+      "1  0.969678    1e-05       0.0303024   1e-05       \n",
+      "2  0.321589    1e-05       0.465421    0.212979    \n",
+      "3  0.248095    0.157832    0.268155    0.325918    \n",
+      "4  0.990022    1e-05       0.00995797  1e-05       \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1072.33   (normalized: -1.07233)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.268359\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.269214   critical probability: 0.965711\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.0117915   variance: 0.0116525   standard deviation: 0.107947\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 64.6206   variance: 2815.95   standard deviation: 53.0655\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 40.6833   variance: 1353.38   standard deviation: 36.7883\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 55.8141   variance: 2173.79   standard deviation: 46.6239\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.86551   variance: 4.83638   standard deviation: 2.19918\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 7.23316   variance: 53.2919   standard deviation: 7.30013\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.14467   variance: 38.9366   standard deviation: 6.23992\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.20339   variance: 8.45628   standard deviation: 2.90797\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 4.25799   variance: 164.722   standard deviation: 12.8344\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.18208   variance: 0.209024   standard deviation: 0.457191\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.3834   variance: 0.541493   standard deviation: 0.735862\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.46769   variance: 0.677099   standard deviation: 0.822861\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.839   variance: 37.1365   standard deviation: 6.09398\n",
+      "coefficient of skewness: -0.110561   coefficient of kurtosis: -1.06927\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.17639   variance: 19.2377   standard deviation: 4.38608\n",
+      "coefficient of skewness: 0.0897507   coefficient of kurtosis: -0.889554\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.546   variance: 43.4575   standard deviation: 6.59223\n",
+      "coefficient of skewness: -0.153054   coefficient of kurtosis: -1.09724\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.9496   variance: 50.6788   standard deviation: 7.11891\n",
+      "coefficient of skewness: -0.200948   coefficient of kurtosis: -1.10551\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 62.1837   variance: 564.066   standard deviation: 23.7501\n",
+      "coefficient of skewness: 0.331579   coefficient of kurtosis: -1.19244\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.3011   variance: 27.8618   standard deviation: 5.27843\n",
+      "coefficient of skewness: 0.160849   coefficient of kurtosis: -0.805722\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.577   variance: 91.0403   standard deviation: 9.5415\n",
+      "coefficient of skewness: -0.0523614   coefficient of kurtosis: -1.0567\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.9383   variance: 111.63   standard deviation: 10.5655\n",
+      "coefficient of skewness: -0.143707   coefficient of kurtosis: -1.07632\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.734239   0.0176366  \n",
+      "_          _          _          0.721583   _          \n",
+      "_          _          _          0.270761   _          \n",
+      "_          _          0.270761   _          _          \n",
+      "0.0176366  _          _          0.741927   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3724\n",
+      "OUTPUT 1 : 0.6276\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3333\n",
+      "OUTPUT 1 : 0.6667\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4903\n",
+      "OUTPUT 1 : 0.5097\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2248\n",
+      "OUTPUT 1 : 0.7752\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8803\n",
+      "OUTPUT 1 : 0.1197\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.372465  0.627535  \n",
+      "1  0.333333  0.666667  \n",
+      "2  0.490302  0.509698  \n",
+      "3  0.224869  0.775131  \n",
+      "4  0.880379  0.119621  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -693.085   (normalized: -0.693085)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.200141\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.200195   critical probability: 0.654564\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.314463   variance: 0.922357   standard deviation: 0.960394\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 5.92123   variance: 42.726   standard deviation: 6.53652\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.19914   variance: 6.32696   standard deviation: 2.51535\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.74674   variance: 5.16151   standard deviation: 2.2719\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.78457   variance: 16.3586   standard deviation: 4.04457\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.40088   variance: 11.059   standard deviation: 3.32551\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.029   variance: 13.5779   standard deviation: 3.68482\n",
+      "coefficient of skewness: -0.0308354   coefficient of kurtosis: -0.221071\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.8544   variance: 14.5911   standard deviation: 3.81983\n",
+      "coefficient of skewness: -0.0472219   coefficient of kurtosis: -0.215015\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.6071   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: 0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.3929   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: -0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.147595  0.507914  \n",
+      "_         _         _         0.108464  _         \n",
+      "_         _         _         0.265433  _         \n",
+      "_         _         0.265433  _         _         \n",
+      "0.507914  _         _         0.655509  _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2421.31   (normalized: -2.42131)\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AIC): -4930.63\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AICc): -4934.78\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BIC): -5146.57\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BICc): -5044.53\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICL): -5144.57\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICLc): -5042.53\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, StateSequence=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the MCEM option would cause the library to crash"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, Algorithm=\"MCEM\", MinNbStateSequence=1, MaxNbStateSequence=10, Parameter=10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the Equilibrium option (stationary) would cause the library to crash"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Equilibrium\", nb_states))   \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change option in means of initial occupancy distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "0.100018     1e-05        1e-05        1e-05        0.899952     \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0            0.755682     1e-05        1e-05        0.244298     \n",
+      "1e-05        0            1e-05        0.661586     0.338394     \n",
+      "1e-05        1e-05        0            0.99997      1e-05        \n",
+      "1e-05        0.181216     0.818764     0            1e-05        \n",
+      "0.272698     0.000102883  0.72719      1e-05        0            \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 97.075   variance: 47986.2   standard deviation: 219.058\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 4\n",
+      "mean: 6.75   variance: 24.9167   standard deviation: 4.99166\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 309.161   variance: 81065   standard deviation: 284.719\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 3\n",
+      "mean: 27.6667   variance: 14.3333   standard deviation: 3.78594\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 58.1834   variance: 4964.5   standard deviation: 70.4592\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 8\n",
+      "mean: 30.875   variance: 540.696   standard deviation: 23.2529\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 53.6105   variance: 1036.52   standard deviation: 32.195\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.75   variance: 271.643   standard deviation: 16.4816\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 9.90762   variance: 5542.22   standard deviation: 74.4461\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 3.34069   variance: 1462.44   standard deviation: 38.2419\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 82\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 113.938   variance: 53024.1   standard deviation: 230.27\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 7\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 5.8292   variance: 762.969   standard deviation: 27.6219\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 175\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.22852   variance: 11.8949   standard deviation: 3.4489\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 498\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 8.95829   variance: 4959.29   standard deviation: 70.4222\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 206\n",
+      "mean: 1.20874   variance: 4.52695   standard deviation: 2.12766\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 14   PARAMETER : 1.60507   PROBABILITY : 0.17199\n",
+      "mean: 21.7273   variance: 44.9286   standard deviation: 6.70288\n",
+      "coefficient of skewness: 1.58567   coefficient of kurtosis: 3.76042\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 21.5   variance: 67   standard deviation: 8.18535\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 12.3974   variance: 67.962   standard deviation: 8.2439\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 1   SUP_BOUND : 4   PROBABILITY : 0.611111\n",
+      "mean: 2.83333   variance: 0.712963   standard deviation: 0.844371\n",
+      "coefficient of skewness: -0.263181   coefficient of kurtosis: -0.597403\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 3.33333   variance: 0.333333   standard deviation: 0.57735\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 2.04248   variance: 0.90766   standard deviation: 0.952712\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.27392   PROBABILITY : 0.0436501\n",
+      "mean: 28.9109   variance: 639.424   standard deviation: 25.2868\n",
+      "coefficient of skewness: 1.77242   coefficient of kurtosis: 4.71143\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 18.8333   variance: 326.167   standard deviation: 18.0601\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 26.0119   variance: 596.83   standard deviation: 24.4301\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 35   variance: 392   standard deviation: 19.799\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 71   PARAMETER : 6.19571   PROBABILITY : 0.113594\n",
+      "mean: 119.347   variance: 425.615   standard deviation: 20.6304\n",
+      "coefficient of skewness: 0.804958   coefficient of kurtosis: 0.970761\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 61.9565   variance: 1416.23   standard deviation: 37.6328\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 63.25   variance: 271.643   standard deviation: 16.4816\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 4   PARAMETER : 1.79517   PROBABILITY : 0.105599\n",
+      "mean: 19.2048   variance: 143.986   standard deviation: 11.9994\n",
+      "coefficient of skewness: 1.49504   coefficient of kurtosis: 3.34924\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 11\n",
+      "mean: 19.5455   variance: 156.473   standard deviation: 12.5089\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 13.8505   variance: 133.352   standard deviation: 11.5478\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.394888   variance: 0.345295   standard deviation: 0.587618\n",
+      "coefficient of skewness: 1.30937   coefficient of kurtosis: 1.2568\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.4   variance: 0.266667   standard deviation: 0.516398\n",
+      "coefficient of skewness: 0.484123   coefficient of kurtosis: -1.95\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.292606   variance: 0.239747   standard deviation: 0.48964\n",
+      "coefficient of skewness: 1.33458   coefficient of kurtosis: 0.754394\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: 1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.787451   variance: 0.169526   standard deviation: 0.411735\n",
+      "coefficient of skewness: -1.35888   coefficient of kurtosis: -0.00513732\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 0.90611   variance: 0.0853907   standard deviation: 0.292217\n",
+      "coefficient of skewness: -2.76565   coefficient of kurtosis: 5.73534\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09297   variance: 0.212714   standard deviation: 0.461209\n",
+      "coefficient of skewness: 1.21609   coefficient of kurtosis: 4.62027\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.1   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: 0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 8.49943   variance: 173.307   standard deviation: 13.1646\n",
+      "coefficient of skewness: 1.48764   coefficient of kurtosis: 1.76481\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 8.6   variance: 145.6   standard deviation: 12.0665\n",
+      "coefficient of skewness: 1.04139   coefficient of kurtosis: -0.944158\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 0.827596   variance: 2.12344   standard deviation: 1.4572\n",
+      "coefficient of skewness: 1.58755   coefficient of kurtosis: 1.70787\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1   variance: 2.66667   standard deviation: 1.63299\n",
+      "coefficient of skewness: 1.1482   coefficient of kurtosis: -1.125\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 20.9918   variance: 461.593   standard deviation: 21.4847\n",
+      "coefficient of skewness: 1.15802   coefficient of kurtosis: 0.759852\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 18.3   variance: 361.344   standard deviation: 19.0091\n",
+      "coefficient of skewness: 0.715145   coefficient of kurtosis: -1.44391\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 48.8286   variance: 677.208   standard deviation: 26.0232\n",
+      "coefficient of skewness: -0.513864   coefficient of kurtosis: -0.827292\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.6   variance: 922.489   standard deviation: 30.3725\n",
+      "coefficient of skewness: -0.906226   coefficient of kurtosis: -0.999491\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 20.8526   variance: 218.207   standard deviation: 14.7718\n",
+      "coefficient of skewness: 1.21048   coefficient of kurtosis: 1.69058\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 21.5   variance: 266.278   standard deviation: 16.318\n",
+      "coefficient of skewness: 1.48107   coefficient of kurtosis: 0.841687\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2733\n",
+      "OUTPUT 1 : 0.4937\n",
+      "OUTPUT 2 : 0.2203\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0127\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0\n",
+      "OUTPUT 1 : 0.5939\n",
+      "OUTPUT 2 : 0.1232\n",
+      "OUTPUT 3 : 0.2827\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1724\n",
+      "OUTPUT 1 : 0.2188\n",
+      "OUTPUT 2 : 0.4206\n",
+      "OUTPUT 3 : 0.1879\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.161\n",
+      "OUTPUT 1 : 0.0315\n",
+      "OUTPUT 2 : 0.2348\n",
+      "OUTPUT 3 : 0.5725\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4569\n",
+      "OUTPUT 1 : 0.4746\n",
+      "OUTPUT 2 : 0.0392\n",
+      "OUTPUT 3 : 0.0105\n",
+      "OUTPUT 4 : 0.0188\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          4          \n",
+      "0  0.273335   0.493714   0.220363   1e-05      0.0125771  \n",
+      "1  1e-05      0.593924   0.123263   0.282794   1e-05      \n",
+      "2  0.172458   0.218856   0.420689   0.187987   1e-05      \n",
+      "3  0.161034   0.0315884  0.234855   0.572513   1e-05      \n",
+      "4  0.456972   0.474691   0.0392654  0.0105606  0.0185114  \n",
+      "\n",
+      "theoretical weights: 0.0849961 0.00827595 0.209917 0.488285 0.208526\n",
+      "\n",
+      "log-likelihood: -1393.33   (normalized: -1.39333)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.259997\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.260005   critical probability: 0.992248\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.086 0.01 0.183 0.506 0.215\n",
+      "\n",
+      "log-likelihood: -1393.22   (normalized: -1.39322)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0271268\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0271197   critical probability: 0.999909\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 1.3309   variance: 3.43597   standard deviation: 1.85364\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 1.09841   variance: 2.32223   standard deviation: 1.52389\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 12.0435   variance: 92.0633   standard deviation: 9.59496\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 26.7307   variance: 360.629   standard deviation: 18.9902\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 118.327   variance: 56098   standard deviation: 236.85\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.92512   variance: 24.6182   standard deviation: 4.96167\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 7.11144   variance: 194.072   standard deviation: 13.931\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 3.99689   variance: 15.2026   standard deviation: 3.89905\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.05188   variance: 5.15176   standard deviation: 2.26975\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 152.55   variance: 69772.7   standard deviation: 264.145\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.33463   variance: 0.553378   standard deviation: 0.743894\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.53872   variance: 1.0599   standard deviation: 1.02951\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.37863   variance: 0.562102   standard deviation: 0.749735\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.1735   variance: 2.69593   standard deviation: 1.64193\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01621   variance: 0.0159436   standard deviation: 0.126268\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.6296   variance: 11.5489   standard deviation: 3.39837\n",
+      "coefficient of skewness: 0.244558   coefficient of kurtosis: 0.0674132\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 12.7957   variance: 29.3875   standard deviation: 5.42103\n",
+      "coefficient of skewness: 0.636295   coefficient of kurtosis: -0.104981\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 16.3403   variance: 13.9054   standard deviation: 3.729\n",
+      "coefficient of skewness: -0.163452   coefficient of kurtosis: 0.102018\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.7789   variance: 30.5359   standard deviation: 5.52593\n",
+      "coefficient of skewness: -0.686732   coefficient of kurtosis: 0.199251\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.48547   variance: 0.57742   standard deviation: 0.759881\n",
+      "coefficient of skewness: 1.82071   coefficient of kurtosis: 4.08199\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.3355   variance: 39.4707   standard deviation: 6.28257\n",
+      "coefficient of skewness: 0.711257   coefficient of kurtosis: 0.717746\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.7229   variance: 107.77   standard deviation: 10.3812\n",
