From 4b2495604ab6e6238fa405070d21238842cf55a9 Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 15 Apr 2020 21:30:31 -0400
Subject: [PATCH 1/7] feat: weights are kept in memory instead of being written
 to disk.

---
 xesmf/backend.py             |  6 ++-
 xesmf/frontend.py            | 76 ++++++++++++------------------------
 xesmf/smm.py                 | 27 ++++++++-----
 xesmf/tests/test_frontend.py | 61 +++--------------------------
 4 files changed, 54 insertions(+), 116 deletions(-)

diff --git a/xesmf/backend.py b/xesmf/backend.py
index 27de0849..61f67404 100644
--- a/xesmf/backend.py
+++ b/xesmf/backend.py
@@ -215,7 +215,8 @@ def esmf_regrid_build(sourcegrid, destgrid, method,
         Offline weight file. **Require ESMPy 7.1.0.dev38 or newer.**
         With the weights available, we can use Scipy's sparse matrix
         mulplication to apply weights, which is faster and more Pythonic
-        than ESMPy's online regridding.
+        than ESMPy's online regridding. If None, weights are stored in
+        memory only.
 
     extra_dims : a list of integers, optional
         Extra dimensions (e.g. time or levels) in the data field
@@ -277,7 +278,8 @@ def esmf_regrid_build(sourcegrid, destgrid, method,
     regrid = ESMF.Regrid(sourcefield, destfield, filename=filename,
                          regrid_method=esmf_regrid_method,
                          unmapped_action=ESMF.UnmappedAction.IGNORE,
-                         ignore_degenerate=ignore_degenerate)
+                         ignore_degenerate=ignore_degenerate,
+                         factors=True)
 
     return regrid
 
diff --git a/xesmf/frontend.py b/xesmf/frontend.py
index 096a1e4b..2f07646e 100644
--- a/xesmf/frontend.py
+++ b/xesmf/frontend.py
@@ -104,7 +104,7 @@ def ds_to_ESMFlocstream(ds):
 
 class Regridder(object):
     def __init__(self, ds_in, ds_out, method, periodic=False,
-                 filename=None, reuse_weights=False, ignore_degenerate=None,
+                 weights=None, ignore_degenerate=None,
                  locstream_in=False, locstream_out=False):
         """
         Make xESMF regridder
@@ -134,16 +134,13 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
             Only useful for global grids with non-conservative regridding.
             Will be forced to False for conservative regridding.
 
-        filename : str, optional
-            Name for the weight file. The default naming scheme is::
-
-                {method}_{Ny_in}x{Nx_in}_{Ny_out}x{Nx_out}.nc
-
-            e.g. bilinear_400x600_300x400.nc
-
-        reuse_weights : bool, optional
-            Whether to read existing weight file to save computing time.
-            False by default (i.e. re-compute, not reuse).
+        weights : None, coo_matrix, dict, str, Dataset, Path,
+            Regridding weights, stored as
+              - a scipy.sparse COO matrix,
+              - a dictionary with keys `row_dst`, `col_src` and `weights`,
+              - an xarray Dataset with data variables `col`, `row` and `S`,
+              - or a path to a netCDF file created by ESMF.
+            If None, compute the weights.
 
         ignore_degenerate : bool, optional
             If False (default), raise error if grids contain degenerated cells
@@ -170,7 +167,6 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
 
         self.method = method
         self.periodic = periodic
-        self.reuse_weights = reuse_weights
         self.ignore_degenerate = ignore_degenerate
         self.locstream_in = locstream_in
         self.locstream_out = locstream_out
@@ -226,14 +222,11 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
         self.n_in = shape_in[0] * shape_in[1]
         self.n_out = shape_out[0] * shape_out[1]
 
-        if filename is None:
-            self.filename = self._get_default_filename()
-        else:
-            self.filename = filename
+        if weights is None:
+            weights = self._compute_weights()  # Dictionary of weights
 
-        # get weight matrix
-        self._write_weight_file()
-        self.weights = read_weights(self.filename, self.n_in, self.n_out)
+        # Convert weights, whatever their format, to a sparse coo matrix
+        self.weights = read_weights(weights, self.n_in, self.n_out)
 
     @property
     def A(self):
@@ -261,49 +254,22 @@ def _get_default_filename(self):
 
         return filename
 
-    def _write_weight_file(self):
-
-        if os.path.exists(self.filename):
-            if self.reuse_weights:
-                print('Reuse existing file: {}'.format(self.filename))
-                return  # do not compute it again, just read it
-            else:
-                print('Overwrite existing file: {} \n'.format(self.filename),
-                      'You can set reuse_weights=True to save computing time.')
-                os.remove(self.filename)
-        else:
-            print('Create weight file: {}'.format(self.filename))
-
+    def _compute_weights(self):
         regrid = esmf_regrid_build(self._grid_in, self._grid_out, self.method,
-                                   filename=self.filename,
                                    ignore_degenerate=self.ignore_degenerate)
-        esmf_regrid_finalize(regrid)  # only need weights, not regrid object
 
-    def clean_weight_file(self):
-        """
-        Remove the offline weight file on disk.
-
-        To save the time on re-computing weights, you can just keep the file,
-        and set "reuse_weights=True" when initializing the regridder next time.
-        """
-        if os.path.exists(self.filename):
-            print("Remove file {}".format(self.filename))
-            os.remove(self.filename)
-        else:
-            print("File {} is already removed.".format(self.filename))
+        w = regrid.get_weights_dict(deep_copy=True)
+        esmf_regrid_finalize(regrid)  # only need weights, not regrid object
+        return w
 
     def __repr__(self):
         info = ('xESMF Regridder \n'
                 'Regridding algorithm:       {} \n'
-                'Weight filename:            {} \n'
-                'Reuse pre-computed weights? {} \n'
                 'Input grid shape:           {} \n'
                 'Output grid shape:          {} \n'
                 'Output grid dimension name: {} \n'
                 'Periodic in longitude?      {}'
                 .format(self.method,
-                        self.filename,
-                        self.reuse_weights,
                         self.shape_in,
                         self.shape_out,
                         self.out_horiz_dims,
@@ -509,3 +475,13 @@ def regrid_dataset(self, ds_in, keep_attrs=False):
             ds_out = ds_out.squeeze(dim='dummy')
 
         return ds_out
+
+    def to_netcdf(self, filename=None):
+        '''Save weights to disk as a netCDF file.'''
+        if filename is None:
+            filename = self._get_default_filename()
+        w = self.weights
+        ds = xr.Dataset({"S": w.data, "col": w.col + 1, "row": w.row + 1})
+        ds.to_netcdf(filename)
+        return filename
+
diff --git a/xesmf/smm.py b/xesmf/smm.py
index 55a3c90e..0a022808 100644
--- a/xesmf/smm.py
+++ b/xesmf/smm.py
@@ -5,9 +5,10 @@
 import xarray as xr
 import scipy.sparse as sps
 import warnings
+from pathlib import Path
 
 
-def read_weights(filename, n_in, n_out):
+def read_weights(weights, n_in, n_out):
     '''
     Read regridding weights into a scipy sparse COO matrix.
 
@@ -31,14 +32,22 @@ def read_weights(filename, n_in, n_out):
     A : scipy sparse COO matrix.
 
     '''
-    ds_w = xr.open_dataset(filename)
-
-    col = ds_w['col'].values - 1  # Python starts with 0
-    row = ds_w['row'].values - 1
-    S = ds_w['S'].values
-
-    weights = sps.coo_matrix((S, (row, col)), shape=[n_out, n_in])
-    return weights
+    if isinstance(weights, (str, Path, xr.Dataset)):
+        if not isinstance(weights, xr.Dataset):
+            ds_w = xr.open_dataset(weights)
+        col = ds_w['col'].values - 1  # Python starts with 0
+        row = ds_w['row'].values - 1
+        S = ds_w['S'].values
+
+    elif isinstance(weights, dict):
+        col = weights['col_src'] - 1
+        row = weights['row_dst'] - 1
+        S = weights['weights']
+
+    elif isinstance(weights, sps.coo_matrix):
+        return weights
+
+    return sps.coo_matrix((S, (row, col)), shape=[n_out, n_in])
 
 
 def apply_weights(weights, indata, shape_in, shape_out):
diff --git a/xesmf/tests/test_frontend.py b/xesmf/tests/test_frontend.py
index 32d3b7de..386acba8 100644
--- a/xesmf/tests/test_frontend.py
+++ b/xesmf/tests/test_frontend.py
@@ -50,7 +50,7 @@ def test_as_2d_mesh():
 methods_list = ['bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s']
 
 @pytest.mark.parametrize("locstream_in,locstream_out,method", [
-                         (False, False, 'conservative'), 
+                         (False, False, 'conservative'),
                          (False, False, 'bilinear'),
                          (False, True, 'bilinear'),
                          (False, False, 'nearest_s2d'),
@@ -75,27 +75,22 @@ def test_build_regridder(method, locstream_in, locstream_out):
     assert 'xESMF Regridder' in str(regridder)
     assert method in str(regridder)
 
-    regridder.clean_weight_file()
-
 
 def test_existing_weights():
     # the first run
     method = 'bilinear'
     regridder = xe.Regridder(ds_in, ds_out, method)
+    fn = regridder.to_netcdf()
 
     # make sure we can reuse weights
-    assert os.path.exists(regridder.filename)
+    assert os.path.exists(fn)
     regridder_reuse = xe.Regridder(ds_in, ds_out, method,
-                                   reuse_weights=True)
+                                   weights=fn)
     assert regridder_reuse.A.shape == regridder.A.shape
 
     # or can also overwrite it
     xe.Regridder(ds_in, ds_out, method)
 
-    # clean-up
-    regridder.clean_weight_file()
-    assert not os.path.exists(regridder.filename)
-
 
 def test_conservative_without_bounds():
     with pytest.raises(KeyError):
@@ -110,7 +105,6 @@ def test_build_regridder_from_dict():
     regridder = xe.Regridder({'lon': lon_in, 'lat': lat_in},
                              {'lon': lon_out, 'lat': lat_out},
                              'bilinear')
-    regridder.clean_weight_file()
 
 
 def test_regrid_periodic_wrong():
@@ -123,9 +117,6 @@ def test_regrid_periodic_wrong():
     rel_err = (ds_out['data_ref'] - dr_out)/ds_out['data_ref']
     assert np.max(np.abs(rel_err)) == 1.0  # some data will be missing
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_periodic_correct():
     regridder = xe.Regridder(ds_in, ds_out, 'bilinear', periodic=True)
@@ -136,9 +127,6 @@ def test_regrid_periodic_correct():
     rel_err = (ds_out['data_ref'] - dr_out)/ds_out['data_ref']
     assert np.max(np.abs(rel_err)) < 0.065
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def ds_2d_to_1d(ds):
     ds_temp = ds.reset_coords()
@@ -164,9 +152,6 @@ def test_regrid_with_1d_grid():
     assert_equal(dr_out['lon'].values, ds_out_1d['lon'].values)
     assert_equal(dr_out['lat'].values, ds_out_1d['lat'].values)
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 # TODO: consolidate (regrid method, input data types) combination
 # using pytest fixtures and parameterization
@@ -202,9 +187,6 @@ def test_regrid_dataarray():
     xr.testing.assert_identical(dr_out_4D['time'], ds_in['time'])
     xr.testing.assert_identical(dr_out_4D['lev'], ds_in['lev'])
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dataarray_to_locstream():
     # xarray.DataArray containing in-memory numpy array
@@ -217,9 +199,6 @@ def test_regrid_dataarray_to_locstream():
     # DataArray and numpy array should lead to the same result
     assert_equal(outdata.squeeze(), dr_out.values)
 
-    # clean-up
-    regridder.clean_weight_file()
-
     with pytest.raises(ValueError):
         regridder = xe.Regridder(ds_in, ds_locs, 'conservative', locstream_out=True)
 
@@ -235,9 +214,6 @@ def test_regrid_dataarray_from_locstream():
     # DataArray and numpy array should lead to the same result
     assert_equal(outdata, dr_out.values)
 
-    # clean-up
-    regridder.clean_weight_file()
-
     with pytest.raises(ValueError):
         regridder = xe.Regridder(ds_locs, ds_in, 'bilinear', locstream_in=True)
     with pytest.raises(ValueError):
@@ -262,9 +238,6 @@ def test_regrid_dask():
     rel_err = (outdata.compute() - outdata_ref) / outdata_ref
     assert np.max(np.abs(rel_err)) < 0.05
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dask_to_locstream():
     # chunked dask array (no xarray metadata)
@@ -274,19 +247,13 @@ def test_regrid_dask_to_locstream():
     indata = ds_in_chunked['data4D'].data
     outdata = regridder(indata)
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dask_from_locstream():
     # chunked dask array (no xarray metadata)
 
     regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)
 
-    outdata = regridder(ds_locs['lat'].data) 
-
-    # clean-up
-    regridder.clean_weight_file()
+    outdata = regridder(ds_locs['lat'].data)
 
 
 def test_regrid_dataarray_dask():
@@ -311,9 +278,6 @@ def test_regrid_dataarray_dask():
     assert_equal(dr_out['lat'].values, ds_out['lat'].values)
     assert_equal(dr_out['lon'].values, ds_out['lon'].values)
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dataarray_dask_to_locstream():
     # xarray.DataArray containing chunked dask array
@@ -323,19 +287,13 @@ def test_regrid_dataarray_dask_to_locstream():
     dr_in = ds_in_chunked['data4D']
     dr_out = regridder(dr_in)
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dataarray_dask_from_locstream():
     # xarray.DataArray containing chunked dask array
 
     regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)
 
-    outdata = regridder(ds_locs['lat']) 
-
-    # clean-up
-    regridder.clean_weight_file()
+    outdata = regridder(ds_locs['lat'])
 
 
 def test_regrid_dataset():
@@ -365,17 +323,12 @@ def test_regrid_dataset():
     assert_equal(ds_result['lat'].values, ds_out['lat'].values)
     assert_equal(ds_result['lon'].values, ds_out['lon'].values)
 
-    # clean-up
-    regridder.clean_weight_file()
-
 
 def test_regrid_dataset_to_locstream():
     # xarray.Dataset containing in-memory numpy array
 
     regridder = xe.Regridder(ds_in, ds_locs, 'bilinear', locstream_out=True)
     ds_result = regridder(ds_in)
-    # clean-up
-    regridder.clean_weight_file()
 
 
 def test_regrid_dataset_from_locstream():
@@ -383,8 +336,6 @@ def test_regrid_dataset_from_locstream():
 
     regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)
     outdata = regridder(ds_locs)
-    # clean-up
-    regridder.clean_weight_file()
 
 
 def test_ds_to_ESMFlocstream():

From cb45eb9d87a982fa82e95e94c5515d131905a4be Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 27 May 2020 14:58:31 -0400
Subject: [PATCH 2/7] added n_s dimension to netcdf weights file. added error
 messages in read_weights. added tests.

---
 xesmf/backend.py             |  4 ++--
 xesmf/frontend.py            |  3 ++-
 xesmf/smm.py                 | 27 ++++++++++++++++++++-------
 xesmf/tests/test_backend.py  | 24 ++++++++++++++++++++++++
 xesmf/tests/test_frontend.py | 21 +++++++++++++++++++++
 5 files changed, 69 insertions(+), 10 deletions(-)

diff --git a/xesmf/backend.py b/xesmf/backend.py
index 61f67404..358b25a6 100644
--- a/xesmf/backend.py
+++ b/xesmf/backend.py
@@ -214,7 +214,7 @@ def esmf_regrid_build(sourcegrid, destgrid, method,
     filename : str, optional
         Offline weight file. **Require ESMPy 7.1.0.dev38 or newer.**
         With the weights available, we can use Scipy's sparse matrix
-        mulplication to apply weights, which is faster and more Pythonic
+        multiplication to apply weights, which is faster and more Pythonic
         than ESMPy's online regridding. If None, weights are stored in
         memory only.
 
@@ -279,7 +279,7 @@ def esmf_regrid_build(sourcegrid, destgrid, method,
                          regrid_method=esmf_regrid_method,
                          unmapped_action=ESMF.UnmappedAction.IGNORE,
                          ignore_degenerate=ignore_degenerate,
-                         factors=True)
+                         factors=filename is None)
 
     return regrid
 
diff --git a/xesmf/frontend.py b/xesmf/frontend.py
index 2f07646e..d8b042e8 100644
--- a/xesmf/frontend.py
+++ b/xesmf/frontend.py
@@ -481,7 +481,8 @@ def to_netcdf(self, filename=None):
         if filename is None:
             filename = self._get_default_filename()
         w = self.weights
-        ds = xr.Dataset({"S": w.data, "col": w.col + 1, "row": w.row + 1})
+        dim = "n_s"
+        ds = xr.Dataset({"S": (dim, w.data), "col": (dim, w.col + 1), "row": (dim, w.row + 1)})
         ds.to_netcdf(filename)
         return filename
 
diff --git a/xesmf/smm.py b/xesmf/smm.py
index 0a022808..faeed6ed 100644
--- a/xesmf/smm.py
+++ b/xesmf/smm.py
@@ -9,13 +9,14 @@
 
 
 def read_weights(weights, n_in, n_out):
-    '''
+    """
     Read regridding weights into a scipy sparse COO matrix.
 
     Parameters
     ----------
-    filename : str
-        Offline weight file generated by ESMPy.
+    weights : str, Path, xr.Dataset
+        Weights generated by ESMF. Can be a path to a netCDF file generated by ESMF, an xarray.Dataset,
+        or a dictionary created by `ESMPy.api.Regrid.get_weights_dict`.
 
     N_in, N_out : integers
         ``(N_out, N_in)`` will be the shape of the returning sparse matrix.
@@ -24,22 +25,34 @@ def read_weights(weights, n_in, n_out):
               N_in = Nx_in * Ny_in
               N_out = Nx_out * Ny_out
 
-        We need them because the shape cannot always be infered from the
+        We need them because the shape cannot always be inferred from the
         largest column and row indices, due to unmapped grid boxes.
 
     Returns
     -------
-    A : scipy sparse COO matrix.
-
-    '''
+    scipy.sparse.coo_matrix
+      Sparse weights matrix.
+    """
     if isinstance(weights, (str, Path, xr.Dataset)):
         if not isinstance(weights, xr.Dataset):
+            if not Path(weights).exists():
+                raise IOError(f"Weights file not found on disk.\n{weights}")
             ds_w = xr.open_dataset(weights)
+        else:
+            ds_w = weights
+
+        if not set(['col', 'row', 'S']).issubset(ds_w.variables):
+            raise ValueError("Weights dataset should have variables `col`, `row` and `S` storing the indices and "
+                                 "values of weights.")
+
         col = ds_w['col'].values - 1  # Python starts with 0
         row = ds_w['row'].values - 1
         S = ds_w['S'].values
 
     elif isinstance(weights, dict):
+        if not set(['col_src', 'row_dst', 'weights']).issubset(weights.keys()):
+            raise ValueError("Weights dictionary should have keys `col_src`, `row_dst` and `weights` storing the "
+                             "indices and values of weights.")
         col = weights['col_src'] - 1
         row = weights['row_dst'] - 1
         S = weights['weights']
diff --git a/xesmf/tests/test_backend.py b/xesmf/tests/test_backend.py
index c7cf848b..be848631 100644
--- a/xesmf/tests/test_backend.py
+++ b/xesmf/tests/test_backend.py
@@ -223,3 +223,27 @@ def test_esmf_locstream():
         ls = esmf_locstream(lon, lat2d)
     with pytest.raises(ValueError):
         ls = esmf_locstream(lon2d, lat)
+
+
+def test_read_weights(tmp_path):
+    fn = tmp_path / "weights.nc"
+
+    grid_in = esmf_grid(lon_in.T, lat_in.T)
+    grid_out = esmf_grid(lon_out.T, lat_out.T)
+
+    regrid_memory = esmf_regrid_build(grid_in, grid_out, method='bilinear')
+    esmf_regrid_build(grid_in, grid_out, method='bilinear', filename=str(fn))
+
+    w = regrid_memory.get_weights_dict(deep_copy=True)
+    sm = read_weights(w, lon_in.size, lon_out.size).todense()
+
+    # Test Path and string to netCDF file against weights dictionary
+    np.testing.assert_array_equal(read_weights(fn, lon_in.size, lon_out.size).todense(), sm)
+    np.testing.assert_array_equal(read_weights(str(fn), lon_in.size, lon_out.size).todense(), sm)
+
+    # Test failures
+    with pytest.raises(IOError):
+        read_weights(tmp_path / "wrong_file.nc", lon_in.size, lon_out.size)
+
+    with pytest.raises(ValueError):
+        read_weights({}, lon_in.size, lon_out.size)
diff --git a/xesmf/tests/test_frontend.py b/xesmf/tests/test_frontend.py
index 386acba8..98b43239 100644
--- a/xesmf/tests/test_frontend.py
+++ b/xesmf/tests/test_frontend.py
@@ -92,6 +92,27 @@ def test_existing_weights():
     xe.Regridder(ds_in, ds_out, method)
 
 
+def test_to_netcdf(tmp_path):
+    from xesmf.backend import esmf_grid, esmf_regrid_build
+
+    # Let the frontend write the weights to disk
+    xfn = tmp_path / 'ESMF_weights.nc'
+    method = 'bilinear'
+    regridder = xe.Regridder(ds_in, ds_out, method)
+    regridder.to_netcdf(filename=xfn)
+
+    grid_in = esmf_grid(ds_in['lon'].values.T, ds_in['lat'].values.T)
+    grid_out = esmf_grid(ds_out['lon'].values.T, ds_out['lat'].values.T)
+
+    # Let the ESMPy backend write the weights to disk
+    efn = tmp_path / 'weights.nc'
+    regrid = esmf_regrid_build(grid_in, grid_out, method=method, filename=str(efn))
+
+    x = xr.open_dataset(xfn)
+    e = xr.open_dataset(efn)
+    xr.testing.assert_identical(x, e)
+
+
 def test_conservative_without_bounds():
     with pytest.raises(KeyError):
         xe.Regridder(ds_in.drop_vars('lon_b'), ds_out, 'conservative')

From b6372874b3a07798d4d335fd58e7e8834b70fbd0 Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 27 May 2020 15:14:54 -0400
Subject: [PATCH 3/7] update `Reuse_regridder` notebook

---
 doc/notebooks/Reuse_regridder.ipynb | 1754 ++++++++++++++++++++++++++-
 1 file changed, 1687 insertions(+), 67 deletions(-)

diff --git a/doc/notebooks/Reuse_regridder.ipynb b/doc/notebooks/Reuse_regridder.ipynb
index 83b8d02f..b664a83b 100644
--- a/doc/notebooks/Reuse_regridder.ipynb
+++ b/doc/notebooks/Reuse_regridder.ipynb
@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -50,14 +48,375 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
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+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       " */\n",
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+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
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+       "\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
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+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
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+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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+       "\n",
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+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
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+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\n",
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+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
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+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
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+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
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+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b1f66790-81a2-47ed-ab7c-08e9e6d5505e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1f66790-81a2-47ed-ab7c-08e9e6d5505e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-25e90012-a93b-4354-b04b-b331eff8dd50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25e90012-a93b-4354-b04b-b331eff8dd50' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-0ca5269e-d52e-47cf-92c7-b7d901e674a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca5269e-d52e-47cf-92c7-b7d901e674a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ea866c0-1193-44ff-8733-0faf940bfccf' class='xr-var-data-in' type='checkbox'><label for='data-7ea866c0-1193-44ff-8733-0faf940bfccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       ...,\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-b7b094b8-bfad-4184-b580-3ad29460fa1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7b094b8-bfad-4184-b580-3ad29460fa1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e384dc6-8052-40ab-9df1-2d61a374a64d' class='xr-var-data-in' type='checkbox'><label for='data-7e384dc6-8052-40ab-9df1-2d61a374a64d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
+       "       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\n",
+       "       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\n",
+       "       ...,\n",
+       "       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\n",
+       "       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\n",
+       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-02a7842a-900b-4848-80ac-0b01bd6c5205' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02a7842a-900b-4848-80ac-0b01bd6c5205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a3205c4-9821-4635-b7f7-345b7f8b3261' class='xr-var-data-in' type='checkbox'><label for='data-9a3205c4-9821-4635-b7f7-345b7f8b3261' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       ...,\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-1ab79659-f147-4a3f-9098-1a873d721de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ab79659-f147-4a3f-9098-1a873d721de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df3f0d43-7914-45d3-9e7f-d5b9fdc98a8f' class='xr-var-data-in' type='checkbox'><label for='data-df3f0d43-7914-45d3-9e7f-d5b9fdc98a8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\n",
+       "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
+       "       ...,\n",
+       "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
+       "       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\n",
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b545b64c-19b9-423b-bd6d-049407ec5c77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b545b64c-19b9-423b-bd6d-049407ec5c77' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7a6d1c6a-def9-40ef-912c-59163b9b35a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a6d1c6a-def9-40ef-912c-59163b9b35a0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (x: 600, x_b: 601, y: 400, y_b: 401)\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 -118.2 -117.8 -117.4 ...\n",
-       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 ...\n",
-       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 -118.8 -118.4 -118.0 ...\n",
-       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
+       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 ... 119.0 119.4 119.8\n",
+       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 ... 59.85 59.85 59.85\n",
+       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 ... 119.2 119.6 120.0\n",
+       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\n",
        "Dimensions without coordinates: x, x_b, y, y_b\n",
        "Data variables:\n",
        "    *empty*"
@@ -81,14 +440,375 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fe2cfedc-8ded-4fcd-ac50-06df9bfcbd83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe2cfedc-8ded-4fcd-ac50-06df9bfcbd83' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 400</li><li><span>x_b</span>: 401</li><li><span>y</span>: 300</li><li><span>y_b</span>: 301</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9a3e5e36-2d78-4404-85b5-7ac5a1ad656a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a3e5e36-2d78-4404-85b5-7ac5a1ad656a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.7 -119.1 ... 119.1 119.7</div><input id='attrs-a3a9c935-fc24-4c10-8948-ef458b60dfd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3a9c935-fc24-4c10-8948-ef458b60dfd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578b17a5-e0c7-4b2f-ba1b-87ed1e6da4cc' class='xr-var-data-in' type='checkbox'><label for='data-578b17a5-e0c7-4b2f-ba1b-87ed1e6da4cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "       ...,\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.8 -59.8 -59.8 ... 59.8 59.8</div><input id='attrs-78ba0346-a9ff-4a8a-8db7-7b9b1e0cf008' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78ba0346-a9ff-4a8a-8db7-7b9b1e0cf008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74c2bcad-54cf-400c-b3c0-07c51a9a3887' class='xr-var-data-in' type='checkbox'><label for='data-74c2bcad-54cf-400c-b3c0-07c51a9a3887' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.8, -59.8, -59.8, ..., -59.8, -59.8, -59.8],\n",
+       "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
+       "       [-59. , -59. , -59. , ..., -59. , -59. , -59. ],\n",
+       "       ...,\n",
+       "       [ 59. ,  59. ,  59. , ...,  59. ,  59. ,  59. ],\n",
+       "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
+       "       [ 59.8,  59.8,  59.8, ...,  59.8,  59.8,  59.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.4 ... 119.4 120.0</div><input id='attrs-398070a7-99f4-482a-a793-b08ff3d16256' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-398070a7-99f4-482a-a793-b08ff3d16256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7bd95ef-5eaf-4f3e-9de4-8de4d46c3550' class='xr-var-data-in' type='checkbox'><label for='data-e7bd95ef-5eaf-4f3e-9de4-8de4d46c3550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       ...,\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-7c3cf8a8-b739-41fb-a10c-c9ae94323767' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c3cf8a8-b739-41fb-a10c-c9ae94323767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b524884-7da0-42bd-8aa9-60a0aaea862d' class='xr-var-data-in' type='checkbox'><label for='data-8b524884-7da0-42bd-8aa9-60a0aaea862d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-59.6, -59.6, -59.6, ..., -59.6, -59.6, -59.6],\n",
+       "       [-59.2, -59.2, -59.2, ..., -59.2, -59.2, -59.2],\n",
+       "       ...,\n",
+       "       [ 59.2,  59.2,  59.2, ...,  59.2,  59.2,  59.2],\n",
+       "       [ 59.6,  59.6,  59.6, ...,  59.6,  59.6,  59.6],\n",
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-37d73cbf-0d4e-46e5-92a8-83a98eceb0ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37d73cbf-0d4e-46e5-92a8-83a98eceb0ed' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8673f580-db3d-4725-a7c9-9f57df803f21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8673f580-db3d-4725-a7c9-9f57df803f21' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (x: 400, x_b: 401, y: 300, y_b: 301)\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -119.7 -119.1 -118.5 -117.9 -117.3 -116.7 -116.1 ...\n",
-       "    lat      (y, x) float64 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 ...\n",
-       "    lon_b    (y_b, x_b) float64 -120.0 -119.4 -118.8 -118.2 -117.6 -117.0 ...\n",
-       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
+       "    lon      (y, x) float64 -119.7 -119.1 -118.5 -117.9 ... 118.5 119.1 119.7\n",
+       "    lat      (y, x) float64 -59.8 -59.8 -59.8 -59.8 ... 59.8 59.8 59.8 59.8\n",
+       "    lon_b    (y_b, x_b) float64 -120.0 -119.4 -118.8 ... 118.8 119.4 120.0\n",
+       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\n",
        "Dimensions without coordinates: x, x_b, y, y_b\n",
        "Data variables:\n",
        "    *empty*"
@@ -119,20 +839,917 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a642077b-7759-4989-8dc6-2e77f7e24aaf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a642077b-7759-4989-8dc6-2e77f7e24aaf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>time</span>: 10</li><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5020ca70-aa91-4e91-80b2-0c4ea9d63900' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5020ca70-aa91-4e91-80b2-0c4ea9d63900' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-7d2d9329-83b3-4f8a-bb66-10012a06f6db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d2d9329-83b3-4f8a-bb66-10012a06f6db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e546cf90-3cff-4b09-bd06-786c07de1027' class='xr-var-data-in' type='checkbox'><label for='data-e546cf90-3cff-4b09-bd06-786c07de1027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       ...,\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-f33a6432-58cb-4942-991e-72e6b8d3b0be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f33a6432-58cb-4942-991e-72e6b8d3b0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a97c15-a4ef-450f-91c0-2c45c568ae0b' class='xr-var-data-in' type='checkbox'><label for='data-29a97c15-a4ef-450f-91c0-2c45c568ae0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
+       "       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\n",
+       "       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\n",
+       "       ...,\n",
+       "       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\n",
+       "       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\n",
+       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-37ce97d2-c864-4b6f-8277-3418056e962a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37ce97d2-c864-4b6f-8277-3418056e962a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0390cd1-3e07-4066-b0fc-18d417157ecd' class='xr-var-data-in' type='checkbox'><label for='data-e0390cd1-3e07-4066-b0fc-18d417157ecd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       ...,\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-63f9c455-4385-4af7-900b-568475ef2144' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63f9c455-4385-4af7-900b-568475ef2144' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0304833-3f67-4057-b09a-83d45dc9b4ab' class='xr-var-data-in' type='checkbox'><label for='data-b0304833-3f67-4057-b09a-83d45dc9b4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\n",
+       "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
+       "       ...,\n",
+       "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
+       "       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\n",
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10</div><input id='attrs-cb3f0775-17e7-40c1-bc59-ac135d9c9548' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb3f0775-17e7-40c1-bc59-ac135d9c9548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09660060-3522-4f8d-aa13-aa3bca9bf91b' class='xr-var-data-in' type='checkbox'><label for='data-09660060-3522-4f8d-aa13-aa3bca9bf91b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 45 46 47 48 49 50</div><input id='attrs-900566b1-2770-48ba-953a-87453271252d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-900566b1-2770-48ba-953a-87453271252d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-706e87da-632e-45ea-bdf2-472bf4640695' class='xr-var-data-in' type='checkbox'><label for='data-706e87da-632e-45ea-bdf2-472bf4640695' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
+       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-6a2d4a93-22f4-4e0b-9c0e-022d60c3a623' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a2d4a93-22f4-4e0b-9c0e-022d60c3a623' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data2D</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 1.869 1.872</div><input id='attrs-f3892f40-3bb8-4aa1-a0b4-ba94327748d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3892f40-3bb8-4aa1-a0b4-ba94327748d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5512f8-7c93-4b86-81cb-b2a5452ad7cd' class='xr-var-data-in' type='checkbox'><label for='data-9f5512f8-7c93-4b86-81cb-b2a5452ad7cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\n",
+       "        1.87234253],\n",
+       "       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\n",
+       "        1.87003418],\n",
+       "       [1.86771234, 1.86457706, 1.86146818, ..., 1.86146818, 1.86457706,\n",
+       "        1.86771234],\n",
+       "       ...,\n",
+       "       [1.86771234, 1.86457706, 1.86146818, ..., 1.86146818, 1.86457706,\n",
+       "        1.86771234],\n",
+       "       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\n",
+       "        1.87003418],\n",
+       "       [1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\n",
+       "        1.87234253]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>data4D</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 934.7 936.2</div><input id='attrs-006d42e1-df56-4f4c-a8e7-dd7926ea1c62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-006d42e1-df56-4f4c-a8e7-dd7926ea1c62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba88f600-f6ba-4343-a589-8a99a7e8d8bd' class='xr-var-data-in' type='checkbox'><label for='data-ba88f600-f6ba-4343-a589-8a99a7e8d8bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[[  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\n",
+       "            1.86931698,   1.87234253],\n",
+       "         [  1.87003418,   1.86695393,   1.86389961, ...,   1.86389961,\n",
+       "            1.86695393,   1.87003418],\n",
+       "         [  1.86771234,   1.86457706,   1.86146818, ...,   1.86146818,\n",
+       "            1.86457706,   1.86771234],\n",
+       "         ...,\n",
+       "         [  1.86771234,   1.86457706,   1.86146818, ...,   1.86146818,\n",
+       "            1.86457706,   1.86771234],\n",
+       "         [  1.87003418,   1.86695393,   1.86389961, ...,   1.86389961,\n",
+       "            1.86695393,   1.87003418],\n",
+       "         [  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\n",
+       "            1.86931698,   1.87234253]],\n",
+       "\n",
+       "        [[  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\n",
+       "            3.73863396,   3.74468505],\n",
+       "         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\n",
+       "            3.73390785,   3.74006836],\n",
+       "         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\n",
+       "            3.72915412,   3.73542468],\n",
+       "         ...,\n",
+       "         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\n",
+       "            3.72915412,   3.73542468],\n",
+       "         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\n",
+       "            3.73390785,   3.74006836],\n",
+       "         [  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\n",
+       "            3.73863396,   3.74468505]],\n",
+       "\n",
+       "        [[  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\n",
+       "            5.60795094,   5.61702758],\n",
+       "         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\n",
+       "            5.60086178,   5.61010254],\n",
+       "         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\n",
+       "            5.59373117,   5.60313702],\n",
+       "         ...,\n",
+       "         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\n",
+       "            5.59373117,   5.60313702],\n",
+       "         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\n",
+       "            5.60086178,   5.61010254],\n",
+       "         [  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\n",
+       "            5.60795094,   5.61702758]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 89.87244122,  89.72721511,  89.58321189, ...,  89.58321189,\n",
+       "           89.72721511,  89.87244122],\n",
+       "         [ 89.76164062,  89.61378848,  89.46718135, ...,  89.46718135,\n",
+       "           89.61378848,  89.76164062],\n",
+       "         [ 89.65019231,  89.49969879,  89.35047252, ...,  89.35047252,\n",
+       "           89.49969879,  89.65019231],\n",
+       "         ...,\n",
+       "         [ 89.65019231,  89.49969879,  89.35047252, ...,  89.35047252,\n",
+       "           89.49969879,  89.65019231],\n",
+       "         [ 89.76164062,  89.61378848,  89.46718135, ...,  89.46718135,\n",
+       "           89.61378848,  89.76164062],\n",
+       "         [ 89.87244122,  89.72721511,  89.58321189, ...,  89.58321189,\n",
+       "           89.72721511,  89.87244122]],\n",
+       "\n",
+       "        [[ 91.74478375,  91.59653209,  91.44952881, ...,  91.44952881,\n",
+       "           91.59653209,  91.74478375],\n",
+       "         [ 91.6316748 ,  91.48074241,  91.33108096, ...,  91.33108096,\n",
+       "           91.48074241,  91.6316748 ],\n",
+       "         [ 91.51790465,  91.36427585,  91.2119407 , ...,  91.2119407 ,\n",
+       "           91.36427585,  91.51790465],\n",
+       "         ...,\n",
+       "         [ 91.51790465,  91.36427585,  91.2119407 , ...,  91.2119407 ,\n",
+       "           91.36427585,  91.51790465],\n",
+       "         [ 91.6316748 ,  91.48074241,  91.33108096, ...,  91.33108096,\n",
+       "           91.48074241,  91.6316748 ],\n",
+       "         [ 91.74478375,  91.59653209,  91.44952881, ...,  91.44952881,\n",
+       "           91.59653209,  91.74478375]],\n",
+       "\n",
+       "        [[ 93.61712627,  93.46584907,  93.31584572, ...,  93.31584572,\n",
+       "           93.46584907,  93.61712627],\n",
+       "         [ 93.50170898,  93.34769633,  93.19498057, ...,  93.19498057,\n",
+       "           93.34769633,  93.50170898],\n",
+       "         [ 93.38561699,  93.22885291,  93.07340887, ...,  93.07340887,\n",
+       "           93.22885291,  93.38561699],\n",
+       "         ...,\n",
+       "         [ 93.38561699,  93.22885291,  93.07340887, ...,  93.07340887,\n",
+       "           93.22885291,  93.38561699],\n",
+       "         [ 93.50170898,  93.34769633,  93.19498057, ...,  93.19498057,\n",
+       "           93.34769633,  93.50170898],\n",
+       "         [ 93.61712627,  93.46584907,  93.31584572, ...,  93.31584572,\n",
+       "           93.46584907,  93.61712627]]],\n",
+       "\n",
+       "\n",
+       "       [[[  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\n",
+       "            3.73863396,   3.74468505],\n",
+       "         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\n",
+       "            3.73390785,   3.74006836],\n",
+       "         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\n",
+       "            3.72915412,   3.73542468],\n",
+       "         ...,\n",
+       "         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\n",
+       "            3.72915412,   3.73542468],\n",
+       "         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\n",
+       "            3.73390785,   3.74006836],\n",
+       "         [  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\n",
+       "            3.73863396,   3.74468505]],\n",
+       "\n",
+       "        [[  7.4893701 ,   7.47726793,   7.46526766, ...,   7.46526766,\n",
+       "            7.47726793,   7.4893701 ],\n",
+       "         [  7.48013672,   7.46781571,   7.45559845, ...,   7.45559845,\n",
+       "            7.46781571,   7.48013672],\n",
+       "         [  7.47084936,   7.45830823,   7.44587271, ...,   7.44587271,\n",
+       "            7.45830823,   7.47084936],\n",
+       "         ...,\n",
+       "         [  7.47084936,   7.45830823,   7.44587271, ...,   7.44587271,\n",
+       "            7.45830823,   7.47084936],\n",
+       "         [  7.48013672,   7.46781571,   7.45559845, ...,   7.45559845,\n",
+       "            7.46781571,   7.48013672],\n",
+       "         [  7.4893701 ,   7.47726793,   7.46526766, ...,   7.46526766,\n",
+       "            7.47726793,   7.4893701 ]],\n",
+       "\n",
+       "        [[ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\n",
+       "           11.21590189,  11.23405515],\n",
+       "         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\n",
+       "           11.20172356,  11.22020508],\n",
+       "         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\n",
+       "           11.18746235,  11.20627404],\n",
+       "         ...,\n",
+       "         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\n",
+       "           11.18746235,  11.20627404],\n",
+       "         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\n",
+       "           11.20172356,  11.22020508],\n",
+       "         [ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\n",
+       "           11.21590189,  11.23405515]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[179.74488244, 179.45443022, 179.16642378, ..., 179.16642378,\n",
+       "          179.45443022, 179.74488244],\n",
+       "         [179.52328123, 179.22757696, 178.93436269, ..., 178.93436269,\n",
+       "          179.22757696, 179.52328123],\n",
+       "         [179.30038461, 178.99939758, 178.70094504, ..., 178.70094504,\n",
+       "          178.99939758, 179.30038461],\n",
+       "         ...,\n",
+       "         [179.30038461, 178.99939758, 178.70094504, ..., 178.70094504,\n",
+       "          178.99939758, 179.30038461],\n",
+       "         [179.52328123, 179.22757696, 178.93436269, ..., 178.93436269,\n",
+       "          179.22757696, 179.52328123],\n",
+       "         [179.74488244, 179.45443022, 179.16642378, ..., 179.16642378,\n",
+       "          179.45443022, 179.74488244]],\n",
+       "\n",
+       "        [[183.48956749, 183.19306418, 182.89905761, ..., 182.89905761,\n",
+       "          183.19306418, 183.48956749],\n",
+       "         [183.26334959, 182.96148481, 182.66216191, ..., 182.66216191,\n",
+       "          182.96148481, 183.26334959],\n",
+       "         [183.03580929, 182.72855169, 182.42388139, ..., 182.42388139,\n",
+       "          182.72855169, 183.03580929],\n",
+       "         ...,\n",
+       "         [183.03580929, 182.72855169, 182.42388139, ..., 182.42388139,\n",
+       "          182.72855169, 183.03580929],\n",
+       "         [183.26334959, 182.96148481, 182.66216191, ..., 182.66216191,\n",
+       "          182.96148481, 183.26334959],\n",
+       "         [183.48956749, 183.19306418, 182.89905761, ..., 182.89905761,\n",
+       "          183.19306418, 183.48956749]],\n",
+       "\n",
+       "        [[187.23425254, 186.93169815, 186.63169144, ..., 186.63169144,\n",
+       "          186.93169815, 187.23425254],\n",
+       "         [187.00341795, 186.69539266, 186.38996114, ..., 186.38996114,\n",
+       "          186.69539267, 187.00341795],\n",
+       "         [186.77123397, 186.45770581, 186.14681775, ..., 186.14681775,\n",
+       "          186.45770581, 186.77123397],\n",
+       "         ...,\n",
+       "         [186.77123397, 186.45770581, 186.14681775, ..., 186.14681775,\n",
+       "          186.45770581, 186.77123397],\n",
+       "         [187.00341795, 186.69539266, 186.38996114, ..., 186.38996114,\n",
+       "          186.69539267, 187.00341795],\n",
+       "         [187.23425254, 186.93169815, 186.63169144, ..., 186.63169144,\n",
+       "          186.93169815, 187.23425254]]],\n",
+       "\n",
+       "\n",
+       "       [[[  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\n",
+       "            5.60795094,   5.61702758],\n",
+       "         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\n",
+       "            5.60086178,   5.61010254],\n",
+       "         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\n",
+       "            5.59373117,   5.60313702],\n",
+       "         ...,\n",
+       "         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\n",
+       "            5.59373117,   5.60313702],\n",
+       "         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\n",
+       "            5.60086178,   5.61010254],\n",
+       "         [  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\n",
+       "            5.60795094,   5.61702758]],\n",
+       "\n",
+       "        [[ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\n",
+       "           11.21590189,  11.23405515],\n",
+       "         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\n",
+       "           11.20172356,  11.22020508],\n",
+       "         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\n",
+       "           11.18746235,  11.20627404],\n",
+       "         ...,\n",
+       "         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\n",
+       "           11.18746235,  11.20627404],\n",
+       "         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\n",
+       "           11.20172356,  11.22020508],\n",
+       "         [ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\n",
+       "           11.21590189,  11.23405515]],\n",
+       "\n",
+       "        [[ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\n",
+       "           16.82385283,  16.85108273],\n",
+       "         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\n",
+       "           16.80258534,  16.83030762],\n",
+       "         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\n",
+       "           16.78119352,  16.80941106],\n",
+       "         ...,\n",
+       "         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\n",
+       "           16.78119352,  16.80941106],\n",
+       "         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\n",
+       "           16.80258534,  16.83030762],\n",
+       "         [ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\n",
+       "           16.82385283,  16.85108273]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[269.61732366, 269.18164533, 268.74963567, ..., 268.74963567,\n",
+       "          269.18164533, 269.61732366],\n",
+       "         [269.28492185, 268.84136544, 268.40154404, ..., 268.40154404,\n",
+       "          268.84136544, 269.28492185],\n",
+       "         [268.95057692, 268.49909637, 268.05141755, ..., 268.05141755,\n",
+       "          268.49909637, 268.95057692],\n",
+       "         ...,\n",
+       "         [268.95057692, 268.49909637, 268.05141755, ..., 268.05141755,\n",
+       "          268.49909637, 268.95057692],\n",
+       "         [269.28492185, 268.84136544, 268.40154404, ..., 268.40154404,\n",
+       "          268.84136544, 269.28492185],\n",
+       "         [269.61732366, 269.18164533, 268.74963567, ..., 268.74963567,\n",
+       "          269.18164533, 269.61732366]],\n",
+       "\n",
+       "        [[275.23435124, 274.78959627, 274.34858642, ..., 274.34858642,\n",
+       "          274.78959627, 275.23435124],\n",
+       "         [274.89502439, 274.44222722, 273.99324287, ..., 273.99324287,\n",
+       "          274.44222722, 274.89502439],\n",
+       "         [274.55371394, 274.09282754, 273.63582209, ..., 273.63582209,\n",
+       "          274.09282754, 274.55371394],\n",
+       "         ...,\n",
+       "         [274.55371394, 274.09282754, 273.63582209, ..., 273.63582209,\n",
+       "          274.09282754, 274.55371394],\n",
+       "         [274.89502439, 274.44222722, 273.99324287, ..., 273.99324287,\n",
+       "          274.44222722, 274.89502439],\n",
+       "         [275.23435124, 274.78959627, 274.34858642, ..., 274.34858642,\n",
+       "          274.78959627, 275.23435124]],\n",
+       "\n",
+       "        [[280.85137881, 280.39754722, 279.94753716, ..., 279.94753716,\n",
+       "          280.39754722, 280.85137881],\n",
+       "         [280.50512693, 280.043089  , 279.5849417 , ..., 279.5849417 ,\n",
+       "          280.043089  , 280.50512693],\n",
+       "         [280.15685096, 279.68655872, 279.22022662, ..., 279.22022662,\n",
+       "          279.68655872, 280.15685096],\n",
+       "         ...,\n",
+       "         [280.15685096, 279.68655872, 279.22022662, ..., 279.22022662,\n",
+       "          279.68655872, 280.15685096],\n",
+       "         [280.50512693, 280.043089  , 279.5849417 , ..., 279.5849417 ,\n",
+       "          280.043089  , 280.50512693],\n",
+       "         [280.85137881, 280.39754722, 279.94753716, ..., 279.94753716,\n",
+       "          280.39754722, 280.85137881]]],\n",
+       "\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "\n",
+       "       [[[ 14.9787402 ,  14.95453585,  14.93053532, ...,  14.93053532,\n",
+       "           14.95453585,  14.9787402 ],\n",
+       "         [ 14.96027344,  14.93563141,  14.91119689, ...,  14.91119689,\n",
+       "           14.93563141,  14.96027344],\n",
+       "         [ 14.94169872,  14.91661646,  14.89174542, ...,  14.89174542,\n",
+       "           14.91661646,  14.94169872],\n",
+       "         ...,\n",
+       "         [ 14.94169872,  14.91661646,  14.89174542, ...,  14.89174542,\n",
+       "           14.91661646,  14.94169872],\n",
+       "         [ 14.96027344,  14.93563141,  14.91119689, ...,  14.91119689,\n",
+       "           14.93563141,  14.96027344],\n",
+       "         [ 14.9787402 ,  14.95453585,  14.93053532, ...,  14.93053532,\n",
+       "           14.95453585,  14.9787402 ]],\n",
+       "\n",
+       "        [[ 29.95748041,  29.9090717 ,  29.86107063, ...,  29.86107063,\n",
+       "           29.9090717 ,  29.95748041],\n",
+       "         [ 29.92054687,  29.87126283,  29.82239378, ...,  29.82239378,\n",
+       "           29.87126283,  29.92054687],\n",
+       "         [ 29.88339744,  29.83323293,  29.78349084, ...,  29.78349084,\n",
+       "           29.83323293,  29.88339744],\n",
+       "         ...,\n",
+       "         [ 29.88339744,  29.83323293,  29.78349084, ...,  29.78349084,\n",
+       "           29.83323293,  29.88339744],\n",
+       "         [ 29.92054687,  29.87126283,  29.82239378, ...,  29.82239378,\n",
+       "           29.87126283,  29.92054687],\n",
+       "         [ 29.95748041,  29.9090717 ,  29.86107063, ...,  29.86107063,\n",
+       "           29.9090717 ,  29.95748041]],\n",
+       "\n",
+       "        [[ 44.93622061,  44.86360755,  44.79160595, ...,  44.79160595,\n",
+       "           44.86360755,  44.93622061],\n",
+       "         [ 44.88082031,  44.80689424,  44.73359067, ...,  44.73359067,\n",
+       "           44.80689424,  44.88082031],\n",
+       "         [ 44.82509615,  44.74984939,  44.67523626, ...,  44.67523626,\n",
+       "           44.74984939,  44.82509615],\n",
+       "         ...,\n",
+       "         [ 44.82509615,  44.74984939,  44.67523626, ...,  44.67523626,\n",
+       "           44.74984939,  44.82509615],\n",
+       "         [ 44.88082031,  44.80689424,  44.73359067, ...,  44.73359067,\n",
+       "           44.80689424,  44.88082031],\n",
+       "         [ 44.93622061,  44.86360755,  44.79160595, ...,  44.79160595,\n",
+       "           44.86360755,  44.93622061]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[718.97952976, 717.81772088, 716.66569513, ..., 716.66569513,\n",
+       "          717.81772088, 718.97952976],\n",
+       "         [718.09312494, 716.91030783, 715.73745076, ..., 715.73745076,\n",
+       "          716.91030783, 718.09312494],\n",
+       "         [717.20153845, 715.99759031, 714.80378015, ..., 714.80378015,\n",
+       "          715.99759031, 717.20153845],\n",
+       "         ...,\n",
+       "         [717.20153845, 715.99759031, 714.80378015, ..., 714.80378015,\n",
+       "          715.99759031, 717.20153845],\n",
+       "         [718.09312494, 716.91030783, 715.73745076, ..., 715.73745076,\n",
+       "          716.91030783, 718.09312494],\n",
+       "         [718.97952976, 717.81772088, 716.66569513, ..., 716.66569513,\n",
+       "          717.81772088, 718.97952976]],\n",
+       "\n",
+       "        [[733.95826996, 732.77225673, 731.59623044, ..., 731.59623044,\n",
+       "          732.77225673, 733.95826996],\n",
+       "         [733.05339838, 731.84593925, 730.64864766, ..., 730.64864766,\n",
+       "          731.84593925, 733.05339838],\n",
+       "         [732.14323717, 730.91420678, 729.69552557, ..., 729.69552557,\n",
+       "          730.91420678, 732.14323717],\n",
+       "         ...,\n",
+       "         [732.14323717, 730.91420678, 729.69552557, ..., 729.69552557,\n",
+       "          730.91420678, 732.14323717],\n",
+       "         [733.05339838, 731.84593925, 730.64864766, ..., 730.64864766,\n",
+       "          731.84593925, 733.05339838],\n",
+       "         [733.95826996, 732.77225673, 731.59623044, ..., 731.59623044,\n",
+       "          732.77225673, 733.95826996]],\n",
+       "\n",
+       "        [[748.93701017, 747.72679258, 746.52676576, ..., 746.52676576,\n",
+       "          747.72679258, 748.93701017],\n",
+       "         [748.01367181, 746.78157066, 745.55984455, ..., 745.55984455,\n",
+       "          746.78157066, 748.01367181],\n",
+       "         [747.08493588, 745.83082324, 744.58727099, ..., 744.58727099,\n",
+       "          745.83082324, 747.08493588],\n",
+       "         ...,\n",
+       "         [747.08493588, 745.83082324, 744.58727099, ..., 744.58727099,\n",
+       "          745.83082324, 747.08493588],\n",
+       "         [748.01367181, 746.78157066, 745.55984455, ..., 745.55984455,\n",
+       "          746.78157066, 748.01367181],\n",
+       "         [748.93701017, 747.72679258, 746.52676576, ..., 746.52676576,\n",
+       "          747.72679258, 748.93701017]]],\n",
+       "\n",
+       "\n",
+       "       [[[ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\n",
+       "           16.82385283,  16.85108273],\n",
+       "         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\n",
+       "           16.80258534,  16.83030762],\n",
+       "         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\n",
+       "           16.78119352,  16.80941106],\n",
+       "         ...,\n",
+       "         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\n",
+       "           16.78119352,  16.80941106],\n",
+       "         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\n",
+       "           16.80258534,  16.83030762],\n",
+       "         [ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\n",
+       "           16.82385283,  16.85108273]],\n",
+       "\n",
+       "        [[ 33.70216546,  33.64770567,  33.59370446, ...,  33.59370446,\n",
+       "           33.64770567,  33.70216546],\n",
+       "         [ 33.66061523,  33.60517068,  33.550193  , ...,  33.550193  ,\n",
+       "           33.60517068,  33.66061523],\n",
+       "         [ 33.61882211,  33.56238705,  33.50642719, ...,  33.50642719,\n",
+       "           33.56238705,  33.61882211],\n",
+       "         ...,\n",
+       "         [ 33.61882211,  33.56238705,  33.50642719, ...,  33.50642719,\n",
+       "           33.56238705,  33.61882211],\n",
+       "         [ 33.66061523,  33.60517068,  33.550193  , ...,  33.550193  ,\n",
+       "           33.60517068,  33.66061523],\n",
+       "         [ 33.70216546,  33.64770567,  33.59370446, ...,  33.59370446,\n",
+       "           33.64770567,  33.70216546]],\n",
+       "\n",
+       "        [[ 50.55324819,  50.4715585 ,  50.39055669, ...,  50.39055669,\n",
+       "           50.4715585 ,  50.55324819],\n",
+       "         [ 50.49092285,  50.40775602,  50.32528951, ...,  50.32528951,\n",
+       "           50.40775602,  50.49092285],\n",
+       "         [ 50.42823317,  50.34358057,  50.25964079, ...,  50.25964079,\n",
+       "           50.34358057,  50.42823317],\n",
+       "         ...,\n",
+       "         [ 50.42823317,  50.34358057,  50.25964079, ...,  50.25964079,\n",
+       "           50.34358057,  50.42823317],\n",
+       "         [ 50.49092285,  50.40775602,  50.32528951, ...,  50.32528951,\n",
+       "           50.40775602,  50.49092285],\n",
+       "         [ 50.55324819,  50.4715585 ,  50.39055669, ...,  50.39055669,\n",
+       "           50.4715585 ,  50.55324819]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[808.85197098, 807.54493599, 806.24890702, ..., 806.24890702,\n",
+       "          807.54493599, 808.85197098],\n",
+       "         [807.85476556, 806.52409631, 805.20463211, ..., 805.20463211,\n",
+       "          806.52409631, 807.85476556],\n",
+       "         [806.85173075, 805.4972891 , 804.15425266, ..., 804.15425266,\n",
+       "          805.4972891 , 806.85173075],\n",
+       "         ...,\n",
+       "         [806.85173075, 805.4972891 , 804.15425266, ..., 804.15425266,\n",
+       "          805.4972891 , 806.85173075],\n",
+       "         [807.85476556, 806.52409631, 805.20463211, ..., 805.20463211,\n",
+       "          806.52409631, 807.85476556],\n",
+       "         [808.85197098, 807.54493599, 806.24890702, ..., 806.24890702,\n",
+       "          807.54493599, 808.85197098]],\n",
+       "\n",
+       "        [[825.70305371, 824.36878882, 823.04575925, ..., 823.04575925,\n",
+       "          824.36878882, 825.70305371],\n",
+       "         [824.68507317, 823.32668165, 821.97972861, ..., 821.97972861,\n",
+       "          823.32668165, 824.68507317],\n",
+       "         [823.66114181, 822.27848262, 820.90746626, ..., 820.90746626,\n",
+       "          822.27848262, 823.66114181],\n",
+       "         ...,\n",
+       "         [823.66114181, 822.27848262, 820.90746626, ..., 820.90746626,\n",
+       "          822.27848262, 823.66114181],\n",
+       "         [824.68507317, 823.32668165, 821.97972861, ..., 821.97972861,\n",
+       "          823.32668165, 824.68507317],\n",
+       "         [825.70305371, 824.36878882, 823.04575925, ..., 823.04575925,\n",
+       "          824.36878882, 825.70305371]],\n",
+       "\n",
+       "        [[842.55413644, 841.19264165, 839.84261148, ..., 839.84261148,\n",
+       "          841.19264165, 842.55413644],\n",
+       "         [841.51538079, 840.12926699, 838.75482511, ..., 838.75482511,\n",
+       "          840.12926699, 841.51538079],\n",
+       "         [840.47055287, 839.05967615, 837.66067986, ..., 837.66067986,\n",
+       "          839.05967615, 840.47055287],\n",
+       "         ...,\n",
+       "         [840.47055287, 839.05967615, 837.66067986, ..., 837.66067986,\n",
+       "          839.05967615, 840.47055287],\n",
+       "         [841.51538079, 840.12926699, 838.75482511, ..., 838.75482511,\n",
+       "          840.12926699, 841.51538079],\n",
+       "         [842.55413644, 841.19264165, 839.84261148, ..., 839.84261148,\n",
+       "          841.19264165, 842.55413644]]],\n",
+       "\n",
+       "\n",
+       "       [[[ 18.72342525,  18.69316981,  18.66316914, ...,  18.66316914,\n",
+       "           18.69316981,  18.72342525],\n",
+       "         [ 18.7003418 ,  18.66953927,  18.63899611, ...,  18.63899611,\n",
+       "           18.66953927,  18.7003418 ],\n",
+       "         [ 18.6771234 ,  18.64577058,  18.61468177, ...,  18.61468177,\n",
+       "           18.64577058,  18.6771234 ],\n",
+       "         ...,\n",
+       "         [ 18.6771234 ,  18.64577058,  18.61468177, ...,  18.61468177,\n",
+       "           18.64577058,  18.6771234 ],\n",
+       "         [ 18.7003418 ,  18.66953927,  18.63899611, ...,  18.63899611,\n",
+       "           18.66953927,  18.7003418 ],\n",
+       "         [ 18.72342525,  18.69316981,  18.66316914, ...,  18.66316914,\n",
+       "           18.69316981,  18.72342525]],\n",
+       "\n",
+       "        [[ 37.44685051,  37.38633963,  37.32633829, ...,  37.32633829,\n",
+       "           37.38633963,  37.44685051],\n",
+       "         [ 37.40068359,  37.33907853,  37.27799223, ...,  37.27799223,\n",
+       "           37.33907853,  37.40068359],\n",
+       "         [ 37.35424679,  37.29154116,  37.22936355, ...,  37.22936355,\n",
+       "           37.29154116,  37.35424679],\n",
+       "         ...,\n",
+       "         [ 37.35424679,  37.29154116,  37.22936355, ...,  37.22936355,\n",
+       "           37.29154116,  37.35424679],\n",
+       "         [ 37.40068359,  37.33907853,  37.27799223, ...,  37.27799223,\n",
+       "           37.33907853,  37.40068359],\n",
+       "         [ 37.44685051,  37.38633963,  37.32633829, ...,  37.32633829,\n",
+       "           37.38633963,  37.44685051]],\n",
+       "\n",
+       "        [[ 56.17027576,  56.07950944,  55.98950743, ...,  55.98950743,\n",
+       "           56.07950944,  56.17027576],\n",
+       "         [ 56.10102539,  56.0086178 ,  55.91698834, ...,  55.91698834,\n",
+       "           56.0086178 ,  56.10102539],\n",
+       "         [ 56.03137019,  55.93731174,  55.84404532, ...,  55.84404532,\n",
+       "           55.93731174,  56.03137019],\n",
+       "         ...,\n",
+       "         [ 56.03137019,  55.93731174,  55.84404532, ...,  55.84404532,\n",
+       "           55.93731174,  56.03137019],\n",
+       "         [ 56.10102539,  56.0086178 ,  55.91698834, ...,  55.91698834,\n",
+       "           56.0086178 ,  56.10102539],\n",
+       "         [ 56.17027576,  56.07950944,  55.98950743, ...,  55.98950743,\n",
+       "           56.07950944,  56.17027576]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[898.7244122 , 897.2721511 , 895.83211891, ..., 895.83211891,\n",
+       "          897.2721511 , 898.7244122 ],\n",
+       "         [897.61640617, 896.13788479, 894.67181346, ..., 894.67181346,\n",
+       "          896.13788479, 897.61640617],\n",
+       "         [896.50192306, 894.99698789, 893.50472518, ..., 893.50472518,\n",
+       "          894.99698789, 896.50192306],\n",
+       "         ...,\n",
+       "         [896.50192306, 894.99698789, 893.50472518, ..., 893.50472518,\n",
+       "          894.99698789, 896.50192306],\n",
+       "         [897.61640617, 896.13788479, 894.67181346, ..., 894.67181346,\n",
+       "          896.13788479, 897.61640617],\n",
+       "         [898.7244122 , 897.2721511 , 895.83211891, ..., 895.83211891,\n",
+       "          897.2721511 , 898.7244122 ]],\n",
+       "\n",
+       "        [[917.44783745, 915.96532091, 914.49528806, ..., 914.49528806,\n",
+       "          915.96532091, 917.44783745],\n",
+       "         [916.31674797, 914.80742406, 913.31080957, ..., 913.31080957,\n",
+       "          914.80742406, 916.31674797],\n",
+       "         [915.17904646, 913.64275847, 912.11940696, ..., 912.11940696,\n",
+       "          913.64275847, 915.17904646],\n",
+       "         ...,\n",
+       "         [915.17904646, 913.64275847, 912.11940696, ..., 912.11940696,\n",
+       "          913.64275847, 915.17904646],\n",
+       "         [916.31674797, 914.80742406, 913.31080957, ..., 913.31080957,\n",
+       "          914.80742406, 916.31674797],\n",
+       "         [917.44783745, 915.96532091, 914.49528806, ..., 914.49528806,\n",
+       "          915.96532091, 917.44783745]],\n",
+       "\n",
+       "        [[936.17126271, 934.65849073, 933.1584572 , ..., 933.1584572 ,\n",
+       "          934.65849073, 936.17126271],\n",
+       "         [935.01708976, 933.47696332, 931.94980568, ..., 931.94980568,\n",
+       "          933.47696333, 935.01708976],\n",
+       "         [933.85616985, 932.28852905, 930.73408873, ..., 930.73408873,\n",
+       "          932.28852905, 933.85616985],\n",
+       "         ...,\n",
+       "         [933.85616985, 932.28852905, 930.73408873, ..., 930.73408873,\n",
+       "          932.28852905, 933.85616985],\n",
+       "         [935.01708976, 933.47696332, 931.94980568, ..., 931.94980568,\n",
+       "          933.47696333, 935.01708976],\n",
+       "         [936.17126271, 934.65849073, 933.1584572 , ..., 933.1584572 ,\n",
+       "          934.65849073, 936.17126271]]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d83a897b-0625-48db-b674-bb514de17552' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d83a897b-0625-48db-b674-bb514de17552' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lev: 50, time: 10, x: 600, x_b: 601, y: 400, y_b: 401)\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 -118.2 -117.8 -117.4 ...\n",
-       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 ...\n",
-       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 -118.8 -118.4 -118.0 ...\n",
-       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
+       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 ... 119.0 119.4 119.8\n",
+       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 ... 59.85 59.85 59.85\n",
+       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 ... 119.2 119.6 120.0\n",
+       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\n",
        "  * time     (time) int64 1 2 3 4 5 6 7 8 9 10\n",
-       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n",
+       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 ... 41 42 43 44 45 46 47 48 49 50\n",
        "Dimensions without coordinates: x, x_b, y, y_b\n",
        "Data variables:\n",
-       "    data2D   (y, x) float64 1.872 1.869 1.866 1.863 1.86 1.857 1.855 1.852 ...\n",
-       "    data4D   (time, lev, y, x) float64 1.872 1.869 1.866 1.863 1.86 1.857 ..."
+       "    data2D   (y, x) float64 1.872 1.869 1.866 1.863 ... 1.863 1.866 1.869 1.872\n",
+       "    data4D   (time, lev, y, x) float64 1.872 1.869 1.866 ... 933.2 934.7 936.2"
       ]
      },
      "execution_count": 4,
@@ -190,7 +1807,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x1198b8940>"
+       "Text(0.5, 1.0, 'extra dimensions to test broadcasting')"
       ]
      },
      "execution_count": 6,
@@ -199,12 +1816,14 @@
     },
     {
      "data": {
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krD9ZCTtRizclGMtzpv8w/Yk83rKydE1/YppuWQnrp49VD6v8ovfFfbfK68S0IbZq2kqD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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e76a4a8>"
+       "<Figure size 864x216 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -243,9 +1862,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Create weight file: bilinear_400x600_300x400.nc\n",
-      "CPU times: user 7.15 s, sys: 458 ms, total: 7.61 s\n",
-      "Wall time: 7.72 s\n"
+      "CPU times: user 3.89 s, sys: 169 ms, total: 4.05 s\n",
+      "Wall time: 4.07 s\n"
      ]
     }
    ],
@@ -264,8 +1882,6 @@
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       bilinear \n",
-       "Weight filename:            bilinear_400x600_300x400.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (400, 600) \n",
        "Output grid shape:          (300, 400) \n",
        "Output grid dimension name: ('y', 'x') \n",
@@ -304,8 +1920,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 429 ms, sys: 153 ms, total: 582 ms\n",
-      "Wall time: 586 ms\n"
+      "CPU times: user 429 ms, sys: 165 ms, total: 594 ms\n",
+      "Wall time: 593 ms\n"
      ]
     }
    ],
@@ -378,7 +1994,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x11a306160>"
+       "Text(0, 0.5, 'output grid indices')"
       ]
      },
      "execution_count": 11,
@@ -387,12 +2003,14 @@
     },
     {
      "data": {
-      "image/png": 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OrVpl3c4tGxuqspzwyxCA1n9aeVvo/SQdmvvgycB+TVbujyRdI+nXkh6U9DZJYyUtk7Qm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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119931710>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -413,9 +2031,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When you open the notebook next time, instead of spending another ~7s on recomputing the weights, you can simply set `reuse_weights=True` to read existing weights from disk.\n",
-    "\n",
-    "The weight file is typically pretty small (due to sparsity), so reading it is almost instantaneous."
+    "To avoid recomputing the same weights later, you can write the weights to disk and reload them later. First write the weights using the `to_netcdf` method. A default file name will be chosen if none is given."
    ]
   },
   {
@@ -427,13 +2043,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7.3M\tbilinear_400x600_300x400.nc\n"
+      "bilinear_400x600_300x400.nc\n"
      ]
     }
    ],
    "source": [
-    "%%bash\n",
-    "du -sh bilinear_400x600_300x400.nc"
+    "fn = regridder.to_netcdf()\n",
+    "print(fn)"
    ]
   },
   {
@@ -445,22 +2061,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reuse existing file: bilinear_400x600_300x400.nc\n",
-      "CPU times: user 23.4 ms, sys: 12.9 ms, total: 36.3 ms\n",
-      "Wall time: 36.2 ms\n"
+      "7.4M\tbilinear_400x600_300x400.nc\n"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "regridder2 = xe.Regridder(ds_in, ds_out, 'bilinear', reuse_weights=True)"
+    "%%bash\n",
+    "du -sh bilinear_400x600_300x400.nc"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second-step, applying those weights to data, is just a matrix multiplication $y=Ax$. With highly-optimized sparse matrix multiplication library, it is blazingly fast."
+    "When you open the notebook next time, simply set the `weights` argument to the path to the netCDF file. The weight file is typically pretty small (due to sparsity), so reading it is almost instantaneous."
    ]
   },
   {
@@ -472,57 +2086,63 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 460 ms, sys: 164 ms, total: 624 ms\n",
-      "Wall time: 628 ms\n"
+      "CPU times: user 24.3 ms, sys: 0 ns, total: 24.3 ms\n",
+      "Wall time: 23 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "dr_out2 = regridder2(ds_in['data4D'])"
+    "regridder2 = xe.Regridder(ds_in, ds_out, 'bilinear', weights=fn)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The retrieved regridder gives the same result as the first regridder."
+    "The second-step, applying those weights to data, is just a matrix multiplication $y=Ax$. With highly-optimized sparse matrix multiplication library, it is blazingly fast."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 426 ms, sys: 172 ms, total: 598 ms\n",
+      "Wall time: 597 ms\n"
+     ]
+    }
+   ],
    "source": [
-    "xr.testing.assert_identical(dr_out, dr_out2)  # they are equal"
+    "%%time\n",
+    "dr_out2 = regridder2(ds_in['data4D'])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For even larger grids, you might spend several minutes computing the weights. But once they are computed, you don't have to do it again."
+    "The retrieved regridder gives the same result as the first regridder."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Remove file bilinear_400x600_300x400.nc\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "xr.testing.assert_identical(dr_out, dr_out2)  # they are equal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# don't have to clean it if you want to use it next time\n",
-    "regridder2.clean_weight_file()  "
+    "For even larger grids, you might spend several minutes computing the weights. But once they are computed, you don't have to do it again."
    ]
   }
  ],
@@ -542,7 +2162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},

