Repository: bird-house/finch (language: python)

Issue:
subset_bbox_dataset fails with output_format=csv
## Description   (updated: changed Finch version from 0.9.1 to 0.9.2)

subset_bbox_dataset fails with output_format=csv

## Environment

* Finch version used, if any: 0.9.2
* Operating System: finch docker image version-0.9.2

## Steps to Reproduce

Run subset_bbox_dataset on the bccaqv2 dataset with output_format='csv'.   Note: bounding box must be small, otherwise it will fail with OOM.

```
Traceback (most recent call last):
  File "/code/finch/processes/wps_base.py", line 43, in _handler_wrapper
    resp = self.wrapped_handler(request, response)
  File "/code/finch/processes/wps_xsubset_bbox_dataset.py", line 94, in _handler
    csv_files, metadata_folder = netcdf_file_list_to_csv(
  File "/code/finch/processes/utils.py", line 489, in netcdf_file_list_to_csv
    concat = pd.concat(data, axis=1)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/util/_decorators.py", line 311, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/reshape/concat.py", line 360, in concat
    return op.get_result()
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/reshape/concat.py", line 591, in get_result
    indexers[ax] = obj_labels.get_indexer(new_labels)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 3721, in get_indexer
    raise InvalidIndexError(self._requires_unique_msg)
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
Traceback (most recent call last):
  File "/code/finch/processes/wps_base.py", line 43, in _handler_wrapper
    resp = self.wrapped_handler(request, response)
  File "/code/finch/processes/wps_xsubset_bbox_dataset.py", line 94, in _handler
    csv_files, metadata_folder = netcdf_file_list_to_csv(
  File "/code/finch/processes/utils.py", line 489, in netcdf_file_list_to_csv
    concat = pd.concat(data, axis=1)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/util/_decorators.py", line 311, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/reshape/concat.py", line 360, in concat
    return op.get_result()
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/reshape/concat.py", line 591, in get_result
    indexers[ax] = obj_labels.get_indexer(new_labels)
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 3721, in get_indexer
    raise InvalidIndexError(self._requires_unique_msg)
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/conda/envs/finch/lib/python3.10/site-packages/pywps/app/Process.py", line 250, in _run_process
    self.handler(wps_request, wps_response)  # the user must update the wps_response.
  File "/code/finch/processes/wps_base.py", line 48, in _handler_wrapper
    raise ProcessError(f"Finch failed with {err!r}")
pywps.app.exceptions.ProcessError: Finch failed with InvalidIndexError Reindexing only valid with uniquely valued Index objects

```


Update to xclim 0.38
This branch is based on extend-dataset.

## Overview

This PR updates finch to use xclim 0.38.

Changes:

No changes were needed in the processes : all changes in finch come from changes in xclim.

For the documentation, the recent addition of a single reference bibfile in xclim made some acrobatics necessary. To correctly render the docstrings we parse from xclim, meaning having correct references, we need to have this reference file. The doc's conf downloads it each time it is run, using the installed xclim version to construct its github url.


Discussion / hints:
FYI there is a new Finch 0.9.2 docker image without numpy downgrade, and that starts properly without error that needed numpy downgrade.
Just tested it, and updated the issue description with the new version


Fix patch:
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index df3a1c0c..da3a8ae1 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -14,7 +14,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.7
+          python-version: 3.8
       - name: Install flake8
         run: pip install flake8
       - name: Run linting suite
@@ -29,7 +29,8 @@ jobs:
       - name: Setup conda and environment
         uses: mamba-org/provision-with-micromamba@main
         with:
-          cache-downloads: true
+          cache-downloads: false
+          cache-env: false
       - name: Install and test finch-wps
         shell: bash -l {0}  # Needed to get into the proper activated env with previous action.
         run: |
diff --git a/CHANGES.rst b/CHANGES.rst
index 417a9933..a1b51065 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -1,6 +1,18 @@
 Changes
 *******
 
+0.10.0 (unreleased)
+===================
+* Generalize ensemble datasets configuration.
+    - Datasets usable by ensemble processes are now specified through a YAML file which is pointed to in the configuration.
+    - As a consequence processes are generated according to the available variables. Similarly for the allowed values of some inputs on these processes.
+    - The output name now includes the dataset name (if a custom name was not specified).
+    - ``finch.processes.xclim`` was removed, there is no static module of processes.
+    - Input "rcp" has been renamed to "scenario".
+    - Input "dataset_name" has been fixed and renamed to "dataset".
+* ``finch.wsgi.application`` had been removed.
+* Update to xclim 0.38.0.
+
 0.9.2 (2022-07-19)
 ==================
 * Fix Finch unable to startup in the Docker image.
diff --git a/docs/Makefile b/docs/Makefile
index cd4cbcf9..764c950d 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -17,4 +17,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" -W $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/source/conf.py b/docs/source/conf.py
index e1207d99..551b8711 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -18,8 +18,13 @@
 # relative to the documentation root, use os.path.abspath to make it
 # absolute, like shown here.
 #
+from datetime import datetime
 import os
+from pathlib import Path
 import sys
+from urllib.request import urlopen
+from xclim import __version__ as xcver
+import warnings
 
 # Add finch to sys.path to avoid having to full
 # install finch for autodoc.
@@ -46,6 +51,7 @@
     "sphinx.ext.imgconverter",
     "nbsphinx",
     "IPython.sphinxext.ipython_console_highlighting",
+    "sphinxcontrib.bibtex"
 ]
 
 # To avoid having to install these and burst memory limit on ReadTheDocs.
@@ -75,14 +81,29 @@
         "slugify",
         "spotpy",
         "statsmodels",
-        "unidecode",
         "zlib",
     ]
 
+# Bibliography stuff, for correct xclim docstring formatting
+# We need to download the reference file from xclim for the correct version.
+bibfile = Path(__file__).parent / 'references.bib'
+if not bibfile.is_file():
+    try:
+        r = urlopen(f"https://github.com/Ouranosinc/xclim/raw/v{xcver}/docs/references.bib")
+    except Exception as err:
+        warnings.warn(f'Unable to download xclim references file, docstrings will be incomplete. (Got {err})')
+    else:
+        with bibfile.open('wb') as f:
+            f.write(r.read())
+
+if bibfile.is_file():
+    bibtex_bibfiles = ["references.bib"]
+    bibtex_reference_style = "author_year"
+
 # Monkeypatch constant because the following are mock imports.
 # Only works if numpy is actually installed and at the same time being mocked.
-#import numpy
-#numpy.pi = 3.1416
+# import numpy
+# numpy.pi = 3.1416
 
 # We are using mock imports in readthedocs, so probably safer to not run the notebooks
 nbsphinx_execute = 'never'
@@ -101,7 +122,7 @@
 
 # General information about the project.
 project = "Finch"
-copyright = "2018-2020, David Huard"
+copyright = f"2018-{datetime.today().year}, David Huard"
 author = "David Huard"
 
 # The version info for the project you're documenting, acts as replacement
@@ -118,7 +139,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
diff --git a/docs/source/configuration.rst b/docs/source/configuration.rst
index cf2b31b2..79ff3335 100644
--- a/docs/source/configuration.rst
+++ b/docs/source/configuration.rst
@@ -45,5 +45,34 @@ Start the service with your custom configuration:
    # start the service with this configuration
    $ finch start -c etc/custom.cfg
 