+      "coefficient of skewness: 0.955382   coefficient of kurtosis: 0.62439\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 23.0924   variance: 43.1067   standard deviation: 6.56557\n",
+      "coefficient of skewness: 0.291014   coefficient of kurtosis: 0.280253\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.3555   variance: 179.578   standard deviation: 13.4007\n",
+      "coefficient of skewness: -0.504688   coefficient of kurtosis: -0.439311\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.493614   variance: 0.604962   standard deviation: 0.777793\n",
+      "coefficient of skewness: 1.86284   coefficient of kurtosis: 4.32088\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         0.382993  _         _         0.200122  \n",
+      "_         _         _         0.562335  0.475463  \n",
+      "_         _         _         0.384526  _         \n",
+      "_         0.562335  0.384526  _         _         \n",
+      "0.200122  0.475463  0.55885   _         _         \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9769\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0115\n",
+      "OUTPUT 3 : 0.0116\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9785\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0214\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2506\n",
+      "OUTPUT 1 : 0.1479\n",
+      "OUTPUT 2 : 0.282\n",
+      "OUTPUT 3 : 0.3195\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          \n",
+      "0  0.976987   1e-05      0.0115013  0.0115013  \n",
+      "1  0.99997    1e-05      1e-05      1e-05      \n",
+      "2  0.978516   1e-05      0.0214642  1e-05      \n",
+      "3  0.250607   0.147907   0.282008   0.319478   \n",
+      "4  0.99997    1e-05      1e-05      1e-05      \n",
+      "\n",
+      "theoretical weights: 0.0849961 0.00827595 0.209917 0.488285 0.208526\n",
+      "\n",
+      "log-likelihood: -1072.59   (normalized: -1.07259)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.790717\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.794581   critical probability: 0.850763\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.086 0.01 0.183 0.506 0.215\n",
+      "\n",
+      "log-likelihood: -1072.2   (normalized: -1.0722)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.00336485\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.00336586   critical probability: 0.999948\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.00227791   variance: 0.00227272   standard deviation: 0.047673\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 59.3439   variance: 1074.29   standard deviation: 32.7763\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 43.4967   variance: 656.924   standard deviation: 25.6305\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 51.9767   variance: 961.984   standard deviation: 31.0159\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.00981   variance: 5.42944   standard deviation: 2.33012\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 8.18432   variance: 118.741   standard deviation: 10.8968\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.29111   variance: 33.8118   standard deviation: 5.81479\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.82594   variance: 39.0785   standard deviation: 6.25128\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 3.73605   variance: 103.886   standard deviation: 10.1924\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.17002   variance: 0.193998   standard deviation: 0.440452\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.37647   variance: 0.512666   standard deviation: 0.716007\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.45579   variance: 0.640436   standard deviation: 0.800272\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.619   variance: 24.8989   standard deviation: 4.98988\n",
+      "coefficient of skewness: -0.164428   coefficient of kurtosis: -0.728907\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.17422   variance: 14.6461   standard deviation: 3.82702\n",
+      "coefficient of skewness: 0.0542412   coefficient of kurtosis: -0.674323\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.4927   variance: 29.3026   standard deviation: 5.41318\n",
+      "coefficient of skewness: -0.241197   coefficient of kurtosis: -0.724883\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.8055   variance: 35.5562   standard deviation: 5.9629\n",
+      "coefficient of skewness: -0.280754   coefficient of kurtosis: -0.797471\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 62.761   variance: 376.175   standard deviation: 19.3952\n",
+      "coefficient of skewness: 0.458389   coefficient of kurtosis: -0.819817\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.22259   variance: 20.9672   standard deviation: 4.579\n",
+      "coefficient of skewness: 0.134399   coefficient of kurtosis: -0.595924\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.3186   variance: 59.8718   standard deviation: 7.73769\n",
+      "coefficient of skewness: -0.153446   coefficient of kurtosis: -0.726919\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.6979   variance: 79.0342   standard deviation: 8.89012\n",
+      "coefficient of skewness: -0.20687   coefficient of kurtosis: -0.78563\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_            0.0229825    _            _            0.0229825    \n",
+      "_            _            _            0.749363     1.11022e-16  \n",
+      "_            _            _            0.727908     _            \n",
+      "_            0.749363     0.727908     _            _            \n",
+      "0.0229825    1.11022e-16  0.0214542    _            _            \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3444\n",
+      "OUTPUT 1 : 0.6556\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.6032\n",
+      "OUTPUT 1 : 0.3968\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.7692\n",
+      "OUTPUT 1 : 0.2308\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2413\n",
+      "OUTPUT 1 : 0.7587\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9862\n",
+      "OUTPUT 1 : 0.0138\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.344475  0.655525  \n",
+      "1  0.603266  0.396734  \n",
+      "2  0.769205  0.230795  \n",
+      "3  0.241372  0.758628  \n",
+      "4  0.986225  0.013775  \n",
+      "\n",
+      "theoretical weights: 0.0849961 0.00827595 0.209917 0.488285 0.208526\n",
+      "\n",
+      "log-likelihood: -693.196   (normalized: -0.693196)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.420884\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.421077   critical probability: 0.5164\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.086 0.01 0.183 0.506 0.215\n",
+      "\n",
+      "log-likelihood: -692.99   (normalized: -0.69299)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0101753\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0101758   critical probability: 0.91965\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.190724   variance: 0.72691   standard deviation: 0.85259\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 16.0954   variance: 142.381   standard deviation: 11.9324\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.36808   variance: 6.79957   standard deviation: 2.6076\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.56234   variance: 2.60968   standard deviation: 1.61545\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.48749   variance: 18.2705   standard deviation: 4.2744\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.48534   variance: 10.3593   standard deviation: 3.21858\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 15.7945   variance: 12.3535   standard deviation: 3.51476\n",
+      "coefficient of skewness: -0.138538   coefficient of kurtosis: 0.0928305\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.582   variance: 12.8657   standard deviation: 3.58688\n",
+      "coefficient of skewness: -0.140303   coefficient of kurtosis: 0.145613\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.9253   variance: 245.161   standard deviation: 15.6576\n",
+      "coefficient of skewness: 0.367878   coefficient of kurtosis: -0.562341\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.0747   variance: 245.161   standard deviation: 15.6576\n",
+      "coefficient of skewness: -0.367878   coefficient of kurtosis: -0.562341\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         0.258791  _         _         0.64175   \n",
+      "_         _         _         0.361894  0.382959  \n",
+      "_         _         _         0.527834  _         \n",
+      "_         0.361894  0.527834  _         _         \n",
+      "0.64175   0.382959  0.217019  _         _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2348.43   (normalized: -2.34843)\n",
+      "\n",
+      "state sequence entropy: 22.1064   (normalized: 0.0221064)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2337.13   (normalized: -2.33713)\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AIC): -4762.25\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AICc): -4766.4\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BIC): -4978.2\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BICc): -4869.82\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICL): -5022.41\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICLc): -4914.03\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, InitialOccupancyMean=20))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change option in estimating occupancy distributions (censoring, etc.?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "1e-05      0.0999969  1e-05      1e-05      0.899973   \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0          1e-05      1e-05      0.335476   0.664504   \n",
+      "0.0097071  0          0.990273   1e-05      1e-05      \n",
+      "1e-05      1e-05      0          0.99997    1e-05      \n",
+      "1e-05      1e-05      0.99997    0          1e-05      \n",
+      "0.272723   1e-05      0.727257   1e-05      0          \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 19.1578   variance: 331.552   standard deviation: 18.2086\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 3\n",
+      "mean: 9.33333   variance: 4.33333   standard deviation: 2.08167\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 0.742908   variance: 493.711   standard deviation: 22.2196\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 1\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 38.7492   variance: 1763.39   standard deviation: 41.9928\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 9\n",
+      "mean: 29.7778   variance: 483.944   standard deviation: 21.9987\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 51.6395   variance: 844.13   standard deviation: 29.0539\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.625   variance: 272.268   standard deviation: 16.5005\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 0.0456987   variance: 11.4282   standard deviation: 3.38057\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1.17019   variance: 7.02821   standard deviation: 2.65108\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 59\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1.00411   variance: 2.74126   standard deviation: 1.65567\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 20\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 4.82983   variance: 353.6   standard deviation: 18.8042\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 187\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.25972   variance: 10.1533   standard deviation: 3.18642\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1.22129   variance: 8.75745   standard deviation: 2.9593\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 205\n",
+      "mean: 1.21463   variance: 4.70861   standard deviation: 2.16993\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 1   SUP_BOUND : 27   PROBABILITY : 0.784059\n",
+      "mean: 21.3855   variance: 4.40207   standard deviation: 2.09811\n",
+      "coefficient of skewness: -0.270776   coefficient of kurtosis: -0.00360327\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 20.6667   variance: 6.33333   standard deviation: 2.51661\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 11.2957   variance: 40.1799   standard deviation: 6.33876\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 1   SUP_BOUND : 21   PROBABILITY : 1\n",
+      "mean: 21   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 21   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 11   variance: 36.6667   standard deviation: 6.0553\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.66822   PROBABILITY : 0.0647549\n",
+      "mean: 25.0938   variance: 372.076   standard deviation: 19.2893\n",
+      "coefficient of skewness: 1.54934   coefficient of kurtosis: 3.59935\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 7\n",
+      "mean: 18   variance: 276.667   standard deviation: 16.6333\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 20.4594   variance: 330.934   standard deviation: 18.1916\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 35   variance: 392   standard deviation: 19.799\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 1   SUP_BOUND : 101   PROBABILITY : 0.951097\n",
+      "mean: 96.1097   variance: 4.65117   standard deviation: 2.15666\n",
+      "coefficient of skewness: -0.41833   coefficient of kurtosis: 0.155\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 48.579   variance: 771.983   standard deviation: 27.7846\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 63.375   variance: 272.268   standard deviation: 16.5005\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 5   PARAMETER : 1.66742   PROBABILITY : 0.107522\n",
+      "mean: 18.8403   variance: 128.721   standard deviation: 11.3455\n",
+      "coefficient of skewness: 1.55135   coefficient of kurtosis: 3.60614\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 11\n",
+      "mean: 19.4545   variance: 142.073   standard deviation: 11.9194\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 13.3357   variance: 122.204   standard deviation: 11.0546\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.294645   variance: 0.313468   standard deviation: 0.559883\n",
+      "coefficient of skewness: 1.91728   coefficient of kurtosis: 3.4391\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: 1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.100019   variance: 0.0900171   standard deviation: 0.300028\n",
+      "coefficient of skewness: 2.66643   coefficient of kurtosis: 5.11033\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.882246   variance: 0.104127   standard deviation: 0.322687\n",
+      "coefficient of skewness: -2.36118   coefficient of kurtosis: 3.61926\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.9   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: -3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 0.927002   variance: 0.0676725   standard deviation: 0.260139\n",
+      "coefficient of skewness: -3.2827   coefficient of kurtosis: 8.77733\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09027   variance: 0.335458   standard deviation: 0.579187\n",
+      "coefficient of skewness: 0.857229   coefficient of kurtosis: 2.50167\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.1   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: 0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 6.22751   variance: 138.531   standard deviation: 11.7699\n",
+      "coefficient of skewness: 1.83581   coefficient of kurtosis: 2.8162\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 6.2   variance: 101.067   standard deviation: 10.0532\n",
+      "coefficient of skewness: 1.08785   coefficient of kurtosis: -1.28817\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 2.10038   variance: 39.6972   standard deviation: 6.30057\n",
+      "coefficient of skewness: 2.66643   coefficient of kurtosis: 5.11035\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 2.1   variance: 44.1   standard deviation: 6.64078\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 21.2227   variance: 331.759   standard deviation: 18.2143\n",
+      "coefficient of skewness: 1.11093   coefficient of kurtosis: 1.06852\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 19.6   variance: 325.378   standard deviation: 18.0382\n",
+      "coefficient of skewness: 0.707661   coefficient of kurtosis: -1.39215\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 50.062   variance: 594.598   standard deviation: 24.3844\n",
+      "coefficient of skewness: -0.63061   coefficient of kurtosis: -0.567965\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.7   variance: 925.789   standard deviation: 30.4268\n",
+      "coefficient of skewness: -0.910424   coefficient of kurtosis: -1.0025\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 20.3874   variance: 238.364   standard deviation: 15.439\n",
+      "coefficient of skewness: 1.12237   coefficient of kurtosis: 1.32975\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 21.4   variance: 250.933   standard deviation: 15.8409\n",
+      "coefficient of skewness: 1.35155   coefficient of kurtosis: 0.609524\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3363\n",
+      "OUTPUT 1 : 0.4672\n",
+      "OUTPUT 2 : 0.1644\n",
+      "OUTPUT 3 : 0.0156\n",
+      "OUTPUT 4 : 0.0165\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.0952\n",
+      "OUTPUT 1 : 0.619\n",
+      "OUTPUT 2 : 0.2857\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1717\n",
+      "OUTPUT 1 : 0.2351\n",
+      "OUTPUT 2 : 0.4124\n",
+      "OUTPUT 3 : 0.1806\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1608\n",
+      "OUTPUT 1 : 0.0315\n",
+      "OUTPUT 2 : 0.2346\n",
+      "OUTPUT 3 : 0.5728\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4551\n",
+      "OUTPUT 1 : 0.477\n",
+      "OUTPUT 2 : 0.0381\n",
+      "OUTPUT 3 : 0.0106\n",
+      "OUTPUT 4 : 0.0192\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          4          \n",
+      "0  0.336323   0.467266   0.164478   0.0156649  0.0162676  \n",
+      "1  0.0952363  0.619035   0.285709   1e-05      1e-05      \n",
+      "2  0.171772   0.235115   0.412427   0.180676   1e-05      \n",
+      "3  0.160898   0.0315762  0.234694   0.572822   1e-05      \n",
+      "4  0.455127   0.477007   0.0381051  0.0106977  0.0190627  \n",
+      "\n",
+      "theoretical weights: 0.0622751 0.0210038 0.212227 0.50062 0.203874\n",
+      "\n",
+      "log-likelihood: -1393.23   (normalized: -1.39323)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0580969\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0579579   critical probability: 0.999588\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.062 0.021 0.196 0.507 0.214\n",
+      "\n",
+      "log-likelihood: -1393.22   (normalized: -1.39322)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0348639\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0348632   critical probability: 0.99985\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 1.9453   variance: 13.5763   standard deviation: 3.68461\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 1.0477   variance: 2.19288   standard deviation: 1.48084\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 12.2463   variance: 95.4752   standard deviation: 9.77114\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 25.5748   variance: 328.574   standard deviation: 18.1266\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 25.9424   variance: 6190.4   standard deviation: 78.6791\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.76433   variance: 23.8147   standard deviation: 4.88003\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.4801   variance: 161.616   standard deviation: 12.7128\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 3.96961   variance: 15.1859   standard deviation: 3.89691\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.1104   variance: 5.81539   standard deviation: 2.41151\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 28.2972   variance: 7031.85   standard deviation: 83.8561\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.35561   variance: 0.599092   standard deviation: 0.77401\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.56108   variance: 1.10517   standard deviation: 1.05127\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.38353   variance: 0.571672   standard deviation: 0.75609\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.15259   variance: 2.65966   standard deviation: 1.63085\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01748   variance: 0.0171739   standard deviation: 0.131049\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.55   variance: 14.2032   standard deviation: 3.76871\n",