From 58369742204223db29df77aa48e4c500a22e59ba Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 27 May 2020 15:23:39 -0400
Subject: [PATCH 4/7] improved test coverage

---
 xesmf/tests/test_backend.py  | 18 ++++++++++++++----
 xesmf/tests/test_frontend.py |  2 ++
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/xesmf/tests/test_backend.py b/xesmf/tests/test_backend.py
index be848631..08081fa2 100644
--- a/xesmf/tests/test_backend.py
+++ b/xesmf/tests/test_backend.py
@@ -7,7 +7,7 @@
                            esmf_regrid_build, esmf_regrid_apply,
                            esmf_regrid_finalize)
 from xesmf.smm import read_weights, apply_weights
-
+import xarray as xr
 from numpy.testing import assert_equal, assert_almost_equal
 import pytest
 
@@ -235,11 +235,17 @@ def test_read_weights(tmp_path):
     esmf_regrid_build(grid_in, grid_out, method='bilinear', filename=str(fn))
 
     w = regrid_memory.get_weights_dict(deep_copy=True)
-    sm = read_weights(w, lon_in.size, lon_out.size).todense()
+    sm = read_weights(w, lon_in.size, lon_out.size)
 
     # Test Path and string to netCDF file against weights dictionary
-    np.testing.assert_array_equal(read_weights(fn, lon_in.size, lon_out.size).todense(), sm)
-    np.testing.assert_array_equal(read_weights(str(fn), lon_in.size, lon_out.size).todense(), sm)
+    np.testing.assert_array_equal(read_weights(fn, lon_in.size, lon_out.size).todense(), sm.todense())
+    np.testing.assert_array_equal(read_weights(str(fn), lon_in.size, lon_out.size).todense(), sm.todense())
+
+    # Test xr.Dataset
+    np.testing.assert_array_equal(read_weights(xr.open_dataset(fn), lon_in.size, lon_out.size).todense(), sm.todense())
+
+    # Test COO matrix
+    np.testing.assert_array_equal(read_weights(sm, lon_in.size, lon_out.size).todense(), sm.todense())
 
     # Test failures
     with pytest.raises(IOError):
@@ -247,3 +253,7 @@ def test_read_weights(tmp_path):
 
     with pytest.raises(ValueError):
         read_weights({}, lon_in.size, lon_out.size)
+
+    with pytest.raises(ValueError):
+        ds = xr.open_dataset(fn)
+        read_weights(ds.drop_vars("col"), lon_in.size, lon_out.size)
diff --git a/xesmf/tests/test_frontend.py b/xesmf/tests/test_frontend.py
index 98b43239..6beab5fe 100644
--- a/xesmf/tests/test_frontend.py
+++ b/xesmf/tests/test_frontend.py
@@ -30,6 +30,7 @@
 ds_locs['lat'] = xr.DataArray(data=[-20, -10, 0, 10], dims=('locations',))
 ds_locs['lon'] = xr.DataArray(data=[0, 5, 10, 15], dims=('locations',))
 
+
 def test_as_2d_mesh():
     # 2D grid should not change
     lon2d = ds_in['lon'].values
@@ -49,6 +50,7 @@ def test_as_2d_mesh():
 # 'patch' is too slow to test
 methods_list = ['bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s']
 
+
 @pytest.mark.parametrize("locstream_in,locstream_out,method", [
                          (False, False, 'conservative'),
                          (False, False, 'bilinear'),

From ce9059391770c08954599870a8926e410e66d9eb Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 27 May 2020 15:48:55 -0400
Subject: [PATCH 5/7] updated notebooks

---
 doc/notebooks/Compare_algorithms.ipynb |  584 +++++-
 doc/notebooks/Curvilinear_grid.ipynb   | 2161 ++++++++++++++++++++--
 doc/notebooks/Dask.ipynb               | 1737 +++++++++++++++++-
 doc/notebooks/Dataset.ipynb            | 2327 +++++++++++++++++++++++-
 doc/notebooks/Pure_numpy.ipynb         |   84 +-
 doc/notebooks/Rectilinear_grid.ipynb   | 1618 +++++++++++++++-
 doc/notebooks/Reuse_regridder.ipynb    |   56 +-
 doc/notebooks/Using_LocStream.ipynb    |  427 ++++-
 8 files changed, 8578 insertions(+), 416 deletions(-)