-
 .. _PyWPS: http://pywps.org/
+
+Content of the configuration file
+---------------------------------
+Configuration sections and values specific to Finch are:
+
+finch
+^^^^^
+:datasets_config: Path to the YAML files defining the available ensemble datasets (see below). The path can be given relative to the "finch/finch/" folder, where `default.cfg` lives.
+:default_dataset: Default dataset to use. Should be a top-level key of the yaml.
+:subset_threads: Number of threads to use when performing the subsetting.
+
+finch:metadata
+^^^^^^^^^^^^^^
+All fields here are added as string attributes of computed indices, in addition to xclim's attributes.
+Finch always adds the following attributes:
+
+:climateindex_package_id: ``https://github.com/Ouranosinc/xclim``
+:product: ``derived climate index``
+
+
+.. _datasetconfig:
+
+Structure of the dataset configuration file
+-------------------------------------------
+The configuration value for `finch, datasets_config`, points to a YAML file that defines each ensemble datasets that are available for ensemble processes. These processes and their default values are generated according to the "allowed_values" of the datasets. This means that a user sees _all_ available values in the process's description, but not all are valid depending on the passed `dataset`.
+
+The YAML file must consist in a mapping of dataset name to dataset configuration, the latter having the same structure as this dataclass:
+
+.. autoclass:: finch.processes.utils.DatasetConfiguration
+
diff --git a/docs/source/notebooks/dap_subset.ipynb b/docs/source/notebooks/dap_subset.ipynb
index a02392f7..186c7ff2 100644
--- a/docs/source/notebooks/dap_subset.ipynb
+++ b/docs/source/notebooks/dap_subset.ipynb
@@ -17,10 +17,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:26.398829Z",
-     "iopub.status.busy": "2022-07-06T15:48:26.398002Z",
-     "iopub.status.idle": "2022-07-06T15:48:29.927355Z",
-     "shell.execute_reply": "2022-07-06T15:48:29.926736Z"
+     "iopub.execute_input": "2022-10-04T17:33:04.521706Z",
+     "iopub.status.busy": "2022-10-04T17:33:04.519495Z",
+     "iopub.status.idle": "2022-10-04T17:33:07.533798Z",
+     "shell.execute_reply": "2022-10-04T17:33:07.532882Z"
     }
    },
    "outputs": [],
@@ -43,10 +43,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:29.931647Z",
-     "iopub.status.busy": "2022-07-06T15:48:29.931347Z",
-     "iopub.status.idle": "2022-07-06T15:48:30.918798Z",
-     "shell.execute_reply": "2022-07-06T15:48:30.917834Z"
+     "iopub.execute_input": "2022-10-04T17:33:07.537228Z",
+     "iopub.status.busy": "2022-10-04T17:33:07.536932Z",
+     "iopub.status.idle": "2022-10-04T17:33:08.422809Z",
+     "shell.execute_reply": "2022-10-04T17:33:08.422051Z"
     }
    },
    "outputs": [
@@ -84,6 +84,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -429,24 +430,24 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-698aba10-a621-4a81-a1d7-6b34a8814664' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-698aba10-a621-4a81-a1d7-6b34a8814664' 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>: 6</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a19f04cc-ef1a-4070-8afa-355f481f9a35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a19f04cc-ef1a-4070-8afa-355f481f9a35' 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'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-82b515d0-b234-496c-80b5-65784aaf75c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82b515d0-b234-496c-80b5-65784aaf75c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c1225d7-a731-4307-9e9b-ba7de795a2de' class='xr-var-data-in' type='checkbox'><label for='data-8c1225d7-a731-4307-9e9b-ba7de795a2de' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-c2e39b3b-1ed7-4363-bbfe-c192763f977b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2e39b3b-1ed7-4363-bbfe-c192763f977b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dbe8a7a-8942-4156-afcf-b7a3b846d01f' class='xr-var-data-in' type='checkbox'><label for='data-2dbe8a7a-8942-4156-afcf-b7a3b846d01f' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00 ... 2065-12-...</div><input id='attrs-7a0dd12d-8273-4a94-b94b-6603942f934b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a0dd12d-8273-4a94-b94b-6603942f934b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6224adac-8c1c-46cb-a69b-5ddac6218da7' class='xr-var-data-in' type='checkbox'><label for='data-6224adac-8c1c-46cb-a69b-5ddac6218da7' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-ddfe3b08-dfea-45c2-a3ed-ec630e7d3b9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ddfe3b08-dfea-45c2-a3ed-ec630e7d3b9b' 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>: 6</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f1072091-5bc0-4d77-b0cb-c900e7f18c7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1072091-5bc0-4d77-b0cb-c900e7f18c7d' 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'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-0167737f-9f02-4d0c-a1b4-809e9a63934e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0167737f-9f02-4d0c-a1b4-809e9a63934e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b79d921-380a-4fe5-a60d-5f51b4289e04' class='xr-var-data-in' type='checkbox'><label for='data-1b79d921-380a-4fe5-a60d-5f51b4289e04' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-198a2b0c-27fe-4c9e-922e-75a31f8c8af7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-198a2b0c-27fe-4c9e-922e-75a31f8c8af7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdc1e6e6-6d19-4186-a284-a2724d32cf03' class='xr-var-data-in' type='checkbox'><label for='data-cdc1e6e6-6d19-4186-a284-a2724d32cf03' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00 ... 2065-12-...</div><input id='attrs-447f6a55-f08e-4314-a48a-c2439bb1752f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-447f6a55-f08e-4314-a48a-c2439bb1752f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-315295cb-f57f-4568-a465-d66ed70fa8f3' class='xr-var-data-in' type='checkbox'><label for='data-315295cb-f57f-4568-a465-d66ed70fa8f3' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2046, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2046, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2065, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46860d0d-166b-4e82-afb3-e30652f271b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46860d0d-166b-4e82-afb3-e30652f271b3' class='xr-section-summary' >Data variables: <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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe4dc0ff-778e-41cf-990d-005522950a19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe4dc0ff-778e-41cf-990d-005522950a19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb7f3d0-fb63-4799-af1d-168771925ba5' class='xr-var-data-in' type='checkbox'><label for='data-4fb7f3d0-fb63-4799-af1d-168771925ba5' 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>_ChunkSizes :</span></dt><dd>[6 2]</dd></dl></div><div class='xr-var-data'><pre>array([[40.822107, 44.533081],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c9396b-e43f-4371-bef0-0964307bab8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c9396b-e43f-4371-bef0-0964307bab8e' class='xr-section-summary' >Data variables: <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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e42b016-c17a-4818-8de3-ae878f58b35d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e42b016-c17a-4818-8de3-ae878f58b35d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f3953e2-961d-4dec-8f6e-4ce796990da2' class='xr-var-data-in' type='checkbox'><label for='data-3f3953e2-961d-4dec-8f6e-4ce796990da2' 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>_ChunkSizes :</span></dt><dd>[6 2]</dd></dl></div><div class='xr-var-data'><pre>array([[40.822107, 44.533081],\n",
        "       [44.533081, 48.244005],\n",
        "       [48.244005, 51.954863],\n",
        "       [51.954863, 55.665632],\n",
        "       [55.665632, 59.376282],\n",
-       "       [59.376282, 63.08676 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0aa5ca2d-4c3d-49f1-97f7-48a41f25c977' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0aa5ca2d-4c3d-49f1-97f7-48a41f25c977' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-564b03d7-6467-4f17-a190-982ef55cd914' class='xr-var-data-in' type='checkbox'><label for='data-564b03d7-6467-4f17-a190-982ef55cd914' 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>_ChunkSizes :</span></dt><dd>[7 2]</dd></dl></div><div class='xr-var-data'><pre>array([[279.375, 283.125],\n",
+       "       [59.376282, 63.08676 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbaa42b4-4d06-433c-a083-486ab89968b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbaa42b4-4d06-433c-a083-486ab89968b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8993db3-a067-40bc-b2be-f62aef1e3701' class='xr-var-data-in' type='checkbox'><label for='data-b8993db3-a067-40bc-b2be-f62aef1e3701' 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>_ChunkSizes :</span></dt><dd>[7 2]</dd></dl></div><div class='xr-var-data'><pre>array([[279.375, 283.125],\n",
        "       [283.125, 286.875],\n",
        "       [286.875, 290.625],\n",
        "       [290.625, 294.375],\n",
        "       [294.375, 298.125],\n",
        "       [298.125, 301.875],\n",
-       "       [301.875, 305.625]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e77d352-f923-4ee7-917b-49cb1a8a6a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e77d352-f923-4ee7-917b-49cb1a8a6a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e745ecfc-0f5d-4480-bb1c-640021647c6b' class='xr-var-data-in' type='checkbox'><label for='data-e745ecfc-0f5d-4480-bb1c-640021647c6b' 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>_ChunkSizes :</span></dt><dd>[7200    2]</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       [301.875, 305.625]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe47f58a-6bff-402b-a928-a1b815d1594f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe47f58a-6bff-402b-a928-a1b815d1594f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935351ad-5031-4a18-8fc1-ec1724a06166' class='xr-var-data-in' type='checkbox'><label for='data-935351ad-5031-4a18-8fc1-ec1724a06166' 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>_ChunkSizes :</span></dt><dd>[7200    2]</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "        cftime.Datetime360Day(2046, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
        "       [cftime.Datetime360Day(2046, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
        "        cftime.Datetime360Day(2046, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
@@ -459,7 +460,7 @@
        "        cftime.Datetime360Day(2065, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
        "       [cftime.Datetime360Day(2065, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
        "        cftime.Datetime360Day(2066, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</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-fe6e91cc-814b-43fb-ba04-ba2e30be6cef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe6e91cc-814b-43fb-ba04-ba2e30be6cef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb4fab2-1866-44f6-b6e1-44a8f8aae496' class='xr-var-data-in' type='checkbox'><label for='data-5cb4fab2-1866-44f6-b6e1-44a8f8aae496' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[302400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fd001db-dcce-4e7d-ba1e-3cb2e8f3007d' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fd001db-dcce-4e7d-ba1e-3cb2e8f3007d' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</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-41307fd9-fa2b-48fd-8fe9-0d40448beff4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41307fd9-fa2b-48fd-8fe9-0d40448beff4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f3ff7b2-447c-4704-b18c-3da960b16912' class='xr-var-data-in' type='checkbox'><label for='data-3f3ff7b2-447c-4704-b18c-3da960b16912' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[302400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35ea7a68-19db-4a46-a813-be28d3860889' class='xr-section-summary-in' type='checkbox'  ><label for='section-35ea7a68-19db-4a46-a813-be28d3860889' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -508,10 +509,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:30.966228Z",
-     "iopub.status.busy": "2022-07-06T15:48:30.962415Z",
-     "iopub.status.idle": "2022-07-06T15:48:30.995794Z",
-     "shell.execute_reply": "2022-07-06T15:48:30.994830Z"
+     "iopub.execute_input": "2022-10-04T17:33:08.460402Z",
+     "iopub.status.busy": "2022-10-04T17:33:08.460110Z",
+     "iopub.status.idle": "2022-10-04T17:33:08.475518Z",
+     "shell.execute_reply": "2022-10-04T17:33:08.474826Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -533,10 +534,10 @@
     "# Use the `remap_label_indexers` function to convert coordinates to *positional* indexes.\n",
     "import datetime as dt\n",
     "coords = dict(lat=45, lon=290)\n",
-    "index, _ = xr.core.coordinates.remap_label_indexers(ds, coords, method=\"nearest\")\n",
+    "index = xr.core.indexing.map_index_queries(ds, coords, method=\"nearest\").dim_indexers\n",
     "\n",
     "# The `nearest` method cannot be used with slices, so we do another call for the time period.\n",
-    "ti, _ = xr.core.coordinates.remap_label_indexers(ds, dict(time=slice(\"2060-01-01\", \"2064-12-30\")))\n",
+    "ti = xr.core.indexing.map_index_queries(ds, dict(time=slice(\"2060-01-01\", \"2064-12-30\"))).dim_indexers\n",
     "\n",
     "# Merge the spatial and temporal indices\n",
     "index.update(ti)\n",
@@ -561,10 +562,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:31.000799Z",
-     "iopub.status.busy": "2022-07-06T15:48:30.999992Z",
-     "iopub.status.idle": "2022-07-06T15:48:31.362033Z",
-     "shell.execute_reply": "2022-07-06T15:48:31.361213Z"
+     "iopub.execute_input": "2022-10-04T17:33:08.478600Z",
+     "iopub.status.busy": "2022-10-04T17:33:08.478080Z",
+     "iopub.status.idle": "2022-10-04T17:33:08.788511Z",
+     "shell.execute_reply": "2022-10-04T17:33:08.786365Z"
     }
    },
    "outputs": [
@@ -602,6 +603,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -940,7 +942,7 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-cd204b96-e4d4-41c1-9879-a6ac605d86cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd204b96-e4d4-41c1-9879-a6ac605d86cf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 1</li><li><span>lat</span>: 6</li><li><span>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ce0ccf6-643c-47e7-a8fc-ca47f8fdfac0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ce0ccf6-643c-47e7-a8fc-ca47f8fdfac0' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <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-03b57efe-68a2-4a55-9758-80aefd088c32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03b57efe-68a2-4a55-9758-80aefd088c32' 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>pr</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-b9352c8b-4f4e-49ad-99d5-3fd63e8c9b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9352c8b-4f4e-49ad-99d5-3fd63e8c9b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72812361-37ac-43db-90f6-60744fd36d94' class='xr-var-data-in' type='checkbox'><label for='data-72812361-37ac-43db-90f6-60744fd36d94' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[3.415346e-05, 2.171099e-05, 1.613796e-05, 4.132092e-05,\n",
+       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-7bcf590c-bfbf-4beb-b020-6aea9857c9ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7bcf590c-bfbf-4beb-b020-6aea9857c9ea' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 1</li><li><span>lat</span>: 6</li><li><span>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b86852f3-ae4c-4c2e-9144-cdfc80ca5ee2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b86852f3-ae4c-4c2e-9144-cdfc80ca5ee2' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <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-9819f2d5-6bb9-4dd0-b221-746d681d73ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9819f2d5-6bb9-4dd0-b221-746d681d73ef' 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>pr</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-8b9c0096-1f14-43df-8068-14e9cd2ae97d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b9c0096-1f14-43df-8068-14e9cd2ae97d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0e2e27b-bf8c-4005-b14b-212fad807119' class='xr-var-data-in' type='checkbox'><label for='data-d0e2e27b-bf8c-4005-b14b-212fad807119' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[3.415346e-05, 2.171099e-05, 1.613796e-05, 4.132092e-05,\n",
        "         3.208220e-05, 5.155802e-06, 2.011657e-06],\n",
        "        [6.732345e-05, 6.915629e-05, 4.905462e-05, 1.920760e-05,\n",
        "         1.399219e-05, 3.948808e-06, 3.293157e-06],\n",
@@ -951,7 +953,7 @@
        "        [2.771616e-06, 0.000000e+00, 2.980232e-08, 1.490116e-08,\n",
        "         0.000000e+00, 1.659989e-05, 2.242625e-05],\n",
        "        [0.000000e+00, 1.043081e-06, 5.215406e-07, 0.000000e+00,\n",
-       "         3.710389e-06, 1.209974e-05, 1.022220e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d751f44-97fe-4e78-91de-66b1d12e8891' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d751f44-97fe-4e78-91de-66b1d12e8891' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "         3.710389e-06, 1.209974e-05, 1.022220e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5590f9e-5e96-4987-8a29-a69f971080c6' class='xr-section-summary-in' type='checkbox'  ><label for='section-b5590f9e-5e96-4987-8a29-a69f971080c6' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -998,10 +1000,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:31.366270Z",
-     "iopub.status.busy": "2022-07-06T15:48:31.365549Z",
-     "iopub.status.idle": "2022-07-06T15:48:31.972215Z",
-     "shell.execute_reply": "2022-07-06T15:48:31.971321Z"
+     "iopub.execute_input": "2022-10-04T17:33:08.797103Z",
+     "iopub.status.busy": "2022-10-04T17:33:08.796454Z",
+     "iopub.status.idle": "2022-10-04T17:33:09.304811Z",
+     "shell.execute_reply": "2022-10-04T17:33:09.302716Z"
     }
    },
    "outputs": [
@@ -1039,6 +1041,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1380,8 +1383,8 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-3bbf2d4e-646d-4674-998e-412dc30157ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3bbf2d4e-646d-4674-998e-412dc30157ef' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ba34b94-0294-408b-b189-d070670172b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ba34b94-0294-408b-b189-d070670172b1' 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'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-7b73d8af-28a8-454e-8dbe-96a2156f9708' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b73d8af-28a8-454e-8dbe-96a2156f9708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2504d269-b770-4836-b441-3e8a4fa6e105' class='xr-var-data-in' type='checkbox'><label for='data-2504d269-b770-4836-b441-3e8a4fa6e105' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-77295c6c-5c95-4324-9644-45339c0c1a1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77295c6c-5c95-4324-9644-45339c0c1a1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-234601c7-0311-4f43-999d-ef451940b3b2' class='xr-var-data-in' type='checkbox'><label for='data-234601c7-0311-4f43-999d-ef451940b3b2' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00</div><input id='attrs-490a018a-89c6-4fab-b105-51a1d83e0626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-490a018a-89c6-4fab-b105-51a1d83e0626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d185341-b056-419e-868d-1a604c121e4f' class='xr-var-data-in' type='checkbox'><label for='data-5d185341-b056-419e-868d-1a604c121e4f' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3945b5c-ae23-4036-9594-325d53423965' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3945b5c-ae23-4036-9594-325d53423965' 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>pr</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-28da3f28-e021-4b1b-8299-6618f0a1af1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28da3f28-e021-4b1b-8299-6618f0a1af1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b69481f-6316-424d-a7fe-dc52509f3a93' class='xr-var-data-in' type='checkbox'><label for='data-0b69481f-6316-424d-a7fe-dc52509f3a93' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[3.415346e-05, 2.171099e-05, 1.613796e-05, 4.132092e-05,\n",