+      "coefficient of skewness: 0.282233   coefficient of kurtosis: 0.0840006\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 12.6615   variance: 28.3586   standard deviation: 5.32528\n",
+      "coefficient of skewness: 0.75025   coefficient of kurtosis: 0.121258\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 16.2741   variance: 15.9659   standard deviation: 3.99574\n",
+      "coefficient of skewness: -0.241461   coefficient of kurtosis: 0.116122\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.9482   variance: 29.7116   standard deviation: 5.45083\n",
+      "coefficient of skewness: -0.800078   coefficient of kurtosis: 0.418936\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.481946   variance: 0.627315   standard deviation: 0.792032\n",
+      "coefficient of skewness: 1.98272   coefficient of kurtosis: 4.86885\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.2737   variance: 51.8996   standard deviation: 7.20414\n",
+      "coefficient of skewness: 0.740713   coefficient of kurtosis: 0.663732\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.5056   variance: 99.6488   standard deviation: 9.98242\n",
+      "coefficient of skewness: 1.01437   coefficient of kurtosis: 0.804815\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.9033   variance: 44.9425   standard deviation: 6.70392\n",
+      "coefficient of skewness: 0.0589027   coefficient of kurtosis: 0.133203\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.8267   variance: 169.61   standard deviation: 13.0234\n",
+      "coefficient of skewness: -0.648995   coefficient of kurtosis: -0.163228\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.490679   variance: 0.658877   standard deviation: 0.811712\n",
+      "coefficient of skewness: 2.0237   coefficient of kurtosis: 5.11657\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.627373  0.13134   \n",
+      "0.273     _         0.38392   _         _         \n",
+      "_         _         _         0.392146  _         \n",
+      "_         _         0.392146  _         _         \n",
+      "0.13134   _         0.5443    _         _         \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9844\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0156\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9523\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0476\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9799\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.02\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2511\n",
+      "OUTPUT 1 : 0.1478\n",
+      "OUTPUT 2 : 0.2818\n",
+      "OUTPUT 3 : 0.3193\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          \n",
+      "0  0.984405   1e-05      1e-05      0.0155748  \n",
+      "1  0.952362   1e-05      0.0476181  1e-05      \n",
+      "2  0.979966   1e-05      0.0200142  1e-05      \n",
+      "3  0.251119   0.147806   0.281817   0.319257   \n",
+      "4  0.99997    1e-05      1e-05      1e-05      \n",
+      "\n",
+      "theoretical weights: 0.0622751 0.0210038 0.212227 0.50062 0.203874\n",
+      "\n",
+      "log-likelihood: -1072.25   (normalized: -1.07225)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.100637\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.100804   critical probability: 0.991741\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.062 0.021 0.196 0.507 0.214\n",
+      "\n",
+      "log-likelihood: -1072.2   (normalized: -1.0722)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.000748833\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.000748947   critical probability: 0.999995\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.00456702   variance: 0.00454616   standard deviation: 0.0674252\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 57.3854   variance: 883.062   standard deviation: 29.7164\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 44.5049   variance: 792.566   standard deviation: 28.1525\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 49.8411   variance: 706.985   standard deviation: 26.5892\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.91024   variance: 4.93843   standard deviation: 2.22226\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 8.4299   variance: 115.502   standard deviation: 10.7472\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.37435   variance: 32.3846   standard deviation: 5.69075\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.97466   variance: 40.018   standard deviation: 6.32598\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 4.13358   variance: 119.699   standard deviation: 10.9407\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.16949   variance: 0.19334   standard deviation: 0.439705\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.37435   variance: 0.50951   standard deviation: 0.7138\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.45373   variance: 0.637134   standard deviation: 0.798207\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.8295   variance: 23.2913   standard deviation: 4.8261\n",
+      "coefficient of skewness: -0.257989   coefficient of kurtosis: -0.582085\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.32654   variance: 13.4149   standard deviation: 3.66264\n",
+      "coefficient of skewness: 0.00952101   coefficient of kurtosis: -0.568178\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.7136   variance: 27.6015   standard deviation: 5.25371\n",
+      "coefficient of skewness: -0.350747   coefficient of kurtosis: -0.543012\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 11.0701   variance: 31.3097   standard deviation: 5.5955\n",
+      "coefficient of skewness: -0.348479   coefficient of kurtosis: -0.618926\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 61.8865   variance: 334.329   standard deviation: 18.2847\n",
+      "coefficient of skewness: 0.574779   coefficient of kurtosis: -0.571632\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.39999   variance: 19.2945   standard deviation: 4.39255\n",
+      "coefficient of skewness: 0.0959192   coefficient of kurtosis: -0.498869\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.6334   variance: 55.8112   standard deviation: 7.47069\n",
+      "coefficient of skewness: -0.24835   coefficient of kurtosis: -0.56732\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 16.0801   variance: 70.2237   standard deviation: 8.37996\n",
+      "coefficient of skewness: -0.268131   coefficient of kurtosis: -0.623359\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.733286   0.0155648  \n",
+      "0.0476081  _          0.0276039  _          _          \n",
+      "_          _          _          0.728847   _          \n",
+      "_          _          0.728847   _          _          \n",
+      "0.0155648  _          0.0200042  _          _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3943\n",
+      "OUTPUT 1 : 0.6057\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2857\n",
+      "OUTPUT 1 : 0.7143\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.7485\n",
+      "OUTPUT 1 : 0.2515\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2413\n",
+      "OUTPUT 1 : 0.7587\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9911\n",
+      "OUTPUT 1 : 0.0089\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           \n",
+      "0  0.394375    0.605625    \n",
+      "1  0.285714    0.714286    \n",
+      "2  0.748509    0.251491    \n",
+      "3  0.241393    0.758607    \n",
+      "4  0.991113    0.00888733  \n",
+      "\n",
+      "theoretical weights: 0.0622751 0.0210038 0.212227 0.50062 0.203874\n",
+      "\n",
+      "log-likelihood: -693.007   (normalized: -0.693007)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0441889\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0441934   critical probability: 0.833494\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.062 0.021 0.196 0.507 0.214\n",
+      "\n",
+      "log-likelihood: -692.999   (normalized: -0.692999)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0279661\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0279683   critical probability: 0.867183\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.24348   variance: 1.19738   standard deviation: 1.09425\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 16.8065   variance: 140.156   standard deviation: 11.8387\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.26661   variance: 6.27801   standard deviation: 2.5056\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.58358   variance: 2.60594   standard deviation: 1.61429\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.61628   variance: 20.4333   standard deviation: 4.52033\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.39104   variance: 9.89405   standard deviation: 3.14548\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 15.9807   variance: 12.3065   standard deviation: 3.50806\n",
+      "coefficient of skewness: -0.149953   coefficient of kurtosis: 0.0889338\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.7814   variance: 12.9768   standard deviation: 3.60233\n",
+      "coefficient of skewness: -0.140755   coefficient of kurtosis: 0.131377\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.2323   variance: 211.456   standard deviation: 14.5415\n",
+      "coefficient of skewness: 0.46967   coefficient of kurtosis: -0.412376\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.7677   variance: 211.456   standard deviation: 14.5415\n",
+      "coefficient of skewness: -0.46967   coefficient of kurtosis: -0.412376\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.152982  0.596738  \n",
+      "0.108661  _         0.462795  _         _         \n",
+      "_         _         _         0.507116  _         \n",
+      "_         _         0.507116  _         _         \n",
+      "0.596738  _         0.242603  _         _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2344.89   (normalized: -2.34489)\n",
+      "\n",
+      "state sequence entropy: 20.1842   (normalized: 0.0201842)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2336.09   (normalized: -2.33609)\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (AIC): -4754.19\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (AICc): -4757.78\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (BIC): -4955.41\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (BICc): -4862.96\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (ICL): -4995.78\n",
+      "\n",
+      "41 free parameters   2 * penalyzed log-likelihood (ICLc): -4903.32\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, Estimator=\"KaplanMeier\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change option in state sequence restoration (Viterbi vs. smoothing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "1e-05     0.099997  1e-05     1e-05     0.899973  \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0         1e-05     1e-05     0.330827  0.669153  \n",
+      "1e-05     0         1e-05     0.99997   1e-05     \n",
+      "1e-05     1e-05     0         0.99997   1e-05     \n",
+      "1e-05     1e-05     0.99997   0         1e-05     \n",
+      "0.334078  1e-05     1e-05     0.665902  0         \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 42.6452   variance: 1548.84   standard deviation: 39.3553\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 3\n",
+      "mean: 9.33333   variance: 4.33333   standard deviation: 2.08167\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 1.04302   variance: 694.887   standard deviation: 26.3607\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 1\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 133.99   variance: 4450.56   standard deviation: 66.7125\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 1\n",
+      "mean: 54   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 59.2976   variance: 2783.15   standard deviation: 52.7556\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 0.0216902   variance: 14.3768   standard deviation: 3.79168\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1.52855   variance: 42.7353   standard deviation: 6.53722\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 54\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1.00363   variance: 2.41484   standard deviation: 1.55398\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 32\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 7.70238   variance: 605.89   standard deviation: 24.6148\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 15\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.14876   variance: 1.97416   standard deviation: 1.40505\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 484\n",
+      "mean: 1.03306   variance: 0.528926   standard deviation: 0.727273\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1.10531   variance: 4.36154   standard deviation: 2.08843\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 393\n",
+      "mean: 1.09924   variance: 1.99268   standard deviation: 1.41162\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 15   PARAMETER : 4.24286   PROBABILITY : 0.521053\n",
+      "mean: 18.9   variance: 7.48485   standard deviation: 2.73585\n",
+      "coefficient of skewness: 1.03748   coefficient of kurtosis: 1.54774\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 19   variance: 13   standard deviation: 3.60555\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 10.148   variance: 33.7613   standard deviation: 5.81045\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 33   SUP_BOUND : 34   PROBABILITY : 0\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 17   variance: 90.6667   standard deviation: 9.5219\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 5   PARAMETER : 1.87866   PROBABILITY : 0.234221\n",
+      "mean: 11.1422   variance: 26.2239   standard deviation: 5.12093\n",
+      "coefficient of skewness: 1.47218   coefficient of kurtosis: 3.23191\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 16   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 7.24768   variance: 27.8932   standard deviation: 5.2814\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 16   PARAMETER : 2.12158   PROBABILITY : 0.0348733\n",
+      "mean: 74.7152   variance: 1683.67   standard deviation: 41.0326\n",
+      "coefficient of skewness: 1.37331   coefficient of kurtosis: 2.82868\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 27   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 49.1232   variance: 1602.6   standard deviation: 40.0325\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 58.125   variance: 387.554   standard deviation: 19.6864\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.31441   PROBABILITY : 0.0308293\n",
+      "mean: 42.3207   variance: 1340.3   standard deviation: 36.6102\n",
+      "coefficient of skewness: 1.74469   coefficient of kurtosis: 4.56552\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 9\n",
+      "mean: 28.6667   variance: 439.25   standard deviation: 20.9583\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 37.4924   variance: 1241.69   standard deviation: 35.2376\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 72   variance: 8   standard deviation: 2.82843\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.31639   variance: 0.298485   standard deviation: 0.546337\n",
+      "coefficient of skewness: 1.59711   coefficient of kurtosis: 2.03051\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: 1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.100016   variance: 0.0900147   standard deviation: 0.300024\n",
+      "coefficient of skewness: 2.66647   coefficient of kurtosis: 5.1105\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.373379   variance: 0.272749   standard deviation: 0.522253\n",
+      "coefficient of skewness: 0.929715   coefficient of kurtosis: -0.296133\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 1.10624   variance: 0.460447   standard deviation: 0.678563\n",
+      "coefficient of skewness: 0.0292603   coefficient of kurtosis: -0.460447\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.9   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: -0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09229   variance: 0.320825   standard deviation: 0.566414\n",
+      "coefficient of skewness: 0.642155   coefficient of kurtosis: 1.95209\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.1   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: 0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 5.72978   variance: 100.296   standard deviation: 10.0148\n",
+      "coefficient of skewness: 1.61552   coefficient of kurtosis: 1.99289\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.7   variance: 87.1222   standard deviation: 9.33393\n",
+      "coefficient of skewness: 1.16737   coefficient of kurtosis: -1.08077\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 3.30041   variance: 98.0207   standard deviation: 9.90054\n",
+      "coefficient of skewness: 2.66648   coefficient of kurtosis: 5.1103\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 3.3   variance: 108.9   standard deviation: 10.4355\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 3.57456   variance: 33.1059   standard deviation: 5.75378\n",
+      "coefficient of skewness: 1.66346   coefficient of kurtosis: 2.53544\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.6   variance: 25.6   standard deviation: 5.05964\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 46.2552   variance: 871.729   standard deviation: 29.5251\n",
+      "coefficient of skewness: -0.349981   coefficient of kurtosis: -1.15396\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.2   variance: 875.956   standard deviation: 29.5965\n",
+      "coefficient of skewness: -0.85303   coefficient of kurtosis: -0.955818\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 41.1401   variance: 999.377   standard deviation: 31.6129\n",
+      "coefficient of skewness: 0.398737   coefficient of kurtosis: -1.076\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 40.2   variance: 842.844   standard deviation: 29.0318\n",
+      "coefficient of skewness: 0.213423   coefficient of kurtosis: -1.49886\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3662\n",
+      "OUTPUT 1 : 0.4624\n",
+      "OUTPUT 2 : 0.1527\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0187\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1212\n",
+      "OUTPUT 1 : 0.5757\n",
+      "OUTPUT 2 : 0.303\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1601\n",
+      "OUTPUT 1 : 0.0931\n",
+      "OUTPUT 2 : 0.2622\n",
+      "OUTPUT 3 : 0.4843\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1603\n",
+      "OUTPUT 1 : 0.0271\n",
+      "OUTPUT 2 : 0.2322\n",
+      "OUTPUT 3 : 0.5802\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3178\n",
+      "OUTPUT 1 : 0.3579\n",
+      "OUTPUT 2 : 0.2195\n",
+      "OUTPUT 3 : 0.0948\n",
+      "OUTPUT 4 : 0.01\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           4           \n",
+      "0  0.366203    0.462481    0.152752    1e-05       0.0185537   \n",
+      "1  0.12121     0.575746    0.303024    1e-05       1e-05       \n",
+      "2  0.160177    0.0931718   0.262248    0.484393    1e-05       \n",
+      "3  0.160347    0.0271556   0.232285    0.580202    1e-05       \n",