diff --git a/doc/notebooks/Compare_algorithms.ipynb b/doc/notebooks/Compare_algorithms.ipynb
index ab8794a4..434f4c24 100644
--- a/doc/notebooks/Compare_algorithms.ipynb
+++ b/doc/notebooks/Compare_algorithms.ipynb
@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "method_list = ['bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s', 'patch']"
@@ -46,9 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -62,9 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ds_in = xe.util.grid_global(20, 15)  # input grid\n",
@@ -86,17 +80,497 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
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+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "}\n",
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+       "}\n",
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+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
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+       "\n",
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+       "}\n",
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+       "}\n",
+       "\n",
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+       "}\n",
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+       "  grid-column: 4;\n",
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+       "}\n",
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+       "  float: right;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-data,\n",
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+       "}\n",
+       "\n",
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+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-38dc4e53-ac73-4b2e-8340-caf46fd32a62' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38dc4e53-ac73-4b2e-8340-caf46fd32a62' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 18</li><li><span>x_b</span>: 19</li><li><span>y</span>: 12</li><li><span>y_b</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad5ecefd-1f6b-4c8f-a020-d60ebc131f1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad5ecefd-1f6b-4c8f-a020-d60ebc131f1b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-170.0 -150.0 ... 150.0 170.0</div><input id='attrs-0f6e926a-7bfd-430d-8c6c-12b2e27d5eb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f6e926a-7bfd-430d-8c6c-12b2e27d5eb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173690be-b5b6-49f4-89e8-ee6c6c60df49' class='xr-var-data-in' type='checkbox'><label for='data-173690be-b5b6-49f4-89e8-ee6c6c60df49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
+       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-82.5 -82.5 -82.5 ... 82.5 82.5</div><input id='attrs-454cf934-37b5-4dfe-855c-2bd3a42ef38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-454cf934-37b5-4dfe-855c-2bd3a42ef38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de2cf556-82d2-40c2-a3d2-0e675913b3da' class='xr-var-data-in' type='checkbox'><label for='data-de2cf556-82d2-40c2-a3d2-0e675913b3da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5,\n",
+       "        -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5],\n",
+       "       [-67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5,\n",
+       "        -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5],\n",
+       "       [-52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5,\n",
+       "        -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5],\n",
+       "       [-37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5,\n",
+       "        -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5],\n",
+       "       [-22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5,\n",
+       "        -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5],\n",
+       "       [ -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,\n",
+       "         -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5],\n",
+       "       [  7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,\n",
+       "          7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5],\n",
+       "       [ 22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,\n",
+       "         22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5],\n",
+       "       [ 37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,\n",
+       "         37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5],\n",
+       "       [ 52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,\n",
+       "         52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5],\n",
+       "       [ 67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,\n",
+       "         67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5],\n",
+       "       [ 82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,\n",
+       "         82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-180 -160 -140 -120 ... 140 160 180</div><input id='attrs-b18f5c3b-0539-4fef-9969-3e5091e320e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b18f5c3b-0539-4fef-9969-3e5091e320e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02f906c8-e7aa-4844-a41a-28d9370b9224' class='xr-var-data-in' type='checkbox'><label for='data-02f906c8-e7aa-4844-a41a-28d9370b9224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180],\n",
+       "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-90 -90 -90 -90 -90 ... 90 90 90 90</div><input id='attrs-0ee818cd-9bd9-4e97-8cf2-05f582a5a063' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ee818cd-9bd9-4e97-8cf2-05f582a5a063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b29f280-e697-423e-9f5c-e3af379cbc50' class='xr-var-data-in' type='checkbox'><label for='data-6b29f280-e697-423e-9f5c-e3af379cbc50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90,\n",
+       "        -90, -90, -90, -90, -90, -90],\n",
+       "       [-75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75,\n",
+       "        -75, -75, -75, -75, -75, -75],\n",
+       "       [-60, -60, -60, -60, -60, -60, -60, -60, -60, -60, -60, -60, -60,\n",
+       "        -60, -60, -60, -60, -60, -60],\n",
+       "       [-45, -45, -45, -45, -45, -45, -45, -45, -45, -45, -45, -45, -45,\n",
+       "        -45, -45, -45, -45, -45, -45],\n",
+       "       [-30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30,\n",
+       "        -30, -30, -30, -30, -30, -30],\n",
+       "       [-15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,\n",
+       "        -15, -15, -15, -15, -15, -15],\n",
+       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+       "          0,   0,   0,   0,   0,   0],\n",
+       "       [ 15,  15,  15,  15,  15,  15,  15,  15,  15,  15,  15,  15,  15,\n",
+       "         15,  15,  15,  15,  15,  15],\n",
+       "       [ 30,  30,  30,  30,  30,  30,  30,  30,  30,  30,  30,  30,  30,\n",
+       "         30,  30,  30,  30,  30,  30],\n",
+       "       [ 45,  45,  45,  45,  45,  45,  45,  45,  45,  45,  45,  45,  45,\n",
+       "         45,  45,  45,  45,  45,  45],\n",
+       "       [ 60,  60,  60,  60,  60,  60,  60,  60,  60,  60,  60,  60,  60,\n",
+       "         60,  60,  60,  60,  60,  60],\n",
+       "       [ 75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,\n",
+       "         75,  75,  75,  75,  75,  75],\n",
+       "       [ 90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,\n",
+       "         90,  90,  90,  90,  90,  90]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-eeaa86c3-130e-4823-8fec-b8cb83bddb89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eeaa86c3-130e-4823-8fec-b8cb83bddb89' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.016 2.009 1.997 ... 2.009 2.016</div><input id='attrs-e94d21a2-2aea-49e6-bcfb-5f8ee7a1c4f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e94d21a2-2aea-49e6-bcfb-5f8ee7a1c4f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daddae96-87bd-4623-97de-22eb4ac30c04' class='xr-var-data-in' type='checkbox'><label for='data-daddae96-87bd-4623-97de-22eb4ac30c04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
+       "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
+       "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
+       "        1.99704154, 2.00851854, 2.01600962],\n",
+       "       [2.1376148 , 2.0732233 , 1.97456981, 1.88781539, 1.85355339,\n",
+       "        1.88781539, 1.97456981, 2.0732233 , 2.1376148 , 2.1376148 ,\n",
+       "        2.0732233 , 1.97456981, 1.88781539, 1.85355339, 1.88781539,\n",
+       "        1.97456981, 2.0732233 , 2.1376148 ],\n",
+       "       [2.34824114, 2.18529524, 1.93564764, 1.71611122, 1.62940952,\n",
+       "        1.71611122, 1.93564764, 2.18529524, 2.34824114, 2.34824114,\n",
+       "        2.18529524, 1.93564764, 1.71611122, 1.62940952, 1.71611122,\n",
+       "        1.93564764, 2.18529524, 2.34824114],\n",
+       "       [2.59145148, 2.31470476, 1.89070418, 1.51784433, 1.37059048,\n",
+       "        1.51784433, 1.89070418, 2.31470476, 2.59145148, 2.59145148,\n",
+       "        2.31470476, 1.89070418, 1.51784433, 1.37059048, 1.51784433,\n",
+       "        1.89070418, 2.31470476, 2.59145148],\n",
+       "       [2.80207782, 2.4267767 , 1.85178201, 1.34614017, 1.14644661,\n",
+       "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
+       "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
+       "        1.85178201, 2.4267767 , 2.80207782],\n",
+       "       [2.923683  , 2.49148146, 1.82931028, 1.24700672, 1.01703709,\n",
+       "        1.24700672, 1.82931028, 2.49148146, 2.923683  , 2.923683  ,\n",
+       "        2.49148146, 1.82931028, 1.24700672, 1.01703709, 1.24700672,\n",
+       "        1.82931028, 2.49148146, 2.923683  ],\n",
+       "       [2.923683  , 2.49148146, 1.82931028, 1.24700672, 1.01703709,\n",
+       "        1.24700672, 1.82931028, 2.49148146, 2.923683  , 2.923683  ,\n",
+       "        2.49148146, 1.82931028, 1.24700672, 1.01703709, 1.24700672,\n",
+       "        1.82931028, 2.49148146, 2.923683  ],\n",
+       "       [2.80207782, 2.4267767 , 1.85178201, 1.34614017, 1.14644661,\n",
+       "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
+       "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
+       "        1.85178201, 2.4267767 , 2.80207782],\n",
+       "       [2.59145148, 2.31470476, 1.89070418, 1.51784433, 1.37059048,\n",
+       "        1.51784433, 1.89070418, 2.31470476, 2.59145148, 2.59145148,\n",
+       "        2.31470476, 1.89070418, 1.51784433, 1.37059048, 1.51784433,\n",
+       "        1.89070418, 2.31470476, 2.59145148],\n",
+       "       [2.34824114, 2.18529524, 1.93564764, 1.71611122, 1.62940952,\n",
+       "        1.71611122, 1.93564764, 2.18529524, 2.34824114, 2.34824114,\n",
+       "        2.18529524, 1.93564764, 1.71611122, 1.62940952, 1.71611122,\n",
+       "        1.93564764, 2.18529524, 2.34824114],\n",
+       "       [2.1376148 , 2.0732233 , 1.97456981, 1.88781539, 1.85355339,\n",
+       "        1.88781539, 1.97456981, 2.0732233 , 2.1376148 , 2.1376148 ,\n",
+       "        2.0732233 , 1.97456981, 1.88781539, 1.85355339, 1.88781539,\n",
+       "        1.97456981, 2.0732233 , 2.1376148 ],\n",
+       "       [2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
+       "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
+       "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
+       "        1.99704154, 2.00851854, 2.01600962]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-89896086-879d-4259-9564-4d138efb14d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89896086-879d-4259-9564-4d138efb14d0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (x: 18, x_b: 19, y: 12, y_b: 13)\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 -90.0 -70.0 -50.0 ...\n",
-       "    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 -82.5 -82.5 -82.5 -82.5 ...\n",
-       "    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 -80 -60 -40 -20 0 20 ...\n",
-       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 -90 -90 -90 -90 ...\n",
+       "    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\n",
+       "    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
+       "    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\n",
+       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n",
        "Dimensions without coordinates: x, x_b, y, y_b\n",
        "Data variables:\n",
-       "    data     (y, x) float64 2.016 2.009 1.997 1.987 1.983 1.987 1.997 2.009 ..."
+       "    data     (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016"
       ]
      },
      "execution_count": 4,
@@ -117,7 +591,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x116fb85f8>"
+       "<matplotlib.collections.QuadMesh at 0x7f61b2da6400>"
       ]
      },
      "execution_count": 5,
@@ -126,12 +600,14 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116e37518>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -142,16 +618,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def regrid(ds_in, ds_out, dr_in, method):\n",
     "    \"\"\"Convenience function for one-time regridding\"\"\"\n",
     "    regridder = xe.Regridder(ds_in, ds_out, method, periodic=True)\n",
     "    dr_out = regridder(dr_in)\n",
-    "    regridder.clean_weight_file()\n",
     "    return dr_out"
    ]
   },
@@ -178,30 +651,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Create weight file: bilinear_12x18_45x90_peri.nc\n",
-      "Remove file bilinear_12x18_45x90_peri.nc\n",
-      "CPU times: user 228 ms, sys: 14.1 ms, total: 242 ms\n",
-      "Wall time: 243 ms\n",
+      "CPU times: user 69.9 ms, sys: 12.4 ms, total: 82.3 ms\n",
+      "Wall time: 98.5 ms\n",
       "\n",
-      "Create weight file: conservative_12x18_45x90.nc\n",
-      "Remove file conservative_12x18_45x90.nc\n",
-      "CPU times: user 160 ms, sys: 5.67 ms, total: 165 ms\n",
-      "Wall time: 176 ms\n",
+      "CPU times: user 109 ms, sys: 0 ns, total: 109 ms\n",
+      "Wall time: 108 ms\n",
       "\n",
-      "Create weight file: nearest_s2d_12x18_45x90_peri.nc\n",
-      "Remove file nearest_s2d_12x18_45x90_peri.nc\n",
-      "CPU times: user 53.7 ms, sys: 2.92 ms, total: 56.6 ms\n",
-      "Wall time: 58.8 ms\n",
+      "CPU times: user 30.3 ms, sys: 60 µs, total: 30.4 ms\n",
+      "Wall time: 30 ms\n",
       "\n",
-      "Create weight file: nearest_d2s_12x18_45x90_peri.nc\n",
-      "Remove file nearest_d2s_12x18_45x90_peri.nc\n",
-      "CPU times: user 23.1 ms, sys: 1.89 ms, total: 25 ms\n",
-      "Wall time: 25.7 ms\n",
+      "CPU times: user 10.1 ms, sys: 0 ns, total: 10.1 ms\n",
+      "Wall time: 9.9 ms\n",
       "\n",
-      "Create weight file: patch_12x18_45x90_peri.nc\n",
-      "Remove file patch_12x18_45x90_peri.nc\n",
-      "CPU times: user 838 ms, sys: 39.3 ms, total: 878 ms\n",
-      "Wall time: 893 ms\n",
+      "CPU times: user 467 ms, sys: 14.7 ms, total: 482 ms\n",
+      "Wall time: 481 ms\n",
       "\n"
      ]
     }
@@ -226,12 +689,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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vBt6ck34bsP/cJxqyCHiA+rcmI7yhOY4zInRib/psay4Hsq6nvLQ5jETHpiSx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\nNLtdAXy81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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1170cf048>"
+       "<Figure size 576x576 with 11 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -266,24 +731,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: bilinear_12x18_9x12_peri.nc\n",
-      "Remove file bilinear_12x18_9x12_peri.nc\n",
-      "Create weight file: conservative_12x18_9x12.nc\n",
-      "Remove file conservative_12x18_9x12.nc\n",
-      "Create weight file: nearest_s2d_12x18_9x12_peri.nc\n",
-      "Remove file nearest_s2d_12x18_9x12_peri.nc\n",
-      "Create weight file: nearest_d2s_12x18_9x12_peri.nc\n",
-      "Remove file nearest_d2s_12x18_9x12_peri.nc\n",
-      "Create weight file: patch_12x18_9x12_peri.nc\n",
-      "Remove file patch_12x18_9x12_peri.nc\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for method in method_list:\n",
     "    ds_coarse[method] = regrid(ds_in, ds_coarse, ds_in['data'], method)"
@@ -296,12 +744,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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CXXLnu9785bmPLWrJ1vl/l/Jeq1YYjdcbil3zvzSZvkCsWQGcGBE3S5oM3CTp8oioXAgX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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117884358>"
+       "<Figure size 576x576 with 11 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -342,7 +792,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},
diff --git a/doc/notebooks/Curvilinear_grid.ipynb b/doc/notebooks/Curvilinear_grid.ipynb
index e1bbd275..5e8bef8a 100644
--- a/doc/notebooks/Curvilinear_grid.ipynb
+++ b/doc/notebooks/Curvilinear_grid.ipynb
@@ -10,9 +10,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -51,16 +49,408 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3affa76d-055b-4574-b4c6-766e5ac9a392' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3affa76d-055b-4574-b4c6-766e5ac9a392' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43b018ac-40df-4c67-954f-339960b155ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43b018ac-40df-4c67-954f-339960b155ed' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-17 00:00:00</div><input id='attrs-d9380283-e36f-4649-b776-b056df9f13f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9380283-e36f-4649-b776-b056df9f13f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b10b2df8-9c09-4e73-b2e6-ab42bb26aeec' class='xr-var-data-in' type='checkbox'><label for='data-b10b2df8-9c09-4e73-b2e6-ab42bb26aeec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1980-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-08-17 00:00:00)], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab8fa961-a68c-4ced-9429-6a678f48d41c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab8fa961-a68c-4ced-9429-6a678f48d41c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28c93c82-2d2e-4d15-a0a8-7ea0f2b32586' class='xr-var-data-in' type='checkbox'><label for='data-28c93c82-2d2e-4d15-a0a8-7ea0f2b32586' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>xv</dd></dl></div><pre class='xr-var-data'>array([[189.222932, 189.389909, 189.558366, ..., 293.779061, 294.027924,\n",
+       "        294.274399],\n",
+       "       [188.96837 , 189.134706, 189.302537, ..., 294.05584 , 294.304444,\n",
+       "        294.55066 ],\n",
+       "       [188.712343, 188.878007, 189.045152, ..., 294.335053, 294.583375,\n",
+       "        294.829293],\n",
+       "       ...,\n",
+       "       [124.04724 , 123.88362 , 123.71852 , ...,  16.831718,  16.58437 ,\n",
+       "         16.339496],\n",
+       "       [123.786864, 123.622542, 123.456725, ...,  17.118145,  16.870437,\n",
+       "         16.625183],\n",
+       "       [123.527984, 123.36296 , 123.196441, ...,  17.402099,  17.154053,\n",
+       "         16.908451]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5e40ca4-1f34-4df4-b11b-84bf781228b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5e40ca4-1f34-4df4-b11b-84bf781228b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb7ee89c-4381-4922-b98e-0a4509fedf55' class='xr-var-data-in' type='checkbox'><label for='data-eb7ee89c-4381-4922-b98e-0a4509fedf55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>yv</dd></dl></div><pre class='xr-var-data'>array([[16.534986, 16.778456, 17.022224, ..., 27.363016, 27.11811 , 26.87289 ],\n",
+       "       [16.693973, 16.938654, 17.183645, ..., 27.584772, 27.338218, 27.091366],\n",
+       "       [16.852192, 17.098089, 17.344309, ..., 27.805843, 27.557646, 27.309156],\n",
+       "       ...,\n",
+       "       [17.31179 , 17.561247, 17.811046, ..., 28.450248, 28.197183, 27.943847],\n",
+       "       [17.155897, 17.40414 , 17.652723, ..., 28.231296, 27.979893, 27.728216],\n",
+       "       [16.999195, 17.246229, 17.493587, ..., 28.0116  , 27.761856, 27.511827]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-a25949a3-8699-4494-8944-2a3a903161e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a25949a3-8699-4494-8944-2a3a903161e9' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0eab5399-1b23-49fd-9bf0-3af747be440e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eab5399-1b23-49fd-9bf0-3af747be440e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcbd37b6-4357-42ca-928d-0ff86e1afaa3' class='xr-var-data-in' type='checkbox'><label for='data-bcbd37b6-4357-42ca-928d-0ff86e1afaa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><pre class='xr-var-data'>[2029500 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-7aa5ce8e-7177-40e1-adab-840b02b1c81d' class='xr-section-summary-in' type='checkbox'  ><label for='section-7aa5ce8e-7177-40e1-adab-840b02b1c81d' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>&quot;4.6.0&quot;</dd><dt><span>history :</span></dt><dd>Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
+       "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
+       "history deleted for brevity</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (time: 36, x: 275, y: 205)\n",
        "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 ...\n",
+       "    yc       (y, x) float64 ...\n",
        "Dimensions without coordinates: x, y\n",
        "Data variables:\n",
-       "    Tair     (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...\n",
+       "    Tair     (time, y, x) float64 ...\n",
        "Attributes:\n",
        "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
        "    institution:               U.W.\n",
@@ -99,36 +489,403 @@
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span>y</span>: 205</li><li><span>x</span>: 275</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-96b8f8b2-1047-4e54-817e-4ac524d81e82' class='xr-array-in' type='checkbox' ><label for='section-96b8f8b2-1047-4e54-817e-4ac524d81e82' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><pre class='xr-array-data'>[2029500 values with dtype=float64]</pre></div></li><li class='xr-section-item'><input id='section-0e9ab8fa-7cd5-4534-977c-ac1109871709' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e9ab8fa-7cd5-4534-977c-ac1109871709' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-17 00:00:00</div><input id='attrs-df93a07e-a8bb-44b4-8eba-bd0dcb99515b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df93a07e-a8bb-44b4-8eba-bd0dcb99515b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51812bbc-6b24-44bc-a466-7c3bc20ce836' class='xr-var-data-in' type='checkbox'><label for='data-51812bbc-6b24-44bc-a466-7c3bc20ce836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1980-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-08-17 00:00:00)], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>189.2 189.4 189.6 ... 17.15 16.91</div><input id='attrs-ca3daf5d-c1f7-469d-9be8-66efa4029136' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca3daf5d-c1f7-469d-9be8-66efa4029136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e76b8f-53be-4f3a-b52d-5041854793c6' class='xr-var-data-in' type='checkbox'><label for='data-68e76b8f-53be-4f3a-b52d-5041854793c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>xv</dd></dl></div><pre class='xr-var-data'>array([[189.222932, 189.389909, 189.558366, ..., 293.779061, 294.027924,\n",
+       "        294.274399],\n",
+       "       [188.96837 , 189.134706, 189.302537, ..., 294.05584 , 294.304444,\n",
+       "        294.55066 ],\n",
+       "       [188.712343, 188.878007, 189.045152, ..., 294.335053, 294.583375,\n",
+       "        294.829293],\n",
+       "       ...,\n",
+       "       [124.04724 , 123.88362 , 123.71852 , ...,  16.831718,  16.58437 ,\n",
+       "         16.339496],\n",
+       "       [123.786864, 123.622542, 123.456725, ...,  17.118145,  16.870437,\n",
+       "         16.625183],\n",
+       "       [123.527984, 123.36296 , 123.196441, ...,  17.402099,  17.154053,\n",
+       "         16.908451]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.53 16.78 17.02 ... 27.76 27.51</div><input id='attrs-197b8a21-691e-4b45-8d34-55d9c61ff0ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-197b8a21-691e-4b45-8d34-55d9c61ff0ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11f9a82-8eba-4e02-9430-15e856139233' class='xr-var-data-in' type='checkbox'><label for='data-e11f9a82-8eba-4e02-9430-15e856139233' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>yv</dd></dl></div><pre class='xr-var-data'>array([[16.534986, 16.778456, 17.022224, ..., 27.363016, 27.11811 , 26.87289 ],\n",
+       "       [16.693973, 16.938654, 17.183645, ..., 27.584772, 27.338218, 27.091366],\n",
+       "       [16.852192, 17.098089, 17.344309, ..., 27.805843, 27.557646, 27.309156],\n",
+       "       ...,\n",
+       "       [17.31179 , 17.561247, 17.811046, ..., 28.450248, 28.197183, 27.943847],\n",
+       "       [17.155897, 17.40414 , 17.652723, ..., 28.231296, 27.979893, 27.728216],\n",
+       "       [16.999195, 17.246229, 17.493587, ..., 28.0116  , 27.761856, 27.511827]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-2668caa6-2297-4e76-8d79-8636de680a2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2668caa6-2297-4e76-8d79-8636de680a2e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
-       "array([[[       nan,        nan, ...,        nan,        nan],\n",
-       "        [       nan,        nan, ...,        nan,        nan],\n",
-       "        ..., \n",
-       "        [       nan,        nan, ...,  26.802619,  27.086035],\n",
-       "        [       nan,        nan, ...,  26.564739,  26.730649]],\n",
-       "\n",
-       "       [[       nan,        nan, ...,        nan,        nan],\n",
-       "        [       nan,        nan, ...,        nan,        nan],\n",
-       "        ..., \n",
-       "        [       nan,        nan, ...,  24.29624 ,  24.614224],\n",
-       "        [       nan,        nan, ...,  24.299677,  24.454399]],\n",
-       "\n",
-       "       ..., \n",
-       "       [[       nan,        nan, ...,        nan,        nan],\n",
-       "        [       nan,        nan, ...,        nan,        nan],\n",
-       "        ..., \n",
-       "        [       nan,        nan, ...,  27.311049,  27.673872],\n",
-       "        [       nan,        nan, ...,  27.008894,  27.23018 ]],\n",
-       "\n",
-       "       [[       nan,        nan, ...,        nan,        nan],\n",
-       "        [       nan,        nan, ...,        nan,        nan],\n",
-       "        ..., \n",
-       "        [       nan,        nan, ...,  28.422736,  28.687212],\n",
-       "        [       nan,        nan, ...,  28.185955,  28.20753 ]]])\n",
+       "[2029500 values with dtype=float64]\n",
        "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
        "Dimensions without coordinates: y, x\n",
        "Attributes:\n",
        "    units:           C\n",
@@ -154,12 +911,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Tv4nd4++3iGWkmZFS0rpuKZ/++3m2blpLb1c7FqsNqy2NgLcXV2YW5cNGM2rUaNIzM8mt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\nVVvx5QURZdmJxqcr8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kju7Hj2j+m7NxVWUuj1nKioKOTk5KCyshJhYWH4+uuv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\nCCMlhZ4QQgghhJGSQk8IIYQQwkj9F7MCx3MjVSf/AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117071ac8>"
+       "<Figure size 864x144 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -192,7 +951,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x11a88f828>"
+       "Text(0, 0.5, 'lat')"
       ]
      },
      "execution_count": 5,
@@ -201,12 +960,14 @@
     },
     {
      "data": {
-      "image/png": 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GaA6oVJRq1zNz9upIiKWNHd4528/16RXGoyGmF7aM0xMevH95iD+7vURtTwG7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qYRxY45TU81ekMDbl04ZTpgd13zw2tmymw6fQnvvuJ0Vk5WnpPt4yQTBkyVC7zpklO5/MwZfutL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\ne5cALwEuBT4vIqc/y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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a4da4e0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -231,16 +992,408 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
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+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-dtype,\n",
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+       "  width: auto;\n",
+       "  z-index: 1;\n",
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+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
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+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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+       ".xr-var-data > table {\n",
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+       ".xr-var-name span,\n",
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+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
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+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
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+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-attrs dd {\n",
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+       "  white-space: pre-wrap;\n",
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+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
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+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-76244af1-e7c2-43a2-bb44-b504b5e9ddaa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-76244af1-e7c2-43a2-bb44-b504b5e9ddaa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8d823a50-7076-4947-96fd-702b1bddbe19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d823a50-7076-4947-96fd-702b1bddbe19' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-17 00:00:00</div><input id='attrs-52034adf-9972-4607-8f33-07f82fe801da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52034adf-9972-4607-8f33-07f82fe801da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f16af355-e57e-4697-908c-60d734c84171' class='xr-var-data-in' type='checkbox'><label for='data-f16af355-e57e-4697-908c-60d734c84171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1980-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-08-17 00:00:00)], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>189.2 189.4 189.6 ... 17.15 16.91</div><input id='attrs-08733b2c-a8d5-4375-9a4d-b98f9e2a2189' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08733b2c-a8d5-4375-9a4d-b98f9e2a2189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd7da8ea-b09e-43da-bd5d-8965925c269d' class='xr-var-data-in' type='checkbox'><label for='data-cd7da8ea-b09e-43da-bd5d-8965925c269d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>xv</dd></dl></div><pre class='xr-var-data'>array([[189.222932, 189.389909, 189.558366, ..., 293.779061, 294.027924,\n",
+       "        294.274399],\n",
+       "       [188.96837 , 189.134706, 189.302537, ..., 294.05584 , 294.304444,\n",
+       "        294.55066 ],\n",
+       "       [188.712343, 188.878007, 189.045152, ..., 294.335053, 294.583375,\n",
+       "        294.829293],\n",
+       "       ...,\n",
+       "       [124.04724 , 123.88362 , 123.71852 , ...,  16.831718,  16.58437 ,\n",
+       "         16.339496],\n",
+       "       [123.786864, 123.622542, 123.456725, ...,  17.118145,  16.870437,\n",
+       "         16.625183],\n",
+       "       [123.527984, 123.36296 , 123.196441, ...,  17.402099,  17.154053,\n",
+       "         16.908451]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.53 16.78 17.02 ... 27.76 27.51</div><input id='attrs-6dfc53fd-0be5-4c00-b6de-41f86b46d76b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dfc53fd-0be5-4c00-b6de-41f86b46d76b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f903dc1-2901-4e4d-8b92-21322278137e' class='xr-var-data-in' type='checkbox'><label for='data-2f903dc1-2901-4e4d-8b92-21322278137e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>yv</dd></dl></div><pre class='xr-var-data'>array([[16.534986, 16.778456, 17.022224, ..., 27.363016, 27.11811 , 26.87289 ],\n",
+       "       [16.693973, 16.938654, 17.183645, ..., 27.584772, 27.338218, 27.091366],\n",
+       "       [16.852192, 17.098089, 17.344309, ..., 27.805843, 27.557646, 27.309156],\n",
+       "       ...,\n",
+       "       [17.31179 , 17.561247, 17.811046, ..., 28.450248, 28.197183, 27.943847],\n",
+       "       [17.155897, 17.40414 , 17.652723, ..., 28.231296, 27.979893, 27.728216],\n",
+       "       [16.999195, 17.246229, 17.493587, ..., 28.0116  , 27.761856, 27.511827]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-ad153725-09f2-40d0-93c5-556a10d4d29c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad153725-09f2-40d0-93c5-556a10d4d29c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ea363a5-ee5e-4a6e-be03-40ee704fb9c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea363a5-ee5e-4a6e-be03-40ee704fb9c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5204b9cd-d9b3-4909-9e59-e22ab719e3fb' class='xr-var-data-in' type='checkbox'><label for='data-5204b9cd-d9b3-4909-9e59-e22ab719e3fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><pre class='xr-var-data'>[2029500 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-52019fd6-386a-4ed6-ba35-fa8a8aaf3b97' class='xr-section-summary-in' type='checkbox'  ><label for='section-52019fd6-386a-4ed6-ba35-fa8a8aaf3b97' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>&quot;4.6.0&quot;</dd><dt><span>history :</span></dt><dd>Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
+       "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
+       "history deleted for brevity</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (time: 36, x: 275, y: 205)\n",
        "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n",
-       "    lon      (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...\n",
-       "    lat      (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    lon      (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    lat      (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
        "Dimensions without coordinates: x, y\n",
        "Data variables:\n",
-       "    Tair     (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...\n",
+       "    Tair     (time, y, x) float64 ...\n",
        "Attributes:\n",
        "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
        "    institution:               U.W.\n",
@@ -286,14 +1439,375 @@
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c1bac052-f40d-4370-971a-13787291406b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c1bac052-f40d-4370-971a-13787291406b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 72</li><li><span>x_b</span>: 73</li><li><span>y</span>: 45</li><li><span>y_b</span>: 46</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-80ee3dee-1c70-4cd8-9484-421afe62d071' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80ee3dee-1c70-4cd8-9484-421afe62d071' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-b857168a-a536-4493-b2d8-75e9175e46c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b857168a-a536-4493-b2d8-75e9175e46c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5cbc0fc-9bc9-43b1-9819-adfc87183598' class='xr-var-data-in' type='checkbox'><label for='data-a5cbc0fc-9bc9-43b1-9819-adfc87183598' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       ...,\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-88.0 -88.0 -88.0 ... 88.0 88.0</div><input id='attrs-f53230c1-ef81-4b97-80f5-c2d602f633ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f53230c1-ef81-4b97-80f5-c2d602f633ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfa9b529-9810-4ae7-bd83-c5193c9da5d2' class='xr-var-data-in' type='checkbox'><label for='data-dfa9b529-9810-4ae7-bd83-c5193c9da5d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-88., -88., -88., ..., -88., -88., -88.],\n",
+       "       [-84., -84., -84., ..., -84., -84., -84.],\n",
+       "       [-80., -80., -80., ..., -80., -80., -80.],\n",
+       "       ...,\n",
+       "       [ 80.,  80.,  80., ...,  80.,  80.,  80.],\n",
+       "       [ 84.,  84.,  84., ...,  84.,  84.,  84.],\n",
+       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-180 -175 -170 -165 ... 170 175 180</div><input id='attrs-ad107d84-bc3d-41f9-a204-e8e061a4bf33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad107d84-bc3d-41f9-a204-e8e061a4bf33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbef9460-af57-4aca-be2b-622a4155c460' class='xr-var-data-in' type='checkbox'><label for='data-fbef9460-af57-4aca-be2b-622a4155c460' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-180, -175, -170, ...,  170,  175,  180],\n",
+       "       [-180, -175, -170, ...,  170,  175,  180],\n",
+       "       [-180, -175, -170, ...,  170,  175,  180],\n",
+       "       ...,\n",
+       "       [-180, -175, -170, ...,  170,  175,  180],\n",
+       "       [-180, -175, -170, ...,  170,  175,  180],\n",
+       "       [-180, -175, -170, ...,  170,  175,  180]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-90 -90 -90 -90 -90 ... 90 90 90 90</div><input id='attrs-1c7b28aa-e874-45b0-bc2d-9661a018aa09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c7b28aa-e874-45b0-bc2d-9661a018aa09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b708c7-7869-438e-a892-d90bbe9fe852' class='xr-var-data-in' type='checkbox'><label for='data-e5b708c7-7869-438e-a892-d90bbe9fe852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-90, -90, -90, ..., -90, -90, -90],\n",
+       "       [-86, -86, -86, ..., -86, -86, -86],\n",
+       "       [-82, -82, -82, ..., -82, -82, -82],\n",
+       "       ...,\n",
+       "       [ 82,  82,  82, ...,  82,  82,  82],\n",
+       "       [ 86,  86,  86, ...,  86,  86,  86],\n",
+       "       [ 90,  90,  90, ...,  90,  90,  90]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-aa7d8247-0918-43d6-af67-717e99f2ce9e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa7d8247-0918-43d6-af67-717e99f2ce9e' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-445aba74-ff1f-4f29-8f94-f585331f95fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-445aba74-ff1f-4f29-8f94-f585331f95fe' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (x: 72, x_b: 73, y: 45, y_b: 46)\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -177.5 -172.5 -167.5 -162.5 -157.5 -152.5 -147.5 ...\n",
-       "    lat      (y, x) float64 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 ...\n",
-       "    lon_b    (y_b, x_b) int64 -180 -175 -170 -165 -160 -155 -150 -145 -140 ...\n",
-       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 -90 -90 -90 -90 ...\n",
+       "    lon      (y, x) float64 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
+       "    lat      (y, x) float64 -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\n",
+       "    lon_b    (y_b, x_b) int64 -180 -175 -170 -165 -160 ... 160 165 170 175 180\n",
+       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n",
        "Dimensions without coordinates: x, x_b, y, y_b\n",
        "Data variables:\n",
        "    *empty*"
@@ -334,16 +1848,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwrite existing file: bilinear_205x275_45x72.nc \n",
-      " You can set reuse_weights=True to save computing time.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "regridder = xe.Regridder(ds, ds_out, 'bilinear')\n",
     "dr_out = regridder(dr)"
@@ -370,36 +1875,493 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span>y</span>: 45</li><li><span>x</span>: 72</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5108e8e3-8d20-4a85-aed6-11f442ab6870' class='xr-array-in' type='checkbox' ><label for='section-5108e8e3-8d20-4a85-aed6-11f442ab6870' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... nan nan nan nan nan nan nan nan</span></div><pre class='xr-array-data'>array([[[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-section-item'><input id='section-f8dc763a-0d4d-4941-9709-47565197104d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8dc763a-0d4d-4941-9709-47565197104d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-17 00:00:00</div><input id='attrs-8a50d2a7-36f4-423f-99d6-36941f21e092' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a50d2a7-36f4-423f-99d6-36941f21e092' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c097f5a-fb69-4941-93df-5771cffa97c9' class='xr-var-data-in' type='checkbox'><label for='data-8c097f5a-fb69-4941-93df-5771cffa97c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1980-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1980-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1981-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-08-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-09-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-10-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-11-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1982-12-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-01-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-02-15 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-03-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-04-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-05-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-06-16 12:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-07-17 00:00:00),\n",
+       "       cftime.DatetimeNoLeap(1983-08-17 00:00:00)], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-a64ee617-2983-4d5c-ae6b-b34c6d1321de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a64ee617-2983-4d5c-ae6b-b34c6d1321de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf627be2-2c56-45ee-a4ed-92f0c68022c3' class='xr-var-data-in' type='checkbox'><label for='data-cf627be2-2c56-45ee-a4ed-92f0c68022c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       ...,\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5],\n",
+       "       [-177.5, -172.5, -167.5, ...,  167.5,  172.5,  177.5]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-88.0 -88.0 -88.0 ... 88.0 88.0</div><input id='attrs-13c1d3c6-524a-40a0-a9ca-1631005d94a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13c1d3c6-524a-40a0-a9ca-1631005d94a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa12bd3-5be2-4fff-b2d8-68ce312fc4b9' class='xr-var-data-in' type='checkbox'><label for='data-9fa12bd3-5be2-4fff-b2d8-68ce312fc4b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-88., -88., -88., ..., -88., -88., -88.],\n",
+       "       [-84., -84., -84., ..., -84., -84., -84.],\n",
+       "       [-80., -80., -80., ..., -80., -80., -80.],\n",
+       "       ...,\n",
+       "       [ 80.,  80.,  80., ...,  80.,  80.,  80.],\n",
+       "       [ 84.,  84.,  84., ...,  84.,  84.,  84.],\n",
+       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-5f30ac66-3db1-4cd0-b8b5-34a0df6c877e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f30ac66-3db1-4cd0-b8b5-34a0df6c877e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.DataArray 'Tair' (time: 36, y: 45, x: 72)>\n",
-       "array([[[  0.,   0., ...,   0.,   0.],\n",
-       "        [  0.,   0., ...,   0.,   0.],\n",
-       "        ..., \n",
-       "        [ nan,  nan, ...,  nan,  nan],\n",
-       "        [ nan,  nan, ...,  nan,  nan]],\n",
-       "\n",
-       "       [[  0.,   0., ...,   0.,   0.],\n",
-       "        [  0.,   0., ...,   0.,   0.],\n",
-       "        ..., \n",
-       "        [ nan,  nan, ...,  nan,  nan],\n",
-       "        [ nan,  nan, ...,  nan,  nan]],\n",
-       "\n",
-       "       ..., \n",
-       "       [[  0.,   0., ...,   0.,   0.],\n",
-       "        [  0.,   0., ...,   0.,   0.],\n",
-       "        ..., \n",
-       "        [ nan,  nan, ...,  nan,  nan],\n",
-       "        [ nan,  nan, ...,  nan,  nan]],\n",
-       "\n",
-       "       [[  0.,   0., ...,   0.,   0.],\n",
-       "        [  0.,   0., ...,   0.,   0.],\n",
-       "        ..., \n",
-       "        [ nan,  nan, ...,  nan,  nan],\n",
-       "        [ nan,  nan, ...,  nan,  nan]]])\n",
+       "array([[[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]])\n",
        "Coordinates:\n",
-       "    lon      (y, x) float64 -177.5 -172.5 -167.5 -162.5 -157.5 -152.5 -147.5 ...\n",
-       "    lat      (y, x) float64 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 -88.0 ...\n",
-       "  * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    lon      (y, x) float64 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
+       "    lat      (y, x) float64 -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\n",
        "Dimensions without coordinates: y, x\n",
        "Attributes:\n",
        "    regrid_method:  bilinear"
@@ -428,12 +2390,14 @@
    "outputs": [
     {
      "data": {
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hXG+T3OPxz/E3t5S+8Q6oVNquEefkHo/KIoRwATh//jx+LtYVPlRMXLwrlV9x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\n8RAEQRAEQWNE4iEIgiAIgsb8P+PmfpbWWBmHAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11aea0da0>"
+       "<Figure size 864x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -443,23 +2407,6 @@
     "dr_out[0].plot.pcolormesh(ax=ax, x='lon', y='lat');\n",
     "ax.coastlines();"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Remove file bilinear_205x275_45x72.nc\n"
-     ]
-    }
-   ],
-   "source": [
-    "regridder.clean_weight_file()  # clean-up"
-   ]
   }
  ],
  "metadata": {
@@ -478,7 +2425,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},
diff --git a/doc/notebooks/Dask.ipynb b/doc/notebooks/Dask.ipynb
index 94f1fa78..5d856a73 100644
--- a/doc/notebooks/Dask.ipynb
+++ b/doc/notebooks/Dask.ipynb
@@ -51,6 +51,422 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4c88a45-9936-44c7-bc88-59a9580b5d86' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4c88a45-9936-44c7-bc88-59a9580b5d86' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-384a34bb-ca24-4b80-8b4c-24ce3d26569b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-384a34bb-ca24-4b80-8b4c-24ce3d26569b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-720cd833-2cbf-4e17-93b3-2ab24a1f8066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-720cd833-2cbf-4e17-93b3-2ab24a1f8066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1687f48-0754-4dee-b255-ddd385cf9609' class='xr-var-data-in' type='checkbox'><label for='data-a1687f48-0754-4dee-b255-ddd385cf9609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-ebe92040-afae-4860-b5d8-71a04c4f2278' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe92040-afae-4860-b5d8-71a04c4f2278' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb43f4d0-a9ea-414c-8015-02eae7571a6c' class='xr-var-data-in' type='checkbox'><label for='data-fb43f4d0-a9ea-414c-8015-02eae7571a6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-6e6ffdca-6928-4264-9a0d-31a4fe2ab68e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e6ffdca-6928-4264-9a0d-31a4fe2ab68e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fede81e7-cc8c-4c18-9129-18495a1ddca6' class='xr-var-data-in' type='checkbox'><label for='data-fede81e7-cc8c-4c18-9129-18495a1ddca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-f5250279-0a73-4aec-a65f-db76c9dbcf45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5250279-0a73-4aec-a65f-db76c9dbcf45' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(500, 25, 53), meta=np.ndarray&gt;</div><input id='attrs-7f6b7004-d3bb-4e22-a6a3-e49ff4d45184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f6b7004-d3bb-4e22-a6a3-e49ff4d45184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-873d3bea-8ff3-479b-a19b-1271f3350d7b' class='xr-var-data-in' type='checkbox'><label for='data-873d3bea-8ff3-479b-a19b-1271f3350d7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'><table>\n",
+       "<tr>\n",
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+       "    <tr><th> Shape </th><td> (2920, 25, 53) </td> <td> (500, 25, 53) </td></tr>\n",
+       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
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+       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-cd142117-9360-43a4-aa6c-e2d81568d3ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd142117-9360-43a4-aa6c-e2d81568d3ae' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
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       "text/plain": [
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        "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
@@ -59,7 +475,7 @@
        "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
        "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
        "Data variables:\n",
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+       "    air      (time, lat, lon) float32 dask.array<chunksize=(500, 25, 53), meta=np.ndarray>\n",
        "Attributes:\n",
        "    Conventions:  COARDS\n",
        "    title:        4x daily NMC reanalysis (1948)\n",
@@ -109,14 +525,17 @@
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        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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@@ -174,7 +593,7 @@
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+       "dask.array<open_dataset-6ea21183781926acde7ac39e7ad66665air, shape=(2920, 25, 53), dtype=float32, chunksize=(500, 25, 53), chunktype=numpy.ndarray>"
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      },
      "execution_count": 4,
@@ -198,21 +617,11 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: bilinear_25x53_59x87.nc\n",
-      "Remove file bilinear_25x53_59x87.nc\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       bilinear \n",
-       "Weight filename:            bilinear_25x53_59x87.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (25, 53) \n",
        "Output grid shape:          (59, 87) \n",
        "Output grid dimension name: ('lat', 'lon') \n",
@@ -231,7 +640,6 @@
     "                   )\n",
     "\n",
     "regridder = xe.Regridder(ds, ds_out, 'bilinear')\n",
-    "regridder.clean_weight_file()\n",
     "regridder"
    ]
   },
@@ -252,12 +660,432 @@
      "output_type": "stream",
      "text": [
       "using dimensions ('lat', 'lon') from data variable air as the horizontal dimensions for this dataset.\n",
-      "CPU times: user 17 ms, sys: 4.58 ms, total: 21.6 ms\n",
-      "Wall time: 18.8 ms\n"
+      "CPU times: user 2.09 ms, sys: 0 ns, total: 2.09 ms\n",
+      "Wall time: 2.06 ms\n"
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+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1f4881b9-c3e8-45a0-8b74-db7024bf0f21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f4881b9-c3e8-45a0-8b74-db7024bf0f21' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f1a61548-e457-49ae-86c2-05466c35ab99' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1a61548-e457-49ae-86c2-05466c35ab99' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-b080276e-c3fc-4dc6-a835-e186b40cdbfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b080276e-c3fc-4dc6-a835-e186b40cdbfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48c541c1-0e39-4a95-94c4-4b8670fef384' class='xr-var-data-in' type='checkbox'><label for='data-48c541c1-0e39-4a95-94c4-4b8670fef384' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-5765109e-4c4f-4350-884b-5417862b0a66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5765109e-4c4f-4350-884b-5417862b0a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05f81fc-d75e-42fa-a0af-de3a1000c339' class='xr-var-data-in' type='checkbox'><label for='data-b05f81fc-d75e-42fa-a0af-de3a1000c339' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-d6b15aa0-da89-47de-93c8-e2039c68ade3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6b15aa0-da89-47de-93c8-e2039c68ade3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db89a181-9c78-4337-8106-4f0726312ef9' class='xr-var-data-in' type='checkbox'><label for='data-db89a181-9c78-4337-8106-4f0726312ef9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-1dd14c7a-2ea7-42b3-ade4-15c4371ab538' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1dd14c7a-2ea7-42b3-ade4-15c4371ab538' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(500, 59, 87), meta=np.ndarray&gt;</div><input id='attrs-045d8081-3ccb-4cf4-b3ea-31a21409b85a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-045d8081-3ccb-4cf4-b3ea-31a21409b85a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-026d0e4d-8397-4bf3-81fc-dd8c48da9cad' class='xr-var-data-in' type='checkbox'><label for='data-026d0e4d-8397-4bf3-81fc-dd8c48da9cad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 119.91 MB </td> <td> 20.53 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (2920, 59, 87) </td> <td> (500, 59, 87) </td></tr>\n",
+       "    <tr><th> Count </th><td> 13 Tasks </td><td> 6 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"161\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <text x=\"35.294118\" y=\"84.382324\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,84.382324)\">2920</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-8b424969-dcd3-4cea-bcd4-b38839646d21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b424969-dcd3-4cea-bcd4-b38839646d21' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 59, lon: 87, time: 2920)\n",
@@ -266,7 +1094,7 @@
        "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
        "  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\n",
        "Data variables:\n",
-       "    air      (time, lat, lon) float64 dask.array<shape=(2920, 59, 87), chunksize=(500, 59, 87)>\n",
+       "    air      (time, lat, lon) float64 dask.array<chunksize=(500, 59, 87), meta=np.ndarray>\n",
        "Attributes:\n",
        "    regrid_method:  bilinear"
       ]
@@ -293,14 +1121,17 @@
        "<table>\n",
        "<tr>\n",
        "<td>\n",
-       "<table>  <thead>    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr><th> Bytes </th><td> 119.91 MB </td> <td> 20.53 MB </td></tr>\n",
        "    <tr><th> Shape </th><td> (2920, 59, 87) </td> <td> (500, 59, 87) </td></tr>\n",
        "    <tr><th> Count </th><td> 13 Tasks </td><td> 6 Chunks </td></tr>\n",
        "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
-       "  </tbody></table>\n",
+       "  </tbody>\n",
+       "</table>\n",
        "</td>\n",
        "<td>\n",
        "<svg width=\"161\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
@@ -358,7 +1189,7 @@
        "</table>"
       ],
       "text/plain": [
-       "dask.array<regrid_numpy, shape=(2920, 59, 87), dtype=float64, chunksize=(500, 59, 87)>"
+       "dask.array<regrid_numpy, shape=(2920, 59, 87), dtype=float64, chunksize=(500, 59, 87), chunktype=numpy.ndarray>"
       ]
      },
      "execution_count": 7,
@@ -379,8 +1210,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 310 ms, sys: 619 ms, total: 929 ms\n",
-      "Wall time: 389 ms\n"
+      "CPU times: user 298 ms, sys: 212 ms, total: 510 ms\n",
+      "Wall time: 154 ms\n"
      ]
     }
    ],
@@ -429,6 +1260,426 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c6962f76-0f2f-47da-82e9-48605d77b933' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6962f76-0f2f-47da-82e9-48605d77b933' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a04195b4-59b7-453b-ab27-df0b8d099ef2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a04195b4-59b7-453b-ab27-df0b8d099ef2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-ad50a2b5-ba2f-454a-a879-bf82acae78b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad50a2b5-ba2f-454a-a879-bf82acae78b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10abaf89-4daa-4dcd-bae3-497c50065363' class='xr-var-data-in' type='checkbox'><label for='data-10abaf89-4daa-4dcd-bae3-497c50065363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-e2bfd819-a069-433f-b637-7790523271ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2bfd819-a069-433f-b637-7790523271ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfc3f8cc-c2f0-4710-bb82-02ccac4146d9' class='xr-var-data-in' type='checkbox'><label for='data-dfc3f8cc-c2f0-4710-bb82-02ccac4146d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-fe012f7b-90bd-4ba4-afa9-93971e0743ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe012f7b-90bd-4ba4-afa9-93971e0743ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a73e6ae-39a6-4bd8-bbd0-9f25ebb2fb7d' class='xr-var-data-in' type='checkbox'><label for='data-5a73e6ae-39a6-4bd8-bbd0-9f25ebb2fb7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-51ccfed3-8883-4d56-81df-ded47413647e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51ccfed3-8883-4d56-81df-ded47413647e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2920, 10, 10), meta=np.ndarray&gt;</div><input id='attrs-408f9de6-1518-4d5c-92f4-70cd71bb3852' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-408f9de6-1518-4d5c-92f4-70cd71bb3852' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bf3e1e8-c224-47c3-9211-0ba206c6b555' class='xr-var-data-in' type='checkbox'><label for='data-5bf3e1e8-c224-47c3-9211-0ba206c6b555' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 15.48 MB </td> <td> 1.17 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (2920, 25, 53) </td> <td> (2920, 10, 10) </td></tr>\n",
+       "    <tr><th> Count </th><td> 44 Tasks </td><td> 18 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
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+       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-233dc0a5-65a1-4894-a3be-a5dc14b9af30' class='xr-section-summary-in' type='checkbox'  checked><label for='section-233dc0a5-65a1-4894-a3be-a5dc14b9af30' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
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@@ -437,7 +1688,7 @@
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        "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
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+       "    air      (time, lat, lon) float32 dask.array<chunksize=(2920, 10, 10), meta=np.ndarray>\n",
        "Attributes:\n",
        "    Conventions:  COARDS\n",
        "    title:        4x daily NMC reanalysis (1948)\n",
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+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-90269549-2b04-4c2e-87f0-072ad3bde1eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90269549-2b04-4c2e-87f0-072ad3bde1eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-067ac8d9-3b05-47e2-8224-d4240dba68db' class='xr-var-data-in' type='checkbox'><label for='data-067ac8d9-3b05-47e2-8224-d4240dba68db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-82bdd7ff-ccb5-4f16-8302-36d54ced748e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82bdd7ff-ccb5-4f16-8302-36d54ced748e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55cdf1df-30c0-4b58-adbf-1b146c862c95' class='xr-var-data-in' type='checkbox'><label for='data-55cdf1df-30c0-4b58-adbf-1b146c862c95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-9706a765-fa1d-4afc-a4a9-ea8816064adf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9706a765-fa1d-4afc-a4a9-ea8816064adf' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>296.1 296.4 296.6 ... 241.0 241.5</div><input id='attrs-6c7d5ca7-bcec-4ca6-bc98-f4940aa2267e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c7d5ca7-bcec-4ca6-bc98-f4940aa2267e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48d57d72-9a1a-4aa8-900d-3b32e56a84b0' class='xr-var-data-in' type='checkbox'><label for='data-48d57d72-9a1a-4aa8-900d-3b32e56a84b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "         296.43398913, 296.19924566],\n",
+       "        [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "         296.07802394, 295.72098714],\n",
+       "        [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "         295.73997197, 295.35693248],\n",
+       "        ...,\n",
+       "        [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "         235.51802332, 238.0780694 ],\n",
+       "        [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "         235.33999633, 237.63241841],\n",
+       "        [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "         235.41999207, 237.49641598]],\n",
+       "\n",
+       "       [[296.25399643, 296.70203773, 297.03166485, ..., 296.06514956,\n",
+       "         296.03998263, 296.01773136],\n",
+       "        [296.2179898 , 296.56767711, 296.82291528, ..., 295.7292558 ,\n",
+       "         295.6800262 , 295.5138904 ],\n",
+       "        [296.23999022, 296.42058286, 296.56714652, ..., 295.50442291,\n",
+       "         295.41998903, 295.19133215],\n",
+       "        ...,\n",
+       "        [245.52028453, 245.73709231, 245.85148963, ..., 231.64759509,\n",
+       "         232.67802699, 234.83033953],\n",
+       "        [243.29994515, 243.61404829, 243.85326489, ..., 231.80653129,\n",
+       "         232.72003168, 234.51923375],\n",
+       "        [242.70001369, 243.03800427, 243.31726258, ..., 232.22256285,\n",
+       "         233.15997775, 234.71925176]],\n",
+       "\n",
+       "       [[296.31998597, 296.35233477, 296.37027072, ..., 296.69703874,\n",
+       "         296.59998477, 296.42993717],\n",
+       "        [296.23999022, 296.37072264, 296.42865111, ..., 296.39312798,\n",
+       "         296.20003046, 295.98447426],\n",
+       "        [296.07996829, 296.20134835, 296.24744824, ..., 296.17051878,\n",
+       "         295.85798008, 295.63218076],\n",
+       "        ...,\n",
+       "        [246.92034241, 246.75294557, 246.50912779, ..., 231.18562131,\n",
+       "         232.24003595, 234.61904532],\n",
+       "        [244.13992017, 244.03040136, 243.89556811, ..., 231.78222568,\n",
+       "         232.82012307, 234.90301222],\n",
+       "        [243.22002405, 243.13672999, 243.05876072, ..., 233.39835074,\n",
+       "         234.45993206, 236.27912467]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[297.62998356, 298.25582152, 298.65503226, ..., 295.7786526 ,\n",
+       "         295.66999665, 295.50299763],\n",
+       "        [297.07004997, 297.7199817 , 298.19914665, ..., 295.58670885,\n",
+       "         295.55000304, 295.2150872 ],\n",
+       "        [296.38994762, 296.98154159, 297.52424514, ..., 295.48204978,\n",
+       "         295.46998597, 295.05016151],\n",
+       "        ...,\n",
+       "        [251.81041188, 251.72296686, 251.55990593, ..., 240.75713161,\n",
+       "         241.37000425, 242.46456685],\n",
+       "        [247.96982451, 247.87036819, 247.69574495, ..., 241.55307104,\n",
+       "         241.93009016, 242.64624471],\n",
+       "        [245.73007797, 245.53418764, 245.25570503, ..., 242.92917313,\n",
+       "         243.20994271, 243.68633428]],\n",
+       "\n",
+       "       [[297.1899857 , 297.6237982 , 297.95503528, ..., 295.4505457 ,\n",
+       "         295.32998658, 295.05487915],\n",
+       "        [296.59005424, 297.09596253, 297.49915579, ..., 295.29059716,\n",
+       "         295.1700073 , 294.7989566 ],\n",
+       "        [295.76992812, 296.23054587, 296.65708274, ..., 295.15442138,\n",
+       "         295.00998537, 294.626334  ],\n",
+       "        ...,\n",
+       "        [252.39037715, 252.04962511, 251.65524335, ..., 241.72163807,\n",
+       "         242.61000973, 243.93278497],\n",
+       "        [249.06987326, 248.66822819, 248.21431545, ..., 242.12742803,\n",
+       "         242.79003593, 243.74867727],\n",
+       "        [247.43005818, 246.93209439, 246.37428385, ..., 242.84748108,\n",
+       "         243.38996739, 244.10871142]],\n",
+       "\n",
+       "       [[297.04997563, 297.38785212, 297.63503348, ..., 296.09892817,\n",
+       "         295.98999483, 295.690696  ],\n",
+       "        [296.4100463 , 296.84401245, 297.17914422, ..., 295.82701626,\n",
+       "         295.79001767, 295.49075727],\n",
+       "        [295.62992871, 296.00626644, 296.35697279, ..., 295.59472609,\n",
+       "         295.56998294, 295.28282378],\n",
+       "        ...,\n",
+       "        [252.51039665, 252.14637887, 251.70761893, ..., 240.09173906,\n",
+       "         240.75006397, 241.87173824],\n",
+       "        [248.92985255, 248.55305942, 248.11071361, ..., 240.21550695,\n",
+       "         240.53002923, 241.23719787],\n",
+       "        [247.01007067, 246.58490681, 246.10268406, ..., 240.91155301,\n",
+       "         241.00997318, 241.45322238]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-91921800-8b0d-4b6b-b31a-d7643b6a8d69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91921800-8b0d-4b6b-b31a-d7643b6a8d69' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 59, lon: 87, time: 2920)\n",
@@ -520,7 +2207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/doc/notebooks/Dataset.ipynb b/doc/notebooks/Dataset.ipynb
index baec0a04..d29d8ba6 100644
--- a/doc/notebooks/Dataset.ipynb
+++ b/doc/notebooks/Dataset.ipynb
@@ -48,6 +48,354 @@
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+       "  background-color: var(--xr-background-color-row-odd);\n",
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+       "\n",
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+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
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+       "\n",
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+       "\n",
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
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+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
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+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0beae9e8-9a8d-4077-9edb-f7c83bf4a011' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0beae9e8-9a8d-4077-9edb-f7c83bf4a011' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-533ecbb6-eacc-4f0d-8a11-9a26ff5c3c5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-533ecbb6-eacc-4f0d-8a11-9a26ff5c3c5c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-475fc162-f488-4940-8391-74c135f53939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-475fc162-f488-4940-8391-74c135f53939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33ddb066-9178-49de-aa60-1a0fee56b423' class='xr-var-data-in' type='checkbox'><label for='data-33ddb066-9178-49de-aa60-1a0fee56b423' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-ead5f8a7-8d95-4a64-8981-eab1ca2fb01b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ead5f8a7-8d95-4a64-8981-eab1ca2fb01b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-443052b8-3e9a-4bf3-b96d-387a8c06dc65' class='xr-var-data-in' type='checkbox'><label for='data-443052b8-3e9a-4bf3-b96d-387a8c06dc65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-b063c5d2-6f3b-4e80-b0f2-bc03f12497ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b063c5d2-6f3b-4e80-b0f2-bc03f12497ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e0879bf-7796-439e-91df-7cac3d544678' class='xr-var-data-in' type='checkbox'><label for='data-0e0879bf-7796-439e-91df-7cac3d544678' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-11798517-608c-4cbc-b011-0b6fb2ead5ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11798517-608c-4cbc-b011-0b6fb2ead5ad' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1aa2289b-603d-427a-907f-8313c07c8122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aa2289b-603d-427a-907f-8313c07c8122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24c5749-e88c-4565-aa6c-74598bb11711' class='xr-var-data-in' type='checkbox'><label for='data-a24c5749-e88c-4565-aa6c-74598bb11711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>[3869000 values with dtype=float32]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-aac9ba94-1672-43b6-8747-4a934afd8877' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aac9ba94-1672-43b6-8747-4a934afd8877' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
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        "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
@@ -82,6 +430,469 @@