+       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-3aae4df3-9932-470c-8009-48be187d49cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3aae4df3-9932-470c-8009-48be187d49cf' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-efe5910b-105d-4b32-bd84-c79068a47809' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efe5910b-105d-4b32-bd84-c79068a47809' 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'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-d5df4476-77c6-441d-b357-13a9180f95f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5df4476-77c6-441d-b357-13a9180f95f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd2986be-d541-4576-8a21-7cad03cf20fa' class='xr-var-data-in' type='checkbox'><label for='data-bd2986be-d541-4576-8a21-7cad03cf20fa' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-aa828ef8-8d65-4dc7-ba88-402abc1cf367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa828ef8-8d65-4dc7-ba88-402abc1cf367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3335f5ff-7e86-4d01-b81d-66cb35b6e2ca' class='xr-var-data-in' type='checkbox'><label for='data-3335f5ff-7e86-4d01-b81d-66cb35b6e2ca' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00</div><input id='attrs-80362245-c94e-4304-b2bb-9d850323bc80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80362245-c94e-4304-b2bb-9d850323bc80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d60064c-ef73-40cb-b0db-ee1b57fdd3ee' class='xr-var-data-in' type='checkbox'><label for='data-9d60064c-ef73-40cb-b0db-ee1b57fdd3ee' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d39b0a3-1485-440e-917e-256a329e858e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d39b0a3-1485-440e-917e-256a329e858e' 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>pr</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-aecad5bc-00f0-486c-871d-e45ee8a7e3b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aecad5bc-00f0-486c-871d-e45ee8a7e3b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02fb8693-081c-4fe2-8873-a309beeb663c' class='xr-var-data-in' type='checkbox'><label for='data-02fb8693-081c-4fe2-8873-a309beeb663c' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[3.415346e-05, 2.171099e-05, 1.613796e-05, 4.132092e-05,\n",
        "         3.208220e-05, 5.155802e-06, 2.011657e-06],\n",
        "        [6.732345e-05, 6.915629e-05, 4.905462e-05, 1.920760e-05,\n",
        "         1.399219e-05, 3.948808e-06, 3.293157e-06],\n",
@@ -1392,7 +1395,7 @@
        "        [2.771616e-06, 0.000000e+00, 2.980232e-08, 1.490116e-08,\n",
        "         0.000000e+00, 1.659989e-05, 2.242625e-05],\n",
        "        [0.000000e+00, 1.043081e-06, 5.215406e-07, 0.000000e+00,\n",
-       "         3.710389e-06, 1.209974e-05, 1.022220e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de7a0dd3-f306-4dd9-8297-c239430fb079' class='xr-section-summary-in' type='checkbox'  ><label for='section-de7a0dd3-f306-4dd9-8297-c239430fb079' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "         3.710389e-06, 1.209974e-05, 1.022220e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17b357d8-2284-4936-9c19-b0b46962fa1d' class='xr-section-summary-in' type='checkbox'  ><label for='section-17b357d8-2284-4936-9c19-b0b46962fa1d' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -1442,10 +1445,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:31.975840Z",
-     "iopub.status.busy": "2022-07-06T15:48:31.975531Z",
-     "iopub.status.idle": "2022-07-06T15:48:32.729390Z",
-     "shell.execute_reply": "2022-07-06T15:48:32.728431Z"
+     "iopub.execute_input": "2022-10-04T17:33:09.314025Z",
+     "iopub.status.busy": "2022-10-04T17:33:09.313290Z",
+     "iopub.status.idle": "2022-10-04T17:33:09.991592Z",
+     "shell.execute_reply": "2022-10-04T17:33:09.989344Z"
     }
    },
    "outputs": [
@@ -1483,6 +1486,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1825,13 +1829,13 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-2d881e39-bc8a-4dcc-aaf5-1772d194095d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d881e39-bc8a-4dcc-aaf5-1772d194095d' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li><li><span>time_1</span>: 1801</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-936f4c3b-941d-4435-abf5-fd3a772a1292' class='xr-section-summary-in' type='checkbox'  checked><label for='section-936f4c3b-941d-4435-abf5-fd3a772a1292' 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'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-e2c4b4a2-57ad-47f2-a2bb-428e34d14ecd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c4b4a2-57ad-47f2-a2bb-428e34d14ecd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c70cc500-e6f4-4081-acc5-083c06013a03' class='xr-var-data-in' type='checkbox'><label for='data-c70cc500-e6f4-4081-acc5-083c06013a03' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-87669b2e-be1e-4776-8506-533a52bcfb34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87669b2e-be1e-4776-8506-533a52bcfb34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f670f317-ed11-43fa-9486-7ebcaa78e943' class='xr-var-data-in' type='checkbox'><label for='data-f670f317-ed11-43fa-9486-7ebcaa78e943' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-0ab3ccf7-91c4-448d-a69f-8dabe58ba25c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ab3ccf7-91c4-448d-a69f-8dabe58ba25c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-651142d4-4cbd-412e-a0cf-3335fd178120' class='xr-var-data-in' type='checkbox'><label for='data-651142d4-4cbd-412e-a0cf-3335fd178120' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-cf3eba71-5c08-43e4-b362-0761c5ee1fff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf3eba71-5c08-43e4-b362-0761c5ee1fff' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li><li><span>time_1</span>: 1801</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0479682f-b8d9-4ece-bdd8-453696f957cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0479682f-b8d9-4ece-bdd8-453696f957cf' 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'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-5aa280f1-7097-42b2-bbcf-725d58c9e8dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aa280f1-7097-42b2-bbcf-725d58c9e8dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a512600f-8169-475e-9cb5-8b6c0ecad146' class='xr-var-data-in' type='checkbox'><label for='data-a512600f-8169-475e-9cb5-8b6c0ecad146' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-91737f25-0843-46d6-855b-2d1a041a387b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91737f25-0843-46d6-855b-2d1a041a387b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a6c63ef-7fbc-466d-8dfd-a893fe1750fc' class='xr-var-data-in' type='checkbox'><label for='data-6a6c63ef-7fbc-466d-8dfd-a893fe1750fc' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-70efd8cd-adaf-4a11-93f2-b6d3dfb284bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70efd8cd-adaf-4a11-93f2-b6d3dfb284bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09ad6e6a-0d19-4c5c-b6f1-90844e75c9e7' class='xr-var-data-in' type='checkbox'><label for='data-09ad6e6a-0d19-4c5c-b6f1-90844e75c9e7' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2064, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddb70084-904f-4462-8d2b-2f4d74b60dbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddb70084-904f-4462-8d2b-2f4d74b60dbd' 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>pr</span></div><div class='xr-var-dims'>(time_1, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b81552cf-c0ce-428f-81d4-d2bc0bdb3de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b81552cf-c0ce-428f-81d4-d2bc0bdb3de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ee8c62-4e99-46e9-95a0-6fc926a8f11c' class='xr-var-data-in' type='checkbox'><label for='data-88ee8c62-4e99-46e9-95a0-6fc926a8f11c' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[1.181662e-05]],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3dde443f-c57a-468b-95f5-915855d930df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3dde443f-c57a-468b-95f5-915855d930df' 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>pr</span></div><div class='xr-var-dims'>(time_1, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b326bf36-f625-4b47-a8b9-66f8884a1c07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b326bf36-f625-4b47-a8b9-66f8884a1c07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d66703b8-65e6-4559-83b1-9f341f7381ec' class='xr-var-data-in' type='checkbox'><label for='data-d66703b8-65e6-4559-83b1-9f341f7381ec' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[1.181662e-05]],\n",
        "\n",
        "       [[1.206994e-06]],\n",
        "\n",
@@ -1839,7 +1843,7 @@
        "\n",
        "       [[5.483627e-06]],\n",
        "\n",
-       "       [[5.592406e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddc26518-dfbb-45f7-bb5a-157af42c5693' class='xr-section-summary-in' type='checkbox'  ><label for='section-ddc26518-dfbb-45f7-bb5a-157af42c5693' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "       [[5.592406e-05]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d86c444-f707-453d-bf3b-0def6187066a' class='xr-section-summary-in' type='checkbox'  ><label for='section-5d86c444-f707-453d-bf3b-0def6187066a' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -1883,10 +1887,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:32.741307Z",
-     "iopub.status.busy": "2022-07-06T15:48:32.740518Z",
-     "iopub.status.idle": "2022-07-06T15:48:32.746976Z",
-     "shell.execute_reply": "2022-07-06T15:48:32.746083Z"
+     "iopub.execute_input": "2022-10-04T17:33:10.011029Z",
+     "iopub.status.busy": "2022-10-04T17:33:10.010252Z",
+     "iopub.status.idle": "2022-10-04T17:33:10.024997Z",
+     "shell.execute_reply": "2022-10-04T17:33:10.022575Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -1913,10 +1917,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:32.753700Z",
-     "iopub.status.busy": "2022-07-06T15:48:32.753139Z",
-     "iopub.status.idle": "2022-07-06T15:48:32.762636Z",
-     "shell.execute_reply": "2022-07-06T15:48:32.761878Z"
+     "iopub.execute_input": "2022-10-04T17:33:10.035988Z",
+     "iopub.status.busy": "2022-10-04T17:33:10.034391Z",
+     "iopub.status.idle": "2022-10-04T17:33:10.054827Z",
+     "shell.execute_reply": "2022-10-04T17:33:10.052330Z"
     }
    },
    "outputs": [
@@ -1962,10 +1966,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:32.768145Z",
-     "iopub.status.busy": "2022-07-06T15:48:32.767803Z",
-     "iopub.status.idle": "2022-07-06T15:48:33.521969Z",
-     "shell.execute_reply": "2022-07-06T15:48:33.520768Z"
+     "iopub.execute_input": "2022-10-04T17:33:10.060246Z",
+     "iopub.status.busy": "2022-10-04T17:33:10.059856Z",
+     "iopub.status.idle": "2022-10-04T17:33:10.900197Z",
+     "shell.execute_reply": "2022-10-04T17:33:10.898052Z"
     }
    },
    "outputs": [
@@ -2003,6 +2007,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -2344,13 +2349,13 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-700efe40-fc4c-423b-b763-1051e7abcd52' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-700efe40-fc4c-423b-b763-1051e7abcd52' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e05e23f-05f3-49c5-927a-aaa3da3b54d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e05e23f-05f3-49c5-927a-aaa3da3b54d8' 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'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-aacde546-6d7c-4d64-83e7-2e7e0e227822' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aacde546-6d7c-4d64-83e7-2e7e0e227822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3774b55-ddf4-4754-b540-daea665ece96' class='xr-var-data-in' type='checkbox'><label for='data-e3774b55-ddf4-4754-b540-daea665ece96' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-bcb4f3d5-1b1b-4a47-8f96-1c3e5d088a4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb4f3d5-1b1b-4a47-8f96-1c3e5d088a4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83bd01ea-7c14-4b11-8b44-f9429a375944' class='xr-var-data-in' type='checkbox'><label for='data-83bd01ea-7c14-4b11-8b44-f9429a375944' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-070e166c-946d-4d99-8ee2-f95f9c769b73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-070e166c-946d-4d99-8ee2-f95f9c769b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cfb532f-0143-4316-8c5e-ebf7ebc7ae5a' class='xr-var-data-in' type='checkbox'><label for='data-1cfb532f-0143-4316-8c5e-ebf7ebc7ae5a' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</pre><div class='xr-wrap' style='display:none'><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-f1bf1f2c-6eac-4668-95f4-b5c983ed6518' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1bf1f2c-6eac-4668-95f4-b5c983ed6518' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6256d308-8c6f-4373-a1d5-c583af09a224' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6256d308-8c6f-4373-a1d5-c583af09a224' 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'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-b3fcd7b2-a215-4337-984d-575d4099c79d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3fcd7b2-a215-4337-984d-575d4099c79d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e262eb9d-09c1-4b2c-8cbc-95a896cac6e7' class='xr-var-data-in' type='checkbox'><label for='data-e262eb9d-09c1-4b2c-8cbc-95a896cac6e7' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-db8f0ef0-e8c3-44ed-b390-f3d45fd123b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db8f0ef0-e8c3-44ed-b390-f3d45fd123b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9172dc56-3c7d-447d-be2a-484d059985aa' class='xr-var-data-in' type='checkbox'><label for='data-9172dc56-3c7d-447d-be2a-484d059985aa' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-9c9ea0f4-a155-43dd-80fc-96951ee1e748' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c9ea0f4-a155-43dd-80fc-96951ee1e748' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a582900f-ff5f-4d6f-9fa0-62cedd9415dd' class='xr-var-data-in' type='checkbox'><label for='data-a582900f-ff5f-4d6f-9fa0-62cedd9415dd' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2064, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-78523658-db74-43a9-bdba-3bffdb25960b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78523658-db74-43a9-bdba-3bffdb25960b' 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>pr</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-258897cf-8e27-422a-9026-8813d035c7ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-258897cf-8e27-422a-9026-8813d035c7ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8085308-e101-4931-b020-ed4bed14e2bf' class='xr-var-data-in' type='checkbox'><label for='data-b8085308-e101-4931-b020-ed4bed14e2bf' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[1.181662e-05]],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfb32b2a-7be9-4004-9858-e027842099f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfb32b2a-7be9-4004-9858-e027842099f5' 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>pr</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-8007c8f5-c5cc-4300-b0ed-d05b3f1e7333' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8007c8f5-c5cc-4300-b0ed-d05b3f1e7333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf613889-0890-4330-b7e8-e627a9642cf8' class='xr-var-data-in' type='checkbox'><label for='data-bf613889-0890-4330-b7e8-e627a9642cf8' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>array([[[1.181662e-05]],\n",
        "\n",
        "       [[1.206994e-06]],\n",
        "\n",
@@ -2358,7 +2363,7 @@
        "\n",
        "       [[1.610816e-05]],\n",
        "\n",
-       "       [[5.483627e-06]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14055b7b-873a-49ae-934c-513b133806ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-14055b7b-873a-49ae-934c-513b133806ff' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "       [[5.483627e-06]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6212ac25-d8f2-4839-b06b-7ce40a4772e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-6212ac25-d8f2-4839-b06b-7ce40a4772e8' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -2410,10 +2415,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:33.526428Z",
-     "iopub.status.busy": "2022-07-06T15:48:33.526103Z",
-     "iopub.status.idle": "2022-07-06T15:48:35.848062Z",
-     "shell.execute_reply": "2022-07-06T15:48:35.846988Z"
+     "iopub.execute_input": "2022-10-04T17:33:10.909485Z",
+     "iopub.status.busy": "2022-10-04T17:33:10.908744Z",
+     "iopub.status.idle": "2022-10-04T17:33:13.027446Z",
+     "shell.execute_reply": "2022-10-04T17:33:13.026715Z"
     }
    },
    "outputs": [
@@ -2451,6 +2456,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -2782,16 +2788,16 @@
        "\n",
        "       [[4.69054 ]]])\n",
        "Coordinates:\n",
-       "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "  * lat      (lat) float64 46.39\n",
        "  * lon      (lon) float64 288.8\n",
+       "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          mm/day\n",
        "    cell_methods:   time: mean (interval: 30 minutes)\n",
-       "    history:        pr=max(0,pr) applied to raw data;\\n[2022-07-06 11:48:34] ...\n",
+       "    history:        pr=max(0,pr) applied to raw data;\\n[2022-10-04 13:33:11] ...\n",
        "    standard_name:  lwe_thickness_of_precipitation_amount\n",
        "    long_name:      Average precipitation during wet days (sdii)\n",
-       "    description:    Annual simple daily intensity index (sdii) : annual avera...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sdii'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d6825878-22cd-4b2b-9f40-f5478b6ba31d' class='xr-array-in' type='checkbox' checked><label for='section-d6825878-22cd-4b2b-9f40-f5478b6ba31d' 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>4.984 5.413 5.488 5.164 4.691</span></div><div class='xr-array-data'><pre>array([[[4.984402]],\n",
+       "    description:    Annual simple daily intensity index (sdii) : annual avera...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sdii'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2d730d63-c432-4df1-999c-dd71c93fdd17' class='xr-array-in' type='checkbox' checked><label for='section-2d730d63-c432-4df1-999c-dd71c93fdd17' 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>4.984 5.413 5.488 5.164 4.691</span></div><div class='xr-array-data'><pre>array([[[4.984402]],\n",
        "\n",
        "       [[5.412616]],\n",
        "\n",
@@ -2799,13 +2805,13 @@
        "\n",
        "       [[5.164405]],\n",
        "\n",
-       "       [[4.69054 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ac459435-c925-41ad-9122-4b1670053b81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac459435-c925-41ad-9122-4b1670053b81' 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'>2060-01-01 00:00:00 ... 2064-01-...</div><input id='attrs-1fcc607b-b711-4598-9ff7-b7efcdc7bd29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fcc607b-b711-4598-9ff7-b7efcdc7bd29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-429775f1-d0bf-47d8-9ae0-f613ed89eaeb' class='xr-var-data-in' type='checkbox'><label for='data-429775f1-d0bf-47d8-9ae0-f613ed89eaeb' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       [[4.69054 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-55f35ace-6db9-4499-93e3-57023d225c18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55f35ace-6db9-4499-93e3-57023d225c18' 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'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-bf69d92a-d063-4c9b-85d0-3047c88d1582' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf69d92a-d063-4c9b-85d0-3047c88d1582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56bbc95b-b55e-434e-9bef-40a68b4c65fa' class='xr-var-data-in' type='checkbox'><label for='data-56bbc95b-b55e-434e-9bef-40a68b4c65fa' 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><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-fa7279e1-70ef-4ebf-86d1-c1030685ff59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa7279e1-70ef-4ebf-86d1-c1030685ff59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-465b4546-8f3d-4669-b2de-6c0fe94e1c10' class='xr-var-data-in' type='checkbox'><label for='data-465b4546-8f3d-4669-b2de-6c0fe94e1c10' 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><div class='xr-var-data'><pre>array([288.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 00:00:00 ... 2064-01-...</div><input id='attrs-459cc159-37be-4299-a1a7-a7dac823b4e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-459cc159-37be-4299-a1a7-a7dac823b4e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85197f81-da22-4c71-a42c-5a0d165042fb' class='xr-var-data-in' type='checkbox'><label for='data-85197f81-da22-4c71-a42c-5a0d165042fb' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2061, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2062, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></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'>46.39</div><input id='attrs-5842791a-3c71-43cd-97f2-01971eb10667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5842791a-3c71-43cd-97f2-01971eb10667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c866afe5-1037-4ef8-8417-f9d6f7f136e6' class='xr-var-data-in' type='checkbox'><label for='data-c866afe5-1037-4ef8-8417-f9d6f7f136e6' 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><div class='xr-var-data'><pre>array([46.388557])</pre></div></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'>288.8</div><input id='attrs-1041c3bf-333a-49e3-a7e2-b48500044430' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1041c3bf-333a-49e3-a7e2-b48500044430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab6e687e-b2fe-4a27-af70-8a3a9031311b' class='xr-var-data-in' type='checkbox'><label for='data-ab6e687e-b2fe-4a27-af70-8a3a9031311b' 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><div class='xr-var-data'><pre>array([288.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c46e5732-9d2f-4502-a454-288f23dac9a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c46e5732-9d2f-4502-a454-288f23dac9a5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;\n",