+      "4  0.317895    0.357923    0.219547    0.0948046   0.00983027  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1393.29   (normalized: -1.39329)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.164562\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.164611   critical probability: 0.996793\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1393.21   (normalized: -1.39321)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0107418\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0107417   critical probability: 0.999986\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 2.66891   variance: 13.7052   standard deviation: 3.70205\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 2.01069   variance: 20.5595   standard deviation: 4.53426\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 3.42567   variance: 15.196   standard deviation: 3.8982\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 11.823   variance: 146.537   standard deviation: 12.1052\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 43.598   variance: 6027.8   standard deviation: 77.6389\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.5987   variance: 20.8907   standard deviation: 4.57064\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.53965   variance: 199.37   standard deviation: 14.1198\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.38623   variance: 15.0431   standard deviation: 3.87855\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.40231   variance: 13.4673   standard deviation: 3.66978\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 42.0107   variance: 5812.08   standard deviation: 76.237\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.32812   variance: 0.475583   standard deviation: 0.689626\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.52273   variance: 0.902345   standard deviation: 0.949918\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.29426   variance: 0.371534   standard deviation: 0.609536\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.15129   variance: 2.79827   standard deviation: 1.6728\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01118   variance: 0.0110572   standard deviation: 0.105153\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 17.3872   variance: 17.5437   standard deviation: 4.18852\n",
+      "coefficient of skewness: 0.0584217   coefficient of kurtosis: -0.423724\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 13.3592   variance: 46.0381   standard deviation: 6.78514\n",
+      "coefficient of skewness: 0.388686   coefficient of kurtosis: -0.886706\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 17.4831   variance: 8.95742   standard deviation: 2.9929\n",
+      "coefficient of skewness: 0.0273391   coefficient of kurtosis: -0.0387641\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.941   variance: 36.0444   standard deviation: 6.0037\n",
+      "coefficient of skewness: -0.204394   coefficient of kurtosis: -0.771918\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.505432   variance: 0.640116   standard deviation: 0.800073\n",
+      "coefficient of skewness: 1.78689   coefficient of kurtosis: 3.54399\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.566   variance: 52.9183   standard deviation: 7.2745\n",
+      "coefficient of skewness: 0.276865   coefficient of kurtosis: -0.513004\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.8642   variance: 128.391   standard deviation: 11.331\n",
+      "coefficient of skewness: 0.333177   coefficient of kurtosis: -0.838437\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.5893   variance: 19.4883   standard deviation: 4.41456\n",
+      "coefficient of skewness: 0.163523   coefficient of kurtosis: 0.0138123\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.4692   variance: 258.561   standard deviation: 16.0798\n",
+      "coefficient of skewness: -0.248885   coefficient of kurtosis: -1.06867\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.511258   variance: 0.660977   standard deviation: 0.813005\n",
+      "coefficient of skewness: 1.81735   coefficient of kurtosis: 3.72311\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.659725   0.161589   \n",
+      "_          _          _          0.61933    _          \n",
+      "_          _          _          0.0959795  _          \n",
+      "_          _          0.0959795  _          _          \n",
+      "0.161589   _          _          0.498136   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9823\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0177\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9696\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0303\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3215\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.4654\n",
+      "OUTPUT 3 : 0.2131\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.248\n",
+      "OUTPUT 1 : 0.1578\n",
+      "OUTPUT 2 : 0.2681\n",
+      "OUTPUT 3 : 0.3261\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.99\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0099\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           \n",
+      "0  0.982333    1e-05       1e-05       0.0176466   \n",
+      "1  0.969678    1e-05       0.0303024   1e-05       \n",
+      "2  0.321589    1e-05       0.465421    0.212979    \n",
+      "3  0.248095    0.157832    0.268155    0.325918    \n",
+      "4  0.990022    1e-05       0.00995797  1e-05       \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1072.33   (normalized: -1.07233)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.268359\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.269214   critical probability: 0.965711\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1072.3   (normalized: -1.0723)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.202107\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.20173   critical probability: 0.977309\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.0117915   variance: 0.0116525   standard deviation: 0.107947\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 64.6206   variance: 2815.95   standard deviation: 53.0655\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 40.6833   variance: 1353.38   standard deviation: 36.7883\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 55.8141   variance: 2173.79   standard deviation: 46.6239\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.86551   variance: 4.83638   standard deviation: 2.19918\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 7.23316   variance: 53.2919   standard deviation: 7.30013\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.14467   variance: 38.9366   standard deviation: 6.23992\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.20339   variance: 8.45628   standard deviation: 2.90797\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 4.25799   variance: 164.722   standard deviation: 12.8344\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.18208   variance: 0.209024   standard deviation: 0.457191\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.3834   variance: 0.541493   standard deviation: 0.735862\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.46769   variance: 0.677099   standard deviation: 0.822861\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.839   variance: 37.1365   standard deviation: 6.09398\n",
+      "coefficient of skewness: -0.110561   coefficient of kurtosis: -1.06927\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.17639   variance: 19.2377   standard deviation: 4.38608\n",
+      "coefficient of skewness: 0.0897507   coefficient of kurtosis: -0.889554\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.546   variance: 43.4575   standard deviation: 6.59223\n",
+      "coefficient of skewness: -0.153054   coefficient of kurtosis: -1.09724\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.9496   variance: 50.6788   standard deviation: 7.11891\n",
+      "coefficient of skewness: -0.200948   coefficient of kurtosis: -1.10551\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 62.1837   variance: 564.066   standard deviation: 23.7501\n",
+      "coefficient of skewness: 0.331579   coefficient of kurtosis: -1.19244\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.3011   variance: 27.8618   standard deviation: 5.27843\n",
+      "coefficient of skewness: 0.160849   coefficient of kurtosis: -0.805722\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.577   variance: 91.0403   standard deviation: 9.5415\n",
+      "coefficient of skewness: -0.0523614   coefficient of kurtosis: -1.0567\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.9383   variance: 111.63   standard deviation: 10.5655\n",
+      "coefficient of skewness: -0.143707   coefficient of kurtosis: -1.07632\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.734239   0.0176366  \n",
+      "_          _          _          0.721583   _          \n",
+      "_          _          _          0.270761   _          \n",
+      "_          _          0.270761   _          _          \n",
+      "0.0176366  _          _          0.741927   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3724\n",
+      "OUTPUT 1 : 0.6276\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3333\n",
+      "OUTPUT 1 : 0.6667\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4903\n",
+      "OUTPUT 1 : 0.5097\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2248\n",
+      "OUTPUT 1 : 0.7752\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8803\n",
+      "OUTPUT 1 : 0.1197\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.372465  0.627535  \n",
+      "1  0.333333  0.666667  \n",
+      "2  0.490302  0.509698  \n",
+      "3  0.224869  0.775131  \n",
+      "4  0.880379  0.119621  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -693.085   (normalized: -0.693085)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.200141\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.200195   critical probability: 0.654564\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -693.023   (normalized: -0.693023)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0766384\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0766333   critical probability: 0.781913\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.314463   variance: 0.922357   standard deviation: 0.960394\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 5.92123   variance: 42.726   standard deviation: 6.53652\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.19914   variance: 6.32696   standard deviation: 2.51535\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.74674   variance: 5.16151   standard deviation: 2.2719\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.78457   variance: 16.3586   standard deviation: 4.04457\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.40088   variance: 11.059   standard deviation: 3.32551\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.029   variance: 13.5779   standard deviation: 3.68482\n",
+      "coefficient of skewness: -0.0308354   coefficient of kurtosis: -0.221071\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.8544   variance: 14.5911   standard deviation: 3.81983\n",
+      "coefficient of skewness: -0.0472219   coefficient of kurtosis: -0.215015\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.6071   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: 0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.3929   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: -0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.147595  0.507914  \n",
+      "_         _         _         0.108464  _         \n",
+      "_         _         _         0.265433  _         \n",
+      "_         _         0.265433  _         _         \n",
+      "0.507914  _         _         0.655509  _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2434.09   (normalized: -2.43409)\n",
+      "\n",
+      "state sequence entropy: 25.5422   (normalized: 0.0255422)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2421.31   (normalized: -2.42131)\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AIC): -4930.63\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AICc): -4934.78\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BIC): -5146.57\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BICc): -5044.53\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICL): -5197.65\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICLc): -5095.61\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, StateSequences=\"ForwardBackward\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change option in estimating occupancy distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "1e-05     0.099997  1e-05     1e-05     0.899973  \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0         1e-05     1e-05     0.330827  0.669153  \n",
+      "1e-05     0         1e-05     0.99997   1e-05     \n",
+      "1e-05     1e-05     0         0.99997   1e-05     \n",
+      "1e-05     1e-05     0.99997   0         1e-05     \n",
+      "0.334078  1e-05     1e-05     0.665902  0         \n",
+      "\n",
+      "recurrent class: states 0 1 2 3 4\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 42.6452   variance: 1548.84   standard deviation: 39.3553\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 3\n",
+      "mean: 9.33333   variance: 4.33333   standard deviation: 2.08167\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 1.04302   variance: 694.887   standard deviation: 26.3607\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 1\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 133.99   variance: 4450.56   standard deviation: 66.7125\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 1\n",
+      "mean: 54   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 59.2976   variance: 2783.15   standard deviation: 52.7556\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 36.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 0.0216902   variance: 14.3768   standard deviation: 3.79168\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 9\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1.52855   variance: 42.7353   standard deviation: 6.53722\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 54\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1.00363   variance: 2.41484   standard deviation: 1.55398\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 32\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 7.70238   variance: 605.89   standard deviation: 24.6148\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 15\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1.14876   variance: 1.97416   standard deviation: 1.40505\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 484\n",
+      "mean: 1.03306   variance: 0.528926   standard deviation: 0.727273\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1.10531   variance: 4.36154   standard deviation: 2.08843\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 393\n",
+      "mean: 1.09924   variance: 1.99268   standard deviation: 1.41162\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 15   PARAMETER : 4.24286   PROBABILITY : 0.521053\n",
+      "mean: 18.9   variance: 7.48485   standard deviation: 2.73585\n",
+      "coefficient of skewness: 1.03748   coefficient of kurtosis: 1.54774\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 19   variance: 13   standard deviation: 3.60555\n",
+      "\n",
+      "state 0 forward sojourn time distribution\n",
+      "mean: 10.148   variance: 33.7613   standard deviation: 5.81045\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "BINOMIAL   INF_BOUND : 33   SUP_BOUND : 34   PROBABILITY : 0\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 33   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 forward sojourn time distribution\n",
+      "mean: 17   variance: 90.6667   standard deviation: 9.5219\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 5   PARAMETER : 1.87866   PROBABILITY : 0.234221\n",
+      "mean: 11.1422   variance: 26.2239   standard deviation: 5.12093\n",
+      "coefficient of skewness: 1.47218   coefficient of kurtosis: 3.23191\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 16   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 forward sojourn time distribution\n",
+      "mean: 7.24768   variance: 27.8932   standard deviation: 5.2814\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 16   PARAMETER : 2.12158   PROBABILITY : 0.0348733\n",
+      "mean: 74.7152   variance: 1683.67   standard deviation: 41.0326\n",
+      "coefficient of skewness: 1.37331   coefficient of kurtosis: 2.82868\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 27   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 forward sojourn time distribution\n",
+      "mean: 49.1232   variance: 1602.6   standard deviation: 40.0325\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 58.125   variance: 387.554   standard deviation: 19.6864\n",
+      "\n",
+      "STATE 4 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.31441   PROBABILITY : 0.0308293\n",
+      "mean: 42.3207   variance: 1340.3   standard deviation: 36.6102\n",
+      "coefficient of skewness: 1.74469   coefficient of kurtosis: 4.56552\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 9\n",
+      "mean: 28.6667   variance: 439.25   standard deviation: 20.9583\n",
+      "\n",
+      "state 4 forward sojourn time distribution\n",
+      "mean: 37.4924   variance: 1241.69   standard deviation: 35.2376\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 72   variance: 8   standard deviation: 2.82843\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.31639   variance: 0.298485   standard deviation: 0.546337\n",
+      "coefficient of skewness: 1.59711   coefficient of kurtosis: 2.03051\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.3   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: 1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.100016   variance: 0.0900147   standard deviation: 0.300024\n",
+      "coefficient of skewness: 2.66647   coefficient of kurtosis: 5.1105\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.373379   variance: 0.272749   standard deviation: 0.522253\n",
+      "coefficient of skewness: 0.929715   coefficient of kurtosis: -0.296133\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 1.10624   variance: 0.460447   standard deviation: 0.678563\n",
+      "coefficient of skewness: 0.0292603   coefficient of kurtosis: -0.460447\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.9   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: -0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 1.09229   variance: 0.320825   standard deviation: 0.566414\n",
+      "coefficient of skewness: 0.642155   coefficient of kurtosis: 1.95209\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.1   variance: 0.322222   standard deviation: 0.567646\n",
+      "coefficient of skewness: 0.0911204   coefficient of kurtosis: -0.0281807\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 5.72978   variance: 100.296   standard deviation: 10.0148\n",
+      "coefficient of skewness: 1.61552   coefficient of kurtosis: 1.99289\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.7   variance: 87.1222   standard deviation: 9.33393\n",
+      "coefficient of skewness: 1.16737   coefficient of kurtosis: -1.08077\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 3.30041   variance: 98.0207   standard deviation: 9.90054\n",
+      "coefficient of skewness: 2.66648   coefficient of kurtosis: 5.1103\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 3.3   variance: 108.9   standard deviation: 10.4355\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 3.57456   variance: 33.1059   standard deviation: 5.75378\n",
+      "coefficient of skewness: 1.66346   coefficient of kurtosis: 2.53544\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 1.6   variance: 25.6   standard deviation: 5.05964\n",
+      "coefficient of skewness: 3.16228   coefficient of kurtosis: 4.3\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 46.2552   variance: 871.729   standard deviation: 29.5251\n",
+      "coefficient of skewness: -0.349981   coefficient of kurtosis: -1.15396\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.2   variance: 875.956   standard deviation: 29.5965\n",
+      "coefficient of skewness: -0.85303   coefficient of kurtosis: -0.955818\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 41.1401   variance: 999.377   standard deviation: 31.6129\n",
+      "coefficient of skewness: 0.398737   coefficient of kurtosis: -1.076\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 40.2   variance: 842.844   standard deviation: 29.0318\n",