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+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8b029423-0029-4a6d-ad96-7f41a3b6bee6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b029423-0029-4a6d-ad96-7f41a3b6bee6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eae506e4-e793-4642-a6d6-3cf5413dcb49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eae506e4-e793-4642-a6d6-3cf5413dcb49' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-bbf20b57-2a64-4137-8702-d4286f80d48d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbf20b57-2a64-4137-8702-d4286f80d48d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7684d907-a8e9-4b93-8234-254cc29d0c07' class='xr-var-data-in' type='checkbox'><label for='data-7684d907-a8e9-4b93-8234-254cc29d0c07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-37483603-a97a-4949-93d8-1c730e5ccba8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37483603-a97a-4949-93d8-1c730e5ccba8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-428abf57-74af-4690-8ce7-729169b00d4b' class='xr-var-data-in' type='checkbox'><label for='data-428abf57-74af-4690-8ce7-729169b00d4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-90dd3a90-7808-4d85-a896-dd115256e263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90dd3a90-7808-4d85-a896-dd115256e263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c544f746-8de0-42b5-9f97-b1b5c61b785c' class='xr-var-data-in' type='checkbox'><label for='data-c544f746-8de0-42b5-9f97-b1b5c61b785c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-3daf5a03-dca0-4f18-b49f-1477d479eda2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3daf5a03-dca0-4f18-b49f-1477d479eda2' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.5 243.5 ... 296.19 295.69</div><input id='attrs-0ca25786-f906-4146-90a3-6233112720a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ca25786-f906-4146-90a3-6233112720a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69af3c1b-f1a5-4fb5-9459-e01b060d133d' class='xr-var-data-in' type='checkbox'><label for='data-69af3c1b-f1a5-4fb5-9459-e01b060d133d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>array([[[241.2    , 242.5    , ..., 235.5    , 238.59999],\n",
+       "        [243.79999, 244.5    , ..., 235.29999, 239.29999],\n",
+       "        ...,\n",
+       "        [295.9    , 296.19998, ..., 295.9    , 295.19998],\n",
+       "        [296.29   , 296.79   , ..., 296.79   , 296.6    ]],\n",
+       "\n",
+       "       [[242.09999, 242.7    , ..., 233.59999, 235.79999],\n",
+       "        [243.59999, 244.09999, ..., 232.5    , 235.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.69998, ..., 295.5    , 295.1    ],\n",
+       "        [296.29   , 297.19998, ..., 296.4    , 296.6    ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[245.79   , 244.79   , ..., 243.98999, 244.79   ],\n",
+       "        [249.89   , 249.29   , ..., 242.48999, 244.29   ],\n",
+       "        ...,\n",
+       "        [296.29   , 297.19   , ..., 295.09   , 294.38998],\n",
+       "        [297.79   , 298.38998, ..., 295.49   , 295.19   ]],\n",
+       "\n",
+       "       [[245.09   , 244.29   , ..., 241.48999, 241.79   ],\n",
+       "        [249.89   , 249.29   , ..., 240.29   , 241.68999],\n",
+       "        ...,\n",
+       "        [296.09   , 296.88998, ..., 295.69   , 295.19   ],\n",
+       "        [297.69   , 298.09   , ..., 296.19   , 295.69   ]]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>celsius</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-31.949997 -30.649994 ... 22.540009</div><input id='attrs-a9214ad1-a683-4467-a620-98df8e24eca7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9214ad1-a683-4467-a620-98df8e24eca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5b1bceb-3ab7-4141-a2c5-c2099e24f36c' class='xr-var-data-in' type='checkbox'><label for='data-b5b1bceb-3ab7-4141-a2c5-c2099e24f36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n",
+       "         -37.649994, -34.550003],\n",
+       "        [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n",
+       "         -37.850006, -33.850006],\n",
+       "        [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n",
+       "         -36.759995, -31.449997],\n",
+       "        ...,\n",
+       "        [ 23.450012,  23.049988,  23.25    , ...,  22.25    ,\n",
+       "          21.950012,  21.549988],\n",
+       "        [ 22.75    ,  23.049988,  23.640015, ...,  22.75    ,\n",
+       "          22.75    ,  22.049988],\n",
+       "        [ 23.140015,  23.640015,  23.950012, ...,  23.75    ,\n",
+       "          23.640015,  23.450012]],\n",
+       "\n",
+       "       [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n",
+       "         -39.550003, -37.350006],\n",
+       "        [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n",
+       "         -40.649994, -37.449997],\n",
+       "        [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n",
+       "         -39.759995, -34.649994],\n",
+       "        ...,\n",
+       "        [ 23.25    ,  22.75    ,  23.049988, ...,  22.25    ,\n",
+       "          21.950012,  21.640015],\n",
+       "        [ 23.049988,  23.549988,  23.640015, ...,  22.450012,\n",
+       "          22.350006,  21.950012],\n",
+       "        [ 23.140015,  24.049988,  24.25    , ...,  23.25    ,\n",
+       "          23.25    ,  23.450012]],\n",
+       "\n",
+       "       [[-30.850006, -30.949997, -30.850006, ..., -38.850006,\n",
+       "         -37.050003, -34.449997],\n",
+       "        [-28.550003, -28.759995, -29.149994, ..., -42.850006,\n",
+       "         -41.149994, -37.449997],\n",
+       "        [-16.950012, -17.649994, -18.949997, ..., -41.949997,\n",
+       "         -39.949997, -34.949997],\n",
+       "        ...,\n",
+       "        [ 22.450012,  22.25    ,  22.25    , ...,  23.140015,\n",
+       "          22.140015,  21.850006],\n",
+       "        [ 23.049988,  23.350006,  23.140015, ...,  23.25    ,\n",
+       "          22.850006,  22.450012],\n",
+       "        [ 23.25    ,  23.140015,  23.25    , ...,  23.850006,\n",
+       "          23.850006,  23.640015]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-29.660004, -30.160004, -31.059998, ..., -28.960007,\n",
+       "         -28.660004, -28.259995],\n",
+       "        [-24.059998, -24.160004, -24.559998, ..., -32.559998,\n",
+       "         -31.86    , -30.460007],\n",
+       "        [-10.459991, -10.959991, -11.459991, ..., -33.759995,\n",
+       "         -31.460007, -27.960007],\n",
+       "        ...,\n",
+       "        [ 21.640015,  22.140015,  24.339996, ...,  22.339996,\n",
+       "          22.23999 ,  21.540009],\n",
+       "        [ 23.640015,  24.73999 ,  25.140015, ...,  22.339996,\n",
+       "          22.339996,  21.640015],\n",
+       "        [ 25.040009,  26.040009,  25.640015, ...,  22.940002,\n",
+       "          22.640015,  22.640015]],\n",
+       "\n",
+       "       [[-27.36    , -28.36    , -29.660004, ..., -29.86    ,\n",
+       "         -29.160004, -28.36    ],\n",
+       "        [-23.259995, -23.86    , -24.660004, ..., -31.86    ,\n",
+       "         -30.660004, -28.86    ],\n",
+       "        [-10.76001 , -11.359985, -11.859985, ..., -32.660004,\n",
+       "         -30.059998, -26.259995],\n",
+       "        ...,\n",
+       "        [ 20.540009,  20.73999 ,  22.23999 , ...,  21.940002,\n",
+       "          21.540009,  21.140015],\n",
+       "        [ 23.140015,  24.040009,  24.440002, ...,  22.140015,\n",
+       "          21.940002,  21.23999 ],\n",
+       "        [ 24.640015,  25.23999 ,  25.339996, ...,  22.540009,\n",
+       "          22.339996,  22.040009]],\n",
+       "\n",
+       "       [[-28.059998, -28.86    , -29.86    , ..., -31.460007,\n",
+       "         -31.660004, -31.36    ],\n",
+       "        [-23.259995, -23.86    , -24.759995, ..., -33.559998,\n",
+       "         -32.86    , -31.460007],\n",
+       "        [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\n",
+       "         -30.559998, -26.86    ],\n",
+       "        ...,\n",
+       "        [ 20.640015,  20.540009,  21.940002, ...,  22.140015,\n",
+       "          21.940002,  21.540009],\n",
+       "        [ 22.940002,  23.73999 ,  24.040009, ...,  22.540009,\n",
+       "          22.540009,  22.040009],\n",
+       "        [ 24.540009,  24.940002,  24.940002, ...,  23.339996,\n",
+       "          23.040009,  22.540009]]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>slice</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.5 243.5 ... 296.79 296.6</div><input id='attrs-97b2fa89-1a0e-4803-a3d0-6af9ac64ff4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97b2fa89-1a0e-4803-a3d0-6af9ac64ff4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b70a19d-3e20-4564-99d1-956baee33b46' class='xr-var-data-in' type='checkbox'><label for='data-9b70a19d-3e20-4564-99d1-956baee33b46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>array([[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    , 238.59999],\n",
+       "       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999, 239.29999],\n",
+       "       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   , 241.7    ],\n",
+       "       ...,\n",
+       "       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    , 294.69998],\n",
+       "       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    , 295.19998],\n",
+       "       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   , 296.6    ]],\n",
+       "      dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-328a94dc-7ec6-43d2-9b11-c04ed9ec7b10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-328a94dc-7ec6-43d2-9b11-c04ed9ec7b10' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
@@ -125,21 +936,11 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: bilinear_25x53_59x87.nc\n",
-      "Remove file bilinear_25x53_59x87.nc\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       bilinear \n",
-       "Weight filename:            bilinear_25x53_59x87.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (25, 53) \n",
        "Output grid shape:          (59, 87) \n",
        "Output grid dimension name: ('lat', 'lon') \n",
@@ -158,7 +959,6 @@
     "                   )\n",
     "\n",
     "regridder = xe.Regridder(ds, ds_out, 'bilinear')\n",
-    "regridder.clean_weight_file()\n",
     "regridder"
    ]
   },
@@ -183,6 +983,538 @@
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-63989e58-bcfb-440f-8a69-fde9dcc9228c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63989e58-bcfb-440f-8a69-fde9dcc9228c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff97bda6-fc6f-49cc-b782-8773ea11a797' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff97bda6-fc6f-49cc-b782-8773ea11a797' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-bd407ce0-9c27-4010-ba2d-d81e5a1c0d05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd407ce0-9c27-4010-ba2d-d81e5a1c0d05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc122e6-efd2-48b7-a516-da43cc54321e' class='xr-var-data-in' type='checkbox'><label for='data-afc122e6-efd2-48b7-a516-da43cc54321e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-d4753736-5990-454d-bb92-6566f2c719ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4753736-5990-454d-bb92-6566f2c719ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e797ecf-37f5-4783-a4f2-34cca4222162' class='xr-var-data-in' type='checkbox'><label for='data-0e797ecf-37f5-4783-a4f2-34cca4222162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-24a8e563-5345-4584-9c2f-3d1278c87440' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24a8e563-5345-4584-9c2f-3d1278c87440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47bf911-3451-4f30-9873-2e51e95059f7' class='xr-var-data-in' type='checkbox'><label for='data-a47bf911-3451-4f30-9873-2e51e95059f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-51abe2ff-3903-46fa-9f89-67c3b56469d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51abe2ff-3903-46fa-9f89-67c3b56469d6' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>296.1 296.4 296.6 ... 241.0 241.5</div><input id='attrs-01a8d3ee-6063-4956-8425-f6e23a5929cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01a8d3ee-6063-4956-8425-f6e23a5929cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-219b2ae3-09de-4f5f-ada5-50467725b638' class='xr-var-data-in' type='checkbox'><label for='data-219b2ae3-09de-4f5f-ada5-50467725b638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "         296.43398913, 296.19924566],\n",
+       "        [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "         296.07802394, 295.72098714],\n",
+       "        [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "         295.73997197, 295.35693248],\n",
+       "        ...,\n",
+       "        [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "         235.51802332, 238.0780694 ],\n",
+       "        [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "         235.33999633, 237.63241841],\n",
+       "        [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "         235.41999207, 237.49641598]],\n",
+       "\n",
+       "       [[296.25399643, 296.70203773, 297.03166485, ..., 296.06514956,\n",
+       "         296.03998263, 296.01773136],\n",
+       "        [296.2179898 , 296.56767711, 296.82291528, ..., 295.7292558 ,\n",
+       "         295.6800262 , 295.5138904 ],\n",
+       "        [296.23999022, 296.42058286, 296.56714652, ..., 295.50442291,\n",
+       "         295.41998903, 295.19133215],\n",
+       "        ...,\n",
+       "        [245.52028453, 245.73709231, 245.85148963, ..., 231.64759509,\n",
+       "         232.67802699, 234.83033953],\n",
+       "        [243.29994515, 243.61404829, 243.85326489, ..., 231.80653129,\n",
+       "         232.72003168, 234.51923375],\n",
+       "        [242.70001369, 243.03800427, 243.31726258, ..., 232.22256285,\n",
+       "         233.15997775, 234.71925176]],\n",
+       "\n",
+       "       [[296.31998597, 296.35233477, 296.37027072, ..., 296.69703874,\n",
+       "         296.59998477, 296.42993717],\n",
+       "        [296.23999022, 296.37072264, 296.42865111, ..., 296.39312798,\n",
+       "         296.20003046, 295.98447426],\n",
+       "        [296.07996829, 296.20134835, 296.24744824, ..., 296.17051878,\n",
+       "         295.85798008, 295.63218076],\n",
+       "        ...,\n",
+       "        [246.92034241, 246.75294557, 246.50912779, ..., 231.18562131,\n",
+       "         232.24003595, 234.61904532],\n",
+       "        [244.13992017, 244.03040136, 243.89556811, ..., 231.78222568,\n",
+       "         232.82012307, 234.90301222],\n",
+       "        [243.22002405, 243.13672999, 243.05876072, ..., 233.39835074,\n",
+       "         234.45993206, 236.27912467]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[297.62998356, 298.25582152, 298.65503226, ..., 295.7786526 ,\n",
+       "         295.66999665, 295.50299763],\n",
+       "        [297.07004997, 297.7199817 , 298.19914665, ..., 295.58670885,\n",
+       "         295.55000304, 295.2150872 ],\n",
+       "        [296.38994762, 296.98154159, 297.52424514, ..., 295.48204978,\n",
+       "         295.46998597, 295.05016151],\n",
+       "        ...,\n",
+       "        [251.81041188, 251.72296686, 251.55990593, ..., 240.75713161,\n",
+       "         241.37000425, 242.46456685],\n",
+       "        [247.96982451, 247.87036819, 247.69574495, ..., 241.55307104,\n",
+       "         241.93009016, 242.64624471],\n",
+       "        [245.73007797, 245.53418764, 245.25570503, ..., 242.92917313,\n",
+       "         243.20994271, 243.68633428]],\n",
+       "\n",
+       "       [[297.1899857 , 297.6237982 , 297.95503528, ..., 295.4505457 ,\n",
+       "         295.32998658, 295.05487915],\n",
+       "        [296.59005424, 297.09596253, 297.49915579, ..., 295.29059716,\n",
+       "         295.1700073 , 294.7989566 ],\n",
+       "        [295.76992812, 296.23054587, 296.65708274, ..., 295.15442138,\n",
+       "         295.00998537, 294.626334  ],\n",
+       "        ...,\n",
+       "        [252.39037715, 252.04962511, 251.65524335, ..., 241.72163807,\n",
+       "         242.61000973, 243.93278497],\n",
+       "        [249.06987326, 248.66822819, 248.21431545, ..., 242.12742803,\n",
+       "         242.79003593, 243.74867727],\n",
+       "        [247.43005818, 246.93209439, 246.37428385, ..., 242.84748108,\n",
+       "         243.38996739, 244.10871142]],\n",
+       "\n",
+       "       [[297.04997563, 297.38785212, 297.63503348, ..., 296.09892817,\n",
+       "         295.98999483, 295.690696  ],\n",
+       "        [296.4100463 , 296.84401245, 297.17914422, ..., 295.82701626,\n",
+       "         295.79001767, 295.49075727],\n",
+       "        [295.62992871, 296.00626644, 296.35697279, ..., 295.59472609,\n",
+       "         295.56998294, 295.28282378],\n",
+       "        ...,\n",
+       "        [252.51039665, 252.14637887, 251.70761893, ..., 240.09173906,\n",
+       "         240.75006397, 241.87173824],\n",
+       "        [248.92985255, 248.55305942, 248.11071361, ..., 240.21550695,\n",
+       "         240.53002923, 241.23719787],\n",
+       "        [247.01007067, 246.58490681, 246.10268406, ..., 240.91155301,\n",
+       "         241.00997318, 241.45322238]]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>celsius</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.98 23.24 23.49 ... -32.14 -31.7</div><input id='attrs-80bdce24-69f4-4d19-ad39-5493d730e692' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80bdce24-69f4-4d19-ad39-5493d730e692' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f72406d6-a526-4c2f-8f0e-9186d8b7dbf3' class='xr-var-data-in' type='checkbox'><label for='data-f72406d6-a526-4c2f-8f0e-9186d8b7dbf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[ 22.98400285,  23.23669915,  23.48890434, ...,  23.32491403,\n",
+       "          23.28399523,  23.04925177],\n",
+       "        [ 22.82801481,  23.03275408,  23.27535111, ...,  22.94262952,\n",
+       "          22.92803004,  22.57099324],\n",
+       "        [ 22.89002377,  22.98556885,  23.15248584, ...,  22.62693524,\n",
+       "          22.58997807,  22.20693858],\n",
+       "        ...,\n",
+       "        [-28.10981478, -27.7891234 , -27.58903202, ..., -39.21370283,\n",
+       "         -37.63197058, -35.0719245 ],\n",
+       "        [-29.87008348, -29.37479887, -28.97624337, ..., -39.33408115,\n",
+       "         -37.80999757, -35.51757549],\n",
+       "        [-30.90996101, -30.27087087, -29.71224358, ..., -39.30207549,\n",
+       "         -37.73000183, -35.65357792]],\n",
+       "\n",
+       "       [[ 23.10400254,  23.55204383,  23.88167095, ...,  22.91515567,\n",
+       "          22.88998873,  22.86773746],\n",
+       "        [ 23.06799591,  23.41768321,  23.67292138, ...,  22.5792619 ,\n",
+       "          22.5300323 ,  22.3638965 ],\n",
+       "        [ 23.08999633,  23.27058897,  23.41715262, ...,  22.35442901,\n",
+       "          22.26999514,  22.04133825],\n",
+       "        ...,\n",
+       "        [-27.62970937, -27.41290159, -27.29850427, ..., -41.50239881,\n",
+       "         -40.4719669 , -38.31965437],\n",
+       "        [-29.85004875, -29.5359456 , -29.29672901, ..., -41.3434626 ,\n",
+       "         -40.42996222, -38.63076015],\n",
+       "        [-30.4499802 , -30.11198963, -29.83273132, ..., -40.92743105,\n",
+       "         -39.99001614, -38.43074214]],\n",
+       "\n",
+       "       [[ 23.16999207,  23.20234088,  23.22027682, ...,  23.54704484,\n",
+       "          23.44999087,  23.27994327],\n",
+       "        [ 23.08999633,  23.22072874,  23.27865721, ...,  23.24313408,\n",
+       "          23.05003657,  22.83448036],\n",
+       "        [ 22.9299744 ,  23.05135446,  23.09745435, ...,  23.02052488,\n",
+       "          22.70798618,  22.48218686],\n",
+       "        ...,\n",
+       "        [-26.22965149, -26.39704832, -26.64086611, ..., -41.96437258,\n",
+       "         -40.90995795, -38.53094857],\n",
+       "        [-29.01007373, -29.11959253, -29.25442578, ..., -41.36776822,\n",
+       "         -40.32987082, -38.24698168],\n",
+       "        [-29.92996985, -30.0132639 , -30.09123317, ..., -39.75164316,\n",
+       "         -38.69006184, -36.87086923]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 24.47998966,  25.10582762,  25.50503836, ...,  22.6286587 ,\n",
+       "          22.52000275,  22.35300373],\n",
+       "        [ 23.92005608,  24.56998781,  25.04915275, ...,  22.43671496,\n",
+       "          22.40000914,  22.0650933 ],\n",
+       "        [ 23.23995372,  23.83154769,  24.37425124, ...,  22.33205589,\n",
+       "          22.31999207,  21.90016761],\n",
+       "        ...,\n",
+       "        [-21.33958202, -21.42702704, -21.59008797, ..., -32.39286229,\n",
+       "         -31.77998965, -30.68542705],\n",
+       "        [-25.18016938, -25.27962571, -25.45424895, ..., -31.59692286,\n",
+       "         -31.21990373, -30.50374918],\n",
+       "        [-27.41991592, -27.61580626, -27.89428887, ..., -30.22082077,\n",
+       "         -29.94005118, -29.46365962]],\n",
+       "\n",
+       "       [[ 24.0399918 ,  24.4738043 ,  24.80504138, ...,  22.3005518 ,\n",
+       "          22.17999269,  21.90488526],\n",
+       "        [ 23.44006035,  23.94596863,  24.34916189, ...,  22.14060326,\n",
+       "          22.02001341,  21.64896271],\n",
+       "        [ 22.61993422,  23.08055197,  23.50708884, ...,  22.00442749,\n",
+       "          21.85999148,  21.4763401 ],\n",
+       "        ...,\n",
+       "        [-20.75961675, -21.10036879, -21.49475055, ..., -31.42835583,\n",
+       "         -30.53998416, -29.21720893],\n",
+       "        [-24.08012063, -24.48176571, -24.93567845, ..., -31.02256587,\n",
+       "         -30.35995796, -29.40131662],\n",
+       "        [-25.71993572, -26.21789951, -26.77571005, ..., -30.30251281,\n",
+       "         -29.76002651, -29.04128248]],\n",
+       "\n",
+       "       [[ 23.89998173,  24.23785823,  24.48503958, ...,  22.94893427,\n",
+       "          22.84000093,  22.5407021 ],\n",
+       "        [ 23.26005241,  23.69401855,  24.02915032, ...,  22.67702236,\n",
+       "          22.64002378,  22.34076337],\n",
+       "        [ 22.47993481,  22.85627254,  23.20697889, ...,  22.44473219,\n",
+       "          22.41998905,  22.13282989],\n",
+       "        ...,\n",
+       "        [-20.63959725, -21.00361502, -21.44237497, ..., -33.05825483,\n",
+       "         -32.39992993, -31.27825565],\n",
+       "        [-24.22014135, -24.59693448, -25.03928029, ..., -32.93448695,\n",
+       "         -32.61996466, -31.91279602],\n",
+       "        [-26.13992323, -26.56508709, -27.04730983, ..., -32.23844089,\n",
+       "         -32.14002072, -31.69677152]]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>slice</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>296.1 296.4 296.6 ... 235.4 237.5</div><input id='attrs-bfcb6161-ff86-4b0e-9fcb-263899421ccd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfcb6161-ff86-4b0e-9fcb-263899421ccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a882350-be46-4edb-ad30-eb6add285f0c' class='xr-var-data-in' type='checkbox'><label for='data-7a882350-be46-4edb-ad30-eb6add285f0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "        296.43398913, 296.19924566],\n",
+       "       [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "        296.07802394, 295.72098714],\n",
+       "       [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "        295.73997197, 295.35693248],\n",
+       "       ...,\n",
+       "       [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "        235.51802332, 238.0780694 ],\n",
+       "       [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "        235.33999633, 237.63241841],\n",
+       "       [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "        235.41999207, 237.49641598]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c654e220-60ef-4191-a0f4-e7cf77587b22' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c654e220-60ef-4191-a0f4-e7cf77587b22' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 59, lon: 87, time: 2920)\n",
@@ -251,6 +1583,529 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       ".xr-section-summary-in:checked + label > span {\n",
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+       "  display: none;\n",
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+       "\n",
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+       "  display: inline-block;\n",
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+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
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+       ".xr-var-dtype,\n",
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+       ".xr-attrs dt {\n",
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+       ".xr-var-name:hover,\n",
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+       "  overflow: visible;\n",
+       "  width: auto;\n",
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+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
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+       "  grid-template-columns: 125px auto;\n",
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+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4cf221c-78a6-4d12-bf1b-cc426273505e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4cf221c-78a6-4d12-bf1b-cc426273505e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-538e426a-d9db-45a5-b37b-47738a5d1edb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-538e426a-d9db-45a5-b37b-47738a5d1edb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-b72ad7d7-fe9d-47ad-a1c4-c84ed6f945f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b72ad7d7-fe9d-47ad-a1c4-c84ed6f945f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-911eba36-a705-492c-bac4-572e5a2ffce6' class='xr-var-data-in' type='checkbox'><label for='data-911eba36-a705-492c-bac4-572e5a2ffce6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-6e5b9806-0809-47e3-8326-dbd39f8ced19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e5b9806-0809-47e3-8326-dbd39f8ced19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc03d6d9-de21-4774-9169-4cb5e3fbdde3' class='xr-var-data-in' type='checkbox'><label for='data-cc03d6d9-de21-4774-9169-4cb5e3fbdde3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-8548bfd0-9e2a-406a-a04a-21fc6d645c86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8548bfd0-9e2a-406a-a04a-21fc6d645c86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85a4ef52-4083-41f3-a1d3-6418aa1da4b8' class='xr-var-data-in' type='checkbox'><label for='data-85a4ef52-4083-41f3-a1d3-6418aa1da4b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-08ed84b1-2286-438d-9b13-d9132ec87a31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08ed84b1-2286-438d-9b13-d9132ec87a31' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(lon, lat, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.09999 ... 295.19 295.69</div><input id='attrs-5e1c2875-69de-4fa9-8674-d2e76253d5a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e1c2875-69de-4fa9-8674-d2e76253d5a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3df5feb3-921c-494e-a7e2-15417c153ae1' class='xr-var-data-in' type='checkbox'><label for='data-3df5feb3-921c-494e-a7e2-15417c153ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>array([[[241.2    , 242.09999, 242.29999, ..., 243.48999, 245.79   ,\n",
+       "         245.09   ],\n",
+       "        [243.79999, 243.59999, 244.59999, ..., 249.09   , 249.89   ,\n",
+       "         249.89   ],\n",
+       "        [250.     , 253.2    , 256.19998, ..., 262.69   , 262.38998,\n",
+       "         262.99   ],\n",
+       "        ...,\n",
+       "        [296.6    , 296.4    , 295.6    , ..., 294.79   , 293.69   ,\n",
+       "         293.79   ],\n",
+       "        [295.9    , 296.19998, 296.19998, ..., 296.79   , 296.29   ,\n",
+       "         296.09   ],\n",
+       "        [296.29   , 296.29   , 296.4    , ..., 298.19   , 297.79   ,\n",
+       "         297.69   ]],\n",
+       "\n",
+       "       [[242.5    , 242.7    , 242.2    , ..., 242.98999, 244.79   ,\n",
+       "         244.29   ],\n",
+       "        [244.5    , 244.09999, 244.39   , ..., 248.98999, 249.29   ,\n",
+       "         249.29   ],\n",
+       "        [249.79999, 252.89   , 255.5    , ..., 262.19   , 261.79   ,\n",
+       "         262.19   ],\n",
+       "        ...,\n",
+       "        [296.19998, 295.9    , 295.4    , ..., 295.29   , 293.88998,\n",
+       "         293.69   ],\n",
+       "        [296.19998, 296.69998, 296.5    , ..., 297.88998, 297.19   ,\n",
+       "         296.88998],\n",
+       "        [296.79   , 297.19998, 296.29   , ..., 299.19   , 298.38998,\n",
+       "         298.09   ]],\n",
+       "\n",
+       "       [[243.5    , 243.09999, 242.29999, ..., 242.09   , 243.48999,\n",
+       "         243.29   ],\n",
+       "        [244.7    , 244.2    , 244.     , ..., 248.59   , 248.48999,\n",
+       "         248.39   ],\n",
+       "        [248.89   , 252.09999, 254.2    , ..., 261.69   , 261.29   ,\n",
+       "         261.38998],\n",
+       "        ...,\n",
+       "        [296.4    , 296.19998, 295.4    , ..., 297.49   , 295.38998,\n",
+       "         295.09   ],\n",
+       "        [296.79   , 296.79   , 296.29   , ..., 298.29   , 297.59   ,\n",
+       "         297.19   ],\n",
+       "        [297.1    , 297.4    , 296.4    , ..., 298.79   , 298.49   ,\n",
+       "         298.09   ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[232.79999, 232.     , 234.29999, ..., 244.18999, 243.29   ,\n",
+       "         241.68999],\n",
+       "        [232.79999, 231.     , 230.29999, ..., 240.59   , 241.29   ,\n",
+       "         239.59   ],\n",
+       "        [233.2    , 230.79999, 231.2    , ..., 239.39   , 240.48999,\n",
+       "         239.89   ],\n",
+       "        ...,\n",
+       "        [295.4    , 295.4    , 296.29   , ..., 295.49   , 295.09   ,\n",
+       "         295.29   ],\n",
+       "        [295.9    , 295.6    , 296.4    , ..., 295.49   , 295.29   ,\n",
+       "         295.69   ],\n",
+       "        [296.9    , 296.4    , 297.     , ..., 296.09   , 295.69   ,\n",
+       "         296.49   ]],\n",
+       "\n",
+       "       [[235.5    , 233.59999, 236.09999, ..., 244.48999, 243.98999,\n",
+       "         241.48999],\n",
+       "        [235.29999, 232.5    , 232.     , ..., 241.29   , 242.48999,\n",
+       "         240.29   ],\n",
+       "        [236.39   , 233.39   , 233.2    , ..., 241.68999, 243.09   ,\n",
+       "         242.59   ],\n",
+       "        ...,\n",
+       "        [295.1    , 295.1    , 295.29   , ..., 295.38998, 294.69   ,\n",
+       "         295.09   ],\n",
+       "        [295.9    , 295.5    , 296.     , ..., 295.49   , 295.09   ,\n",
+       "         295.69   ],\n",
+       "        [296.79   , 296.4    , 297.     , ..., 295.79   , 295.49   ,\n",
+       "         296.19   ]],\n",
+       "\n",
+       "       [[238.59999, 235.79999, 238.7    , ..., 244.89   , 244.79   ,\n",
+       "         241.79   ],\n",
+       "        [239.29999, 235.7    , 235.7    , ..., 242.68999, 244.29   ,\n",
+       "         241.68999],\n",
+       "        [241.7    , 238.5    , 238.2    , ..., 245.18999, 246.89   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [294.69998, 294.79   , 295.     , ..., 294.69   , 294.29   ,\n",
+       "         294.69   ],\n",
+       "        [295.19998, 295.1    , 295.6    , ..., 294.79   , 294.38998,\n",
+       "         295.19   ],\n",
+       "        [296.6    , 296.6    , 296.79   , ..., 295.79   , 295.19   ,\n",
+       "         295.69   ]]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>celsius</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-31.949997 -30.649994 ... 22.540009</div><input id='attrs-8b8a029c-d1f7-49fc-8a8e-5c306e7e9a44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b8a029c-d1f7-49fc-8a8e-5c306e7e9a44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd87e8a4-fd81-4419-8d6e-255fa6a7fda3' class='xr-var-data-in' type='checkbox'><label for='data-dd87e8a4-fd81-4419-8d6e-255fa6a7fda3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n",
+       "         -37.649994, -34.550003],\n",
+       "        [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n",
+       "         -37.850006, -33.850006],\n",
+       "        [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n",
+       "         -36.759995, -31.449997],\n",
+       "        ...,\n",
+       "        [ 23.450012,  23.049988,  23.25    , ...,  22.25    ,\n",
+       "          21.950012,  21.549988],\n",
+       "        [ 22.75    ,  23.049988,  23.640015, ...,  22.75    ,\n",
+       "          22.75    ,  22.049988],\n",
+       "        [ 23.140015,  23.640015,  23.950012, ...,  23.75    ,\n",
+       "          23.640015,  23.450012]],\n",
+       "\n",
+       "       [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n",
+       "         -39.550003, -37.350006],\n",
+       "        [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n",
+       "         -40.649994, -37.449997],\n",
+       "        [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n",
+       "         -39.759995, -34.649994],\n",
+       "        ...,\n",
+       "        [ 23.25    ,  22.75    ,  23.049988, ...,  22.25    ,\n",
+       "          21.950012,  21.640015],\n",
+       "        [ 23.049988,  23.549988,  23.640015, ...,  22.450012,\n",
+       "          22.350006,  21.950012],\n",
+       "        [ 23.140015,  24.049988,  24.25    , ...,  23.25    ,\n",
+       "          23.25    ,  23.450012]],\n",
+       "\n",
+       "       [[-30.850006, -30.949997, -30.850006, ..., -38.850006,\n",
+       "         -37.050003, -34.449997],\n",
+       "        [-28.550003, -28.759995, -29.149994, ..., -42.850006,\n",
+       "         -41.149994, -37.449997],\n",
+       "        [-16.950012, -17.649994, -18.949997, ..., -41.949997,\n",
+       "         -39.949997, -34.949997],\n",
+       "        ...,\n",
+       "        [ 22.450012,  22.25    ,  22.25    , ...,  23.140015,\n",
+       "          22.140015,  21.850006],\n",
+       "        [ 23.049988,  23.350006,  23.140015, ...,  23.25    ,\n",
+       "          22.850006,  22.450012],\n",
+       "        [ 23.25    ,  23.140015,  23.25    , ...,  23.850006,\n",
+       "          23.850006,  23.640015]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-29.660004, -30.160004, -31.059998, ..., -28.960007,\n",
+       "         -28.660004, -28.259995],\n",
+       "        [-24.059998, -24.160004, -24.559998, ..., -32.559998,\n",
+       "         -31.86    , -30.460007],\n",
+       "        [-10.459991, -10.959991, -11.459991, ..., -33.759995,\n",
+       "         -31.460007, -27.960007],\n",
+       "        ...,\n",
+       "        [ 21.640015,  22.140015,  24.339996, ...,  22.339996,\n",
+       "          22.23999 ,  21.540009],\n",
+       "        [ 23.640015,  24.73999 ,  25.140015, ...,  22.339996,\n",
+       "          22.339996,  21.640015],\n",
+       "        [ 25.040009,  26.040009,  25.640015, ...,  22.940002,\n",
+       "          22.640015,  22.640015]],\n",
+       "\n",
+       "       [[-27.36    , -28.36    , -29.660004, ..., -29.86    ,\n",
+       "         -29.160004, -28.36    ],\n",
+       "        [-23.259995, -23.86    , -24.660004, ..., -31.86    ,\n",
+       "         -30.660004, -28.86    ],\n",
+       "        [-10.76001 , -11.359985, -11.859985, ..., -32.660004,\n",
+       "         -30.059998, -26.259995],\n",
+       "        ...,\n",
+       "        [ 20.540009,  20.73999 ,  22.23999 , ...,  21.940002,\n",
+       "          21.540009,  21.140015],\n",
+       "        [ 23.140015,  24.040009,  24.440002, ...,  22.140015,\n",
+       "          21.940002,  21.23999 ],\n",
+       "        [ 24.640015,  25.23999 ,  25.339996, ...,  22.540009,\n",
+       "          22.339996,  22.040009]],\n",
+       "\n",
+       "       [[-28.059998, -28.86    , -29.86    , ..., -31.460007,\n",
+       "         -31.660004, -31.36    ],\n",
+       "        [-23.259995, -23.86    , -24.759995, ..., -33.559998,\n",
+       "         -32.86    , -31.460007],\n",
+       "        [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\n",
+       "         -30.559998, -26.86    ],\n",
+       "        ...,\n",
+       "        [ 20.640015,  20.540009,  21.940002, ...,  22.140015,\n",
+       "          21.940002,  21.540009],\n",
+       "        [ 22.940002,  23.73999 ,  24.040009, ...,  22.540009,\n",
+       "          22.540009,  22.040009],\n",
+       "        [ 24.540009,  24.940002,  24.940002, ...,  23.339996,\n",
+       "          23.040009,  22.540009]]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>slice</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.5 243.5 ... 296.79 296.6</div><input id='attrs-9ad92705-8ba9-4155-8b16-8c3d981b9dc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ad92705-8ba9-4155-8b16-8c3d981b9dc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edd0a098-9226-4ae2-a352-fbaf30eb059e' class='xr-var-data-in' type='checkbox'><label for='data-edd0a098-9226-4ae2-a352-fbaf30eb059e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>array([[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    , 238.59999],\n",
+       "       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999, 239.29999],\n",
+       "       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   , 241.7    ],\n",
+       "       ...,\n",
+       "       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    , 294.69998],\n",
+       "       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    , 295.19998],\n",
+       "       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   , 296.6    ]],\n",
+       "      dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-27105d88-3b26-4428-8040-fa330e47be8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27105d88-3b26-4428-8040-fa330e47be8a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
@@ -305,6 +2160,454 @@
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+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ec4f9cae-25d3-4d8c-9cd5-eb706a22294b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec4f9cae-25d3-4d8c-9cd5-eb706a22294b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea538137-84c4-4020-a7b2-b91810322578' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea538137-84c4-4020-a7b2-b91810322578' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-bccebec7-0755-444c-b396-77562aaa0e4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bccebec7-0755-444c-b396-77562aaa0e4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea3aac31-15cd-4cf5-b1e8-b50b4ab2646e' class='xr-var-data-in' type='checkbox'><label for='data-ea3aac31-15cd-4cf5-b1e8-b50b4ab2646e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-4a996840-6c79-440c-b0b5-b69089b9e3b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a996840-6c79-440c-b0b5-b69089b9e3b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6d7d0a8-7f6d-418d-9038-98d67577d78d' class='xr-var-data-in' type='checkbox'><label for='data-f6d7d0a8-7f6d-418d-9038-98d67577d78d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-9a50181e-c75c-4b36-aefc-a38a1ed7dcb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a50181e-c75c-4b36-aefc-a38a1ed7dcb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c70a5f8-8def-43b9-9db8-329db19eddb5' class='xr-var-data-in' type='checkbox'><label for='data-6c70a5f8-8def-43b9-9db8-329db19eddb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-7c8abf90-260e-4f3c-8eb7-35543e006230' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c8abf90-260e-4f3c-8eb7-35543e006230' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>celsius</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.98 23.24 23.49 ... -32.14 -31.7</div><input id='attrs-3a9161d4-d6be-43d6-94d4-9fd080679b12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a9161d4-d6be-43d6-94d4-9fd080679b12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-705015b1-5065-4fea-ab26-9080d83c417e' class='xr-var-data-in' type='checkbox'><label for='data-705015b1-5065-4fea-ab26-9080d83c417e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[ 22.98400285,  23.23669915,  23.48890434, ...,  23.32491403,\n",
+       "          23.28399523,  23.04925177],\n",
+       "        [ 22.82801481,  23.03275408,  23.27535111, ...,  22.94262952,\n",
+       "          22.92803004,  22.57099324],\n",
+       "        [ 22.89002377,  22.98556885,  23.15248584, ...,  22.62693524,\n",
+       "          22.58997807,  22.20693858],\n",
+       "        ...,\n",
+       "        [-28.10981478, -27.7891234 , -27.58903202, ..., -39.21370283,\n",
+       "         -37.63197058, -35.0719245 ],\n",
+       "        [-29.87008348, -29.37479887, -28.97624337, ..., -39.33408115,\n",
+       "         -37.80999757, -35.51757549],\n",
+       "        [-30.90996101, -30.27087087, -29.71224358, ..., -39.30207549,\n",
+       "         -37.73000183, -35.65357792]],\n",
+       "\n",
+       "       [[ 23.10400254,  23.55204383,  23.88167095, ...,  22.91515567,\n",
+       "          22.88998873,  22.86773746],\n",
+       "        [ 23.06799591,  23.41768321,  23.67292138, ...,  22.5792619 ,\n",
+       "          22.5300323 ,  22.3638965 ],\n",
+       "        [ 23.08999633,  23.27058897,  23.41715262, ...,  22.35442901,\n",
+       "          22.26999514,  22.04133825],\n",
+       "        ...,\n",
+       "        [-27.62970937, -27.41290159, -27.29850427, ..., -41.50239881,\n",
+       "         -40.4719669 , -38.31965437],\n",
+       "        [-29.85004875, -29.5359456 , -29.29672901, ..., -41.3434626 ,\n",
+       "         -40.42996222, -38.63076015],\n",
+       "        [-30.4499802 , -30.11198963, -29.83273132, ..., -40.92743105,\n",
+       "         -39.99001614, -38.43074214]],\n",
+       "\n",
+       "       [[ 23.16999207,  23.20234088,  23.22027682, ...,  23.54704484,\n",
+       "          23.44999087,  23.27994327],\n",
+       "        [ 23.08999633,  23.22072874,  23.27865721, ...,  23.24313408,\n",
+       "          23.05003657,  22.83448036],\n",
+       "        [ 22.9299744 ,  23.05135446,  23.09745435, ...,  23.02052488,\n",
+       "          22.70798618,  22.48218686],\n",
+       "        ...,\n",
+       "        [-26.22965149, -26.39704832, -26.64086611, ..., -41.96437258,\n",
+       "         -40.90995795, -38.53094857],\n",
+       "        [-29.01007373, -29.11959253, -29.25442578, ..., -41.36776822,\n",
+       "         -40.32987082, -38.24698168],\n",
+       "        [-29.92996985, -30.0132639 , -30.09123317, ..., -39.75164316,\n",
+       "         -38.69006184, -36.87086923]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 24.47998966,  25.10582762,  25.50503836, ...,  22.6286587 ,\n",
+       "          22.52000275,  22.35300373],\n",
+       "        [ 23.92005608,  24.56998781,  25.04915275, ...,  22.43671496,\n",
+       "          22.40000914,  22.0650933 ],\n",
+       "        [ 23.23995372,  23.83154769,  24.37425124, ...,  22.33205589,\n",
+       "          22.31999207,  21.90016761],\n",
+       "        ...,\n",
+       "        [-21.33958202, -21.42702704, -21.59008797, ..., -32.39286229,\n",
+       "         -31.77998965, -30.68542705],\n",
+       "        [-25.18016938, -25.27962571, -25.45424895, ..., -31.59692286,\n",
+       "         -31.21990373, -30.50374918],\n",
+       "        [-27.41991592, -27.61580626, -27.89428887, ..., -30.22082077,\n",
+       "         -29.94005118, -29.46365962]],\n",
+       "\n",
+       "       [[ 24.0399918 ,  24.4738043 ,  24.80504138, ...,  22.3005518 ,\n",
+       "          22.17999269,  21.90488526],\n",
+       "        [ 23.44006035,  23.94596863,  24.34916189, ...,  22.14060326,\n",
+       "          22.02001341,  21.64896271],\n",
+       "        [ 22.61993422,  23.08055197,  23.50708884, ...,  22.00442749,\n",
+       "          21.85999148,  21.4763401 ],\n",
+       "        ...,\n",
+       "        [-20.75961675, -21.10036879, -21.49475055, ..., -31.42835583,\n",
+       "         -30.53998416, -29.21720893],\n",
+       "        [-24.08012063, -24.48176571, -24.93567845, ..., -31.02256587,\n",
+       "         -30.35995796, -29.40131662],\n",
+       "        [-25.71993572, -26.21789951, -26.77571005, ..., -30.30251281,\n",
+       "         -29.76002651, -29.04128248]],\n",
+       "\n",
+       "       [[ 23.89998173,  24.23785823,  24.48503958, ...,  22.94893427,\n",
+       "          22.84000093,  22.5407021 ],\n",
+       "        [ 23.26005241,  23.69401855,  24.02915032, ...,  22.67702236,\n",
+       "          22.64002378,  22.34076337],\n",
+       "        [ 22.47993481,  22.85627254,  23.20697889, ...,  22.44473219,\n",
+       "          22.41998905,  22.13282989],\n",
+       "        ...,\n",
+       "        [-20.63959725, -21.00361502, -21.44237497, ..., -33.05825483,\n",
+       "         -32.39992993, -31.27825565],\n",
+       "        [-24.22014135, -24.59693448, -25.03928029, ..., -32.93448695,\n",
+       "         -32.61996466, -31.91279602],\n",
+       "        [-26.13992323, -26.56508709, -27.04730983, ..., -32.23844089,\n",
+       "         -32.14002072, -31.69677152]]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>slice</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>296.1 296.4 296.6 ... 235.4 237.5</div><input id='attrs-4896b678-5410-4d24-b750-afb05c7ef91f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4896b678-5410-4d24-b750-afb05c7ef91f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7596f001-5eab-4255-b334-7cd7d3233d2e' class='xr-var-data-in' type='checkbox'><label for='data-7596f001-5eab-4255-b334-7cd7d3233d2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "        296.43398913, 296.19924566],\n",
+       "       [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "        296.07802394, 295.72098714],\n",
+       "       [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "        295.73997197, 295.35693248],\n",
+       "       ...,\n",
+       "       [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "        235.51802332, 238.0780694 ],\n",
+       "       [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "        235.33999633, 237.63241841],\n",
+       "       [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "        235.41999207, 237.49641598]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-87a9de04-9ff8-40c3-897b-300d81353a5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87a9de04-9ff8-40c3-897b-300d81353a5b' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 59, lon: 87, time: 2920)\n",
@@ -346,7 +2649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/doc/notebooks/Pure_numpy.ipynb b/doc/notebooks/Pure_numpy.ipynb
index fa1ad0c2..c2f84041 100644
--- a/doc/notebooks/Pure_numpy.ipynb
+++ b/doc/notebooks/Pure_numpy.ipynb
@@ -19,9 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# not importing xarray here!\n",
@@ -60,7 +58,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1180e7b38>"
+       "<matplotlib.collections.QuadMesh at 0x7f8f5b75af40>"
       ]
      },
      "execution_count": 2,
@@ -69,12 +67,14 @@
     },
     {
      "data": {
-      "image/png": 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4r3v6+6yT5O+AjcCFVXXL0HmGlOQ3wMaqGvlA1xBX7n0+zuCsUFU/AX47dI7loKqOVNXe\nbvl14CDTTzmfdbqP8ZjqVie711l550OSdcDHgK8NneUPzRDlPt9HFUgAJLkcuBJ4Ztgkw+mmIvYB\nR4Enq+psPRdfBj4D/M/QQZaJAn6YZE/3cS7z8g+qWlaSrAQeAz5VVa8NnWcoVfVOVX2E6ae9NyU5\n66btktwCHK2qPUNnWUb+vPt3cRNwbze1O6chyt2PKtCcuvnlx4BvV9X3hs6zHFTVq8BO4Mahswzg\nWuCvu3nmR4Drk3xr2EjDqqqXup9HgX9gepp7TkOUe5+PM9BZpvsj4teBg1X1paHzDCnJJUlWdcvn\nMX3zwS+GTXXmVdXnqmpdVV3OdE/8qKo+PnCswSQ5v7vZgCTnA38FzHun3Rkv96o6AfzfxxkcBB6t\nqgNnOsdykOQ7wL8AH0hyOMnfDp1pQNcCn2D66mxf97p56FADWQPsTLKf6YuhJ6vqrL8NUPwR8HSS\n54FngX+sqn+ab7AfPyBJDfIPqpLUIMtdkhpkuUtSgyx3SWqQ5S5JDbLcJalBlrskNeh/AQpSL+Dh\nv1MWAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117adbba8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -100,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "grid_in = {'lon': np.linspace(0, 40, 5),\n",
@@ -127,21 +125,11 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: bilinear_4x5_41x51.nc\n",
-      "Remove file bilinear_4x5_41x51.nc\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       bilinear \n",
-       "Weight filename:            bilinear_4x5_41x51.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (4, 5) \n",
        "Output grid shape:          (41, 51) \n",
        "Output grid dimension name: ('lat', 'lon') \n",
@@ -155,7 +143,6 @@
    ],
    "source": [
     "regridder = xe.Regridder(grid_in, grid_out, 'bilinear')\n",
-    "regridder.clean_weight_file()\n",
     "regridder "
    ]
   },
@@ -202,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x118c42e10>"
+       "<matplotlib.collections.QuadMesh at 0x7f8f5adc65e0>"
       ]
      },
      "execution_count": 6,
@@ -211,12 +198,14 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1121f96a0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -251,9 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# cell centers\n",
@@ -275,7 +262,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x118c94a20>"
+       "Text(0, 0.5, 'lat')"
       ]
      },
      "execution_count": 8,
@@ -284,12 +271,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118c67f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -310,9 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lon_out_b = np.linspace(-30, 40, 36)  # bounds \n",
@@ -332,9 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "grid_in = {'lon': lon, 'lat': lat,\n",
@@ -356,21 +341,11 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: conservative_4x5_35x35.nc\n",
-      "Remove file conservative_4x5_35x35.nc\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       conservative \n",
-       "Weight filename:            conservative_4x5_35x35.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (4, 5) \n",
        "Output grid shape:          (35, 35) \n",
        "Output grid dimension name: ('lat', 'lon') \n",
@@ -384,7 +359,6 @@
    ],
    "source": [
     "regridder = xe.Regridder(grid_in, grid_out, 'conservative')\n",
-    "regridder.clean_weight_file()\n",
     "regridder "
    ]
   },
@@ -424,7 +398,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x118d636a0>"
+       "Text(0, 0.5, 'lat')"
       ]
      },
      "execution_count": 13,
@@ -433,12 +407,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118d84d68>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -485,7 +461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},
diff --git a/doc/notebooks/Rectilinear_grid.ipynb b/doc/notebooks/Rectilinear_grid.ipynb
index f41ff39c..ff4d1296 100644
--- a/doc/notebooks/Rectilinear_grid.ipynb
+++ b/doc/notebooks/Rectilinear_grid.ipynb
@@ -49,6 +49,354 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-258ea453-fa8d-4454-91ae-44c6cbd47cf8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-258ea453-fa8d-4454-91ae-44c6cbd47cf8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2771d163-5e06-475e-9f11-567637a035c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2771d163-5e06-475e-9f11-567637a035c7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-1872fae5-0e3d-4962-ac4c-27434f532f8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1872fae5-0e3d-4962-ac4c-27434f532f8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a31ce2b-ce49-4470-b506-af6a24911f71' class='xr-var-data-in' type='checkbox'><label for='data-5a31ce2b-ce49-4470-b506-af6a24911f71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-ddc91d6e-c555-4ed4-a4c3-6524ebf17f13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddc91d6e-c555-4ed4-a4c3-6524ebf17f13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdae9cb9-ed7c-40fa-a190-763a7f628b0c' class='xr-var-data-in' type='checkbox'><label for='data-bdae9cb9-ed7c-40fa-a190-763a7f628b0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-28dd73b8-46f5-4722-bc5c-8db0752c3b46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28dd73b8-46f5-4722-bc5c-8db0752c3b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-087bd472-081b-4e9b-b22a-ab76094ce857' class='xr-var-data-in' type='checkbox'><label for='data-087bd472-081b-4e9b-b22a-ab76094ce857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-693bcbce-6e75-420b-826c-aaa142080125' class='xr-section-summary-in' type='checkbox'  checked><label for='section-693bcbce-6e75-420b-826c-aaa142080125' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66c2486f-52df-4dc0-ae29-574460154716' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66c2486f-52df-4dc0-ae29-574460154716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4af7d003-d47e-4648-88b4-6e810a105b3e' class='xr-var-data-in' type='checkbox'><label for='data-4af7d003-d47e-4648-88b4-6e810a105b3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><pre class='xr-var-data'>[3869000 values with dtype=float32]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-de2281eb-a683-4c05-b8ae-6f38527dc0f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de2281eb-a683-4c05-b8ae-6f38527dc0f2' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
@@ -99,7 +447,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -180,6 +528,355 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-13cf86c8-b783-402f-96a9-5f9a424a9d88' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13cf86c8-b783-402f-96a9-5f9a424a9d88' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7d93b275-b38d-4c8e-926a-5b85b85ed68b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d93b275-b38d-4c8e-926a-5b85b85ed68b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-4723e3f1-1acc-47b2-92b5-b82796242162' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4723e3f1-1acc-47b2-92b5-b82796242162' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fda68a36-71ad-4709-bd0c-d9a0e1549936' class='xr-var-data-in' type='checkbox'><label for='data-fda68a36-71ad-4709-bd0c-d9a0e1549936' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-c0ef002d-5a56-4105-9a8b-ecff9db03cf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0ef002d-5a56-4105-9a8b-ecff9db03cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ab6c825-46e3-41c1-a8c5-9a9f97d8330a' class='xr-var-data-in' type='checkbox'><label for='data-6ab6c825-46e3-41c1-a8c5-9a9f97d8330a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-e56c85f1-f8dc-4adc-97ef-8d49b80a4596' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e56c85f1-f8dc-4adc-97ef-8d49b80a4596' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c8678126-45bc-4e3d-8d80-3d7d9a57cfca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8678126-45bc-4e3d-8d80-3d7d9a57cfca' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:  (lat: 59, lon: 87)\n",
@@ -222,20 +919,11 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Create weight file: bilinear_25x53_59x87.nc\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "xESMF Regridder \n",
        "Regridding algorithm:       bilinear \n",
-       "Weight filename:            bilinear_25x53_59x87.nc \n",
-       "Reuse pre-computed weights? False \n",
        "Input grid shape:           (25, 53) \n",
        "Output grid shape:          (59, 87) \n",
        "Output grid dimension name: ('lat', 'lon') \n",
@@ -268,33 +956,529 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c4fdabe-6f52-4482-8e86-eee0333bdbf2' class='xr-array-in' type='checkbox' ><label for='section-9c4fdabe-6f52-4482-8e86-eee0333bdbf2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>296.1 296.4 296.6 296.9 296.9 296.8 ... 241.3 241.0 240.9 241.0 241.5</span></div><pre class='xr-array-data'>array([[[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "         296.43398913, 296.19924566],\n",
+       "        [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "         296.07802394, 295.72098714],\n",
+       "        [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "         295.73997197, 295.35693248],\n",
+       "        ...,\n",
+       "        [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "         235.51802332, 238.0780694 ],\n",
+       "        [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "         235.33999633, 237.63241841],\n",
+       "        [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "         235.41999207, 237.49641598]],\n",
+       "\n",
+       "       [[296.25399643, 296.70203773, 297.03166485, ..., 296.06514956,\n",
+       "         296.03998263, 296.01773136],\n",
+       "        [296.2179898 , 296.56767711, 296.82291528, ..., 295.7292558 ,\n",
+       "         295.6800262 , 295.5138904 ],\n",
+       "        [296.23999022, 296.42058286, 296.56714652, ..., 295.50442291,\n",
+       "         295.41998903, 295.19133215],\n",
+       "        ...,\n",
+       "        [245.52028453, 245.73709231, 245.85148963, ..., 231.64759509,\n",
+       "         232.67802699, 234.83033953],\n",
+       "        [243.29994515, 243.61404829, 243.85326489, ..., 231.80653129,\n",
+       "         232.72003168, 234.51923375],\n",
+       "        [242.70001369, 243.03800427, 243.31726258, ..., 232.22256285,\n",
+       "         233.15997775, 234.71925176]],\n",
+       "\n",
+       "       [[296.31998597, 296.35233477, 296.37027072, ..., 296.69703874,\n",
+       "         296.59998477, 296.42993717],\n",
+       "        [296.23999022, 296.37072264, 296.42865111, ..., 296.39312798,\n",
+       "         296.20003046, 295.98447426],\n",
+       "        [296.07996829, 296.20134835, 296.24744824, ..., 296.17051878,\n",
+       "         295.85798008, 295.63218076],\n",
+       "        ...,\n",
+       "        [246.92034241, 246.75294557, 246.50912779, ..., 231.18562131,\n",
+       "         232.24003595, 234.61904532],\n",
+       "        [244.13992017, 244.03040136, 243.89556811, ..., 231.78222568,\n",
+       "         232.82012307, 234.90301222],\n",
+       "        [243.22002405, 243.13672999, 243.05876072, ..., 233.39835074,\n",
+       "         234.45993206, 236.27912467]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[297.62998356, 298.25582152, 298.65503226, ..., 295.7786526 ,\n",
+       "         295.66999665, 295.50299763],\n",
+       "        [297.07004997, 297.7199817 , 298.19914665, ..., 295.58670885,\n",
+       "         295.55000304, 295.2150872 ],\n",
+       "        [296.38994762, 296.98154159, 297.52424514, ..., 295.48204978,\n",
+       "         295.46998597, 295.05016151],\n",
+       "        ...,\n",
+       "        [251.81041188, 251.72296686, 251.55990593, ..., 240.75713161,\n",
+       "         241.37000425, 242.46456685],\n",
+       "        [247.96982451, 247.87036819, 247.69574495, ..., 241.55307104,\n",
+       "         241.93009016, 242.64624471],\n",
+       "        [245.73007797, 245.53418764, 245.25570503, ..., 242.92917313,\n",
+       "         243.20994271, 243.68633428]],\n",
+       "\n",
+       "       [[297.1899857 , 297.6237982 , 297.95503528, ..., 295.4505457 ,\n",
+       "         295.32998658, 295.05487915],\n",
+       "        [296.59005424, 297.09596253, 297.49915579, ..., 295.29059716,\n",
+       "         295.1700073 , 294.7989566 ],\n",
+       "        [295.76992812, 296.23054587, 296.65708274, ..., 295.15442138,\n",
+       "         295.00998537, 294.626334  ],\n",
+       "        ...,\n",
+       "        [252.39037715, 252.04962511, 251.65524335, ..., 241.72163807,\n",
+       "         242.61000973, 243.93278497],\n",
+       "        [249.06987326, 248.66822819, 248.21431545, ..., 242.12742803,\n",
+       "         242.79003593, 243.74867727],\n",
+       "        [247.43005818, 246.93209439, 246.37428385, ..., 242.84748108,\n",
+       "         243.38996739, 244.10871142]],\n",
+       "\n",
+       "       [[297.04997563, 297.38785212, 297.63503348, ..., 296.09892817,\n",
+       "         295.98999483, 295.690696  ],\n",
+       "        [296.4100463 , 296.84401245, 297.17914422, ..., 295.82701626,\n",
+       "         295.79001767, 295.49075727],\n",
+       "        [295.62992871, 296.00626644, 296.35697279, ..., 295.59472609,\n",
+       "         295.56998294, 295.28282378],\n",
+       "        ...,\n",
+       "        [252.51039665, 252.14637887, 251.70761893, ..., 240.09173906,\n",
+       "         240.75006397, 241.87173824],\n",
+       "        [248.92985255, 248.55305942, 248.11071361, ..., 240.21550695,\n",
+       "         240.53002923, 241.23719787],\n",
+       "        [247.01007067, 246.58490681, 246.10268406, ..., 240.91155301,\n",
+       "         241.00997318, 241.45322238]]])</pre></div></li><li class='xr-section-item'><input id='section-ce5bc250-f8b2-491d-a064-613dbc5872ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce5bc250-f8b2-491d-a064-613dbc5872ad' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-148e6206-1626-49af-9a78-d08aa3539ddb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-148e6206-1626-49af-9a78-d08aa3539ddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26509b62-0005-40d5-884a-724c053df1ad' class='xr-var-data-in' type='checkbox'><label for='data-26509b62-0005-40d5-884a-724c053df1ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-d91c728b-5fb8-4881-a300-7916e1b44844' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d91c728b-5fb8-4881-a300-7916e1b44844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2d24f2-eef4-46c5-9151-8cd9d731ee49' class='xr-var-data-in' type='checkbox'><label for='data-4f2d24f2-eef4-46c5-9151-8cd9d731ee49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
+       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
+       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
+       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
+       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
+       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
+       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
+       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
+       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-3bdac40b-7fba-417e-89ea-b758c8f973ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bdac40b-7fba-417e-89ea-b758c8f973ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ebff354-c038-4319-b35a-0b8cbfc329dc' class='xr-var-data-in' type='checkbox'><label for='data-6ebff354-c038-4319-b35a-0b8cbfc329dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
+       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
+       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
+       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
+       "       72., 73., 74.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b9f832ad-b34f-455b-8e17-1681fe199f89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b9f832ad-b34f-455b-8e17-1681fe199f89' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.DataArray 'air' (time: 2920, lat: 59, lon: 87)>\n",
-       "array([[[296.133997, 296.386693, ..., 296.433989, 296.199246],\n",
-       "        [295.978009, 296.182748, ..., 296.078024, 295.720987],\n",
+       "array([[[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\n",
+       "         296.43398913, 296.19924566],\n",
+       "        [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\n",
+       "         296.07802394, 295.72098714],\n",
+       "        [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\n",
+       "         295.73997197, 295.35693248],\n",
        "        ...,\n",
-       "        [243.27991 , 243.775195, ..., 235.339996, 237.632418],\n",
-       "        [242.240033, 242.879123, ..., 235.419992, 237.496416]],\n",
+       "        [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\n",
+       "         235.51802332, 238.0780694 ],\n",
+       "        [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\n",
+       "         235.33999633, 237.63241841],\n",
+       "        [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\n",
+       "         235.41999207, 237.49641598]],\n",
        "\n",
-       "       [[296.253996, 296.702038, ..., 296.039983, 296.017731],\n",
-       "        [296.21799 , 296.567677, ..., 295.680026, 295.51389 ],\n",
+       "       [[296.25399643, 296.70203773, 297.03166485, ..., 296.06514956,\n",
+       "         296.03998263, 296.01773136],\n",
+       "        [296.2179898 , 296.56767711, 296.82291528, ..., 295.7292558 ,\n",
+       "         295.6800262 , 295.5138904 ],\n",
+       "        [296.23999022, 296.42058286, 296.56714652, ..., 295.50442291,\n",
+       "         295.41998903, 295.19133215],\n",
        "        ...,\n",
-       "        [243.299945, 243.614048, ..., 232.720032, 234.519234],\n",
-       "        [242.700014, 243.038004, ..., 233.159978, 234.719252]],\n",
+       "        [245.52028453, 245.73709231, 245.85148963, ..., 231.64759509,\n",
+       "         232.67802699, 234.83033953],\n",
+       "        [243.29994515, 243.61404829, 243.85326489, ..., 231.80653129,\n",
+       "         232.72003168, 234.51923375],\n",
+       "        [242.70001369, 243.03800427, 243.31726258, ..., 232.22256285,\n",
+       "         233.15997775, 234.71925176]],\n",
+       "\n",
+       "       [[296.31998597, 296.35233477, 296.37027072, ..., 296.69703874,\n",
+       "         296.59998477, 296.42993717],\n",
+       "        [296.23999022, 296.37072264, 296.42865111, ..., 296.39312798,\n",
+       "         296.20003046, 295.98447426],\n",
+       "        [296.07996829, 296.20134835, 296.24744824, ..., 296.17051878,\n",
+       "         295.85798008, 295.63218076],\n",
+       "        ...,\n",
+       "        [246.92034241, 246.75294557, 246.50912779, ..., 231.18562131,\n",
+       "         232.24003595, 234.61904532],\n",
+       "        [244.13992017, 244.03040136, 243.89556811, ..., 231.78222568,\n",
+       "         232.82012307, 234.90301222],\n",
+       "        [243.22002405, 243.13672999, 243.05876072, ..., 233.39835074,\n",
+       "         234.45993206, 236.27912467]],\n",
        "\n",
        "       ...,\n",
        "\n",
-       "       [[297.189986, 297.623798, ..., 295.329987, 295.054879],\n",
-       "        [296.590054, 297.095963, ..., 295.170007, 294.798957],\n",
+       "       [[297.62998356, 298.25582152, 298.65503226, ..., 295.7786526 ,\n",
+       "         295.66999665, 295.50299763],\n",
+       "        [297.07004997, 297.7199817 , 298.19914665, ..., 295.58670885,\n",
+       "         295.55000304, 295.2150872 ],\n",
+       "        [296.38994762, 296.98154159, 297.52424514, ..., 295.48204978,\n",
+       "         295.46998597, 295.05016151],\n",
+       "        ...,\n",
+       "        [251.81041188, 251.72296686, 251.55990593, ..., 240.75713161,\n",
+       "         241.37000425, 242.46456685],\n",
+       "        [247.96982451, 247.87036819, 247.69574495, ..., 241.55307104,\n",
+       "         241.93009016, 242.64624471],\n",
+       "        [245.73007797, 245.53418764, 245.25570503, ..., 242.92917313,\n",
+       "         243.20994271, 243.68633428]],\n",
+       "\n",
+       "       [[297.1899857 , 297.6237982 , 297.95503528, ..., 295.4505457 ,\n",
+       "         295.32998658, 295.05487915],\n",
+       "        [296.59005424, 297.09596253, 297.49915579, ..., 295.29059716,\n",
+       "         295.1700073 , 294.7989566 ],\n",
+       "        [295.76992812, 296.23054587, 296.65708274, ..., 295.15442138,\n",
+       "         295.00998537, 294.626334  ],\n",
        "        ...,\n",
-       "        [249.069873, 248.668228, ..., 242.790036, 243.748677],\n",
-       "        [247.430058, 246.932094, ..., 243.389967, 244.108711]],\n",
+       "        [252.39037715, 252.04962511, 251.65524335, ..., 241.72163807,\n",
+       "         242.61000973, 243.93278497],\n",
+       "        [249.06987326, 248.66822819, 248.21431545, ..., 242.12742803,\n",
+       "         242.79003593, 243.74867727],\n",
+       "        [247.43005818, 246.93209439, 246.37428385, ..., 242.84748108,\n",
+       "         243.38996739, 244.10871142]],\n",
        "\n",
-       "       [[297.049976, 297.387852, ..., 295.989995, 295.690696],\n",
-       "        [296.410046, 296.844012, ..., 295.790018, 295.490757],\n",
+       "       [[297.04997563, 297.38785212, 297.63503348, ..., 296.09892817,\n",
+       "         295.98999483, 295.690696  ],\n",
+       "        [296.4100463 , 296.84401245, 297.17914422, ..., 295.82701626,\n",
+       "         295.79001767, 295.49075727],\n",
+       "        [295.62992871, 296.00626644, 296.35697279, ..., 295.59472609,\n",
+       "         295.56998294, 295.28282378],\n",
        "        ...,\n",
-       "        [248.929853, 248.553059, ..., 240.530029, 241.237198],\n",
-       "        [247.010071, 246.584907, ..., 241.009973, 241.453222]]])\n",
+       "        [252.51039665, 252.14637887, 251.70761893, ..., 240.09173906,\n",
+       "         240.75006397, 241.87173824],\n",
+       "        [248.92985255, 248.55305942, 248.11071361, ..., 240.21550695,\n",
+       "         240.53002923, 241.23719787],\n",
+       "        [247.01007067, 246.58490681, 246.10268406, ..., 240.91155301,\n",
+       "         241.00997318, 241.45322238]]])\n",
        "Coordinates:\n",
        "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
        "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