-       "[2022-07-06 11:48:34] sdii: SDII(pr=pr, thresh=&#x27;1 mm/day&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>long_name :</span></dt><dd>Average precipitation during wet days (sdii)</dd><dt><span>description :</span></dt><dd>Annual simple daily intensity index (sdii) : annual average precipitation for days with daily precipitation over 1 mm/day. this indicator is also known as the &#x27;simple daily intensity index&#x27; (sdii).</dd></dl></div></li></ul></div></div>"
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ead2b746-d59c-42e8-8e39-9088b67883ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ead2b746-d59c-42e8-8e39-9088b67883ca' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;\n",
+       "[2022-10-04 13:33:11] sdii: SDII(pr=pr, thresh=&#x27;1 mm/day&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>long_name :</span></dt><dd>Average precipitation during wet days (sdii)</dd><dt><span>description :</span></dt><dd>Annual simple daily intensity index (sdii) : annual average precipitation for days with daily precipitation over 1 mm/day. this indicator is also known as the &#x27;simple daily intensity index&#x27; (sdii).</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'sdii' (time: 5, lat: 1, lon: 1)>\n",
@@ -2819,13 +2825,13 @@
        "\n",
        "       [[4.69054 ]]])\n",
        "Coordinates:\n",
-       "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "  * lat      (lat) float64 46.39\n",
        "  * lon      (lon) float64 288.8\n",
+       "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          mm/day\n",
        "    cell_methods:   time: mean (interval: 30 minutes)\n",
-       "    history:        pr=max(0,pr) applied to raw data;\\n[2022-07-06 11:48:34] ...\n",
+       "    history:        pr=max(0,pr) applied to raw data;\\n[2022-10-04 13:33:11] ...\n",
        "    standard_name:  lwe_thickness_of_precipitation_amount\n",
        "    long_name:      Average precipitation during wet days (sdii)\n",
        "    description:    Annual simple daily intensity index (sdii) : annual avera..."
@@ -2842,6 +2848,13 @@
     "out = resp.get(asobj=True)\n",
     "out.output.sdii"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -2860,7 +2873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/finch-usage.ipynb b/docs/source/notebooks/finch-usage.ipynb
index eb57f5cc..945f85e8 100644
--- a/docs/source/notebooks/finch-usage.ipynb
+++ b/docs/source/notebooks/finch-usage.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:39.569814Z",
-     "iopub.status.busy": "2022-07-06T15:48:39.568439Z",
-     "iopub.status.idle": "2022-07-06T15:48:42.452021Z",
-     "shell.execute_reply": "2022-07-06T15:48:42.450951Z"
+     "iopub.execute_input": "2022-10-04T17:33:16.406620Z",
+     "iopub.status.busy": "2022-10-04T17:33:16.404846Z",
+     "iopub.status.idle": "2022-10-04T17:33:19.269650Z",
+     "shell.execute_reply": "2022-10-04T17:33:19.268944Z"
     }
    },
    "outputs": [],
@@ -45,10 +45,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:42.455275Z",
-     "iopub.status.busy": "2022-07-06T15:48:42.455016Z",
-     "iopub.status.idle": "2022-07-06T15:48:42.459215Z",
-     "shell.execute_reply": "2022-07-06T15:48:42.458676Z"
+     "iopub.execute_input": "2022-10-04T17:33:19.273231Z",
+     "iopub.status.busy": "2022-10-04T17:33:19.272959Z",
+     "iopub.status.idle": "2022-10-04T17:33:19.277196Z",
+     "shell.execute_reply": "2022-10-04T17:33:19.276649Z"
     }
    },
    "outputs": [
@@ -82,7 +82,7 @@
       "    data_validation : {'log', 'warn', 'raise'}string\n",
       "        Whether to log, warn or raise when inputs fail data validation checks.\n",
       "    variable : string\n",
-      "        Name of the variable in the NetCDF file.\n",
+      "        Name of the variable in the input files.\n",
       "    output_name : string\n",
       "        Filename of the output (no extension).\n",
       "    output_format : {'netcdf', 'csv'}string\n",
@@ -122,10 +122,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:42.481839Z",
-     "iopub.status.busy": "2022-07-06T15:48:42.481241Z",
-     "iopub.status.idle": "2022-07-06T15:48:45.030009Z",
-     "shell.execute_reply": "2022-07-06T15:48:45.029331Z"
+     "iopub.execute_input": "2022-10-04T17:33:19.314448Z",
+     "iopub.status.busy": "2022-10-04T17:33:19.314184Z",
+     "iopub.status.idle": "2022-10-04T17:33:21.589891Z",
+     "shell.execute_reply": "2022-10-04T17:33:21.589244Z"
     }
    },
    "outputs": [],
@@ -139,10 +139,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:45.033248Z",
-     "iopub.status.busy": "2022-07-06T15:48:45.032957Z",
-     "iopub.status.idle": "2022-07-06T15:48:45.037530Z",
-     "shell.execute_reply": "2022-07-06T15:48:45.036722Z"
+     "iopub.execute_input": "2022-10-04T17:33:21.593022Z",
+     "iopub.status.busy": "2022-10-04T17:33:21.592758Z",
+     "iopub.status.idle": "2022-10-04T17:33:21.597008Z",
+     "shell.execute_reply": "2022-10-04T17:33:21.596269Z"
     }
    },
    "outputs": [
@@ -151,7 +151,7 @@
      "output_type": "stream",
      "text": [
       "Process status:  ProcessSucceeded\n",
-      "Link to process output:  https://pavics.ouranos.ca/wpsoutputs/1c86f7ea-fd43-11ec-9e63-484d7ef8d8d1/frost_days_sres_a2_experiment_20460101_20650101.nc\n"
+      "Link to process output:  https://pavics.ouranos.ca/wpsoutputs/a2fd4640-440a-11ed-bc1e-484d7ef8da38/frost_days_sres_a2_experiment_20460101_20650101.nc\n"
      ]
     }
    ],
@@ -173,10 +173,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:45.040607Z",
-     "iopub.status.busy": "2022-07-06T15:48:45.040356Z",
-     "iopub.status.idle": "2022-07-06T15:48:45.093625Z",
-     "shell.execute_reply": "2022-07-06T15:48:45.092931Z"
+     "iopub.execute_input": "2022-10-04T17:33:21.600235Z",
+     "iopub.status.busy": "2022-10-04T17:33:21.599968Z",
+     "iopub.status.idle": "2022-10-04T17:33:21.724908Z",
+     "shell.execute_reply": "2022-10-04T17:33:21.723504Z"
     }
    },
    "outputs": [
@@ -214,6 +214,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -562,16 +563,16 @@
        "        [251., 250., ..., 156., 103.]]])\n",
        "Coordinates:\n",
        "    height   float64 2.0\n",
-       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat      (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon      (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          days\n",
        "    cell_methods:   time: minimum (interval: 30 minutes) time: sum over days\n",
        "    history:        tas=max(195,tas) applied to raw data; min of 194.73 detec...\n",
        "    standard_name:  days_with_air_temperature_below_threshold\n",
        "    long_name:      Number of frost days (tmin &lt; 0 degc)\n",
-       "    description:    Annual number of days with minimum daily temperature belo...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'frost_days'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1bf20a1e-c27f-48e1-888c-5c16750bf691' class='xr-array-in' type='checkbox' checked><label for='section-1bf20a1e-c27f-48e1-888c-5c16750bf691' 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>122.0 121.0 98.0 47.0 33.0 19.0 ... 249.0 229.0 193.0 156.0 103.0</span></div><div class='xr-array-data'><pre>array([[[122., 121., ...,  19.,   9.],\n",
+       "    description:    Annual number of days with minimum daily temperature belo...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'frost_days'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-391782f1-18d2-49a2-a2ce-020d6baa2ae2' class='xr-array-in' type='checkbox' checked><label for='section-391782f1-18d2-49a2-a2ce-020d6baa2ae2' 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>122.0 121.0 98.0 47.0 33.0 19.0 ... 249.0 229.0 193.0 156.0 103.0</span></div><div class='xr-array-data'><pre>array([[[122., 121., ...,  19.,   9.],\n",
        "        [156., 168., ..., 113.,  93.],\n",
        "        ...,\n",
        "        [227., 227., ..., 194., 133.],\n",
@@ -595,7 +596,7 @@
        "        [163., 172., ...,  91.,  59.],\n",
        "        ...,\n",
        "        [225., 225., ..., 161., 107.],\n",
-       "        [251., 250., ..., 156., 103.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-84a7cd68-189f-4881-b6ff-911ce6f8430e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84a7cd68-189f-4881-b6ff-911ce6f8430e' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-67ac3122-97aa-48ba-8b80-54d1346af0ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67ac3122-97aa-48ba-8b80-54d1346af0ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f907e3d4-8de4-4428-8d8a-da0eb7a4a7af' class='xr-var-data-in' type='checkbox'><label for='data-f907e3d4-8de4-4428-8d8a-da0eb7a4a7af' 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>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-4e182342-18c8-4263-94e6-86504b798003' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e182342-18c8-4263-94e6-86504b798003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6fae988-23e5-409c-a5bd-3f28e3919454' class='xr-var-data-in' type='checkbox'><label for='data-e6fae988-23e5-409c-a5bd-3f28e3919454' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "        [251., 250., ..., 156., 103.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c0b047f9-ed27-4577-8a5f-848777f7369a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0b047f9-ed27-4577-8a5f-848777f7369a' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b2fbc39-2f93-447f-bb0e-72b3a8757190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2fbc39-2f93-447f-bb0e-72b3a8757190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c744f71d-3674-4c4a-9419-04d18e2e1739' class='xr-var-data-in' type='checkbox'><label for='data-c744f71d-3674-4c4a-9419-04d18e2e1739' 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>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-6021b547-a206-4f29-b184-5e61911e3a3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6021b547-a206-4f29-b184-5e61911e3a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e8720ab-12a6-40d7-97aa-234775cc60bb' class='xr-var-data-in' type='checkbox'><label for='data-6e8720ab-12a6-40d7-97aa-234775cc60bb' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-8ec91e68-97f3-4840-bf32-7805c8a8958d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ec91e68-97f3-4840-bf32-7805c8a8958d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-469da278-7ec0-4557-8103-85752a952e0a' class='xr-var-data-in' type='checkbox'><label for='data-469da278-7ec0-4557-8103-85752a952e0a' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-d18b89e8-d318-4654-b991-9eb5492814f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d18b89e8-d318-4654-b991-9eb5492814f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a0b2eb8-7f57-4c84-b911-1b9ebc7f610a' class='xr-var-data-in' type='checkbox'><label for='data-5a0b2eb8-7f57-4c84-b911-1b9ebc7f610a' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -615,8 +616,8 @@
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-4a95e3ba-5cc9-4122-98a9-40bd37481b7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a95e3ba-5cc9-4122-98a9-40bd37481b7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71819aed-a4ac-4e95-826a-87646d3bdc0b' class='xr-var-data-in' type='checkbox'><label for='data-71819aed-a4ac-4e95-826a-87646d3bdc0b' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-cd5caa18-06ea-47a0-b4c3-b1c680a397f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd5caa18-06ea-47a0-b4c3-b1c680a397f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84108d1a-454b-4495-a5b6-91b22b9edd3b' class='xr-var-data-in' type='checkbox'><label for='data-84108d1a-454b-4495-a5b6-91b22b9edd3b' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1effadf3-a3e7-45d2-b2b3-bb10c6440991' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1effadf3-a3e7-45d2-b2b3-bb10c6440991' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
-       "[2022-07-06 11:48:43] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div></li></ul></div></div>"
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e33eeb2d-fffd-4a98-97d6-03638f1b1782' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e33eeb2d-fffd-4a98-97d6-03638f1b1782' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
+       "[2022-10-04 13:33:20] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'frost_days' (time: 20, lat: 6, lon: 7)>\n",
@@ -647,9 +648,9 @@
        "        [251., 250., ..., 156., 103.]]])\n",
        "Coordinates:\n",
        "    height   float64 ...\n",
-       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat      (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon      (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          days\n",
        "    cell_methods:   time: minimum (interval: 30 minutes) time: sum over days\n",
@@ -684,10 +685,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:45.102286Z",
-     "iopub.status.busy": "2022-07-06T15:48:45.102035Z",
-     "iopub.status.idle": "2022-07-06T15:48:45.145259Z",
-     "shell.execute_reply": "2022-07-06T15:48:45.143777Z"
+     "iopub.execute_input": "2022-10-04T17:33:21.738891Z",
+     "iopub.status.busy": "2022-10-04T17:33:21.738603Z",
+     "iopub.status.idle": "2022-10-04T17:33:21.778344Z",
+     "shell.execute_reply": "2022-10-04T17:33:21.777607Z"
     }
    },
    "outputs": [
@@ -725,6 +726,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1046,12 +1048,12 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:     (time: 20, lat: 6, lon: 7)\n",
+       "Dimensions:     (lat: 6, lon: 7, time: 20)\n",
        "Coordinates:\n",
        "    height      float64 2.0\n",
-       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat         (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon         (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Data variables:\n",
        "    frost_days  (time, lat, lon) timedelta64[ns] 122 days 121 days ... 103 days\n",
        "Attributes: (12/20)\n",
@@ -1067,7 +1069,7 @@
        "    NCO:                      4.0.9\n",
        "    climateindex_package_id:  https://github.com/Ouranosinc/xclim\n",
        "    product:                  derived climate index\n",
-       "    institute_id:             CCCS</pre><div class='xr-wrap' style='display:none'><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-7d0e4a5e-4668-465f-b855-f94ad9e657f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7d0e4a5e-4668-465f-b855-f94ad9e657f4' 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>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57df9f7b-bb30-4050-9c45-9cfaeda1608b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57df9f7b-bb30-4050-9c45-9cfaeda1608b' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c215322e-9f93-492e-8971-b220ae40a307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c215322e-9f93-492e-8971-b220ae40a307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-143b2dca-b637-4d99-8ea4-816a6e99637c' class='xr-var-data-in' type='checkbox'><label for='data-143b2dca-b637-4d99-8ea4-816a6e99637c' 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>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-49fb416f-f4d1-4792-8c94-211992633973' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49fb416f-f4d1-4792-8c94-211992633973' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7a98148-a78f-4cdf-921e-163dfe0ed5f3' class='xr-var-data-in' type='checkbox'><label for='data-f7a98148-a78f-4cdf-921e-163dfe0ed5f3' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "    institute_id:             CCCS</pre><div class='xr-wrap' style='display:none'><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-6005dd86-13a6-48a9-958c-12b8d86ee937' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6005dd86-13a6-48a9-958c-12b8d86ee937' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-237ef6ae-5737-42a6-9c88-2526b9b6c455' class='xr-section-summary-in' type='checkbox'  checked><label for='section-237ef6ae-5737-42a6-9c88-2526b9b6c455' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db30c1b8-184d-4e81-bc56-f09d01f16df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db30c1b8-184d-4e81-bc56-f09d01f16df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d45ecae3-5578-4e96-b3f7-8b561e28e586' class='xr-var-data-in' type='checkbox'><label for='data-d45ecae3-5578-4e96-b3f7-8b561e28e586' 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>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-65ad81d6-3a70-433f-a673-6234d17aea3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65ad81d6-3a70-433f-a673-6234d17aea3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e3eb13e-2f1f-44a2-9e2c-968dad68ea48' class='xr-var-data-in' type='checkbox'><label for='data-8e3eb13e-2f1f-44a2-9e2c-968dad68ea48' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-d23d2cd3-eabb-4138-a578-dd70c8017518' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d23d2cd3-eabb-4138-a578-dd70c8017518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d24b01b-6b85-4031-af32-0faae1ca986b' class='xr-var-data-in' type='checkbox'><label for='data-3d24b01b-6b85-4031-af32-0faae1ca986b' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-524697ce-fe50-4833-b6db-d07ff8ec419a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-524697ce-fe50-4833-b6db-d07ff8ec419a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09cebe2f-6813-467d-b2de-56365a5a1307' class='xr-var-data-in' type='checkbox'><label for='data-09cebe2f-6813-467d-b2de-56365a5a1307' 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><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -1087,8 +1089,8 @@
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-0c96f29a-4ab1-4c06-b54e-115356e2ef65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c96f29a-4ab1-4c06-b54e-115356e2ef65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdbf92e7-ff76-4697-85f5-e86367c5af85' class='xr-var-data-in' type='checkbox'><label for='data-bdbf92e7-ff76-4697-85f5-e86367c5af85' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</pre></div></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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-1b80955d-3981-4f58-a28c-1258a83a71af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b80955d-3981-4f58-a28c-1258a83a71af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acd8d73a-fadf-4afe-bee3-ea0cfcce304d' class='xr-var-data-in' type='checkbox'><label for='data-acd8d73a-fadf-4afe-bee3-ea0cfcce304d' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-013fd458-ebe8-40ad-b5de-f6212eb4d01f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-013fd458-ebe8-40ad-b5de-f6212eb4d01f' 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>frost_days</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2f85676c-949a-4852-ba2b-897fa938d94a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f85676c-949a-4852-ba2b-897fa938d94a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5f261da-662a-4c3d-9792-6d574a8b03f7' class='xr-var-data-in' type='checkbox'><label for='data-e5f261da-662a-4c3d-9792-6d574a8b03f7' 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>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
-       "[2022-07-06 11:48:43] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div><div class='xr-var-data'><pre>array([[[10540800000000000, 10454400000000000, ...,  1641600000000000,\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d8c1bb0-5b4b-4f22-9565-1a2e0b6391f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d8c1bb0-5b4b-4f22-9565-1a2e0b6391f7' 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>frost_days</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7320b01-bc7e-4baa-af51-8e2371e318c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7320b01-bc7e-4baa-af51-8e2371e318c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5465219a-e1a4-4592-bb0f-4370dbde7545' class='xr-var-data-in' type='checkbox'><label for='data-5465219a-e1a4-4592-bb0f-4370dbde7545' 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>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
+       "[2022-10-04 13:33:20] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div><div class='xr-var-data'><pre>array([[[10540800000000000, 10454400000000000, ...,  1641600000000000,\n",
        "           777600000000000],\n",
        "        [13478400000000000, 14515200000000000, ...,  9763200000000000,\n",
        "          8035200000000000],\n",
@@ -1128,17 +1130,17 @@
        "        [19440000000000000, 19440000000000000, ..., 13910400000000000,\n",
        "          9244800000000000],\n",
        "        [21686400000000000, 21600000000000000, ..., 13478400000000000,\n",