+      "coefficient of skewness: 0.213423   coefficient of kurtosis: -1.49886\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3662\n",
+      "OUTPUT 1 : 0.4624\n",
+      "OUTPUT 2 : 0.1527\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0187\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1212\n",
+      "OUTPUT 1 : 0.5757\n",
+      "OUTPUT 2 : 0.303\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1601\n",
+      "OUTPUT 1 : 0.0931\n",
+      "OUTPUT 2 : 0.2622\n",
+      "OUTPUT 3 : 0.4843\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1603\n",
+      "OUTPUT 1 : 0.0271\n",
+      "OUTPUT 2 : 0.2322\n",
+      "OUTPUT 3 : 0.5802\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3178\n",
+      "OUTPUT 1 : 0.3579\n",
+      "OUTPUT 2 : 0.2195\n",
+      "OUTPUT 3 : 0.0948\n",
+      "OUTPUT 4 : 0.01\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           4           \n",
+      "0  0.366203    0.462481    0.152752    1e-05       0.0185537   \n",
+      "1  0.12121     0.575746    0.303024    1e-05       1e-05       \n",
+      "2  0.160177    0.0931718   0.262248    0.484393    1e-05       \n",
+      "3  0.160347    0.0271556   0.232285    0.580202    1e-05       \n",
+      "4  0.317895    0.357923    0.219547    0.0948046   0.00983027  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1393.29   (normalized: -1.39329)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.164562\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.164611   critical probability: 0.996793\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1393.21   (normalized: -1.39321)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.0107418\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0107417   critical probability: 0.999986\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 2.66891   variance: 13.7052   standard deviation: 3.70205\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 2.01069   variance: 20.5595   standard deviation: 4.53426\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 3.42567   variance: 15.196   standard deviation: 3.8982\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 11.823   variance: 146.537   standard deviation: 12.1052\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 43.598   variance: 6027.8   standard deviation: 77.6389\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.5987   variance: 20.8907   standard deviation: 4.57064\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.53965   variance: 199.37   standard deviation: 14.1198\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.38623   variance: 15.0431   standard deviation: 3.87855\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.40231   variance: 13.4673   standard deviation: 3.66978\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 42.0107   variance: 5812.08   standard deviation: 76.237\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.32812   variance: 0.475583   standard deviation: 0.689626\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.52273   variance: 0.902345   standard deviation: 0.949918\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.29426   variance: 0.371534   standard deviation: 0.609536\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.15129   variance: 2.79827   standard deviation: 1.6728\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.01118   variance: 0.0110572   standard deviation: 0.105153\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 17.3872   variance: 17.5437   standard deviation: 4.18852\n",
+      "coefficient of skewness: 0.0584217   coefficient of kurtosis: -0.423724\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 13.3592   variance: 46.0381   standard deviation: 6.78514\n",
+      "coefficient of skewness: 0.388686   coefficient of kurtosis: -0.886706\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 17.4831   variance: 8.95742   standard deviation: 2.9929\n",
+      "coefficient of skewness: 0.0273391   coefficient of kurtosis: -0.0387641\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.941   variance: 36.0444   standard deviation: 6.0037\n",
+      "coefficient of skewness: -0.204394   coefficient of kurtosis: -0.771918\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.505432   variance: 0.640116   standard deviation: 0.800073\n",
+      "coefficient of skewness: 1.78689   coefficient of kurtosis: 3.54399\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.566   variance: 52.9183   standard deviation: 7.2745\n",
+      "coefficient of skewness: 0.276865   coefficient of kurtosis: -0.513004\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.8642   variance: 128.391   standard deviation: 11.331\n",
+      "coefficient of skewness: 0.333177   coefficient of kurtosis: -0.838437\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 22.5893   variance: 19.4883   standard deviation: 4.41456\n",
+      "coefficient of skewness: 0.163523   coefficient of kurtosis: 0.0138123\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 32.4692   variance: 258.561   standard deviation: 16.0798\n",
+      "coefficient of skewness: -0.248885   coefficient of kurtosis: -1.06867\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.511258   variance: 0.660977   standard deviation: 0.813005\n",
+      "coefficient of skewness: 1.81735   coefficient of kurtosis: 3.72311\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.659725   0.161589   \n",
+      "_          _          _          0.61933    _          \n",
+      "_          _          _          0.0959795  _          \n",
+      "_          _          0.0959795  _          _          \n",
+      "0.161589   _          _          0.498136   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9823\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0177\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9696\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0303\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3215\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.4654\n",
+      "OUTPUT 3 : 0.2131\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.248\n",
+      "OUTPUT 1 : 0.1578\n",
+      "OUTPUT 2 : 0.2681\n",
+      "OUTPUT 3 : 0.3261\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.99\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0099\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0           1           2           3           \n",
+      "0  0.982333    1e-05       1e-05       0.0176466   \n",
+      "1  0.969678    1e-05       0.0303024   1e-05       \n",
+      "2  0.321589    1e-05       0.465421    0.212979    \n",
+      "3  0.248095    0.157832    0.268155    0.325918    \n",
+      "4  0.990022    1e-05       0.00995797  1e-05       \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -1072.33   (normalized: -1.07233)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.268359\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.269214   critical probability: 0.965711\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -1072.3   (normalized: -1.0723)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.202107\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.20173   critical probability: 0.977309\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.0117915   variance: 0.0116525   standard deviation: 0.107947\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 64.6206   variance: 2815.95   standard deviation: 53.0655\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 40.6833   variance: 1353.38   standard deviation: 36.7883\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 55.8141   variance: 2173.79   standard deviation: 46.6239\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.86551   variance: 4.83638   standard deviation: 2.19918\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 7.23316   variance: 53.2919   standard deviation: 7.30013\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.14467   variance: 38.9366   standard deviation: 6.23992\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.20339   variance: 8.45628   standard deviation: 2.90797\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 4.25799   variance: 164.722   standard deviation: 12.8344\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.18208   variance: 0.209024   standard deviation: 0.457191\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.3834   variance: 0.541493   standard deviation: 0.735862\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.46769   variance: 0.677099   standard deviation: 0.822861\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.839   variance: 37.1365   standard deviation: 6.09398\n",
+      "coefficient of skewness: -0.110561   coefficient of kurtosis: -1.06927\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.17639   variance: 19.2377   standard deviation: 4.38608\n",
+      "coefficient of skewness: 0.0897507   coefficient of kurtosis: -0.889554\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.546   variance: 43.4575   standard deviation: 6.59223\n",
+      "coefficient of skewness: -0.153054   coefficient of kurtosis: -1.09724\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.9496   variance: 50.6788   standard deviation: 7.11891\n",
+      "coefficient of skewness: -0.200948   coefficient of kurtosis: -1.10551\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 62.1837   variance: 564.066   standard deviation: 23.7501\n",
+      "coefficient of skewness: 0.331579   coefficient of kurtosis: -1.19244\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.3011   variance: 27.8618   standard deviation: 5.27843\n",
+      "coefficient of skewness: 0.160849   coefficient of kurtosis: -0.805722\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.577   variance: 91.0403   standard deviation: 9.5415\n",
+      "coefficient of skewness: -0.0523614   coefficient of kurtosis: -1.0567\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.9383   variance: 111.63   standard deviation: 10.5655\n",
+      "coefficient of skewness: -0.143707   coefficient of kurtosis: -1.07632\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          _          _          0.734239   0.0176366  \n",
+      "_          _          _          0.721583   _          \n",
+      "_          _          _          0.270761   _          \n",
+      "_          _          0.270761   _          _          \n",
+      "0.0176366  _          _          0.741927   _          \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3724\n",
+      "OUTPUT 1 : 0.6276\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3333\n",
+      "OUTPUT 1 : 0.6667\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.4903\n",
+      "OUTPUT 1 : 0.5097\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2248\n",
+      "OUTPUT 1 : 0.7752\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8803\n",
+      "OUTPUT 1 : 0.1197\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0         1         \n",
+      "0  0.372465  0.627535  \n",
+      "1  0.333333  0.666667  \n",
+      "2  0.490302  0.509698  \n",
+      "3  0.224869  0.775131  \n",
+      "4  0.880379  0.119621  \n",
+      "\n",
+      "theoretical weights: 0.0572991 0.0330041 0.0357457 0.462551 0.4114\n",
+      "\n",
+      "log-likelihood: -693.085   (normalized: -0.693085)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.200141\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.200195   critical probability: 0.654564\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.057 0.033 0.016 0.492 0.402\n",
+      "\n",
+      "log-likelihood: -693.023   (normalized: -0.693023)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.0766384\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.0766333   critical probability: 0.781913\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.314463   variance: 0.922357   standard deviation: 0.960394\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 5.92123   variance: 42.726   standard deviation: 6.53652\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 2.19914   variance: 6.32696   standard deviation: 2.51535\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.74674   variance: 5.16151   standard deviation: 2.2719\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 2.78457   variance: 16.3586   standard deviation: 4.04457\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.40088   variance: 11.059   standard deviation: 3.32551\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 16.029   variance: 13.5779   standard deviation: 3.68482\n",
+      "coefficient of skewness: -0.0308354   coefficient of kurtosis: -0.221071\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.8544   variance: 14.5911   standard deviation: 3.81983\n",
+      "coefficient of skewness: -0.0472219   coefficient of kurtosis: -0.215015\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 51.6071   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: 0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 48.3929   variance: 413.732   standard deviation: 20.3404\n",
+      "coefficient of skewness: -0.370427   coefficient of kurtosis: -1.06606\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         _         _         0.147595  0.507914  \n",
+      "_         _         _         0.108464  _         \n",
+      "_         _         _         0.265433  _         \n",
+      "_         _         0.265433  _         _         \n",
+      "0.507914  _         _         0.655509  _         \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2434.09   (normalized: -2.43409)\n",
+      "\n",
+      "state sequence entropy: 25.5422   (normalized: 0.0255422)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2421.31   (normalized: -2.42131)\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AIC): -4930.63\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (AICc): -4934.78\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BIC): -5146.57\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (BICc): -5044.53\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICL): -5197.65\n",
+      "\n",
+      "44 free parameters   2 * penalyzed log-likelihood (ICLc): -5095.61\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", \"Ordinary\", nb_states, \"Irreducible\", Nbiteration=300, OccupancyMean=\"Estimated\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other options in estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function Estimate in module openalea.sequence_analysis.estimate:\n",
+      "\n",
+      "Estimate(obj, *args, **kargs)\n",
+      "    Estimate\n",
+      "    \n",
+      "    * Estimation of distributions.\n",
+      "    * Estimation of 'top' parameters.\n",
+      "    * Estimation of a renewal process from count data.\n",
+      "    * Estimation of (hidden) Markovian models.\n",
+      "    \n",
+      "    :Usage:\n",
+      "    .. doctest::\n",
+      "        :options: +SKIP\n",
+      "    \n",
+      "        >>> Estimate(histo, \"NON-PARAMETRIC\")\n",
+      "        >>> Estimate(histo, \"NB\", MinInfBound=1, InfBoundStatus=\"Fixed\")\n",
+      "        >>> Estimate(histo, \"MIXTURE\", \"B\", dist,..., MinInfBound=1,\n",
+      "            InfBoundStatus=\"Fixed\", DistInfBoundStatus=\"Fixed\")\n",
+      "        >>> Estimate(histo, \"MIXTURE\", \"B\", \"NB\",..., MinInfBound=1,\n",
+      "                InfBoundStatus=\"Fixed\", DistInfBoundStatus=\"Fixed\",\n",
+      "                NbComponent=\"Estimated\", Penalty=\"AIC\")\n",
+      "        >>> Estimate(histo, \"CONVOLUTION\", dist,MinInfBound=1, Parametric=False)\n",
+      "        >>> Estimate(histo, \"CONVOLUTION\", dist,InitialDistribution=initial_dist,\n",
+      "                Parametric=False)\n",
+      "        >>> Estimate(histo, \"COMPOUND\", dist, unknown, Parametric=False,\n",
+      "                MinInfBound=0)\n",
+      "        >>> Estimate(histo, \"COMPOUND\", dist, unknown,\n",
+      "                InitialDistribution=initial_dist, Parametric=False)\n",
+      "    \n",
+      "        >>> Estimate(top, MinPosition=1, MaxPosition=5, Neighbourhood=2,\n",
+      "                EqualProba=True)\n",
+      "    \n",
+      "        >>> Estimate(timev, type, NbIteration=10,Parametric=True)\n",
+      "        >>> Estimate(timev, type, InitialInterEvent=initial_dist,\n",
+      "                NbIteration=10, Parametric=True)\n",
+      "    \n",
+      "        >>> Estimate(seq, \"NONHOMOGENEOUS_MARKOV\", MONOMOLECULAR, VOID,\n",
+      "                Counting=False)\n",
+      "        >>> Estimate(seq, \"SEMI-MARKOV\", Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_MARKOV\", nb_state, structure,\n",
+      "                SelfTransition=0.9, NbIteration=10,\n",
+      "                StateSequences=\"Viterbi\", Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_MARKOV\", hmc, Algorithm=\"Viterbi\",\n",
+      "                NbIteration=10, Order=2, Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_MARKOV\", \"NbState\", min_nb_state,\n",
+      "                max_nb_state, Penalty=\"AIC\", Order=2, Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_MARKOV\", \"NbState\", hmc, state,\n",
+      "                max_nb_state, Penalty=\"AIC\", SelfTransition=0.9, Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_SEMI-MARKOV\", nb_state, structure,\n",
+      "                OccupancyMean=20, NbIteration=10, Estimator=\"PartialLikelihood\",\n",
+      "                StateSequences=\"Viterbi\", Counting=False)\n",
+      "        >>> Estimate(seq, \"HIDDEN_SEMI-MARKOV\", hsmc, Algorithm=\"Viterbi\",\n",
+      "            NbIteration=10, Counting=False)\n",
+      "    \n",
+      "    :Arguments:\n",
+      "    \n",
+      "    * histo (histogram, mixture_data, convolution_data, compound_data),\n",
+      "    * dist (distribution, mixture, convolution, compound),\n",
+      "    * unknown (string): type of unknown distribution: \"Sum\" or \"Elementary\".\n",
+      "    * top (tops),\n",
+      "    * timev (time_events, renewal_data),\n",
+      "    * type (string): type or renewal process: \"Ordinary\" or \"Equilibrium\".\n",
+      "    * seq (discrete_sequences, markov_data, semi-markov_data),\n",
+      "    * states, ... (array(int)):  new states corresponding to a partition of the\n",
+      "      original state space,\n",
+      "    * hmc (hidden_markov),\n",
+      "    * structure (string): structural properties of  the underlying Markov chain:\n",
+      "      \"Irreductible\" or \"LeftRight\" (i.e. a succession of transient states and a\n",
+      "      final absorbing state),\n",
+      "    * nb_state (int): number of states with 2 <= nb_state <=  15,\n",
+      "    * min_nb_state (int): minimum number of states,\n",
+      "    * max_nb_state (int): maximum number of states with 2 <= min_nb_state < max_nb_state <= 15\n",
+      "      or (number of states of the initial hidden Markov chain hmc) < max_nb_state<= 15.\n",
+      "    * state (int): state to be duplicated,\n",
+      "    * hsmc (hidden_semi-markov).\n",
+      "    \n",
+      "    :Optional Arguments:\n",
+      "    \n",
+      "    **distribution case**\n",
+      "    \n",
+      "    * MinInfBound (int): lower bound to the range of possible values (0 - default\n",
+      "      value - or 1). This optional argument cannot be used in conjunction with the\n",
+      "      optional argument InitialDistribution.\n",
+      "    * InfBoundStatus (string): shifting or not of the distribution: \"Free\" (default\n",
+      "      value) or \"Fixed\". This optional argument cannot be used if the second mandatory\n",
+      "      argument giving the model type is \"NON-PARAMETRIC\" (\"NP\").\n",
+      "    * DistInfBoundStatus (string): shifting or not of the subsequent components of\n",
+      "      the mixture: \"Free\" (default value) or \"Fixed\". This optional argument can\n",
+      "      only be used if the second mandatory argument giving the distribution type is \"MIXTURE\".\n",
+      "    * NbComponent (string): estimation of the number of components of the mixture:\n",
+      "      \"Fixed\" (default value) or \"Estimated\". This optional argument can only be\n",