@@ -348,7 +1532,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -386,6 +1570,349 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-06166135-9bde-4a49-80e0-8ad2a8bb9841' class='xr-array-in' type='checkbox' ><label for='section-06166135-9bde-4a49-80e0-8ad2a8bb9841' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2013-01-01 2013-01-01T06:00:00 ... 2014-12-31T18:00:00</span></div><pre class='xr-array-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-section-item'><input id='section-219deba6-587d-454e-90c2-a720037d44c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-219deba6-587d-454e-90c2-a720037d44c6' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-4086ca7f-e6b6-48ca-aac1-f7f56089856e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4086ca7f-e6b6-48ca-aac1-f7f56089856e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c022c2c4-a655-4218-aa63-831072fb9d66' class='xr-var-data-in' type='checkbox'><label for='data-c022c2c4-a655-4218-aa63-831072fb9d66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b4f114e9-48e8-4894-8c49-53b176ba4233' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4f114e9-48e8-4894-8c49-53b176ba4233' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.DataArray 'time' (time: 2920)>\n",
        "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
@@ -433,7 +1960,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff41e255cc0>]"
+       "[<matplotlib.lines.Line2D at 0x7ffab0a3d3a0>]"
       ]
      },
      "execution_count": 12,
@@ -442,7 +1969,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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AfdmNSYHSGZNL6xSr9khfYISqtgFGWJ8RkXbAhZgV0inAq5btJcSDVVu9PVTcN/OO3ZE3YF6+xnyAe83SEuE7DyO8m0Rk1GRXTMDcNdnk5puHgVOtlp+vZO9KvQfa3kheDMm83uE9ZTcbYnlF3fjRRA55fLhjEhH/h7YnA37jLt25E72E5fJNO7j+g4ls91j9evHGrwuK5IlYGgkSB3KfiNQVkW4icoz98mjXSlX3w5S/PV1VG6hqfYwR/usA36OqaidOqmi9FDgTeM/a/h5wlvX+TOBTVd2tqouA+UAYb+KHz03uvt/ctovvphTmE1IPj/vVPoIpKK+NWuC5Clm6YQdjrVQliXi6rHK5cp70wq8sWGceNs6veWXkfDo+8lNMz5+QaLJ35UapifIdk3V3Shwv19rXRi1g664chk5bzcbtexgxy2QTCPLwj9cm3WauTA+V7VH/HsmwGatp//CPvjYiJ08NnQ0YtW1ZJ4gb7z+BXzGC4VHr7yMxDjlMVYfaH1T1B0w9kbiISKaITMaowIar6jigsaquss61CmhkNW8KOJXjy/EIcBSRa0VkgohMWLcuvnteecV3XHvckP0GF+aUmrFia+FN63GOZCd8OXn5/DwrOh3JMc+M5CIrMWOqZpWqRnVx+2eT+cjy2DnsyTC7ThDsn2D11l0c99yoiH3OSUcsQ7hzz1aHPe2zCcsivmPAsNmexy9ev533x3oWxiv8/gQTLyZKZobEjGMZbdluhk1fxZo4kyvbs6wsE8SIfitwGLBEVXsBXTBqKT/Wi8iDIpIlIi1F5AFgQ4z2BahqnlVrvRnQTUQ6xGju9ViJGr2q+oaqdlXVrg0bRpX03WvwWj14kafKwnWF6oG3xiwqtCF4tJ+0NLk0EgvWbWfzjtiBZYnKDz+7yo0fG03qN3+vSHrlVNbJy9eCmJ8gOIW4e5UXS3Vz2ktj+HOhuf2dXlL5Hl9tf4eXhxVAzxheUTZ/J5nWJB4iwoOD/ZN2jlu0kdy8fK7/cBIXeLjBJ6vyLW0EESC7VHUXgIhUVtXZmOqEflyEKXv7jfVqSGFUeiBUdTMwCmPbWCMi+1rfvy9mdQJmxdHccVgzILG0nWWAPxduKFDlJIOfmtqtHop1Yy9Yl/rU7AAZcZYYixLMRXT8c6M9ty/bWPZnfKniqnf/4oAHf0jJua79oNDE6eW+/dZvC6O2HfPMyKhtqQjKu+6DIObWolMhQ9gRQ2C+PnpBwUTLjlzfk5vPG78uICcvP6ZrcBC6PfkzT3w/M6lzpJIgAmS5iNTBJEYcLiKDifGgVtWNqnqrqnZR1UNU9TZVjRvSLCINre9BRKpi6ojMxtRjv8JqdgWFHmDfAReKSGURaQW0AbxdN8owF77xZ4EqJxlmeHhQQbR6KJZB9KEAAVZFIZ6KyivaPRYLy9ksLx243WTdqCoTl2yMa+B+8ee5EY4LD35T9JT6ZaF4YKZI3LW8LURtbd7bvy/iqaGzuTZGNH1Q1mbv5i2f5JIlQRAj+tmqullVHwH6AYMoNGQXICKPxDtXnDb7AiNFZCrwF8YG8j3QHzhRROYBJ1qfUdUZwOfATGAYcJNVc2SvY+3WXezOLdq//qCrhoaXl8nGIuQuSoRYD47Q7z71BDH0fvP3Cv7x2tiYXnJPfD+zIPbDZuzC6NVyUPfpH6av9ozjKU0s3rCdUXNiC99cl6u8be8ZGee4skjMeiAikgFMVdUOAKrqrRsw/FNE/ENBzRTmQnwM8Ko6FWNfcW/fgMnu63XMk8CTMb5zr6DbUyM4vm0jBl15WMLHBqkYOHt1egPwfp/vr6Jr28+/pkhI0XAbupdt3MH2Pbm03adWwbbF1irO1tl7Cfl0zIRfDRh8WFK43cS9cNuWynP4UUwBoqr5VmBfC1WNF0HzJlAzQJuQNDCiDBdW+nLi8hL9flVFyoL+JA38PHNNQZp8Z0Ex+3r4LVbmpGlS4WdALy1UyIyv9d/ocgopzwGscSsSYlRLM0RkPKaoFACqeoazkao+muK+7fXM80lhbjNs+iq671e/mHpTNLq2rMuEJdEJF4vKxAdP4NAnUut6+9lfy7iwW4uUnrM04zR0e9VYgULHBtsG4n4IvvTLPPchvpSV5+eAcztxz5fRmQyc+OV+c+J04vhiwrJynRw0iAAJBUMJEUu9tHLzTq7/cBJH7B8pQOaszmbDtt0c0bpBwba5HoLo3d8X8cj/0u/Ncfh+9VImQBrWrEz9GpU99zWtU7XIfvVex6kq+eodOFbWifc8e+S7GUxYstHVNvKgeJ5zpYFhtx3NKS/Gz/0G8Oolh3Bqx33jCpBEk4SOnrsubs2dmz+eRLO61ejbu23g8+7JzadShaKUc0otQYzoo4HFQEXr/V9AdBL9kJQTxLi8xOHiet7rf3Dyi79y8VvjItqe9MKvUcenQ3i027dW1LYW9arx5fU9eOrsjkmf/4dbjwbglzuj41JH392zyOf1iod4+ofZ7H//0HKZwdcvJuizv4yW+t0/FjN9hTFnuj2KbBKRH4k+dFPBGZ2bRNh04nFy+30CtSvKcPAregbw+/z1fD91Fa+PTkx1FzSuK90EiUS/BvgS+K+1qSnGpTck7fjfpfas3rk8/mtx5Ex//KKNnjl30uFVdcJBjbj2mOhCled3bU7XrHr0cKyUXrigc5G+o0pFk+psv4Y1+P6WoyL2BdFN+2FnDj7lxV+52arz8O7viwETKb9p+x5WbdlJXr6ytQTyZ81dk82TQ2amTBXi9xC896tpUSVfFZi9emtUqvREViAlIYQTEVrznuxdsNI85xDfat1AsOSfEe3j7L/EMdm747PJZPUdwjgPT7bofiTUjbQR5K67CTgS2AqgqvMoTCdSgIg0cH2+VEQGWqlESv96txQS66rZS+1Y4+jTv7z9Horq8uvHAY1r8NYVh9HzwOhIf/unb1KnSsG2s7s046HT2iX8Pc6HQoemtRnyLyNEHj/LJCx45PTEzwnwuaWnnr06m+/tVPKOa9/tqZ/p8fQvPDR4Op0e+algxTJp6SbP1Bnbd+cyPYWFhi4fNJ43f1vE2hTl7Yr1ENzj+n9mrdrKKS/+xq2fTo7Y/s3fKwjK4hj1XDo0Db5K8GL6o95JuO3/sE2jGhHb3SpfgIrOyUecB3Oiq6lEBM7X1jW94I34cV9lSYDsVtWCKatVD8Sr+z852jwIXIbJwnsi8HyS/dwr8ZMfW3YUzoLdaSUijy8euf39LUa1VKdaJc7o3MSzTeUKmYy+uyeDbzoSgKuOzIpq8+m13fngav98mDUrR5rs2jepzeL+fbise0sATu20b1G6z449eVH5uOwrl69akAL/60nmBs/Nz2f6ii2c8+ofPPvT3Kjz3fTxJE57aYxnbYrrPphAv28TC7aLFdzpx5qtu8jxyQuVyIIgXsxDEGJlADjr4Ngz/nb71uKcLoVt6lWPrI5dwxoTh7eKrBjRsl41IHqldGTriHluwsxelZj32Yo0FZ4qCbWgF0EEyGgRuR+oKiInAl8A//No5/ylzgHOUdX3gIsxUeUhCeK3cHtyaDD7hd8KZkcCRXeC4DTmOYd183pVI9q1rF+dzs3rWH0T3rmqMG7lodPa0X2/+hzdpnAVY9tUnjuvM9MeOSmuq20yAtPL0QC81S+qsM5KU/7nwg0FuZ5sJliqxByPvDA/zljDB3/GTgjoJtH/6ueZazj8qRG+aT1izYrTpSvwU8vEew7+6/g2PH/BwSx86lT6ndaOMff2imoz54lT+Pia7hHbbj/xAJ/vM1945sFNuPKILMbdHxliFu+xvCFB9e+U5ekpeVtaBEgQL6y+wNXANOA6YCjwlke7qiLSBSOUMlV1O4Cq5ohIGE7sICcvn5y8fKpVin35/e5ld1GoRLnl47/jNwrAPaccSJtGkaE/Z3ZuUpB65NSOsVcEBzmMnBcfXuhG+9Ptx7A+O9KTLAh1qlVMqL2TZxzlV9/7Y3HBe6cMsA2Xgyev5H6rfvfkZZu58I0/mf34KQU2mpLmDSv31C8+sUEl8ezxU8vEMwbbAi0jQ7j6qFaebSpXiL7utlqqeb1qBRUSu7SoU7D6al63GnedHJ3S766TD2TK8s0RCUVLmnXZu/luykquOiKrYFtp8e0I4oWVj6nD8TjGpfc99Z7CrMKoqp4FNjoSINYHSnd+ghSzafueiBQcE5dsjJjJXvH2eNo99GPc88z0qfGc7CTR77yJcsJBjTmxXePIbe0as7h/HyY8eAL3nBzbLdGpmnE+fA9oXDNh4QHmoeEMhisqD383o8CwvshDf//fX6M9ZrxmhM6UITl5+Rz17+TqP+Tk5TNpaQCX6CT0+MkIl87Naid8jCq8dXlXjjnAO1N2smP9+Qs6c2pH42HVrG41rjoyi7O7NOUaD4cPMO7g7/9fsLJC5x3arOB99UqZPHl2rOThRedfn/zN49/PjMgIUVpiS4J4YfUBFgADgZeB+SLS291OVXu5XnZh681AVAGq8kyXx4dz+SCT13HknLX847WxEW56fwTMrvv88Gj9Ovirtpyoxq4k6KaXhwE8HnWq+s/4G9SoHDeGokntKjH3FyddWtTx3H7WK79HbYt379oPaOekYfOOHJYnqQ9/9sc5nPPqH7zhIcC8vt+LDdt2c9W7fyXVDz92B0wPP8Nh+N6ndhVOaNeY964KnobndB87mxe1qlSkdwezEs5XpWaVirxwwcHUjjF2m9WtVuCYEYvbHGqy6Y+ezCWHtwzcr0SwPf92OZxfyswKBHgO6KWqPVX1WKAX8IJfYxHpKiJni8jpItLWqvGRWD7uMoxdJnX8YhOIddU75mZNJPXD3DXZnPvaH777g8S25eZrQvrsy3tkBW+MibtoVCs5ASAi9DutHXd7qBKKm/rVvQMUndjeV0s3Rg9n+4b+ZfaaAhuTc4VVlIDELTtyyOo7pMD7ys6obFe08yPWs2XQmEVMjamXL/qTKWh9keqVKzD5oRN55txOBU4XfpOiGlWi1by200RQ3FH1QQjyHfvUqkLPAxvyxfU90poKxx47zglJWbKBrFVVZ4azhRTW5ChARI7FCJvNwKHA70BdEckBLlPVZe5jyiNPfD8r6XN4Bf4lyvdTV/L5hOA5phSlcoWMiFlkt1b1GL/IOxN/y/rVk+4j4KvXTob61SslbOwMcv/HmvXZN/TwmWsKtiUb/zB/XeSkI+gzKtbDJdGSsEe1bsCY+f6BcE6CrkDAeOyd17V5zDatG9Wgh0eqnm6t6jHxwROo7GNzuqHn/hGfOzY1qrXTOwVfuQD8fMex7MrJ47SXxgDm2jkvbWaG8O5V6a+ibQsnp1ddviqqyj1fTuXcQ5txeAmlNAqyApkhIkNF5EoRuQLjgfWXiJwjIuc42r0I9FbVE4BDgBxVPRKTLXdQynteStixJ5esvkPI6juEpRt2FJTnLCpLYvjM2wR5LjldfYOgCpUc/vD71KrCs+d6B/y13SdezsySZWK/Ewv02Ps1CCbokp0/Fto7Cs8UZMY4bPpqNmzzju9wZykOGrwXa3Ia7xzuVOSJRDwnIkC8sB/0YITHT7cd4zuzr1+jcoELr5t7T4m0vbWoX43F/fvQO45Th5vWjWrQon61gs/xnELSRaZ1CZzjSRXGzF/PFxOXc8Ebf/LKyPkpqTeSKEEESBVgDaaueU9MOdt6wOnAaY52mapqO40vBVoCqOpwPGqVlxec+ars/EE2RallEUQ4+C3FMzOkINlb83rVPNv4nxOOP6gwPvSQlnUibh4npT2BIxTOtBvVqkzXlnXjto8VTxME+3dzFmp6e8xix/7o32zLjhyu/3AiV78XfeMPnbYqygYWVG0RewUSW4Aks2rak2SA6v9uOarANvHOlYeRkaDar02jGpx5cGKrjHg4J1VVPLy90oXzHreFvlO456uyO6dQYD/z4xx+cqx+i4u4KixVvSrguSaIyCBgBHAmpiQtIlINKB3+jWkg1v326P8KK/h9N2UlfXu3jVtdLzPALNPvAVHVWtJv252b8INAMRHd3042/fOLqXjotHZc1iM9xsJUYvc/Q4Qvb+jhmdLFybQkI8ft38SZmHHM/EJh4lUqeMN2s/JY5mFT8SpbOndNsJLCThuHO1V9vGeyO9rMgY8AACAASURBVHYlVq0WN1t3xXe2jDe8nzirA7d9NpmGNePbpNwMvyM6R1qyOL0DK1dMXfLCfWtXiTlpUY10YQa3CgsGezxLxi3cUKzqrLgCxCoXewuQ5WzvTueOiRG5BjgC+Bl4224KeOcbKAfEmu2NcEU33/zxJCYtjV2QJhldfG5+foH/e6JGtppVKkTEpfj1o3m9apGpH0opdv+Ly9bo6cbr2OQVTX7jRybvlle21lQZZfPylQqZheeKF2zpVmEVlUVPn0qr+4ZGbf/EFfDn5vTOTRLysipOKqco++2AczvRpHZVLh00zreN81ewhb6zmFV+vnpORsct2li6BAgmceIgjO3DV8mpqjnAqx7bdwKJhd6WIUbHSPXgzl0UT3gExU845OUrlawJU6LR5m61VCfLp3/+k71p/cAPcb+7tFHcyde8LovzWnmVkbWj33PzlXXZuyNm3UW1J7hLAOTmK07NS7wViFdur6LgJwA7NE08VqSkubR7C+pVq8QeS7g2qFGZgRceXOTznd+1edxcafmqZFqj2E6J4yy25a4qaZNMMG1RCCJSd6nqQFUdqaqj7Ze7kYjUEJHHRGS6iGwRkXUi8qeIXBmkIyLSXERGisgsEZkhIrda2w+2zjNZRCaISDfHMfeJyHwRmSMixbrKycnLR1X5z4jCwjqJThrdtozTXxrD9R96p5/I6jukIIvuph3eHka5+VqgRoiVQjoI/zzKBFpVyMxgxqMnc4JlHykj8qOA4kp7HS+Q0Gu/06B92JM/M2be+oJjimpP+HhcZAJNd3LEeHYFdymAVONn+C7NPHFWR+446cCCFUijmpWLFOjqxLkq9MI5XLzSofglRC3u6xtEgPxHRB4WkR4icoj98mj3EcbF9xRMxPpATELFXiLyVIDvyQXuVNWDgO7ATSLSDhgAPKqqBwMPWZ+x9l0ItLe+81URKRZby+otu2jzwA98PD5eld/YOO0U01dsYdqKLQW+/l4c8vhwcvPyffXSzkEXxOd9/P2epeaByAdN9coVqJCREfi8pYF2TUyalOuOMS6d3axke8+eV7RU8vHwmhA61VZBTFKXDhrHi9aEpKgrkOqVI2+BTo/8FPE51eEKf953PFMfOYksH4eL8oQdj5GKTA72/eRHvJX+rhzv8ZEqFWRQggiQjhjbRn9MnMdzmHQlbrJU9V1VXa6qzwNnWKnfr8IkV4yJqq5S1UnW+2xgFsZ7SwE7aVJtwFb8nQl8qqq7VXURMB9Iv1M2ha62g/+ObRCPh/Oh4hWc5kXv/wSrsBbkgZVIIGDTuiYxYs0qxbtELip1qlVicf8+9GobWXmgQY3CbK6prDaYn68FBZkKtxW+D+rUMHaBWTkWVYAsXB/bDTzZSoLukq771K5CrSoVS00esHSS6Hh55txOAPTusE9BqYHLLQeUinFWIDZjfDQJXhkSoGiZm5MhiAA5G9hPVY91pCk5zqPddhE5CkBETgc2QkEurYSuvIhkAV2AccBtwDMisgwjuO6zmjUFnEEXyykmd2F7du7+sSYtSczG4ZxlBL1A82KUuXXiNYzaN4lfe+GR09t55iW655QDeemiLhzZuvS78HphX1+nbj4vX5ny8EkpOX++Kvd+NS1im3O15l657crJ8/yNgjrPZfUdwru/L4rYtjs3jyF2PRMXl7z1Jy8Mn5u0bchP/vTwqLPh5tLuZbvufJB66DbVK2VSy3JJ9po8xCuAZg+XWIZ2L/Lylfx85acZq4sURpAoQQTIFMA7UVAk1wPPi8hm4F6M5xYi0hB4JWiHRKQG8BVwm6puBW4AblfV5sDtFAYlev2aUb+UVdBqgohMWLcu+doGUFhG1m1wTDRNdzortXmpmm7u1TrucVce2cozmVzlCpmc3rlJWlM2pBPbScCZf6t6pcyYOZESweuntLdt3ZVDv8HRNUC8fv9EnBTcZYljrVp+n7+B/4yYx9M/xE6DEo9GNb1Xre7gPS+yUpS9oKQIsgKxc6pVq1yhwCU/X7VAYNiqq4pxzlVUZ5W8fOXN3xZy7QcTadtvWJHOkQhBBEhjYLaI/Cgi39kvdyNVnaqq3VS1jqoepapzre3rVHVgkM6ISEWM8PhIVb+2Nl8B2O+/oFBNtRxw5kJoRqF6y9mvN1S1q6p2bdgw8YSBbqav2MJdX0wBko+8/c7hhpfsc9mdEsRr/HkZUP/udyKTHzoxuS8vA9x6fBtG392T/RsWVqhLpgyuG68b/vzDzPB87sc5/LkwMsjUb/KQyJzCHSDpl4vq2GdGBj9pHHp38K4dnioX19JMkBWIrbaqUjGjQODk5ivndW3G1Ue14vYT25iGPqfa15rgKLDeJ0NBLPLyNbCWIhUEMdk/nPZeAFbZ20HALMuGYrMSEwU/CjgOsN2evgM+FpHngSZAG2B8uvvp9JKanUCCRC+GTV/NRd3sZX1yEsRtxPTyPvIKUqzrqvBWXsnIEFrWrx6xMkul4ddrxVfXcql0e0KBv65aVQOls4FooeUnQJZsSE0u09F39+RdR60UJ2V1ZZoI1QN4OFXKNLYg1cgkiJUrZNLPUcbZLx4nw7FqKcoiJF+VLycGz4GXLEEi0UeLSEugjar+nMbI8iMxXlvTRMQuwHw/xoD/H6uU7i7gWqtfM0Tkc2AmxoPrJlVNq9Jv3prspFNyO4mwgSR5/2W6vDq8Ip/jOH7sFTgfdG9fGTyFeDy+8Lhp7dWEZ4yI7wpEGeeTwNKN+wzp1nm3rF89IbXrO1cdRuUKGVz8ptHjF06WyiYNAkTHOwNYW1l52I53OXKAv7utfY+qFs0FfYGrENaWHTnUTmNsSJBI9GswD+16wP4YQ/XrgL8PaBFQ1TH4T8MP9TnmSUyyxmLh1IHBPKCCkmyshhP38tqrHnayHjjljfo1Ek+X4ceHY6PtX7Fcnv2ew/n5hSlp4uE+x8rNyeXzCoJfAJsTu0xsrwMjH5xBZvClmaOt2I8gudXAZG2Y8vBJ1PJISV+1UiafXNOdi96MrNR4YOOaLNu4E1X1nATG4xNXaEHnx35KSZE1P4L8ojdh7A7jAFR1nohEiVRXZt4oHDaNMou7lGyDGpWLpKd0o6q+9auD4jbwealNUum2WtY5yhEI9sRZHXjw20Ij94099y+I+j28Vb1AKwKvx2pevkm5/elf0RmaJy7xriyYr+q7L+o7XQIqUY+dopAXI87g5l6tGbtwA41d7uGPndme9dnJ3yclTYUAFS8LVyDmOsVy0vDyXDuydQN+nrUW1bKR9SGIANmtqnvspb+lSvL6z06PcQ6l0BBebjiqdf2C5IPJ4FdzIxHcka1eq5sgiRr3Bsbff3yBiyXApd1bRgiQu046sECA/OOQZoFVSm7yFSa4hMFhWXX5a/EmvvLRU89enR3Ytlacz5fu+5lATHeyRSdeNcYh8WJlZZmC4lUpOL68CJDRInI/UFVETgRuxOTFiiCBrL1lkqUehshkvbAApi3fwgVv/Bm/YRzirS5uO6FNwumxyyteAZSXdW/JB38uYd/aVcjIEI5r24glG7Zz/EGNaFK7CivjpHv3yj2Wrxpll8gomIgl8Q84zl9c2AbgdLqelwfs37Wov43z+ERP0bJ+tZQ5TAQliFm1L6YGyDRMxt2hqvqAX2MRaSwig0TkB+tzOxG5OiW9LUGO8XCF9MtJlQinvzymyMfWc3hQxXMxrFmlYkT0a+dmZS+pXTp59Iz2PHpGe7664QjAGNhH3NmT+jUq88d93ua+nnHqyHvZQBKxQzmj5r2I9SxvkWA9mHjYdTGcNpCXLuqS0u8oDzSsUZnDW9XjhfODJVsceVdPLjm80LnA1vSoJi6s/Z4BXvbQVBFEgNyiqm+q6nmqeq6qvmknOvThXeBHjGstwFxMNHm5w+3bb1NcFfucDyO3F5YbVY1o/8olXunM9l4yMoQrjsiiSZ2qMdu1clQ4fP3SQ+l/Tkfftl73bTUrXXKQGWrlOAWMnAIqe1dkSvjPr+sR9/yJYH+T0wZSWtOulyQVMjP47LoegZMttmpQnSfPLhxD9h2qqmzfE7++ipPNPlVI2zzwA2uz0+NgEUSAXOGx7coY7Ruo6udYqd9VNRdIf0x9KaIoxXCKgnMyGyTIyTmhaVa3/Ce/SwdPntWh4H2VipkxBY6XkLDTyQSZFMbL2Gqff83WXXR0JU2sH2f1kii2Ki6IF1ZI0XHaQPoMjK+deP78wuSgG7b7a0TSpdryFSAicpGI/A9o5YxAF5GRQKwyZdtFpD7WpEVEugPJlXsrYzijcq85ulWMlkXnufM6F8xWHuxzUCAPq1TVetibcbui+qmkRMws0i1DWjc2q9OfZ8UvPxrvF7XPvXJzdGxSqh0malWx8zqFYygd3NRrfy7q1jxhG0pQVWVOCuy1XsQyov8BrAIaYDLw2mQDU2McdycmSnx/EfkdaAicm2Q/yxTOh/ll3bPo2KwO//rk75R+R9t9a3Jc20Z8+tcyzj+sOdM9aga4sWePPcpATfPSirsao5/czhDxtFEk8mCv4RE/4MR+yLijwI89oGEgh4muLesWeImd0blJRGqdy7q3pFWD6sxctZXrjtmPLEt1F65A0sPdJ5tcYp9acRwPD54Rq3kBdhXRZnWrxgxy9nLrTwW+I1RVl2AqCSakTFXViSJyLHAgZhI1x6pWuNfgfMiImJvzz4Uboor9BKH7fvU8bS15+crjZ3XgluPbUKtKxUArELtaWafQgF5kbLWSnZTRL4VHhph0Je7HbSKOcC3rVWf6Cv/aE8s27SSr7xAecqTI6NNx3wL71r+Oa83AX+b7Hv/B1YezdVdOQdzG3DWFLsSPO1R1TlLheRjij72i/Wlm/BUqmLo3A87txMnt9qHzYz/5tktXnZCUJ7cQkSnAPZhKhtPLg/BI1BvCKUDsB3tRPS4/vbZHQWSvkz25pv55U0sHH6TCWfsmtfnqhh7c7eOvH+KPnUSw0A1XrM/RbT+4upu1AvHwwkpAgpx7aLOY++3cV59PKAxUdNrfLoiTOqRqpcyIoL8gHmLt9o1fEiAkCYqgeTy/a/O46UrS5YmVjuxIZ2ByU30uIn+JyF0iUqaT4HgVr4+VfdTpqZOI2+aHVx/uub1xrSoF7qU27qh4pxfWyxf7u1ce2rJeSrPQ7i0MvKgLkx86sUBg2D+rl0A4uk1DMkQ8o4ndKqwLD2uOH73aNmLCgyfwwdXB66Q5T59ohtwgQ/W+U+OnbQ8pOl7Pi94d9mHwTUcmfK6DmxdW4chJk+ox5ggTkUwR+TCRE6rqElUdoKqHAhcDnYBFcQ4r1Xi50zmToV3QNfIhcGPP/Qve2891u6a4Hw1qVOKoNv6uf4e2rMv1xxae1y7RauP0wjqtU7R7ZXHVBi+vVMzMoE61SgUrSvs+93vmZohZubpTf7hVjfFWtw1qVOboNrHjTZxqNKeAapBgrq8gAiSea3FIcnj9BH067Uvn5nWiVqQP9jko5rnu610o7NPlQBNTgFjZbRuKSEI+gSKSJSL3AJ8CbTEqrTKLpzHU8SC48sisiH3OGb49ozj+oMZJ92P/hoUrG/eDyL75DwpVDGnF/j2b1DaqQz8bSGaGkJevEcntBt90ZNSKZXAKUuE440GC2MIeP7M94x+IVova/1s8NdXLF3fhnatSl8k4pBCvcC57kjHgH534vW9hMdjeHfeNaHdg48j4M6fHYLpUWEFSmSwGfreKSBXkCnbV7ChARMYBFTHFn85T1YUp6GeJ8uLwuRGfT27fOCJfUaUYqoJEM+DWrVaRTT4BQcfGiHy2nyHu50fVipnsLIbSlnsLzepW5e6TD+Tk9rZNxLtdhcwM8vKVEbPXFmw7oHHNwIkSE8E5FoPYWKpUzPSsLGgf+VSM4EjwXuGGpAavOiG2GjQjQyLUku6f+tubjmRnTh7Dpq+mU7PaVK1UuFp0q7xTRRAl6Urge6ttTcfLjytU9RBVfbo8CA+IDtA54aDGEYE5sVwz/fbV9HHRHHvf8cx87GTPfX7lRMEpQCK/78/7j+f8rs245PCWvseGBEdEuKlXa1o3MpUN/SYIGSLk5muEqjEjI/qmj+f8kCju8XZ+12hDfBWfdPGFaTRCdWdJ4TWcnIuHTB91JRiniHrVK3Hx4S3o0LQ21SsVPmP8io0lS5CCUo8meM5NIjIIaKKqvUWkHdBDVQfFO7Cs4H74e6kNbBWG+Ijocw9txju/L47a7ndzx8O2cdhdufjwFnRsWpvaVSsy4NzOMY4MSQZbgLRqUJ03Lz+04CFcIUPIz1cObFyzINuyV2xIqlPsu1cgXpOOPi7VR8GxBUFsKe1SSAJ4qUSdxcecE5Z4VSCdK5BduenRQsSKRH/R+vs/VyS6Z010B+9SznJhuQvC2OoLGy+1gT07cP7gC546tSAy3T3JO8Wn1nRQCsaY9X1Pnd2xzFeAK0tUrpBB60Y1C2quZ2aYFcjvCwrT6meIRJWyTfVk3z0rdQ/NxrUq+6q5Ch9IoQQpKbxWCs4x45yQxpt7VHcIkN05xW9E/8D6+ywmEt398qPc5cJyzhJPbt84SvLXqxbtY1DgrePadp7lsdXbITBuP+EAHjm9fcTxHZsmFux3QOMa7FOrCveGMR7Fyp48M7TdK8cKmUJefj4LHSVGjWdW5I3sV9q0qLg9tN3CIpZNrqWVFqNapbJdObAsM3jyiqhteT4rkHhi3unMk64A0FiR6BOtv6Od20WkOXAhMNrrOMphLqxWDaqzaelmwHvGWLVSJov79yGr75CCbce1bcSQaauiVBQHNK4ZVdXsjIObRMVmfHXDEZ6eE52b1aaOh8CqVqkCf3oEHIakl13WzK5KxcjfLzND2LY7ct4kIlFJFOvXqMTqrdGZUkfceWyR+rPClRfLLTBiCZAnzu7ASe0bh558JYjXCsQZS+RcYeYnoGt016VJFYEijUSkgYjcICK/AqOAWD6pdxCZC+t94JYA39FcREaKyCwRmeFMGS8it4jIHGv7AMf2+0RkvrXP2/KcAgZe1IWs+pFJyyr6GD/tugnPnd+Z0Xf3jGnTsMeCM/Cw4DwVMjxrSA+++Sje+7/ggWUh6cW+Md11zBeu2x6RMPG3e3oB0XEffiosWxWWKMNdKTD83L29qFapAqd08LaPhBQPXrnGJMLu4b09HsW+AhGRmsDZmGDAA4BvgP1UNWZ+BVWdVMRcWLnAndbxNYGJIjIcI6zOBDqp6m67HrtlnL8QaI+xt/wsIgdYsSsppVndavTtfRDXfzixYNm4b+2qLN0YmSL5734nkmkJlioVM2lZP1owOJn80EkJzSJCSh8HWL7353X1jygHaG6ph9yR6amuKuheYfTpuC/P/DjHd39I6aKuR0oSpyed8/dLpGzE7jStQGIpO9cC44EHgTGqqiJytl9jETnHZ9cBIoKqxqyJrqqrMNl/UdVsEZkFNAWuAfqr6m5rn+1YfybwqbV9kYjMB7oBY2N9T1Gxfzf7fvdagdStnlgNhtpVY+evCSn9NK9XLUolGQt3Nt+rjszi3q+mMfWRk3jv98U8N3xuRMBoorjPn+Va3YZVjUs3bqHQY7/6EdH/RfXaS9cKJJYK636gCvAacJ+I7B+jLcDpMV6nJdIpEckCugDjMKufo0VknIiMFhE7BLYpsMxx2HJrm/tc14rIBBGZsG7dukS6EXmegndGgrhv1JCQIBzftlFBMsvj2jbigsNasLh/H5NR2ZqUeKUu6dMpWrXUpUWdQNuchCuQ0s1dJ0U6wbi99oo6ASh2G4iqvqCqh2OSIwrwLdBERO4VkQM82l8V4/V/QTskIjWAr4DbVHUrZpVUF+gO3I1J0ih4p42J0geo6huq2lVVuzZsGDunUJx+RXxunuKa0yF7BxkZJhBxzhOn8OblXSP2rcveDcCmHdGV5Z45t1PUtnev6hZVifKBOPmRQvlRuqnvyl/Wr0+7iM+J2D0APru2OwAjZq/l76Wpz4IQdxqtqgtV9UlV7QgcBtQGfkh5TwARqYgRHh85VF7Lga/VMB7jHtzA2u5UPDfDRM0XC8+eZ4LzEnW3DQkBk5TQrY6wA0u98mO5Vw5VKmZQu2pFeh4YmaQzngtuuAIpOyzu34eOSdbuOXy/+gWThl1piAVJyOFbVacB0zDqrZRirSoGAbNceba+BY4DRlkrn0rAeoyn18ci8jzGiN4GY7NJK/aKsnbVigz919E0retfEzskpCh4qSncwqafVUSqUoXI7X7egYXnDgXI3kqqsx5AeuqBFJUjgcuA40RksvU6FXgb2E9EpmOy+15hrUZmAJ8DM4FhwE3p8MCy6b5fPVo3qsHtJxZq79o1qRUawkM8+en2YxI+xnbn9hpTzgf/97ccxcVWloFKDlvcdcfsF+VODDDtkZO4uVdrADZ6qMdCyh5dW9YN3Nae9KbDbJuWkFMR6QC0wxjhAVDV92Mdo6pj8C+vcKnPMU8CTxaxmwlRs0pFfr6jaMFdIXsfzsnel9cHqwr9+JkduHTQOM+4IOf5OjjUposdST3vO9Xb/lGzSsWCQEfbzhJSdvntnl7Ur5GYxyekZ/UZV4CIyGnAUFUNpEATkYeBnhgBMhToDYzBBBSGhOwVNKlTqNrsmlUvRstCqlU2qwevIlMiQpPaVbj5uDYR2ycv2xzo3ImU0g0pWb6/5Siyd0UXsbNJ1IGnTaMazFu7LS0qrCArkAuB/4jIV8A7qjorTvtzgc7A36p6lYg0Bt5Ksp8hIWWKouSTsj2qvKKRAf64r+ipatzeWiGllw5pcsxJxwokiBfWpZiYjAXAOyIy1oqt8KsJstNareSKSC1MQOJ+KetxSEgZoU2jxNKR2DPEeGVui0IFr1J3IXsVJbUCQVW3WiuQqpjU7GcDd4vIQFV9ydV8gojUAd4EJgLbKAbvqJCQ0sY3Nx3JthiqCDf2DZ5IepP9GlZn4brtHN+2Ucx28byzQso/JbICEZHTReQb4BdMqdpuqtobo6a6y91eVW9U1c2q+jpwIsZr6qoU9zskpNRTo3IF9qntX0XSjV1Bzmk/iYcduRyvsmGmtQJx180OCUmGICuQ84AXVPVX50ZV3SEinhHmItIJyLLPLyKt4+XCCgnZ22lerxqvXnIIR+7fIPAxtlYi3qLFnnzGS3USUv6wy0KkYxUapKTt5TH2jXBvE5G3gU7ADKyiUpgUI6EACQmJw6k+5Wb9sFNbxDObFNQaDJM/73UU1qwpWrnsWMRK555NZG4psT4LoKrqV3Wmu6q289kXEhKSQoLWp8uQxO0rIeWDFy88mFdGzqdxreDq1KDEqkhYVGXpWBFpp6ozi3h8SEhIQNpbLp/nHBKzTE+BpAnFx95H9/3q032/+mk5d6wVSC3L+8ozCkpVN/oc+h5GiKwGdlO4YolOJxoSEpIUTetUDVSPJFRhhaSDWDaQjzF1PCZSqLqyUfxjO97G5LSaRqENJCQkpASxVVgaSpCQFBJLhXWa9bdVgudcqqrfJdWrkJCQlCKhCiskDQQKJBSRuph06c7kiL/6NJ8tIh8D/8OosOz2oRdWSEgJYQcp2i6dISGpIEgyxX8Ct2IKNk3GVAYci6nR4UVVjOA4ybEtdOMNCSlB7DTv6SptGrJ3EmQFciumEuGfqtpLRNoCj/o1DqPOQ0JKH0e0bkDnZrW501VzOyQkGYIIkF2quktEEJHKqjpbRHxHoYgM9Ni8BZigqoOL3NOQkJAiU6NyBQbffFRJdyOknBEkRedyKznit8BwERlM7NrjVYCDgXnWqxNQD7haRF5Msr8hISEhIaWEIKlMzrbePiIiI4HamBKyfrQGjlPVXAAReQ34CZNYcVpy3Q0JCQkJKS0EMaJ3BNpaH2ep6ug4hzQFqmPUVljvm6hqnoiE9TRDQkJCygmxItFrA4OBFsAUTCBhRxFZCpypqlt9Dh0ATBaRUdYxxwBPiUh14OcU9j1hJk6cuF5ElhTx8AbA+lT2J0WE/UqMsF+JEfYrMUprvyC5vrX02ih+kamWMXwPcI9dD11EMoD+QFVVvcXvm0RkX6AbRoCMV9VYNpMygYhMUNWuJd0PN2G/EiPsV2KE/UqM0tovSE/fYqmwTgA62cIDQFXzReR+PGwZItLW8tA6xNq0zPq7j4jso6qTUtbrkJCQkJASJ5YA2WMbwp2oaq6PLeMO4FrgOY99in/gYUhISEhIGSSWAKkiIl2ITKKI9bmyu7GqXmv97ZW67pUq3ijpDvhQJvslIouBf6pqcdvFyuT1shERBdqo6vw098emTF+vEqC09gvS0LdYNpCRsQ70ExQich4wTFWzReRB4BDgcVX9O9nOhpQfSlCAePWlMvAqRm1bD5gP3K+qPzjaVAOeBc4HKgJTVPUYa59gbIP/tJoPAu5Vn5tLRI4HXsE4qIwDrlTVQM4dQQWIiGQBi4CKXpqEVCAilYCpQA1VbebYngW8AxwOLAVu9vudE712IaWLWNl4i7qS6KeqX4jIUcDJmJvudcxgCgkpjVTA2OyOxTzwTgU+F5GOqrrYavOG1e4gYCMmWNbmWuAsoDNGXTscWIgZ9xGISANMXrh/YhKOPg58hskxV9a4G1gL1HBt/wSTL+9U6/WliLRR1XUe5wh87UJKIaoa84UZ4JmOz7WAd2K0/9v6+zRwsXNb+Apf9gtYDJxgva8MvIjJcLDSel/Z2tcTWA7ciXlYrQKuKob+TQX+Yb0/ENgK1PJp+wdwrePz1ZjccV5trwX+cHyuDuwE2gbslwKtrfd9gL+tvi0DHnG0W2q13Wa9eqT4+rQCZgG9geWO7QdgkqnWdGz7Dbg+2WsXvkrfK0gqkwrAeBHpJCInAX9hikz5sUJE/otZ6g+11ANBvqfEsZbTpQoROaik++CFiNxpjYdUXLcHMDPwgzEz0W7Ag479+2AyIDTFPGBesUoMePXrLRHZ7POaGqQzItIY8yCcYW06HFgCPCoi60Vkmoj8w3FIe0yslM0Uxj8k1wAAIABJREFUa5v7vOJuq6rbgQVe7QOwHbgcqIMRJjeIyFnWvmOsv3VUtYaqjvXoz8UisjXG9WoR47tfAu7HCD8n7YGFqprt2OZ5PRzto65disdXSrFi5Oz3paZvJdGXuA92Vb0PuBejq30X6KOqL8c45HzgR+AUVd2M0SnfnXxX04eInCki72EeXqUGEXkJI4SzSrgrBYjISSLyI2ZMXA6mXnGSp70EeExV16pRczyKqWppk2Ptz1HVoZgZdURCTxE5TkQmA9WAAapax+MVt6yyiFQEPgLeU9XZ1uZmQAdMdoUmwM3Aew7hXoPCzAtY72vYN7RrfLnb2u1rxuubG1UdparTVDVfVadiVEfHJnCKHsAG4GCf67XU6yARORuooKrfeOxO9P9zt+9gbUvl+EoJjjH2mhXOUCr6VpLPryCpTI4B/gM8BnQEXhaR/1Of4EBV3YGj9oeqrsKoHUoVIiKqqiLSC6OmywF6iMgSVd1Ukn1ybKoHbAJOEJEPVLVEUsFYD8KKwEOYB9TTQCXgMOuBm5vkjdQEM8O3WWJts9mgkYbgHTj07iJSAzMbfhwYj3m4V1NV5yomLlag7AeYANqbHbt2YsbHE1Y/RltOJidh1DjbMKpdm1rANq/xZf11trXbZ5MgInI4xgDdAfN7VAa+iNE+6fElJqPEAIxtwwv3tYDY/982oJZlkH8Is5LaiVH1pWp8JU2qxlgK+1Mqnl9BVEvPAuep6tOqejHGmPhLeruVXlw30iKMsf9ujKoi7iw13X0SkUxr85/Aa5gZepuS7Jeq7gEGq+rR1ipgE3ChtSpI9uZeSWSqhBbEzvjs7F8GRpgsw9gDHsD8jveLyHYR2eZ4zYhxHsF4ADXG2D5yHLvjqb5mEDn760yh+ss9vrKdba0H8v6O9onwMfAd0FxVa2MMz7YaI+I38RlfuUA767hNrmu1zUeF1QbIAn4TkdWYyeK+IrLaWinPAPYTEeeKw3k93MwAOtvjC6Mam0pqx1dSuMeYqi7DOEFcIKY+UnH3p/Q8v+IZSXAY0B3b6peEwSYVL8zM8hvgdmAf175/Y2ZBTUuoT7dhEk+CmVEOweSveQh4AjgHaFhC12pfx/aK1t/hQO8innsxhUb0JzDG1IbW/zsGM9sHy4juOnYDkQbjxsD3mMwJ9rYBwOfWewnQn9cxAruGx76KGNfefphV+5EYQdDW2n89ZiXSFCPAtmJWAl7jq7+1/x+Y0gf/xmE0Bq4EFsfop9OIvha4wnrfzfr8ofW5GpCHseWkbHxZ//8+jtc5GGG/j/2ssK7js9b/dzaw2eu8Vr/+BtYAXTCrzhnAjcmOrxSM/RuxnChSNcbScE+W+PMr0c6/XxI/Zgov/tkYJ4BeGD/1lzH6X3t/J+BD4BzXcWkbHD59OtTa96j19yLroTMLaFSC16qzfT0w6o+3gJOKeP7FFAqQKsBAjKpzlfW+irWvJ5YAwejRX7cejDsxenj7fAOAtx2fMzCqsPYB+tIS82DeRaHX0jbgEkeb9hjX1O3ATOBs5/iwvj8bM6v/xDm+rIfiJY7x9Qgw2/ofRgFZjnP1Az6K0VenADnX+h+zMQ+3l7EEiLX/MYx9IRfzQEz5+MJbwGdZ/9dOYI79O1v7jraurXN8TbOu/RbrOiY9vpIY9/YYW231MyVjLI33ZIk+v2IFEn7n3mR1/BcAVT3D88BSjIj0B+aq6tsi0hJzA7ZX1f9ztLkV4/EzBjhMVf9dAn06SFX/KSITMDdhTcyyPg+4QVV3pbNPMfrlvlYfAUtV9T4RyVBH3rQ09usiVf1ERD4Ftqjqddb2GsCvGO+tEaq6W0ReAd5U1cnp7pfVh6THl4j8BNyqqrPS2KdwfMXuW6kcY6Xx+RVL2k3CSLOeGMNpT8zs8Fjg2OKcFRRBUovXZ4xh7mfH9raYIK4zHNsaYXzo1wHPpEqCJ9inLzEG18uAfzv2/QB0KUXXqjcwAmu1UEz9qm793Qcza27jaHMh8D7Gi+sxzEOxcXGNt5IcXwn2qdjGV5LXKm3jK07fStUYK43jy37FMqJ3xcR7PICRwqOAnao6WuMXlSppKjo/qHUVMTfODhE50/q8CrPUbieGGhiPs2kYXefdruOLq0+/YPz4P1LVex2Hna2pTwlTpGtlbasKfIqZuaYaz36p6nZrNroak37kLUebT4GnMKvlhhj9+ZpUd0xMNHmBMbo0jK8E+1Rs46uo18rals7x5du3kh5j4nLbLw3jy5cA0q8ZxiD4MmY5mfaZQBKSuqvV1/8AR1Fo1MuwJTFwFaYkry3V78YyyGIMhCm1MSTRp4et95l229LUL7tvxdkvR98yHO2XYmbT+wCH231PQ78EY5j+BBjj3lcS4yvJPqVtfKWiX2kcXzH7VsJj7BBM0b33icz+USLjK8grSCDhclU9D7O8/TBe+5LAkr79Mcav7zFeHTdj3EHRQt1pVUyQ4yrgDRFpgvH+yLHa5arq2lLSp1yrXZ6mUPebqn7ZfSvOfqmpR1MDo+O1+TfwO0Y3XcVqm/IZlxp2WB8bisgNVr8zHd9XbOMrBX1Ky/hKVb/svqWyX0H6VhJjzBr7D2CE2qeqern9v1sroRIZX4H6noZ7rUQQkd4Yd8hNYioiPg9co6rbrP2P1a9fv19WVlZJdjMkJCSkzDFx4sT1qtrQvT1uJHppRUSOBXap6jgAtVJvi8jRmJXSSuAJERmMcX1rnZWVxYQJE0qqyyEhISFlEhHxLDdQJpIcOhGRmiLyNSaY5jqxkupZ0aJgUm1fqao9MInZrsIEMV1cIh0OCQkJKaeUOQGCyVP0C3ApZpVxHhTq7lV1hqqOtNqOxvi453icJyQkJCQkCcqEABGRy0XkWBGpoybh21sYb4W5QFcROcBq505nfBLmf0w4UV1IahkwbDZ3fj4lfsOQkJAyQ6kVIJZnwr5isp5egUkF8YqINFDVXWqSr43F5P85H4xnhIhUFpETRGQiJmPovarqTi8dkgRDp61i/bZgiYEXrNvG+f8dy6ujFvDVpOVp7llIiOHrScvZtjstlXxDHJRKAeJw96sJrFDV4zG5fDYC/7Xbqeo8TLBjExFpLaZ4VT7GBfRhVT1DC2s6hKSALTtyuPGjSfzfu39F7Zu3JpvF67dHbHt48AzGL9qY0j5s251LVt8hDJ1mqgQMHDGPrL5DWLs17Rk4QsoAI2ev5Y7Pp3D/19NKuivlnlIlQESkgog8BTxleVkdiBWFqqYOw7+AI6x9WNu/wZQ8HYZJbNZaTZGd74v9H9gL2JNnQgZWbHIXooMTX/iVns+OSnsflm4wbvx9vzJZ1p8fPheAu74MVHAwpIwwbuEGsvoOYcmG7fEbO7jKmtzMXZPNb/O8yrCHpIpSI0AsoTARqItJnW0XSeklIt2gIHjnMUw2U/u48zDpVkZiwvdTkoRub2Lw5BV8Mt6z+FwUb41ZCMCG7Xs4/3VTJfWgfsO4+eNJUW3/XrqJMfPXe55n7ppsNu/Yk3Bf56/N5tSBvwGwdVcuu3MLY81+nRs+LMoTl789HoCxCzZ47t+yMydqxetk9upsLhs0ni07iu5DM3X5ZmasDDXgfpSmOJB84FlV/QBARLoArTD57V8DDrVcdb/BCJVWqroIk3b5FFX9rYT6Xea59VOTSPSibrFKYBv+O3phwfvxi41qamdOHt9PjS46efarf/ie56QXfqVVg+qMvKtnQn094flfIz6//4ene3pIOWB3rlntPjd8Lhd6jM3Oj/4EwG/39KJ5vWq+57FXzX58MWEZe/LyaVa3GsceEBkrd8bLvwOwuH+fhPq+t1BqViCY1cfnUlgt7Xeghaq+C2SKyC2Wq24zIM8SHqjqb6HwKF04VwWxWBRj9ujmr8Ub6frEz1Hbd+akJc9eSCliXXZsh42Vm6PVqYlw95dTeeCb6Vzx9ngmLfWuBrtkw3Zmr94asW3hum3kxhFO5Z1SI0BUdYeq7nbkvzkRk5IYTDDgQSLyPSZfTLS+JKTUkL0r104CF8WWnTks27jDc18sznt9rKfnl23/cLMrJ4+de0LhUprYvjuX4TNTnhyZPXn5zFy51Xe/PaH5de46+gz8jR17Cr2zdrkmIOe8+ofn2D32mVGc8mLhPHXF5p0c99xonv5h7/bRKTUCxEZEMi1VVWNMvWcwcRz3Y8qB9lTVZ0qqf+WNOauTC5Fx3ow2U5Zt5suJ3i67hzw+nKMHjPTcl0raP/wjHR75Me3fExKce76ayjXvT2DBum0JH/vjjNW++y4bNJ5TB/7mazC/w4o/uvzt8cxYuZV2DxWOi2s/mBjVfuxCb5uLk/XWqijVHoZljVInQDC2kIrAeqCTteroB+Sr6hhVXVGivStnrHa4vr40Yl6BUfK+r6dx40fRN5ebl36ZH7Xt6vcm0G/wdM/2efnFk7wzL1+L7btCgmF7U+3YHTnrX7F5J1l9hzB4sv+t/ZBjPOX4qI1mrfJehfg95Bes2+bpePG6w87nh70aVvbuMVbqBIjladUFEzh4B/CNql6hqv5r1JAi4wzdf274XK54Zzx5+con45cydJr/rM9md05qdcCfjF/Kpu2Je2fZeKkfVNVzpRSSOsYv2shG63ebsXILX05cHuVl55f4e45lWxg8eWXc7xkxaw1tHvjB0zPqqaH+6qQhHk4eG33G2a9z17EnNz+mje7q90xS1ukr9u7HUqkTIBbLMa65x6vqoJLuTHkhq+8QXnDZDNzJX3bl5DFuUeQSPjcv39eVsUKmO3uMIdHJ/xcTljFg2Gzu+3oaXR4fHrHvuynxHyw2XquOV0ctoN1DP/o+MELio6q8PWYRa7O9gzXP/+9YLn7zTwD6DBzDXV9M4VAPpwfvc0dvcxun12w1M/4Rs025i//8PC9o1wG4ycPN/K4v/FPrPPDNNHoVQ0xTWafUCBARqSIi40VkCqZoShU1RevrichwEZln/a3rOOY+EZkvInNE5OSS633p53crHuM/I2LfeIJE3dADfpxDn4FjmL822l6SEZV+zLAnN7GVyd1fTuXVUQs89832UU14MWtVdB+/s2a2fg+/kPgsWr+dx76fyY0fRj+I8y2hPdtlTwuqQrTHW4ZjKF3nYZuAwhXzfB87StCYj9/mrWPJBn9nji98bHixjPV7I6VGgAC7geNUtTNwMHCKiHQH+gIjVLUNMML6jIi0wxS4bw+cArzqcAEOcTHgxzme290CINfjpp+8bDMA67JTP4NPtafU9R9GP3jsfzF/7/a4TAp7XGzZGf2Azvv/9s47TIoqe9jvmQQzzMCQhpxzRkCigBgQRMXFsKhrwEVdxTWuGZXFdUXd9dNd07rqiru6Zsyg8hNRRFRQCRJWQRQQFZUkcZi53x9V1V1dXd1VnaaLmfs+zzzTU32r+kzXrXvuPfeEBIvS7a8w7vnm7XtY891OKkPnh/uitdJwYt1Lt0wIgO1a8TnzkY/8Cevg/RiBsYmwfXc5n8ZwFz7YCIwCMctJWtOKfPNHAeOBmebxmcCJ5uvxGOUf95kxIV8CA6tQ5GqBc/3g3CvY8PPuUBu3DcOP16fmhXL9LO98RTEWOa44Z70nPbAw5DZsl/+nX/bxn0U6CDFR3IZn+3funBDYV63W2H7Bv41VzJDb3uaYu98NBQz6uc9i9sZ9Ca5w00VOTrSQVi622cuj91ncOOvRD/nV/QtjbvofTARGgUDIhfczjAy7b5nVBpsopTYDmL/LzOYtgA220zeaxzRu+JyZCfDd9rCp570vfgw/2C6X8Dvji8WKTdv5ySW+Y+bC9Uye+bEpk38N8p0joeKSr7eyyxzUlDLyK23cuptLn/qMqS+uyEhcQnUkZDr64ReWfB05abD3Aef3v9fFyeLHX/ZF9LFrzJxm1mfs2BvbDLXCI61IoquhRHHb8rNikS584pOI/ysWSzca/8PYew7++OdAKRClVIVSqi9GtPlAEekZp7nbqBLVe0TkfBFZLCKLt2ypubmSYpqjHd+iAq60bS7eN+/LuAN4AosDVw5UKldzxc0vf87cVcbxRFYg8di0bQ+/fmgRI+98J5Sj67zHdYnjRDnpgQ8i/o631/HZhm2hDXG7orG/3u1YtfSe9qbrtR5d8BWffrMtrmyxXHzTRW6O8GacmJRlG7ehlGLEHfN4oQaULwiUArFQSm0D3sHY2/heRJoBmL+t0WYj0Mp2WkuMCoXOaz2klBqglBrQuHFUTfgaQyx/dadycO6BbLKliXC7wiceD7QXX/24y1MJJao/YqW2sDZmdXyIsZJ4e3V6Vl/LN8ZeFUx9cUXIccO+yb5iU/Q5sRwyLKa/utJTFre4pHSSkyNxE49e8cxSDlQqvvl5N1e5ZId+ZMFXKX3+qs07PFO7VCWBUSAi0lhESs3XhcBRwGqMaPSzzWZnAy+Zr18GJpoFpNoBnYDkdsYCzOSZi5n0r9T/rVib1c5nttJlcLXa3DcvMw+n18CR6EM3dMbbqYhTIzjqrvmc+5j/1VesW1ReUcnpD38Y+ttNebvZ+t0iwNOx0nzyQ39ZpZMlVyRu6OAv+w6ErMXWRGXNdztpe+1rrP5uB7f4UILxGHvPexzx13dSukY6CVI23mbATNOTKgd4Rin1qoh8gJFk8bfAN4RroH8uIs8AK4EDwBRbHq1qw9xV6Zklrt3iHhT1vcNmHc+GvDBGWu1U8Ro4dumcVlXOjr3l/PHllUw7oTsltfNjtnGam86wKZNEORjidHJyhHfWxDeFO/cFXzHjmOy5tFJh597gBMUGRoEopZZhRKA7j/8EHBnjnFuBWzMsWuCZuXA9Qzs0pFOTkoTPtVK5W7jpj0wpDot4dutZn1Z/O3JV4yeD7MPvruP5TzbSukERlx7VCTdDYqy9imT58KufWbYxNZNoppnvoTwgOn18qo4mQSYwJixN8tz88ueM+9uCbIuRNM8sjq0kLn86drSwJjmcVsr/LPqaO+Y40oCYy8JYg1+sbMvubf3Ldutrwa4Ht9VHETRnEG113nLTCiTA+HlIrTZeRXOCzJKv/QdVjeqSfkeImlZL3e5QMf6+95n64oqoLABWuEOsHvjYwvUJfJ5/Pgx4dtvCfO9Y5VdtqXd27TtQrRMuagUSYOLZWvcdqOCIv77DO0mWcVVK8eoy/zmmkuWcoW3Ter1Hzzk0rdcDeNJnOd/qgn1esnRDtMlox95yDlQos6374Jeq910QWTz1KM82flTBtFfCG+V/fn2V5wpsw8+7E97/SWQFmEm0Agkw8Sr7bdq6h3VbdnHji+E011t37efON1Zz5iORG5l3vhGdpbTdda9z8ZOfpk9YoFFxQdSxOrVyKamdnq22u3/dFxFhYLsGUe/deXLvpK/rtgfzw869cWtQHMx4jT29p73JvabHnWXCcg5YLgHZgaJzk+KEytB+euPRNCqu5dku0f2M7XvKeejd+Onhh98xj36OBKJeBMXioBVIoPF+Su39+ZBb3uK+eWt57wsjSK6iUvH+lz9y37xI80Sm4iAO69go6ti4Xs1ZPPUoZl86POXrH9HNSELwzAVDoj7rlAGt3E7xhWWz/vKHX0JxL6f/80Mu+PcS3+V5M4lSKuHklPGINQi2v+61KHdvq6s4u0wi+iMbcTddmtb13fbiUR0pLTI8zVo1KIzbNtH/xav12Y+GXfQ3b9/D6u92uLrSBxWtQAJMPPfWS54yVg/xZkQPzl/r6lbpdN1NB/P+cLjrIN69eV1q5eVSrzDsCrps2mhXZeOFPV7k4bMHRL3foE70CsgP1uB81F3zGWbGkHxjZmpVCuat+YGnP/6GTdv28MziDTGvkynueut/dJ46O201TWL1mEoF326PjONQyljBOjfZP3MxfcXiQAazWJ47rJ3r8Vgmnrouq+E/HNMFMfvWoW2iV7eR101QQI/2820m6CG3vc2Yu99zTT2fshwZQiuQABNvlmcVsok3I4pVOjRdqUEsHjqzP+0a1WFoh4Yx2zQvDc/s6tbO59qxXRP+nDyb3aR2fi7r/nwsU8d1Y+lNowH492+Ty6U584Ovo/NxWZvICib962OueX45pz20iKufW8ae/RWUV1RyzXPLIiL1LT7/dju3vLrSdRD7dtuehCOJn/rYUFq/pMn/P96kw/nWg/PXct+8tVHpZtbHSYXu5P0vY7uBX3h4B9/XcePG47q5Hrf+jy4O1/ZjezVL6fMSNWEls/qavcLbdBoU12CtQA5C5tke5h/iDEaxclilu++N7tHU+DwRju/TPGa79TPGhezSPVvUo0fzSDPDquljWDndvazLaQNbU9vhAZOTI0we3p56pvmhcYm3DTsWzgHS+ubsD6o18CsUC774kacXb+C6F6KzCU/8xyIeWfAVO1wG/KEz3ubQW/0VWnLi97aVV1Ry/uOLQ6uoqOsEw3wOQIOi+KvGP4zuzKyLhvKbwa0BmD6+R8T7IsJVx3ThycmDIo6feqi7SbNl/UgTVZuGRRF/e33HicZEzcnQPlpQrFxagQSYWCk+4tWO9sOa76KLLqWLgtxwl2rfqE7ctved3i/0+s3LR1BYkEtRQR4D2hg1w+ZeMZLjejdj6c2juW1CL8/Pzs9JvjvblbJ9v8EemW+5Y+7ZX8EH64yBZNHanzjv8cURdutQdYs0rfQSvcz0V1by5srvGXHnPNf347mVpnt1anHIdPegQy8X145lJRzSuj5/OrEXS28ezVlD2ka1mTKqI0MdJtGRnd3dva3bOWVUB9bPGMf8q0ZFvN/Oo88GBb0CqcF88f1O5vhYpsZ6mL1yR3mdP+mxj32d78XT5w/ms5uOjjh243Hd+O1h7RjVpTF3/bpv3PML8sLdr1NZcej1MxcM4ctbx9KxrJh7T+8XsX8Sj/p1Cph8mLtN3Au72aDz1Nm2IlTRD+r1s5aHPGv2V1Ty1srv2VMevdnufMa//MHdpOgXv2PGmu/jTxAyNXttVq92zPe2+qwU6MTeh/32AzunD2oden1Cn+a0NlccbRu6K4opozpyi2OVk21eWfotba99LaKYV1BWkVqBZIDnl2wMJZDbW17BH55dGhGsdvT/e9e1cp6Tm1763P0Nn7PETHta1q9TQKnDBFFaVMCNx3XnX5MG0rdVadzz7UFZYhspcnKEvNzkuubU47ondZ4Tq46FW5ldZ+lWiBzorL0Pu/LZubeco+6an5JMm7bt4ZrnlnmmLPeKEYgXc5DKxLahixu3GxeMbB/xeUtvGs3cK0a4tk21D589tC1/O83IkFSpFCf0ac7T5w/m5P4tXdvn5ggn9PFXVugJm9nsb6cd4iuOJBkeMPvg+h/D+ez0CqQac+WzS0PFYmbMXs1zSzbypwRSNCil2LRtj+sGLfhbgVRWqoTMEXd7rBacdCwrpnWDIu+GcaifpNdUJjiya5nrcTcffreH1zq0/0BlKPmj3fyVbAW9xet/DimM615YxtOLN9Dphtlxz4m3wnh79fceiiz5gWmfS/EoN64b243DzYwCo7qWUa8on45l7nncxKUTX3ZUp4TkyrE5RIgIg9o3dL2uRb2i/KiVtRtdmxoyj+jcmBP6NPcVR5IMlmXW7s0WDPURoGSK1YVF6yI32RJJ+WDxyIKv4iocP3ph255yvk7AU6ZeYT55OeJaE92NuVeM9H3teLx1ufvMs6qJN6BYWKuSDT9HK3ZLqdz79hehY3YPHL9mRztf/rCTkx/8wLuhg3grkMXr46eNcZ7aqayYL3ya3hIJbntskj+Puc5NiqOOTR7enrvnfsFVx3RxPadOQaSzRZ45AucmEP1YWlRAj+Z1Kamdx6J17ulVGhbX4onJg+jVsp7v6yZDrtl3rOwAoFcg1Yq5K78PFdVJJZ01GLlzvFYrfsb4xxauTyivkEKR7zAb3Xx8esxB8ejUpCSpLMLxuGdi34RTqKS6eWzdky02d+BUA+i274n04vJb2jfep3opMmc6/yZ1Y+9rOElnsCPAs78bQhuXvYriWnmsnzGOKaM6up73/EVDI/4+qlsZkw9rl3B/fu2S4Tz4m/6hv+37dBbDOjaibox09+nCmtzYJ3eVSrFl5z7aXvsaY+95j+27y9m83d1ikUm0AkmRjVt3M/nxxRx/7wLm/29LxKCRTL4aZ01pN/xc123mFv+akZvah3dp7OrxAtG+9UFjfN8WHGlGrff2OTtMNTWHdU/sKxl75lY3ZXKgopIj/vqOa4nUXfsOcNIDCyOO+VVy8bqH1/9pn+WCt5eUnVQVyFPnDw69fvScARzaNn5QXyy6OqLQ83JzmHpcdxomYWKyr1q6N/cf3Z5OLBkORIwt4TxmqzbvYNRf32HIbVVfSE0rkBT56Zf9tteRMRl2E5KVcdZr8M/1s78RJ8q2uJZhlayd5501NPKakfmk6hTkxVzy33xC5lcm6aKkdh7/muSdgPGNz1Mr3GUpCHtFPLsThNt937q7nHVbdrnGktwwK/qY33rf8aLEvUx1TkWXyBwo2X0ei8HtG9KvteF40a1Z4oP1Q2f25/kLh3o3TICigrCVv1Ze1Q2X9v5iPYb2Oi6VKnKP03KMyHRNeCdagaRIPFvkb2eG3WU3bdvD0x9/Q7vrXo97PT922lifWGHbOI9XWdD1mkox0p4qPYYYj5w9gKEdEk9DUtXYzT2jurhvkKcTN2vVDpvbpdv9+Hj9zzHfW+yS4j5WVcm4cjkE8zJhOdOOJBI498u+sMktVoR5vGwFAA+e2Z8ZE3rRrF78nFRujO7RlP5mDFG6sD+PtRKclMVjwiHxPb3sXcJS+uUReyDw25nRJYkzXdLXiVYgKRJvmHZGIt8994sYLcP4MVPEMq0fqFShYTNR+3u3ZnUjggCt69x+UmQAX1AiYL2wvseq2mt0W2HYJxdu9+OiJ4ycR9tcYiScpqRk5XA6RXj1r/IkP9fJNWPcU9XMmBA/a3JZSW0mDmwdt022SNcKZM2fxnC2xx6dve9YVolrX1gWOlYR4z6uDC6uAAAgAElEQVTZlXhVoBVIipxss1M7H05nzqPN2yP3N5wP+7fb9rBpq/tGmD1SOtaqxz5IrYuRBysWrRoURZg3LhhhzCBPHdCKc4a2DbkoBsX7wwu3VCSZxE2x2j/aTQz77PbB+Wsj+kOy6bqfXRJZ3dH5/3vNT77+KfFVTiI4U4kcDPz3vMHMvnQ4tfKN4bJPy3oJpYp3Uisvl7zc+HfCftesrAf2icb+Cvcs0Yk4PaSDwCgQEWklIvNEZJWIfC4il5rH+4rIIhH5TEQWi8hA2znXiciXIrJGRNyTKGWI55ZsZOfe8pRm5PZn+0BFJUNnvM2vH1rk2nbSYx/zrJkJdu5Kd3v9gUoVWvX4MXecObhNzPcs10QRYdoJPejfpjRK5iBTywxSLC00Yk16tjBs6snY1v3gpqgqPFYg9iFkxuzVnPHwh/yw05hk7HOJbPeDM4HmBw4TVI6HifSa56P3XuJxdPcmTOjXwrUWjBtenx9EhnRoSLdmdUMmrHVJmBKdOD0enXhNfPb6jLnJNGlVIGKQbGGGA8CVSqluwGBgioh0B+4A/qiU6gvcZP6N+d5EoAcwBrhfRNJnpIzDso3b+MOzS7l+1grvxnGwDzDOZH5uXPXcMlZs2u5rs9LPzPuWE3t6trGw9hSCUgnNi36tS7n5+O7MME1w1mbo1WPCsQPpHMsqlYpKt17hcLv0YuHan7jyGaMGfLIrEKfzhDNtTbpzXf3zrAHcdWpf6iaRZuRgw/rqdvo0E1m54Do3Kebo7k0AGN7J2D/M8+h8Xt3Fmmg4qchg6nw30qpAlDG6vJjkuZuVUp+Yr3cCq4AWGKs5a9pYD7DqsI4HnlJK7VNKfQV8CSSXzztBLO3/XYp+1142cjeO+/sCX+3SPc6faG769Wie2aCpdCEiTBrWLirVin0TWUQiciWlglJGESrnMQunAol1v62CTl57EQvX/hjhlWN8nuKe/3PfZ9u4dTfbdu9PKqDRTqyBL1Gvv4ORXA+zkxNrstK+UTHDTOcBK1mj1wrE6i6/iRFXdu5j0RvoAFVdqDATJqxFIpJS4WoRaQscAnwIXAbcKSIbgL8A15nNWgD26j4bzWPOa51vmr4Wb9mSXP1wJ7mh1AKRD/k1zyW2/M/kZMFt+IkVuWvn3atGRaXGBhjTsynrZ4wLJaM72BDHbzAG8T//yjvLrx8qlYpyn7UrDae+KK+odF2V+N2zOf2fHzL4tv+LOOaW0NHisNvnMfLOd1ixabuv68cilv65/lj3uhx2po7zbhNkvFYNdkpq54WUtVt2B+89EOOcBV/+mICExgrkh5176XD963yUQCBxsmRCgYwCPhCRtSKyTESWi8gyz7NMRKQYeB64TCm1A7gQuFwp1Qq4HHjEaupyetSdUko9pJQaoJQa0Lixe4rnRFBKcdIDRnoJZ+BUomaHXTaTR6qWhfpFkSYEN1NTh8bRUb3nDmsXkdyudcOiqNTY1YHrju1Gx7Ji+rYOJ3gsLUqf2cVtRVFm1id5939bGP3/3o14Tyn3jfdE9tScpszyA+4nWxHK2/eU8+qyzf4/wIVYlSS93HOrA7k+ygVMM6Pd6xXmh5REpVKhXF89zRV8rHiv2uZGvVLJZTKoqFTcMWcNFZWKU/+ReBqcRMmEAhkLdACOAI4HjjN/eyIi+RjK4wml1Avm4bMB6/WzhM1UGwH7fktLwuatjPH4B1+HXqcaODVjdrhMqJ9cTPG4+IjIBHNuE1k388VNx3fnurEH98zQD31blTL3ipGU1AoHhqXTzOf2rFtmv9krogftWHE6Sinf+0ydHRkBYtVvT1eEcv829Wnf2D3DwcG4OZ4oBT5MWKPMpJwi4eetolJxWKdGzL1iBKcMMLMAx7iUVWCrUqmkSk9XKPjqx8x60tlJmwIREWufYmeMH6/zBWN1sUopdZftrW8BK3PfEYBl5H0ZmCgitUSkHdAJ+IgMc/PL4ejiVOs72N18U93cLHD4qLuloEgkmVx1xa6oE82XFY9dLhur8fRArNllpYJ31vgztToVTaoTGi+ev3BowrNie2xRqxSzN2cb536aG2Fnk/DzZpklO5aVhPpfrL0o632F9z6JGxWVlaGsF1VBOrPxPomx2lhCpClJzL/bu51kYxhwJrBcRD4zj10PnAfcIyJ5wF7gfACl1Oci8gywEsODa4pSKjnfR5/c9db/0nq9nXuTK7LjhtM+6xaIVhNmiYlw+dGd03at611Sj1gDh5sicStUZZ1jz6EVD+cl9ibp+psIfhTIiX2NssbLp40mR4QeN78BwDFm6eODlU4+8svZA1gHtWvI+L7NucKlnzWMUcrAUjqqMjmPx8feXx/x92vLNjOud2p14OORNgWilDrO/N1ORBpgrAh8R7UopRYQeyugv9tBpdStwK0Jipo0f4vh4ZIsn3wT3nS1svkmi3N14ZYTJ1UPHE1s3MwG1vPvNg6Ux/CgqFRE1X6PhfOyb2So/rYdZ6oTN+6eaBRwKslwltqqpmvTulw9pgv9WsdOl2J/xArycrjH/C6i2wlr/jSGLlPnRBw/rncz7n9nLQqVcDoigG8dwcpTnvyEcb2TD3r0Iu17ICIyGZgPzAGmmb9vSvfnBIFjejRJy3UqKxX3zvsypWs4VyBugYR+EjXWBI7v05z7zwjXY19wTWRd7Fm2dOCppLSvUIqtu/bz9OINUe89umC96zmVlSqU4sQL5wz1L2+md4XsRrwUK69dchiPnjMg6vi7V43izYDUfUmViw7vyOD2sR0GLBOUH286t9xaluNFZQwni6CRiYJSlwKHAouUUqNEpCvwxwx8TtYpSIPve3lFJf1ueSvl6zhXIN/8HFlMqrhWHj6cSGoEfz8tclbYsn6kbf4Q2wyzeWnyqTcqlWK5w212YNsGfLT+55gpQ7zqmTuvX1XMmGC4O8czYfVoXs81Tuhgdf1OBuspTPbWhPZAlIpp5oxFSa0830GO6SITQ8pepdReABGppZRaDXgHIASc/yz6OupYsukm7Jz/+GJ27k39pud5aIcrR3f2bFOTeWnKMADOHmKkd/nveYP5x5n9Gd29ia9yv7v3R/cFV0848xb4WQzWrR1/fleVQceWUrXHNDQuyUwJ14MZy/zYoSzaZd6NIkf1xFD5XRJXQo2ycD8yMaJsFJFSjIj0t0TkJarAvTbTTH0xOm3J+zGCfBJ5sOb59Lhxw55/yMvDqqJSRbRJpwdSdaBPq1KWTxvNTcf3AIz8R8f0aIqIhNxxndx3ej/X4xZuM8jQJqmPwcFrLyTeCmTKKPd06skSDp4Na63Xfn9YWj+jOtCgTgEzzx3I/We4bttGsezm0cy+dHj4gM0ElugeSKxAx1mfbnQ9ng7SrkCUUr9SSm1TSk0DbsRwzT0x3Z8TBHa5zDoBWlVZxtFwh/ETJWtvcsNBHhWcCUpq53sq4nOHtQu9Hte7GTPPjZ09x20AsMqf+vFmsrK/eqGUilohX3Jkpxitk8NKrWLfAymr4syvBwsjOzemns/cYHm5OREJPkPdT8GcFd5OEfZg2Fi16y9/eimff5uak04sMmrTUErNV0q9rJTy55dYTbAnlstPMH+OX0qL8iPMIH7y9NgHrWR8zDVEuUTG0zduOuJws2iXn/0LL6856xqrNu+MWiGn22HCSpOSap13TXysOJJKBbfPWe3R2khm6Ydf0mAmd0OPIhnAPjjPnDTQd6rrRHj0nEND64+3Lh/hawWSrmJBNZkCh+KNNcjniLsfv5UVeO4q7+zLXnfUGsvdItDTGTTasn4hXcyod7e8TprUeeOyEbw0ZVhoQrLeZ10Wv675mXr2tQLJAPZVR6sGRbx+yXA6lnkHISVCQW4Ofz21D31bldKuUR3PAUOpcGzIsI7VP29RpsjPi/yeYz2/OSKuq4xEBnavyO1Kpfhg7U9R8SBPTh7kKzXOXaf2YXD7Blw3titvXj4ilH4cDJfc964exYtThrHgmiOoY6aA0SuQzNClaQl9WpWG+tPEGHWBnLRqYJjLbzouvrt5pmqlZ8KNt8Zj93YSMWzFh7ZtkFTqk6fOH8zf3/6C97+MLAxUXlHJ8E6NGd6pcdRnxqJnC8PF8ncj07vBWpOwTEPN6xn2f3FZJ7QoLWTLzn2uJqxEgjnLSuLvMazbsovT/rmIY3uFI7zPGtImlAxz1kVD+dX9C2OdzoR+LZnQr2Xo71q2TXvLHdepxKq6ZGpNw60/xaOspDarbxlDrbwcpr+6Mma7TCkQvQLxwTc/7fZuZMOeqjlVU8Lg9g15YvLgKFu7MxOw5woERYM6BayfMS6kdDT+efzcgZzSv2UoHUw4p1F021d+fxgihheWU4c471OLOHEmXrNKC3uFPLuCSnST209PTWf6F000yWxd1c7P9VxxahNWFnl/bbS7brxU4Cf3D8/qrAfaz5716lvGxHxv3W2R6QgaOHLp2PdAPrz+SO8P0yTEiM6NufOUPqH7aT2vbvnFGtQpIDfHMGE5XXmdzUd0jq3M6xXls37GOM/62/bBw66gGvhI/hchm48+OjKOvJrUcVME95/RL6ka7H85pU/otZ8UNMmgFYgP3Oy+9gH7H2dG+nwP7RCumWANOFceHT+WslFxgaff/20TwsWPOjlSeVsDR/2ifJq4zDwPkkq0gSdUmMpSIHH2QCoqYYctYWbL+oVRCmdtihmd7TI55SkscO9PPVvUZbyZ8DDyOjrVTbZx60/W+PPCRUM5bWC4gqZXeph2jcLmR6fFIl3oPRAf/Lwr2gvZnsemXaPYUadWh6gfI/tmIjiLRrnRrF5VxaDUTKyV57E9DXfeWKYDawVy6VOfhY69efkIFq+PTLW95JvUU29v3Bo2sfrJuHzusHYRex8W1r8yfXyPuOcvmXqULg2QIdy6k+WM0a91fVrVL+K/H30DGOmJ7Nx5cm8++WYr//1oA4e0Lg15/EHmvOe0AvGBM437HSf35j5b8sN4MRX+N02NdjMm9GLH3nL+/Hq0D/jo7t7psJ0fd9HhHbj/nbW0qUH5iDJJaVEBC689goama3as+5uXI1FmgxyRqIE3L0dS9mzaYfPx9xP/EevzLGXYq0X8uvcNi3UKk0zh1p/s98vefZxtTxnQilMGtOLyozpTUjs/ot6Q3kQPEAJ8bdtYj/fQxpoRju3pVAZGJ5k4sDXnj3D3koo3u7RmKc5OddUxXZh10VDG9MxcTYCaRvPSwtAK1LolLUoL6dWiHoe2NXJG5ZqK4bSB4aKZORJtJEqkzrYfnArqwsOj+5I98tmOPQ+TJjhEKhCxvXZvX1a3NoUFuRTVCltJtAkrQPRqGTlDc9t8NGagKuIm33x8d/74iuFq17ReetNAWH3M+rwXpwyjUXEBIhKRXVaTXqwHurAgl1dsuaHycoQDFSoiHUmORKc3SXeRL+cEwqmgCvNzQ+7cTlLNJKtJHTeTqD2eyN5fvDyv7IkaM1VsTCsQHxTXygv5v4/q0piuTSNncG4xGDk5ApUq4oGeNKwdBXk53DBrRVREs1diPi8KzQ14K/1431alKV1P4w/r4a7tyFuVm2usQP6z6JvQMWNj3VGGtjy9M0PnCsSpUOLlaAq31RokW6z5bkfUMbv1KdKEFf9ahTannEyVO9YmLB80tKUicdYeB2hStxbXjOkaccya+TkfxVP6t+Kiwzvwe1uyuztO7s0gR5GaB87ol1BCvC5NS7j71325/eTevs/RpM5eUwHUdtSGyRWh3OnCmxMdnd6+sbsDxuD2DZKSx0uBxBt0JpoePq0b+EtFrkk/n9qqlFrYV632++ml5u0rlEytQLQC8cGRXcOVB92W9yISZWu+69S+dG1aEjELAEMBXT2ma4QHxQSXdOFjezVzraX8r3MO5cnzBrnKeeIhLULZXjVVg/VgOl2wc0R4ZWm4ikFv0+zprOgXaxP+qfOHJCXP/6363nH9yPfjmT1O7t+S9TPG6TofWcSt4mOFbQlinyAk4nyRqRVIYExYItIKeBxoClQCDyml7jHf+z1wMXAAeE0pdbV5/Drgt0AFcIlS6o1MyHbdsV1pUCefv7z5v5DWb1FayKZteyLaTRrWlkamh8qYnk0ZE7VRHsmVR3dm1/4K8hLIjDuqa1lCsmsyi+VpVbcw8lFa56iR/vLFxv6IcwWSbmPRhq2RfdIZ8KprigWb/S7eUh3LwjFfdv3vdOONR03YAzkAXKmU+kRESoAlIvIW0AQYD/RWSu0TkTIAEekOTAR6AM2BuSLSWSmV9m8qPzcnFLhnPf9upqybj4/vP+/k92mu2aCpekZ0asx5w9v5zi/mHB8Kzb2TK47uzOvLN7P6O/8lbd1wrjhOG9iaG1/63Pa+jt8IMh3LivlsQ9iM1aK0kMM6RQcmA6EEl36o9isQpdRmYLP5eqeIrAJaAOcBM5RS+8z3rDzY44GnzONficiXwEDgg0zI53zsMlXnQ3NwkZebww3j/OWsgug9j3tP78esTzdx0eEdyBFY/d1OTnIJ8vNLnYLIR9q5utUKJNg4XaytbLsWydZ5SbezhkUgF7Qi0hY4BPgQ6AwMF5EPRWS+iBxqNmsBbLCdttE85rzW+SKyWEQWb9mSfPnYMMYSRNcX1yRDt2Z1+eiGI7nqmC48OXkQzUsLmTKqIyISGuyLa0WnIHlycvS+1x0nRTtMXDCyfdzP1/oj2ExylJpuVT8yADjR+2cpoIVrf4xIq5MuAjcKikgx8DxwmVJqB8YqqT4wGLgKeEaMnUC3rzLKpKyUekgpNUApNaBx4/QlgjtlQPKzRE3NpqykNlNGdQylXbeYvXwzAB+tj05v4hbLc1L/lozqEtmnj/bIVqBXIMHGHudx16l9mD6+Z8T7fuq82HntEqPe+o69B1ixMf1lbQOlQEQkH0N5PKGUesE8vBF4QRl8hLHB3sg83sp2ekvgWzKE88adM7QtRQW5TNW1xTVpYqn5gK92iQVwLngnHNKC3ByJ2otz25uLuI7WHwcNE/q1jJkQ0y91a+eHPLfSHbQKAVIg5qriEWCVUuou21svAkeYbToDBcCPwMvARBGpJSLtgE7AR5mSzwrAsgL1RISV08cweXh8k4GmZnLBiOT7hdtjbrd9N6xTwO9Mt3FnHja3vbmLDu9A16Yl5rW1BqlpWO6+mUiAGZhNdGAYcCawXESsFKbXA48Cj4rICmA/cLYyik1/LiLPACsxPLimZMIDy2Jguwbcf0Y/jtButBofnDGoDf94dx3g393ynKFteWzhetfszvaHf8mNR4de2xXI7EuHR2SJtrh6TFdq5eWy+rudrPk+NS8vTfbp07IeZwxqk/B5mTBfBkaBKKUWELso2m9inHMrcGvGhHJwbC+dkFDjj3JbJt4lNx7l65zj+zTnsYXrKXEJBrVMqGWOIL9Zn24KvY6VJBEiq2Rqgk3HsuK45a9fuviwmO+50bZhEet/2l3tVyAaTbWhQ+Pi0Gu3VYEb1gPuDDa0uP+MfvRJMseZrt9x8DDn0uFpDTC1VqnJugDHQysQjSYgWA94rBQVqayA0502XpM5EslMkQiZiDzQCkSjyRC3nNiT+Wt+8G5oYj3gyRSY8qpWqRWIJhOr0MB4YWk01Y0zB7fh4bMP9W5oYgWnxjJhuXGpmQ5nULuGcdtlalarOXjIxCa67lUaTUCwioydOqCVR8sw3ZqZOdo8rObW4HGqDoCtcSQyIUkUbcLSaAJCvcJ8vrrt2ASjja2N9/itLOuFjgOpeViJFJ01a9KBXoFoNAEi0VQVVnOvSWaona42WOOw4oqKa6d/vaBXIBrNQYxVGrkgL77isVYeut55zePe0/uxbOM2GtQp8G6cIFqBaDQHMSM6N+Z3Iztw3vB2cduFVyCamka9wnyGd0pfIlk7WoFoNAcxuTnCtWO7erazTGOZ3FDV1Dz0HohGUwMIGbi0/tCkEa1ANJoagGXC0isQTTrRCkSjqQFY+bj2O4uyazQpoBWIRlMDaFLXyOJbVlI7y5JoqhN6E12jqQEMaNuAB3/Tn8O7ZMYbR1Mz0QpEo6khjOkZv166RpMo2oSl0Wg0mqTQCkSj0Wg0SSGqBrn1icgW4OskT28E/JhGcdKFlisxtFyJoeVKjKDKBanJ1kYpFbWBVqMUSCqIyGKl1IBsy+FEy5UYWq7E0HIlRlDlgszIpk1YGo1Go0kKrUA0Go1GkxRagfjnoWwLEAMtV2JouRJDy5UYQZULMiCb3gPRaDQaTVLoFYhGo9FokkIrEI1Go9EkhVYgGo1Go0kKrUBsiFW2LUCISLdsy+CGiFwpIqPN14H53kSknu11YOSC4MkDun8lQ1D7WDZk0QoEEJHxIjIT6JNtWeyIyN+B10WkbZZFCSEio0XkDeAa4CwAFQBPDBE5QkQ+Ax4QkeshGHKB7l+JENT+BcHtY9nsXzU2G6+IiFJKicgo4BagHBgiIl8rpbZmUybboQbAVuAoEfm3UmpftuQC8oGbgJHAbUABcKiI5AMHsvkgiUgxcD3GffwImCkiRUqpqVmUSfevBOQiwP0LgtfHgtK/auQKxPEgfQUcA1wFDAJ6Z1smEck1Dy8CHgDOADplUy6l1H7gJaXUcKXU6xgDz0SlVHmWlUcOUAxsAD5VSm0AJgO/FpGuWZJJ968E5Qpq/zJlDFQfC1L/qnEKREQuBl4QkctFpKlSar1SarNS6m3ge2CkiLTIkkyXiUhzpVSFiBQAY4BZwDxgoohMEJEqqwjk+K6aKaU+No/nK6XmA+tEZGxVyWOT6yIROQlAKVUJKKAxxkOOUmodxvc23WxfZbZh3b+SkitQ/cuUIZB9LGj9q0YpEBH5FXA28DcMTT1VRPramjwBdMbQ5PbzMtY5HDL1Aa4Xkf7mjGyxUupH4AvgEuBWoKo6qvO7ukFELBvrARFpgJHZuKIq5DFlKhGRBzFMHTNFJA9AKfU9sBK4zNb8WmCQiPSoqhms7l9JyxWI/mXKFdg+FsT+VaMUCMYX+4BSah4wDWP5d4n1plJqGfAx0FOMDbNrzOOZ7BxuMl1ovjdORN7D2FB8EcPksCODsnjJdSkY34dS6megEBgFoWV+RlFK7QTmK6WaAq8C99neng70FZFjRaSWOWt8FcO2XlXo/pWaXFntX+ZnB7mPBa5/VUsF4tS4tr/XAacDKKW+Bl4D6ojICbbm/8Wwbz6NkT8/LRo8QZlKRWQIcA+wUCnVVyl1FtAUSKvbZYrf1X+AgSJS23yYqkKul83flwGniUgnU8ZfgDuAiRiz7OnAcGBzOuXykDVr/StBmaqsfyUoV5X1Lw/ZAtXHgti/LKqlAsExI7Bp4OeA3SIy3vx7M/AO0F0MijEequVAb6XUVY7zq0qmt4ERwBNKqWtsp/1KKfVpGmRJVq53ML8r81gh8BSZMTO4yqWU2iUiOUqp74D7gYdtbZ4C/oxhhmkMjDVND2lFRKwHM9cuG1nsXwnKVGX9K9nvyjyWyf4VU7Zs9zFxuFUHoX/FRClVbX6AAcCz5pd4GJBrHs8xfwswCZhDOJHkVcA083UeUBYQmW42X+dabYMklyVbVcplky3H1v4bYAjG7HmQJXsG5BKgCGOGt8D5Xjb6V4oyZax/pUOuDPavuLJluY/1A+YCj9v/92z1Lz8/1WIFYmrfGcCDGDbJ74GLgdYQ8qIAY0bzBobmfkhEmgOHYPhQo5Q6oJT6ISAyHTDbVag0Lt/TJZclW1XKpZSqNGdZ9Wyn3g68D7wL1Dbbpn3GpQx2m382FpELTblzbZ9XZf0rDTJlpH+lSy5LtnTK5Ue2bPQxs+/fgKHUnlJKnWX97+ZKKCv9yxdVqa0y+QOMBeqbr5uZN6PY9v5088s/BCOA6k8Yy7/7ycBMJ6gyVQO55gDDbe1XA38B8jPcv8SU6W6MldEyoDTL/StwMgVZLp+y/TEbfcz83Edsfx+CsaKwVhq3ZOs7iyt3tj44DV/4SMzlpOP4cAz3vw/MTjIKw3f7SaCjo21RdZepOssFdAdaZbp/EWnOeBFoCfwdmAG0AeqYsnWoqv4VFJmCLFc6ZMtUH3P2ffOz5wB/xfCkehXDLffEqnwmE/4/si1AEl98CfAC8DPwKOEZq2W77AGMMl9PwrAntrOdn4n9hMDJVM3lyuTs2VU2873OwF3m6+MxXF6XOs6vsv6VTZmCLFeaZMvUqjGeXGdg7IGMMP++APgX0KYqvrNkfg7GPZD9GF4kvwG+BU6BsO1eKfW5MvykAeZj3LByCNkTM+EOGESZqrNcmQwuc5XN5Fugs4i8jGHSmI/hi49NtirrX1mWKchypUO2TPWxmHIppZ4ATlVKvWsemothrqqKZzIpDgoFIiJnichIESlVRsK3hzG+3P8BA0Sks9nO6e88GuN/3AkRG8TVUiYtV2Zlw1Bk32L44/dXSh0PtBSR/umWLYgyBVmuIMuWSN9XRgClxdEYKVR+yYRc6SCwNdHNgaQphu2vEliLYSe8VBnpFxAjwOdsYK9S6k/msVoYNvTbgU3A1Uqp1dVVJi1Xlci2Tyl1i3msnlJqu+06EX9XN5mCLFeQZUuh7+dgbO7fg+E+fE26+346CeQKxObuVwJsUkodCVyEYTf8h9VOKfUFsARoLiIdzYGnEsMF9Gal1AlpVB6Bk0nLVWWyNTNlKwT2mtfIMdukS3kETqYgyxVk2VLo+7UxVhybMPr++CArDwhYPRAxEpdNB3JF5HWgLmYUqlLqgIhcAnwrIiOVka0TpdQsMaqqzcHwVhillFqOEY1ZLWXScmVXNmBVuswJQZQpyHIFWbY0yXWEUmolxool+KgA7OQbypqRwFKM+gTnYQTtjMFYxg20tbsQmGf7+xRgF/BP0h9FHjiZtFzVR7YgyhRkuYIsW1DlyvRP1gWwfZHDgTNtf99vftnnAEvMYzkYdsVnMN06zfOG1xSZtFzVR7YgyhRkuYIsW1DlyvRP1gWwfeFFQC3CuY/OAG4zX38G/N58PWsXBHkAAAJmSURBVAD4b02VSctVfWQLokxBlivIsgVVrkz/BGYTXSm1Wym1T4X9r48GtpivJwHdRORVjPQWn9RUmbRc1Ue2IMoUZLmCLFtQ5co0gdpEh1BqZQU0IZyXfydGQfuewFdKqU01XSYtV/WRLYgyBVmuIMsWVLkyRWBWIDYqMWpB/Aj0NrX2jUClUmpBlr78IMqk5ao+sgVRpiDLFWTZgipXZsi2Dc3tBxiMcSMWAL/NtjxBlUnLVX1kC6JMQZYryLIFVa5M/AQyEl1EWgJnYiQ825dteSCYMoGWKxmCKFsQZYLgygXBlS2ocmWCQCoQjUaj0QSfIO6BaDQajeYgQCsQjUaj0SSFViAajUajSQqtQDQajUaTFFqBaDQajSYptALRaDKAiDQUkc/Mn+9EZJP5+hcRuT/b8mk06UC78Wo0GUZEpgG/KKX+km1ZNJp0olcgGk0VIiKHm+ktEJFpIjJTRN4UkfUiMkFE7hCR5SIyR0TyzXb9RWS+iCwRkTdEpFl2/wuNxkArEI0mu3QAxgHjgf9gFBvqBewBxplK5O/AyUqp/sCjwK3ZElajsRO4bLwaTQ1jtlKqXESWA7kYpU3BKOfbFuiCkcX1LRHBbLM5C3JqNFFoBaLRZJd9AEqpShEpV+FNyUqM51OAz5VSQ7IloEYTC23C0miCzRqgsYgMARCRfBHpkWWZNBpAKxCNJtAopfYDJwO3i8hSjPKoQ7MrlUZjoN14NRqNRpMUegWi0Wg0mqTQCkSj0Wg0SaEViEaj0WiSQisQjUaj0SSFViAajUajSQqtQDQajUaTFFqBaDQajSYp/j9/CvRuoA1mzwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -459,37 +1986,6 @@
     "plt.subplot(2,1,2)\n",
     "dr_out.sel(lon=260, lat=40).plot()  # output data"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean-up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "xESMF saves the regridder to the current directory so you don't need to re-compute it next time (see [Save time by reusing regridder](./Reuse_regridder.ipynb)). If you don't need it anymore, you can just delete it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Remove file bilinear_25x53_59x87.nc\n"
-     ]
-    }
-   ],
-   "source": [
-    "regridder.clean_weight_file()  # regridder.c + TAB would bring-up the command"
-   ]
   }
  ],
  "metadata": {
@@ -508,7 +2004,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},
diff --git a/doc/notebooks/Reuse_regridder.ipynb b/doc/notebooks/Reuse_regridder.ipynb
index b664a83b..fe660f1e 100644
--- a/doc/notebooks/Reuse_regridder.ipynb
+++ b/doc/notebooks/Reuse_regridder.ipynb
@@ -383,31 +383,31 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b1f66790-81a2-47ed-ab7c-08e9e6d5505e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1f66790-81a2-47ed-ab7c-08e9e6d5505e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-25e90012-a93b-4354-b04b-b331eff8dd50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25e90012-a93b-4354-b04b-b331eff8dd50' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-0ca5269e-d52e-47cf-92c7-b7d901e674a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca5269e-d52e-47cf-92c7-b7d901e674a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ea866c0-1193-44ff-8733-0faf940bfccf' class='xr-var-data-in' type='checkbox'><label for='data-7ea866c0-1193-44ff-8733-0faf940bfccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ada84263-2be6-431e-a20b-5016c5679cc3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ada84263-2be6-431e-a20b-5016c5679cc3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7131a835-8654-492e-8b41-07728dc97556' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7131a835-8654-492e-8b41-07728dc97556' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-deeb70c1-d875-4b38-b534-481c9deb1385' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-deeb70c1-d875-4b38-b534-481c9deb1385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f52096d-62f3-48d4-a4a5-0589f635c522' class='xr-var-data-in' type='checkbox'><label for='data-0f52096d-62f3-48d4-a4a5-0589f635c522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       ...,\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
-       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-b7b094b8-bfad-4184-b580-3ad29460fa1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7b094b8-bfad-4184-b580-3ad29460fa1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e384dc6-8052-40ab-9df1-2d61a374a64d' class='xr-var-data-in' type='checkbox'><label for='data-7e384dc6-8052-40ab-9df1-2d61a374a64d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-7c85c487-e614-463b-8bec-aec7665a03f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c85c487-e614-463b-8bec-aec7665a03f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ca3475f-1ed1-4f0b-bd2f-3fff0265372a' class='xr-var-data-in' type='checkbox'><label for='data-1ca3475f-1ed1-4f0b-bd2f-3fff0265372a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
        "       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\n",
        "       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\n",
        "       ...,\n",
        "       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\n",
        "       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\n",
-       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-02a7842a-900b-4848-80ac-0b01bd6c5205' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02a7842a-900b-4848-80ac-0b01bd6c5205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a3205c4-9821-4635-b7f7-345b7f8b3261' class='xr-var-data-in' type='checkbox'><label for='data-9a3205c4-9821-4635-b7f7-345b7f8b3261' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-f19a3d93-9cf1-4076-a867-6b5d43edf449' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f19a3d93-9cf1-4076-a867-6b5d43edf449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa8ef71a-91fb-4181-a5b2-b601ac9b01ce' class='xr-var-data-in' type='checkbox'><label for='data-aa8ef71a-91fb-4181-a5b2-b601ac9b01ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       ...,\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
-       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-1ab79659-f147-4a3f-9098-1a873d721de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ab79659-f147-4a3f-9098-1a873d721de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df3f0d43-7914-45d3-9e7f-d5b9fdc98a8f' class='xr-var-data-in' type='checkbox'><label for='data-df3f0d43-7914-45d3-9e7f-d5b9fdc98a8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-9d732b01-eec4-481a-9267-532fb6bd0886' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d732b01-eec4-481a-9267-532fb6bd0886' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2231c8f4-4be0-4392-acf6-85a2070cf330' class='xr-var-data-in' type='checkbox'><label for='data-2231c8f4-4be0-4392-acf6-85a2070cf330' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
        "       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\n",
        "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
        "       ...,\n",
        "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
        "       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\n",
-       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b545b64c-19b9-423b-bd6d-049407ec5c77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b545b64c-19b9-423b-bd6d-049407ec5c77' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7a6d1c6a-def9-40ef-912c-59163b9b35a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a6d1c6a-def9-40ef-912c-59163b9b35a0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-678337b4-48ed-44f3-b83f-e24069b2f46d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-678337b4-48ed-44f3-b83f-e24069b2f46d' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e555ed21-9007-4657-a9c2-9da465c4a3ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e555ed21-9007-4657-a9c2-9da465c4a3ea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -775,31 +775,31 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fe2cfedc-8ded-4fcd-ac50-06df9bfcbd83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe2cfedc-8ded-4fcd-ac50-06df9bfcbd83' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 400</li><li><span>x_b</span>: 401</li><li><span>y</span>: 300</li><li><span>y_b</span>: 301</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9a3e5e36-2d78-4404-85b5-7ac5a1ad656a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a3e5e36-2d78-4404-85b5-7ac5a1ad656a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.7 -119.1 ... 119.1 119.7</div><input id='attrs-a3a9c935-fc24-4c10-8948-ef458b60dfd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3a9c935-fc24-4c10-8948-ef458b60dfd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578b17a5-e0c7-4b2f-ba1b-87ed1e6da4cc' class='xr-var-data-in' type='checkbox'><label for='data-578b17a5-e0c7-4b2f-ba1b-87ed1e6da4cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-98dba8ca-8773-4c94-9ee8-d0531533041f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98dba8ca-8773-4c94-9ee8-d0531533041f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 400</li><li><span>x_b</span>: 401</li><li><span>y</span>: 300</li><li><span>y_b</span>: 301</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5be84c98-283f-4503-a42d-3ce6f50a56e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5be84c98-283f-4503-a42d-3ce6f50a56e4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.7 -119.1 ... 119.1 119.7</div><input id='attrs-cfba391b-fc74-4bc5-96fa-9598cbb31218' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfba391b-fc74-4bc5-96fa-9598cbb31218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8522563-de4f-48bf-8fa0-65a54ff93709' class='xr-var-data-in' type='checkbox'><label for='data-c8522563-de4f-48bf-8fa0-65a54ff93709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
        "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
        "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
        "       ...,\n",
        "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
        "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\n",
-       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.8 -59.8 -59.8 ... 59.8 59.8</div><input id='attrs-78ba0346-a9ff-4a8a-8db7-7b9b1e0cf008' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78ba0346-a9ff-4a8a-8db7-7b9b1e0cf008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74c2bcad-54cf-400c-b3c0-07c51a9a3887' class='xr-var-data-in' type='checkbox'><label for='data-74c2bcad-54cf-400c-b3c0-07c51a9a3887' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.8, -59.8, -59.8, ..., -59.8, -59.8, -59.8],\n",
+       "       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.8 -59.8 -59.8 ... 59.8 59.8</div><input id='attrs-4483fa44-4938-487b-8ad2-381c646c6160' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4483fa44-4938-487b-8ad2-381c646c6160' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca1b160a-04fa-4e81-bd68-5ae3d1509ae4' class='xr-var-data-in' type='checkbox'><label for='data-ca1b160a-04fa-4e81-bd68-5ae3d1509ae4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.8, -59.8, -59.8, ..., -59.8, -59.8, -59.8],\n",
        "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
        "       [-59. , -59. , -59. , ..., -59. , -59. , -59. ],\n",
        "       ...,\n",
        "       [ 59. ,  59. ,  59. , ...,  59. ,  59. ,  59. ],\n",
        "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
-       "       [ 59.8,  59.8,  59.8, ...,  59.8,  59.8,  59.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.4 ... 119.4 120.0</div><input id='attrs-398070a7-99f4-482a-a793-b08ff3d16256' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-398070a7-99f4-482a-a793-b08ff3d16256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7bd95ef-5eaf-4f3e-9de4-8de4d46c3550' class='xr-var-data-in' type='checkbox'><label for='data-e7bd95ef-5eaf-4f3e-9de4-8de4d46c3550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
+       "       [ 59.8,  59.8,  59.8, ...,  59.8,  59.8,  59.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.4 ... 119.4 120.0</div><input id='attrs-8dab6107-b8f9-4236-9cf9-2e38a4f8f025' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dab6107-b8f9-4236-9cf9-2e38a4f8f025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-010ff6b6-4b1e-4c15-a819-0b0fc5f0fe51' class='xr-var-data-in' type='checkbox'><label for='data-010ff6b6-4b1e-4c15-a819-0b0fc5f0fe51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
        "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
        "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
        "       ...,\n",
        "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
        "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\n",
-       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-7c3cf8a8-b739-41fb-a10c-c9ae94323767' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c3cf8a8-b739-41fb-a10c-c9ae94323767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b524884-7da0-42bd-8aa9-60a0aaea862d' class='xr-var-data-in' type='checkbox'><label for='data-8b524884-7da0-42bd-8aa9-60a0aaea862d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-c6dbb4a4-303d-4daf-8269-9ef21c27ab14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6dbb4a4-303d-4daf-8269-9ef21c27ab14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5dea4f4-8bd0-4e3a-aa0b-34d92cdb03c9' class='xr-var-data-in' type='checkbox'><label for='data-e5dea4f4-8bd0-4e3a-aa0b-34d92cdb03c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
        "       [-59.6, -59.6, -59.6, ..., -59.6, -59.6, -59.6],\n",
        "       [-59.2, -59.2, -59.2, ..., -59.2, -59.2, -59.2],\n",
        "       ...,\n",
        "       [ 59.2,  59.2,  59.2, ...,  59.2,  59.2,  59.2],\n",
        "       [ 59.6,  59.6,  59.6, ...,  59.6,  59.6,  59.6],\n",
-       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-37d73cbf-0d4e-46e5-92a8-83a98eceb0ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37d73cbf-0d4e-46e5-92a8-83a98eceb0ed' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8673f580-db3d-4725-a7c9-9f57df803f21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8673f580-db3d-4725-a7c9-9f57df803f21' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-8937f0e1-5302-4b55-a59c-2ddd2be8fbf4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8937f0e1-5302-4b55-a59c-2ddd2be8fbf4' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-4887d35a-c111-4d09-a4cd-dc34f7c6e106' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4887d35a-c111-4d09-a4cd-dc34f7c6e106' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1174,33 +1174,33 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a642077b-7759-4989-8dc6-2e77f7e24aaf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a642077b-7759-4989-8dc6-2e77f7e24aaf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>time</span>: 10</li><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5020ca70-aa91-4e91-80b2-0c4ea9d63900' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5020ca70-aa91-4e91-80b2-0c4ea9d63900' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-7d2d9329-83b3-4f8a-bb66-10012a06f6db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d2d9329-83b3-4f8a-bb66-10012a06f6db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e546cf90-3cff-4b09-bd06-786c07de1027' class='xr-var-data-in' type='checkbox'><label for='data-e546cf90-3cff-4b09-bd06-786c07de1027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ce6ff7db-096a-48df-9db5-111086d1aa7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce6ff7db-096a-48df-9db5-111086d1aa7f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>time</span>: 10</li><li><span>x</span>: 600</li><li><span>x_b</span>: 601</li><li><span>y</span>: 400</li><li><span>y_b</span>: 401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6776c026-81c8-4e4f-9e12-3bea49287374' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6776c026-81c8-4e4f-9e12-3bea49287374' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-119.8 -119.4 ... 119.4 119.8</div><input id='attrs-d2d05324-2548-417f-802d-9c7b6c5fd319' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2d05324-2548-417f-802d-9c7b6c5fd319' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b339ba-07a7-4c29-bacc-856e52fefa22' class='xr-var-data-in' type='checkbox'><label for='data-48b339ba-07a7-4c29-bacc-856e52fefa22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       ...,\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
        "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\n",
-       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-f33a6432-58cb-4942-991e-72e6b8d3b0be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f33a6432-58cb-4942-991e-72e6b8d3b0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a97c15-a4ef-450f-91c0-2c45c568ae0b' class='xr-var-data-in' type='checkbox'><label for='data-29a97c15-a4ef-450f-91c0-2c45c568ae0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
+       "       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.85 -59.85 ... 59.85 59.85</div><input id='attrs-50b5b462-2798-40f7-aa5e-93064627e239' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50b5b462-2798-40f7-aa5e-93064627e239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905b75dc-6d82-4522-b8f7-11888b24bfaa' class='xr-var-data-in' type='checkbox'><label for='data-905b75dc-6d82-4522-b8f7-11888b24bfaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\n",
        "       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\n",
        "       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\n",
        "       ...,\n",
        "       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\n",
        "       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\n",
-       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-37ce97d2-c864-4b6f-8277-3418056e962a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37ce97d2-c864-4b6f-8277-3418056e962a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0390cd1-3e07-4066-b0fc-18d417157ecd' class='xr-var-data-in' type='checkbox'><label for='data-e0390cd1-3e07-4066-b0fc-18d417157ecd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
+       "       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-120.0 -119.6 ... 119.6 120.0</div><input id='attrs-109e525d-a077-4ca0-88b1-36589e277bc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-109e525d-a077-4ca0-88b1-36589e277bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e4cb6f-c36a-46cc-91c8-c556aeb7da1a' class='xr-var-data-in' type='checkbox'><label for='data-85e4cb6f-c36a-46cc-91c8-c556aeb7da1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       ...,\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
        "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\n",
-       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-63f9c455-4385-4af7-900b-568475ef2144' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63f9c455-4385-4af7-900b-568475ef2144' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0304833-3f67-4057-b09a-83d45dc9b4ab' class='xr-var-data-in' type='checkbox'><label for='data-b0304833-3f67-4057-b09a-83d45dc9b4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
+       "       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... 60.0 60.0</div><input id='attrs-9c76a1ee-bd66-4329-9c9c-c467b94b61a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c76a1ee-bd66-4329-9c9c-c467b94b61a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0079aab-1bb8-4329-aa0b-f344a3fb91cd' class='xr-var-data-in' type='checkbox'><label for='data-c0079aab-1bb8-4329-aa0b-f344a3fb91cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\n",
        "       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\n",
        "       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\n",
        "       ...,\n",
        "       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\n",
        "       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\n",
-       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10</div><input id='attrs-cb3f0775-17e7-40c1-bc59-ac135d9c9548' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb3f0775-17e7-40c1-bc59-ac135d9c9548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09660060-3522-4f8d-aa13-aa3bca9bf91b' class='xr-var-data-in' type='checkbox'><label for='data-09660060-3522-4f8d-aa13-aa3bca9bf91b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 45 46 47 48 49 50</div><input id='attrs-900566b1-2770-48ba-953a-87453271252d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-900566b1-2770-48ba-953a-87453271252d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-706e87da-632e-45ea-bdf2-472bf4640695' class='xr-var-data-in' type='checkbox'><label for='data-706e87da-632e-45ea-bdf2-472bf4640695' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10</div><input id='attrs-f7c419b4-2b33-4fb8-b781-fe879f9e4126' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7c419b4-2b33-4fb8-b781-fe879f9e4126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b6d9388-3058-45ee-8fbe-78962515607d' class='xr-var-data-in' type='checkbox'><label for='data-9b6d9388-3058-45ee-8fbe-78962515607d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 45 46 47 48 49 50</div><input id='attrs-d8d32ccc-33fa-409c-9a95-22dba22a9a6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8d32ccc-33fa-409c-9a95-22dba22a9a6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8db3a458-4275-4146-9009-ab96483b13ba' class='xr-var-data-in' type='checkbox'><label for='data-8db3a458-4275-4146-9009-ab96483b13ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
        "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
-       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-6a2d4a93-22f4-4e0b-9c0e-022d60c3a623' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a2d4a93-22f4-4e0b-9c0e-022d60c3a623' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data2D</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 1.869 1.872</div><input id='attrs-f3892f40-3bb8-4aa1-a0b4-ba94327748d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3892f40-3bb8-4aa1-a0b4-ba94327748d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5512f8-7c93-4b86-81cb-b2a5452ad7cd' class='xr-var-data-in' type='checkbox'><label for='data-9f5512f8-7c93-4b86-81cb-b2a5452ad7cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\n",
+       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-18863b66-4160-4a04-9df4-33b951cece32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18863b66-4160-4a04-9df4-33b951cece32' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data2D</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 1.869 1.872</div><input id='attrs-ea97cbdf-1ea7-43f5-bc34-12c5fda0836d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea97cbdf-1ea7-43f5-bc34-12c5fda0836d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eecaea7-fde3-42a0-a62e-2c0ba79ed2b4' class='xr-var-data-in' type='checkbox'><label for='data-6eecaea7-fde3-42a0-a62e-2c0ba79ed2b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\n",
        "        1.87234253],\n",
        "       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\n",
        "        1.87003418],\n",
@@ -1212,7 +1212,7 @@
        "       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\n",
        "        1.87003418],\n",
        "       [1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\n",
-       "        1.87234253]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>data4D</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 934.7 936.2</div><input id='attrs-006d42e1-df56-4f4c-a8e7-dd7926ea1c62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-006d42e1-df56-4f4c-a8e7-dd7926ea1c62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba88f600-f6ba-4343-a589-8a99a7e8d8bd' class='xr-var-data-in' type='checkbox'><label for='data-ba88f600-f6ba-4343-a589-8a99a7e8d8bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[[  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\n",
+       "        1.87234253]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>data4D</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.872 1.869 1.866 ... 934.7 936.2</div><input id='attrs-e55fb10e-bc9c-4ea5-9faf-ab4dbe37edf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e55fb10e-bc9c-4ea5-9faf-ab4dbe37edf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7474c40-7e06-48a6-8e71-28b2661a0069' class='xr-var-data-in' type='checkbox'><label for='data-a7474c40-7e06-48a6-8e71-28b2661a0069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[[  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\n",
        "            1.86931698,   1.87234253],\n",
        "         [  1.87003418,   1.86695393,   1.86389961, ...,   1.86389961,\n",
        "            1.86695393,   1.87003418],\n",
@@ -1734,7 +1734,7 @@
        "         [935.01708976, 933.47696332, 931.94980568, ..., 931.94980568,\n",
        "          933.47696333, 935.01708976],\n",
        "         [936.17126271, 934.65849073, 933.1584572 , ..., 933.1584572 ,\n",
-       "          934.65849073, 936.17126271]]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d83a897b-0625-48db-b674-bb514de17552' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d83a897b-0625-48db-b674-bb514de17552' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "          934.65849073, 936.17126271]]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-67781e4c-f848-4405-aad5-76bc030c87c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67781e4c-f848-4405-aad5-76bc030c87c4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1816,7 +1816,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x216 with 4 Axes>"
       ]
@@ -1862,8 +1862,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.89 s, sys: 169 ms, total: 4.05 s\n",
-      "Wall time: 4.07 s\n"
+      "CPU times: user 4.19 s, sys: 228 ms, total: 4.41 s\n",
+      "Wall time: 4.43 s\n"
      ]
     }
    ],
@@ -1920,8 +1920,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 429 ms, sys: 165 ms, total: 594 ms\n",
-      "Wall time: 593 ms\n"
+      "CPU times: user 408 ms, sys: 205 ms, total: 613 ms\n",
+      "Wall time: 612 ms\n"
      ]
     }
    ],