-       "          8899200000000000]]], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f71180a-ea2f-4be6-bff1-d8adefbdec92' class='xr-section-summary-in' type='checkbox'  ><label for='section-4f71180a-ea2f-4be6-bff1-d8adefbdec92' class='xr-section-summary' >Attributes: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Services (CCCS)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Canadian Centre for Climate Services</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:43:58 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v tasmin http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/tasmin.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "          8899200000000000]]], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04d2e566-9aea-4bfa-a6d1-c31aded425b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-04d2e566-9aea-4bfa-a6d1-c31aded425b5' class='xr-section-summary' >Attributes: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Services (CCCS)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Canadian Centre for Climate Services</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:43:58 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v tasmin http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/tasmin.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd><dt><span>climateindex_package_id :</span></dt><dd>https://github.com/Ouranosinc/xclim</dd><dt><span>product :</span></dt><dd>derived climate index</dd><dt><span>institute_id :</span></dt><dd>CCCS</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:     (time: 20, lat: 6, lon: 7)\n",
+       "Dimensions:     (lat: 6, lon: 7, time: 20)\n",
        "Coordinates:\n",
        "    height      float64 ...\n",
-       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat         (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon         (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Data variables:\n",
        "    frost_days  (time, lat, lon) timedelta64[ns] ...\n",
        "Attributes: (12/20)\n",
@@ -1185,7 +1187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.6"
   }
  },
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diff --git a/docs/source/notebooks/subset.ipynb b/docs/source/notebooks/subset.ipynb
index 135175fc..c817d62b 100644
--- a/docs/source/notebooks/subset.ipynb
+++ b/docs/source/notebooks/subset.ipynb
@@ -6,10 +6,10 @@
    "id": "5ef8b9ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:48.337501Z",
-     "iopub.status.busy": "2022-07-06T15:48:48.336715Z",
-     "iopub.status.idle": "2022-07-06T15:48:48.359405Z",
-     "shell.execute_reply": "2022-07-06T15:48:48.357461Z"
+     "iopub.execute_input": "2022-10-04T17:33:25.232397Z",
+     "iopub.status.busy": "2022-10-04T17:33:25.231555Z",
+     "iopub.status.idle": "2022-10-04T17:33:25.256004Z",
+     "shell.execute_reply": "2022-10-04T17:33:25.253649Z"
     },
     "nbsphinx": "hidden"
    },
@@ -26,9 +26,9 @@
    "source": [
     "# Spatial subsets and averages\n",
     "\n",
-    "Finch includes processes to subset and average gridded data. Subsetting processes return a rectangular portion of the original grid. This portion can either be defined as a bounding box over geographic coordinates (longitude, latitude), or as a polygon outline. \n",
+    "Finch includes processes to subset and average gridded data. Subsetting processes return a rectangular portion of the original grid. This portion can either be defined as a bounding box over geographic coordinates (longitude, latitude), or as a polygon outline.\n",
     "\n",
-    "When subsetting a polygon or over a curvilinear grid, the subset usually does not align with the grid dimensions. In this case, the subset functions will return data over a rectangular array, but mask grid cells that fall outside the subset. "
+    "When subsetting a polygon or over a curvilinear grid, the subset usually does not align with the grid dimensions. In this case, the subset functions will return data over a rectangular array, but mask grid cells that fall outside the subset."
    ]
   },
   {
@@ -37,10 +37,10 @@
    "id": "8f4fd0c3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:48.368568Z",
-     "iopub.status.busy": "2022-07-06T15:48:48.367912Z",
-     "iopub.status.idle": "2022-07-06T15:48:49.417536Z",
-     "shell.execute_reply": "2022-07-06T15:48:49.416828Z"
+     "iopub.execute_input": "2022-10-04T17:33:25.266089Z",
+     "iopub.status.busy": "2022-10-04T17:33:25.265596Z",
+     "iopub.status.idle": "2022-10-04T17:33:26.386410Z",
+     "shell.execute_reply": "2022-10-04T17:33:26.385517Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "id": "500631a5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:49.420275Z",
-     "iopub.status.busy": "2022-07-06T15:48:49.420016Z",
-     "iopub.status.idle": "2022-07-06T15:48:51.017920Z",
-     "shell.execute_reply": "2022-07-06T15:48:51.017201Z"
+     "iopub.execute_input": "2022-10-04T17:33:26.390490Z",
+     "iopub.status.busy": "2022-10-04T17:33:26.390238Z",
+     "iopub.status.idle": "2022-10-04T17:33:28.060660Z",
+     "shell.execute_reply": "2022-10-04T17:33:28.059768Z"
     }
    },
    "outputs": [],
@@ -84,10 +84,10 @@
    "id": "f7a203aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:51.021766Z",
-     "iopub.status.busy": "2022-07-06T15:48:51.021182Z",
-     "iopub.status.idle": "2022-07-06T15:48:51.024599Z",
-     "shell.execute_reply": "2022-07-06T15:48:51.023988Z"
+     "iopub.execute_input": "2022-10-04T17:33:28.064004Z",
+     "iopub.status.busy": "2022-10-04T17:33:28.063716Z",
+     "iopub.status.idle": "2022-10-04T17:33:28.067022Z",
+     "shell.execute_reply": "2022-10-04T17:33:28.066519Z"
     }
    },
    "outputs": [],
@@ -113,10 +113,10 @@
    "id": "eee118da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:51.027622Z",
-     "iopub.status.busy": "2022-07-06T15:48:51.027024Z",
-     "iopub.status.idle": "2022-07-06T15:48:51.594940Z",
-     "shell.execute_reply": "2022-07-06T15:48:51.594250Z"
+     "iopub.execute_input": "2022-10-04T17:33:28.069837Z",
+     "iopub.status.busy": "2022-10-04T17:33:28.069609Z",
+     "iopub.status.idle": "2022-10-04T17:33:28.679642Z",
+     "shell.execute_reply": "2022-10-04T17:33:28.678317Z"
     }
    },
    "outputs": [],
@@ -131,10 +131,10 @@
    "id": "961b3e52",
    "metadata": {
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-     "iopub.status.idle": "2022-07-06T15:48:52.462427Z",
-     "shell.execute_reply": "2022-07-06T15:48:52.461660Z"
+     "iopub.execute_input": "2022-10-04T17:33:28.685281Z",
+     "iopub.status.busy": "2022-10-04T17:33:28.684805Z",
+     "iopub.status.idle": "2022-10-04T17:33:29.605163Z",
+     "shell.execute_reply": "2022-10-04T17:33:29.604334Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -143,7 +143,7 @@
    "outputs": [
     {
      "data": {
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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -168,7 +168,7 @@
     "\n",
     "The subset processes of finch also support subsetting from a polygon input. Polygons are passed as GeoJSON or zipped-ShapeFiles files. They can be either local or remote.\n",
     "\n",
-    "In the next example, we retrieve a dataset of polygons from a geospatial data server. Communication with this server uses the Web Feature Services (WFS) standard, an Open Geospatial Consortium (OGC) protocol. \n",
+    "In the next example, we retrieve a dataset of polygons from a geospatial data server. Communication with this server uses the Web Feature Services (WFS) standard, an Open Geospatial Consortium (OGC) protocol.\n",
     "\n",
     "We then inspect and select the polygon using GeoPandas, write it down in a local file and send the subsetting request to finch.\n",
     "\n",
@@ -181,10 +181,10 @@
    "id": "e2bc916c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:52.471309Z",
-     "iopub.status.busy": "2022-07-06T15:48:52.470827Z",
-     "iopub.status.idle": "2022-07-06T15:48:52.706626Z",
-     "shell.execute_reply": "2022-07-06T15:48:52.705343Z"
+     "iopub.execute_input": "2022-10-04T17:33:29.615949Z",
+     "iopub.status.busy": "2022-10-04T17:33:29.615395Z",
+     "iopub.status.idle": "2022-10-04T17:33:29.919073Z",
+     "shell.execute_reply": "2022-10-04T17:33:29.917103Z"
     }
    },
    "outputs": [],
@@ -215,10 +215,10 @@
    "id": "288f4067",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:52.711227Z",
-     "iopub.status.busy": "2022-07-06T15:48:52.710912Z",
-     "iopub.status.idle": "2022-07-06T15:48:54.880394Z",
-     "shell.execute_reply": "2022-07-06T15:48:54.879700Z"
+     "iopub.execute_input": "2022-10-04T17:33:29.927303Z",
+     "iopub.status.busy": "2022-10-04T17:33:29.926673Z",
+     "iopub.status.idle": "2022-10-04T17:33:32.389711Z",
+     "shell.execute_reply": "2022-10-04T17:33:32.389030Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -406,10 +406,10 @@
    "id": "22746b90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:54.882950Z",
-     "iopub.status.busy": "2022-07-06T15:48:54.882727Z",
-     "iopub.status.idle": "2022-07-06T15:48:54.911187Z",
-     "shell.execute_reply": "2022-07-06T15:48:54.910443Z"
+     "iopub.execute_input": "2022-10-04T17:33:32.392649Z",
+     "iopub.status.busy": "2022-10-04T17:33:32.392403Z",
+     "iopub.status.idle": "2022-10-04T17:33:32.422241Z",
+     "shell.execute_reply": "2022-10-04T17:33:32.421645Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -485,10 +485,10 @@
    "id": "da924370",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:54.913872Z",
-     "iopub.status.busy": "2022-07-06T15:48:54.913641Z",
-     "iopub.status.idle": "2022-07-06T15:48:55.223556Z",
-     "shell.execute_reply": "2022-07-06T15:48:55.222828Z"
+     "iopub.execute_input": "2022-10-04T17:33:32.425860Z",
+     "iopub.status.busy": "2022-10-04T17:33:32.425562Z",
+     "iopub.status.idle": "2022-10-04T17:33:32.516552Z",
+     "shell.execute_reply": "2022-10-04T17:33:32.515822Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -517,10 +517,10 @@
    "id": "b9f968d5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:55.226388Z",
-     "iopub.status.busy": "2022-07-06T15:48:55.226161Z",
-     "iopub.status.idle": "2022-07-06T15:48:56.070269Z",
-     "shell.execute_reply": "2022-07-06T15:48:56.069100Z"
+     "iopub.execute_input": "2022-10-04T17:33:32.519740Z",
+     "iopub.status.busy": "2022-10-04T17:33:32.519495Z",
+     "iopub.status.idle": "2022-10-04T17:33:33.068506Z",
+     "shell.execute_reply": "2022-10-04T17:33:33.067752Z"
     }
    },
    "outputs": [],
@@ -535,10 +535,10 @@
    "id": "ea7a2700",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:56.076321Z",
-     "iopub.status.busy": "2022-07-06T15:48:56.076008Z",
-     "iopub.status.idle": "2022-07-06T15:48:56.269768Z",
-     "shell.execute_reply": "2022-07-06T15:48:56.268975Z"
+     "iopub.execute_input": "2022-10-04T17:33:33.071803Z",
+     "iopub.status.busy": "2022-10-04T17:33:33.071513Z",
+     "iopub.status.idle": "2022-10-04T17:33:33.267504Z",
+     "shell.execute_reply": "2022-10-04T17:33:33.266754Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -547,7 +547,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -570,9 +570,9 @@
    "source": [
     "## Averaging over polygons\n",
     "\n",
-    "Finch also provides an `average_subset` process. The call is similar, but it instead averages the data over each polygon in the GeoJSON. In this case, in opposition to `subset_shape`, the intersection between polygon and grid cells is exact: partial overlaps between the polygon and grid cell outline are considered, as well as holes within the polygon itself. \n",
+    "Finch also provides an `average_subset` process. The call is similar, but it instead averages the data over each polygon in the GeoJSON. In this case, in opposition to `subset_shape`, the intersection between polygon and grid cells is exact: partial overlaps between the polygon and grid cell outline are considered, as well as holes within the polygon itself.\n",
     "\n",
-    "The computation of these overlaps can be time-consuming, especially when the polygon is defined at high resolution. In turn, long execution time can cause time-outs on the server side. As the second example below demonstrates, a strategy to speed up execution is to simplify the polygon shape. \n",
+    "The computation of these overlaps can be time-consuming, especially when the polygon is defined at high resolution. In turn, long execution time can cause time-outs on the server side. As the second example below demonstrates, a strategy to speed up execution is to simplify the polygon shape.\n",
     "\n",
     "\n",
     "### Basic averaging"
@@ -584,10 +584,10 @@
    "id": "2955da6e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:48:56.272698Z",
-     "iopub.status.busy": "2022-07-06T15:48:56.272455Z",
-     "iopub.status.idle": "2022-07-06T15:49:02.600748Z",
-     "shell.execute_reply": "2022-07-06T15:49:02.599868Z"
+     "iopub.execute_input": "2022-10-04T17:33:33.270383Z",
+     "iopub.status.busy": "2022-10-04T17:33:33.270173Z",
+     "iopub.status.idle": "2022-10-04T17:33:34.570874Z",
+     "shell.execute_reply": "2022-10-04T17:33:34.568727Z"
     }
    },
    "outputs": [],
@@ -603,10 +603,10 @@
    "id": "6fec7d59",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:02.603843Z",
-     "iopub.status.busy": "2022-07-06T15:49:02.603593Z",
-     "iopub.status.idle": "2022-07-06T15:49:02.812428Z",
-     "shell.execute_reply": "2022-07-06T15:49:02.811689Z"
+     "iopub.execute_input": "2022-10-04T17:33:34.581493Z",
+     "iopub.status.busy": "2022-10-04T17:33:34.579248Z",
+     "iopub.status.idle": "2022-10-04T17:33:34.788106Z",
+     "shell.execute_reply": "2022-10-04T17:33:34.787328Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -615,7 +615,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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EmAs8KIR4Xko5qRJXKeWvgV8DrFixYvy5oz7z0DlNkVLi9DspDaq6GbLLkNllVDv72O1ViYNUK886PZ0sKV7Cm91v0uZqY2nJ0hMfpJMQwUiQI84jrK1cy8FetdKxcMOnIOiB258DIfjwunp+v6GF37x+mK9fc3KERYk4A/xOCPHbY1+pGIwQwowKMg/HpeZuBaKfPwacdexxUsr9qLTeaD+1HiFEuXb+ckD32NB5x+IOugnLMFlaER8ZVoS1hnrzAPbBTAzCkFLlmTfoxel3srx0OQJBu6s9Zdd6J9I22EYwEmSmdSaHetyU56Zj7twKRzdA6wYAyvIsvHdpFX/Z0ka/239SxpVI6uxZ4G/a6yXUesnxDm1TRCgv6weA/VLKH8S91Qmcp31+IXBI239GdPFfCFELzAZaRjn106hghfbxqWSP/WRx0003sXr1ag4cOEBVVRUPPPDAqR6SzmlGtFjTGgkRNGaAKR2sNVQJVUtTklmS0qLNqBCgJreGsqwyPXWWZA45DgHEijVXFIfAp3XSfOO+2H53nFdPIBzh92+0nJRxJZI6G2GCI4R4BHgxBWNZC9wC7BZC7NC2fQn4d+A+LagMAXdo760DviCECAIR4ONSyn5tjPcDv5RSbgX+F3hUCPFh4ChwQwrGflJ45JGT0ztC58wl2iKgMBIimJaHGcBaTVG4l84BF7NnVKZ0lhENYhXZFVTnVOuBJsk0O5oxCAM1OXU09bZw0zwtgVN3Dhz6B/Tsg9J5NBRnc9n8Mv6wsYWPnldPjsWc0nFNxr15JlCT7IFIKddLKYWUcpGUcon2ek7bvlxKuVhKebaUcpu2/0NSyvnafsuklE/GnesjWpBBSmmTUl4kpZypfRwYYwg6Omc8ziEVaIrDfsLpWkmZtQYjYaSrh7LM8pSmzqIzmvKscj3QpIAmRxPVOdX0DUbwhyLMN2m/yyu+B+ZM2PDj2L4fO6+BwaEQj2w+mvJxJbJG4xJCDEZfwDNMVR6so6NzSojOaEojPmTGcKAB1S4g21hCr7eXYDiYkut3ebowCRPFGcVU5VQxMDSAN3jy1E9nOofsh0Z4nFWHW8FiheI5sOxW2P0YONWMdXG1lbWNhdz/+hH8odT2ITphoJFS5kgpc+Nes45Np+no6JweRNdoKiI+DJlaoMmLBpp+zLKQiIzQ7e1OyfU73Z2UZpViNBipyqkCdIlzsvCH/Rx1HVXS5l61jJ7vOQwlc0EIWP1xkBI2/jx2zJ3nNdLr8vPEW6lVGiYyo3kpkW06OjrTn2iLgKqIG1NWodporQZUX5pIQKulSZHEucvTRXlWOQDVOeq6uvIsObQ4W4jISGxGU5lnwdj/tgo0oGauC6+Hbb+PCQTWNhaysDKPX73WTDiSOpeGMQONEMIihCgAioQQ+ZpnWIFWTFmRshHp6OikDKffiUlYKMJNWo4WaMwZyKwSaox9+HyqliZVyrNooHlyewe5xjJAn9Eki5GKMzcrCv0w5ITiucM7rfm0qqnZohSrQgjuPL+BFpuXv+9JzSwWxp/RfBTYBszRPkZfTwE/S9mIdHR0UsZgYJA0MkkT4eHUGSCsNTSYBrA5MzEKY0pmNMFIkF5vL9mmYu76yw6e2+kgNy2Xdrc+o0kGTfYmTAYTVdk1NPe5OTtbU5yVzBneqWwBNF4Mb/4Sgqq2/dL5ZdQXZfGL15pS5j03ZqCRUt4npZwBfE5KWS+lnKG9Fkspf5qS0eiMSVtbGxdccAFz585l/vz53HfffSc+SEfnGBx+B2nSor7IiDMyt1ZTaein0+GnLKssJYGm19tLREYwS5Weax3w6sqzJNLkaKIut44uZ5BAKMI8k/Y7jJ/RAKz9DHj6YOefADAaBB89r549HYOsb+pPydgSEQP8RAixQAhxoxDig9FXSkajMyYmk4nvf//77N+/n02bNvGzn/2Mffv2neph6ZxmKOfmNPXFiEBTQ3G4l44BDxXZFSlJnUXNNGXQCsBRmx5okkmTo4mZ1pnDirNQK2QWQnbxyB3rzoGKZbDhJ6C1BLh2aSWluemjthBIBomIAb4K/ER7XYCy6b86JaPRGZPy8nKWLVsGQE5ODnPnzqWj4+T0d9c5c3D6naSFtH/7YwKNSQbB3av60qRgRhOtofH7lM9a64CH6pxqutxdKW9NMB04aD+YstSUN+ilw91Bg7WBQ1qgsbqbj5/NgFKgrf0MDByGt58FIN1k5GPnNVBhzSAQiiR9fIm0CbgeWAxsl1LeLoQoBe5P+khOI76z+Tu8PfB2Us85p2AO/3VWYuVJLS0tbN++nbPPPntC12h3tfPckef4yMKPYBCTqdXVOd1x+p1YQ1pPo/hAkxdXS2Mooc/bRyAcIM2YlrRrR2dJLk82Vxv+yXrHEsqzKgjJEN2e7pjc+UxkQ8cGPvriR/n2um9zVcNVST9/s0PNRBrzG3lqn1spzmwHYdGNox8w9yrInwHrfwRzrwYhuH3tjKSPK0oidxuflDIChIQQuShTyvqUjUhnXNxuN9dddx0/+tGPyM3NPfEBGsFIkM+99jl+sv0nHHYcTuEIdVJFKBKaUvfDiIzgDDhJD2pP1cfMaEDV0phkIRIZm4Ekiy5PFwWWAvz9Xfw47We8V7yKRevacaanzx7c9yAAfznwl5ScP9rsLJo6W1XkA/+gKtQcDYMR1nwKOt+ClvUpGVM8icxotgohrMBvUKozN7A5lYOa7iQ680g2wWCQ6667jve///28973vndCx9++6n722vQAcdh6mMb8xFUPUSSGfevlTlGaW8rU1X5vU8Z6gh4iMkBWIOjePFAOAmtGEA2sAVUtTm1s7lSGPICptli0qqNSKHiIBJbFuc7WxmtVJu9Z0osnexIbODdTm1rKzbycHBg4wu2B2Uq9xyHEIi9FCaUY5h/v2cntJVHE2SuosypKb4dX/UWabM85J6niOZdwZjeao/D9SSoeU8pfAJcCtJ7tHjY7qI/LhD3+YuXPn8tnPfnZCx+7t38uvdv2KS2ovQSBodqZmwU8ntezt38v+gf2TPj5arGmNhAkZ0sGcMfxmWhYys5AaQz9eb2pqaTrdnZRnlWPWvNSqRR9OdyZmg/mMLtp8+O2HSTem89MLf0qaIY3HDj6W9Gs0O5qZkTeDdoefQDjCnKjH2WhrNFHMGXD2R6HpBejek/QxxTNuoJFq5erJuK9bpJS7UjoinVF54403eOihh3j55ZdZsmQJS5Ys4bnnnjvhcUOhIb60/ksUZhTy1dVfpSK7Qk+dnYZ4g17sfjvdnskX1Q36VZ/4wkiQYFrece8Law31aQPYnBmYhCmpggApJd2ebgrSSykKKwltjaGPtgEfldmVZ2wtjWPIwTPNz/Du+ndTl1fHZTMu49nDzybd363J3sTM/JkxIUB1qAWyiiHq/jAWKz4M5qwRZpupIJE1mk1CiJUpHYXOCVm3bh1SSnbt2sWOHTvYsWMHV1xxxQmP+/H2H3PYeZhvrP0Geel5NFgbOOzUA83pRnR2MTA0QCAcmNQ5oj5nxRE/4XTr8TtYa6gSfXSkoJbG7rczFB4inULKhQ1Qabqj/e6kSJwf3Psgjx54NBlDTSp/PfRX/GE/H5j7AQBumHUDnqCH546c+CExUZx+J72+3pgjAECeq3n8tFmUzAJYfivs/is4UufinEiguQAVbJqFELuEELuFEPqs5jRgc9dmHtr3EO+b/T7WVKi8e0NeAy3OlneEnPRMIt66v9c7uSax0dRZaWQImWE9foe86lgtTWV2ZVJTZ9EaGhHOp0ILNGkEcdk6YoFmstJfKSW/2/M7frsnJY1/J00wEuSR/Y+wunx1bE10cfFiZubPTGpQjAoBoh5nVVZNcTZe2iyeVR9XkudNv0jamI4lkUBzOUpldiFwFfBu7aPONMYdcPPlN75MbW4tdy+/O7Z9Rt4MApFAyvvC6ySX+N9Xj7dnUueItggoj/gwZBQcv4O1FrMMgKefkszypAaaaKAM+q1UCBvSmA6AcLRSlVOFJ+jB7rdP6tw93h5sQzY63B1TSi0mmxdaXqDX18sH5n0gtk0IwY2zbmT/wH729u9NynWa7JriLH8mTb1uVhf5IOAeaT0zHtZqWHA9bHsQvKlp15WIM0ArUA1cqH3uTeQ4nVPLd7Z8hx5vD99a9y0yzZmx7Q3WBgB9neY0o8MVF2g8kws0I5ybs0cLNMO1NFmGYvp8fQyFhiZ1rWOJzmjcnmw1o6laAUBJqJtckzLXnKwgIP6Gvb13+xRHmhyklDy07yHqcutYV7luxHtX1l9JhimDRw8mZ1bT5Ggi25xNYXoxh/s8rMzS/j4SndEArB1ptplsEnUG+C/gi9omM/DHlIxmmpOqqt5kER3fy0df5smmJ/nwgg+zuHjxiH3q81QJlK48O73o9HRSkqlqTibbK2bQP4hZZFCEG3P2KIvEcRJns1TvJ6uWpsvTRYYpA4c9RIFwIWpVKrda9CFCwxLnybDXtheTMJFhymBbz7akjHeq7OzbyR7bHt4/9/3HFUfnpOVwxYwreP7I87gCrilfq8nRRIO1gdYBH4FwhLkG7aEk0RkNQOl8aLxEmW2GJrcGOB6JzEzeg7Kc8QBIKTuBnKSPZJpjsViw2WzTNthIKbHZbBjTjNyz8R7mFMzhzsV3Hrdfdlo2JZkl+ozmNKPD3cHM/JnkmHMmPaNx+p2YycQigiOcm2PkDfelCQesQPIkzp3uTiqyKgg5tFlLQQOhrDKqRS8+r1LATTbQ7Onfw8z8mSwuXjxtZjQP7XuInLQcrm4Y3a3rhtk34Av5ePbws1O6jpSSJkeTanamKc6qQi2QXTayTioRLrkHbvk/MCXPDSJKIgWbASmlFEJIACFEVtJHoc5bDfwBKAMiwK+llPcJIZYAvwQsQAj4uJRysxDiEuB/gTQgAPynlPLlUc77NeDfgT5t05eklBOWfFRVVdHe3k5fX9+Jdz5FWCwWftv2W1wBF795128wG82j7teQpyvPTjc63Z0sKFxAT1bPpNdoHH4HZqnVzox2E7LkIjPyqYn087ZH3fyTtZbX5emiPLucsFM7X14lhvxaql19bLSHKcksmVSgkVKy17aXS2ovoTSrlF/s+AWDgUFy0xJ3zUg2ne5OXjz6IrfOv3VE2jqe+YXzmVc4j0cPPMr7Zr8PVbI4cWxDNhx+BzPzZ3Kww40QUcXZBGYzUUrnT2oMiZBIoHlUCPErwCqE+HfgQyiXgGQTAv5DSvmWECIH2CaEeAFl4nmPlPJ5IcQV2tfnA/3AVVLKTiHEAuAfQOUY5/6hlPLeqQzObDYzY0bqvICSwTPNz/C31r/x2eWfZVb+rDH3a7A28Pihx4nIiO55dhrgCXpw+B1UZFfQ4e6Y/Iwm4MQc1h4+xnjaFdYaGgMDrHemYzIkr5amy9PFnIJ5ePy96tEwtxJDQR117S/xlwEvVblVk1qjaXe1MxgYZEHRAqpzqpFIdvTu4Nyqc5My7snw57f/jEBw0+ybxt3vxlk38rWNX2NH3w6Wliyd1LWiirMGawMbtruosVow2A5C7a2TOl+qSEQMcC/wV+BxYBbw31LKnyR7IFLKLinlW9rnLmA/KnBIIPp4kgd0avts19J4AHsBixAiPdnjOl3o9nTz7Te/zbKSZXxw3vhdHGbkzcAX8k0rhY7O2ERv9pXZlZRmlU56RjPoHxzduTmePNWXpsPhpyIrOe0CvEEvDr+DLENxrIaG3Eqw1lLCAJ02J9U51ZMKNFFbpfmF81lYtBCTMPFWz1tTHvNk8Qa9/PXQX7m49mLKs8vH3ffyGZeTbc7msQOTdwqIKs6iqbPVhW4Ieic3o0khiT7O7gZeB/6lfZ5StHbRS4E3gbuA7wkh2oB7GRYlxHMdyl3aP8YpP6nVAP1WCDHBxOX0JyIjfPmNLxOWYb657psYDcZx948pz/T02WlB9GZfkV1BaWYp/b5+gpHghM/j8DtIC54g0FhrKQn30D7gTVpfmugDjTFSQIWwEbQUgtkC+bUYiOC3HaU6p5peX++EVW57bXtJM6TRmN9IpjmTuYVzT+k6zVPNT+EKuGIFmvEMeEYusmeaM7my/kr+0fIPHEOOSV2vydFEfno+ueZ8jvR7WJGl1VhNRHF2EkhEdfYRlInme1EtAzYJIT6UqgEJIbJRs6e7pJSDwJ3A3VLKauBu4IFj9p8PfAfVeno0fgE0AEuALuD7Y1z3DiHEViHE1um8DjMaTzU9xZtdb/L5lZ+nOqd6zP0e29rG6v95iVKLkrFGrcV1pjfxM5qyrDIkkj7vxP5GIzLCYGCQ9GivkTEDTQ1pkSHw2ijJKE+KNUy0hiYUyFM1NLlahju/DoBsXwfFlgpg4hLnPf17mFMwB7NBpQSXlSxjd/9u/OGxnjlTR0RGeHj/wywsWnic2nNbq50V33yBF/aNnI3eMOsGApEATzc/PalrHnIcojG/kVabl2BYMteg/fyKk2vaOVUSmdH8J7BUSnmblPJWYDlK7px0hBBmVJB5WEr5hLb5ViD6+WPAWXH7VwH/B3xQSjnqXVNK2SOlDGutDn4Tf/wx+/1aSrlCSrmiuLh4tF2mLVt7tlKSUcJ1M68bc5/DfW7++6m9dDmH6LYbKbAU6DOa04ROdycWo4UCSwGlmaXAxIs23UE3ERkhczTn5nisw8qzbGMJA0MD+EK+SY8dhmdkQ75cKoQNc77Wd8aqnKGrRS+mSBHAhAJbREbYZ9vHvMJ5sW1LS5cSjASTVgw5EdZ3rKd1sJUPzP3AcYv7D6w/TETCgxtaRmyfXTCbJcVLeOzgYxNWtEopaXY0j7CeqQy2qrTkaM4Pp5BEAk07EC/2dgFJbx6hOUU/AOyXUv4g7q1O4Dzt8wuBQ9r+VuBvwBellG+Mc974ROl7gNTalJ4C2l3tVOdWj6lcCYYj3P3oztjXh3rd1OfV6xLn04ROdycV2RVsbbUz6FYqpokKApxDyhXAGokQESZIyx59x7iiTWNYFXVGiy0nS5enC6MwYndmUGEYQGgyanIriBjMVIs+gn51rYkoz1qcLXhDXhYULYhtiy6qv9V78tdpHtr3ECWZJVxSd8mI7R0OH//Y20NRdjrrm/pp6feMeP+G2TfQMtjClu4tE7pet6cbT9Cj1md6XQgBua7msXvQnEISCTQdwJtCiK9pxZubgCYhxGeFEBPzqx+ftcAtwIVCiB3a6wqUNPn7QoidwLeBO7T9Pwk0Al+J278EQAhxvxBihbbfd+P82S5Apd/OKNpd7VRlj92d8KcvN7GzzcF3r1+ExWygqddNg7WBZmfztK0L0hmmw91BRXYFX3xiNw+tdwATn9FE7WcKI0ECaXnK22o08oaLNkMBNeuZavqsy9NFaWYpdvsA2XghT0udGYzI3CqqRS82p4lsc/aEAk28ECBKgaWAGXkzTrog4JD9EJu6NnHTnJtiabwof9jYgpSSX39wOUaD4JHNI80r31X7LnLTcifcPuCQ4xAQFQK4qctPV4qzRMw0TzKJyJubtVeUp7SPSS3alFKuB8YSky8fZf9vAt8c41wfifv8lqQMcJriD/vp9fWO2QZ3+1E7P32lifcureSqxRX88rVmDvW6ubRhBq6Ai35fP8WZp1eq8J1Gh7uDhUULeXnAS1hayCzLnLBiMOrcXBTxEx4vrZJhRabnUhvp522P+hefqiCgy61qaEIHNal07nAVgrGgljpHO2/avVTlVE0o0Ozp30OGKYMZeSPLDpaVLOOfLf8kHAmfUBiTLB7e/zAWo4XrZ14/YrsvEObPm9u4dH4Zy2ryuWRuKY9ta+ez75pFukmNzWKycE3jNTzy9iP0+/opyihK6Jrx0uaDPTtYXeCB9qHTc0YjpbxnvNfJGKTO2MQvFB+Lxx/i7r/soCzXwteuUU99jSXZNGszGtCVZ9MdV8DFYGAQq7kMfyhCj1NZ+E90RhP1OSsLDyEt4wsvhbWWBrOdfmc6aYa0KQeaTk8nZZnDDc/Ii3sostZSLfpotXknLHHea9vL3IK5GA1Gjtq8dDrUWtKy0mW4gq7YjTjVDAwN8EzzM1zVcBVWi3XEe/+3vQOnL8jta1UwvPnsGgY8Af6+Z+SDwvWzricUCfFk05MJX7fJ3kRJZgkZxhylOMvUzjkNZzSJqM5WCCH+TwjxliYR3qW3CZg+RP8xR1ObffNv+2kd8PL9GxeTazGDlCzID9Ph8FGeoRZideXZ9CZ6kzdp3mPeQJhCS8nE12i0GU259CJGs5+Jx1pDlaGPdsdQrEh0soQiIXq9veSaiyklroYmSn4tVumkz2ajKqeKDncH4Ug4ofO+PfA284vUA9TH/7SNu/6yA1AzGjh56zSPHXiMQCTA++e+f8R2KSW/33CE+RW5rKxTP/N1jUXUFGTy8Jsj02f1efWsLFvJXw/+lYiMnPCa/b5+9tj20GhtpMXmIRSRzDFqv6dppjiDxNZoHgZ+h6pVuSrupTMNiAaaY1NnL+3v4ZHNR7njnHpW1WsGiq98iw+9eTn5DOJwWchJy9FnNNOcaKCJBK2xbdmmwgkba0bXaCoiXkxZozg3x2OtHlFLM5VA0+vtJSIjmCOq4ZkUBsiJ0+doEmeD8yiVWVUEI8GE+u00O5rxh/0sKFyALxBmf5eLHUcdDAXDVGZXUpJRwvae1NfTBMNB/nzgz6ytWBvLEkR5o8nGwR43t6+dERPqGAyCm86qYfORAZp6Rxpq3jjrRjrcHWzs3Djqtdpd7Ty490Fuff5WLnz0Qo44j7CidAUHNY+zimCrWmNLn35WlIkEmj4p5dNSyiNSytboK+Uj00mIdnc7FqOFQsuwG2+/289/Pb6LueW5fPZdmhVN88vwr3sxRgLMEh009XqU8kwPNNOa6E3e6xn27kojn35f/4Sa1zn9Tswic2zn5nisNaRHfEifg2JL2ZRSZ7Fjw1YqsBHOLAFj3NKwtQ6ACtmLBeVOncg6TUwIUDSffV1OwhFJIBxhd4cTIQTLSpexrXdbysUur7W/Rr+vf0TPmSi/e+MIRdlpXLV4pEPADSuqMBvFcbOai2ouosBSEGuKJqWkyd7EL3f+khueuYHLn7ice7feiyfo4c4ld/L41Y/zkYUf4WCPG4OA3MFD03J9BhITA3xVCHE/8BIQq4KKq3PROYV0uDqoyqmKPTFJKfnC47sZHArx8EeWqAVHVw88cYdKWQy202jsoqlPrdO82vbqKR2/zvh0uDvIMGXQ7zRjNfjwRwQibCUiI/T7+inLKkvoPFHn5mwxpNr3jkd8XxpjCXa/HW/QO6ZB5HhE2wz4fapY02A9RrSSP1xLI4MqALa72zlr9HK3GHv695BjzqEmp4aXd7fEtm9pGWBlXQFLS5by95a/0+npHHX9Mlls7NxIljmLVeWrRmxv6ffw8oFePnXhTPU/GInA28/ArMspyk7n0vllPL6tnf+6bA4WsxIFmI1mrm28lgf3Psh3t3yXf7X/i9bBVgSCxcWL+dyKz3FhzYXHpckP9biYUWDBYDsEjRem7HudConMaG5HVdVfxnDa7N0pHJPOBGh3t4/4R/rLljZe3N/D5y+dzeyyHPUH/n93gN8N738UTBkszezjUI+qpRkYGpi0/YVO6ul0qxtlu8PHH7Lu43/MDxDyq9nNRAQBTr8Ts7SoL05UzBcncTaG1c1/sumzaKAZdGdRZRzAYD1mLTGzkIg5k2rRh8uThUmYEp7RzCuahxCC3e1OSnLSaSjOYmuL6tK5vFQJVVMtc97cvZllJcswGUY+sz+4sQWTQfCBs1XQ5u1n4dEPwlsPAvD+s2sZHArx7K6RNUrXz1KqtUf2P0JFVgVfWfUVXrrhJR664iFunX/rqGuxB3tcrMkfhHBg2lnPREkk0CzWKuZvlVLerr1SZkGjkzhSSlVDo63PtPR7+Pqz+1jTUMiHNJUL638Ah1+Fy7+jbMALG5ht6qG5zx1rgqanz6YvnR5VrNlu91EfaWGuqROvT+XgJyJxdvqdmMNan5ET9SmJzWj6CQZUu4DJps863Z0UWArodkQowzZSCAAgBCK/jhpDH+0DfiqyK04YaALhAAftB2P1M7s6nCyqymNlXQFbWwaIRCSN1kayzdkpFQT0entpGWzhrLKRsy/XUJDHtrZz5cJySnK14L5d6xW542EAVtUXUF+cxZ/eHLkKUZ1TzRPXPMGr//Yqv37Xr7lx9o3jlh/4Q2FabF6WxxRn0zN1lkig2SSEmHfi3XRONna/HW/IS1V2FaFwhLsf3YHJILj3hsUYDAKOboJXvg0LroNlmqNzYSNVkQ5abR6qsusAvdvmdKbD3UF5VgV2h53s8CClDOB0TdwdwBlwYg5pNSUnCjQZ+ci0HGqNNrxT7EvT5emiPKsc10AP6QRGSps1hLWWGaZ+Wm2J1dIctB8kFAmxoGgBbn+I5j43CyutrKgrYHAoRFOfG6PByJKSJSmd0UQr+VeWrxyx/a/b2nH7QzFJM65uaHpBBdnO7dCzDyEEN59Vw1tHHezvGhxxfH1ePXnpeQmNoaXfSzgimR3tqjlN12gSCTTrgB1CiAOatHm3Lm+eHsQrzn7+ajPbjzr4xrULqLBmgHcA/vph9XT67h8NV4IXzSTP34lRhvD5csgwZehWNNOUwcAgroCLHFMJJRFlommVTgYcQTJMGRNOnaUFtb+BEwUaIRDWahrTbPQ5zKQb0yc9o4kGGga1+phjZzQA+bVUyh5abZ6Eamn29CsXqfmF89nT4URKWFSdF5MQb2kZAJTM+bDzMPYh+6TGfiK2dG8hJy2HOfnDN/dIRPLghhaW1VhZXG1VG3f+GWQErv8tGEyw808AXL+8ijSTgT8dIwqYCDHFWaBFecelpaQv5ZRJJNBcBswE3sXw+owub54GRJ8yfZ487nvpEFcvruCaJZUgJTz1CXD3qD9uS1y3wcKZGGSYGtHD4T4vM/Jm6KmzaUr05m6OFFIl+offGOymJLMk4UATc24OagqsRFr8WmuoEv202ydfSyOlpMvdRX56KYVhbfx5owWaOixyCLe9h6rsKgYDg7G6n9HYa9tLgaWA8qxydrer/RblDVGT7qU4Jz22TrOsVNXTpKptwObuzSwvXT7CfeCVA7202LzDsxkpVdqsehXUrIKZl8LOv0A4hDUzjXcvLOf/tnfg8SeuIIznUI8Lg4AcV9O0LNSMkogzQCtQDVyofe5N5Did1BN98vvX/jAWk4FvXKOZC775KzjwHFzydahcNvKgokYAGgydHOpx05DXoBdtTlOiN/dQIJ/KuEBjDfZRNIGiTVfARURGyAiewLk5HmsNxZFe2u2Tr6Wx++0MhYewUBjX8GwUqyTNxbkg0InVrKTA481q9tr2Mq9QCQF2tjuotGZQ+OyHEE98hJV1+bEZzYKiBZgN5pQEmm5PN22utuPWZ373RgtluRYuW6CpAdu3gO0QLNWKOZfcDJ5eVW6Acgpw+0M8s3NyM8aDPW4aCtIx2JqmbdoMEnMG+CqqLUC04ZgZ+GMqB6WTGO3udgothXTaI8woziIv06xywP/8Msy6HFbdefxBhTMBWJ7ZT1Ofm3prPT3eHtwB90kevc6JiM5oPJ7cEYGmTAyQbSpKuGhz0K/WAKyRMBEMkJ57giMAaw0ZYTcMOSm2VMR6ykyEqOLMEMmnQtiIGMyQNcrCdkzi3Aeaym2sdRpv0Euzoznm2Ly7w8nS8gz1d9+6kbOqs2m3++hy+kg3prOgaEFK1mk2d28GGBFoDvW4WN/Uzy2razEbtVvr9j+CORPmv0d9PfNdkFkYEwUsr81ndmkOf9o8ufTZwV4XawqcEAme3jMalLX+1YAHQGufPP1KT9+BRBVnbQNeqvMzYWgQHrsdskvg2p+P7tBryYXsUuan99LUM6w8O+I8cpJHr3MiOt2dZJoy6XMYaUizQ6YyWywTA6SLAvq8fQnZtUR9zgojIYJpuWBI4N8+b7gvTaahGKffOeGHkWh7gaDfqhqe5VSMfm3rcKDxe63A2I7RB+wHiMgI8wvn4/QGabV5OT+/GyIhCPs5J1MFqGj6bGnJUvbZ9k25p86xbO7ajDXdysz8mbFtv9vQQrrJwE1naZLmgAf2PAHzrh2u1jelwcIbVcbBO6BEAWfXsKvdGUsDJoo/FKbV5mV5hjazPZ1nNEBAqvJaCSCEmJ6rTe9AOtwdVGZX0W73UZOfAc/eDY5WuO7+8YvyCmdSRydH+j3U5qhcsq48m360u9uVtNnho9Zog5K5RNJyKBcDiJCVsAxjG7Kd8DxR+5miiJ9wujWxi8cVbaZL1WwtWo2fKNEZmdudQ6Vh4PhizSjp2cjMQqpFL90OSaGlcMwZTbwQYFeHA4ClhuGHpDrvLjLTjGzV0mfLS5cTkiF29yWvA72Uks3dm1lZthKDULdQhzfAE2+1c+2SSgqyNBn5/mcg4BpOm0VZcrOqednzOADvWVZJhtnInzZPzHDlcJ+HcEQyy9ABCCiaNdVvLWUkEmgeFUL8CrAKIf4deBG4P7XD0jkRwUiQLk8XVnMpgXCE833/gD1/hfO/BLVrxj+4qJFi/1EC4QiRQD5phrTTTnkWCAcIhAMn3vE0ptPdSVV2FR12H6WyD/KqEbkVlIsBwgH1hJzIOk10YV05N1sTu7g2y6gSfWSG55JuTOeloy9NaPxdni4yTBn0OQ1UGQYQo0iboyjHaBtHB8aXOO+17aUks4TizGJ2aTOA6qEDkFUChTMxtm1iWU0+W7QZzeLixQhEUutp2t3tdHm6WFk2LGv+85Y2hoIRbl9XN7zj9j9C/gyoXTvyBOWLoHQh7FDqs1yLmasXV/DUjk5cQ8GExzFCcZZfB2kTd244WSQiBrgX+CuqxfJs4L+llD9O9cB0xqfb3U1ERkinmEbRzln7/xdmnAvnJNCLrnAm6UEn+QxypH+I2rza025G870t3+PCxy5ka/fWk37tk9EsTkpJp7uTsqxy+pwu8kI2sKpAU21yxIo2E1GeRVNn5REv4kT2M1EyC5DmTGaYbPQ4I6ytWMtLR19KyFk4SlTa3O3wUCRHKdaMJ7+WWkMvRweUxHnMQNO/N1aoubvdSV1hJmk9O5TopXY1tG1iRW0eb3cPMjgUJC89j8b8xqSu00TrZ6LrM6FwhD9saGF1fSFzyrT1r4Ej0PI6LHn/6CnsJTdD51vQux9QogBvIMyTOxJfCzvU48ZoEGQPHprW6zOQmBjgO1LKF6SU/yml/JyU8gUhxHdOxuB0xqbNrf4RI8EC3m98Sf0tv/c3kEijpyKVV64XXRzqVcqz021Gs39gP06/k39/4d95qumpEx+QJA7aD3L+o+fzdPPTKb3OYGAQd9BNlrGYEgYwEFHrJrmVlIm4os0EAk1UDFAZ8ZzYuTmKEAhrDfVmO+12HxfVXkSvt5e9/YmnzzrdnZRnlzPk6MFEeHRpc5T8OooifbT1u6jOqabH03PcjNUVcNEy2DJCCLC8Ig36DkDFMqhZA0NOzs3rJyJh+1EHoOppdvbtnJAJ6Xhs7t5MoaUwtr75z309dDqHuH1t3fBOOx8BBCy5afSTLLxB1dRos5pFVXksqMzl4U2tCT/IHOxx0VBgxjAwPds3x5NI6uySUbZdnuyB6EyMqPzT57VSI3qhoAFyEjNYpFBJnJdm9tPcq5RnHe6OpC+YppJuTzcXVF/A8pLlfPmNL3PfW/dN6Gl7sjzT/AwDQwN85Y2v8Nzh51J2nVgfmvgaGms15FaQH7HT74B0Y3pCNjTOgJM0kUmh8GDKTjDQAFhrqDb00W73cV7VeZiEiRePvpjw4d2ebkoyysj0aWMcTdocu1YtJhnC4O6mNKMSiTxOUr3fpp7+5xfOp9/tp8Ph4/zsTkBCxVI1owHmh/ZgNIjYOs2ykmV4Q14O2A8k/r2PgZSSLV1bWFm2MmZk+/s3WqguyOCiuWoti0hEBZCGC0Z1QgAgu1jV1OxSNTXKKaCWt7tdbG9zjHv95j43D21qZVurnXVWpxJCnK4zGiHEnUKI3cDs+IZnQogjgO4McIrpcHdgNpgZcFiYYerHoPX1SAhrLRjMLMns41CvUp5JJC3OllQNN6kEI0H6fH3Myp/FLy75BdfNvI77d9/P5177XEqDpZSSF1pf4Kyys1haspQvrf8SL7S+kJJrRW+ywSErlUSLHVWgMRAhPNhDaWZpQms0Dr8DE1nkCi+GjAkEmrxqirW+NLlpuawoW8FLR19K6InbG/Ri99vJMhQP19CMO6MZVp6ZIkpdd2z6bI9NCQHmFc4bLtQ0aDPxiqXq7zqngvSON5lXnjvsEBAt3ExCf5rWwVZ6fb2x9Zm9nU42twxw6+o6jAYtRXbkNXC2qbTZeCy5SRVVazU1Vy+pIDvdxMObRkqdOx0+/rqtnc/+ZQer/+dlLvr+a3zlyT2kmwxcU6XZ15zGM5o/oRwAnmZkw7PlUsrjmy/onFTaXUqR1Dbgo4Le2D9qQhhNUNhAo6GL5j43M3JPL3PNPm8fERmhPKscs8HMV1d/lc+t+Bwvtr7Ih/7+Ifq8fSm57v6B/XS4O3h3/bv52UU/Y0HRAj7/2ud55egrSb9WNNB4vLlUGaI36irIrQBU0WahJTF3gJHOzQkUa0ax1pAZdiH9gwz6QlxcczGtg60JFfhGZ1rGSAEVsWLNcQJNVOJs6CXoV2M8NtDs7d9LZXYl+ZZ8drU7EQIqvG+rAJxdrNZCalfD0Y2sqLWyo81BIBShLKuMiqyKpAgCjq2feXFfL0LAdcviZi47HgZLHsw5gcn9zEshoyBWU5OdbuKaJRU8u6uTJ7d38KX/280F977Kmv99mc89tpNXD/axvC6fb79nIa9+7nze+MKFLE7rAmGY1oozGCfQSCmdUsoWKeVN8Q3PpJQDqRiIEKJaCPGKEGK/EGKvEOIz2vYlQohNQogdQoitQoiztO2XCCG2ad5r24QQozZiEEIUCCFeEEIc0j5O4D9t+tLubqcquwq3vRuL9Mf+UROmsJHyUDveQBizLMEojCl1CHD6nQQjiStqxiN6EyvPUlXkQghunX8r911wH83OZm5+7mYODEw9TXIsL7a+iFEYuaD6ArLMWfzi4l8wp2AOn33ts7ze/npSr9Xp7iTbnE2vw8DMdDtkl4IpPRZoVNFmYcJrNKZEnZvj0STOlaKfNruXC2ouAEhIfRYt8AwHrJQLGxFTxvjXzqtGIqgWfdgH08kwZRznDrDXNiwE2NXuoKE4G3P3djWbiVKzGlxdnFvsZSgYYW+nmvksK13GWz1vTVnIsaV7CyUZJdTmqv+3jYf7mV+RS35U0uxzKFnzwhvAbBn/ZKY0WKTV1PiUSu7ms2vwhyLc9ZcdPL2jk/qiLL7y7nk8/5lz2Pr/LuZnNy/j5rNrqCvKUqm7vv1QUH/ia51ippOVTAj4DynlXGAV8AnNNfq7wD1SyiXAf2tfA/QDV0kpFwK3Ag+Ncd4vAC9JKWeimrd9IXXfwsmj3dVOWVYFlmhhm3ZTSJiimeT42jESprXfT3VOdcpmNMFwkKufvJrf7v5tUs4XrTg/tunXBTUX8OBlDxKRET74/Ad5re21pFwPhtNmK8tWYtUkwjlpOfzykl8y0zqTu165a8wWvJOh0z3cHqDWaIsVUEZnBeVa0WaPt+eEa1MOvyNx5+Z44mpp2u0+SjJLWFy8OKFAE/0d+Xw5VBpsiLzq0dVXUUxpkFtJvamPNruPqpyqEYHGPmSnw93BgqIFSCnZ1eHk7HID2I+MtFmqUes0y1HrOdtahws3bUM2jromb2AppWRL9xZWlqv1maFgmLdaHayOtkoHVRsTGjpx2izKMTU18yvy+N3tK3ni42vY8d+X8MBtK/nwuhnMLc9VjuzH0vv2tE+bwTQKNFLKLinlW9rnLmA/UIkqFI16ZuQBndo+2zWXAoC9gEUIkT7Kqa8BHtQ+fxC4NiXfwElkMDDIYGCQbEMpVUJLE00kdQbKXDMSpFr00tSrum2mKtDs6NvBwNAAB+0Hk3K+sQINwNzCuTxy5SPU5dXx6Vc+zUP7HkqKHLnJ0UTLYAuX1I7UxuSl5/HrS35NbV4tn3750zHp61Tp8HTEAk0ZfUoIAMrC32ihTAxgCFsJRUIMDI2fZHAGnKSFEnRujiduRtNu9wKq3XA0hTgeXe4ujMKIfTCDGqMdMd76jIbIr6XeZFPtArJH1tLEWjcXzqdn0E+fy8+5Wdr78TOaknlgySO3dwu1hZmxdZpkNEI77DyMbcgWS5u91WonEI6wuiEu0Ox4WI0hfkzjUbYIShfE1GcAF8wuYVlNPibjCW7PIT8MHJ72QgCYRoEmHiFEHbAUeBO4C/ieEKINuJdhz7V4rgO2Syn9o7xXKqXsAhXMQGtMfhrT4VL/5KZIkfKHgomnzjSJ8+KMPpo0QcDRwaMEw8lJb8UTfdKPBoip0u3pJi89b8zWwiWZJfzu0t9xYfWFfHfLd/nOlqmr8V9ofQGB4MKa4zO0VouV31zyGyqyK/jES5+YsomjlJIOVwdlmRX0uXzkB3uHZzRCQF60aFPrtDmOICAiIwz6B0kPTMC5OUpWMdJkod5ko92uRBYX1VwEwMtHXx730E5PJ6WZpXQ5AkoMkECgIb+OStnD0QGvahfgbo/N1qKy6nmF89jZ7gBgAdqDUfmS4XMYDMop+egmVtQWsLXFjpSSGXkzyEvPm9I6TXR9JioE2HjYhtEgWFmnCSx690PHNlj6gfFnb/EIoWY1HdvU7GQi9B8CGT4zZjRCiPdq6xtOIcSgEMIlhBg80XGTRQiRjSoOvUtKOQjcCdwtpawG7gYeOGb/+cB3gI9O8bp3aGtAW/v6UrOYnCyiPlBBv5Vq0UskoxDSsyd2Ek3ivDzLppRn1nrCMkzr4MRsMBJhQ+cGYPLNs46l29NNWeb4Uu5McybfP//7/Nvsf+Ph/Q+zz7ZvStd8ofUFlpcupyijaNT3CzMKuf9d91OaWcqdL97Jrr7JCzOdfifekJdMUUwRg5hkYERqVORWakWb6nc+nrmmK+BCIifm3By7kEDkVdOQNhCb0dTk1jAzfyYvto4vc+5yd1GeXU6PfRBrxD6+tDmKtRZr2Eav3UFldhX+sJ9+n1Lc7bHtoS63juy0bHa3OzEaBGWe/UrWf2xr6trVYDvEuvIINk+AI/0eDMLA0pKlU3oI2NK9hfKscqqy1feysdnGwso8cixmtcP2P6ramEX/NrETL7xxRJ+ahOnTAtMZMqP5LnC1lDJPSpkrpcyRUiZg/zpxhBBmVJB5WEr5hLb5ViD6+WPAWXH7VwH/B3xQSjnWSnaPEKJc278c6B1tJynlr7WW1SuKi8dunTodiOauXa48agx9iPwJrs+A8kLLLGSOuVvNaFKkPHMMOdhn20dOWg4DQwNJkR/HmmmdAIMw8OllnybDlMGf9k/wnziOw87DNDmauLj24nH3K84s5v533U9+ej4fe+Fjkw5uHR4VkA2RgmHX5uiMBiCnnHIxgNOlbAfHm9FE7WesUfNNS2KdG2NYa6gSwzMagItrLmZ77/ZYEBiNLk8XZZllSFc3BmSCMxo1Ky+N9JFlUDUp0fTZvv59zC8abt08qzQHY9f249tggCrcBFaZDgHDBpvLSpbROtg67rjHIiIjan1Gq5/x+EPsaHMMp83CQVUTM+syyBr9YWRMsouVq7PWpyZheveDMMYeGqcziQSaHinl/lQPRKjqpweA/VLKH8S91Qmcp31+IXBI298K/A34opTyjXFO/TQqWKF9PHll5Cmi3dVOXnoe3Q6oM9kQE02bRSmcSU2kE6cvSI6xEoFIuhXNpu5NSCRX1ateeVFH36nQ5ekadX1mNHLTcrm64WqeP/L8CdcyxiL69H5xzfiBBqA0q5QHLn2AnLQc7njhjkm5YkeLNYP+uD401rhAk1tBYcRGv0NgNpjHVZ5FA01BOETQlKOk7RPBWkNJpIe2AW9sreuimouQSF5te3XUQ0KREL3eXnLMJZRKbfzjSZtj1xqupYkEVDqqzdVGr7eXXl8vCwo1IUC7gzUlIRjsUI4Ax1KxFEwWyhxvkZ9pPr6eZhKzmkP2Qzj8jtj6zNZWO6GIHBYCHHoBPH0qbTYZltwM7m44PAGpfN/bUNig1IjTnEQCzVYhxF+EEDdpabT3CiHeO9bOQogfJ/D65iiHrgVuAS7UpMw7hBBXAP8OfF8IsRP4NnCHtv8ngUbgK3H7l2hjuF8IsULb73+BS4QQh1AuB/+bwPc8rYlKm9sHPJRG+iYuBIhS1EjBkEqVtQ0EqciuSLoVzcbOjeSk5cQW0SfT1yQeT9CDK+CiPPvEM5ooN8+5mUAkwOMHH5/UNV9sfZHFxYspzSpNaP+K7Aruv/R+fEEfjx18bMLXi/WhcedQbYyroYmSW4mJEIHB/hN22oz6nBXJAKFEDTXjsdaQFXIQCXiwe9X63az8WVRlV42pPuvz9hGWYdJkXMOzcQw1Y8SKNnvxeHMwCANtrrbY+sz8ovm02304vEHWjSYEiGJKg8oViKMbWVFXwFZNeTavYB4Wo2VSgoBj62c2NtswGwUrtPbRbP+jMvZsHM1IJQGOqakZFynhyOvQtvm0WJ8BSOTxJhfVVfNdcdskw+msY7kGJUMejy8AX47fIKVcD4y1grb82A1Sym8CowUspJQfifvcBlx0gvGcVnS4O5iTP4ft9g7MIjhxaXOUwpmkDf2RHLw0p0B5JqVkQ+cGzi47m+oc9UQ+2d7zUaI1NCdao4mn3lrP6vLV/PnAn7ltwW2YDeaEj21ztbF/YD+fW/G5CY2zOqeaFWUr2NCxAVaeeP942l3t5KTl0Os0cLHFAYa8kSkvrZYmN9hPQUbpuDY00RYBpeEhpGUSJWRxyrNtrXYumVeKEIKLai7i4bcfxhVwkZM2sj1V9GFChPITK9aMkl2GNKZTF+6jfSBIeVY57a52BAKDMDCnYA4v73MAME82qULF8kWjn6t2Nbz+fdack8YL+zz0ufwU56SzsHghGzo3EI6ER7RgPhGbuzdTnVMde8DZ2NzPkmormWkmcPfBoX+oRoMTnTFGMaWp2pttv1M1NaOtpQV9sPuv8OYvoWePCkwrbp/c9U4yibg33z7K60PjHPJDKeWD472AXyXvW3hnEY6E6XB3UGgpIz+gpaGsdZM7maY8m5/eGzPXbHG2JM188MjgEbo93ayuWE1xZjEmg2nKgoCoci3bWESHI/H1nvfPfT+93t4JW93H0mYnWJ8ZjbUVa2l2Nk84Xdjp6aQyu5I2u1fV0MSnzSAWaMq1TpuJrNFURHyIiQgBomiBZlb6AC/sGw5oF9deTCgSGrVQNfowMTSUqxqeWfISE6sYDAhrNTPTbBwd8MRqafbY9tBgbSDDlMGuDgdpRgNFrn3qaT5tjPZYNatBRliXoVKX21pV+uz6mddz2HmY3+/9fcI/gnAkzLbubbHZzOBQkN0dzuG02a6/KL+xJVM0TDmmpibGYBe89A344Xx4+pNqRnP1T+Cz+6Bh1Dr1aUciqjOLEOITQoifCyF+G32Nc8hfxznXVQBSyh9NfKg6AL3eXkKREBZKhqXNk02daW2dV+XalCDAWk8gEkiaOiwqa15TsQaDMFCRVZG0Gc2fN7q44Huv8pctiRXgnVN1DtU51RMWBbzQ+gLzC+dTmZ3AE/kxrKtcB8AbneMtIR5Pp7uTiixVQ1NO30ghAIxwB0gnnx5vz5i1QtFAUx7xYEzUuTkeLdCcXzLES/t7CUfUdRYVL6Ioo2hUk83ow8CgK5tak10VayZ8vVpqDf0jamn29e8bdgRoczKnLBtj5/bR12eiVJ8FwkC9ZxfpJkOsP83lMy7nktpL+OmOnybsHvG2/W1cQVdM1rzlyAARCasaCtVNf/sfoXIFlEwxjVW+GErmD9fUtG2Bv34YfrQAXv++km3f+gzc+QYs+yCYM6Z2vZNIIms0DwFlwKXAa0AV4Bpn/5e0OpgRCCE+BPxo4kPUiScqbRahguFAM5F/5Hjy60AYWaDNaKK258lap9nYuZGanBqqclR+viJ76oGmy9OFQRho7zcRjET4r8d385Un9xAIjV8dbxAGbppzE9t7tyfcKbLL3cXu/t2Tms0AzMibQXlWOW90JB5opFSuxSUZ5Qx4AhQEe46f0WQVIw0myoUNQ8RKMBLE7rePej6n34lZZFIo3JizC0fdZ1yySsCYxrK8QWyeAG8dVdcxCAMXVl/I+o71DIWGRhzS5emiwFJAtzNClXEgsbRZlPw6SiLdHLWpWhq7347db2dB4QIiEcmeDifnlg6Btx8qxymKTM+BskUY2zaxuNoac3IWQvCVVV8hLy2PL63/UkLN87Z0jew/s6HZRprJwLKafOjcrmxgju2iORnia2p+eQ48cDEc+iec9VH49Ha46U+q51SiNTrTiEQCTaOU8iuAR0t7XQksHGf/u4EXhBCxZtpCiC9q288b8yidhIhKm4d8Wg1NdunkfY5MaZBfR73opM/lpyhN3dCSoTwLhoNs7t7M6orVsW0V2RVTFgN0e7opySyhyxnkumVVfPTceh7a1MoH7n+Tfvdo9brDXNt47YSkztGn9Xg3gCP9Ht71w9f4264Tp8OEEKypWMOmrk0J+7w5/A58IR8WQxE5eEkPu49/kDAYIadMK9pUazdjpc+cASdpZJMnPIjMSaTODAbIq6bWqBa/X9g3fJ2Lai7CF/IdZ73T5VaqwE6HT6nOEpE2R8mvJTPsAv8gBWnDgo/5RfNpsXlw+UOstowjBIindg10bOXsmmz2dA7iDaiUcL4ln3vW3MNB+0F+vuPnJxzS5u7N1OXWUZypyh42NttYXpOPxWyEg/8ABMy7NvHvcTwW3Qhp2RDwwOXfU+mxy74NBTOSc/5TRCKBJvof4hBCLEDZwNSNtbOU8jngY8DzQogFQogfAe8GzpVSto91nE5itLvbNWsP1f1wQu0BRqNoJiUB9Y/b5ZCUZpYmZUazo28HvpBvZKDJqqDf13/cE/BE6PJ0UZpZRp/LT1V+Bl+8Yi73vW8JO9sdXP2T9TH7+NHIScvhmoZreP7I89h8thNe64XWF5iVPytmoAjw6NY2Dva4+eQjb/HghpYTnmNd5TrcQXfCBZzRtKUxVDi6tFlD5FZSY3Lg86o1irEEAQ6/A5O0YEROrFgzHmsNZlc7qxuK+Ofe7liabmXZSnLSco5b9+r0dFKeVY7N7iA7MjixGY11WHkmwmoGZjKYmJU/K9a6eXbkEBjMyrplPGpWQWiI83M7CUckO7RGaADnVZ/HdTOv43d7fzeu3DkUCfFW71ux2YzDG2B/9+Bw/czhV1XAS7Rz6YnILoH/eBs+uRXOvkPNzM4AEgk0v9Ycj7+CqknZx7Cx5ahIKV8CbgNeBeqBi6SUo8/tdSaEMtMso8MepNrQN3HrmWMpbCTL3YqBSMyKJhnKs42dGzEKY+wfFNSMBqZmRdPt6cZqVi5CFVaVo75mSSWP37kGIQTX/3IDT24fe43pprk3EYwE+evBMZcSAbUWtqN3x4jZjJSSv+3qYlV9ARfNKeWrT+/le/94e1wvtbPLz8YojAmnz6KBJuDPo2K0Ys0ouRWUG+wMetQi+1gS50k7N8djrQbHUS6ZV0qLzUtTrxsAs9HMeVXn8Wr7qzEBiZSSbk83BellWEPR1G4C0uYocX1p/D413ln5s0gzprGr3YnFbKDAsQdK55+4fkQz2JwX3IMQxNZpovznyv+kPKuc/7f+/+ENekc9xT7bPjxBDyvL1frMpsMDSIkKNH4XdGyF+vMT//4SIT1HzSTPIBJRnd0vpbRLKV+TUtZLKUuklL8ca/84i5rnUdLoi4DeVFvXvFOI1tB02AYpjvRPXggQpWgWIuxnhmmAQz3DEuepdqvc0LmBRcWLYtLXLz+5mz67evqe7DpNREbo9nRjEeppsjJvOGW4oDKPpz65liXVVu76yw6+9bd9hMLHfw/1efWsrVjLXw78Zdx01ktHX0IieVftsKp/d4eTowNe3ru0il9+YBk3nVXNz15p5vN/3TXqtUDNohYXL2Z9x/qEvsfoz8blymWGSSswHTXQVFIUsdFrN2ISpjEDjdPvxDQZ5+Z4rDXg6eOSmcoQ5J9x6bOLay7G6XeyrWcbEJf6E3E1NJOc0fQ6BJXZlawsVTf53R0OFpTnYOjaObojwLFkl0BhIxmdm5ldmsPW1pEFu1nmLL659pu0u9r5/tbvj3qKmL+ZNoaNzf1kmI0srrJC6walNqvXVwRORCKqs1IhxANCiOe1r+cJIT481v5RixrtY5qUMivV1jXvJFTDs0pwtqs+8pOtoYmiSZzXWO009SnlmS/kS6hF8FhEbWeiabNWm4c/bjrKdm2iNFlV28DQAMFIEGMkn/8yPcKKf1wNnmE7kaLsdP74kbO5dXUtv3n9CLf/fgsO7/GLvTfPvZk+X9+4fl0vtr5IfV499db62La/7epijqGD618+D9PR9Xz7PQv59EUzeWxbO3c8tA1fIDzqudZVrmP/wP6ErE863B3kpuXS44TZFjsY0yFrFEuknHLS5RDeQbsq2hxjjcbhd0zOuTmefLU+UDZ0hMVVeSMCzZrKNViMllj6bNQamoms0WTkQ3ous9JtHB3w8ucr/8ynln2KUDjCno5Bzitygd85vuIsnprVcHQTZ9VaeavVftwDwYqyFdw6/1YePfjoqFLtLd1baLQ2UpihHm42Hraxoi6fNJNBpc1MFqUG0xmXROZnvwf+AVRoXx9EOSqPihDihGW3ieyjczzeoJeBoQFyTaXKOh6SkDpTgWZJZh+HeuKUZ1NIn0VtZ9ZUKM+p1w+pG2y/04JJmCadOosGv3Agj6WGJtL798FD16pmUxpmo4F7rlnAd69bxJuHB7j6p2/wdvfIifS6ynXU5NTw8P7Rq7AHhgbY2rP1uLTZs7u6uKN4DwafDZ76JCLg4bOXzOKb1y7glQO93Hz/Juye4wPb2sq1AAn1q+l0R2tofNSZBlTaabQ0iiZxzgv2UZgxujtAOBLGFXCRFh3SZAPNjPMAAYf+ySXzStnZ5qBnUK2zZZgyWFu5lpeOvqRmnG71OwoFrFQwiRmNEGCtpcFko3XAi9ViJd2YTnOfB18wzKoMzfQ1URv+2jUw5OD8AhueQJi3u48XzH5y6SdptDby1Q1fjcnBQQlatvduj8ma+1x+Dva449ZnXoPqs6d907HpQCKBpkhK+SgQAZBShoDRH90Uc4UQu8Z57QYm6DqnA8MzAbMsolpo3qBTTZ1lFYElj5nGbjocPioy1fmm0m0zajsTrX1YrwWaTodfrS9NckYTDVBeXw4VBrsKkr1vw8M3gN89Yt8bV1bz54+uYigY5n2/3oTbP1yEahAGbp57Mzv7drKnf89x13n56MtEZGREoNnR5qDD4eNcw07V7dLRCi9/A4APrKrlF+9fxt7OQa775YaY03GUOQVzKLAUJFRP0+HuoDK7kna7V9XQjCIEAEY0QMsZo9OmO+ievHNzPNnFULUCDv6dS+YpR4YX949Un/V6e9nbvzc2o/F4cqg2DiCzSibuxZVfSyW9HLUN/xx3aa0BZgYPgSkjcesVbZ1mmdYILSpzjifdmM63130b+5Cdb236Vmz7HtsefCFfbJ1x02EVOFfXF4K7F3r3Jn995gwlkUDjEUIUomxnEEKsAsaW9sAc4KpxXu8G1kxhzO9YotLmcCCfatGHFIaJPS2OhhBQOJOKkDp3v9NMgaVg0jOaeNsZk8FEOCLZ0BwNNL4p1dJEK+ydg5mUMgCzL4Prf6vqDv58EwRHqtmW1eTzy1uW4/AG+b9jBALXNFxDpilzVKnzC60vUJNTw6z84T7sz+7qosjopdCxC5bdCmfdAW/+ClrVLOWyBeX88cNn0+/yc90vNoyYRRmEgbUVa9nQsWHctS8pJZ3uTgotZbiGQhSGeseukdJmNKXCToYopNvTfZwoIepzZg1HnZutY177hMy6DDq3MyvTRW1hJv/cOxxozq06F5Mw8eLRF+l0d5JhyqDPYaTWnFjDs+PIr6Mw1E33oI8hLUju7nCSlWYkz75H2c4kavWSXwc55Vj7tlJpzWBL6+iapLmFc/nY4o/xfMvzPH/keQA2d21GIFhRqmwTNx62kZ1uYmFlHhz5lzpQDzQJkUig+SxKbdYghHgD+APwqbF2llK2JvDSZc6TIFqs6fHkUSP6VJAxJu7bNSZFM7F6WwBo6nMp5dkkJc7xtjOgnkQHh0Ksqi8gGJYUpJVNOtB0e7vJMGXgtXtIJwA5FTDvarj25+of/7HblF17HEurrSyszOMPG1pG3Iiz07K5tvFanm95fsTaidPvZHPXZi6uvVj1ZAciEclzu7v4cOVRhIxA48Vw0VdVEHj6k8qDCjhrRgGPfWwNAsENv9zIm4eHJdRrK9di99vZbxvbCH1gaICh8BAWikgnQGagf+w1uJwyJIJyYUNErPjD/hFpn+j3AlAQCREyZaq6qcky6zIAxKEXuGRuKRubbbFZYl56HivLVvLS0ZdiLRw6nUNUGCZYrBnFWos54qcYJ20Dalazs93JoopsRPeuxNdnQD1I1ayG1o2sqFWFm2OpBD+88MMsLFrINzd9k15vL5u7NzMrf1asdfemZhtnzShQnS8Pv6L858oXT/z7eweSiOrsLVSh5RpUc7H5UsrJd3XSmTTtrnayzFn02k3Um22IqdbQRClsxOTpJtfgjynPmp3Nk2qBHG87AyptJgT820r1ZG4RhfT5+hKqyD6Wbk83ZVlliEEtUGlP9Sx+H1z5fTj4PDxxB0SGM7tCCD64upZDvW42HR6ZNrlpzk2EIqERUudX2l4hJEMj1GZvHbXT5RziCssedXOpXK68u67+Mdia4NX/ie07uyyHxz++hpKcdG757eZYz/rVFasRiHHVZ9EAbAjnUyaiirMxpMFGM2SXjOy0eUz6LBpoiiMBQmnWMa+bEKXzVWA9+HcumVdKIBzhtQPDDQIvrr2Y1sFWtvZspTy7nE6Hj6Jw38SkzVHiXJyPDngJhCLs7xrkwiI7BL2JKc7iqV0Drk7OL/XRM+gf0VsnHpPBxLfWfYtAOMCX13+ZnX07Y+sz3c4hDvd7VNpMSrU+M+NcVTyrc0ISUZ0ZgStQMuV3AZ8SQnw21QPTOZ4Od4fyf7L7qKJ36kKAKFHlWb4jVkvjCrgm1SBqQ+eGEbYzrzf1M78iV6UbAGNELaRORhDQ5e6i2FJKbmiUHicrPwKXfB32PgHPfEbdDDSuWlyBNdPMHza2jDhfXV4d6yrX8eiBR2MtrF9ofYGKrArmFc6L7ffsri7STILqgQ1Qf8Fw2qbhAuU5teEnKn2nUWnN4K8fW0O6ycBjW1UxbIGlgHmF88Zdp4k2PAsM5Y/e8OwYRG6FVrQ5ei1NNHVWHPEjp5I2AzUzmHUpHH6V5RUWCrLSRphsXlB9AQKB0++kxFJGwOPAEvFOekYDKtC02rwc7HERCEVYaW5R7ycqBIiirdOcbToIEOtPMxoz8mZw9/K72di1EX/YP9wW4LD6faxuKISBw+Bs00QSOomQSOrsGVTxZSGQE/fSOcm0u9qpzK6kb8BOfmRg6tLmKJrybEVWP029bmbmq6//1f6vCZ0mGA6ypXtLLG3m8YfYftTOusZiKq2Zah+/FZicxLnL00W2qZjy6NN+dEYTZe1n4Nz/hO0PwT++FAs2FrORf1tRzT/39dDlHPk0e/McJXV+ofUFXAEXGzs3jkibhbW02fvr3Bjc3SptFs+7vgnZZfDkJyA0bIGTn5XGusYiXjvYF5sZrq1cy86+nceluKJ0uNTPxOnOpiFN+x7HEgMA5FZSYbAz6BndHWAwoNaJKiLe5FSuz7ocgl5MR9/gwjklvPx2L0FNLlycWcziYpVGyjQWxfWhmUygUX/XDaZ+jg54Y44ADaFDkJ6r2jdPhJJ5YMmj3LGdHIvpuMLNY3nfnPexqnwVJmFieZnqULKx2UZehpm55blK1gzqoUMnIRIJNFVSyvdKKb8qpbwn+kr5yHRGIKWk3d1OaWYF6V5tNjBVxVmUgnpAMC+th9YBLwsLl7KsZBnf3/Z9er2jdr4elajtTDRt9uYRG8Gw5JzGQjL+7zZuyHwLj0c9o0x0nSYQDmAbsmERhZSJASWEyB6lEdkF/w/OvhM2/Rxe+XZs8wdW1RKRkkfeHOn2vLZyLbW5tTz89sO81v4awUhwhNpsS8sAvS4/N+RpTr+Nx7Q2suTBVT9Sxoqvjyz6O392MV3OIQ72KEXcusp1RGSEN7veHPV77HR3Yk230uOAORan6rcy3owgt4JiaaPfmY5RGI8LNMPOzV5Mk3FuPpa6dWDOiqXPBodCbD4yPDu4qEb9bEyRwrg+NJNInaVlQlYJsy12Wm0ednc4yMswk23bpdZEJlo1bzBA9SrVCK02n9cO9MZ8z0bdXRj44fk/5KErHiI3TaUlNx62cfaMAowGoQJNbpXqbqmTEIn8xp4XQrzrxLvppJJ+Xz/+sJ9MQ1x7gGSlzswWsNZQI5Un1NEBH99Y+w2C4SD3bLwn4bWaqO1MNK/9+qF+0k0GVuTYYP/TXGbeEaulmWigiRYkypCVcgaIZJaMrjwSAi77H1h6C/zru/DGfQBUF2Ry4ewS/rS5bYTTc9TVeVffLn6181eUZJSwqHi4mdbfdnVhMRuY7X5TPRkfO4sClVJa9D4VaLp3xzafO0sVWr52UAXrhUULyTHnjJk+6/B0UJFdQduAlxkmG+SUjy/2yK0gM+Jm0OGkOKN41NRZmsiiULiTE2jMFpUuPPgPzm0swmI2jDDZvLL+Ss4uO5scZk1tRgOQX0etoY9WbUazrDID0b1n4uszUWpWQf9BPnG2la7BIb7+zL5xd89Oy2ZBkfJSaxvw0jbgU2mzSBhaXlduAKehi/KpIpFAswn4PyGETwgxqFvJnBqiqSZDKIk1NPEUzaRwSD3tH+pxU5Nbw2eWfYZ/tf+Lp5ufTugUx9rOrD/Uz1kzCkhvUzfWSoONTnuA0qzSCbs4R9d0gv5cyg12DOPdwISAq+6DBdfBC/8NW+4H4INr6uh3+3l+z8j1oWsariHLnEXLYAsX116MQah/i1A4wvN7urhiVi7Gtk3Hz2biuex/VMfDJz8eU76V52UwuzSH1w6qBwOTwcSqilWs71g/avCO70NTIfpP3P5Bm+3kBPsoGqVo0+l3YiYbq3BPzrl5NGZdCoPtZNj3s66xeITJZnFmMfdfej8udzZVBhtSGFVacTLk11IW6aF9wMeBbhcXWPshEpyY4iyeWjXLXiEOcOd5Dfx5SxvP705snXCjph5c01AE3btUB0xd1jwhEgk03wdWA5m6lcypo82lFpX9Q1aqRB/SmDb5f+LRKJyJZfAwBhGJmSbePPdmlpUs4zubvzNuF0cA+5B9hO1Mt3OIQ71uzplZpJ4AgZJInyoKnUQDtG6vSgt5PDlUGe2IvFFmFvEYjPCeX6le7M//F7i6OaexiLrCTP6wsXXErlGpM4zspLn5yAD97gA3l7Som9yx6zPxZBYo5Vv3Ltjw49jm82cXs+WIHY8mBV5XuY5eby9NjqYRh8dqaNLL8QXDFIZG6UNzLDnKRr9MDJAzSqdNp9+JUVowE558seaxzLxUfTz4d941r5RO5xB7O0c+d3Y4fNSbHYic8sm3NrbWkhvsJRIOEIpIlptVp8wJCwGiVCxVdj6tG7n7klksrsrjC0/spjOBLq2bmm0UZqUxqzRbqc1AFwJMkEQCzSFgj5yM1lUnaURraAZd2dQZtafdZDq8FjUigl6W5vk41KtsOgzCwDfXfpOQDPG1jV8bN4X2ZtebI2xn1jcplc66hiI4ogJNXqCHQChEQXrphMUA0WLNAWcmpdhUDc2JMJrh0m8r48Ptf8RgENyyuo5trXb2do5ckP/Yoo/xlVVfYXnp8ti2Z3Z1kZlmZEngLTBnxtRLYzLvatWX5NX/hT61pnPerGIC4Qgbm7WnYu3ns6Fzw4hDbUM2/GE/6RRiIELWUM+JpcHRls4MYBHHd9p0+p1Td24+lpxSNas48HcumluCQTAifQYq0FSb7JNPmwHk12KQ4VgKri5wEDILJy+AMaUrd4OjGzAbDdz3vqUEwxE+++iOWNfQ0ZBSsqHZxqr6QiUQOfwqFM9VPwedhEnkTtUFvCqE+KIQ4rPRV7IHIoSoFkK8IoTYL4TYK4T4jLZ9iRBikxBihxBiqxDiLG17oba/Wwjx03HO+zUhRId2/A4hxBXJHvvJoN3VTklmCZ32EPWmfkQy02YQU56tzhuIzWgAqnOr+cyyz7C+Yz1PNj055uEbu461nemjKDuNOcZ21Q2xfAlGGaSIQTIMxfR5J1ZLE+3a6LS7yJTe0ddKRqOoEerOgbcehEiE65dXkWE28tAxsxqrxcqNs28ckTb7+54uLppbiunwS6pmIhErlSu+pxpXPfUJiIRZUVdAZpqRV7V1mrKsMhqtjcfV08QCb6iAYhwYZCiB1NlwS2djJB9fyIcrOOzl5Qw4MYW0GUWyAg3A7MuhYxuFOFlemz/CZBOgw641PJuKa0VM4qz+jjL7dqpZyVTWRWpWQ9cu8LupK8ria1fPZ9PhAX71r7HtllpsXroHh1Tb5uAQHN2op80mQSKB5gjwEpBGauXNIeA/pJRzgVXAJ4QQ81C9b+6RUi4B/pvhXjhDqB45n0vg3D+UUi7RXs8lf+ipp93VrtXQeKmQSayhiaLV0iy09HG43zPiKe+mOTexonQF393y3VFdnaO2M6vKV2EymJBSsr7JxtrGIgxa2owlqtVthejHFClEIifkEN3t7aYsqxzhihZrTuAmtvw2cByFw6+Ql2Hm2qWVPLmjA6d37DYBG5pt2L1BbqwPgP3I+GmzeLJL4PLvQvsWePOXpJkMrGko4tUDcTLnirVs69k2ogdKNJXoH8qLa3h2gqd3cwYyo4AywwChoKpTiv+ZOvwOzMEpOjePxqxLARkz2dzfNRir4A+FI3QP+rCGJthZ81i0B6k6Qx8rKiyIvrcnvz4TpXY1yDC0K+v/G5ZXceWicn7wz4PsbHOMekhsJtpQqI4LDeltASZBIs4A94z2SvZApJRdmgsBUkoXsB+oRHmsRdeE8oBObR+PlHI9KuCc8XS4O6jKqcI2YFNdC5NVQxMlpxzSsqkXnQRCkdiNA1QK7etrv05YhkdNoUVtZ1aVK7v0t7td9Lv9rGvU1mestbHF2AphI+hXN8WJCAK63d3kpxVTHHMETnBGAzD3KpV22fZ7AD64upahYITHtrWNecizuzrJTjexKqx1XxxPCHAsC69XNScvfQMGDnPe7GLa7T6O9HsAJakORoJs7dkaOyQ6o3G6cphtcaiNJ5rRoDpt1pocDPm0ok1tnSbq3Jwe1H5XyQw0ZYtU6nIUk81el5+8yCBm6Z+ctDlKbhUII1fXBrljphtkZPKKsyhVZynJ+NFNgHKN+Pa1CynJSeczf94eW0eLZ0NzPyU56dQXZam0mTBC7dqpjeMdyLRs4yaEqAOWAm+iWhJ8TwjRBtwLfHESp/yk5hz9W61b6GjXvENLzW3t6+sbbZdThj/sp9fbS35aGUUhLU2R7NSZEFDYSGlQ3XwPxaXPAKpzqrlr2V280fHGcSm00WxnAM5pKICW9TDjnNh6Q2O6Ha9XPTckKgiQUtLl6SLTUBRXrFk+/kHxmNJhyc1w4Dlw9TC3PJeVdfk8tKmVyCj5+UAowj/29nDJvFLMR15WdUYF9aOceAyEgHf/QAkI3voD52sy51c1y5ZlpcvIMGWMSJ91ujspsBTQ7YgwJ0MrKDyRGAAgt5xKo51BtyrajCrPXAGVQssIalLuZAaaqEtA8yvMsJqYWZIdM9nscPimLm0GJSLIq2JVvptlpikKAaJYcqFsIbQMy8vzMs388N+WcHTAy9ee3jtidyklmw4PsLohuj7zmlrnsehaqIky7QKNECIbeBy4S0o5CNwJ3C2lrAbuBh6Y4Cl/ATQAS1DrTaO20pNS/lpKuUJKuaK4eJRGU6eQTncnEkmaLI6roalL/oWKZpLtagEYsU4T5X1z3sfKspXHpdBGs51pLMmmzHcIhhxQd64qbEzLoTHNjs2ZgVEYExYEDAYG8Ya8mGUBZWiBJhExQDzLblOigB1/BOCDq+totXl57dDxDxVvNPXj9AW5al6BmpElmjaLJ7dCPUE3v0J1QSb1xVkxmXO6MZ2VZStHtHfucHfEpM31ZruSSqdlJXSdEq1o0yAMsUATtZ/JC6cg0IBapwm4oWU9l8wrZXPLAA5vgA67L65Yc4rO4vm1qh1D53b1+85JgspyzruhdX1sVgNwdn0hn7igkce2tfPsruGHn6ZeN/1uv/I38zmg8y1dbTZJEvE6S0KlV2IIIcyoIPOwlPIJbfOtQPTzx4CzRjt2LKSUPVLKsJQyAvxmosdPB6LtAWSwYLiGJtmpM4DCmRgG26nJIaY8i8cgDNyz5h6VQtugUmjH2s4MBcNsPmIbTpuBmtEIAXlV1JgG6LAHKM0sTXhGEw1qMmSlTAwQySiceLOpqChgmxIFXDq/jOKcdP6woeW4XZ/d1UWOxcS69EPKxHEygQZUcWPXTvDYOG9WMZsO22K292sr1nLUdZSjg6p2qdPdSUV2hbpRMwEzytxKcsIOBpxeiizDEmdnIOrcHCBsSAdzxuS+h7GYca7qC6O5BIQjklcO9Cr5emxGk8CMbDystWBvVTf4qc5moqz+hAqAz39+hPnqpy+ayZJqK198YjcdmuR5RP1My3qVvtOFAJMikRnNm0KIx4QQVwiRulJY7dwPAPullD+Ie6sT5R4NcCFKbj2R88bnWN4DHN/papoTlTYPefNUHxpzpmpYlmyKGgHJ2vxBmkeZ0YBKod29/G7e6HyDJw49cZztzFutdoaCEVU/c+R1KGwcXk+xVlMm++iw+5TD7wQDzZBP1dAYTlRDMxbLb1NPyEdeJc1k4Oazanj1YN+IBlv+UJh/7uvm0vllpB15GYxpynplMtRfAEg48irnzy7BH4rEmmetq1TnfKPzDSIyouxnzMoVuTDcm/iDhPazzQqoos3ozyrm3BwOEkq3Tm7842HOUDfdg39ncWUeJTnp/HNvj6qhSXNoLain+DeaXwueXuWQXZmkQJOWpcxXu3bC9j/GNivJ8xIiEcndf1aS5w1NNiqtGVQXZMCR15TEvWplcsbxDiORQDML+DVwC9AkhPi2EGLWCY6ZDGu1a1x4jBT534HvCyF2At8G7ogeIIRoAX4A3CaEaNdUaggh7hdCrNB2+64QYrcQYhdwASr9dlrR4eog3ZhOnyOdBrMNYa1Njf2FJnFeqplrjlU382+z/42VZSv53tbv8cShJ0bazjT1YzIIzq7Lg9YN6sk3Sl4V+cFefMEwRellCYsBoq4ATlc2VUb7xNNmUea8W6WkNFHAzWfXYBSCP745LHV+/WA/rqEQVy4qh6aXlCQ2kRTWaFQsVSnD5lc4e0YB6SZDLH1Wk1tDdU41b3S8gc1nIxAJkIbqL5g91J34bCCulibXXBRLnUUDTYkcImJJctosyqxLwXEUQ//bXDyvlNcO9nGkz0Od2a7GNdW/0fj08FQVZ/EsuE79Xl/6+og24LWFWXz9mgVsbhngZ680senIMfUztWum1tPnHUwiqjMppXxBSnkT8BFUKmuzEOI1IcQJKtgSR0q5XkoppJSL4qXI2vblUsrFUsqzpZTb4o6pk1IWSCmzpZRVUsp92vaPSCm3ap/fIqVcqJ33ainl5BrWn0La3Ura3O7wUWvsT74QIIpmEjjL1I0nEKbLObqgzyAMfH3N14nICM8efvY425llNflk2/ZAwKXSVVHyqrAEHWQwRKaxmF5vb8yefzy6Pd2YDCb6B82UMDAxxVk8ZosSBbz9N3D3Uppr4dIFZfxlSxu+gEqjPLurk7wMM+uKh5RR5mTTZqAWtGecC82vYDEZWN1QOKKHy9qKtWzu3kzLYIvaECogDw+mkCcxIQCMaOlsEQXHBZryiC959jPHMmvYJeCSeaV4A2E2HbFRaRiYXB+aY4n/O09W6gxUALz8O+C1wWvfHfHWe5dVcvXiCn7wwkEc3qDyNxvshP6DetpsCiSyRlMohPiMEGIrqmblU0AR8B/A8X1wdZJOu6udypxK2mxeyiI9qVmfAfXknltFdUQt0j83jhdUVU4Vn12u6naj6zN2T4A9nU7WzSxSqQY4JtCocVcIG8ZIIREZiVnLjEeXp4vSzFJsdje5EefUFpmX3aqJAh4G4IOranH6gjyzs5OhYJgX9vVw2fwyzEdeUftPJdCASp8NtoOtifNmFXO43xNL1a2rXIcv5OPZw88CMOSzUpVAH5oRxGxobBgjVjxBD+6AW1ujEZRFvBiTYag5GrkVyk354N9Z01BIVpoRKaEoMsVizSjRWrH8uuS0OYinfDEsvxU2/yrm4gBK8vzN9yyg0qrWtFY3FOq2M0kgkdTZRlQdy7VSyiullE9IKUPajOGXqR2eTrQ9QHlWJZ7BftVMKtnFmvEUNVLga+XCOSXc+88DsdqP0bhx9o18a923uHnOzQC80dyPlGiB5nXldpwdp+DTnnIrRT9hrS9NIus03Z5uSjPLMPs0afdkZzQAxbNUHYQmCjhrRgFzynJ4cGMLrx7owxMI8+7F5dD0okrRlcyd/LUAGi5UH5tf4byom7OmdFtZthKTwRTrUe8czGJepkPtn+iMxpKLTMuhXAwXbfZ4e3AMJdm5eSxmXQ5tm0n3Ozh/dgkGIuQG+6YmbY6SXaLWRZI5m4nnwq+otgd//8KIRnm5FjP337qCL185VwWcw6+qOqzSBakZxzuARALNbCnlN6SU7ce+IaX8TgrGpBOH0+/EE/SQYyxVXTUhdakzgMKZCFsT//OeBaQZDfznYzvH9IIyCANXN1xNXrq6wa0/1E+OxcSisgwlH42fzUAs0NSn2fFOoC9Nt6ebXHMx5UyihmY0lt+uqv1b/oUQgltW17K3c5B7/3mAgqw0Vtdqza0aL5r6OkPBDPVE3vwyM4qyqCnI5LUD6veYac5keclyfCEfBZYCuhwR5mQ41HF5ic9aRW4FdeaRRZvOgBOzyFLOzcmWNscTdQloeoFL5pVq9jnh5MxohIDrfwvnf2nq5xqNrCK44IvQ/DIceH7EW3PLc/nIOfUqAB15Tc1mkukt+A5jzJ+cEOIZIcTTwFNCiKePfZ3EMb6jiSrOjOHC5PehGY2imeAfpNQwyNeuns/WVju/e+PICQ+TUvL6oX7WNBRi6noLQj4la44npxyEkVnpTmyDmRiE4YSCgHAkTI+3Bwuq4Rkw9ZvY3KtUXYkmCrh2SSU5FhNNvW4uW1Cmxu8fnHraLEr9BdCyHhEJcd6sYjY02/CHNJlzpaoyj9oL1ZvtSjY8kVRRbgUVRgcutwo03d5uBv2DGCMZWAgmv4YmnvIlykX8wPNcvrCML63T3KmSsUYDql6nOBXaI42VH4HiOaoja1yH1Bj9B8HVpdvOTJHxQvS9qOLGsV46J4FoDU0okJ/aGpoohY3qo+0Q71laycVzS/nePw6MWsAZT4vNS4fDx7qZxZpbszjeqsNogtwK6swDdDmCyiT0BDOaPl8fYRnGEMmPCzRTSJ2BEgUsvhn2PwvuPrLSTVy/XN0Y372wHJpfUlYjyVr8bbhQCSPat3LerGK8gTBbtXbC0UBTllVOl3NI+ZxZqyc2k8qtjBVtCgQ9nh4cfkfynZtHw2CAWe+C5pdJJ8w1M7TZb7ICTaoxmlUvIfsR2Piz49+Prs/oQoApMWagkVK+Nt7rZA7yncxwe4Bcag02pCUPMqypu6Bmrkn/IeUF9d4FZKQZ+dw4KTRQbs0A50QLNcsWjv5UnldFBf2quVdWxQndAaJ1IaFAHuViAJmeA+lJ8HRdfquyiNFEAZ++cCbfuHYBq+oL1fpM1crk/ZxnnKs8tg6/wuqGQtKMwzLnmdaZLCtZxuy8pYQjUtXQTLTQMbecvPAA/U4/hRmF9Hh7VIuAVDg3j8asy9UM8OgGcGoZ9mSkzk4WDRfC7CvhX/fC4DECmMOvqgxCft2pGNkZw3ips0e1j7s1n7ARr5M3xHc27a52CiwF9DgkjWlaDU0qya1SqRubasxVkmPhnqvns6PNwW9ePzzmYa8f6qcqP4PaXAFtb46sn4knr4qCUC9uf4jijBMXbUYDjdebQ63ZgZhsDc2xFM+GmjWx9gH5WWncsqoWg7dfWZ4kK20GKmBVLIPml8lKN7FyRn5M5iyE4MHLH2RBrpIK5wx1JS4EiJJbgYEIGf5+iiwldHu71RpNKpybR6P+PFWgefAf4OxQbRIseam9ZrK59JvqwePFrw1vC4e0ts3nn6pRnTGMlzr7jPbx3cBVo7x0TgLt7naqcqo4OuBVqbNUps1ApUIKG6B/2IDh6sUVXDa/jB/88yCHeo63pglpjb3WNRYh2rdAODBOoKkm29+DgQiZhiJ6vb2EIse75kaJFms6BrNUseZU02bxLL8NBg4PW+UAHI7Kmifg1pwIDRdCxzbwOTh/VgkHelwjuju2231Y8JPmH5jEjGa4libXXESnuxNXwIU5Fc7No5GWpYLNgeeVlDu3MjUFxamkoB5WfxJ2/RnatqhtXTvUTE0PNFNmvNRZl/axdbTXyRviO5toH5qjNg/F4Z6TM4UvbATbcKCJ1hZkW0z8x2M7CUWNGjV2tjtx+UOarPlfan1jrG6UeVUYZIhiHJgihYRl+Lhe9/F0ebrIMefQ44RiBpKbkpl3NVisMVEAoNJmmYVqkTuZNFygvLJaXue82Urm/K+Dw8Wb7QNeqgwJ9qE5Fi34lgo7FkNBzD8tJc7NYzHrUrXO0bohOdLmU8E5/6EEK89/HiKR4YeOsR6adBImkYLNVUKILVony4AQIiyEGDzRcTpTJxgJ0u3ppthSQdpQP2YZSK3iLErRTLC3jFDhFGWn841rFrCr3cmv/jUyhbb+UD9CwNoGbX2mYunYVura03ql6CccUDfA8dJn3Z5uyrLK6HG4yQtNwRVgNMwZsPgm2P8MePrVzaXpJWi4KPlS1qqVKqXU/DIzS7Ipz7PE1mkA2uw+FmZps8VJz2hsmCL5hKVStFnDmmnkSQk0l6mPXtvptT4TT3o2XHyPMvHc+SclBChbmBpfwXcYifw3/RS4CWVmmYGyoflJKgelo+j2dBOWYdIpGlacpbKGJkrRLPX0PTBS1nzlonKuXFTOj148yNvdw88a65v6WFCRR74poNJDx8qa49HWH2aYB/B61aL+eIKAbk83hZYScoIDGIhMvYbmWGKigD9B907VdjrZaTNQ6qa6c6D5FYQQnD+7mPWH+glqs8N2u5c50WLNiSq2MvKRJgvlYoBwYHhtJD8SJiJMk/dqmwh5VVC6cPjz05VFN6r2Di9+TVtr1GXNySChxzYpZRNg1Oz2f4cyp9RJMVFpsyFcSFWshibFazQAlcuVSurNXxz31jeuWUBehpnPPbaTYDiC2x9i+1GHSpsd3aTsXY4t1IxHe9qdk+nE7sxCIMad0XR5usg2Fsc1PEvy03LJXKhepdJnh15U26LV/Mmm4QKVXho4wnmzinFpPzuAtgEfDWa7SjvmTDCYCoHIKafW7GBoKDu2uTgcUM7NJ2u9ZLY2qzldZzQw7IPm6VdrjfX6rS4ZJBJovEKINGCHEOK7Qoi7gZPwiKQTfdJXHlgnMdAUNqi+Hdt+r+SdcRRkpfHNaxewp2OQX7zazKZmG6GI1GTN/wKDGWpWjX1uSy5Y8qg32+l0BCnOLB4z0PhCPhx+B2kUUCq0rpPJTJ1FWXE7DDTDpp8rD6zskuRfA4ZvWodfYU1jESaD4NUDvfhDYXpcQ1SKPnWTNpomfu7cSqqMjlinTYBiOUTEYk3O2BNh3rWqrULZwpN3zVRQuQyW3aJSnbVJ8w1+R5NIoLkFMAKfBDxANXBdKgelo2h3tWMymLA7M2kw2yCr+OSkQQAu+H9KFPD0p8A/sljzsgXlXL24gh+/dIjfbTiCxWxgeV2+KtSsWnHiMebVUClstNu9VGZXjukOMNzwLG+4PXAqnpbnXaPkuL6B5Mqaj6VoppKPN79CrsXMstp8XjvYR6djSJlRhvsmLm2OkltBKTYGnMMN4crDQydnfSZK2QL4YjtULDl510wVV/4APvHmyft/O8NJpE1Aq5TSJ6UclFLeI6X8rJZK00kxbw+8TWV2JW32IRrM/SdHCBDFnAHX/AwcbSNrCzTuuXo+1sw03miycdaMQtJDbiUHTUShk1dFcbiXwaEQxRllY85ootLm4FAelUY70piemhtnVBQAqQ00QkDD+UqZFwlz3qxi9nYOsv2omq3l+rsm35UytwJr2EaPM0iBpQAQlEt36pybx8KUfnKvlyqM5tN7rWmaMV7B5qiFmnrB5slhd99u3uh8gytnXEnbgJdKTkINzbHUrIJVd8KW32i2MsPkZ6XxP+9VKZLzZhUrWauMjL8+EyWvipyAkjRnG1RXyNFqaaIzGpcnmxlmJyIZzbTG4pzPwaXfVus1qaT+AhhyQOcOztdkzg+/eRQjYdK8PVOY0VRikkHS/HaKM0pIF1kUCG9qnZt1dBJkvBlNtFDz79rr/drrOeCvqR/aOxcpJT9660cUWAr4wNxb6LR7KAj1nhzF2bFc+BXInwFPfxICI1sGXDKvlKc+sZYPrKpRT+nG9MRa3VqrSQsOko0XkywgLMP0efuO263b041AYHNmUGG0p3aRObtYrUul2qG3/gJAQPPLzCvPpTgnnW2tdqqMdoQMT2FGE+1LM4A1rRSzyNGcm/VAo3PqGa9gM1qYuVZK+Xkp5W7t9QXg0pM3xHceG7s2srl7M3csugP3kJGCcD9GGT65qbMoaZlwzU9VXc1L3zju7cXVVtJNRjXjqTlbGVaeCC0lUSFshANWYHSJc5eni+KMYrqdQUqkLTVCgJNNViGUL4LDSuZ87kw1q1mUE62hmWS6JtrSWQzwrvLbmSVuI4uTvEajozMGiTy+ZQkh1kW/EEKsQVedpYyIjHDfW/dRkVXBDbNuoG3AF9ce4CSnzqLUrYOV/w5v/lJJmI/FOwA9u6EuwQpq7am91jSAz6vqPkYTBHR5uijNKqN30EteqD/5NTSnivoLoG0z+F2x9Nm8aB+ayf6OtdlemRgg4i8my6PNZFJpwKqjkyCJBJoPAz8TQrQIIY4APwc+lOyBCCGqhRCvCCH2CyH2CiE+o21fIoTYJITYIYTYKoQ4S9teqO3vFkL8dJzzFgghXhBCHNI+TutHvBdaX2CfbR+fWPoJ0oxpyuPMEC3WrDt1A7v4a2r94KlPQNA38r2oV9h4hZrxaE/t8zKdOAbVM8toM5oeTw/5aSVYpQuTDJ7e9RnxNFyoikRb3mBdYxEGAQ1pmnx7sjOarGKkwUS5GKDLOUTEq9Ud6TManWlAIqqzbVLKxcAiYImUcomU8q0UjCUE/IeUci6wCviEEGIe8F3gHinlEuC/ta8BhoCvAJ87wXm/ALwkpZwJvKR9PS0JRoL8ZPtPaLQ2cuWMKwFoG/BSJfqQiFOrgknPhqt/qlydX/nWyPeOvK5a4lYsS+xc2WVgMNOY7qDTEaIk4/i+NFJKujxdZBgKKUtlDc2poGaVcsg+/Ar5WWn88N+WcHahV8nXzRmTO6fBiMgpp87spNs5BD7tZ6YHGp1pQMIrn5q82ZmqgUgpu6IBTErpAvYDlYAEosZZeUCnto9HSrkeFXDG4xrgQe3zB4Frkzvy5PFU01O0Drby6aWfxmgwAirQzE4bUIqrUy0drT9PtUHe+LNhh1tQM5qaVWBKS+w8BgPkVlBtsNHh8FGeXU6Xe2QfELvfjj/sxxTJpyxWQ3OGBBpTOtSugWZl2njNkkry/N2TFwJEySmnymSn0+nD6HeobXqg0ZkGTMsm2EKIOmAp8CZwF/A9IUQbquvnFyd4utI4J+ouIEVl31NjKDTEL3b8gsXFizm/+vzY9qMDXupM/adufeZYLvk65FTAUx+H4BC4eqDv7Yk73OZVUxLpY8AToDSz/LjUWVTaHA5ah+1nktWLZjrQcCH0H1D9W0DVK01W2hxFK9pst/uwBDUvOj3Q6EwDpl2gEUJkA48Dd0kpB4E7gbullNXA3cADKbruHdoa0Na+vuOltqnmkbcfodfXy13L7kLE1Yq02b2UR3pOjeJsNCy5cPV9qpf6a/878fWZKNZqrEGtlsaoamnCkXDs7Wix5pAvlzqzQ3mApcoa5lTQMGxHg5SqM+VUZzS5lRSE+2mxubEKzc1BDzQ604CETJU0pVld/P5Syj8kezBCCDMqyDwspXxC23wrw03YHgPun+Bpe4QQ5VLKLiFEOdA72k5Syl8DvwZYsWLF2D2LU8BgYJD7d9/Pusp1rChbEds+FAwzMOghz9J/ampoxqLxYlh6C7xxn6qbSc+FssUTO0deFRlDvRgJY5aFhGSIPl8fZVllwPCMxunKpDZtECzloKUTzwhK5kF2qUqfzbwUQr4kBJoK0iJD5EgvecKNxIBIH6Ndg47OSSSRfjQPoVJW64CV2mvFuAdNAqEe4x8A9kspfxD3VicQ9eq+ENWuYCI8jQpWaB+fmso4U8Hv9/yewcAgn1n2mRHb2+0+KkQ/Ajl9ZjRRLv2WWtRvexNq107cCDKvCiHDlGInMkotTZe7i3RjOj0OE5WGJPehmQ4IoWTOh18Fh2pUlozUGWhFm3gIpeelvgBVRycBEvkrXIEq2vy4lPJT2uvTKRjLWpSB54WalHmHEOIK4N+B7wshdgLfBu6IHiCEaAF+ANwmhGjXVGoIIe4XQkSD4f8ClwghDgGXaF+nhN/s+g0HBg5M6Jg+bx9/3P9HLp9xOXMK5ox4r23Ae+praMbCkgdX3ac+j6aBJkJ8LY1Pq6WJU551e7spzyqn2+mnWNrOnBqaeBouUP1vDjynvk5C6gxU0aZVuIlY9LSZzvQgkcfQPUAZ0HWiHaeCpiAby8hq+RjH1I2x/SNxn9uAFHSyGsnA0AB/3P9Hfr7j59y+4HY+uvijpBtPrBL71a5fEQwH+eSSTx733lFN2gxMr9RZlFnvgo/+C4rnnHjfY9FuqvOznLRrtTTxgabL00VRRgm7fQGsUfv8M41oL/odD6uPU57RxNnQ4NbXZ3SmDYnMaIqAfUKIfwghno6+Uj2w040CSwFPXvMkV9RfwW92/4brn76erd1bxz2mbbCNxw8+znWzrqMm9/gZS9uAlxnGPqTBNH1vtOWLJye71vrKz0x30O0IU5RRNMIdoNvdTa6pmBx8mCO+My91BpBTBiXzwdUFaTkw1d4x2WVIBOXCRp7wYMjSA43O9CCRQPM1VO3Jt4Hvx710jiHfks+31n2LX138K4KRILf/43a+vvHruAKuUff/6Y6fYjKY+Oiijx73npSSra12ZqfbEXlVZ9ZCOKg+HxkF1JgG1FpUdkVsjSYYCdLn6yOdQspinTXPwEADw2lHa/XUnalNaYjsEmaYnViFW3du1pk2JOIM8Npor5MxuNOVNZVreOLqJ7h13q08fuhxrn3yWl46+tKIfd4eeJvnjjzHB+Z9gOLM4uPO8cqBXna0OViQaZ9+6zPJwlpNueyj3+2nNKM8ljrr9fYikYiIdbjh2ZlUQxNPtOvmVNdnouRWUGVykC88unOzzrQhEdXZKiHEFs1TLCCECAshBk/G4E5nMs2ZfG7l5/jTFX8i35LPXa/cxWdf/WzMDv/Hb/2Y3LRcbl9w+3HHRiKS7/3jILWFmRSGplENTbLJq1btD4AcUwldni4iMhJzCQj586g402c0tWvAZEmej11uJZWin1w8+hqNzrQhkdTZT4GbULLiDOAj2jadBJhfNJ9H3v0Idy27i3+1/4trnryGe7fcy+sdr/PhhR8mN+34Oodnd3exv2uQ/7ygGuE5RX1oTgZ5VWT5ugCpamkiIfq8fXR7VQ2N25NDg0VLO+acgaozUG0YbvsbnHsiy74EySmnNNiuPtcDjc40ISGRvda62SilDEspfwecn9JRnWGYDWY+vPDDPH7148wpnMOD+x6kJKOEm+bcdNy+wXCEH/zzAHPKcriiOqg2nsEzGmPIQy5eZFDdFDs9nbFiTcdgJnVpDsgqSdxH7XSkakXyXA9yK1QDNdADjc60IRF5s1cIkQbsEEJ8FyVz1vvRTILa3FoeeNcD/KP1H1RkVZBhOt6p97Gt7bTYvDxw6woMzh1q4xkbaJQbdY3RxpBvFqCKNrvcXVjTrXT3Rqgw2M/MGppUEa9O1AONzjQhkRnNLdp+nwQ8QDVwXSoHdSYjhOCyustYVLzouPeGgmHue+kgy2vzuXBOyXDF+BmbOovW0gzG+tJ0ubvo8nRRllVGl2OI4kj/9JV2T0fi17L0QKMzTTjhjEZK2SqEyADKpZT3nIQxvWP5w8YWegb93Pe+pcpY096iFoqzS0/10FKDVqA4O8PJ35wRCgoK6HB30O3tpsRSQSAc0TprTsJ54J2KHmh0piGJqM6uAnYAf9e+XqIXbCYf11CQn7/azLmzillVX6g29uxRabOp1ldMVzKLwJjODNMA7XYvldmVdLo76XZ3k2ksxIKf9KDzzFWcpQI90OhMQxIt2DwLcABIKXegnJx1kshvXj+Cwxvk85fOVhv6m5Th4vz3nNJxpRSDAfIqqRD99Lr8lGWWc9B+EFfQhVkWDBdrnqk1NKnAnDEcYCx5p3YsOjoaiQSaUCo7a+qAze3ngdcPc+XCchZUajeHN38JxjRY8aFTO7hUk1dFYbgXKSHXVIJtSBVoylD+cMMzfUYzMXIrVZA509wkdE5bEgk0e4QQNwNGIcRMIcRPgA0pHtc7ip+90owvGObuS5TyCp9dGS0uuB5yztD1mSh5NeQOKTmzWRbGNvt9OVSbtOcbXQwwMXIr9LSZzrQikUDzKWA+4AceAQZR7ZV1kkCHw8cfN7Vy/fIqGkuy1ca3HoKgF1bdeWoHdzLIq8Ls68VMCELDN0eXO5uZlmig0eXNE2LdZ1XLbR2daUIiqjMv8P+0l84YRCKS23+/hQtmF/P+VbWYjYk1nLrvxYMAfOZibTYTDsHmX0PdOVB+vAT6jCOvCoGkXAww5FMqNKMw0j9ooS7NCYY8ZcCpkzi1q0/1CHR0RjBmoDmRskxKeXXyh3P6MjgUJBiO8LVn9vHQplb+35VzuWB2iZIpj0Fzn5u/bmvntjUzqLRqxZtvPwvONrj8Oydp5KcYTeK8KNvFoEvN6EozS+lq81ORMaCnzXR0zgDGm9GsBtpQ6bI3GbspmQ5gzUzj4Y+czYv7e/n2c/v50O+3cs7MIr585Txml+WMeswP/nkQi9nIxy9oGN646RfKYHHWZSdn4KcarWhzTqaTfzkk+dZ8SjJLOeDyU5R+BrZw1tF5BzJefqcM+BKwALgP1Qa5X28TMDZCCC6ZV8o/7vr/7d15dJXVvcbx7xPCIFMQCYqESYQFiogVUauiVbquQ3tRV6VWRbE4VK1TvW2Ve1tbe52Hq15bh4vzDFYrdahVF+rSViuwUERUVKpMIqABbAKI+d0/9o4cMTEk5Jx35+T3WSuL97znfU+evJx99vvus9+9R/Or7+3EawsrOeS6F5j0yBxWfLbuK9u+sXgVj89Zykn7DqBH5zhx2OKZsPBl2PMnrafHULxi2aHdpyyurGZMvzHsuk1o+un6+cde0ThXBOqtaOIAmn8xsxOAvYB3gecknVmwdC1Uu9ISJu47gOd//h2O37s/U15dyHeufI6bnn+PdRvCgIdXPPU23Tq25aTRO2zc8eUbw0yLI47NKHkG2naATj2pKFnJ0lXVXDDqv9inxzhK2UCHdSv9HhrnisA3dgaQ1B44jDBNQH/geuDh/McqDlt3asdv/n1nxu/dj0ufmMdlT77Fva98wJG7VfDCO8uZdOgQunZoGzZevQTmPgKjToEOX586oKiVVVC+/mNqDD5atZYlldX0pBJhfkXjXBGo94pG0p2E+2W+BfzWzPYws9+Z2eJ8BJHUR9J0SfMkzZV0dlw/QtLLkmZLmiFpVM4+F0h6V9Lbkv6tntf9jaTFcf/Zkg7NR/5vMrC8M5NP2IN7Ju5Jp3alXPfsfLbt2p7j9+6/caNXJ0PNF6GiaW3KKihbvwyARZ9Ws3RVdc4Uzt4ZwLmW7puuaMYTRmseDJyV03tKgJlZc592bwDOM7NZkroAMyU9DVxBqOiejJXEFcABknYCjibc47M98IykwWa1k3F8xf+Y2VXNnLfR9h3Ug8fP2o/HXl9Cn+4d6dA2fg+zvgpm3A5DDoPuA7INmYVufekw/2nAWPRpFYsr1zJ4q9VQg1/ROFcE6q1ozGzzbgRpJma2lDDXDWa2RtI8oDdgQG2lVgYsictjgQfMbB2wQNK7hDHZ/l7I3F+xdlWD40u1KRFjR2xylj5nClR/AnudnsdwCSuroGRDNd21hsWV1SyprGa/DquhCr9Z07kiUNDKZHNJ6g/sRuhWfQ5wpaSFwFXABXGz3oTu17UWxXV1+amk1yXdJik/Y3NUfQI3j4anL4Sams3fzyx0AthueJg/vjWKE6AN67Tmy6azfm0roW1H6NAt02jOuS2XXEUjqTPwR+AcM1sNnAaca2Z9gHOBW2s3rWN3q2PdjcBAYAThiunqen7vKfE7oBnLly9vfPD2XWHggfDStfDwybBhXYO7APD+dFj+VriaKdbpABoSK5qdO60OTWefVtOr5NPQbNZaj4lzRSSpikZSW0Ilc6+Z1fZuO4GNPd2mEprHIFzB9MnZvYKNzWpfMrNlsat2DfB/Oftvut0tZjbSzEaWl5c3PnybUjjsGjjoQnjjIbj7yDA4ZkNevhE69YRhRzb+dxaLsr4ADGr/KW99tIZ/rf+CHjUr/PsZ54pEMhWNQm+DW4F5ZnZNzlNLgP3j8oHA/Lg8DThaUntJA4BBwD/qeN3cRv4jgDeaO3vOL4P9fgZHToaFr8BtB2+cjrkuK+bD/L/CHidBafu8xUpex+5QuhV92nxCZdXnAHRdv9zvoXGuSDQ4qGYB7UPo6TZH0uy4bhJwMnCdpFJgLXAKgJnNlTQFeJPQY+2M2h5nkiYDN5nZDOAKSSMIzWr/BE7N+18y/KgwvP8Dx8HkMXDsVOi169e3ay1zzjREgrIKtrXQZClq2GqtjwrgXLFIpqIxsxepfzy13evZ52Lg4jrWn5SzPL5ZAjbWgNEw8Sm45wdw+6Ew7k7YcczG56s+gdn3wS7joHMTmuqKTbc+dKv8GIAerEa2wSsa54pEMk1nRannUDjpGdh6ANw7DmbdtfG5WXfFOWd+kl2+lJRV0LEqfMVWUeo3azpXTLyiybeuveDEJ2CH/WHamTD9Evji8zDnzIDRsN0uWSdMQ1kfSquX0571DO34WVjn99A4VxS8oimEDl3hmCkw4jh4/nK49buwenHrvUGzLnG6gKGdPmNgh9VhnV/ROFcUkvmOpui1aQtjbwgTfT13aWhOG1Tn8GytU7yX5ujBMGztOvisLXTskXEo51xz8IqmkCQ44HzoPTJ0ACjxC8ovfVnRCBZ8FprN/Pg4VxS8osnCoDENb9PadO0NCFYtClMm+D00zhUNP2V0aShtB122g8qFoaLxrs3OFQ2vaFw6yipg1Yde0ThXZLyicekoq4CP3oAN1d7jzLki4hWNS0dZnzAvD/g9NM4VEa9oXDrKcgbj9isa54qGVzQuHbGLM+Df0ThXRLyicenoFq9oVAKdt802i3Ou2XhF49JRe0XTqWcYScE5VxS8onHp6NAN2nX2ZjPnioyPDODSIUH5ENhmx6yTOOeakVc0Li3HPOjNZs4VGa9oXFo6+YjNzhUb/47GOedcXnlF45xzLq+SqWgk9ZE0XdI8SXMlnR3Xj5D0sqTZkmZIGpWzzwWS3pX0tqQ6ZxGT1F3S05Lmx3+3LtTf5JxzLqGKBtgAnGdmQ4G9gDMk7QRcAfzWzEYAv46Pic8dDewMHAz8QVKbOl73fOBZMxsEPBsfO+ecK5BkKhozW2pms+LyGmAe0BswoGvcrAxYEpfHAg+Y2TozWwC8C4zi68YCd8blO4HD8/IHOOecq1OSvc4k9Qd2A14BzgGeknQVoWL8dtysN/Byzm6L4rpNbWtmSyFUZpJ61vM7TwFOAejbt++W/xHOOeeAhK5oaknqDPwROMfMVgOnAeeaWR/gXODW2k3r2N2a+nvN7BYzG2lmI8vLy5v6Ms455zaR1BWNpLaESuZeM3s4rj4BODsuTwUmx+VFQM648lSwsVkt1zJJveLVTC/g44ZyzJw5c4WkD5ryNwA9gBVN3LcQUs8HnrE5pJ4P0s+Yej5IK2O/+p5IpqKRJMLVyjwzuybnqSXA/sBzwIHA/Lh+GnCfpGuA7YFBwD/qeOlphMrqsvjvow1lMbMmX9JImmFmI5u6f76lng88Y3NIPR+knzH1fNAyMkJCFQ2wDzAemCNpdlw3CTgZuE5SKbCW+D2Kmc2VNAV4k9Bj7Qwz+wJA0mTgJjObQahgpkiaCHwIHFW4P8k551wyFY2ZvUjd37sA7F7PPhcDF9ex/qSc5ZXAQc2R0TnnXOMl1xmgCNySdYAGpJ4PPGNzSD0fpJ8x9XzQMjIisyZ31HLOOeca5Fc0zjnn8sormkaKveNckfP/5y3nx9DV8oqm8ZKflUtSj/hvXWO/JUFSWc5yih9IyZcNSSPrG+kiEV5WtlALKCebJfnClApJe0uaClwlaafU3pgKOkq6n3ivUG1375RI2lPSo8BkST+W1N4S+qJQ0ihJ9wCXStpFUnJlRNLOkv4GXAh0yzjO13hZ2XKpl5PGSq4QpSieNd4APEG4C/ds4MfxuSTOMiyoig97SDoNIKUPSknDgd8DDxFGeTgQ2DHTUJGkEkkXEkaeeJLQ9f8MYNdMg9XtbOARM/u+mb0D6bwPvaxsuZTLSVMlcWBbgF2Bd8zsduBq4GFgrKTBZmYpFCBJbeIQO8uAicBpkrqZWU0qBYgwuva7ZnY38DTQgXATLZDtB5GZ1RCGNZpgZvcS7s/qB6R2Nt6DMKbfDfHxEZIqgK3i40yOYc57bDiJlpXajKmWlZwrvz1ItJw0VSofQEmRdLikSZIOi6tmAyMlDTSzfwGvAjOAUyGcIWWY8dCY4Ys4SnV/4J/A88D5MXNNofNtkvF7cdVjwBGSLgbmEManu17SL6HwxzF+SF8i6Ydx1f3Aa7GZYiWwBuhVyEybysk4Lq6qAkYDB8YmvlOB/wauhcIew9gENVLSs8CYuHo2oazskEJZ2STjQTFDMmWlnmOYVDlpDl7R5JBULulPwM+AT4DbJf3AzJYTBvs8M25aCTwDdIxnRllmvEPSEfG5nYD3zWwR4UzodGCqpPYKA5ZmlfE2SePM7CPCKA+lwCQz2wu4A9hX0t4FzvcYYQqKOcBN8f+5Kn4IrYvHqwJ4u1C5Gsh4s6QfxSafOwlNK0+Z2cHAfwLDJB1SyIzxA297oBzYQ1JfM1tBaO45K25WSUZlpY6MoyT1BpA0hATKSh3HsJ+ZLSNMk9KGDMtJc/KK5qsGAi+Z2Wgzuwk4jzA1AYSz3SGSDopnPSsJ89+sSiDjL+JzHwCDJU0DriScqX0QJ4f7POOM5wKY2RvAEGBh3HYmYUTtdQXM1x14yMz2N7P7CbO2bjrF91BgmZm9I6mLcqYQzzBjl/jc/wIdCR9OmNli4EUgiyvXHQiVcXvggLjuQWCopDEZl5VatRnbsfGq4UNCWfkz2ZaV3HxticfQzN4kvAcXxW2yKCfNptVXNJKOl3SApI6E/8y74vo2hAE734ybzgEeIAzwuSPhMlyEN2/WGefETbsQRrt+H9jdzL4P9JFU51hxBc74ek7b8l+B38THtdNxryxQvi5m9raZ3RHXnwlcBOwi6bicXbYBqiRNAP4Wn89r2/hmZBwm6YTYJHU6MEHSCIUvs8cQmoHynW9/5XS5jb/zQeAjYKCkAYSTiNvIrqzUl3EZMEDSDkBfQjlZQAHLymbmGxLL0aMUuJzkjZm1uh/Cm74XMB14ljBe0L1Aj/h8m/jvccCUTfb9BaFn0ovA0NQyAl03eZ2yBDNuBdwNPEVoVtkpo3zlhJ5lewAjCWe1e8fnLidcIdwGDM/wGNaVcZ/43I+ASwgfSDsXKN/NMV95fP5a4DDC2fifCFOqnxifuyCjstJQxvmEKUO6FKKsNDLfI/EYTiBcJd4H/CWf5aQQP5kHKPgfvPHDbzBwT1wuJTRHPLzJNncB4+Lydjmv0S7RjLVv3BKgJNGM29Yew9xjmmW+nH0mA9fH5X2AH6ZyDOvKGB8ro3yPxsenEqbyeJhwZv44MCrnNbIsK/VlfIJwFZP3srIF+UbWHr98lpNC/SQzTUC+KcxncxHQRtITQFfgCwAz2yDpLGCJpP3N7Pm422fAAkkXAUdKOtjMFpnZ+tQz5iNfM2Y8xMwWEppbMs0nqcQ29jT6nDDBHmb2UnNna+6Mcftm74G0mfmWShpB+ML614Sz8onAz4EDJM0xs+qMy8o3ZTxI0lwzW5tovgNjvmryUE4KLuuarhA/hBk6XwNuJJw5vAAcTPhCMPfs6zRgelxuQ5ho7QPCG6DcM6adsYn5tiPcUDiLcLXQKcFjWLCMjcj3U+DxuNw1Z/0IoGMixzCTjKnny+In8wAF+SNhP2B8zuM/xII8AZgZ15XEAj2FcKPewPjB+C3P2DIyNiHf9jHjL4E9Ez2GBc3YyHxTgQFxXWkhjl9LyJh6vix+Wkuvs5mE6Zxr77x9CehroVdPG0lnWmiaqABqzOwDM3vPzM4xs1mescVkbGy+JTHj5Wb2SgHytYSMjcm3wcwWQGgOKkC2lpIx9XwF1yoqGgs34q2zjQPnfRdYHpdPJPT5f4xwr8xMKPwwD56x+PO1hIyNzDer0PlaQsbU82Wh1XQGgC/v6TBgW2BaXL0GmAQMAxZYuPkNi9eynrHlZUw9X0vImHq+lpAx9XyF1CquaHLUEPqqrwCGx7OKXxGaKF6s/U/PmGfccqnng/Qzpp4P0s+Yer7CaeqXOy31B9iL8AZ4EZiYdR7P2DrztYSMqedrCRlTz1eoH8WD0WooDKk+HrjGzJIcN8gzbrnU80H6GVPPB+lnTD1fobS6isY551xhtbbvaJxzzhWYVzTOOefyyisa55xzeeUVjXPOubzyisY551xeeUXjXMYkdZN0elzeXtJDWWdyrjl592bnMiapP/CYmQ3LOotz+dCqxjpzLlGXAQMlzSZMMzzUzIZJmgAcTpjTZxhwNWHGxfHAOuBQM/tE0kDg94Rpn6uAk83srUL/Ec7Vx5vOnMve+cB7ZjaCMLtirmHAMcAo4GKgysx2A/4OHB+3uQU408x2B/6DMP+Jc8nwKxrn0jbdzNYAayStAv4c188hDNTYGfg2MDVnpPn2hY/pXP28onEubbnjY9XkPK4hlN8SoDJeDTmXJG86cy57a4AuTdnRzFYDCyQdBWECLUm7Nmc457aUVzTOZczMVgIvSXoDuLIJL3EsMFHSa8BcYGxz5nNuS3n3Zuecc3nlVzTOOefyyisa55xzeeUVjXPOubzyisY551xeeUXjnHMur7yicc45l1de0TjnnMsrr2icc87l1f8DgAFSziSfEq4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -637,10 +637,10 @@
    "id": "88713a7e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:02.815447Z",
-     "iopub.status.busy": "2022-07-06T15:49:02.815217Z",
-     "iopub.status.idle": "2022-07-06T15:49:02.832983Z",
-     "shell.execute_reply": "2022-07-06T15:49:02.832287Z"
+     "iopub.execute_input": "2022-10-04T17:33:34.791134Z",
+     "iopub.status.busy": "2022-10-04T17:33:34.790885Z",
+     "iopub.status.idle": "2022-10-04T17:33:34.806616Z",
+     "shell.execute_reply": "2022-10-04T17:33:34.805984Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -681,6 +681,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1018,7 +1019,7 @@
        "    institution:     Norwegian Climate Centre\n",
        "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
        "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative</pre><div class='xr-wrap' style='display:none'><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-b1560d3d-8461-41d4-994d-c02ca838ad00' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1560d3d-8461-41d4-994d-c02ca838ad00' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea0b77cc-f6ef-4d6c-8670-1448650a7847' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea0b77cc-f6ef-4d6c-8670-1448650a7847' class='xr-section-summary' >Coordinates: <span>(5)</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'>2020-01-01 ... 2050-01-01</div><input id='attrs-8f92dd61-b45c-4997-81cc-f7eb4c4bb339' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f92dd61-b45c-4997-81cc-f7eb4c4bb339' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e70a3600-4860-4fbe-85cb-185c1c65a1cf' class='xr-var-data-in' type='checkbox'><label for='data-e70a3600-4860-4fbe-85cb-185c1c65a1cf' 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><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "    regrid_method:   conservative</pre><div class='xr-wrap' style='display:none'><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-ae89d9b1-5398-4622-8e42-6a97ff847a12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae89d9b1-5398-4622-8e42-6a97ff847a12' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f60ead2d-57cd-4e7b-9672-55cd81350c8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f60ead2d-57cd-4e7b-9672-55cd81350c8e' class='xr-section-summary' >Coordinates: <span>(5)</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'>2020-01-01 ... 2050-01-01</div><input id='attrs-7d84d59f-363d-40ac-94e1-2cc660cacc10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d84d59f-363d-40ac-94e1-2cc660cacc10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc05a700-bf1d-407e-83a0-1f49222e87d8' class='xr-var-data-in' type='checkbox'><label for='data-bc05a700-bf1d-407e-83a0-1f49222e87d8' 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><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
@@ -1033,7 +1034,7 @@