+      "      used if the second mandatory argument giving the distribution type is \"MIXTURE\".\n",
+      "      the number of estimated components is comprised between 1 and a maximum number\n",
+      "      which is given by the number of specified parametric distributions in the\n",
+      "      mandatory arguments (all of these distributions are assumed to be unknown).\n",
+      "    * Penalty (string): type of penalty function for model selection: \"AIC\"\n",
+      "      (Akaike Information Criterion), \"AICc\" (corrected Akaike Information Criterion\n",
+      "      - default value) or \"BIC\" (Bayesian Information Criterion). This optional\n",
+      "      argument can only be used if the second mandatory argument giving the distribution\n",
+      "      type is \"MIXTURE\" and if the optional argument NbComponent is set at \"Estimated\".\n",
+      "    * Parametric (bool): reestimation of a discrete nonparametric or parametric\n",
+      "      distribution (default value: True). This optional argument can only be used if\n",
+      "      the second mandatory argument giving the distribution type is \"CONVOLUTION\"\n",
+      "      or \"COMPOUND\".\n",
+      "    * InitialDistribution (distribution, mixture, convolution, compound): initial\n",
+      "      distribution for the EM deconvolution-type algorithm. This optional argument\n",
+      "      can only be used if the second mandatory argument giving the distribution type\n",
+      "      is \"CONVOLUTION\" or \"COMPOUND\". This optional argument cannot be used in\n",
+      "      conjunction with the optional argument MinInfBound.\n",
+      "    \n",
+      "    .. note:: the optional arguments MinInfBound and InitialDistribution are mutually exclusive.\n",
+      "    \n",
+      "    \n",
+      "    **top case**\n",
+      "    \n",
+      "    * MinPosition (int): lower position taken into account for the estimation of 'top' parameters.\n",
+      "    * MaxPosition (int): upper position taken into account for the estimation of 'top' parameters.\n",
+      "    * Neighbourhood (int): neighbourhood taken into account for the estimation of 'top' parameters.\n",
+      "    * EqualProba (bool): growth probabilities of the parent shoot and of the offspring shoots equal or not (default value: False).\n",
+      "    \n",
+      "    **renewal case**\n",
+      "    \n",
+      "    * InitialInterEvent (distribution, mixture, convolution, compound): initial inter-event distribution for the EM algorithm.\n",
+      "    * NbIteration (int): number of iterations of the EM algorithm.\n",
+      "    * Parametric (bool): reestimation of a discrete nonparametric or parametric distribution (default value: False).\n",
+      "    \n",
+      "    **markovian case**\n",
+      "    \n",
+      "    * Counting (bool): computation of counting distributions (default value: True).\n",
+      "    * Order (int): Markov chain order (default value: 1). This optional argument can only be used if the second mandatory argument giving the model type is \"MARKOV\", \"NONHOMOGENEOUS_MARKOV\" or \"HIDDEN_MARKOV\".\n",
+      "    * MaxOrder (int): maximum order of the Markov chain (default value: 4). This optional argument can only be used if the second mandatory argument giving the model type is \"MARKOV\".\n",
+      "    * Penalty (string): type of penalty function for model selection: \"AIC\" (Akaike Information Criterion), \"AICc\" (corrected Akaike Information Criterion) or \"BIC\" (Bayesian Information Criterion). This optional argument can only be used if the second mandatory argument giving the model type is \"MARKOV\" (default value: \"BIC\") and if the optional argument MaxOrder is set or else, if a new set of states is given (defining a partition of the original state space) or else, if the second mandatory argument giving the model type is \"HIDDEN_MARKOV\" and the third \"NbState\" (default value: \"AICc\").\n",
+      "    * Algorithm (string): type of algorithm: \"ForwardBackward\" (the default) or \"Viterbi\". This optional argument can only be used if the second mandatory argument giving the model type is \"HIDDEN_MARKOV\" or \"HIDDEN_SEMI-MARKOV\".\n",
+      "    * NbIteration (int): number of iterations of the estimation algorithm.\n",
+      "    * SelfTransition (real): self-transition probability. This optional argument can only be used if the second mandatory argument giving the model type is \"HIDDEN_MARKOV\" and if the initial model used in the iterative estimation procedure (EM algorithm) is only specified by its number of states, its structural properties and eventually its order.\n",
+      "    * OccupancyMean (int/real): average state occupancy. This optional argument can only be used if the second mandatory argument giving the model type is \"HIDDEN_SEMI-MARKOV\" and if the initial model used in the iterative estimation procedure (EM algorithm) is only specified by its number of states and its structural properties.\n",
+      "    * Estimator (string): type of estimator: \"CompleteLikelihood\" (the default) or \"PartialLikelihood\". In this latter case, the contribution of the time spent in the last visited state is not taken into account inthe estimation of the state occupancy distributions. This optional argument can only be used if the second mandatory argument giving the model type is \"HIDDEN_SEMI-MARKOV\" and the optional argument Algorithm is set at \"ForwardBackward\".\n",
+      "    * StateSequences (string): Computation of the optimal state sequences: no computation (the default), \"ForwardBackward\" or \"Viterbi\". This optional argument can only be used if the second mandatory argument giving the model type is \"HIDDEN_MARKOV\" or \"HIDDEN_SEMI-MARKOV\" and if the optional argument Algorithm is not set at \"Viterbi\".\n",
+      "    \n",
+      "    \n",
+      "    :Returned Object:\n",
+      "    \n",
+      "    **distribution case**\n",
+      "    \n",
+      "    In case of success of the estimation procedure, the type of the returned object\n",
+      "    (chosen among distribution, mixture, convolution or compound) is given by the\n",
+      "    second mandatory argument. Otherwise no object is returned. The returned object\n",
+      "    of type distribution, mixture, convolution or compound contains both the estimated\n",
+      "    distribution and the data used in the estimation procedure. In the case of\n",
+      "    mixtures, convolutions, or compound (or stopped-sum) distributions, the returned\n",
+      "    object contains pseudo-data computed as a byproduct of the EM algorithm which\n",
+      "    can be extracted by the function ExtractData.\n",
+      "    \n",
+      "    **top case**\n",
+      "    \n",
+      "    In case of success of the estimation procedure, an object of type top_parameters\n",
+      "    is returned, otherwise no object is returned. The returned object of type top_parameters\n",
+      "    contains both the estimated model and the data used for the estimation.\n",
+      "    \n",
+      "    **renewal case**\n",
+      "    \n",
+      "    In case of success of the estimation procedure, an object of type renewal is\n",
+      "    returned, otherwise no object is returned. The returned object of type renewal\n",
+      "    contains both the estimated renewal process and the count data used in the\n",
+      "    estimation procedure.\n",
+      "    \n",
+      "    **markovian case**\n",
+      "    \n",
+      "    In case of success of the estimation procedure, the type of the returned object\n",
+      "    (chosen among markov, semi-markov, hidden_markov, hidden_semi-markov) is given\n",
+      "    by the second mandatory argument. Otherwise no object is returned. If the\n",
+      "    second mandatory argument is \"NONHOMOGENEOUS_MARKOV\", in case of success\n",
+      "    of the estimation procedure, the returned object is of type markov. If the\n",
+      "    second mandatory argument is \"NONHOMOGENEOUS_MARKOV\", the subsequent\n",
+      "    arguments chosen among \"VOID\" (homogeneous state), \"MONOMOLECULAR\" or\n",
+      "    \"LOGISTIC\", specify the evolution of the self-transition probabilities\n",
+      "    as a function of the index parameter. The returned object of type markov,\n",
+      "    semi-markov, hidden_markov or hidden_semi-markov contains both the estimated\n",
+      "    distribution and the data used in the estimation procedure. In the case of\n",
+      "    the estimation of a hidden Markov chain or a hidden semi-Markov chain,\n",
+      "    the returned object contains pseudo-data (optimal state sequences\n",
+      "    corresponding to the observed sequences used in the estimation procedure)\n",
+      "    computed as a byproduct of the EM algorithm which can be extracted by the\n",
+      "    function ExtractData.\n",
+      "    \n",
+      "    \n",
+      "    :Background:\n",
+      "    \n",
+      "    The aim of the model of 'tops' is to related the growth of offspring shoots\n",
+      "    to the growth of their parent shoot in the case of immediate branching. A\n",
+      "    model of 'tops' is defined by three parameters, namely the growth probability\n",
+      "    of the parent shoot, the growth probability of the offspring shoots (both in\n",
+      "    the sense of Bernoulli processes) and the growth rhythm ratio offspring\n",
+      "    shoots / parent shoot.\n",
+      "    \n",
+      "    :Description (markovian case):\n",
+      "    \n",
+      "    In the case of hidden Markovian models (second mandatory argument \"HIDDEN_MARKOV\"\n",
+      "    or \"HIDDEN_SEMI-MARKOV\"), either the forward-backward algorithm or the Viterbi\n",
+      "    algorithm can be used for estimation. The Viterbi algorithm should only be\n",
+      "    used for the estimation of hidden Markovian models based on an underlying\n",
+      "    \"left-right\" Markov chain (i.e. constituted of a succession of transient states\n",
+      "    and a final absorbing state). Hence, in this case, the model structure is\n",
+      "    implicitly \"LeftRight\" and should not be given as argument (only the number of\n",
+      "    states should be given as argument). Since the optimal state sequences are computed\n",
+      "    by the Viterbi algorithm, the optional argument StateSequences cannot be used if\n",
+      "    the optional argument Algorithm is set at \"Viterbi\".\n",
+      "    \n",
+      "    .. seealso::\n",
+      "    \n",
+      "        :func:`~openalea.stat_tool.data_transform.ExtractData`,\n",
+      "        :func:`~openalea.stat_tool.data_transform.ExtractDistribution`.\n",
+      "        :func:`~openalea.sequence_analysis.data_transform.AddAbsorbingRun`,\n",
+      "        :func:`ModelSelectionTest`.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(Estimate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimate HSCM with manual initialization from file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "0.694432   0.0999981  0.20555    1e-05      1e-05      \n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "0          0.431991   0.567989   1e-05      1e-05      \n",
+      "0          0          0.499998   1e-05      0.499992   \n",
+      "0          0          0          0.99999    1e-05      \n",
+      "0          0          0          0          1          \n",
+      "0          0          0          0          1          \n",
+      "\n",
+      "transient class: state 0\n",
+      "transient class: state 1\n",
+      "transient class: state 2\n",
+      "transient class: state 3\n",
+      "recurrent class: state 4 (absorbing state)\n",
+      "\n",
+      "probability of no-occurrence of state 0: 0.305568\n",
+      "\n",
+      "time up to the first occurrence of state 0 distribution\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of state 0 frequency distribution - sample size: 7\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of no-occurrence of state 1: 0.600011\n",
+      "\n",
+      "time up to the first occurrence of state 1 distribution\n",
+      "mean: 5.37165   variance: 18.8147   standard deviation: 4.33759\n",
+      "\n",
+      "time up to the first occurrence of state 1 frequency distribution - sample size: 4\n",
+      "mean: 6.75   variance: 24.9167   standard deviation: 4.99166\n",
+      "\n",
+      "probability of no-occurrence of state 2: 0.200024\n",
+      "\n",
+      "time up to the first occurrence of state 2 distribution\n",
+      "mean: 10.9192   variance: 141.993   standard deviation: 11.9161\n",
+      "\n",
+      "time up to the first occurrence of state 2 frequency distribution - sample size: 8\n",
+      "mean: 10.625   variance: 166.839   standard deviation: 12.9166\n",
+      "\n",
+      "probability of no-occurrence of state 3: 0.200012\n",
+      "\n",
+      "time up to the first occurrence of state 3 distribution\n",
+      "mean: 31.0019   variance: 278.57   standard deviation: 16.6904\n",
+      "\n",
+      "time up to the first occurrence of state 3 frequency distribution - sample size: 8\n",
+      "mean: 30.5   variance: 555.429   standard deviation: 23.5675\n",
+      "\n",
+      "time up to the first occurrence of state 4 distribution\n",
+      "mean: 53.7018   variance: 886.315   standard deviation: 29.771\n",
+      "\n",
+      "time up to the first occurrence of state 4 frequency distribution - sample size: 8\n",
+      "mean: 36.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "probability of leaving state 0: 0.138604\n",
+      "\n",
+      "state 0 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 0 recurrence time frequency distribution - sample size: 44\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 1: 0.0412602\n",
+      "\n",
+      "state 1 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 1 recurrence time frequency distribution - sample size: 92\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 2: 0.0496392\n",
+      "\n",
+      "state 2 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 2 recurrence time frequency distribution - sample size: 151\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "probability of leaving state 3: 0.0346981\n",
+      "\n",
+      "state 3 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 3 recurrence time frequency distribution - sample size: 178\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time distribution\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "state 4 recurrence time frequency distribution - sample size: 500\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 2   PARAMETER : 3.48885   PROBABILITY : 0.400848\n",
+      "mean: 7.21481   variance: 13.0094   standard deviation: 3.60686\n",
+      "coefficient of skewness: 1.10606   coefficient of kurtosis: 1.79663\n",
+      "\n",
+      "state 0 sojourn time frequency distribution - sample size: 7\n",
+      "mean: 7.28571   variance: 15.9048   standard deviation: 3.98808\n",
+      "\n",
+      "final run - state 0 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 17   PARAMETER : 2.15715   PROBABILITY : 0.229641\n",
+      "mean: 24.2364   variance: 31.512   standard deviation: 5.61355\n",
+      "coefficient of skewness: 1.37333   coefficient of kurtosis: 2.81318\n",
+      "\n",
+      "state 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 24   variance: 42   standard deviation: 6.48074\n",
+      "\n",
+      "final run - state 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 10   PARAMETER : 0.765176   PROBABILITY : 0.0701318\n",
+      "mean: 20.1454   variance: 144.661   standard deviation: 12.0275\n",
+      "coefficient of skewness: 2.2879   coefficient of kurtosis: 7.84825\n",
+      "\n",
+      "state 2 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 19.875   variance: 174.696   standard deviation: 13.2173\n",
+      "\n",
+      "final run - state 2 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.22179   PROBABILITY : 0.0420701\n",
+      "mean: 28.8201   variance: 661.279   standard deviation: 25.7153\n",
+      "coefficient of skewness: 1.8098   coefficient of kurtosis: 4.91233\n",
+      "\n",
+      "state 3 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 19.3333   variance: 351.867   standard deviation: 18.7581\n",
+      "\n",
+      "final run - state 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 35   variance: 392   standard deviation: 19.799\n",
+      "\n",
+      "absorption probability of state 4: 1\n",
+      "\n",
+      "state 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "final run - state 4 sojourn time frequency distribution - sample size: 8\n",
+      "mean: 63.5   variance: 273.143   standard deviation: 16.527\n",
+      "\n",
+      "number of runs of state 0 per length 100 sequence distribution\n",
+      "mean: 0.69443   variance: 0.212197   standard deviation: 0.460648\n",
+      "coefficient of skewness: -0.844158   coefficient of kurtosis: -1.2874\n",
+      "\n",
+      "number of runs of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 0.233333   standard deviation: 0.483046\n",
+      "coefficient of skewness: -1.0351   coefficient of kurtosis: -1.41429\n",
+      "\n",
+      "number of runs of state 1 per length 100 sequence distribution\n",
+      "mean: 0.399984   variance: 0.239997   standard deviation: 0.489895\n",
+      "coefficient of skewness: 0.408317   coefficient of kurtosis: -1.83328\n",
+      "\n",
+      "number of runs of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.4   variance: 0.266667   standard deviation: 0.516398\n",
+      "coefficient of skewness: 0.484123   coefficient of kurtosis: -1.95\n",
+      "\n",
+      "number of runs of state 2 per length 100 sequence distribution\n",
+      "mean: 0.799974   variance: 0.160016   standard deviation: 0.40002\n",
+      "coefficient of skewness: -1.49979   coefficient of kurtosis: 0.24938\n",
+      "\n",
+      "number of runs of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 3 per length 100 sequence distribution\n",
+      "mean: 0.797624   variance: 0.16142   standard deviation: 0.401771\n",
+      "coefficient of skewness: -1.48156   coefficient of kurtosis: 0.195012\n",
+      "\n",
+      "number of runs of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of runs of state 4 per length 100 sequence distribution\n",
+      "mean: 0.916526   variance: 0.0765063   standard deviation: 0.276598\n",
+      "coefficient of skewness: -3.01178   coefficient of kurtosis: 7.07082\n",
+      "\n",
+      "number of runs of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.8   variance: 0.177778   standard deviation: 0.421637\n",
+      "coefficient of skewness: -1.77878   coefficient of kurtosis: -0.075\n",
+      "\n",
+      "number of occurrences of state 0 per length 100 sequence distribution\n",
+      "mean: 5.01004   variance: 20.0746   standard deviation: 4.48047\n",
+      "coefficient of skewness: 0.718669   coefficient of kurtosis: 0.34569\n",
+      "\n",
+      "number of occurrences of state 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.1   variance: 22.9889   standard deviation: 4.79467\n",
+      "coefficient of skewness: 0.512743   coefficient of kurtosis: -1.3736\n",
+      "\n",
+      "number of occurrences of state 1 per length 100 sequence distribution\n",
+      "mean: 9.694   variance: 153.565   standard deviation: 12.3921\n",
+      "coefficient of skewness: 0.698661   coefficient of kurtosis: -1.00396\n",
+      "\n",
+      "number of occurrences of state 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 9.6   variance: 167.6   standard deviation: 12.946\n",
+      "coefficient of skewness: 0.804284   coefficient of kurtosis: -1.35587\n",
+      "\n",
+      "number of occurrences of state 2 per length 100 sequence distribution\n",
+      "mean: 16.0856   variance: 176.401   standard deviation: 13.2816\n",
+      "coefficient of skewness: 1.41899   coefficient of kurtosis: 3.56837\n",
+      "\n",
+      "number of occurrences of state 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 206.1   standard deviation: 14.3562\n",
+      "coefficient of skewness: 1.28981   coefficient of kurtosis: 0.223948\n",
+      "\n",
+      "number of occurrences of state 3 per length 100 sequence distribution\n",
+      "mean: 21.0189   variance: 437.962   standard deviation: 20.9275\n",