@@ -2003,7 +2003,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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"iVBORw0KGgoAAAANSUhEUgAAAZgAAADUCAYAAACh1jX2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAdkElEQVR4nO3df9QU1Z3n8fdHUEQjDhgkBCRgJDo4MaMSolEzbpwRJuqgRleSMyOTOEt+qKPZk81g3J04mbNncZOYxMzGXY2OSIzir6xEZY0/F50oiD/REAaiRhmJYHARjUHR7/5Rt2Px0E8/xdO/qrs/r3P6dPXtrqpb5WN9uLduVSkiMDMza7Sd2l0BMzPrTg4YMzNrCgeMmZk1hQPGzMyawgFjZmZN4YAxM7OmcMAAkmZIWiVpjaS57a5PM0h6VtIKSY9JWp7KRkm6Q9Lq9D4y9/vz0v5YJWl6rvzQtJw1ki6WpFQ+TNLCVL5U0sRWb2MRkq6QtF7Sk7myluwHSbPTOlZLmt2aLS6mn/1ygaR/S38zj0n6RO67rt8vkvaRdI+klZKeknROKu/5v5fCIqKnX8AQ4JfAvsAuwOPAlHbXqwnb+Szw7j5l/x2Ym6bnAhem6SlpPwwDJqX9MyR9tww4HBCwGPjzVP5F4H+m6VnAwnZvcz/74WPAIcCTrdwPwCjg6fQ+Mk2PbPf+GGC/XAB8ucpve2K/AGOBQ9L0HsC/pm3v+b+Xoi+3YGAasCYino6IN4BrgZltrlOrzATmp+n5wIm58msjYktEPAOsAaZJGguMiIgHIvu/4Ko+81SWdQNwTOVfaWUSEUuAjX2KW7EfpgN3RMTGiHgZuAOY0fgtHJx+9kt/emK/RMS6iHgkTW8GVgLj8N9LYQ6Y7A/m+dzntams2wTwU0kPS5qTysZExDrI/mcC9k7l/e2TcWm6b/k280TEVmATsFcTtqMZWrEfOvXv7CxJT6QutEpXUM/tl9R1dTCwFP+9FOaAyZqsfXXj/XOOiIhDgD8HzpT0sRq/7W+f1NpX3bgfG7kfOnH/XAK8H/hjYB3wrVTeU/tF0ruAG4FzI+KVWj+tUta1+6UIB0z2L4N9cp/HAy+0qS5NExEvpPf1wI/JugZfTM130vv69PP+9snaNN23fJt5JA0F9qR4l0u7tWI/dNzfWUS8GBFvRcTbwGVkfzPQQ/tF0s5k4XJ1RNyUiv33UpADBh4CJkuaJGkXshNti9pcp4aStLukPSrTwLHAk2TbWRmdMhu4OU0vAmalES6TgMnAstQdsFnSYamf+PQ+81SWdQpwd+pv7gSt2A+3A8dKGpm6mo5NZaVVOYgmJ5H9zUCP7Je0DZcDKyPiotxX/nspqt2jDMrwAj5BNkLkl8D57a5PE7ZvX7LRLY8DT1W2kayv9y5gdXoflZvn/LQ/VpFGvKTyqWQHml8C/wQole8KXE92YnMZsG+7t7uffXENWXfPm2T/SjyjVfsB+GwqXwN8pt37osB+WQCsAJ4gOxCO7aX9AhxJ1i31BPBYen3Cfy/FX5WNNDMzayh3kZmZWVM4YMzMrCkcMGZm1hQOGDMza4quDhjtwE0sc1e3W473S3XeL9V5v2yvl/dJ1waMpCHA/yC7cn0K8ClJU2rM0rN/BAPwfqnO+6U675ft9ew+6dqAobdvYmlm1nZdex2MpFOAGRHxN+nzXwEfiYizcr+ZQ/rXxU7DRxw6dM+9qy6rqA+O27Ou+ctow4YNjB49ut3VKB3vl+q8X7bX7fvk4Ycffikiqm7g0FZXpoUGvFlcRFwKXAowbOzkGDv7O3Wt8KX0/uy84+pajplZp5D0q/6+6+YusrbdLG7i3FuZOPfWVqzKzKy0ujlg2n4TSweNmfWyru0ii4itks4iuwPpEOCKiHiqHXWphIy7zsysl3RtwABExG3Abe2uR4WDxsx6STd3kZWWu87MrBc4YNrIQWNm3cwBUwIOGjPrRg6YpAwXSTpozKybdO2V/Dtq6tSpsXz5coDSHOQ9GMDMyk7SwxExtep3DphMPmDyyhA2DhozK6taAeMusgGU4eDurjMz60RuwST9tWDyynKQL0PomZmBu8gKKRIwFQ4aM7OMA6aAHQmYCgeNmfU6B0wBgwmYvDKEjYPGzFrNJ/lboAwHdw8GMLMycQsmqbcFk1eWg3wZQs/Mupu7yApoZMBUOGjMrNs5YApoRsBUOGjMrFs5YApoZsDklSFsHDRm1ig+yV8iZTi4ezCAmbWCWzBJq1oweWU5yJch9MysM7mLrIB2BEyFg8bMOlWpusgk7SPpHkkrJT0l6ZxUPkrSHZJWp/eRuXnOk7RG0ipJ03Plh0pakb67WJJS+TBJC1P5UkkTW72dO+LZeceV4uDurjMza6SWt2AkjQXGRsQjkvYAHgZOBP4a2BgR8yTNBUZGxN9JmgJcA0wD3gvcCXwgIt6StAw4B3gQuA24OCIWS/oicFBEfF7SLOCkiDitVr3a2YKppgwH+jKEnpmVW6laMBGxLiIeSdObgZXAOGAmMD/9bD5Z6JDKr42ILRHxDLAGmJaCakREPBBZSl7VZ57Ksm4Ajqm0bjpFGQ7ubtGYWT3aOoosdV0dDCwFxkTEOshCCNg7/Wwc8HxutrWpbFya7lu+zTwRsRXYBOxVZf1zJC2XtHzDhg2N2agGcteZmXWytgWMpHcBNwLnRsQrtX5apSxqlNeaZ9uCiEsjYmpETB09evRAVW4bB42ZdaK2BIykncnC5eqIuCkVv5i6vSrnadan8rXAPrnZxwMvpPLxVcq3mUfSUGBPYGPjt6S1HDRm1knaMYpMwOXAyoi4KPfVImB2mp4N3Jwrn5VGhk0CJgPLUjfaZkmHpWWe3meeyrJOAe6OLhqP7aAxs07QjlFkRwL3ASuAt1PxV8nOw1wHTACeA06NiI1pnvOBzwJbybrUFqfyqcCVwHBgMXB2RISkXYEFZOd3NgKzIuLpWvUq2yiyHVGWg3wZQs/MWssXWhbQyQFT4aAxs1ZzwBTQDQFT4aAxs1ZxwBTQTQFT4aAxs2ZzwBTQjQFT4aAxs2ZxwBTQzQFT4aAxs0ZzwBTQCwGTV4awcdCYdb667kUm6QhJu6fpv5R0kaT3NbqS1lplOLj7Ohqz7lbkQstLgN9K+hDwFeBXZDeWtA7nCzbNrJmKBMzWdBX8TOC7EfFdYI/mVstayUFjZs0wtMBvNks6D/gr4ChJQ4Cdm1sta4dKyLT7IF9ZfxlCz8wGb8CT/JLeA3waeCgi7pM0ATg6Irqqm6zXTvIX1e6wAQeNWZnVdZI/In5NdufjYanoJeDHjauelVkZDu7uOjPrTEVGkf0HsqdC/q9UNA74382slJWLz9GY2WAUOcl/JnAE8ApARKzmnadNWg9x0JjZjigSMFsi4o3Kh/QAL1+d2cMcNGZWRJFRZP9X0leB4ZL+DPgi8JPmVss6QT5k2nmg96gzs3Iq0oKZC2wge0DY54DbgP/czEpZ5ynDwd0tGrNyKTJMeXfgdxHxVvo8BBgWEb9tQf1axsOUG6csB/kyhJ5Zt6trmDJwF9kjiSuGA3c2omLWnXyOxsygWMDsGhGvVj6k6d2aVyXrFg4as95WJGBek3RI5YOkQ4HX612xpCGSHpV0S/o8StIdklan95G5354naY2kVZKm5+siaUX67mJJSuXDJC1M5UslTay3vjZ4laBpd9g4aMxaq0jAnAtcL+k+SfcBC4GzGrDuc4CVuc9zgbsiYjJZt9xcAElTgFnAgcAM4PvpPBBkd3qeA0xOrxmp/Azg5YjYD/g2cGED6msN0O6QAQeNWasUuVXMQ8ABwBfIhij/YUQ8XM9KJY0HjgN+kCueCcxP0/OBE3Pl10bEloh4BlgDTJM0FhgREQ+kuz1f1WeeyrJuAI6ptG6s/crQmgEHjVmz9XsdjKSPR8Tdkk7u89VkSUTETXWs9ztkz5bJ3/Z/TESsA4iIdZIqdwsYBzyY+93aVPZmmu5bXpnn+bSsrZI2AXuR3Uft9yTNIWsBMWHChDo2xwbDd2826261LrT8E+Bu4IQq3wUwqICRdDywPiIelnR0kVn6WX9/5bXm2bYg4lLgUsiGKReoizWBg8asO/UbMBHxtfT+mQav8wjgLyR9AtgVGCHph8CLksam1stYYH36/Vpgn9z844EXUvn4KuX5edamW9vsCWxs8HZYg/nOAGbdpd8LLSX9x1ozRsRFda88a8F8OSKOl/QN4DcRMU/SXGBURHxF0oHAj4BpwHvJBgBMjoi3JD0EnA0sJbvDwPci4jZJZwIfjIjPS5oFnBwR/75WXXyhZfm0u0VT4aAx61+tCy1rdZFVzo/sD3wYWJQ+nwAsaVz1fm8ecJ2kM4DngFMBIuIpSdcBPwe2AmdW7ipANvDgSrKLPxenF8DlwAJJa8haLrOaUF9rMnedmXW2IreK+SnwyYjYnD7vAVwfETNqzthh3IIpv3YHTYWDxuwd9d4qZgLwRu7zG8DEBtTLbId4eLNZZylyu/4FwDJJPyYbiXUS2TUnZm3hwQBmnWHALjL4/e1hjkwfl0TEo02tVRu4i6xzlaU14aCxXlSri6xowAwBxpBr8UTEcw2rYQk4YDqfg8as9eoKGElnA18DXgTeIruIMSLioEZXtJ0cMN3DQWPWOvUGzBrgIxHxm2ZUriwcMN3HQWPWfIO9DqbieWBTY6tk1nweDGDWXkUC5mngXkm3AlsqhY24kt+sVZ6dd1zbWzQOGus1RQLmufTaJb3MOpLvDGDWWoVGkfUCn4PpPe0OmgoHjXWyQZ3kl/SdiDhX0k+ofqv7v2hsNdvLAdO7HDRmgzfYk/wL0vs3G18ls/LwYACz5nAXWeIWjFW4RWNWXN1X8vcCB4z15aAxG5gDpgAHjPXHQWPWPwdMAQ4YG4iDxmx7gx1FVnX0WIVHkVkvK0PYOGisDAb7wLFvAt8CngFeBy5Lr1eBJxtdSbNOUoaDux98ZmVX5GaXSyLiYwOVdTq3YGywynKQL0PoWe+p95HJoyXtm1vYJGB0nRX6A0k3SPqFpJWSDpc0StIdklan95G5358naY2kVZKm58oPlbQifXexJKXyYZIWpvKlkibWU1+zWvwoZ7PqigTMl8hudnmvpHuBe4Bz61zvd4H/ExEHAB8CVgJzgbsiYjJwV/qMpCnALOBAYAbw/fQANIBLgDnA5PSakcrPAF6OiP2AbwMX1llfswE5aMy2VfSJlsOAA9LHX0TEllq/H2BZI4DHgX0jt3JJq4CjI2KdpLHAvRGxv6TzACLiv6Xf3Q5cADwL3JNCCkmfSvN/rvKbiHhA0lDg18DoqLGx7iKzZijDgb4MoWfda1BdZJI+nt5PBo4D3p9ex6WywdoX2AD8s6RHJf1A0u7AmIhYB5De906/H0f2TJqKtalsXJruW77NPBGxlex5NntV2cY5kpZLWr5hw4Y6NsmsujIc3N2isXapdS+yPwHuBk6o8l0AN9WxzkOAsyNiqaTvkrrD+qF+1t9fea15ti2IuBS4FLIWTK1Kmw2WHxNgvarfgImIr0naCVgcEdc1cJ1rgbURsTR9voEsYF6UNDbXRbY+9/t9cvOPB15I5eOrlOfnWZu6yPYENjZwG8x2mIPGek3Nk/wR8TZwViNXGBG/Bp6XtH8qOgb4ObAImJ3KZgM3p+lFwKw0MmwS2cn8ZakbbbOkw9LosdP7zFNZ1inA3bXOv5i1kgcDWK8och3MfyG70HIh8FqlPCIG3SKQ9MfAD8iekPk08BmysLsOmED2BM1TK+uQdD7wWWArcG5ELE7lU4ErgeHAYrJut5C0K9njBg4ma7nMioina9XJJ/mtncpwoC9D6FnnqeteZJKeqVIcEbFvlfKO5YCxditDyICDxnaMb3ZZgAPGysJBY52k3hZMtSHJm4AVEbG+yncdyQFjZeOgsU5Qb8DcChxOdgU/wNHAg8AHgK9HxIJ+Zu0oDhgrKweNlVmtgKl1HUzF28AfRsSLaWFjyG7R8hFgCdnJdDNrkvyBvZ1h4+HNtqOK3ItsYiVckvXAB9IIrzebUy0zq6YMB3cPb7aiirRg7pN0C3B9+vxJYEm6vcv/a1rNzKwqX7BpnaLIORgBJwNHkt2C5X7gxm67cNHnYKxTtTtoKhw0vcnDlAtwwFinc9BYOzhgCnDAWDcpQ9g4aHpDvU+0NLMOU4aDuwcD2IABI+mcImVmVi6+qaa1W5GT/I9ExCF9yh6NiIObWrMWcxeZdbuyHOTLEHrWOIO60DI9gvjTwCRJi3Jf7QH8prFVNLNm8/Bma7Va18H8DFgHvBv4Vq58M/BEMytlZs3jOwNYq3gUWeIuMutV7W7RVDhoOlO9N7vczDvPs98F2Bl4LSJGNLSWbeaAsV7noLHBqOtmlxGxR5+FnQhMa1DdzKwkfI7GGm1QXWSSHoyIw5pQn7ZxC8ZsW+0OmgoHTbnV1YLp88CxnYCpvNNlZmZdyi0aq1eRK/lPyL2mk40im1nPSiV9SdJTkp6UdI2kXSWNknSHpNXpfWTu9+dJWiNplaTpufJDJa1I312cbsyJpGGSFqbypZIm1lNfs17mCzZtsFo+ikzSOLI7Mk+JiNclXQfcBkwBNkbEPElzgZER8XeSpgDXkJ33eS9wJ9nzaN6StAw4h+wJm7cBF0fEYklfBA6KiM9LmgWcFBGn1aqXu8jMiinLQb4MoWd13otM0r6SfiJpg6T1km6WtG+ddRoKDJc0FNgNeIGsVTQ/fT8fODFNzwSujYgtEfEMsAaYJmksMCIiHkiPDriqzzyVZd0AHFNp3ZhZfdyisaKKdJH9CLgOGEvWgrierEUxKBHxb8A3gefILuTcFBE/BcZExLr0m3XA3mmWccDzuUWsTWXj0nTf8m3miYitwCZgr751kTRH0nJJyzds2DDYTTLrSQ4aG0iRgFFELIiIren1Q+o4yZ/OrcwEJpEF1u6S/rLWLFXKokZ5rXm2LYi4NCKmRsTU0aNH1664mVXloLH+FAmYeyTNlTRR0vskfQW4NZ2UHzWIdf4p8ExEbIiIN4GbgI8CL6ZuL9L7+vT7tcA+ufnHk3WprU3Tfcu3mSd1w+0JbBxEXc2sIAeN9TXgMGWgcnL8c33KP0vWKtjR8zHPAYdJ2g14HTgGWA68BswG5qX3m9PvFwE/knQRWYtnMrAsneTfLOkwYClwOvC93DyzgQeAU4C7u+0Rz2Zl5XudWUWRW8XsGhG/G6hsh1Yq/QNZcG0FHgX+BngX2bmeCWQhdGpEbEy/P58s0LYC50bE4lQ+FbgSGA4sBs6OiJC0K7AAOJis5TIrIp6uVSePIjNrjrK0Jhw0zVHvvciqPQ9mu7JO54Axay4HTXca7PNg3kM2Gmu4pIN558T5CLKhxWZmhfnOAL2n1jmY6cBfk508vyhXvhn4ahPrZGZdzEHTO4p0kX0yIm5sUX3axl1kZu3T7rABB81g1XWzS+CPJB3YtzAivl53zczMyA7u7Q4Zt2gar0jAvJqb3hU4HljZnOqYWa9y11n32eGbXUoaBiyKiOkD/riDuIvMrFzaHTQVDpra6rrZZRW7seMXV5qZ7RDfGaDzFXng2AreuY/XEGA04PMvZtYSvjNA5yoyiux9uY9bgRfTHYq7irvIzDpDWVoTDppMXVfypwV8CDgqfVwSEU80sH6l4IAx6ywOmnKo94Fj5wBXkz2fZW/gaklnN7aKZmY7xudoyq9IF9kTwOER8Vr6vDvwQEQc1IL6tYxbMGadrSwH+TKEXivVe6GlgLdyn9+i+gO9zMzaxoMByqfIMOV/BpZKukDSBcCDwOVNrZWZWR3KcHB311nxk/yHAEeStVyWRMSjza5Yq7mLzKw7leUgX4bQa4a6R5H1AgeMWXdz0DSHA6YAB4xZb3DQNJYDpgAHjFnvKUPYdHrQNPpeZGZmXaEMB/duHgzQtICRdIWk9ZKezJWNknSHpNXpfWTuu/MkrZG0StL0XPmhklak7y6WpFQ+TNLCVL5U0sTcPLPTOlZLmt2sbTSzzucLNpunmS2YK4EZfcrmAndFxGTgrvQZSVOAWcCBaZ7vSxqS5rkEmANMTq/KMs8AXo6I/YBvAxemZY0CvgZ8BJgGfC0fZGZm1ThoGq9pARMRS4CNfYpnAvPT9HzgxFz5tRGxJSKeAdYA0ySNBUZExAORnSy6qs88lWXdAByTWjfTgTsiYmNEvAzcwfZBZ2ZWlYOmcYpcyd9IYyJiHUBErJO0dyofR3YBZ8XaVPZmmu5bXpnn+bSsrZI2AXvly6vMsw1Jc8haR0yYMGHwW2VmXcd3BqhfWU7yV7v1TNQoH+w82xZGXBoRUyNi6ujRowtV1Mx6TxkO7p3Yoml1wLyYur1I7+tT+Vpgn9zvxgMvpPLxVcq3mUfSUGBPsi65/pZlZjZo7jrbca0OmEVAZVTXbODmXPmsNDJsEtnJ/GWpO22zpMPS+ZXT+8xTWdYpwN3pPM3twLGSRqaT+8emMjOzujloimvaORhJ1wBHA++WtJZsZNc84DpJZwDPAacCRMRTkq4Dfk721MwzI6JyB+cvkI1IGw4sTi/Ibri5QNIaspbLrLSsjZL+EXgo/e7rEdF3sIGZWV0qIdPug3yZz9H4Sv7EV/KbWb3aHTbQ+qDxlfxmZi1QhlZEmbrO3IJJ3IIxs0Yqy0G+2aHnm10W4IAxs2bo9qBxwBTggDGzZurWoHHAFOCAMbNWKUPYNCpofJLfzKxEemUwgFswiVswZtYOZWjNwOBDz11kBThgzKydOjVoHDAFOGDMrAw6LWgcMAU4YMysbMoQNgMFjU/ym5l1oE4fDOAWTOIWjJmVWRlaM7B96LmLrAAHjJl1grIFTa2AafUjk83MrA5le0xALQ4YM7MOlO+qamfY7PKe/Q7t7zuf5Dcz63BlGAxQjQPGzKwLlOVRznkOGDOzLlKmoHHAmJl1oTIEjU/ym5l1sXYOBnALxsysR7S6ReOAMTPrIa3sOvOV/ImkzcCqdtejhN4NvNTuSpSQ90t13i/bK/U+qXUdSxFbN63nrd9uUrXvfA7mHav6u91BL5O03Ptle94v1Xm/bK+X94m7yMzMrCkcMGZm1hQOmHdc2u4KlJT3S3XeL9V5v2yvZ/eJT/Jbz5L0s4j4aIOXORH4aET8qM7lfB1YEhF39ik/GvhyRBzfp3wqcHpE/O0OrOMC4NWI+GZ/6zOrh0/yW89qdLgkE4FPA4MOGElDIuLvd2SeiFgODPqBRju6PrMi3EVmPUvSq+n9aEn3SrpB0i8kXS1J6btnJV0oaVl67ZfKr5R0St9lAfOAoyQ9JulLfda3k6TvS3pK0i2SbqssI63n7yXdD5yaX76kGale9wMn97MtR0u6JU1fIOmKtE1PS/rb3O/Ol7RK0p3A/rny/Po+LOlnkh5P27yHpCGSviHpIUlPSPpc+u1YSUvS9j4p6ag6/pNYl3ELxixzMHAg8ALwL8ARwP3pu1ciYpqk04HvAMdXXwQAc6nShZWcTNbC+SCwN7ASuCL3/e8i4kjIQiW97wpcBnwcWAMsLLg9BwD/DtgDWCXpEuAgYFba1qHAI8DD+Zkk7ZLWcVpEPCRpBPA6cAawKSI+LGkY8C+Sfpq26faI+K+ShgC7Fayf9QC3YMwyyyJibUS8DTxGFgQV1+TeD69jHUcC10fE2xHxa+CePt9XC48DgGciYnVkJ0x/WHBdt0bEloh4CVgPjAGOAn4cEb+NiFeARVXm2x9YFxEPAUTEKxGxFTgWOF3SY8BSYC9gMvAQ8Jl0PueDEbG5YP2sB7gFY5bZkpt+i23/34gq01tJ/0BL3Wm7FFhH1audc17rp3wwI3H6256BlqV+fiPg7Ii4fbsvpI8BxwELJH0jIq4aRH2tC7kFYzaw03LvD6TpZ4HKLTZmAjun6c1k3VLV3A98Mp2LGQMcXWDdvwAmSXp/+vyp4tXezhLgJEnDJe0BnNDP+t4r6cMA6fzLUOB24AuSdk7lH5C0u6T3Aesj4jLgcuCQOupnXcYtGLOBDZO0lOwfZJUD/GXAzZKWAXfxTuvjCWCrpMeBKyPi27nl3AgcAzwJ/CtZV9OmWiuOiN9JmgPcKuklspD6o8FsREQ8ImkhWRfgr4D7qvzmDUmnAd+TNJzs/MufAj8g6zZ8JLXYNgAnkoXkf5L0JvAqcPpg6mbdydfBmNUg6VlgajqX0YjlvSsiXpW0F7AMOCKdjzHrOm7BmLXWLZL+gOyczT86XKybuQVjZmZN4ZP8ZmbWFA4YMzNrCgeMmZk1hQPGzMyawgFjZmZN8f8BXaKWPou7mXQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2086,8 +2086,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 24.3 ms, sys: 0 ns, total: 24.3 ms\n",
-      "Wall time: 23 ms\n"
+      "CPU times: user 33.7 ms, sys: 2.3 ms, total: 36 ms\n",
+      "Wall time: 33.1 ms\n"
      ]
     }
    ],
@@ -2112,8 +2112,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 426 ms, sys: 172 ms, total: 598 ms\n",
-      "Wall time: 597 ms\n"
+      "CPU times: user 739 ms, sys: 191 ms, total: 929 ms\n",
+      "Wall time: 926 ms\n"
      ]
     }
    ],
diff --git a/doc/notebooks/Using_LocStream.ipynb b/doc/notebooks/Using_LocStream.ipynb
index 72423843..ed6950a1 100644
--- a/doc/notebooks/Using_LocStream.ipynb
+++ b/doc/notebooks/Using_LocStream.ipynb
@@ -57,7 +57,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1205a8ed0>"
+       "<matplotlib.collections.QuadMesh at 0x7f0edf4cf4c0>"
       ]
      },
      "execution_count": 3,
@@ -66,7 +66,7 @@
     },
     {
      "data": {
-      "image/png": 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XLuCDpJVRkGL9HCGpX9IuwO7A5ZN8dhtE9CyCIAgaMGDISuJZlOzPHAAcCVwn6epsOwHYXdKx+fMPga8DmNkNkpYBN5JWUh3bCSuhYAY1FqMm1q0bWwc+Oux3imzEae29JwBvfbajn2DICW7jpJNThnUVhxqt2xl+dH5z5qwpl7M23uYMF2xDi4r5DTp6kTWPFG19Dzl6jAe9oDjFMgB61zgBfdZ618YJquM9qDnpvGBKXn6jfV6go6JtaLZThmPytCXdRTmAKwIamlPMcO1WRV3DSJ9nc8oAhmcXbYNbOL+vucXfSE9/8f9nyLknHt61+BcxOH9uwebpMdZuXbQNLS1esEVbFgMn9XUX67du2KmL87ueDAyVrnaqIsozs0vw5yEAPtfkmJOAk0ozn2Y2qWEot6EIpg2voQimBq+hCCaXUesq3dqJpAOq2CaLTaqxCIIgmAxqLsrLtjbznxVtk8KMGYYKgiCYLtLS2bI5i/Y8a0vaH3gmsFjSe+p2zccd1J4corEIgiBwKBtmsvYNQ/WRfEr1kDQaNR4GXj1VhUZjEQRB0IDlpbOtaJeLcjO7GLhY0jfM7M7pKrdlYyHplRXyWGdm505SfYIgCNrOJC6dnUrWSPokyengQM1oZs+disLKehZfBX5M86VfAAcC0VgEQbAJIUZLRHdl+6eBM4HvAS8F3gocBfx1qgorayzOM7M3tUog6duTWJ8gCIK2U/M624oOCH60pZmdLumddUNTF09VYS0bCzN7fVkGVdJMBhL09tYJdXqLoh15EWaAnm4n+I5j63KOXz9UvERDjhBoZKT4w3GFeo4UYXS4mJ8XQMeLoNM7UBRczR4YLNi6u4oZrl7bX7CtmztQsHlCMQ9PrJjsRZt1OYGmZjnX0AskNFLNr5r34Nez1hH0efrMAUfk1+vYnJEI73v3hHVDxdhADBZjCDEy2wn60+tfA+tzfteOENT7/Xdvsa5gG55bPMGVO1Ubfunud36b/UPFMpx7Z8i5J0Yc0aB3b08WM6BnUbuY90h6CcmH1NKpKqzyBLekZwI71x9jZt+cgjoFQRC0lU5eOlvHxyUtAN5L0lfMB97dmEiS49OhwKiZPdQqQaXGQtK3gN2Aq0mh/iD11EobC0kLSVGhnpCPeRNwC2msbWdSSMHXmNmDVeoSBEEw1ZhV6Fm0WZRnZj/Nb1cCB7VIenfeWlW4G9ixVXlVexb7AnuaTSTy9aN8DviZmb1aUh8wm+RI60IzO1nSccBxwL9sQN5BEARTQgfrLCbKTWb25FYJJP2+LJOqZ3s9KeD4hJA0n7Ra6nQAMxvMXZ3DgDNysjOAl0807yAIgqkiDUN1lW4zhP2b7cgP8C3T1CjTWfyENHQ0D7hR0uXAo24jzezQZsdmdiUt5fq6pCcBVwLvBLYxs3tyHvfUfLs75R8DHAPQs5Uz8xcEQTBFlPUs2u1IcAK8H/hYozHPd/wYeI6ZFVc3NFA2DPWpDavbuPyfArzdzC6T9DnSkFMlzOw04DSAgcds3xGhBYMg2PSZQKS8tiFpG+ATwHZm9mJJewL7m9npDUmfJekkMzux7thtgfNJEfwq0bJpNLOL8/rdQ2rv620V8l9BCjt4Wf7836TG415JS3KllwD3Va1wEATBVGMmhka7W27Do23vWXyD9Ie/Xf78B+BdTrpDgSdJOhVA0u7AJcAXzeyjVQurerYvcGwvLjvIzP4C/FnSY7PpeaQIUOeQ1Ibk1x9XrEcQBMG0MGoq3drMVma2DBgFMLNhxlarPkoeYnoFsJOks4ALgPeb2VcmUljZnMU/AW8DdpN0bd2uecCvK5bxduDMPJHyJ+CNpEZqmaSjgbuAw8syEUZvz9h16HJEZmry3Q30FMVBc/qKEbsGuovpPKHeoKPE8p4yvB/T6sGiEO6h1bOKZTxSVHHZYLGMQRzBnDNgN+CIoTybFhYPXlvMjvUjxXK7vOiDgLdcXU5adwjYORdPeykn8GTXsCNGK+oVXaGeN8LgCfXcpfhuXYo2L8petzNy7IkBaSLKk3MNzbnWw46wtNuJztjn/EZ65xQr2eVEhfTunW5HZVlVWGfT+Oc8E4ahgNWStiTfJZKeQVpGO446N+aXAx8AfgXsUrPXxwZvRdmcxXeA84B/Z/xcwyoze6BKAWZ2NWnpbSPPq3J8EATB9DMjfEO9hzRKs5ukXwOL8V2U1/sK+Lxjq0SZu4+VklYBT5xOV7hBEATtxIBhz5dLHSMl+6cSSd3As/P2WFJf+BYzK3QFzewjk1FmqSjPzEYlXSNpRzO7azIKDYIg6HQmYxhK0g4kTxfbkuYWTjOzz0naB/gyybX4MPA2M7s8H3M8cDRpQPMdZnZ+Y75mNiLpMDP7DHBDlfOR9HnHvBK4wsxK542rKriXADdkncXqugqX6SyCIAhmHFZhArviMNQw8F4zu0rSPOBKScuBU4CPmNl5kg7Jn5+Tl78eQYpRsR1wgaQ9zMyZCePXkr5Acp1U/798VZO6DACPA76fP7+K1NAcLekgM/NWUj1K1cZiUroxQRAEM4WypbHDFUR5WXxcEyCvknQTsD1ppGt+TraA5LsJkneLs8xsPXC7pNuA/YBLneyfmV/rl78a0Cz40WOA5+ZVU0j6EvBz0mrX68rOpVJjYWYXZwHI07LpcjMLbUQQBJskRmUX5UslXVFnPi2LiQtI2hl4MnAZSQ9xvqRPkVaH1v74twd+W3fYimwr1tGslfNAj+2BOYytmJpDEvSNSHLW5o2nqtfZ1wCfBC4iTaT8p6T3m9l/T7CyQRAEM4KyOYm8MHiFmR1clpekuSS19LvM7GFJHwfebWY/yP+vpwPPx/cM666TlvRvbr2aC+1OAa6WdFEu50DgE5LmkLQXLak6DHUi8LRab0LS4px5NBZBEGxy2CQunZXUS2oozjSzH2bzUSQ/eZDmEP4rv18B7FB3+FLGhqgaWV33foAUXvWmZvXIUfXOJQ1rCTjBzGp5v7/sPKo2Fl0Nw073U139PSlI40V5fd3efE911o8UT9378nsc8Z9n8wR9XrrZPUWRkxfF7iHHtn5dNQHeiBNlzPMS1t9XLcoeC4umteuLX3/PGn8pYddQ8bp6IrqqGifvWDmiME+lKcfLvnfPjzp3hmfrcurSs96pi/ds6NTP0Toy2uOIAXv822/UiYrnudIeGXJEpM41HOl3ohfOKqab5fyuvXt0wEk3t7f4m/OO9eYQPIHspGAV5iwquPuQJFKv4aYG8dvdpGWvF5HmGG7N9nOA72TXHNsBu5PEdMUqmn26oaxP5eNb1eV5wK5m9lFJO0rar7YKq4yqjcXPJJ0PfDd/fi1wbsVjgyAIZhQTmLMo4wDgSOA6SVdn2wnAm4HPSeohPcsdA2BmN0haRnKLNAwc22QllMdskqfvZnyRtHz3uaRJ8VWkHs/TWhzzKFUnuN8v6VWkExdpEufsKscGQRDMRMrci1RxP2Jml9C8z/zUJsecBJxUlrek6xjrs3aTFNwFV+R1PN3MnlILdGRmD9bFsyilcgxuM/sBE3BnGwRBMFMxVLo0dqT98SxeWvd+GLi3tiy2CUNZ+V3zJbWY7ISwCpXOVtIrJd0qaaWkhyWtkvRw1UKCIAhmFJZ6DmVbm/m4md2Zt/81s2FJ32qR/vPA2cDWkk4iuSn/RNXCqvYsTgFeZmZNZ9qDIAg2JWaAI8G96j/k+Q93aAvAzM6UdCVpklvAyyfyn161sbg3GoogCDYXDDFSstqpbP9UkX1HnQDMqhvhETBIjizakH5R3cf7GFuohKRFVT2IV20srpD0PeBHjI/B/cPmhwRBEMxcnFXW4/dPTzWK5Zr9O/Dvkv7dzI6vcMiVpOoK2BF4ML9fSIontEuVcqs2FvOBNcAL6+sMTFtjIY3XLXjaBHlRcfCDsPQ6C+S9td1dzk/Cy69quZ7Oom/OqoLNW49+f8+cgu2RRwYKtuE1xa91ZKi46GG9E+yme7ajF+l1Vu7NKtoG5zXRWTiOBHqciEqeZqFrqFoAI40U07lfkyeB6C4OJ3gBh0aKlxqnWFe40e1oL/yATY7N05UUfx4JR38hZw7THD0Gjs7CC4hUr3d61Obcj/2O9sj7/c/tLf5A5vQ42gvv4kwR1YIftRczO17SFiQtxkCd/ZcN6XYBkPRl4BwzOzd/fjFJNV6Jqktn39hqv6Tjc2sXBEEw87HJWTo7lUj6R5IKfClwNfAMksPBZo4En2Zmb619yB5vWy21HcdkDbqVhkUNgiCYSYyMquU22iSM8DTyTpKg7s7sVPDJwF9bpP+bpA9K2lnSTpJOJHnjqMRkNRZtv2pBEASThTEjls6uM7N1AJL6zexmUtS8ZryOJNw7O2+Ls60SlUV5JbRrricIgmAKKG8MOqCxWCFpIWnh0XJJD9Lc6SB51dM7m+0vI3oWQRAEjVj5MNRIm4ehzOwVZvaQmX0Y+FeSw8KXN6aT9OGyvKqkmayexffLkwRBEMwMasNQLdO0sWchqQu41syekOpiF7dI/o8lHjdECuX64VZlVnX3cYqk+ZJ6JV0o6W+SXl/bb2aVJeNBEAQzgU6eszCzUeAaSTtWSP5VYF6LbW5O05KqPYsXmtkHJL2CFJzjcOAXwLcrHh8EQTCjKJuI7YCJ2iXADZIupy4QkpkdWp/IzD4yGYVVbSxqIVkOAb5rZg/ICdwylQhfiFeFbhWP8wR4PU46L4CRJ7ar6ifG82Tp5ecFhBmaVRS9DQ4Vv8J164o2DTpCsdXFdLa6WMb62Y4Asq9oG17ki6bWqVhO/4PFdL2PFG1dFQV4XiChwdmO2M5xyOwGP3LSDc0t2oZnF23rHcFc38piIV1OupF+p9x5xfMdXuhf666+aqEPuhwBXo9zrBcgq2pQowX9xZBbi/pXF2wLe4sKzbndRaHegHPBhmzqgh9ZyZxE2f5pYFIagapUbSx+IulmYC3wtuza1gu+FgRBsAlQZZip7RPcF0vaCdjdzC6QNJsU12JKqDRnYWbHAfsD+5rZEMn1x2FVjpV0h6TrJF0t6YpsWyRpeXZ7vjxL1oMgCDqCNMFdvrUTSW8G/hv4SjZtT1pGOyVUneCeDRwLfCmbtgP2nUA5B5nZPmZWO+Y44EIz2x24MH8OgiDoGGy0q3RrM8eSopc+DGBmtwJbNyaStFXD59dL+rykYzSB+YSqZ/t1kvvbZ+bPK4CPVy3E4TDgjPz+DJy1wUEQBG2jQq+i3T0LYL2ZPTqrl+NZeLX6eV2aD5Jigl8JvAA4tWphVRuL3czsFGAIwMzWUn3AzoCfS7pS0jHZto2Z3ZPzugenNQTILd8Vkq4YWrmmYnFBEASTgFXYSpC0g6RfSLpJ0g2S3pnt38tD81fnofqr6445XtJtkm6R9KIW2V8sqRbX4gUkvdtPvGrUvX8l8EozOwP4Oybb6ywwKGkWY7Fbd6MurkUJB5jZ3ZK2JknSb65aOTM7jRzMY+4eS9rfjgdBsNlQvhqqUjbDwHvN7CpJ84ArJS03s9fWEkj6NLAyv9+TJJDbizTcf4GkPczMW+Z2HHA0cB3wFuBc4L+cdLMkPZnUOeg2s9UAZjYkeY7yfao2Fh8CfgbsIOlM0jjZG6ocaGZ359f7JJ0N7AfcK2mJmd0jaQkpelMQBEHHMBkK7jxyUhtFWSXpJtJE9I0Aec7gNYy5FT8MOMvM1gO3S7qN9J95qZP3qKQzgMtID/K3mLmDY/cwNtz0QN1/75akxqwSVeNZLJd0FclfuoB3mtnfyo6TNAfoyhdpDil40keBc4CjgJPz64+rVjgIgmDKqTLMlPYvra3yzJyWR0QKSNqZ5Eb8sjrzs0hhq2/Nn7cHflu3f0W2efm9BPgy8EfS//Iukt5iZueNq2ZyX+7xEHBgk30FKjUWufV7MbCrmX1U0o6S9jOzy0sO3QY4O0+49wDfMbOfSfodsEzS0aSwfqXxMCQbJ/zxxHLN8ARzrojOMXlCvYHuojioxwlnNuoI8AZHi8ughx2bFz3MizLmRfzz3E6us2KYNz1SLLdrrSMadGwjjlDPBvzvxEs7NFTM07lcjLgR66pNlw06IrqhBUWbc/ndKHvDc5xznuv04oeL9bPu4q3Wt7J4qDeL6F0X9frXunfAeVD0fp6AaUQAACAASURBVOrOCVYVvY44FfKOnd9blGIt7isqL7foLQr1BlTtgXedTZZ7u0ZE+bSsAFaY2cGluUlzgR8A7zKzej9Nr6MuJnaTQps1W58mrTS9LZexG/A/wHleYkn7AjuQehO3ZpfmlSeDq17pL5KiCD6X1DNYRTrxp7U6yMz+BDzJsd8PPK9qJYMgCKadsraz4vOqpF7S/+WZZvbDOnsPacL5qXXJV5D+0Gsspbnb8ftqDUXmTzhD+pKeTWpYHspl/RrYQtIQcKSZ/bnKeVRdDfV0MzuWrNo2swcBxyFCEATBJoKpfCshj8qcDtxkZo3LVJ8P3GxmK+ps5wBHSOqXtAspvnazEZwbJJ0r6Q2SjiKthPqdpFdKemVdus8CLzaz5wNPAYbM7ADgpFy3SlRtLIYkdTO2Gmox7Y9XHgRBMCVUUnBXy+oAkq7huXVLZQ/J+45g/BAUZnYDsIw0Af4z4NgmK6EABoB7gWcDzyGFVF0EvAx4aV26bjOrhVu9C9gpl7WcJvMhHlWHoT5PCsO3taSTgFcDH6xaSBAEwYzCgDJHgRUcCZrZJTSZ/DCzNzSxn0R66i/L+42lFUhcIel0kreMw4CL4FHPHJV9SZU2FjnIxu3AB0jzDAJebmY3VS0kCIJgJiH8RQ7j0rTfN9QuwNuBnan7L290UU7SYLyZ5IHjAuBrtaRAK9HfOEobi7yW99Nmtj9QWVAXBEEwo+n8gBY/Is05/IQW0wLZ+esXHfta4M6qhVUdhvq5pFcBP2wi+giCINh0mKRhqClmnZl9vixRXrb7AdLKqx1Ifv7+CHzZzL5RtbCqjcV7gDnAsKR1pF6amdn8qgVtLF2ycToDLwDLcBMvkF7AIU8/Mb+vuC58lqepqK6QdyguIvP0GF5ApAEnIExfd1E/MTpSzE/e2n9v/b3zKNBTXAZPzzrvWncxON8JDOXoL4bmF20j/cUK9TiailFnZbgXJGmkeGkYnuuU6wV3cnQMXb3F773L04YMFoeBhxYWjx2Z5RzsPB9aj/OljPh/VKPOH1h/f1Gz0N9btLnaC8c2zwlqtO3sVQXbNgNF24KeYqAjT1PR5dyf3n3Su1H3Ygmd37P4nKQPkRwFPvrnYGZXNaQ7kzTnfDBJLT4HOAv4YHYlckKVwqoquOdVSRds3ngNRRDMWDq/sXgieaUVY48ZxpjrkBo71/UgTpX0OzP7mKQ3klZdTV5jIekpjnklcKeZVfYtEgRBMGMo01FUDKU8hbyC5FXD6VePY7Wk/2Nml0h6GfAAPDofXfkkJqLgfgrJuyGkFu0aYEtJbzWznzc9MgiCYKZh4IyEjaNs/zRwDbCQckesbwX+S9IewPXAm+BRvdz/rVpY1cbiDuDoLBipudF9P/Ax4IfUBdcIgiAIpoVtgJuzr736OYtxS2fN7FqS51oa7H8laegqUbWxeFytociF3CjpyWb2pwn0YoIgCGYMpTqL6alGKz40nYVVbSxukfQl0gw6wGuBP0jqJ0fPC4Ig2HRQxy+dNbOLJe0E7G5mF0xUkT1RqvqGegNwG/Au4N0k74ZvIDUUzXylB0EQzFwmIazqVCLpzcB/A1/Jpu1JQr0poerS2bWSvgj81MxuadhddFAfBEEww+l0dx/AsaS5iMsAzOzWHL56HA0eaAvUu01vRdWls4cCnyQpynaRtA/wUccHyZTRqxG2njUm8vHEQkNeFBv8gEOe6G1eT1FsNN+xeUKgISuWMep03DyR39qR3kr18/DEha4ob9CxOd1o63bKdealupwI7N3rmgjF+os2L1DSSLdTnx4n8JIj3vOe8kZnOddmXvG763YCBvU4ArzubqfOTYSghWNnF1c3DvRv+Ahub48vRvPEql5gIi9olhdQbKC7eG22mVUU2y0deLBg28JRcw6oeM4DXUWbF2DJu8dGKg+OTJDqkfLayXozG6zNG+f4GF6tXtYiDyMtUiplIjG49yN7KzSzq3OIwCAIgk2SGbB09mJJJwCzJL0AeBvJT9Q4JuCdtiVVm+VhM/OCQAZBEGyaTELwoynmOFIMi+tInmXPNbMTmyWWtI2k0yWdlz/vmUNbV6JqY3G9pL8DuiXtLuk/gd9ULSQIgmDG0eET3MDbzeyrZna4mb3azL4q6Z0t0n8DOB/YLn/+A2nRUiWqNhZvB/YiCT++Czw8kUKCIAhmEsoK7rKtzRzl2N7QIv1WZraM7Ecqu2qq7Imx6mqoNcCJeQuCINjk6YDVTi6SXgf8HWmx0Tl1u+YB97c4dLWkLRkLj/0Mko+/SrRsLCT9hBadrelcDRUEQTCtdO5qqN8A9wBbAZ+us68Crm1x3HuBc4DdJP0aWEwKkV2Jsp7Fp/LrK4FtgW/nz68j+YsKgiDY9JgkR4KSdgC+Sfr/HAVOM7PP5X1vB/4ZGAb+x8w+kO3HA0eThojeYWbnj6ua2Z2kCHf7T+iUzK6U9GzgsSRvJbfkKHqVaNlYmNnFufIfM7MD63b9RNIvJ1LRIAiCzZBh4L1mdpWkecCVkpaTnAAeBuxtZutrYrrspPUI0hzxdsAFOUDRRkd5knQN8D3ge2b2x4keX1VnsVjSrmb2p1zoLqQuzLTRrdFxAjlPtDbcRJQ35EbYKj4WzOouCqe26C0Ki2Y7Ydk8wZAr1PPERj2O2MhJ57F2VlHQ97/zFhRsDz5STNfzcLFcORHYvIhz3qpBN6Ib0L22eC7miPpwBIFelL3RBc4jXU/R1u1Eu+vv86KyFcs15wS9SHIDfcUHs97ZjrjTiS630LF5IsvZPcXfm/dbBRh2fnOe6NMrp7/Liajn2Bb3FUV5W/UWbXMc5aYnyut1IuUNWfGvaZ0Vz8O7xyaNSRiGMrN7SENGmNkqSTeR3HK8GTjZLN0JZlZzM34YcFa23y7pNpLG7dINOYUGDiX59VsmaZTUcCwzs7uqHFx1NdS7gYskXSTpIuAXQKslWuOQ1C3p95J+mj8vkrRc0q35dYuqeQVBEEwHsvINWCrpirrtmKb5JSHzk0nuOfYAniXpMkkXS3paTrY98Oe6w1ZkW2Ne3ZK+3WhvhZndaWanmNlTSRPkewO3Vz2+6mqon0naHXhcNt1caxEr8k7gJqAWs/s44EIzO1nScfnzv0wgvyAIgqmlbE4i7V9hZgeXZSVpLvAD4F1m9nB2zbEF8AzgaaSn/V3xPZ8X+jBmNiJpsaS+CpHy6uuxMykO92tJcyIfqHps2Wqop9SCf+fG4ZpWaZrksRR4CXAS8J5sPgx4Tn5/BsmNSDQWQRB0BmM9h6ZUXVorqZfUUJxZ57RvBfBDMzPg8jwstFW271B3+FLg7iZZ3wH8Oi+ffXS83MxObVKPy4Be4PvA4bVphaqU9Sy+Luk5tI7zcTqpa9WMz5Jar3l1tm3yWB5mdo/nKTEIgqCtTMKcRY5xfTpwU8Of+I+A55KG9/cgOWn9G2lp63cknUqa4N4duLxJ9nfnrYvx/6/NOMrMbq6QzqWssVgAXEnrxuKvzXZIeilwX16y9ZyJVi6P/x0DMG/b2RM9PAiCYIMQk+ZI8ADgSOA6SVdn2wnA14CvSboeGCT9kRtwg6RlwI2klVTHNlsJZWYfqVSDMR6UdDqwnZm9OK+82t/MTq9ycNnS2Z0nWJlGDgAOlXQIMADMz5My90paknsVS2gScNzMTgNOA9hmz0UdqqcMgmCTZBL+cczsEpo/bL++yTEnkYbtXSR91sze1Uw03UIs/Q3g64x54vgDaUXUxjcWG4uZHQ8cD5B7Fu8zs9dL+iTJr8nJ+fXHU1mPIAiCCTGJcxZTwLfy66dapiqylZkty6I/zGxYcoLzNGFKG4sWnEya/T8auAs4vOyAbo2yRe+a0ozXjRbXYnvBihb0FPPy9BPeWvGqwY+qrgEfdNaUr3fOw8tvp1kPFGyji4srogsrE4CHuovDnKOPFMtwJR9dE7hTvKSeTqOv2K/vGihe697+4rp8OfXxgv70eUGNnHReUCNPjzG7r/ib2WZ2MXjkdgNFFzwLnd+zFwjI+72NNHlYfWS4KIpZ7+gTepw8u53lP7MdPcdWPUVNxcLu4rl0OeM0fU65XU65XnCzXit+7+usr2CbNDrU3YeZXZlfL663Z7X4EcDF3nFMpW+oycTMLmIseNL9wPMmuwyvoQiCINgQZkDwIyRtRXrYfh1Jj3F2i+TvYQp9Q9UqJODvgV3N7KOSdgS2NbNms/RBEAQzmw7tWWS3Ia8gCev2IDUQu5rZ0lbHZZcjU+Mbqo4vkiQozwU+SvJu+AOSmCQIgmDTorPnLO4jLaf9IHCJmZmkVzRLLOmVTXbtIYk67UdLqjYWTzezp0j6PYCZPShpCgcLgyAI2kw1BXc7OIE0N/ElkibjeyXpX9ZinwGT2lgMSepmbGJkMe28VEEQBFNMWYTtdkXgNrPPAJ/J7kFeRxL4bSfpX4CzzewPDenfOBnlVnUk+HnSuNjWkk4CLgE+MRkVCIIg6Eg6PAa3mf3JzE4ysyeSpgQWAOdNVXlVHQmeKelK0gomAS83s5umqlJBEATtpBaDu2WaDhpbMbPrgOtIQ1RTQpkjwUV1H+8Dvlu/z8yKi/yDIAg2BcJnxDjKehZXki6ZgB2BB/P7hSQx3S5TWrs6umXM7R4LFtPtfJNb9BQDFYEvrJvXVQw84wuiikIgT7zkCes8Ed2IM/JXVR+ybqTaFNOOjlBvzpLiNbhz3qKC7YF1RR9cK9cUhV5rVlWMiAT0zSkKuxbMXluwze0v1nF2T/E78a7D6sH+gs0T2w04+XliO4+B7uJvYVF/UYy2uL8oytu69+GCrf73XMP7rXqsHi2eL8CA83v1BHxVA3N5dZzn2Lx7Z0DF790VGDrljnozAo6p26buH72DV0NtMJKeAOxJcr8EgJl9s8qxLecszGwXM9sVOB94mZltZWZbAi+l4gx6EATBjKTD5ywkvVRS1XlnJH0I+M+8HQScQoqeV4mqBT3NzM6tfTCz84BnVy0kCIJgRpHnLMq2NnMEcKukUyQ9vkL6V5Pmnf+SV0g9CfC7qA5VG4u/SfqgpJ0l7STpROD+qoUEQRDMNEpDqrZ/NdTrSbGE/kiKPXSppGOywttjrZmNAsOS5pPmoXetWl7VxuJ1JD8iZ5PW9G6dbUEQBJsmHT4MBWBmD5O8aZwFLCG5AblK0tud5FdIWgh8lTQffRXNAysVqLp09gFSHO0gCILNgk5fOivpZcCbgN1Ibsv3M7P7JM0GbiLNTTyKmb0tv/2ypJ8B883s2qrlVXUk+Av8IBvPrVpQEATBjKFKz6H9PYvDgc+Y2S/rjWa2RtKbvAMk7Q3sTP7vl/SYyfYN9b669wPAq0gh/4IgCDY5BKhkWW7Z/qnGzP6hxb4LG22SvgbsDdzAmLumyfUNVQu2UcevJTULsBEEQTDz6dCehaRVDaWLMT2cmdn8Joc+w8z23NByqw5D1au3uoCnAttuaKEbQo9G2KZnLKiTF3HLE9BBM8FQNZsnGFrnRB7rrjqA6fzARiuvMyji1c+ry9Z9xehmcxcUBWBr5xadCd+9bkHBdlvPlgXb+kFfXLj1gmLZO8x9qGDbqq8oZtuytyi0XD9a/NneO1i8P0YckWB/V/E3MuwI1LxIctv2F4V1nhDUE55VxRPLefnN6yqKGpvZvahz3rXxmO8I8Gar+LvxfnO9ONH4nHSeUHWIalEme+Vfh8mgdM6iQmORo9d9k/R/OQqcZmafk/Rh4M3AX3PSE2ryhBz29GhgBHiHmZ1fn6eZNVvtVMalkvY0sxs35OCqw1D1Su5h4HbSyQRBEGx6TF48i2HgvTnw0DzgSknL877PmNm4ONqS9iTpJ/YCtgMukLSHmY3UpZlvZg83PMSPVb25G6YzSA3GX4D1jPVE9q5yIlUbi8eb2bhHDEmVxRxBEAQzjkkYhjKze4B78vtVkm4ihT9txmHAWWa2Hrhd0m3AfsCldWm+Q/KiUf8QX1+rZtqJrwFHkhwOTngtV9XG4jfAUxpslzq2IAiCTYLJXjoraWeSiO4y4ADgnyX9A3AFqffxIKkh+W3dYStoaFzM7KX5daK++e4ys3MmeMyjlHmd3ZZU0VmSnsxYCzYfKHqcC4Ig2ESoOAy1VNIVdebTzOy0QlppLkk89648hPQl4GOknsDHgE+TNBPeZFLTmkjaAtid8Y4Bf9kk+c2SvgP8hDQMVUs/KauhXgS8AVgKnFpnX8UU+k0PgiBoK2ZpK0sDK8zs4FbJJPWSGooza3/MZnZv3f6vAj/NH1cAO9QdvhS4u0m+/0gSSy8FrgaeQRrxaaZ/m0VqJF5YfxZMxtJZMzsDOEPSq8zsB1UyDIIgmOmIyRmGkiTgdOAmMzu1zr4kz2dActFxfX5/Dimu9qmkCe7dae6S452kCHm/NbODJD0O+EizumxseNWyYajXm9m3gZ0lvccp/FTnsCAIghnPJM1ZHECeVJZ0dbadALxO0j6kJ/s7gLcAmNkNkpYBN5JWUh1bvxKqgXVmtk4SkvrN7GZJj21aX+nzjnklcIWZ/bjsRMqGoebk17nOvvaL3YMgCKaCSXIUaGaX4M9DnOvYasecBJxUIfsV2THgj4Dlkh6kyZBVZgB4HPD9/PlVJDX30ZIOMrN3tSqsbBjqK/ntBWb26/p9kg5odexk06dhtu998NHPXqS8ZnQ5q8QGHHFWnyMietiKK4Q9UZ4npvIYciLqecI6j9ldxchjVfGEXZ5tQU9R5LSwtxgNrsd5rLpv7ZyCDWDpnJUF2+5z7ivYvAhs/Y6gss8RXy7pK4r8PLGXF+XQ+z69a7O4pygu9ISgXuRDT3jp/S49AZ1X564mj7VeWi9Pr96eKHWOE+3Oiyzo3TteuiHnt+4K8DrgUbTTI+WZ2Svy2w9n/30LgJ+1OOQxwHPNbBggT7L/HHgBaTltS6pKh/+zom0ckgYkXS7pGkk3SPpIti+StFzSrfl1i4r1CIIgmBY0aqVbW+snPVHS4ZIOB+43s3PMrNUT5faMjRaR32+Xh7lK4/mWzVnsDzwTWNwwZzEfKunx15NaskfyioBLJJ0HvBK40MxOlnQccBzwLxXyC4IgmB461zfUAuDHwI7ANaRhridKugs4LMe48DgFuFrSRfmYA4FPSJoDXFBWbtmcRR9pvqIHqPdH8jApRF9LzMyAmsOf3rwZSaX4nGw/A7iIaCyCIOgUJs/dx1TwMZKQ77k58h05FvfJpLkOL/ARZna6pHNJinCR/FHV5jjeX1Zo2ZzFxcDFkr5hZndWPZN6JHWTZOmPAf6vmV0maZvasjEzu0fS1k2OPQY4BmDr7aqKzYMgCDaOtHS2pDVo3zDU84G9aw0FgJmNSjoBZ+5B0uPySqmax40/59dtJW1rZldVKbTqP/AaSZ8kObeqVwqWBj/K42H75Fn7syU9oWKZZCXkaQCP3XugA6a8giDYbOjQYShgsDZJXY+ZDUuOS2B4D+mh+9POPqO5iG8cVRuLM4HvkZxXvRU4ijHXupUws4fyWNnBwL01UYqkJaTA4UEQBB1DBw9DDTS4X6ohoLB808yOya8HbUyhVRuLLfN41zvrhqZKgx9JWgwM5YZiFqn79B8kleJRpDG2o0iTNUEQBJ2BASMlrUHZ/qnjHsa7X6rnL80Oyqumfpa9336Q5Aj2Y2b2+yqFVm0saguw75H0EpLwY2mF45aQ3IV0k5bpLjOzn0q6FFgm6WjgLlIs2ZYIG7cO3NNZNAs646f11tsXF3h56+O9df5eUBdvvb1Hs6BNVcr1ztnTfHhr6Od1FXUNvh6jWrld8uNhzeoulr1Lf7EzuXPv3wq2qkFwqgau8vJbNTrLObaoo6oaRGuoq1jGOisGlfJ1FhP2HD0OL+CQq59wNDtznO+533l89mo44urOilTVR1XVaFTVKG0IpT2LKSu5NRvRQ/hXM/u+pP9D8vv3KeDLwNOrHFy1sfh4Xq71XpK+Yj7QUu0HYGbXklzyNtrvB55XsewgCILpp5ojwbYh6WPAh2vuQCTNBz7XwgdU7UniJcCXzOzHOWJfJSo1y2b2UzNbaWbXm9lBZvZUYLeqhQRBEMwo8tLZsq3N9ACXS9pb0guB35FWnjbjfyV9BXgNcG4OYFe5a7Yx61HfA3x2I44PgiDoUMoV2u1WcJvZ8ZIuJAVTehA40Mxua3HIa0gLjD6V55GXUEFfUWNjGot2DdkFQRBMPWXTRxs3vbTRSDoQ+BzwUeCJwBckvalOaDcOM1tDXeyK+pCvVdiYxqL9nbAgCIIpIA0zlfQs2jxnQZqgPtzMbgSQ9Erg/5E8y046Zb6hVuE3CiJFXQqCINg0KVVwT081WrB/fawLM/thFUnDhlLm7mNeq/1BEASbKh0sygMe9Y4BgKRvmtk/5JWmU0I4XAqCIPDo0KWzks5pNAEHZZdKmNmhU1HujGksejAWdo25PZlQ8CNnKn7IEV15YraqYqPqdSn2XYtyLR9PINjvCK68dB6eoGm04vk+dqA4Lza723eJf+farQq2B0aKwRf3GVhRsM1xxH/rPHHWRojCFnevLthGnd/HYEWBoBeAyAvE5InMvPp5AjWvjGbHewLUAddWPOdeefUulrHOsXnfSdX6ebe3J1gcceo3KViFsKrt61ksJYVd/S/SlRKwL77vp0lj6uSPQRAEM5lRK9/aw74kPcWJwEozuwhYa2YXZ3dMU8KM6VkEQRBMH9axq6Gya/LPSPp+fr2Xafgvj55FEARBI0aakyjbSpC0g6RfSLoph5Z+Z8P+90kySVvV2Y6XdJukWyS9qGkVzVaY2eHAecC3N/xkqxE9iyAIAgeVeJUt258ZBt5rZldJmgdcKWm5md0oaQfgBSRnqilPaU/gCFLsoO2ACyTtUb/yqREz+x/gf6pUZmOInkUQBIHHJPQszOyeWiQ6M1sF3ARsn3d/BvgA46fzDwPOMrP1ZnY7cBspDGrbicYiCIKgkerDUEslXVG3HdMsS0k7k7xwXybpUOB/zeyahmTbMxb2FGAFY41LW4lhqCAIggaElQ9DpdVQK8zs4NL8pLnAD0ihHYZJK5le6BZdpO1+RSAaiyAIAp9JEuVJ6iU1FGdmlxxPBHYBrlHStiwFrpK0H6knsUPd4UtJwebazoxpLLoFC7vGvpxB54samoCAzhNErbNiZLuNicTlRVZzo+c51faEeiPOA4YvzirOhXmisNldRRFdM7FXI9v3PFSwPbHf/03/rmengu3uoYUF2x1Diwq23XqL3gvmdTniLOe+9c7E+za7nevf7Xwp66wYSc4TCPrCOqdgry7esV66Jvl516HPEdt1eYK5ium6nDp6gj7v91q8I2DE+9OtGDGwe6qWr9aGoVqmqbQaSsDpwE1mdmo6zK4Dtq5Lcwewr5n9LauzvyPpVNIE9+7A5Rt0DpPMjGksgiAIppXJ6VkcABwJXCfp6mw7wczO9bO0GyQtIym0h4FjW62Emk6isQiCIHAoXzpbnoeZXUJJ7B8z27nh80nASeW5Ty/RWARBEDRSZWls++NZTCvRWARBEHiUxrOIxiIIgiAYLZlkt/ZHP5pOorEIgiBoZJJWQ21KRGMRBEFQoIIL8hiG6kzMbJy2wusANguI5K1T9wPCFFeBdzklecGFBp3ASV6wlm5PI7ARXlc8HYhX7pAVv2rv3BZ2ramU37yuQbc+O/UUyxmYdXvBdnPPlgXbqpFiWPc/Dy8o2J7Sv7Jgm9/VX7A9OLquYKuqJfB0FrO9IFrONex1giStqbj6sV/VAiw1wzu/qlTVXngMuUMy1bQXVQOZecGUJjs42TjKhpliGCoINgyvoQiCGYkBIyWNQdn+TYwpdSTYzJe7pEWSlku6Nb9uMZX1CIIgmBgVnAhuZnMWU+11tubL/fHAM4Bjs7/244ALzWx34ML8OQiCoHMobSzaXcHpZUobixa+3A8DzsjJzgBePpX1CIIgmBAGjIy03kY7wgvHtDFtg8z1vtyBbczsHkgNiqStmxxzDHAMwHbbR+iNIAimi1BwNzIt/8D1vtzN7OGqx5nZaWa2r5ntu+WiaCyCIJhGYs5iHFPes2j05Z7N90paknsVS4D7proeQRAElTGwkdbDTLaZDUNN9Wqogi/3zDnAUfn9UcCPp7IeQRAEEyOL8sq2zYip7lm4vtyBk4Flko4G7gIOL8vI0LjgRp6Qp3cCAWF6VTTOdoIV9TpiKk9E11sxSJIn/PMEc57Ir8/xiewFWOpyzs3DCwA1oGKAnwGn3PpAVDX65QR2AnbuKQrmtu8uiv/uGnmgYHtgpHhsr3P9H7GiSPDukeJ1XeRcr0VdxXrP7Roo2DyGnIBIvXIEkI5A0BP0DVQU5XU1ec4bdfJ0gwttBN7197RxnlDPC9rUWzHgk5duSok5i3FMaWNR4sv9eVNZdhAEwQZjlDsSjJ5FEATB5o1h5XMWnRHAbtqIxiIIgqCR8DpbIBqLIAgCj7Jhps2ssQjxQhAEQSOWhqHKtjJa+Mf7mKRrJV0t6eeStqs75nhJt0m6RdKLpvAsJ0Q0FkEQBB42Wr6V08w/3ifNbG8z2wf4KfBvAHnfEcBewMHAF6WN9Fs/SURjEQRBUMCw0fKtNJcm/vEaPFnMYcwt4WHAWWa23sxuB24D9pvUU9tAYs4iCIKggW56WDO6ktma1zTN3+xugBFJV9SZTzOz07z0Df7xkHQS8A/ASuCgnGx74Ld1h63ItrYzgxoLGyfE8wR4XnQz8IVAnqivG0eA50SJ8x4oRipGFPMjexUFZaNWTOdF9/POwytjwDm2mYixkM7Nr9gzbiYUc/N0hGu79cx1bH6NGllvRbHdvK71BduQcy73jhaFdbOdiIZVz88Txs3u6ivYRlzRWrEM79y8qHa1PY30OL9N7/p79R7yloc6vyU8UWrF+27ASbeuYhS6XueenQx24fHczs3sxdPc/aM2yl3cBvAPZnZ/WX6efzwzOxE4UdLxwD8DH8LX89CaRQAACl5JREFUpXXETHoMQwVBEDTwe37VtZbVrLFV7v6/cBdbsS0VGwrPP1493wFeld+vAHao27cUuHtClZ8iorEIgiBowMys1rtopNaruIObtyrLp5l/PEm71yU7FB4t6BzgCEn9knYBdgcu34hTmTRm0DBUEATB9PF7ftW1kK1G19iqcXMXtV7FKnuwtFdBc/94R0t6LDAK3Am8FcDMbpC0DLiRtJLqWOsQqXg0FkEQBA5mZk/RgePmLmq9ikd4qLRXkfNo5h/v3BbHnASctCF1nkpiGCoIgqAJjXMXE5mr2NSIxiIIgqAJ9XMXE5mr2BSJxiIIgqAFtd7Fn7hxs+1VQDQWQRAELan1Lv68GfcqAGQzxHOipL+SVg1sBfytzdUpo9Pr2On1g86vY6fXDzq/jlNZv53MbPFkZZaXwM43s5WTledMY8Y0FjUkXWFm+7a7Hq3o9Dp2ev2g8+vY6fWDzq9jp9cvGE8MQwVBEASlRGMRBEEQlDITGwvXo2OH0el17PT6QefXsdPrB51fx06vX1DHjJuzCIIgCKafmdizCIIgCKaZaCyCIAiCUjqusWgR4HyRpOWSbs2vW9QdM20BzlvU75OSbs5B2M+WtLCT6le3/32STNJWdbZpDRDfqo6S3p7rcYOkU9pRxxbf8T6SfivpaklXSNqv7pjpvoYDki6XdE2u40eyvVPuk2b164j7JNgAzKyjNmAJ8JT8fh7wB2BP4BTguGw/DviP/H5P4BqgH9gF+CPQ3Yb6vRDoyfb/6LT65c87AOeTxY3tqF/JNTwIuADoz/u27qRrCPwceHG2HwJc1MZrKGBuft9LCtX5jA66T5rVryPuk9gmvnVcz8KaBDgnBTI/Iyc7A3h5fj+tAc6b1c/Mfm5mtficvyVFuOqY+uXdnwE+wPgwjdMeIL5FHf8JONnM1ud997Wjji3qZ8D8nGwBYxHM2nENzcweyR9782Z0zn3i1q9T7pNg4nRcY1FPQ4DzbczsHkg3M7B1TrY98Oe6w6YtwHljAPY63gScl993RP0kHQr8r5ld05CsbfWDwjXcA3iWpMskXSypFgC5I64h8C7gk5L+DHwKOL6d9ZPUnQPq3AcsN7OOuk+a1K+ejrhPgmp0bGMhJ8B5s6SObcrXAzern6QTSRGuzuyU+uX6nAj8m5fUsU3LemrnGvYAW5CGK94PLMs+edp+DXP9/gl4t5ntALybFC6TdtXPzEbMbB/S0/l+kp7QIvm017FV/TrlPgmq05GNhfwA5/dKWpL3LyE9rUAbApw3qR+SjgJeCvy9mdV+6J1Qv91I48DXSLoj1+EqSdu2o35N6kiuyw/zEMblpJCTW7Wjjk3qdxRQe/99xoZJ2nINa5jZQ8BFwMF00H3SpH4dc58EE6TdkyaNG+kJ45vAZxvsn2T8xN0p+f1ejJ8Y+xNTP3Hn1e9gUtzcxQ32jqhfQ5o7GJvgntb6lVzDtwIfze/3IA1LqFOuIWnu4jn5/fOAK9t4DRcDC/P7WcCvSH/AnXKfNKtfR9wnsW3Ad9ruChQqBP+H1P28Frg6b4cAWwIXArfm10V1x5xIWj1xC3m1Shvqd1v+c6vZvtxJ9WtI82hjMd31K7mGfcC3geuBq4DndtI1zPYr85/aZcBT23gN9wZ+n+t4PfBv2d4p90mz+nXEfRLbxLdw9xEEQRCU0pFzFkEQBEFnEY1FEARBUEo0FkEQBEEp0VgEQRAEpURjEQRBEJQSjUUQBEFQSjQWmwCSHilPtVH5nytpYd7etgHHP0fSTyeYfqWkc5vs/4akV0+0HjORfC2eWff53ZLukvSFdtYr2PyIxiIoxcwOseSyYSEw4cZiA/mVmR0ylQVI6pnK/CeJ5wCPNhZm9hl8H19BMKVEY7GJUheopxZkZotsv0jSf+TANH+Q9Kxsny1pWU7/vez5dd+87w6lYEknA7vl4D+fbOwxSPqCpDfk9wfnIDeXAK+sSzNH0tck/U7S7yUdVuFclPO+UdL/MOZJFUlPzR5qr5R0fp1fpKflc7k01/X6bH+DpO9L+gnw82b1yR5TP5nt10p6S7YvkfTLfA2ur12/JvV+YS7/qlzm3Gz/t5zv9ZJOy84SkfSOfI7XSjpLyePtW4F35/KalhUEU067JeSxbfwGPOLYrgWend9/lOzniOTQ7dP5/SHABfn9+4Cv5PdPIHkE3Td/voPk0G9n4Pq6Mp4D/LTu8xeANwADJJcOu5P8LC2rpQM+Abw+v19ICiw0p6Hujfm+ElgOdAPbAQ8BrybFSPgN2c8Q8Frga/n99cAz8/uTa/XO9VtBdoPRrD7AMcAHs70fuILks+i9wInZ3g3Ma/KdbAX8snZuwL8w5vKi3gXHt4CX5fd3Mxb4qeZX6cPA+xryfgPwhXb/7mLbvLaZ0A0PJoikBaQ/m4uz6QySl9QaNc+pV5IaAEh+jz4HYGbXS7p2I6rwOOB2M7s11+fbpD9fSJHSDpX0vvx5ANiR5KSvGQcC3zWzEeBuSf8v2x9LatiW54fzbuAepVCd88zsNzndd0hO7GosN7MHSurzQmDvurmRBaTG73fA17JX2h+Z2dVN6vwMUvS3X+e69QGX5n0HSfoAMBtYBNwA/ITUwJ8p6UfAj1pcjyCYdqKx2DxZn19HGPsNePEEyhhm/FDmQN37Zk7HBLzKzG6ZYFlefgJuMLP9xxnr4k43YXVZffLQ0NvN7PxCodKBwEuAb0n6pJl9s0ndlpvZ6xqOHQC+SOq1/VnShxm7bi8hNYyHAv8qaa+S8wiCaSPmLDZBzGwl8GDdGPeRwMUtDgG4BHgNgKQ9gSc6aVaRYlLXuBPYU1J/7s08L9tvBnaRtFv+XP+HeT7w9rpx+idXOKVfAkfkeYQlpFjdkLyTLpa0f86rV9JeZvYgsErSM3K6I1rk3aw+5wP/lHsQSNojz2/sBNxnZl8lBT96SpN8fwscIOkx+fjZkvZgrGH4W57DeHXe3wXsYGa/IIW+XQjMpXjNg6AtRM9i02C2pBV1n08lBer5sqTZpNgAbyzJ44vAGXn4qeZaemV9AjO7X9Kv82TxeWb2fknLctpb83GY2TpJ/7+9O1ZpIAqiMPwfUATxBawFK0sfINj6DNoIgqCIhaBg5wMotrEUbBQrbUWsbCwMNlaxjoVCbGIxFncEhZAFNSzi+apli7uzIeywM5PcZeBc0hMlEX3skrYL7AN3+YBu87VE1M8ZMAe0KD2Fq7xOL8tEB5msRnLte2AJaEp6pfRpXvqsOyieQ0qJ7jbPdyj7WTeATUlvQBdY7LdoRHRUmv3Hksby9E5EPEhq5r20KWUtKCW0o7wPAXsR8ZyN+JNsvK9FxHXFZ2U2FP6LcgPK9A8wmg/6KcpeCNMR0ashlgalqVuVRAatMRER3TzeAiYjYv2XQqxVJqHZiFitOxb7P/xmYR/GgcssuwhYqSNRpB4wI+kivv9bi3lJ25Tv+CNlgujPk7RBGac9rTsW+1/8ZmH2Q5JuKOO1ny1ERKuOeMyGwcnCzMwqeRrKzMwqOVmYmVklJwszM6vkZGFmZpXeAYkTVs+8ToQrAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -112,16 +112,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwrite existing file: bilinear_25x53_1x6.nc \n",
-      " You can set reuse_weights=True to save computing time.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "regridder = xe.Regridder(airtemps, ds_locs, 'bilinear', locstream_out=True)"
    ]
@@ -151,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x12076a0d0>"
+       "<matplotlib.collections.QuadMesh at 0x7f0edf449ca0>"
       ]
      },
      "execution_count": 7,
@@ -160,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -193,16 +184,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwrite existing file: nearest_s2d_1x6_25x53.nc \n",
-      " You can set reuse_weights=True to save computing time.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "regridder_back_s2d = xe.Regridder(airtemps_locs, airtemps, 'nearest_s2d', locstream_in=True)"
    ]
@@ -215,17 +197,356 @@
     {
      "data": {
       "text/html": [
-       "<pre>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:  (locations: 6, time: 2920)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "    lon      (locations) int64 220 230 240 250 260 270\n",
-       "    lat      (locations) int64 20 30 40 50 60 70\n",
-       "Dimensions without coordinates: locations\n",
-       "Data variables:\n",
-       "    air      (time, locations) float64 292.8 288.9 268.1 ... 268.4 255.5 236.8\n",
-       "Attributes:\n",
-       "    regrid_method:  bilinear</pre>"
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       ".xr-wrap {\n",
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+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-549b0bd9-b460-44e1-988e-19580e5810b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-549b0bd9-b460-44e1-988e-19580e5810b5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>locations</span>: 6</li><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fd2f7cac-dad5-4edb-9794-5c87051146bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd2f7cac-dad5-4edb-9794-5c87051146bf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-01c8ce7e-da94-4061-80d3-67e215b64d8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01c8ce7e-da94-4061-80d3-67e215b64d8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f1c2be-c112-4636-9d14-c0f47fcd6935' class='xr-var-data-in' type='checkbox'><label for='data-87f1c2be-c112-4636-9d14-c0f47fcd6935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>220 230 240 250 260 270</div><input id='attrs-f8db0191-5a8a-4ab3-9c67-acf20fbd8b81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8db0191-5a8a-4ab3-9c67-acf20fbd8b81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bddb0ed-1cda-4a0b-b350-56aebfb8f489' class='xr-var-data-in' type='checkbox'><label for='data-7bddb0ed-1cda-4a0b-b350-56aebfb8f489' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([220, 230, 240, 250, 260, 270])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(locations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>20 30 40 50 60 70</div><input id='attrs-a7ec7a40-2265-49b6-9a10-121607063205' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7ec7a40-2265-49b6-9a10-121607063205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7960b54-d99c-4913-b431-215b7e938d95' class='xr-var-data-in' type='checkbox'><label for='data-f7960b54-d99c-4913-b431-215b7e938d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([20, 30, 40, 50, 60, 70])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d55807a1-a997-4872-9a32-a261b82bfd6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d55807a1-a997-4872-9a32-a261b82bfd6b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, locations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>292.8 288.9 268.1 ... 255.5 236.8</div><input id='attrs-7e3c934a-84a9-490a-9a5c-d45969b94470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e3c934a-84a9-490a-9a5c-d45969b94470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1003e950-2c83-4278-aa97-499a0f9c07ef' class='xr-var-data-in' type='checkbox'><label for='data-1003e950-2c83-4278-aa97-499a0f9c07ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[292.79000854, 288.8999939 , 268.1000061 , 269.79000854,\n",
+       "        247.69999695, 247.88999939],\n",
+       "       [293.        , 289.79000854, 262.3999939 , 267.69998169,\n",
+       "        246.        , 246.29998779],\n",
+       "       [292.29000854, 289.5       , 256.69998169, 269.8999939 ,\n",
+       "        244.5       , 243.88999939],\n",
+       "       ...,\n",
+       "       [296.29000854, 290.58999634, 263.19000244, 266.19000244,\n",
+       "        259.79000854, 234.78999329],\n",
+       "       [296.48999023, 289.48999023, 261.08999634, 270.88998413,\n",
+       "        259.98999023, 237.98999023],\n",
+       "       [297.19000244, 289.58999634, 260.79000854, 268.38998413,\n",
+       "        255.48999023, 236.78999329]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-312120fa-1155-4b96-8531-f0387f356371' class='xr-section-summary-in' type='checkbox'  checked><label for='section-312120fa-1155-4b96-8531-f0387f356371' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -275,7 +596,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x124b779d0>"
+       "<matplotlib.collections.QuadMesh at 0x7f0ede9ecbb0>"
       ]
      },
      "execution_count": 11,
@@ -284,7 +605,7 @@
     },
     {
      "data": {
-      "image/png": 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C4D7gHbZ/VGccERFj1e/9/CejI+sptk+0vaS8Ph+43vYi4PryOiKiu3gUSxuSFki6QdJWSVsknVPK/1HS5rLcJ2lzwzEXSNom6S5Jp3X+wipT0eyzDHhNWV8D3Ah8eAriiIgYhjp1w3cPcJ7tTZIOAW6RtM72O3/6TtJfAI+V9eOA5cDxwDHAdZIW2x7/ExKHUXfN38BXJd0iaWUpO9r2DoDyc06rAyWtlLRR0sa9u56sOcyIiCYdqPnb3mF7U1nfBWwF5g1tlyTgHcDnStEy4Arbu23fC2wDlnbmgp6t7pr/ybYflDQHWCfpztEeaHs1sBpg5sL5fd76FhFdxeAO9/aRtBA4CVjfUPwqYKftu8vrecDNDdu30/Bl0Um11vxtP1h+PgRcTfUNtlPSXIDy86E6Y4iIGB+NYuHIoRaKsqxseSZpFnAlcK7txxs2vYv9tf6hN21WS+W3tpq/pIOBabZ3lfU3AB8DrgVWABeXn9fUFUNExLiNLuU+3NCZpSVJM6gS/2W2r2oonw78JvDyht23AwsaXs8HHhxlxGNSZ7PP0cDVVZMW04HLbX9Z0r8BayWdBdwPvL3diZ4/6zE+/Kp/GVcQH//GGeM6rttMZNBYvw8Qy2CwSgZ+dVgH6tulTf9SYKvtS5o2vw640/b2hrJrgcslXUJ1w3cRsGHikTxXbcnf9neBE1qUPwKcWtf7RkRMWOcGeZ0MnAnc3tCd80Lb/0LVq6exyQfbWyStBe6g6il0dh09fSAjfCMiWurEIC/bN9G6HR/b/2WY8lXAqom/+8iS/CMiWsmzfSIiBk+reyj9JMk/IqLZKAdx9bIk/4iI51Dfd5VK8o+IaCU1/9423vEBMHhjBAZxPED6/sewuvzjUMYQzLf9wHiOH1VWkPTx0ZRFRPSFoX7+7ZapDNE28E/jPX60w0Zf36LsjeN904iIbie3X7rAzZJeMZ4DR2z2kfQ+4PeBF0q6rWHTIcC/jucNIyJ6Qnck93ZOAd4j6XvAk1QDymz7Ze0ObNfmfznwJeDPePaMW7ts/3CcwUZERGeMuwVmxORv+zGqGWbeBVCey38QMEvSLNv3j/eNIyK6WZc067Qk6dDyaOhd4z3HqHr7SHozMPSUuYeAF1DNSHP8eN84IqJrmW5/vMPlwJuAW3h2A5XK6xe2O8Fou3r+KfBK4DrbJ0k6hfLXQEREX+rimr/tN5Wfx0qaTfXo54PGco7RJv9nbD8iaZqkabZvSFfPiOhn3dzsM0TS7wHnUE36spmqkv5NRvHY/NEm/0fLNGRfBy6T9BDVs6b72qANEBvLhDH9MiBsECeCycCvUeqB5E+V+F8B3Gz7FEkvBj46mgNH+2lfBvwEeD/wZeAe4M3jCDQiojd4FMvUe8r2UwCSZtq+E3jRaA4cVc3f9pMNL9eMPb6IiN7RRYO42tku6XCqkb7rJP2IUc75226Q1y5af78NDSQ4dKyRRkT0hO7u7QOA7d8oq38i6QbgMKrWmbba9fM/ZIKxRUT0pB6p+f+U7a+NZf++f6pnRMS49FjyH6sk/4iIZr3T5j9uSf4REa30efIffcfuiIgBon3tl7bnkBZIukHSVklbJJ3TsO0PJd1Vyj/RUH6BpG1l22n1XF2P1Pxnag+LDvz+s8rufvr5UxTN6Ix2gFgvDgaDwZsdrN9nAcvAr9rsAc6zvUnSIcAtktYBR1ONn3qZ7d3loZlIOg5YTvXctGOA6yQttj260YhjUHvNX9IBkr4t6Yvl9WxJ6yTdXX4eUXcMERFj1oFBXrZ32N5U1ndRPRBzHvA+4GLbu8u2h8ohy4ArbO+2fS+wDVjauYvabzKafc6huuAh5wPX214EXM+z5wmIiJh6o5jFa6w3hCUtBE4C1gOLgVdJWi/paw2zcc0DGufk3V7KOq7W5C9pPvDrwN83FC9j/yjhNcBb64whImJcRlfzP1LSxoZlZatTlWejXQmcW57DPx04gupBbB8E1pYJ2Vs1ttVy67nuNv+/Aj5ENe3jkKNt74DqT6Khtq5m5Ze4EmDOMT1xayIi+snoUu7DtpeMtIOkGVSJ/zLbV5Xi7cBVZRL2DZL2AUeW8gUNh89nlI9rGKvaav6S3gQ8ZPuW8Rxve7XtJbaXHDY7yT8iJo/oWG8fAZcCW21f0rDpn4DXln0WAwcCDwPXAsslzZR0LNVz+jd09OKKOrPqycBbJJ1BNcnAoZI+C+yUNLfU+udSzQwWEdE9OjfI62TgTOB2SZtL2YXAPwD/IOk7wNPAivJXwBZJa4E7qHoKnV1HTx+oMfnbvgC4AEDSa4AP2H63pE8CK4CLy89r6oohImLcOpD8bd9E63Z8gHcPc8wqYNXE331kU9GecjHVzY2zgPuBt4/nJM39/oeT8QBTq9V4gPT97w0D3/e/z0f4Tkryt30jcGNZf4RRTDEWETGV8myfiIhBlOQfETFgPLrePL0syT8iopXU/CMiBk/a/CMiBlGSf0TEgBnlUzt7WZJ/REQTkWafntfPg8H6aeBXK704GCwDv/pHkn9ExCBK8o+IGEBJ/hERA6ZzT/XsWkn+ERGtJPlHRAyePN4hImIApdknImLQZJBXRMSASvIfDK0Gg/XiwK9W+mkwWAZ+xWTICN+IiAGlff2d/ZP8IyKapc0/ImIwpdknImIQ9XnyH93jFSMiBozcfml7DmmBpBskbZW0RdI5pfxPJP2HpM1lOaPhmAskbZN0l6TT6rq+1PwjIlrpTM1/D3Ce7U2SDgFukbSubPtL23/euLOk44DlwPHAMcB1khbbfm6XsQlKzT8iopmrxzu0W9qext5he1NZ3wVsBeaNcMgy4Arbu23fC2wDlk78gp4rNf8RZCKY7pO+/92nHyd4GUM//yMlbWx4vdr26pbnlBYCJwHrgZOBP5D0O8BGqr8OfkT1xXBzw2HbGfnLYtxS84+IaMVuv8DDtpc0LMMl/lnAlcC5th8HPg38PHAisAP4i6FdW0XS8WujxuQv6SBJGyTdWm50fLSUz5a0TtLd5ecRdcUQETFenbjhCyBpBlXiv8z2VQC2d9rea3sf8Hfsb9rZDixoOHw+8GCnrqlRnTX/3cBrbZ9A9e12uqRXAucD19teBFxfXkdEdA+PcmlDkoBLga22L2kon9uw228A3ynr1wLLJc2UdCywCNgwwatpqbY2f9sGnigvZ5TFVDc0XlPK1wA3Ah+uK46IiPHo0PP8TwbOBG6XtLmUXQi8S9KJVDnxPuA9ALa3SFoL3EHVU+jsOnr6QM03fCUdANwC/ALwN7bXSzra9g6o7oRLmjPMsSuBlQBzjsl96YiYXJ1I/rZvonU7/rBPZbS9Clg18XcfWa03fEub1olU7VZLJb10DMeuHrqJctjsJP+ImERmtDd8e9ak9Pax/ShV887pwM6h9q7y86HJiCEiYiw6dcO3W9XZ2+coSYeX9ecBrwPupLqhsaLstgK4pq4YIiLGrQM3fLtZne0pc4E1pd1/GrDW9hclfQtYK+ks4H7g7TXGMCn6ZSKYDPyaWv008KvXZTKXCbB9G9VotubyR4BT63rfiIgJszOZS0TEQOrv3J/kHxHRSpp9IiIGjYE0+0REDKD+zv1J/hERraTZJyJiAKW3T0TEoOmDQVztJPnXJAO/pla/DPzqVb0+u1c1yKu/s3+Sf0REK31eV0jyj4hoITX/iIhBkzb/iIhBlGf7REQMpjT7REQMGHdsDt+uleQfEdFKav4REQOov3N/kv9kajXwq5VuGgzWauDXcLp9QFgvDvxqNbsXZIavyaB93f1/Y6ImZQL3iIieYqpBXu2WNiQtkHSDpK2Stkg6p2n7ByRZ0pENZRdI2ibpLkmndeyamqTmHxHRRLhTg7z2AOfZ3iTpEOAWSets3yFpAfB6qrnMq/eVjgOWA8cDxwDXSVpsu/WfgBOQmn9ERCt2+6XtKbzD9qayvgvYCswrm/8S+BDPvruwDLjC9m7b9wLbgKWdvKwhSf4REa2MLvkfKWljw7JyuNNJWgicBKyX9BbgP2zf2rTbPOCBhtfb2f9l0VFp9omIaDbU5t/ew7aXtNtJ0izgSuBcqqagi4A3tNp1mGg6Lsk/IqKFTvX2kTSDKvFfZvsqSb8IHAvcKglgPrBJ0lKqmv6ChsPnAw92JJAmafaJiHiOUTT5jKLNX1V2vxTYavsSANu3255je6HthVQJ/5dsfx+4FlguaaakY4FFwIY6rjA1/y7UixPBQG9OBtOLff+hdf//bu/731MTvJhOjfA9GTgTuF3S5lJ2oe2WA2hsb5G0FriDqnno7Dp6+kCSf0REax2oA9i+idbt+I37LGx6vQpYNfF3H1ltzT7DDW6QNFvSOkl3l59H1BVDRMR4yW679LI62/yHBje8BHglcHYZwHA+cL3tRcD15XVERHfpQJt/N6st+Y8wuGEZsKbstgZ4a10xRESMiw1797VfetiktPk3Dm4Ajra9A6ovCElzhjlmJbASYM4xuTUREZOsx2v27dTe1bNxcIPtx0d7nO3VtpfYXnLY7CT/iJhkafYZv+bBDaV4p6S5Zftc4KE6Y4iIGDMD+9x+6WF19vZ5zuCG4lpgRVlfAVxTVwwREeNj8L72Sw+rsz2l5eAG4GJgraSzqB5l+vYaY+gbvTrwK6InmZ6/odtObcm/zeCGU+t634iIjujxNv12cic1IqKVJP+IiEHT+7152knyj4hoZqDPJ3BP8o+IaCU1/4iIQeP09omIGDgG93g//naS/CMiWunxEbztJPn3sF4Y+JXZvSZPZvfqsLT5R0QMGDu9fSIiBlJq/hERg8Z4by3zpneNJP+IiGZDj3TuY0n+ERGt9HlXz9pn8oqI6DUGvM9tl3YkLZB0g6StkrZIOqeU/w9Jt0naLOmrko5pOOYCSdsk3SXptLquMck/IqKZOzaZyx7gPNsvAV4JnC3pOOCTtl9m+0Tgi8BHAMq25cDxwOnApyTV0l83yT8iogXv3dt2aXsOe4ftTWV9F7AVmNc0n/nBVH9sACwDrrC92/a9wDZgaUcvrJB7oDuTpB8A3wOOBB6e4nDa6fYYuz0+6P4Yuz0+6P4Y64zvBbaPmsgJJH2ZKsZ2DgKeani92vbqYc65EPg68FLbj0taBfwO8Bhwiu0fSPrfwM22P1uOuRT4ku0vjPtihtETN3yH/iElbbS9ZKrjGUm3x9jt8UH3x9jt8UH3x9jt8dk+vZPnkzQLuBI4d6jWb/si4CJJFwB/APwxrWc/rKWGnmafiIgaSZpBlfgvs31Vi10uB95W1rcDCxq2zQcerCOuJP+IiJpIEnApsNX2JQ3lixp2ewtwZ1m/FlguaaakY4FFwIY6YuuJZp8GLdvSuky3x9jt8UH3x9jt8UH3x9jt8XXKycCZwO2SNpeyC4GzJL0I2Ed1P/O9ALa3SFoL3EHVU+hs27UMNe6JG74REdFZafaJiBhASf4REQOoq5L/CEOhZ0taJ+nu8vOIhmMmZSh0m/g+KenOMlz7akmHT0V8I8XYsP0DkizpyIayKf8dlm1/WGLYIukT3RSfpBMl3VyG42+UtLThmMn+Nz5I0gZJt5YYP1rKu+VzMlx8XfM5CcB21yzAXOCXyvohwL8DxwGfAM4v5ecDHy/rxwG3AjOBY4F7gAOmIL43ANNL+cenKr6RYiyvFwBfoQyY67Lf4SnAdcDMsm1Ol8X3VeCNpfwM4MYp/DcWMKuszwDWUz06oFs+J8PF1zWfkyzurpq/hxkKTTXkeU3ZbQ3w1rI+aUOhR4rP9ldt7ym73UzVN3fS4xspxrL5L4EP8exBI13xOwTeB1xse3fZ9lCXxWfg0LLbYezvez0V/8a2/UR5OaMspns+Jy3j66bPSXRZs0+jMhT6JKpaw9G2d0D14QTmlN3mAQ80HLad/YluMuNr9LvAl8r6lMUHz45R0luA/7B9a9Nu3fI7XAy8StJ6SV+T9Ioui+9c4JOSHgD+HLhgKuOTdEDpOvgQsM52V31OhomvUdd8TgZVVyb/VkOhh9u1RVntfVeHi0/SRVR9cy+byvhKLD+NscR0EeXJgc27tiibit/hdOAIquaBDwJrywCZbonvfcD7bS8A3k81cIepis/2XldPhJwPLJX00hF2n/QYR4qvmz4ng0lhhAAAAAJISURBVKzrkr9aD4XeKWlu2T6XqjYBkzgUuk18SFoBvAn4bdtD/3EnPb5hYvx5qrbUWyXdV+LYJOn5UxHjML/D7cBVpclgA9XglyO7KL4VwND659nfLDEl/8ZDbD8K3Ej1+N+u+ZwME19XfU4G3lTfdGhcqGoA/xf4q6byT/LsG1mfKOvH8+wbRd+l/htZreI7nWpE3lFN5ZMa30gxNu1zH/tv+HbL7/C9wMfK+mKqZgB1UXxbgdeU9VOBW6bw3/go4PCy/jzgG1QJtVs+J8PF1zWfkyzuuuT/q1R/7t0GbC7LGcDPAtcDd5efsxuOuYiqd8BdlN4YUxDftpKshsr+diriGynGpn1+mvy76Hd4IPBZ4DvAJuC1XRbfrwK3lCS1Hnj5FP4bvwz4donxO8BHSnm3fE6Gi69rPidZnMc7REQMoq5r84+IiPol+UdEDKAk/4iIAZTkHxExgJL8IyIGUJJ/dDVJT7TfKyLGKsk/ImIAJflHT1Dlk5K+I+l2Se8s5a+RdKOkL5RnxV9WngkUESPotQncY3D9JnAicALVM3/+TdLXy7aTqB4R8CDwr1STZt80FUFG9IrU/KNX/CrwOVdPi9wJfA0YeuzzBtvbbe+jemzAwimKMaJnJPlHrxipKWd3w/pe8hdtRFtJ/tErvg68s0wSchTwamDDFMcU0bNSQ4pecTXwK1RP1TTwIdvfl/TiqQ0rojflqZ4REQMozT4REQMoyT8iYgAl+UdEDKAk/4iIAZTkHxExgJL8IyIGUJJ/RMQA+v8e7GrhPiFH+AAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -311,16 +632,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwrite existing file: nearest_d2s_1x6_25x53.nc \n",
-      " You can set reuse_weights=True to save computing time.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "regrid_back_d2s = xe.Regridder(airtemps_locs, airtemps, 'nearest_d2s', locstream_in=True)"
    ]
@@ -350,7 +662,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x126b35e90>"
+       "<matplotlib.collections.QuadMesh at 0x7f0ede9b19d0>"
       ]
      },
      "execution_count": 14,
@@ -359,7 +671,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -403,16 +715,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwrite existing file: nearest_s2d_1x6_1x6.nc \n",
-      " You can set reuse_weights=True to save computing time.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "regrid_l2l = xe.Regridder(ds_locs, ds_locs2, 'nearest_s2d',\n",
     "                          locstream_in=True, locstream_out=True)"
@@ -443,7 +746,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1249f00d0>"
+       "<matplotlib.collections.QuadMesh at 0x7f0ede8f4a00>"
       ]
      },
      "execution_count": 18,
@@ -452,7 +755,7 @@
     },
     {
      "data": {
-      "image/png": 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+bcD/ADCzJyTdAjxJ1tPpOjNLWhVMvY5KDmRv1Dsl/RBQBvolfcHMfmqe43LOOWD2MqGtGQRnZvcxdzsCwJ8dZp8byard26qjqpXM7AYzW2Vma8j68n7dE4NzrtO0o7dSp+m0koNzznW2Bkc/L3QdmxzM7BvAN+Y5DOecO4Bf7Mc559ycvOTgnHPuAAbE6cJuT2iEJwfnnGuCX+zHOefcnLzNwTnn3IHM2xycc84dpJWD4DqZJwfnnGuSJwfnnHMHMETivZWcc84dzBuknXPOHcC8Qdo559xczJODc865A/kgOOeccwcx8AbphaDWAzteJZLhmML2Qbq2w46fXExcCRm8N2LTW4dIexKGHiqw5X29JFMFwu6Y4MUCaX+NLa8PSXsiep4NmDl/GgTp3iLBoipdj3Wx6QcLWMEIl1ZItncRjYs9FwUU90J6wQS1RExOFin2V6hORsT7ItLBmHQyZHx5TGFbiXhplQ9837f4503ns3trP5ee+SLFIKaaFjitew+VtEA1LbBxfIhimLCmZw+9ayvsmO5lbKbMWFxmrFJmed8YU3GRpaUxLlq6ha/tPIfvG9zEGYO7WBKNEWAsLk2wr1rm9J49rCzuZVetly2VAVaV97G90k9/sUJ3WKWvMMNrhvbykE4DIFLMjy26n9uDS4j6EpZGo0yeW+SS/k1c0LWJKy98mjv2XkSglMv6NvLk9Eqe6RvjtUPP8eT4CordNS5evoXYAr63a4Rl/WNs2rOInq4K41NlqlHK8Pl7GJsuM7qjl55nI0qnjzOxLEKbysT9CcFMQLR6gj2beknLondDwORpKWNnFmBkht5vd7Ho6RrTIwV2XwRcuYiky+h/tovSXmPnqwvQWyLaUiQ5Xaz5TMLWK0pMLw8pL51iZqxE1FOjtrtMOFhl8R29jJ4hel6C0mhK3B0QT4sX3j3M9OqYwsAU9mI3w4+FVAZE/wtidG2BfRfGDC4bY+fOPqj2EQ5OUSrVmHhoACXZL8rqINhFBWz5NKtX7GD7RB+7nlxEvLTK4pFxeopVNnctoq9/mmKY0LV8DzvHeukJjDBISZYHlIs1zhnawVA0RSUtsLZ7J/ftOpPVp+/ijP7dbJ4cYG3fHh7btYJqUuCpiWUMRgOcs2QH355Yy3QSsbJvlHN6t7GytJc39q9n50wvhSDl+hX/yn2TZzMadxEqJbWAC/q3MBZ3MRBO8ctr7uHhyTV8X8/zfG9mGe89+xHu33U6k7Uia0o7ecOZz5BaQBQkvGXp03zu26+jZ9E0l614gYlamUDGmxY9xeBF09zz0hn0lSucf942XpoaYNvqPl7RM85weZKJuMTu6W7OGdzOS1ODPL93iHVDu/nezhEKF0wxumEEC0IK05CUYOdFBSrn1UjHilBKWPll2PiuHuIlEYXuKVYsHmXTk8uIxgImLqyBQdeGItMjfVT7W/SlY1m7w8luwScH55w70by3knPOuQMYp0aD9Mlfceaccy2VNUg3shz1maTVku6WtF7SE5Kuz9dfLOlbkh6R9KCky+r2uUHSBklPS7qqXa/SSw7OOdekFrY5xMCHzexhSX3AQ5LuAv4Y+D0z+xdJP5Tff6Ok84BrgPOBFcBXJZ1lZknLIsp5cnDOuSaYQdqi3kpmthXYmt8el7QeWElWezXbhD4AbMlvXw3cbGYVYKOkDcBlwDdbElAdTw7OOdekJsY5DEt6sO7+TWZ201wbSloDXALcD3wIuFPSn5BV/39/vtlK4Ft1u23O17WcJwfnnGtSE9VKu8zs0qNtJKkXuBX4kJmNSfoD4NfM7FZJPwp8FngLzNlNqi0da71B2jnnmmSmhpZGSIrIEsMXzey2fPUHgdnbf09WdQRZSWF13e6reLnKqaU8OTjnXBOMxhJDI8lBkshKBevN7JN1D20B3pDffjPwTH77duAaSSVJa4F1wAMte3F1vFrJOeea0dpZWa8APgA8LumRfN1HgV8E/kxSAZgBrgUwsyck3QI8SdbT6bp29FQCTw7OOde8FtXym9l9zN2OAPCqw+xzI3BjayI4PE8OzjnXpFNhhLQnB+eca5JPvDcPJJWBe4ESWXz/YGYfm9+onHMu43MrtYCkHklBfvssSe/Mu20dSQV4s5m9ErgYeJuk17YzTueca5gBpsaWBazdXVnvBcqSVgJfA34W+Osj7WCZifxulC+nQCHOObdQWNrYspC1OznIzKaA9wB/bmbvBs476k5SmHfr2gHcZWb3H/T4tflMhQ+mk5NtCdw55+bWunEOnaztyUHS5cBPAv+crztqO4eZJWZ2Mdnov8skXXDQ4zeZ2aVmdmnQ09PyoJ1z7oiswWUBa3dyuB64AfhSPnjjFcDdje5sZvuAbwBva094zjnXJGvt9Bmdqq29lczsXrJ2h9n7zwG/eqR9JI0ANTPbJ6mLbLKpP2pnnM4515QFXipoRFuTg6SzgP8ErKk/lpm9+Qi7LQc+LykkK9ncYmZfbmeczjnXnIVdKmhEu8c5/D3wl8BngIbm/zCzx8jmNHfOuc60wHsiNaLdySE2s0+3+RjOOXfizI5zOMm1u0H6f0v6ZUnLJQ3NLm0+pnPOtZVZY8tC1u6Swwfzvx+pW2fAK9p8XOeca58F/sXfiHb3Vlrbzud3zrl5cQpUK7W7t1IE/BLw+nzVN4C/MrNaO4/rnHNtYyBvkD5unyabG+lT+f0P5Ot+oc3Hdc65Nln4k+o1ot3J4dX57Kqzvi7p0TYf0znn2usUaHNod2+lRNIZs3fy6TPacr1T55w7YVo0t5Kk1ZLulrRe0hOSrs/X/52kR/Ll+brrSyPpBkkbJD0t6aojPLckrT7Wl9juksNHgLslPUc2pPB0smm7nXNu4WpdySEGPmxmD0vqAx6SdJeZ/djsBpL+KzCa3z4PuAY4H1gBfFXSWWZ2yI9uMzNJ/8hhrkV9NO3urfQ1SeuAs8mSw1NmVmnnMZ1zrq1aOAjOzLYCW/Pb45LWAyuBJyH79Q/8KDA75dDVwM359+hGSRuAy4BvHuYQ35L0ajP7drOxtSU5SHqzmX1d0nsOeugMSZjZbe04rnPOnQjt6K0kaQ3Z1EH116+5EthuZs/k91cC36p7fHO+7nDeBPwHSS8Ak2Q/0s3MLjpaPO0qObwB+DrwI3M8ZoAnB+fcqWBY0oN1928ys5sO3khSL3Ar8CEzG6t76MeBv63fdI5jHKmS6+3NBFuvLcnBzD6W3/yEmW2sf0ySD4xzzi1oarzNYZeZXXrE58rGg90KfLG+VkVSgewqmvVtBpuB+kbmVcCWOZ6zP08y4w1HepB291a6dY51/9DKA4RVCKcFyop6aQSVvWVKT3UxvQTClVOoK2b3q2vYrhIKjWIpJqwCMwGFZdOoFjCzxEhmChSihGCwSlSKmTp/hqQ/Ju1OWLdsJ+e88kW6X7kHUlG+dA9rh3dz+sgeFCUsGRin3FulsHKKoeFxgsEqA0OTxMsq9C2aohzUuHB4K+VFM3SHVZ7dN8yLo4M8P7mYxdEkG8eHmKoVeXbnMGNxmWf3DZOauOGsf+Gtg9/lspEXKAYJF/a/RHdY5f6Na1jZPcrOah8Ay6O9vKf3BS7vf5Z1/TuZSiJe072B87tf4vSuPQwVJvn5kXtZ1bWX4dIEb+5/krWlnRSClJkk4qxoB5tqiwB4Xe/T7Ir7iNOQlyqDhKRsiwd5ct9SUgsYDCfpD6d5cusyyoqZiEtUxktsnhhgRXmUqJC1jaUmVvWNMtQ7RVBIefvyJzl78U7Crpips6qsWrQvu9buadOURqZ46+u+QxIHpENVlpy+h4l1MdYXky6qsXxklPEzUna8KmJ6WBSmRDQuCIyxdQk7vz8BE8XuKlo3QTJV4IV3FBl83XbCoQrVF3uxOGCwdworpiSVkIl3jTH0mu1MLYNtV8Doa2fY9e5pps+s0D0ySX/vDPFgwo5XGyPvfZEdPzPN6KUVeoYnWTO4FwKjb/k4xWLMuuFdpJeMU7h8L1NnVwnOmCCNjGS6wIWDWzl9YC/hGRP0DU7zmqUvMNI1SamrxujOPkpRTC0JKYQp541sp1IrEAYp5wztIMB4fO9yXpoaYEW0j96oyo7RPqbiIr+79stctehxzh3azoqe7MfmSHGcqTjitNIeRooTLO8eI5DxU33Psi/pIZDxrpGH6QuqLI1G2Rt3896Bh1hR2Esg4+zurTw2sZqRcIz+cJpvjp3JO/ofZV+tiwsGt/Gbr7iDclBjMJpmaWmMKwe+x1Bhgr7Fk3xw3f28cfBpimHM6d276QuneWpsCWZi51gv71r8EG8YfobxqTLnDmzn4W2rWL9jCdtG+6mkBZ7esYQwTHlm1zC95QrlQo2RNXsY+ZFNTLxlgulhqF04SVhIiYZmWL5iL9veV2Xgwl10D05TKsVMVEooFtUVNcBYtmwflWFj8rSEwnQLv3hMjS1HkbcpfBZYb2afPOjht5C1026uW3c7cI2kUv5Dex3wwBxP/Tf534eAB+uW2ftH1a42h3PIWtMHDmp36AfK7Timc86dEK29BOgVZIODH6/rrvpRM/sKWa+k+iol8itq3kLWYB0D1x2mp9IP53/X5pOdrqPJ7952tTmcDfwwMMiB7Q7jwC+26ZjOOXditCg5mNl9HObKQWb2M4dZfyNwYyPPL+kXyC7XvAp4BHgt8H+AHzjavu1qc/gn4J8kXW5mh+ti5ZxzC9ICmlvpeuDVwLfM7E15rc7vNbJjuwfBfUfSdWRVTPuLNGb2c20+rnPOtc/CmT5jxsxmJCGpZGZPSTq7kR3b3SD9v4BlwFXAPWRFm2NuPXfOufkma3zpAJslDQL/CNwl6Z+Yo3fTXNpdcjjTzN4v6Woz+7ykvwHubPMxnXOuvRbIrKxm9u785scl3Q0MAHc0sm+7k8PsdRv2SboA2AasafMxnXOuvTqjVNAUM7unme3bnRxukrQI+B2y/rm9wO+2+ZjOOddWC6hB+pi1e+K9z+Q378WvG+2cOxl0TntCW7W1QVrSf8kbQ2bvL5L0B+08pnPOtV2LrufQydrdW+ntZrZv9o6Z7QV+qM3HdM659joFkkO72xzCvG9tBUBSF1Bq8zGdc66tToVqpXYnhy8AX5P0P8jy6M8Bn2/zMZ1zzh2ndjdI/7Gkx8hmFwT4fTM74jiH/Jqn/5Ns8FxKNv/5n7UzTueca5h5b6VW+Q4QkZUcvtPA9oe7puqT7QzSOecadgpUK7W7t9KPks01/j6y66DeL+l9R9rHzLaa2cP57XFg9pqqzjnXGbxB+rj9NvBqM9sBIGkE+CoNXvDnMNdUdc65eSO8QboVgtnEkNtNg6WVI1xTFUnXAtcCRH2LWhSqc841yJPDcbtD0p28fDWjHwO+crSdDndN1Vn5BbpvAuheuvoU+Jiccx3jFBkh3e7eSh+R9F6yS+GJrOfRl460z1Guqeqcc/PPeysdPzO7lawU0KgjXVPVOefmnZccjpGkceaulRNgZtZ/uH2PdE1V55zrCC1KDkca1yXpPwK/Qta9/5/N7Dfy9TcAPw8kwK8ebezYsWrXNaT72vG8zjk371rbTXXOcV3AUuBq4CIzq0haAiDpPOAasksvrwC+KuksM0taFlGu3RPvOefcSadVlwk9wriuXwL+cHZeurpen1cDN5tZxcw2AhuAy1r/Cj05OOdc05Q2tgDDkh6sW6497HMeOK7rLOBKSfdLukfSq/PNVgKb6nbbTJsGCZ+I6TOcc+7k0ni10i4zu/RoGx08rktSAVgEvBZ4NXCLpFcwd3tsW5rHPTk451wzWjw1xmHGdW0GbjMzAx6QlALD+frVdbuvAra0LpqXebWSc841QU0sR32uw4/r+kfgzfk2ZwFFYBdwO3CNpJKktcA6svnrWs5LDs4516zWlRzmHNcFfA74nKTvAlXgg3kp4glJtwBPkvV0uq4dPZXAk4NzzjWtVYPgjjKu66cOs8+NwI2tieDwPDk451yzfPoM55xzB/CJ95xzzs3Jk4NzzrmDecnBOefcoU6B5KCsd9TCNXLeYvuJL76VyaTE3mo3qYk4DSiHMQPFaSbjIgBTcZFARjms7b9fCFLKYY2JWok4DSgEKamJ/uIMqYmCUvZVu/ZvF+Q/F3bN9NAfVfY/X5CPk2CjIpIAABUbSURBVA9lJCZmkohqWqAYxMRpSIpITZTDGqkFFIKEACOQkZqopgUGomkqaYGZJGIgmgZgMilm+5voLlSZSSIA+qIZKkmBnkIWQymISU2kFlCzgN5ClbFamUDp/ttdYZXUAhLEZFwiUEpXWKM3rFAKYqbTIosKk0wl+fsUZK+rrJgZK+z/W0sL9IUzzFiBibhMKYippAVqFlKzkOFogr1xDyXVSAiopAVCGQUlxBYyViszVJykkhaI0xCAUhhTSwMWR5OMJV3sq3UxXJzghakhlpTGqVn2HsQWMhkXWdk1yvaZPkphTIAxmRQpBjG9YTWPJSC1gK6wmr22pLh/XX9hhgQxnUSUghiA0VoX+6pdrO7et/+8mk4iCkFCatlQoP7CDNNpRDGIiZStn8g/n1IYMxkXqaYFFhcnSRGT+fmWmugrVAiUsrfaQzGISRE9eayzn0OI7X++nkKFKD+napa9h/2FmTyuIoFSYgupJAW6whopIjHt/yxCGbU0ZDCaomYh1bRAgJEiCnnsg9EUKQFdQZV9te7s/CCgkhTy1ztNlJ+nu2s9AHSFNcpBjVJQo5YWSBE7qn3Ze5NELI4mCZUSKKWSRnQHVfbEPXQHVSaSEt1hNXtNabj/HANI8tc4lRaJlBApoWbh/v+37iDbrxzU2FHrpyuoUg5qjMZd+8/drnybsbhMagGB0v2f/ez7DPBXl37hoUZGLB9J95LVds57f72hbb/zl79+3MebL15ycM65ZhjeW8k559yBhLc5OOecm4snB+eccwfTAm+rbYQnB+eca0aLZ2XtVJ4cnHOuSfIGaeeccwfzBmnnnHOH8uTgnHPuAD7xnnPOuTl5cnDOOVfPB8E555ybk9KTPzsE8x2Ac84tKNbEchSSVku6W9J6SU9Iuj5f/3FJL0l6JF9+qG6fGyRtkPS0pKta/fJmdVzJQdLngB8GdpjZBfMdj3POHayF4xxi4MNm9rCkPuAhSXflj/03M/uTA44rnQdcA5wPrAC+KuksM0taFlGuE0sOfw28bb6DcM65w2pRycHMtprZw/ntcWA9sPIIu1wN3GxmFTPbCGwALjvm13EEHZcczOxeYM98x+Gcc4cja2xp6jmlNcAlwP35ql+R9Jikz0lalK9bCWyq220zR04mx6zjkoNzznU0yxqkG1mAYUkP1i3XzvWUknqBW4EPmdkY8GngDOBiYCvwX2c3nTui1uu4NodG5G/wtQC9y3rmORrn3Cmn8a/jXUe7EpykiCwxfNHMbgMws+11j/934Mv53c3A6rrdVwFbGo6mCQuy5GBmN5nZpWZ2aXlRab7Dcc6dQmbHObSiWkmSgM8C683sk3Xrl9dt9m7gu/nt24FrJJUkrQXWAQ+06KUdYEGWHJxzbt6YZUtrXAF8AHhc0iP5uo8CPy7pYrIyyvPAf8gObU9IugV4kqyn03Xt6KkEHZgcJP0t8EayurrNwMfM7LPzG5Vzzr2sVSOkzew+5m5H+MoR9rkRuLE1ERxexyUHM/vx+Y7BOeeO6OQfIN15ycE55zqagZKTPzt4cnDOuWad/LnBk4NzzjXLZ2V1zjl3qNb1VupYnhycc65JXnJwzjl3oAYn1VvoPDk451wThPdWcs45Nwd5m4NzzrkDeLWSc865Q7V0bqWO5cnBOeea5L2VnHPOHcinz3DOOTcnr1bqfAWlrCztI0HQDRNxme6wSqCUShpRS0MCGeWgxq5aL71hhXJQYyaNqKQFBgrT1CxkNO5ioDDNVFKkFMRESqikBYZLRYajCWoWUktDoiBhTVdIQkBsIf3hNH3hTPa4hSQWkCKCvMVqIikRKaEUxJSDGgmirJhAKTULmUpKlIMa40mZvnAGgEgJM1agO6gykZSZSSNqFtIXzmT7pdnH1h1WqFm4//nKqjFjEakFLIlCBsIpEgJGoy5CjEApSwujbK0tojvI3ofEgv3H3BX3sby4jyAvM1fSiIFwigEgVMpMGkEINQvpDioMhNP7P4dKGlEKatQsZLgwTkJAzUJC0uz9V41IMTUrMJ6WiZQwlRb3xxWSUg5qDKUTJKWAnqDCUGGSUlBjKi1m2yclovyzWRyNk1rAQDjFeFomxOjNP4eptLj/PQlJmbEIgLJqjCbd++OMlOyPI1Kyf91o3A3Mvs4qAEOFCfbEvZSD2gHHCTG6gwoJwf73YCIpEyhlNO6mHNRYVJgktYCZrgJlxeyJe4iUTcEfBTHdQZXUAqbSIgCJBYRKs884qDJjhf3nSZCvB/a/d5U02r/d/udVwkSSvc/1+/QFM0ylJYYKE0ym2YWyVhX37D9egNGTv57N1SG6gwpLo9H9n2ekhJCUqJAQKWa4MJ6frxFD4QQVi/a/ryEpy6O9VCwiwAiVEpDuvz973ncHFdL8+WclFpAQUFRMSTXG0y7KqrE0GqVmIQHGosIkNQv3n6eREmrFMP9fLJAgQoxIMQnB/vemJU7+3LDwk4Nzzp1o3pXVOefcoTw5OOecO4AB6VG3WvA8OTjnXBOEofTkzw7BfAfgnHMLjlljy1FIWi3pbknrJT0h6fqDHv9PkkzScN26GyRtkPS0pKva8OoALzk451xzWlutFAMfNrOHJfUBD0m6y8yelLQa+EHgxdmNJZ0HXAOcD6wAvirpLDNrYVesjJccnHOuSTJraDkaM9tqZg/nt8eB9cDK/OH/BvwGB3acvRq42cwqZrYR2ABc1srXNsuTg3PONavxaqVhSQ/WLdce7iklrQEuAe6X9E7gJTN79KDNVgKb6u5v5uVk0lJereScc01pauK9XWZ26dE2ktQL3Ap8iKyq6beBt8616dwBtZ4nB+eca4YBLZxbSVJElhi+aGa3SboQWAs8KglgFfCwpMvISgqr63ZfBWxpWTB1vFrJOeea1Ko2B2Xf/p8F1pvZJwHM7HEzW2Jma8xsDVlC+D4z2wbcDlwjqSRpLbAOeKAdr9FLDs4516zWjZC+AvgA8LikR/J1HzWzr8x9WHtC0i3Ak2TVT9e1o6cSeHJwzrnmGJC2JjmY2X3M3Y5Qv82ag+7fCNzYkgCOwJODc8415dS4ElxHtjlIels++m+DpN+a73icc+4AadrYsoB1XHKQFAJ/AbwdOA/48XxUoHPOzb/ZaqVGlgWs45ID2Wi/DWb2nJlVgZvJRgU651wHMLC0sWUB68TkcNQRgJKunR1xOLW3ckKDc865Vk2818k6MTkcdQSgmd1kZpea2aXdi0onKCznnOOUqVbqxN5KJ2wEoHPOHZMFXipoRCcmh28D6/LRfy+RTU/7E/MbknPOzbIF3xOpER2XHMwslvQrwJ1ACHzOzJ6Y57Cccy5jeHKYL/nQ8TmHjzvn3LzzaiXnnHOH8OTgnHPuQAu/J1IjPDk451wzDGyBD3BrhCcH55xrVuLJwTnnXD3zrqzOOefm4g3SzjnnDmZecnDOOXeghT+pXiM6ceI955zrXAYkSWPLUUhaLeluSeslPSHp+nz970t6TNIjkv5V0oq6fW7IL4T2tKSr2vUyPTk451wTDLDUGloaEAMfNrNzgdcC1+UXN/v/zOwiM7sY+DLwuwD5Y9cA5wNvAz6VXyCt5Tw5OOdcM6x1F/sxs61m9nB+exxYD6w0s7G6zXp4+bIFVwM3m1nFzDYCG8gukNZy3ubgnHNNarBU0BRJa4BLgPvz+zcCPw2MAm/KN1sJfKtut0MuhtayeGyBN6xIGgeenucwhoFd8xwDeBwew9w8jpedbmYjx/MEku4gey2NKAMzdfdvMrOb5njOXuAe4EYzu+2gx24Aymb2MUl/AXzTzL6QP/ZZ4CtmdusxvJQjOhlKDk+b2aXzGYCkB+c7Bo/DY/A4Tgwze1srn09SBNwKfPHgxJD7G+CfgY9xAi+G5m0Ozjk3TyQJ+Cyw3sw+Wbd+Xd1m7wSeym/fDlwjqZRfEG0d8EA7YjsZSg7OObdQXQF8AHhc0iP5uo8CPy/pbCAFXgD+HwAze0LSLcCTZD2drjOzo/eZPQYnQ3I4pP5uHnRCDOBx1PMYXuZxdCgzuw/QHA8d9mJnZnYjcGPbgsot+AZp55xzredtDs455w7hycE559whPDk455w7xIJIDnl3r/mO4dwOiOHDkt6a356390TSQN3t+YzDzwv8vHDt0dHJQdLVkj4PvHKe4/hz4Cv58Pb5OP5bJd0J/CbZcHpsHnoSSHpz3t3u05I+Oo9x+HmBnxeuvTquK6skmZlJehPw+0ANuFzSC2a290TGULdqCNgLvEXS/zKzyomIAYjIZmN8A/D/AkXg1fmIyvhE/gPmw/s/SvaZPAB8XlK3mf3OCTq+nxf4eeFOnI4qORz0z7cRuAr4CPAa4KITHUPdVLjfAj4N/CTZiMQTEoOZVYF/MrMrzewrZF9E15hZ7QR/AQRAL7AJ+I6ZbQJ+AfgxSeecgOP7eYGfF+7E6pjkIOlXgNsk/ZqkZWb2fD6d7deB7cAbJLVl9sE5YviQpBVmlkgqks2b/iXgbrKh6++RdFyTdzUQw69JWm5m387XR2Z2D/CcpLe349gHxfHLkt4LYGYp2ZTBI2RfBpjZc2TvySfy7dtSx+znxSEx+HnhToiOSA6S3g18EPj/yX4J/o6ki+s2+SJwFtkvxfr9WnbiHRTDK4GPSnpV/ivtQTPbBTwD/CrZ6MSWn/RzvA+/LWm2Xj2WNEQ2lL4tw+XzGPok/SVZtcXnJRUAzGw72ZD9D9Vt/lvAaySd345frH5ezBnDKX9euBOjI5ID2T/3p83sbuDjZFUHvzr7oJk9BnwbuCBv/PrNfH0rT7y5Yvil/LF3SPo3soa/fySrThib60naEMP1kL1WM9sDdJHP7Z4X61sqv+DIPWa2jOwKVH9R9/AngIsl/ZCkUv7L8ctkdeDt4OfF4WM4lc8LdwKc0ORw8C+6uvvPAT8BYGYvkE1P2yPpnXWb/y1Zfebfkc+lfiy/EJuMYVDS5cCfAf/HzC42s58GlgHH3IXxON+HLwCXSSrn/4TH7Ahx3J7//RDw48pniDSzCeCPyS5T+FFJnwCuBLYeTxxHiOuEnRdNxtCW86LJGNp2Xhwhjnk9L9yJdaJLDgf8kqj7hfcPwJSkq/P7W4FvAOcp00v2j/g4cJGZfeSg/dsVw9eB15PNs/6bdbu928y+cwzHPpYYvkH+PuTruoCbaU0VwpxxmNmkpMDMtgGfAj5Tt83NwH8hqz4ZAd6eVy0cM0mzX+phfRycwPOiyRjacl4c6/uQr2vZeXG4OE70eeHmmZm1fQEuBf6e7B/5dUCYrw/yvwJ+FriDlycD/Ajw8fx2AVgyTzF8LL8dzm47XzHMxtHOz6MulqBu+xeBy8l+Gb9mNtbjjEFAN9kv//sOfuxEnBfHGUNLzotWxNCK8+JocZyo88KXzlnaWnLIf939IfCXZHWQ24FfAU6D/b0dIPvVcyfZL6KbJK0gu5ZqLd8uNrMd8xRDnG+X2DEW11sVw2wcxxJDo3GYWZr/Ih+o2/WPgH8H7iW77CGWfxMcK8tM5XdHJP1SHmNY99xtOy9aEMNxnxetimE2jmONoZE4TtR54TpIu7MP8HZgUX57Odkvk966xz9BduJfQjao6A/IisyfogW/kj2GY4rjDuDKuu2fAv4EiFoYh/Lj/ylZ6eUxYPAEfyYeQ+Nx/N6JOC986Zyl9U+Yjdp8zRzrryTrbvfN/AR8E1nf6L8Bzjxo226P4fhjaEUcwHnA6lbGwYFVE/9Idh3cPwf+EDgd6MnjOKNdn8mpHEMr4mjVeeFL5y6teyLoA24D9gCf4+Vfp7N1lecDb8pv/yzwP4G1dfsfV32+x9DyOFr1y3jOOPLHzgI+md/+EbJuoI8etH/bPpNTLYYWxdGy0oovnb20ss2hStaL46eALcD74eX6dDN7wrJ+2gD35CdpDbJ+2daa7nceQ+viaNWAqjnjyG0BzpJ0O1n1xD1kffipi6Ntn8kpGEMr4mjbQDvXWY4rOUj6aUlvkDRo2aRjnwG+CnwPuFTSWfl2B/c7f2t+7HE4oEHWYzgOCy0OsoS0hawf/6vM7EeAVZJedbxxeAydF4dbWJq+hnT+xbKMrA4yBZ4lq5O83rKpBMgHx3wQmDGzP8jXlcjquf8IeAn4DTN76piC9hgWehwVM/v9fN2AmY3WPc8B9z2GY9MpcbiFq6mSQ133uj7gJTP7AeCXyeov/2p2OzN7BngIWCHpzPyLKCXrOvkxM3vncXwpewwLP47leRxdwEz+HEG+zbF+KXsMHRaHW9gaup6Dskm2PgGEkr4C9JOPxDSzWNKvAlskvcGyGSIxsy8pu0rWHWS9YN5kZo+TjWZtmsdw8sYBrD/WKguPofPicCcJO0qLNVmXt0fJ5q3/RbIBL28jGx15Wd12vwTcXXf//cAk8N85/tHNHoPH4TEsgDh8OXmWo2+Q1Ut/oO7+p/IT7GeAh/J1AVn95i3k3SHz/a5sSZAeg8fhMSyIOHw5eZZG2hweAm7Ry1e/+nfgNDP7a7Li63+0rAi6CkjMbCOAmf2bmf1bA8/fCI/B4/AYFkYc7iRx1ORgZlNmVrGX+zf/ILAzv/2zwLmSvkw2DcPD7QjSY/A4PIaFEYc7eTTUIA37p+81YCkvz+s+TnZx8QuAjWb2Ussj9Bg8Do9hwcXhFr5murKmZPP/7wIuyn+F/GcgNbP7TtAJ5zF4HB7DwojDLXTNNFAAryU7+e4Dfr4djSAeg8fhMZwccfiysJemRkhLWgV8gGxyrkrTmagFPAaPw2NYGHG4ha3p6TOcc86d/E70NaSdc84tAJ4cnHPOHcKTg3POuUN4cnDOOXcITw5uQZC0WNIj+bJN0kv57QlJn5rv+Jw72XhvJbfgSPo4MGFmfzLfsTh3svKSg1vQJL0xHwWMpI9L+rykf5X0vKT3SPpjSY9LukNSlG/3Kkn3SHpI0p2Sls/vq3Cu83hycCebM4B3AFcDXyC7dsGFwDTwjjxB/DnwPjN7FfA54Mb5Cta5TtXwxHvOLRD/YmY1SY8DIdkVziC74t0a4GyyCejukkS+zdZ5iNO5jubJwZ1sKgBmlkqq2cuNainZ+S7gCTO7fL4CdG4h8Gold6p5GhiRdDmApEjS+fMck3Mdx5ODO6WYWRV4H/BHkh4FHgG+f36jcq7zeFdW55xzh/CSg3POuUN4cnDOOXcITw7OOecO4cnBOefcITw5OOecO4QnB+ecc4fw5OCcc+4Qnhycc84d4v8CVEo7oSwBX6sAAAAASUVORK5CYII=\n",
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+bcD/ADCzJyTdAjxJ1tPpOjNLWhVMvY5KDmRv1Dsl/RBQBvolfcHMfmqe43LOOWD2MqGtGQRnZvcxdzsCwJ8dZp8byard26qjqpXM7AYzW2Vma8j68n7dE4NzrtO0o7dSp+m0koNzznW2Bkc/L3QdmxzM7BvAN+Y5DOecO4Bf7Mc559ycvOTgnHPuAAbE6cJuT2iEJwfnnGuCX+zHOefcnLzNwTnn3IHM2xycc84dpJWD4DqZJwfnnGuSJwfnnHMHMETivZWcc84dzBuknXPOHcC8Qdo559xczJODc865A/kgOOeccwcx8AbphaDWAzteJZLhmML2Qbq2w46fXExcCRm8N2LTW4dIexKGHiqw5X29JFMFwu6Y4MUCaX+NLa8PSXsiep4NmDl/GgTp3iLBoipdj3Wx6QcLWMEIl1ZItncRjYs9FwUU90J6wQS1RExOFin2V6hORsT7ItLBmHQyZHx5TGFbiXhplQ9837f4503ns3trP5ee+SLFIKaaFjitew+VtEA1LbBxfIhimLCmZw+9ayvsmO5lbKbMWFxmrFJmed8YU3GRpaUxLlq6ha/tPIfvG9zEGYO7WBKNEWAsLk2wr1rm9J49rCzuZVetly2VAVaV97G90k9/sUJ3WKWvMMNrhvbykE4DIFLMjy26n9uDS4j6EpZGo0yeW+SS/k1c0LWJKy98mjv2XkSglMv6NvLk9Eqe6RvjtUPP8eT4CordNS5evoXYAr63a4Rl/WNs2rOInq4K41NlqlHK8Pl7GJsuM7qjl55nI0qnjzOxLEKbysT9CcFMQLR6gj2beknLondDwORpKWNnFmBkht5vd7Ho6RrTIwV2XwRcuYiky+h/tovSXmPnqwvQWyLaUiQ5Xaz5TMLWK0pMLw8pL51iZqxE1FOjtrtMOFhl8R29jJ4hel6C0mhK3B0QT4sX3j3M9OqYwsAU9mI3w4+FVAZE/wtidG2BfRfGDC4bY+fOPqj2EQ5OUSrVmHhoACXZL8rqINhFBWz5NKtX7GD7RB+7nlxEvLTK4pFxeopVNnctoq9/mmKY0LV8DzvHeukJjDBISZYHlIs1zhnawVA0RSUtsLZ7J/ftOpPVp+/ijP7dbJ4cYG3fHh7btYJqUuCpiWUMRgOcs2QH355Yy3QSsbJvlHN6t7GytJc39q9n50wvhSDl+hX/yn2TZzMadxEqJbWAC/q3MBZ3MRBO8ctr7uHhyTV8X8/zfG9mGe89+xHu33U6k7Uia0o7ecOZz5BaQBQkvGXp03zu26+jZ9E0l614gYlamUDGmxY9xeBF09zz0hn0lSucf942XpoaYNvqPl7RM85weZKJuMTu6W7OGdzOS1ODPL93iHVDu/nezhEKF0wxumEEC0IK05CUYOdFBSrn1UjHilBKWPll2PiuHuIlEYXuKVYsHmXTk8uIxgImLqyBQdeGItMjfVT7W/SlY1m7w8luwScH55w70by3knPOuQMYp0aD9Mlfceaccy2VNUg3shz1maTVku6WtF7SE5Kuz9dfLOlbkh6R9KCky+r2uUHSBklPS7qqXa/SSw7OOdekFrY5xMCHzexhSX3AQ5LuAv4Y+D0z+xdJP5Tff6Ok84BrgPOBFcBXJZ1lZknLIsp5cnDOuSaYQdqi3kpmthXYmt8el7QeWElWezXbhD4AbMlvXw3cbGYVYKOkDcBlwDdbElAdTw7OOdekJsY5DEt6sO7+TWZ201wbSloDXALcD3wIuFPSn5BV/39/vtlK4Ft1u23O17WcJwfnnGtSE9VKu8zs0qNtJKkXuBX4kJmNSfoD4NfM7FZJPwp8FngLzNlNqi0da71B2jnnmmSmhpZGSIrIEsMXzey2fPUHgdnbf09WdQRZSWF13e6reLnKqaU8OTjnXBOMxhJDI8lBkshKBevN7JN1D20B3pDffjPwTH77duAaSSVJa4F1wAMte3F1vFrJOeea0dpZWa8APgA8LumRfN1HgV8E/kxSAZgBrgUwsyck3QI8SdbT6bp29FQCTw7OOde8FtXym9l9zN2OAPCqw+xzI3BjayI4PE8OzjnXpFNhhLQnB+eca5JPvDcPJJWBe4ESWXz/YGYfm9+onHMu43MrtYCkHklBfvssSe/Mu20dSQV4s5m9ErgYeJuk17YzTueca5gBpsaWBazdXVnvBcqSVgJfA34W+Osj7WCZifxulC+nQCHOObdQWNrYspC1OznIzKaA9wB/bmbvBs476k5SmHfr2gHcZWb3H/T4tflMhQ+mk5NtCdw55+bWunEOnaztyUHS5cBPAv+crztqO4eZJWZ2Mdnov8skXXDQ4zeZ2aVmdmnQ09PyoJ1z7oiswWUBa3dyuB64AfhSPnjjFcDdje5sZvuAbwBva094zjnXJGvt9Bmdqq29lczsXrJ2h9n7zwG/eqR9JI0ANTPbJ6mLbLKpP2pnnM4515QFXipoRFuTg6SzgP8ErKk/lpm9+Qi7LQc+LykkK9ncYmZfbmeczjnXnIVdKmhEu8c5/D3wl8BngIbm/zCzx8jmNHfOuc60wHsiNaLdySE2s0+3+RjOOXfizI5zOMm1u0H6f0v6ZUnLJQ3NLm0+pnPOtZVZY8tC1u6Swwfzvx+pW2fAK9p8XOeca58F/sXfiHb3Vlrbzud3zrl5cQpUK7W7t1IE/BLw+nzVN4C/MrNaO4/rnHNtYyBvkD5unyabG+lT+f0P5Ot+oc3Hdc65Nln4k+o1ot3J4dX57Kqzvi7p0TYf0znn2usUaHNod2+lRNIZs3fy6TPacr1T55w7YVo0t5Kk1ZLulrRe0hOSrs/X/52kR/Ll+brrSyPpBkkbJD0t6aojPLckrT7Wl9juksNHgLslPUc2pPB0smm7nXNu4WpdySEGPmxmD0vqAx6SdJeZ/djsBpL+KzCa3z4PuAY4H1gBfFXSWWZ2yI9uMzNJ/8hhrkV9NO3urfQ1SeuAs8mSw1NmVmnnMZ1zrq1aOAjOzLYCW/Pb45LWAyuBJyH79Q/8KDA75dDVwM359+hGSRuAy4BvHuYQ35L0ajP7drOxtSU5SHqzmX1d0nsOeugMSZjZbe04rnPOnQjt6K0kaQ3Z1EH116+5EthuZs/k91cC36p7fHO+7nDeBPwHSS8Ak2Q/0s3MLjpaPO0qObwB+DrwI3M8ZoAnB+fcqWBY0oN1928ys5sO3khSL3Ar8CEzG6t76MeBv63fdI5jHKmS6+3NBFuvLcnBzD6W3/yEmW2sf0ySD4xzzi1oarzNYZeZXXrE58rGg90KfLG+VkVSgewqmvVtBpuB+kbmVcCWOZ6zP08y4w1HepB291a6dY51/9DKA4RVCKcFyop6aQSVvWVKT3UxvQTClVOoK2b3q2vYrhIKjWIpJqwCMwGFZdOoFjCzxEhmChSihGCwSlSKmTp/hqQ/Ju1OWLdsJ+e88kW6X7kHUlG+dA9rh3dz+sgeFCUsGRin3FulsHKKoeFxgsEqA0OTxMsq9C2aohzUuHB4K+VFM3SHVZ7dN8yLo4M8P7mYxdEkG8eHmKoVeXbnMGNxmWf3DZOauOGsf+Gtg9/lspEXKAYJF/a/RHdY5f6Na1jZPcrOah8Ay6O9vKf3BS7vf5Z1/TuZSiJe072B87tf4vSuPQwVJvn5kXtZ1bWX4dIEb+5/krWlnRSClJkk4qxoB5tqiwB4Xe/T7Ir7iNOQlyqDhKRsiwd5ct9SUgsYDCfpD6d5cusyyoqZiEtUxktsnhhgRXmUqJC1jaUmVvWNMtQ7RVBIefvyJzl78U7Crpips6qsWrQvu9buadOURqZ46+u+QxIHpENVlpy+h4l1MdYXky6qsXxklPEzUna8KmJ6WBSmRDQuCIyxdQk7vz8BE8XuKlo3QTJV4IV3FBl83XbCoQrVF3uxOGCwdworpiSVkIl3jTH0mu1MLYNtV8Doa2fY9e5pps+s0D0ySX/vDPFgwo5XGyPvfZEdPzPN6KUVeoYnWTO4FwKjb/k4xWLMuuFdpJeMU7h8L1NnVwnOmCCNjGS6wIWDWzl9YC/hGRP0DU7zmqUvMNI1SamrxujOPkpRTC0JKYQp541sp1IrEAYp5wztIMB4fO9yXpoaYEW0j96oyo7RPqbiIr+79stctehxzh3azoqe7MfmSHGcqTjitNIeRooTLO8eI5DxU33Psi/pIZDxrpGH6QuqLI1G2Rt3896Bh1hR2Esg4+zurTw2sZqRcIz+cJpvjp3JO/ofZV+tiwsGt/Gbr7iDclBjMJpmaWmMKwe+x1Bhgr7Fk3xw3f28cfBpimHM6d276QuneWpsCWZi51gv71r8EG8YfobxqTLnDmzn4W2rWL9jCdtG+6mkBZ7esYQwTHlm1zC95QrlQo2RNXsY+ZFNTLxlgulhqF04SVhIiYZmWL5iL9veV2Xgwl10D05TKsVMVEooFtUVNcBYtmwflWFj8rSEwnQLv3hMjS1HkbcpfBZYb2afPOjht5C1026uW3c7cI2kUv5Dex3wwBxP/Tf534eAB+uW2ftH1a42h3PIWtMHDmp36AfK7Timc86dEK29BOgVZIODH6/rrvpRM/sKWa+k+iol8itq3kLWYB0D1x2mp9IP53/X5pOdrqPJ7952tTmcDfwwMMiB7Q7jwC+26ZjOOXditCg5mNl9HObKQWb2M4dZfyNwYyPPL+kXyC7XvAp4BHgt8H+AHzjavu1qc/gn4J8kXW5mh+ti5ZxzC9ICmlvpeuDVwLfM7E15rc7vNbJjuwfBfUfSdWRVTPuLNGb2c20+rnPOtc/CmT5jxsxmJCGpZGZPSTq7kR3b3SD9v4BlwFXAPWRFm2NuPXfOufkma3zpAJslDQL/CNwl6Z+Yo3fTXNpdcjjTzN4v6Woz+7ykvwHubPMxnXOuvRbIrKxm9u785scl3Q0MAHc0sm+7k8PsdRv2SboA2AasafMxnXOuvTqjVNAUM7unme3bnRxukrQI+B2y/rm9wO+2+ZjOOddWC6hB+pi1e+K9z+Q378WvG+2cOxl0TntCW7W1QVrSf8kbQ2bvL5L0B+08pnPOtV2LrufQydrdW+ntZrZv9o6Z7QV+qM3HdM659joFkkO72xzCvG9tBUBSF1Bq8zGdc66tToVqpXYnhy8AX5P0P8jy6M8Bn2/zMZ1zzh2ndjdI/7Gkx8hmFwT4fTM74jiH/Jqn/5Ns8FxKNv/5n7UzTueca5h5b6VW+Q4QkZUcvtPA9oe7puqT7QzSOecadgpUK7W7t9KPks01/j6y66DeL+l9R9rHzLaa2cP57XFg9pqqzjnXGbxB+rj9NvBqM9sBIGkE+CoNXvDnMNdUdc65eSO8QboVgtnEkNtNg6WVI1xTFUnXAtcCRH2LWhSqc841yJPDcbtD0p28fDWjHwO+crSdDndN1Vn5BbpvAuheuvoU+Jiccx3jFBkh3e7eSh+R9F6yS+GJrOfRl460z1Guqeqcc/PPeysdPzO7lawU0KgjXVPVOefmnZccjpGkceaulRNgZtZ/uH2PdE1V55zrCC1KDkca1yXpPwK/Qta9/5/N7Dfy9TcAPw8kwK8ebezYsWrXNaT72vG8zjk371rbTXXOcV3AUuBq4CIzq0haAiDpPOAasksvrwC+KuksM0taFlGu3RPvOefcSadVlwk9wriuXwL+cHZeurpen1cDN5tZxcw2AhuAy1r/Cj05OOdc05Q2tgDDkh6sW6497HMeOK7rLOBKSfdLukfSq/PNVgKb6nbbTJsGCZ+I6TOcc+7k0ni10i4zu/RoGx08rktSAVgEvBZ4NXCLpFcwd3tsW5rHPTk451wzWjw1xmHGdW0GbjMzAx6QlALD+frVdbuvAra0LpqXebWSc841QU0sR32uw4/r+kfgzfk2ZwFFYBdwO3CNpJKktcA6svnrWs5LDs4516zWlRzmHNcFfA74nKTvAlXgg3kp4glJtwBPkvV0uq4dPZXAk4NzzjWtVYPgjjKu66cOs8+NwI2tieDwPDk451yzfPoM55xzB/CJ95xzzs3Jk4NzzrmDecnBOefcoU6B5KCsd9TCNXLeYvuJL76VyaTE3mo3qYk4DSiHMQPFaSbjIgBTcZFARjms7b9fCFLKYY2JWok4DSgEKamJ/uIMqYmCUvZVu/ZvF+Q/F3bN9NAfVfY/X5CPk2CjIpIAABUbSURBVA9lJCZmkohqWqAYxMRpSIpITZTDGqkFFIKEACOQkZqopgUGomkqaYGZJGIgmgZgMilm+5voLlSZSSIA+qIZKkmBnkIWQymISU2kFlCzgN5ClbFamUDp/ttdYZXUAhLEZFwiUEpXWKM3rFAKYqbTIosKk0wl+fsUZK+rrJgZK+z/W0sL9IUzzFiBibhMKYippAVqFlKzkOFogr1xDyXVSAiopAVCGQUlxBYyViszVJykkhaI0xCAUhhTSwMWR5OMJV3sq3UxXJzghakhlpTGqVn2HsQWMhkXWdk1yvaZPkphTIAxmRQpBjG9YTWPJSC1gK6wmr22pLh/XX9hhgQxnUSUghiA0VoX+6pdrO7et/+8mk4iCkFCatlQoP7CDNNpRDGIiZStn8g/n1IYMxkXqaYFFhcnSRGT+fmWmugrVAiUsrfaQzGISRE9eayzn0OI7X++nkKFKD+napa9h/2FmTyuIoFSYgupJAW6whopIjHt/yxCGbU0ZDCaomYh1bRAgJEiCnnsg9EUKQFdQZV9te7s/CCgkhTy1ztNlJ+nu2s9AHSFNcpBjVJQo5YWSBE7qn3Ze5NELI4mCZUSKKWSRnQHVfbEPXQHVSaSEt1hNXtNabj/HANI8tc4lRaJlBApoWbh/v+37iDbrxzU2FHrpyuoUg5qjMZd+8/drnybsbhMagGB0v2f/ez7DPBXl37hoUZGLB9J95LVds57f72hbb/zl79+3MebL15ycM65ZhjeW8k559yBhLc5OOecm4snB+eccwfTAm+rbYQnB+eca0aLZ2XtVJ4cnHOuSfIGaeeccwfzBmnnnHOH8uTgnHPuAD7xnnPOuTl5cnDOOVfPB8E555ybk9KTPzsE8x2Ac84tKNbEchSSVku6W9J6SU9Iuj5f/3FJL0l6JF9+qG6fGyRtkPS0pKta/fJmdVzJQdLngB8GdpjZBfMdj3POHayF4xxi4MNm9rCkPuAhSXflj/03M/uTA44rnQdcA5wPrAC+KuksM0taFlGuE0sOfw28bb6DcM65w2pRycHMtprZw/ntcWA9sPIIu1wN3GxmFTPbCGwALjvm13EEHZcczOxeYM98x+Gcc4cja2xp6jmlNcAlwP35ql+R9Jikz0lalK9bCWyq220zR04mx6zjkoNzznU0yxqkG1mAYUkP1i3XzvWUknqBW4EPmdkY8GngDOBiYCvwX2c3nTui1uu4NodG5G/wtQC9y3rmORrn3Cmn8a/jXUe7EpykiCwxfNHMbgMws+11j/934Mv53c3A6rrdVwFbGo6mCQuy5GBmN5nZpWZ2aXlRab7Dcc6dQmbHObSiWkmSgM8C683sk3Xrl9dt9m7gu/nt24FrJJUkrQXWAQ+06KUdYEGWHJxzbt6YZUtrXAF8AHhc0iP5uo8CPy7pYrIyyvPAf8gObU9IugV4kqyn03Xt6KkEHZgcJP0t8EayurrNwMfM7LPzG5Vzzr2sVSOkzew+5m5H+MoR9rkRuLE1ERxexyUHM/vx+Y7BOeeO6OQfIN15ycE55zqagZKTPzt4cnDOuWad/LnBk4NzzjXLZ2V1zjl3qNb1VupYnhycc65JXnJwzjl3oAYn1VvoPDk451wThPdWcs45Nwd5m4NzzrkDeLWSc865Q7V0bqWO5cnBOeea5L2VnHPOHcinz3DOOTcnr1bqfAWlrCztI0HQDRNxme6wSqCUShpRS0MCGeWgxq5aL71hhXJQYyaNqKQFBgrT1CxkNO5ioDDNVFKkFMRESqikBYZLRYajCWoWUktDoiBhTVdIQkBsIf3hNH3hTPa4hSQWkCKCvMVqIikRKaEUxJSDGgmirJhAKTULmUpKlIMa40mZvnAGgEgJM1agO6gykZSZSSNqFtIXzmT7pdnH1h1WqFm4//nKqjFjEakFLIlCBsIpEgJGoy5CjEApSwujbK0tojvI3ofEgv3H3BX3sby4jyAvM1fSiIFwigEgVMpMGkEINQvpDioMhNP7P4dKGlEKatQsZLgwTkJAzUJC0uz9V41IMTUrMJ6WiZQwlRb3xxWSUg5qDKUTJKWAnqDCUGGSUlBjKi1m2yclovyzWRyNk1rAQDjFeFomxOjNP4eptLj/PQlJmbEIgLJqjCbd++OMlOyPI1Kyf91o3A3Mvs4qAEOFCfbEvZSD2gHHCTG6gwoJwf73YCIpEyhlNO6mHNRYVJgktYCZrgJlxeyJe4iUTcEfBTHdQZXUAqbSIgCJBYRKs884qDJjhf3nSZCvB/a/d5U02r/d/udVwkSSvc/1+/QFM0ylJYYKE0ym2YWyVhX37D9egNGTv57N1SG6gwpLo9H9n2ekhJCUqJAQKWa4MJ6frxFD4QQVi/a/ryEpy6O9VCwiwAiVEpDuvz973ncHFdL8+WclFpAQUFRMSTXG0y7KqrE0GqVmIQHGosIkNQv3n6eREmrFMP9fLJAgQoxIMQnB/vemJU7+3LDwk4Nzzp1o3pXVOefcoTw5OOecO4AB6VG3WvA8OTjnXBOEofTkzw7BfAfgnHMLjlljy1FIWi3pbknrJT0h6fqDHv9PkkzScN26GyRtkPS0pKva8OoALzk451xzWlutFAMfNrOHJfUBD0m6y8yelLQa+EHgxdmNJZ0HXAOcD6wAvirpLDNrYVesjJccnHOuSTJraDkaM9tqZg/nt8eB9cDK/OH/BvwGB3acvRq42cwqZrYR2ABc1srXNsuTg3PONavxaqVhSQ/WLdce7iklrQEuAe6X9E7gJTN79KDNVgKb6u5v5uVk0lJereScc01pauK9XWZ26dE2ktQL3Ap8iKyq6beBt8616dwBtZ4nB+eca4YBLZxbSVJElhi+aGa3SboQWAs8KglgFfCwpMvISgqr63ZfBWxpWTB1vFrJOeea1Ko2B2Xf/p8F1pvZJwHM7HEzW2Jma8xsDVlC+D4z2wbcDlwjqSRpLbAOeKAdr9FLDs4516zWjZC+AvgA8LikR/J1HzWzr8x9WHtC0i3Ak2TVT9e1o6cSeHJwzrnmGJC2JjmY2X3M3Y5Qv82ag+7fCNzYkgCOwJODc8415dS4ElxHtjlIels++m+DpN+a73icc+4AadrYsoB1XHKQFAJ/AbwdOA/48XxUoHPOzb/ZaqVGlgWs45ID2Wi/DWb2nJlVgZvJRgU651wHMLC0sWUB68TkcNQRgJKunR1xOLW3ckKDc865Vk2818k6MTkcdQSgmd1kZpea2aXdi0onKCznnOOUqVbqxN5KJ2wEoHPOHZMFXipoRCcmh28D6/LRfy+RTU/7E/MbknPOzbIF3xOpER2XHMwslvQrwJ1ACHzOzJ6Y57Cccy5jeHKYL/nQ8TmHjzvn3LzzaiXnnHOH8OTgnHPuQAu/J1IjPDk451wzDGyBD3BrhCcH55xrVuLJwTnnXD3zrqzOOefm4g3SzjnnDmZecnDOOXeghT+pXiM6ceI955zrXAYkSWPLUUhaLeluSeslPSHp+nz970t6TNIjkv5V0oq6fW7IL4T2tKSr2vUyPTk451wTDLDUGloaEAMfNrNzgdcC1+UXN/v/zOwiM7sY+DLwuwD5Y9cA5wNvAz6VXyCt5Tw5OOdcM6x1F/sxs61m9nB+exxYD6w0s7G6zXp4+bIFVwM3m1nFzDYCG8gukNZy3ubgnHNNarBU0BRJa4BLgPvz+zcCPw2MAm/KN1sJfKtut0MuhtayeGyBN6xIGgeenucwhoFd8xwDeBwew9w8jpedbmYjx/MEku4gey2NKAMzdfdvMrOb5njOXuAe4EYzu+2gx24Aymb2MUl/AXzTzL6QP/ZZ4CtmdusxvJQjOhlKDk+b2aXzGYCkB+c7Bo/DY/A4Tgwze1srn09SBNwKfPHgxJD7G+CfgY9xAi+G5m0Ozjk3TyQJ+Cyw3sw+Wbd+Xd1m7wSeym/fDlwjqZRfEG0d8EA7YjsZSg7OObdQXQF8AHhc0iP5uo8CPy/pbCAFXgD+HwAze0LSLcCTZD2drjOzo/eZPQYnQ3I4pP5uHnRCDOBx1PMYXuZxdCgzuw/QHA8d9mJnZnYjcGPbgsot+AZp55xzredtDs455w7hycE559whPDk455w7xIJIDnl3r/mO4dwOiOHDkt6a356390TSQN3t+YzDzwv8vHDt0dHJQdLVkj4PvHKe4/hz4Cv58Pb5OP5bJd0J/CbZcHpsHnoSSHpz3t3u05I+Oo9x+HmBnxeuvTquK6skmZlJehPw+0ANuFzSC2a290TGULdqCNgLvEXS/zKzyomIAYjIZmN8A/D/AkXg1fmIyvhE/gPmw/s/SvaZPAB8XlK3mf3OCTq+nxf4eeFOnI4qORz0z7cRuAr4CPAa4KITHUPdVLjfAj4N/CTZiMQTEoOZVYF/MrMrzewrZF9E15hZ7QR/AQRAL7AJ+I6ZbQJ+AfgxSeecgOP7eYGfF+7E6pjkIOlXgNsk/ZqkZWb2fD6d7deB7cAbJLVl9sE5YviQpBVmlkgqks2b/iXgbrKh6++RdFyTdzUQw69JWm5m387XR2Z2D/CcpLe349gHxfHLkt4LYGYp2ZTBI2RfBpjZc2TvySfy7dtSx+znxSEx+HnhToiOSA6S3g18EPj/yX4J/o6ki+s2+SJwFtkvxfr9WnbiHRTDK4GPSnpV/ivtQTPbBTwD/CrZ6MSWn/RzvA+/LWm2Xj2WNEQ2lL4tw+XzGPok/SVZtcXnJRUAzGw72ZD9D9Vt/lvAaySd345frH5ezBnDKX9euBOjI5ID2T/3p83sbuDjZFUHvzr7oJk9BnwbuCBv/PrNfH0rT7y5Yvil/LF3SPo3soa/fySrThib60naEMP1kL1WM9sDdJHP7Z4X61sqv+DIPWa2jOwKVH9R9/AngIsl/ZCkUv7L8ctkdeDt4OfF4WM4lc8LdwKc0ORw8C+6uvvPAT8BYGYvkE1P2yPpnXWb/y1Zfebfkc+lfiy/EJuMYVDS5cCfAf/HzC42s58GlgHH3IXxON+HLwCXSSrn/4TH7Ahx3J7//RDw48pniDSzCeCPyS5T+FFJnwCuBLYeTxxHiOuEnRdNxtCW86LJGNp2Xhwhjnk9L9yJdaJLDgf8kqj7hfcPwJSkq/P7W4FvAOcp00v2j/g4cJGZfeSg/dsVw9eB15PNs/6bdbu928y+cwzHPpYYvkH+PuTruoCbaU0VwpxxmNmkpMDMtgGfAj5Tt83NwH8hqz4ZAd6eVy0cM0mzX+phfRycwPOiyRjacl4c6/uQr2vZeXG4OE70eeHmmZm1fQEuBf6e7B/5dUCYrw/yvwJ+FriDlycD/Ajw8fx2AVgyTzF8LL8dzm47XzHMxtHOz6MulqBu+xeBy8l+Gb9mNtbjjEFAN9kv//sOfuxEnBfHGUNLzotWxNCK8+JocZyo88KXzlnaWnLIf939IfCXZHWQ24FfAU6D/b0dIPvVcyfZL6KbJK0gu5ZqLd8uNrMd8xRDnG+X2DEW11sVw2wcxxJDo3GYWZr/Ih+o2/WPgH8H7iW77CGWfxMcK8tM5XdHJP1SHmNY99xtOy9aEMNxnxetimE2jmONoZE4TtR54TpIu7MP8HZgUX57Odkvk966xz9BduJfQjao6A/IisyfogW/kj2GY4rjDuDKuu2fAv4EiFoYh/Lj/ylZ6eUxYPAEfyYeQ+Nx/N6JOC986Zyl9U+Yjdp8zRzrryTrbvfN/AR8E1nf6L8Bzjxo226P4fhjaEUcwHnA6lbGwYFVE/9Idh3cPwf+EDgd6MnjOKNdn8mpHEMr4mjVeeFL5y6teyLoA24D9gCf4+Vfp7N1lecDb8pv/yzwP4G1dfsfV32+x9DyOFr1y3jOOPLHzgI+md/+EbJuoI8etH/bPpNTLYYWxdGy0oovnb20ss2hStaL46eALcD74eX6dDN7wrJ+2gD35CdpDbJ+2daa7nceQ+viaNWAqjnjyG0BzpJ0O1n1xD1kffipi6Ntn8kpGEMr4mjbQDvXWY4rOUj6aUlvkDRo2aRjnwG+CnwPuFTSWfl2B/c7f2t+7HE4oEHWYzgOCy0OsoS0hawf/6vM7EeAVZJedbxxeAydF4dbWJq+hnT+xbKMrA4yBZ4lq5O83rKpBMgHx3wQmDGzP8jXlcjquf8IeAn4DTN76piC9hgWehwVM/v9fN2AmY3WPc8B9z2GY9MpcbiFq6mSQ133uj7gJTP7AeCXyeov/2p2OzN7BngIWCHpzPyLKCXrOvkxM3vncXwpewwLP47leRxdwEz+HEG+zbF+KXsMHRaHW9gaup6Dskm2PgGEkr4C9JOPxDSzWNKvAlskvcGyGSIxsy8pu0rWHWS9YN5kZo+TjWZtmsdw8sYBrD/WKguPofPicCcJO0qLNVmXt0fJ5q3/RbIBL28jGx15Wd12vwTcXXf//cAk8N85/tHNHoPH4TEsgDh8OXmWo2+Q1Ut/oO7+p/IT7GeAh/J1AVn95i3k3SHz/a5sSZAeg8fhMSyIOHw5eZZG2hweAm7Ry1e/+nfgNDP7a7Li63+0rAi6CkjMbCOAmf2bmf1bA8/fCI/B4/AYFkYc7iRx1ORgZlNmVrGX+zf/ILAzv/2zwLmSvkw2DcPD7QjSY/A4PIaFEYc7eTTUIA37p+81YCkvz+s+TnZx8QuAjWb2Ussj9Bg8Do9hwcXhFr5murKmZPP/7wIuyn+F/GcgNbP7TtAJ5zF4HB7DwojDLXTNNFAAryU7+e4Dfr4djSAeg8fhMZwccfiysJemRkhLWgV8gGxyrkrTmagFPAaPw2NYGHG4ha3p6TOcc86d/E70NaSdc84tAJ4cnHPOHcKTg3POuUN4cnDOOXcITw5uQZC0WNIj+bJN0kv57QlJn5rv+Jw72XhvJbfgSPo4MGFmfzLfsTh3svKSg1vQJL0xHwWMpI9L+rykf5X0vKT3SPpjSY9LukNSlG/3Kkn3SHpI0p2Sls/vq3Cu83hycCebM4B3AFcDXyC7dsGFwDTwjjxB/DnwPjN7FfA54Mb5Cta5TtXwxHvOLRD/YmY1SY8DIdkVziC74t0a4GyyCejukkS+zdZ5iNO5jubJwZ1sKgBmlkqq2cuNainZ+S7gCTO7fL4CdG4h8Gold6p5GhiRdDmApEjS+fMck3Mdx5ODO6WYWRV4H/BHkh4FHgG+f36jcq7zeFdW55xzh/CSg3POuUN4cnDOOXcITw7OOecO4cnBOefcITw5OOecO4QnB+ecc4fw5OCcc+4Qnhycc84d4v8CVEo7oSwBX6sAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -484,7 +787,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,