+      "coefficient of skewness: 1.04806   coefficient of kurtosis: 0.390499\n",
+      "\n",
+      "number of occurrences of state 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 18.6   variance: 376.044   standard deviation: 19.3919\n",
+      "coefficient of skewness: 0.700214   coefficient of kurtosis: -1.49566\n",
+      "\n",
+      "number of occurrences of state 4 per length 100 sequence distribution\n",
+      "mean: 48.1915   variance: 619.948   standard deviation: 24.8988\n",
+      "coefficient of skewness: -0.576308   coefficient of kurtosis: -0.816286\n",
+      "\n",
+      "number of occurrences of state 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.8   variance: 929.289   standard deviation: 30.4842\n",
+      "coefficient of skewness: -0.913914   coefficient of kurtosis: -1.00596\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.8963\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0244\n",
+      "OUTPUT 3 : 0\n",
+      "OUTPUT 4 : 0.0793\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2577\n",
+      "OUTPUT 1 : 0.5061\n",
+      "OUTPUT 2 : 0.2141\n",
+      "OUTPUT 3 : 0.0113\n",
+      "OUTPUT 4 : 0.0108\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3134\n",
+      "OUTPUT 1 : 0.6256\n",
+      "OUTPUT 2 : 0.0458\n",
+      "OUTPUT 3 : 0.015\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1746\n",
+      "OUTPUT 1 : 0.2133\n",
+      "OUTPUT 2 : 0.4221\n",
+      "OUTPUT 3 : 0.1897\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.1605\n",
+      "OUTPUT 1 : 0.0315\n",
+      "OUTPUT 2 : 0.2342\n",
+      "OUTPUT 3 : 0.5736\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          4          \n",
+      "0  0.896356   1e-05      0.024402   1e-05      0.0792224  \n",
+      "1  0.257717   0.506118   0.214173   0.011306   0.0106861  \n",
+      "2  0.313478   0.625697   0.0458074  0.0150069  1e-05      \n",
+      "3  0.174687   0.213384   0.422198   0.189721   1e-05      \n",
+      "4  0.160542   0.0315415  0.234264   0.573643   1e-05      \n",
+      "\n",
+      "theoretical weights: 0.050105 0.0969425 0.160855 0.210187 0.481911\n",
+      "\n",
+      "log-likelihood: -1393.47   (normalized: -1.39347)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.538042\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.539802   critical probability: 0.969512\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.051 0.096 0.159 0.186 0.508\n",
+      "\n",
+      "log-likelihood: -1393.21   (normalized: -1.39321)\n",
+      "maximum possible log-likelihood: -1393.2   (information: -1.3932)\n",
+      "deviance: 0.010631\n",
+      "\n",
+      "chi-square test (4 degrees of freedom)\n",
+      "chi-square value: 0.0106381   critical probability: 0.999986\n",
+      "reference chi-square value: 9.48773   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.802947   variance: 3.50148   standard deviation: 1.87122\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.7   variance: 4.9   standard deviation: 2.21359\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 5.7393   variance: 21.1711   standard deviation: 4.60121\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 5.9   variance: 24.5444   standard deviation: 4.95424\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 12.34   variance: 87.37   standard deviation: 9.34719\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 10\n",
+      "mean: 10.2   variance: 37.2889   standard deviation: 6.10646\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 28.0414   variance: 219.637   standard deviation: 14.8202\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 10\n",
+      "mean: 25.7   variance: 183.567   standard deviation: 13.5487\n",
+      "\n",
+      "time up to the first occurrence of output 4 distribution\n",
+      "mean: 13.9478   variance: 5697.26   standard deviation: 75.4802\n",
+      "\n",
+      "time up to the first occurrence of output 4 frequency distribution - sample size: 4\n",
+      "mean: 5.25   variance: 29.5833   standard deviation: 5.43906\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 4.43669   variance: 21.9315   standard deviation: 4.68311\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 224\n",
+      "mean: 4.13839   variance: 15.3126   standard deviation: 3.91313\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 5.74424   variance: 184.42   standard deviation: 13.5801\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 195\n",
+      "mean: 3.56923   variance: 39.7722   standard deviation: 6.30652\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 3.85977   variance: 15.6332   standard deviation: 3.95388\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 216\n",
+      "mean: 4.04167   variance: 21.8448   standard deviation: 4.67384\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 2.29263   variance: 7.64644   standard deviation: 2.76522\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 320\n",
+      "mean: 2.25937   variance: 12.8134   standard deviation: 3.57958\n",
+      "\n",
+      "output 4 recurrence time distribution\n",
+      "mean: 18.4217   variance: 7865.9   standard deviation: 88.6899\n",
+      "\n",
+      "output 4 recurrence time frequency distribution - sample size: 1\n",
+      "mean: 4   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 1.47305   variance: 1.44985   standard deviation: 1.2041\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 1.45912   variance: 1.52838   standard deviation: 1.23628\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.80616   variance: 2.02075   standard deviation: 1.42153\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 118\n",
+      "mean: 1.73729   variance: 1.99022   standard deviation: 1.41075\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.4223   variance: 0.655215   standard deviation: 0.809454\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 1.41935   variance: 0.608714   standard deviation: 0.780201\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 2.06683   variance: 2.56301   standard deviation: 1.60094\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 159\n",
+      "mean: 2.04403   variance: 2.80185   standard deviation: 1.67387\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 2.5   variance: 0.5   standard deviation: 0.707107\n",
+      "\n",
+      "output 4 sojourn time distribution\n",
+      "mean: 1.05934   variance: 0.0626113   standard deviation: 0.250223\n",
+      "\n",
+      "output 4 sojourn time frequency distribution - sample size: 5\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "final run - output 4 sojourn time frequency distribution - sample size: 0\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 15.7494   variance: 9.05478   standard deviation: 3.00912\n",
+      "coefficient of skewness: 0.119808   coefficient of kurtosis: 0.00140247\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 8.1   standard deviation: 2.84605\n",
+      "coefficient of skewness: 0.19954   coefficient of kurtosis: -1.17884\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 11.4656   variance: 20.8057   standard deviation: 4.56132\n",
+      "coefficient of skewness: 0.634883   coefficient of kurtosis: -0.120908\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 11.8   variance: 30.1778   standard deviation: 5.49343\n",
+      "coefficient of skewness: 0.552139   coefficient of kurtosis: -1.34827\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 16.3388   variance: 11.3516   standard deviation: 3.36921\n",
+      "coefficient of skewness: -0.139868   coefficient of kurtosis: 0.24889\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 17.6556   standard deviation: 4.20185\n",
+      "coefficient of skewness: -0.649044   coefficient of kurtosis: -0.869825\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 15.6128   variance: 20.7419   standard deviation: 4.55433\n",
+      "coefficient of skewness: -0.487928   coefficient of kurtosis: 0.0536395\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.1   variance: 34.5444   standard deviation: 5.87745\n",
+      "coefficient of skewness: -1.30266   coefficient of kurtosis: 0.19092\n",
+      "\n",
+      "number of runs of output 4 per length 100 sequence distribution\n",
+      "mean: 0.47295   variance: 0.526727   standard deviation: 0.725759\n",
+      "coefficient of skewness: 1.62849   coefficient of kurtosis: 2.81505\n",
+      "\n",
+      "number of runs of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 23.44   variance: 30.2232   standard deviation: 5.49756\n",
+      "coefficient of skewness: 0.306511   coefficient of kurtosis: 0.110952\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 23.4   variance: 40.4889   standard deviation: 6.36309\n",
+      "coefficient of skewness: 0.299779   coefficient of kurtosis: -0.977912\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 20.9762   variance: 88.1781   standard deviation: 9.39032\n",
+      "coefficient of skewness: 1.03936   coefficient of kurtosis: 1.07784\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 20.5   variance: 123.167   standard deviation: 11.098\n",
+      "coefficient of skewness: 1.30068   coefficient of kurtosis: 0.0179219\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 23.0989   variance: 34.7081   standard deviation: 5.89136\n",
+      "coefficient of skewness: 0.344837   coefficient of kurtosis: 0.443507\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 22.6   variance: 38.2667   standard deviation: 6.18601\n",
+      "coefficient of skewness: 0.574946   coefficient of kurtosis: -1.10525\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 31.9835   variance: 142.541   standard deviation: 11.9391\n",
+      "coefficient of skewness: -0.44279   coefficient of kurtosis: -0.561928\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 33   variance: 225.778   standard deviation: 15.0259\n",
+      "coefficient of skewness: -1.03193   coefficient of kurtosis: -0.581789\n",
+      "\n",
+      "number of occurrences of output 4 per length 100 sequence distribution\n",
+      "mean: 0.501351   variance: 0.618837   standard deviation: 0.786662\n",
+      "coefficient of skewness: 1.76871   coefficient of kurtosis: 3.55744\n",
+      "\n",
+      "number of occurrences of output 4 per sequence frequency distribution - sample size: 10\n",
+      "mean: 0.5   variance: 0.5   standard deviation: 0.707107\n",
+      "coefficient of skewness: 1.17851   coefficient of kurtosis: -0.5\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_         0.707175  0.66209   _         _         \n",
+      "_         _         0.179041  _         0.582428  \n",
+      "_         _         _         0.551105  _         \n",
+      "_         _         _         _         0.383922  \n",
+      "_         _         _         _         _         \n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9793\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0103\n",
+      "OUTPUT 3 : 0.0104\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9782\n",
+      "OUTPUT 1 : 0\n",
+      "OUTPUT 2 : 0.0217\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2525\n",
+      "OUTPUT 1 : 0.1475\n",
+      "OUTPUT 2 : 0.2812\n",
+      "OUTPUT 3 : 0.3188\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          2          3          \n",
+      "0  0.99997    1e-05      1e-05      1e-05      \n",
+      "1  0.97934    1e-05      0.0103213  0.0103283  \n",
+      "2  0.99997    1e-05      1e-05      1e-05      \n",
+      "3  0.978205   1e-05      0.021775   1e-05      \n",
+      "4  0.252545   0.147524   0.28128    0.318651   \n",
+      "\n",
+      "theoretical weights: 0.050105 0.0969425 0.160855 0.210187 0.481911\n",
+      "\n",
+      "log-likelihood: -1072.99   (normalized: -1.07299)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 1.57957\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 1.59084   critical probability: 0.661468\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.051 0.096 0.159 0.186 0.508\n",
+      "\n",
+      "log-likelihood: -1072.2   (normalized: -1.0722)\n",
+      "maximum possible log-likelihood: -1072.2   (information: -1.0722)\n",
+      "deviance: 0.000271727\n",
+      "\n",
+      "chi-square test (3 degrees of freedom)\n",
+      "chi-square value: 0.000271754   critical probability: 0.999999\n",
+      "reference chi-square value: 7.81473   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.00205244   variance: 0.00204822   standard deviation: 0.0452573\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0   variance: 0   standard deviation: 0\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 59.4588   variance: 925.348   standard deviation: 30.4195\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 8\n",
+      "mean: 42.375   variance: 411.411   standard deviation: 20.2833\n",
+      "\n",
+      "time up to the first occurrence of output 2 distribution\n",
+      "mean: 43.7799   variance: 536.598   standard deviation: 23.1646\n",
+      "\n",
+      "time up to the first occurrence of output 2 frequency distribution - sample size: 9\n",
+      "mean: 38.3333   variance: 393   standard deviation: 19.8242\n",
+      "\n",
+      "time up to the first occurrence of output 3 distribution\n",
+      "mean: 52.2495   variance: 926.982   standard deviation: 30.4464\n",
+      "\n",
+      "time up to the first occurrence of output 3 frequency distribution - sample size: 9\n",
+      "mean: 36.3333   variance: 373.75   standard deviation: 19.3326\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.59068   variance: 3.40334   standard deviation: 1.84482\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 604\n",
+      "mean: 1.57616   variance: 3.03067   standard deviation: 1.74088\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 6.74959   variance: 37.8178   standard deviation: 6.14962\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 67\n",
+      "mean: 6.1194   variance: 48.1976   standard deviation: 6.94245\n",
+      "\n",
+      "output 2 recurrence time distribution\n",
+      "mean: 4.20277   variance: 28.29   standard deviation: 5.31883\n",
+      "\n",
+      "output 2 recurrence time frequency distribution - sample size: 139\n",
+      "mean: 3.61871   variance: 10.5999   standard deviation: 3.25575\n",
+      "\n",
+      "output 3 recurrence time distribution\n",
+      "mean: 3.25921   variance: 11.2631   standard deviation: 3.35606\n",
+      "\n",
+      "output 3 recurrence time frequency distribution - sample size: 154\n",
+      "mean: 3.05844   variance: 5.85931   standard deviation: 2.4206\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 6.05219   variance: 200.544   standard deviation: 14.1614\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 107\n",
+      "mean: 4.6729   variance: 129.052   standard deviation: 11.3601\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 2\n",
+      "mean: 57   variance: 1568   standard deviation: 39.598\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 1.17114   variance: 0.195402   standard deviation: 0.442043\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 57\n",
+      "mean: 1.29825   variance: 0.320175   standard deviation: 0.56584\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 1\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 2 sojourn time distribution\n",
+      "mean: 1.3682   variance: 0.505988   standard deviation: 0.711328\n",
+      "\n",
+      "output 2 sojourn time frequency distribution - sample size: 100\n",
+      "mean: 1.45   variance: 0.65404   standard deviation: 0.808728\n",
+      "\n",
+      "final run - output 2 sojourn time frequency distribution - sample size: 3\n",
+      "mean: 1   variance: 0   standard deviation: 0\n",
+      "\n",
+      "output 3 sojourn time distribution\n",
+      "mean: 1.46117   variance: 0.665883   standard deviation: 0.816016\n",
+      "\n",
+      "output 3 sojourn time frequency distribution - sample size: 103\n",
+      "mean: 1.50485   variance: 0.703408   standard deviation: 0.838694\n",
+      "\n",
+      "final run - output 3 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2   variance: 2   standard deviation: 1.41421\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 10.5507   variance: 23.1583   standard deviation: 4.81231\n",
+      "coefficient of skewness: -0.186006   coefficient of kurtosis: -0.737076\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.9   variance: 26.7667   standard deviation: 5.17365\n",
+      "coefficient of skewness: -0.813106   coefficient of kurtosis: -1.11134\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 6.08107   variance: 13.6385   standard deviation: 3.69304\n",
+      "coefficient of skewness: 0.034168   coefficient of kurtosis: -0.685049\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 5.8   variance: 19.2889   standard deviation: 4.39191\n",
+      "coefficient of skewness: 0.688976   coefficient of kurtosis: -0.140103\n",
+      "\n",
+      "number of runs of output 2 per length 100 sequence distribution\n",
+      "mean: 10.3574   variance: 26.4746   standard deviation: 5.14535\n",
+      "coefficient of skewness: -0.259347   coefficient of kurtosis: -0.731147\n",
+      "\n",
+      "number of runs of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.3   variance: 38.0111   standard deviation: 6.16532\n",
+      "coefficient of skewness: -0.426213   coefficient of kurtosis: -1.24839\n",
+      "\n",
+      "number of runs of output 3 per length 100 sequence distribution\n",
+      "mean: 10.6549   variance: 33.1973   standard deviation: 5.76171\n",
+      "coefficient of skewness: -0.320355   coefficient of kurtosis: -0.799926\n",
+      "\n",
+      "number of runs of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 10.7   variance: 39.1222   standard deviation: 6.25478\n",
+      "coefficient of skewness: -0.717339   coefficient of kurtosis: -0.966584\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 63.32   variance: 342.607   standard deviation: 18.5097\n",
+      "coefficient of skewness: 0.510067   coefficient of kurtosis: -0.81747\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 61.4   variance: 517.6   standard deviation: 22.7508\n",
+      "coefficient of skewness: 0.758395   coefficient of kurtosis: -1.06565\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 7.10994   variance: 19.5515   standard deviation: 4.42171\n",
+      "coefficient of skewness: 0.11819   coefficient of kurtosis: -0.605049\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 7.5   variance: 28.9444   standard deviation: 5.38\n",
+      "coefficient of skewness: 0.147165   coefficient of kurtosis: -0.928174\n",
+      "\n",
+      "number of occurrences of output 2 per length 100 sequence distribution\n",
+      "mean: 14.1132   variance: 54.4356   standard deviation: 7.37805\n",
+      "coefficient of skewness: -0.170628   coefficient of kurtosis: -0.735517\n",
+      "\n",
+      "number of occurrences of output 2 per sequence frequency distribution - sample size: 10\n",
+      "mean: 14.8   variance: 86.4   standard deviation: 9.29516\n",
+      "coefficient of skewness: -0.447805   coefficient of kurtosis: -1.44578\n",
+      "\n",
+      "number of occurrences of output 3 per length 100 sequence distribution\n",
+      "mean: 15.4568   variance: 73.4596   standard deviation: 8.57086\n",
+      "coefficient of skewness: -0.243379   coefficient of kurtosis: -0.791275\n",
+      "\n",
+      "number of occurrences of output 3 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.3   variance: 87.5667   standard deviation: 9.35771\n",
+      "coefficient of skewness: -0.945543   coefficient of kurtosis: -0.869543\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_            0.0206296    1.11022e-16  _            _            \n",
+      "_            _            0.0206296    _            0.726796     \n",
+      "_            _            _            0.021765     _            \n",
+      "_            _            _            _            0.72566      \n",
+      "_            _            _            _            _            \n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9999\n",
+      "OUTPUT 1 : 0.0001\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.3713\n",
+      "OUTPUT 1 : 0.6287\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.9882\n",
+      "OUTPUT 1 : 0.0118\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.7655\n",
+      "OUTPUT 1 : 0.2345\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "OUTPUT 0 : 0.2424\n",
+      "OUTPUT 1 : 0.7576\n",
+      "\n",
+      "observation probability matrix\n",
+      "\n",
+      "   0          1          \n",
+      "0  0.99999    1e-05      \n",
+      "1  0.371328   0.628672   \n",
+      "2  0.988283   0.0117166  \n",
+      "3  0.765547   0.234453   \n",
+      "4  0.242439   0.757561   \n",
+      "\n",
+      "theoretical weights: 0.050105 0.0969425 0.160855 0.210187 0.481911\n",
+      "\n",
+      "log-likelihood: -693.367   (normalized: -0.693367)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.764356\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.764902   critical probability: 0.381799\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "restoration weights: 0.051 0.096 0.159 0.186 0.508\n",
+      "\n",
+      "log-likelihood: -692.985   (normalized: -0.692985)\n",
+      "maximum possible log-likelihood: -692.985   (information: -0.692985)\n",
+      "deviance: 0.000448302\n",
+      "\n",
+      "chi-square test (1 degree of freedom)\n",
+      "chi-square value: 0.000448305   critical probability: 0.983107\n",
+      "reference chi-square value: 3.84146   reference critical probability: 0.05\n",
+      "\n",
+      "time up to the first occurrence of output 0 distribution\n",
+      "mean: 0.16065   variance: 0.583715   standard deviation: 0.764012\n",
+      "\n",
+      "time up to the first occurrence of output 0 frequency distribution - sample size: 10\n",
+      "mean: 0.1   variance: 0.1   standard deviation: 0.316228\n",
+      "\n",
+      "time up to the first occurrence of output 1 distribution\n",
+      "mean: 16.9476   variance: 176.602   standard deviation: 13.2892\n",
+      "\n",
+      "time up to the first occurrence of output 1 frequency distribution - sample size: 10\n",
+      "mean: 17.1   variance: 136.989   standard deviation: 11.7042\n",
+      "\n",
+      "output 0 recurrence time distribution\n",
+      "mean: 1.937   variance: 4.78686   standard deviation: 2.18789\n",
+      "\n",
+      "output 0 recurrence time frequency distribution - sample size: 499\n",
+      "mean: 1.95992   variance: 4.902   standard deviation: 2.21405\n",
+      "\n",
+      "output 1 recurrence time distribution\n",
+      "mean: 1.73388   variance: 3.99427   standard deviation: 1.99857\n",
+      "\n",
+      "output 1 recurrence time frequency distribution - sample size: 481\n",
+      "mean: 1.67568   variance: 4.96126   standard deviation: 2.22739\n",
+      "\n",
+      "output 0 sojourn time distribution\n",
+      "mean: 3.2727   variance: 32.2377   standard deviation: 5.67783\n",
+      "\n",
+      "output 0 sojourn time frequency distribution - sample size: 158\n",
+      "mean: 3.13924   variance: 33.127   standard deviation: 5.7556\n",
+      "\n",
+      "final run - output 0 sojourn time frequency distribution - sample size: 6\n",
+      "mean: 2.16667   variance: 3.76667   standard deviation: 1.94079\n",
+      "\n",
+      "output 1 sojourn time distribution\n",
+      "mean: 3.14033   variance: 9.33678   standard deviation: 3.05561\n",
+      "\n",
+      "output 1 sojourn time frequency distribution - sample size: 155\n",
+      "mean: 3.09677   variance: 9.19187   standard deviation: 3.03181\n",
+      "\n",
+      "final run - output 1 sojourn time frequency distribution - sample size: 4\n",
+      "mean: 2.75   variance: 2.91667   standard deviation: 1.70783\n",
+      "\n",
+      "number of runs of output 0 per length 100 sequence distribution\n",
+      "mean: 15.796   variance: 13.5033   standard deviation: 3.67468\n",
+      "coefficient of skewness: -0.167225   coefficient of kurtosis: 0.239642\n",
+      "\n",
+      "number of runs of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 16.4   variance: 9.6   standard deviation: 3.09839\n",
+      "coefficient of skewness: -0.832647   coefficient of kurtosis: -0.205208\n",
+      "\n",
+      "number of runs of output 1 per length 100 sequence distribution\n",
+      "mean: 15.5747   variance: 13.9623   standard deviation: 3.73662\n",
+      "coefficient of skewness: -0.170552   coefficient of kurtosis: 0.290764\n",
+      "\n",
+      "number of runs of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 15.9   variance: 10.5444   standard deviation: 3.24722\n",
+      "coefficient of skewness: -0.107574   coefficient of kurtosis: -0.151205\n",
+      "\n",
+      "number of occurrences of output 0 per length 100 sequence distribution\n",
+      "mean: 52.2812   variance: 270.628   standard deviation: 16.4508\n",
+      "coefficient of skewness: 0.289249   coefficient of kurtosis: -0.607469\n",
+      "\n",
+      "number of occurrences of output 0 per sequence frequency distribution - sample size: 10\n",
+      "mean: 50.9   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: 0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "number of occurrences of output 1 per length 100 sequence distribution\n",
+      "mean: 47.7188   variance: 270.628   standard deviation: 16.4508\n",
+      "coefficient of skewness: -0.289249   coefficient of kurtosis: -0.607469\n",
+      "\n",
+      "number of occurrences of output 1 per sequence frequency distribution - sample size: 10\n",
+      "mean: 49.1   variance: 375.433   standard deviation: 19.3761\n",
+      "coefficient of skewness: -0.41822   coefficient of kurtosis: -1.35608\n",
+      "\n",
+      "distances between observation distributions for consecutive states\n",
+      "_          0.628662   0.0117066  _          _          \n",
+      "_          _          0.616956   _          0.128889   \n",
+      "_          _          _          0.222736   _          \n",
+      "_          _          _          _          0.523108   \n",
+      "_          _          _          _          _          \n",
+      "\n",
+      "sequence length frequency distribution - sample size: 10\n",
+      "mean: 100   variance: 0   standard deviation: 0\n",
+      "\n",
+      "cumulative length: 1000\n",
+      "\n",
+      "information of the sequences in the iid case: -3158.39 (-3.15839)\n",
+      "\n",
+      "log-likelihood of the state sequences: -2309.03   (normalized: -2.30903)\n",
+      "\n",
+      "state sequence entropy: 20.4417   (normalized: 0.0204417)\n",
+      "\n",
+      "log-likelihood of the observed sequences: -2299.49   (normalized: -2.29949)\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (AIC): -4674.98\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (AICc): -4678.06\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (BIC): -4861.47\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (BICc): -4783.88\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (ICL): -4902.36\n",
+      "\n",
+      "38 free parameters   2 * penalyzed log-likelihood (ICLc): -4824.76\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "hsmc1 = Estimate(seq1v, \"HIDDEN_SEMI-MARKOV\", hmsc_init, NbIteration=300)\n",
+    "print(hsmc1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Export states\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openalea.sequence_analysis import ExtractData\n",
+    "seg = ExtractData(hsmc1) # Data and segmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[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], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\n",
+      "[[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, 0, 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Restored states are added as the first variable to seq_1v, see\n",
+    "print(seg[0][0:10])\n",
+    "print(seq1v[0][0:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seg_dir = \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(seg_dir): \n",
+    "    # if the seg_dir directory is not present  \n",
+    "    # then create it. \n",
+    "    os.makedirs(seg_dir) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export into file\n",
+    "\n",
+    "seg_file = \"seq1v_\" + str(nb_states) + \"s_LR_segm.seq\"\n",
+    "output_Rpyseq1_file = seg_dir + os.sep + seg_file[0:-4] + \".csv\"\n",
+    "WriteRSequence(seg, output_Rpyseq1_file, RestoredStatesHeader())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(output_Rpyseq1_file, index_col=0, comment=\"#\", usecols=range(6))\n",
+    "var_names = [' axillary shoot type',' lateral flowering', ' terminal flowering']\n",
+    "df = df[var_names]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add sequence identifiers to DF\n",
+    "seqid = []\n",
+    "for i in range(len(seq1v)):\n",
+    "    seqid += [i+1] * len(seq1v[i])\n",
+    "seqid\n",
+    "df[\"seqid\"] = seqid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seqint.pyseq_data_frame import PySeqDataFrame\n",
+    "pyd = PySeqDataFrame(df, seq_index_name=\"seqid\")\n",
+    "pyd.seq_index_name\n",
+    "pyd.col_to_seq(var_names)\n",
+    "seqc = pyd.get_input_sequence(var_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert(str(seqc[0]) == str(seq1v[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embed HSCM within Model class for automatic parameter visualization\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/scratch/Results/2AmEGv.hsmc\n",
+      "HIDDEN_SEMI-MARKOV_CHAIN\n",
+      "\n",
+      "5 STATES\n",
+      "\n",
+      "INITIAL_PROBABILITIES\n",
+      "\n",
+      "0.694432     0.099998     0.20555     1e-05     9.99999999995e-06\n",
+      "\n",
+      "TRANSITION_PROBABILITIES\n",
+      "\n",
+      "0.0     0.431991     0.567989     1e-05     1.00000000001e-05\n",
+      "\n",
+      "0.0     0.0     0.499998     1e-05     0.499992\n",
+      "\n",
+      "0.0     0.0     0.0     0.99999     9.99999999995e-06\n",
+      "\n",
+      "0.0     0.0     0.0     0.0     1.0\n",
+      "\n",
+      "0.0     0.0     0.0     0.0     1.0\n",
+      "\n",
+      "STATE 0 OCCUPANCY_DISTRIBUTION\n",
+      "\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 2   PARAMETER : 3.48885   PROBABILITY : 0.400848\n",
+      "\n",
+      "STATE 1 OCCUPANCY_DISTRIBUTION\n",
+      "\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 17   PARAMETER : 2.15715   PROBABILITY : 0.229641\n",
+      "\n",
+      "STATE 2 OCCUPANCY_DISTRIBUTION\n",
+      "\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 10   PARAMETER : 0.765176   PROBABILITY : 0.0701318\n",
+      "\n",
+      "STATE 3 OCCUPANCY_DISTRIBUTION\n",
+      "\n",
+      "NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.22179   PROBABILITY : 0.0420701\n",
+      "\n",
+      "3 OUTPUT_PROCESSES\n",
+      "\n",
+      "OUTPUT_PROCESS 1 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.8963\n",
+      "\n",
+      "OUTPUT 1 : 0\n",
+      "\n",
+      "OUTPUT 2 : 0.0244\n",
+      "\n",
+      "OUTPUT 3 : 0\n",
+      "\n",
+      "OUTPUT 4 : 0.0793\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.2577\n",
+      "\n",
+      "OUTPUT 1 : 0.5061\n",
+      "\n",
+      "OUTPUT 2 : 0.2141\n",
+      "\n",
+      "OUTPUT 3 : 0.0113\n",
+      "\n",
+      "OUTPUT 4 : 0.0108\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.3134\n",
+      "\n",
+      "OUTPUT 1 : 0.6256\n",
+      "\n",
+      "OUTPUT 2 : 0.0458\n",
+      "\n",
+      "OUTPUT 3 : 0.015\n",
+      "\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.1746\n",
+      "\n",
+      "OUTPUT 1 : 0.2133\n",
+      "\n",
+      "OUTPUT 2 : 0.4221\n",
+      "\n",
+      "OUTPUT 3 : 0.1897\n",
+      "\n",
+      "OUTPUT 4 : 0.0003\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.1605\n",
+      "\n",
+      "OUTPUT 1 : 0.0315\n",
+      "\n",
+      "OUTPUT 2 : 0.2342\n",
+      "\n",
+      "OUTPUT 3 : 0.5736\n",
+      "\n",
+      "OUTPUT 4 : 0.0002\n",
+      "\n",
+      "OUTPUT_PROCESS 2 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9999\n",
+      "\n",
+      "OUTPUT 1 : 0\n",
+      "\n",
+      "OUTPUT 2 : 0\n",
+      "\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9793\n",
+      "\n",
+      "OUTPUT 1 : 0\n",
+      "\n",
+      "OUTPUT 2 : 0.0103\n",
+      "\n",
+      "OUTPUT 3 : 0.0104\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9999\n",
+      "\n",
+      "OUTPUT 1 : 0\n",
+      "\n",
+      "OUTPUT 2 : 0\n",
+      "\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9782\n",
+      "\n",
+      "OUTPUT 1 : 0\n",
+      "\n",
+      "OUTPUT 2 : 0.0217\n",
+      "\n",
+      "OUTPUT 3 : 0.0001\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.2525\n",
+      "\n",
+      "OUTPUT 1 : 0.1475\n",
+      "\n",
+      "OUTPUT 2 : 0.2812\n",
+      "\n",
+      "OUTPUT 3 : 0.3188\n",
+      "\n",
+      "OUTPUT_PROCESS 3 : CATEGORICAL\n",
+      "\n",
+      "STATE 0 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9999\n",
+      "\n",
+      "OUTPUT 1 : 0.0001\n",
+      "\n",
+      "STATE 1 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.3713\n",
+      "\n",
+      "OUTPUT 1 : 0.6287\n",
+      "\n",
+      "STATE 2 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.9882\n",
+      "\n",
+      "OUTPUT 1 : 0.0118\n",
+      "\n",
+      "STATE 3 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.7655\n",
+      "\n",
+      "OUTPUT 1 : 0.2345\n",
+      "\n",
+      "STATE 4 OBSERVATION_DISTRIBUTION\n",
+      "\n",
+      "OUTPUT 0 : 0.2424\n",
+      "\n",
+      "OUTPUT 1 : 0.7576\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_path = base_path + os.sep + \"Results\" \n",
+    "model = Model(pyd, output_process_name=var_names, init_hsmc_file=init_file, output_path=output_path)\n",
+    "model.iterate_em(300)\n",
+    "model.hsmm.save(os.path.join(output_path, 'seq1v_' + str(nb_states) + 's_LR.hsmc'))\n",
+    "model.print_hsmc_file(verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:*** ERROR : non-existing characteristic distribution\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from seqint import html_report\n",
+    "\n",
+    "output_path = \".\" + os.sep + \"tmp_dir\"\n",
+    "\n",
+    "if not os.path.exists(output_path):\n",
+    "    os.mkdir(output_path)\n",
+    "    \n",
+    "report = html_report.Htmlreport(model, output_path=output_path)\n",
+    "report.make_html(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Removing some garbage files*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "report_prefix = report._html_report_file_path[0:-11]\n",
+    "report_prefix = report_prefix.split(\"/\")[2]\n",
+    "import glob\n",
+    "for f in glob.glob(\".\" +os.sep +  \"*.dat\"):\n",
+    "    os.remove(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Report printed in ./tmp_dir/2AmEGv-report.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Report printed in \" + str(report._html_report_file_path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "#shutil.rmtree(tempdir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dill\n",
+    "# dill.dump_session('notebook_env.db')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sequence_analysis/tutorials/sim_v_5s_LR.hsmc b/sequence_analysis/tutorials/sim_v_5s_LR.hsmc
new file mode 100755
index 00000000..d2af7aa3
--- /dev/null
+++ b/sequence_analysis/tutorials/sim_v_5s_LR.hsmc
@@ -0,0 +1,172 @@
+HIDDEN_SEMI-MARKOV_CHAIN
+
+5 STATES
+
+INITIAL_PROBABILITIES
+0.983184     0.0167856    1e-005       1e-005       1e-005
+
+TRANSITION_PROBABILITIES
+0            0.344161     0.655819     1e-005       1e-005
+0            0            0.529125     1e-005       0.470865
+0            0            0            0.999756     0.000243555
+0            0            0            0            1
+0            0            0            0            1
+
+STATE 0 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 1.214   PROBABILITY : 0.164955
+# mean: 7.14556   variance: 37.2559   standard deviation: 6.10376
+# coefficient of skewness: 1.82256   coefficient of kurtosis: 4.96919
+# coefficient of variation: 0.854203
+
+# state 0 sojourn time frequency distribution - sample size: 116
+# mean: 7.50862   variance: 42.7391   standard deviation: 6.53751
+
+# final run - state 0 sojourn time frequency distribution - sample size: 0
+
+STATE 1 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 9.51496   PROBABILITY : 0.318924
+# mean: 21.3196   variance: 63.7131   standard deviation: 7.98205
+# coefficient of skewness: 0.660368   coefficient of kurtosis: 0.646281
+# coefficient of variation: 0.374399
+
+# state 1 sojourn time frequency distribution - sample size: 43
+# mean: 21.2326   variance: 67.3732   standard deviation: 8.20812
+
+# final run - state 1 sojourn time frequency distribution - sample size: 0
+
+STATE 2 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 4.28511   PROBABILITY : 0.180089
+# mean: 20.5092   variance: 108.331   standard deviation: 10.4082
+# coefficient of skewness: 0.970925   coefficient of kurtosis: 1.40943
+# coefficient of variation: 0.507489
+
+# state 2 sojourn time frequency distribution - sample size: 95
+# mean: 20.6421   variance: 107.658   standard deviation: 10.3758
+
+# final run - state 2 sojourn time frequency distribution - sample size: 1
+# mean: 32   variance: 0   standard deviation: 0
+
+STATE 3 OCCUPANCY_DISTRIBUTION
+NEGATIVE_BINOMIAL   INF_BOUND : 1   PARAMETER : 0.670372   PROBABILITY : 0.0389384
+# mean: 17.5459   variance: 424.925   standard deviation: 20.6137
+# coefficient of skewness: 2.44319   coefficient of kurtosis: 8.95261
+# coefficient of variation: 1.17485
+
+# state 3 sojourn time frequency distribution - sample size: 55
+# mean: 5.6   variance: 30.6519   standard deviation: 5.53641
+
+# final run - state 3 sojourn time frequency distribution - sample size: 40
+# mean: 14.4   variance: 26.4   standard deviation: 5.13809
+
+
+3 OUTPUT_PROCESSES
+
+OUTPUT_PROCESS 1 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.837599
+OUTPUT 1 : 0.043112
+OUTPUT 2 : 0.00187668
+OUTPUT 3 : 0.0144115
+OUTPUT 4 : 0.103
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.198527
+OUTPUT 1 : 0.657862
+OUTPUT 2 : 0.108052
+OUTPUT 3 : 0.0299297
+OUTPUT 4 : 0.00562959
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.376836
+OUTPUT 1 : 0.57858
+OUTPUT 2 : 0.0352186
+OUTPUT 3 : 0.00909654
+OUTPUT 4 : 0.000268293
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.124423
+OUTPUT 1 : 0.22101
+OUTPUT 2 : 0.462468
+OUTPUT 3 : 0.192089
+OUTPUT 4 : 1e-005
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.145512
+OUTPUT 1 : 0.0419245
+OUTPUT 2 : 0.206654
+OUTPUT 3 : 0.6059
+OUTPUT 4 : 1e-005
+
+# observation probability matrix
+
+#    0            1            2            3            4
+# 0  0.837599     0.043112     0.00187668   0.0144115    0.103
+# 1  0.198527     0.657862     0.108052     0.0299297    0.00562959
+# 2  0.376836     0.57858      0.0352186    0.00909654   0.000268293
+# 3  0.124423     0.22101      0.462468     0.192089     1e-005
+# 4  0.145512     0.0419245    0.206654     0.6059       1e-005
+
+OUTPUT_PROCESS 2 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.995312
+OUTPUT 1 : 0.0012052
+OUTPUT 2 : 0.0034729
+OUTPUT 3 : 1e-005
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.969859
+OUTPUT 1 : 0.00451974
+OUTPUT 2 : 0.0107587
+OUTPUT 3 : 0.0148629
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.99997
+OUTPUT 1 : 1e-005
+OUTPUT 2 : 1e-005
+OUTPUT 3 : 1e-005
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.975647
+OUTPUT 1 : 0.00283823
+OUTPUT 2 : 0.0215051
+OUTPUT 3 : 1e-005
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.251944
+OUTPUT 1 : 0.147155
+OUTPUT 2 : 0.273018
+OUTPUT 3 : 0.327883
+
+OUTPUT_PROCESS 3 : CATEGORICAL
+
+STATE 0 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.99889
+OUTPUT 1 : 0.00110962
+
+STATE 1 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.301492
+OUTPUT 1 : 0.698508
+
+STATE 2 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.985937
+OUTPUT 1 : 0.014063
+
+STATE 3 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.742475
+OUTPUT 1 : 0.257525
+
+STATE 4 OBSERVATION_DISTRIBUTION
+OUTPUT 0 : 0.244794
+OUTPUT 1 : 0.755206
+
+# observation probability matrix
+
+#    0           1
+# 0  0.99889     0.00110962
+# 1  0.301492    0.698508
+# 2  0.985937    0.014063
+# 3  0.742475    0.257525
+# 4  0.244794    0.755206
+