From 0a07d6ea4345abaed5bbacbcc26a3975897d03b0 Mon Sep 17 00:00:00 2001
From: David Huard <huard.david@ouranos.ca>
Date: Wed, 27 May 2020 15:51:17 -0400
Subject: [PATCH 6/7] updated Backend notebook

---
 doc/notebooks/Backend.ipynb | 45 +++++++++++++++----------------------
 1 file changed, 18 insertions(+), 27 deletions(-)

diff --git a/doc/notebooks/Backend.ipynb b/doc/notebooks/Backend.ipynb
index 30155dae..e03afbd4 100644
--- a/doc/notebooks/Backend.ipynb
+++ b/doc/notebooks/Backend.ipynb
@@ -21,9 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -53,9 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ds_in = xe.util.grid_2d(-120, 120, 0.4,  # longitude range and resolution\n",
@@ -106,9 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "grid_in = esmf_grid(lon_in.T, lat_in.T)\n",
@@ -159,9 +153,10 @@
       "text/plain": [
        "  C_CONTIGUOUS : True\n",
        "  F_CONTIGUOUS : False\n",
-       "  OWNDATA : False\n",
+       "  OWNDATA : True\n",
        "  WRITEABLE : True\n",
        "  ALIGNED : True\n",
+       "  WRITEBACKIFCOPY : False\n",
        "  UPDATEIFCOPY : False"
       ]
      },
@@ -187,6 +182,7 @@
        "  OWNDATA : False\n",
        "  WRITEABLE : True\n",
        "  ALIGNED : True\n",
+       "  WRITEBACKIFCOPY : False\n",
        "  UPDATEIFCOPY : False"
       ]
      },
@@ -209,14 +205,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "filename = 'test_weights.nc' # weight filename\n",
     "if os.path.exists(filename):\n",
-    "    os.remove(filename) # ESMPy will crash if the file exists"
+    "    os.remove(filename) # ESMPy will complain if the file exists"
    ]
   },
   {
@@ -235,8 +229,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.06 s, sys: 382 ms, total: 7.44 s\n",
-      "Wall time: 7.57 s\n"
+      "CPU times: user 4.24 s, sys: 220 ms, total: 4.46 s\n",
+      "Wall time: 4.47 s\n"
      ]
     }
    ],
@@ -329,8 +323,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.35 s, sys: 662 ms, total: 3.01 s\n",
-      "Wall time: 3.09 s\n"
+      "CPU times: user 1.33 s, sys: 548 ms, total: 1.88 s\n",
+      "Wall time: 1.88 s\n"
      ]
     }
    ],
@@ -359,6 +353,7 @@
        "  OWNDATA : False\n",
        "  WRITEABLE : True\n",
        "  ALIGNED : True\n",
+       "  WRITEBACKIFCOPY : False\n",
        "  UPDATEIFCOPY : False"
       ]
      },
@@ -463,8 +458,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 443 ms, sys: 165 ms, total: 609 ms\n",
-      "Wall time: 620 ms\n"
+      "CPU times: user 529 ms, sys: 195 ms, total: 725 ms\n",
+      "Wall time: 722 ms\n"
      ]
     }
    ],
@@ -505,9 +500,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.testing.assert_equal(data_out_scipy, data_out_esmpy)  # exactly the same"
@@ -516,9 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "os.remove(filename)  # clean-up"
@@ -541,7 +532,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.2"
   },
   "toc": {
    "nav_menu": {},

From 21b0c7bbfc954868e8a40d2de47ba102bfa03e51 Mon Sep 17 00:00:00 2001
From: Raphael Dussin <raphael.dussin@gmail.com>
Date: Mon, 17 Aug 2020 15:35:59 -0400
Subject: [PATCH 7/7] re-enable legacy args

---
 xesmf/frontend.py            | 36 ++++++++++++++++++++++++++++++++++--
 xesmf/tests/test_frontend.py | 23 +++++++++++++++++++++++
 2 files changed, 57 insertions(+), 2 deletions(-)

diff --git a/xesmf/frontend.py b/xesmf/frontend.py
index d8b042e8..882f5634 100644
--- a/xesmf/frontend.py
+++ b/xesmf/frontend.py
@@ -104,6 +104,7 @@ def ds_to_ESMFlocstream(ds):
 
 class Regridder(object):
     def __init__(self, ds_in, ds_out, method, periodic=False,
+                 filename=None, reuse_weights=False,
                  weights=None, ignore_degenerate=None,
                  locstream_in=False, locstream_out=False):
         """
@@ -134,6 +135,17 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
             Only useful for global grids with non-conservative regridding.
             Will be forced to False for conservative regridding.
 
+        filename : str, optional
+            Name for the weight file. The default naming scheme is::
+
+                {method}_{Ny_in}x{Nx_in}_{Ny_out}x{Nx_out}.nc
+
+            e.g. bilinear_400x600_300x400.nc
+
+        reuse_weights : bool, optional
+            Whether to read existing weight file to save computing time.
+            False by default (i.e. re-compute, not reuse).
+
         weights : None, coo_matrix, dict, str, Dataset, Path,
             Regridding weights, stored as
               - a scipy.sparse COO matrix,
@@ -167,6 +179,7 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
 
         self.method = method
         self.periodic = periodic
+        self.reuse_weights = reuse_weights
         self.ignore_degenerate = ignore_degenerate
         self.locstream_in = locstream_in
         self.locstream_out = locstream_out
@@ -222,12 +235,27 @@ def __init__(self, ds_in, ds_out, method, periodic=False,
         self.n_in = shape_in[0] * shape_in[1]
         self.n_out = shape_out[0] * shape_out[1]
 
-        if weights is None:
+        # some logic about reusing weights with either filename or weights args
+        if reuse_weights and (filename is None) and (weights is None):
+            raise ValueError("to reuse weights, you need to provide either filename or weights")
+
+        if not reuse_weights and weights is None:
             weights = self._compute_weights()  # Dictionary of weights
+        else:
+            weights = filename if filename is not None else weights
+
+        assert weights is not None
 
         # Convert weights, whatever their format, to a sparse coo matrix
         self.weights = read_weights(weights, self.n_in, self.n_out)
 
+        # follows legacy logic of writing weights if filename is provided
+        if filename is not None and not reuse_weights:
+            self.to_netcdf(filename=filename)
+
+        # set default weights filename if none given
+        self.filename = self._get_default_filename() if filename is None else filename
+
     @property
     def A(self):
         message = (
@@ -265,11 +293,15 @@ def _compute_weights(self):
     def __repr__(self):
         info = ('xESMF Regridder \n'
                 'Regridding algorithm:       {} \n'
+                'Weight filename:            {} \n'
+                'Reuse pre-computed weights? {} \n'
                 'Input grid shape:           {} \n'
                 'Output grid shape:          {} \n'
                 'Output grid dimension name: {} \n'
                 'Periodic in longitude?      {}'
                 .format(self.method,
+                        self.filename,
+                        self.reuse_weights,
                         self.shape_in,
                         self.shape_out,
                         self.out_horiz_dims,
@@ -479,7 +511,7 @@ def regrid_dataset(self, ds_in, keep_attrs=False):
     def to_netcdf(self, filename=None):
         '''Save weights to disk as a netCDF file.'''
         if filename is None:
-            filename = self._get_default_filename()
+            filename = self.filename
         w = self.weights
         dim = "n_s"
         ds = xr.Dataset({"S": (dim, w.data), "col": (dim, w.col + 1), "row": (dim, w.row + 1)})
diff --git a/xesmf/tests/test_frontend.py b/xesmf/tests/test_frontend.py
index 6beab5fe..7f2e54c4 100644
--- a/xesmf/tests/test_frontend.py
+++ b/xesmf/tests/test_frontend.py
@@ -90,9 +90,32 @@ def test_existing_weights():
                                    weights=fn)
     assert regridder_reuse.A.shape == regridder.A.shape
 
+    # this should also work with reuse_weights=True
+    regridder_reuse = xe.Regridder(ds_in, ds_out, method,
+                                   reuse_weights=True, weights=fn)
+    assert regridder_reuse.A.shape == regridder.A.shape
+
     # or can also overwrite it
     xe.Regridder(ds_in, ds_out, method)
 
+    # check legacy args still work
+    regridder = xe.Regridder(ds_in, ds_out, method, filename='wgts.nc')
+    regridder_reuse = xe.Regridder(ds_in, ds_out, method,
+                                   reuse_weights=True,
+                                   filename='wgts.nc')
+    assert regridder_reuse.A.shape == regridder.A.shape
+
+    # check fails on non-existent file
+    with pytest.raises(OSError):
+        regridder_reuse = xe.Regridder(ds_in, ds_out, method,
+                                       reuse_weights=True,
+                                       filename='fakewgts.nc')
+
+    # check fails if no weights are provided
+    with pytest.raises(ValueError):
+        regridder_reuse = xe.Regridder(ds_in, ds_out, method,
+                                       reuse_weights=True)
+
 
 def test_to_netcdf(tmp_path):
     from xesmf.backend import esmf_grid, esmf_regrid_build