        "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2048-01-01T00:00:00.000000000&#x27;, &#x27;2049-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed4deebe-ea65-4126-8548-910c2a0d4fbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed4deebe-ea65-4126-8548-910c2a0d4fbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5096ba83-33ba-4418-a737-d9b53e19ff8b' class='xr-var-data-in' type='checkbox'><label for='data-5096ba83-33ba-4418-a737-d9b53e19ff8b' 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><div class='xr-var-data'><pre>array([-73.725043, -73.298828, -73.694512])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4002f27d-20ae-4439-a27a-d29deb51afb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4002f27d-20ae-4439-a27a-d29deb51afb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a94eaf07-e0b6-4a33-b41d-ec7ef01f654a' class='xr-var-data-in' type='checkbox'><label for='data-a94eaf07-e0b6-4a33-b41d-ec7ef01f654a' 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><div class='xr-var-data'><pre>array([45.605235, 45.386636, 45.507791])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-17d4e206-0417-44ae-b8ba-3a3a75cc9ec5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17d4e206-0417-44ae-b8ba-3a3a75cc9ec5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2d42047-0766-4634-871c-8f1c223bcf41' class='xr-var-data-in' type='checkbox'><label for='data-a2d42047-0766-4634-871c-8f1c223bcf41' 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><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RES_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c101149f-b876-4d3b-9cfc-86f9ee7eeb14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c101149f-b876-4d3b-9cfc-86f9ee7eeb14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8008499a-392e-4723-9eec-c655c0a0c6ed' class='xr-var-data-in' type='checkbox'><label for='data-8008499a-392e-4723-9eec-c655c0a0c6ed' 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><div class='xr-var-data'><pre>array([&#x27;Laval&#x27;, &#x27;Montérégie&#x27;, &#x27;Montréal&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d92d8d0-cc8f-4a85-a8b9-f98d4638d654' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d92d8d0-cc8f-4a85-a8b9-f98d4638d654' 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>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3536b033-45dd-4562-a305-6020d46428b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3536b033-45dd-4562-a305-6020d46428b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-847dabe2-674e-4118-ad46-96895dd639c0' class='xr-var-data-in' type='checkbox'><label for='data-847dabe2-674e-4118-ad46-96895dd639c0' 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>K</dd><dt><span>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[280.6498 , 280.49625, 280.9249 ],\n",
+       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-408ae745-09ef-47e4-9ca4-221a6c83f105' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-408ae745-09ef-47e4-9ca4-221a6c83f105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6809ea5-6b01-4016-af20-fc02fe274860' class='xr-var-data-in' type='checkbox'><label for='data-c6809ea5-6b01-4016-af20-fc02fe274860' 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><div class='xr-var-data'><pre>array([-73.725043, -73.298828, -73.694512])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32d2f8f3-b438-41cf-be4c-fe65ef3cc9c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32d2f8f3-b438-41cf-be4c-fe65ef3cc9c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48d490f6-16de-4104-a6ec-6f11ad92a43e' class='xr-var-data-in' type='checkbox'><label for='data-48d490f6-16de-4104-a6ec-6f11ad92a43e' 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><div class='xr-var-data'><pre>array([45.605235, 45.386636, 45.507791])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-935e8d49-bdf0-4f70-875f-270eadaacc9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-935e8d49-bdf0-4f70-875f-270eadaacc9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df4345d4-4a95-4a35-a3cf-6983d83f725a' class='xr-var-data-in' type='checkbox'><label for='data-df4345d4-4a95-4a35-a3cf-6983d83f725a' 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><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RES_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cf0b629-ff6b-4656-b908-829af6fd647c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cf0b629-ff6b-4656-b908-829af6fd647c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91968398-2932-4542-af28-1f518b389545' class='xr-var-data-in' type='checkbox'><label for='data-91968398-2932-4542-af28-1f518b389545' 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><div class='xr-var-data'><pre>array([&#x27;Laval&#x27;, &#x27;Montérégie&#x27;, &#x27;Montréal&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57b072e4-b452-4bb7-90e5-a4340d5e3e5a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57b072e4-b452-4bb7-90e5-a4340d5e3e5a' 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>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1446310-de0c-4cd6-a25a-6f835bf986c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1446310-de0c-4cd6-a25a-6f835bf986c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04eb8abc-b276-4be2-8561-2f78ac2489bb' class='xr-var-data-in' type='checkbox'><label for='data-04eb8abc-b276-4be2-8561-2f78ac2489bb' 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>K</dd><dt><span>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[280.6498 , 280.49625, 280.9249 ],\n",
        "       [280.5494 , 280.34033, 280.8113 ],\n",
        "       [280.88556, 280.74432, 281.14438],\n",
        "       [282.2344 , 282.1285 , 282.4849 ],\n",
@@ -1063,7 +1064,7 @@
        "       [282.05273, 281.84796, 282.31708],\n",
        "       [282.09875, 281.92465, 282.37344],\n",
        "       [283.1577 , 282.97922, 283.41135],\n",
-       "       [282.7805 , 282.59613, 283.04648]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70e9914e-3241-4ac8-a2dc-14071146e008' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70e9914e-3241-4ac8-a2dc-14071146e008' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
+       "       [282.7805 , 282.59613, 283.04648]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49c4103b-04f5-4e92-bd29-f557471bd591' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49c4103b-04f5-4e92-bd29-f557471bd591' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
        "2016-01-19T05:16:03: Interpolate to nrcan_livneh grid.\n",
        "2016-02-10T09:50:15: Bias correction using nrcan_livneh.</dd><dt><span>institution :</span></dt><dd>Norwegian Climate Centre</dd><dt><span>source :</span></dt><dd>NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r112, f19L26);  ocean: MICOM (MICOM-noresm-ver1_cmip5-r112, gx1v6L53);  sea ice: CICE (CICE4-noresm-ver1_cmip5-r112);  land: CLM (CLM4-noresm-ver1_cmip5-r112)\n",
        "30-day moving window 50-bins quantile mapping with detrending.</dd><dt><span>redistribution :</span></dt><dd>Redistribution prohibited. For internal use only.</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
@@ -1114,10 +1115,10 @@
    "id": "023b40ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:02.836842Z",
-     "iopub.status.busy": "2022-07-06T15:49:02.836315Z",
-     "iopub.status.idle": "2022-07-06T15:49:03.948574Z",
-     "shell.execute_reply": "2022-07-06T15:49:03.946980Z"
+     "iopub.execute_input": "2022-10-04T17:33:34.809519Z",
+     "iopub.status.busy": "2022-10-04T17:33:34.809288Z",
+     "iopub.status.idle": "2022-10-04T17:33:36.144944Z",
+     "shell.execute_reply": "2022-10-04T17:33:36.144091Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -1307,10 +1308,10 @@
    "id": "e976d3e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:03.951978Z",
-     "iopub.status.busy": "2022-07-06T15:49:03.951731Z",
-     "iopub.status.idle": "2022-07-06T15:49:04.109084Z",
-     "shell.execute_reply": "2022-07-06T15:49:04.108288Z"
+     "iopub.execute_input": "2022-10-04T17:33:36.148501Z",
+     "iopub.status.busy": "2022-10-04T17:33:36.148200Z",
+     "iopub.status.idle": "2022-10-04T17:33:36.281451Z",
+     "shell.execute_reply": "2022-10-04T17:33:36.280617Z"
     }
    },
    "outputs": [
@@ -1326,7 +1327,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1347,10 +1348,10 @@
    "id": "a9dde0db",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:04.111831Z",
-     "iopub.status.busy": "2022-07-06T15:49:04.111578Z",
-     "iopub.status.idle": "2022-07-06T15:49:04.302295Z",
-     "shell.execute_reply": "2022-07-06T15:49:04.301635Z"
+     "iopub.execute_input": "2022-10-04T17:33:36.284846Z",
+     "iopub.status.busy": "2022-10-04T17:33:36.284545Z",
+     "iopub.status.idle": "2022-10-04T17:33:36.469215Z",
+     "shell.execute_reply": "2022-10-04T17:33:36.468565Z"
     }
    },
    "outputs": [],
@@ -1367,10 +1368,10 @@
    "id": "08e3483f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:04.304967Z",
-     "iopub.status.busy": "2022-07-06T15:49:04.304749Z",
-     "iopub.status.idle": "2022-07-06T15:49:05.173724Z",
-     "shell.execute_reply": "2022-07-06T15:49:05.173009Z"
+     "iopub.execute_input": "2022-10-04T17:33:36.472146Z",
+     "iopub.status.busy": "2022-10-04T17:33:36.471912Z",
+     "iopub.status.idle": "2022-10-04T17:33:37.344021Z",
+     "shell.execute_reply": "2022-10-04T17:33:37.343296Z"
     },
     "tags": [
      "nbval-ignore-output"
@@ -1410,10 +1411,10 @@
    "id": "81b47e25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:05.176786Z",
-     "iopub.status.busy": "2022-07-06T15:49:05.176536Z",
-     "iopub.status.idle": "2022-07-06T15:49:05.181474Z",
-     "shell.execute_reply": "2022-07-06T15:49:05.180776Z"
+     "iopub.execute_input": "2022-10-04T17:33:37.347196Z",
+     "iopub.status.busy": "2022-10-04T17:33:37.346824Z",
+     "iopub.status.idle": "2022-10-04T17:33:37.351526Z",
+     "shell.execute_reply": "2022-10-04T17:33:37.350872Z"
     }
    },
    "outputs": [
@@ -1438,10 +1439,10 @@
    "id": "db34519a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:05.184194Z",
-     "iopub.status.busy": "2022-07-06T15:49:05.183961Z",
-     "iopub.status.idle": "2022-07-06T15:49:06.447446Z",
-     "shell.execute_reply": "2022-07-06T15:49:06.446652Z"
+     "iopub.execute_input": "2022-10-04T17:33:37.354212Z",
+     "iopub.status.busy": "2022-10-04T17:33:37.353885Z",
+     "iopub.status.idle": "2022-10-04T17:33:38.627220Z",
+     "shell.execute_reply": "2022-10-04T17:33:38.626601Z"
     },
     "tags": [
      "nbval-skip"
@@ -1464,10 +1465,10 @@
    "id": "46026a4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:06.450381Z",
-     "iopub.status.busy": "2022-07-06T15:49:06.450147Z",
-     "iopub.status.idle": "2022-07-06T15:49:06.501622Z",
-     "shell.execute_reply": "2022-07-06T15:49:06.500884Z"
+     "iopub.execute_input": "2022-10-04T17:33:38.630611Z",
+     "iopub.status.busy": "2022-10-04T17:33:38.630087Z",
+     "iopub.status.idle": "2022-10-04T17:33:38.673308Z",
+     "shell.execute_reply": "2022-10-04T17:33:38.672605Z"
     },
     "tags": [
      "nbval-skip"
@@ -1508,6 +1509,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1852,7 +1854,7 @@
        "    institution:     Norwegian Climate Centre\n",
        "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
        "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative</pre><div class='xr-wrap' style='display:none'><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-bf3b8f5e-4d1f-49e4-96e1-cc1e86a79e4a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf3b8f5e-4d1f-49e4-96e1-cc1e86a79e4a' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-252a1794-c4a4-46ba-8188-22318caaa238' class='xr-section-summary-in' type='checkbox'  checked><label for='section-252a1794-c4a4-46ba-8188-22318caaa238' class='xr-section-summary' >Coordinates: <span>(12)</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'>2020-01-01 ... 2050-01-01</div><input id='attrs-8133541d-dcbc-4737-82e2-5d9ec8eda07c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8133541d-dcbc-4737-82e2-5d9ec8eda07c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e163af7-f0bc-4d97-bc01-926e01f55bc3' class='xr-var-data-in' type='checkbox'><label for='data-5e163af7-f0bc-4d97-bc01-926e01f55bc3' 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><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "    regrid_method:   conservative</pre><div class='xr-wrap' style='display:none'><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-1686b2be-ef97-45ae-b862-643bc48ea934' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1686b2be-ef97-45ae-b862-643bc48ea934' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b773af34-a7cb-4b5d-859e-8fda72fb831c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b773af34-a7cb-4b5d-859e-8fda72fb831c' class='xr-section-summary' >Coordinates: <span>(12)</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'>2020-01-01 ... 2050-01-01</div><input id='attrs-82a4da5f-b528-47a6-a25a-7aafc974c6fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82a4da5f-b528-47a6-a25a-7aafc974c6fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbc19c91-72e2-4a03-8601-81dd7b27b45d' class='xr-var-data-in' type='checkbox'><label for='data-dbc19c91-72e2-4a03-8601-81dd7b27b45d' 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><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
@@ -1867,28 +1869,28 @@
        "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2048-01-01T00:00:00.000000000&#x27;, &#x27;2049-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-00f7d609-4f10-47e4-960c-7cc4a403bd85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00f7d609-4f10-47e4-960c-7cc4a403bd85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed2cc5ae-7c0d-4b4a-a819-61eb7acc0c61' class='xr-var-data-in' type='checkbox'><label for='data-ed2cc5ae-7c0d-4b4a-a819-61eb7acc0c61' 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><div class='xr-var-data'><pre>array([-76.756984, -79.111026, -77.932891, -78.559943, -78.87371 , -76.160389,\n",
-       "       -77.094422])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a571397-c160-4fc7-8069-5ffd6de64bf7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a571397-c160-4fc7-8069-5ffd6de64bf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-688b8331-145f-4bea-96be-2c4fb452792c' class='xr-var-data-in' type='checkbox'><label for='data-688b8331-145f-4bea-96be-2c4fb452792c' 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><div class='xr-var-data'><pre>array([48.152176, 48.747089, 48.673527, 47.129865, 48.132749, 46.906679,\n",
-       "       46.426215])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-e9863c59-73af-42ea-98c9-17a24d8df90e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9863c59-73af-42ea-98c9-17a24d8df90e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6b1b735-2fdb-4a23-b7de-b2f8bce88b97' class='xr-var-data-in' type='checkbox'><label for='data-f6b1b735-2fdb-4a23-b7de-b2f8bce88b97' 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><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NO_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ca7ad25-a949-4029-8988-1df5d7d2ff5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca7ad25-a949-4029-8988-1df5d7d2ff5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99b43427-4154-448a-854a-b9af7d2e7b58' class='xr-var-data-in' type='checkbox'><label for='data-99b43427-4154-448a-854a-b9af7d2e7b58' 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><div class='xr-var-data'><pre>array([&#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0030 000&#x27;,\n",
-       "       &#x27;50 02 0040 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_DE_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9ce8d3b-a2c4-4c50-9cbc-fee20a75b9ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9ce8d3b-a2c4-4c50-9cbc-fee20a75b9ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f65a3d0-ebe4-49ec-ba83-67607ccc4525' class='xr-var-data-in' type='checkbox'><label for='data-9f65a3d0-ebe4-49ec-ba83-67607ccc4525' 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><div class='xr-var-data'><pre>array([&#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
+       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f66d839-a777-4e15-9a13-8b33a8c639de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f66d839-a777-4e15-9a13-8b33a8c639de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6a212a8-4da4-4647-8b37-c56ba5ca7624' class='xr-var-data-in' type='checkbox'><label for='data-b6a212a8-4da4-4647-8b37-c56ba5ca7624' 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><div class='xr-var-data'><pre>array([-76.756984, -79.111026, -77.932891, -78.559943, -78.87371 , -76.160389,\n",
+       "       -77.094422])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-99e79ee9-35f5-47aa-8bf5-a9ac1c1d0665' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99e79ee9-35f5-47aa-8bf5-a9ac1c1d0665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da568242-1d45-4b5b-8518-e58489ecef2b' class='xr-var-data-in' type='checkbox'><label for='data-da568242-1d45-4b5b-8518-e58489ecef2b' 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><div class='xr-var-data'><pre>array([48.152176, 48.747089, 48.673527, 47.129865, 48.132749, 46.906679,\n",
+       "       46.426215])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-2ca50e3a-bee4-4e4e-b9b4-f8dea7a9253f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ca50e3a-bee4-4e4e-b9b4-f8dea7a9253f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfa13c80-2229-40af-bad4-8cf07e8f997a' class='xr-var-data-in' type='checkbox'><label for='data-cfa13c80-2229-40af-bad4-8cf07e8f997a' 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><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NO_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e1f4a23-43a1-4f20-ae45-85ecabba6b08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e1f4a23-43a1-4f20-ae45-85ecabba6b08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdd5b9c9-d15e-43cd-b6b3-979418edf51f' class='xr-var-data-in' type='checkbox'><label for='data-bdd5b9c9-d15e-43cd-b6b3-979418edf51f' 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><div class='xr-var-data'><pre>array([&#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0030 000&#x27;,\n",
+       "       &#x27;50 02 0040 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_DE_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf993225-2f14-4b2d-aeab-80c5900177be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf993225-2f14-4b2d-aeab-80c5900177be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceedd480-5e8b-4159-83ab-e97e97ea0e85' class='xr-var-data-in' type='checkbox'><label for='data-ceedd480-5e8b-4159-83ab-e97e97ea0e85' 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><div class='xr-var-data'><pre>array([&#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
        "       &#x27;Municipalité régionale de comté&#x27;,\n",
        "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
        "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
        "       &#x27;Municipalité régionale de comté géographique&#x27;,\n",
        "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
-       "       &#x27;Municipalité régionale de comté&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-80058070-df9f-4d7a-9f40-08fd88e496a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80058070-df9f-4d7a-9f40-08fd88e496a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a3a642a-c65e-47a1-8ab8-53ed624a51c3' class='xr-var-data-in' type='checkbox'><label for='data-0a3a642a-c65e-47a1-8ab8-53ed624a51c3' 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><div class='xr-var-data'><pre>array([&#x27;89&#x27;, &#x27;87&#x27;, &#x27;88&#x27;, &#x27;85&#x27;, &#x27;86&#x27;, &#x27;83&#x27;, &#x27;84&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2df1d16-f397-42b0-82a2-35c31ea4f10b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2df1d16-f397-42b0-82a2-35c31ea4f10b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31fef36e-6012-471f-829c-f69c199a180f' class='xr-var-data-in' type='checkbox'><label for='data-31fef36e-6012-471f-829c-f69c199a180f' 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><div class='xr-var-data'><pre>array([&quot;La Vallée-de-l&#x27;Or&quot;, &#x27;Abitibi-Ouest&#x27;, &#x27;Abitibi&#x27;, &#x27;Témiscamingue&#x27;,\n",
-       "       &#x27;Rouyn-Noranda&#x27;, &#x27;La Vallée-de-la-Gatineau&#x27;, &#x27;Pontiac&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b7fa8b0-1bd0-4c12-98c9-c8e56e27fddf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b7fa8b0-1bd0-4c12-98c9-c8e56e27fddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b90b5c6-e577-44e7-a2ff-819f564430e5' class='xr-var-data-in' type='checkbox'><label for='data-5b90b5c6-e577-44e7-a2ff-819f564430e5' 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><div class='xr-var-data'><pre>array([&#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;07&#x27;, &#x27;07&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5205a8e5-f070-4550-aaa5-06c7d5b1a905' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5205a8e5-f070-4550-aaa5-06c7d5b1a905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102893df-9a13-4a3b-a90e-1148a9222fed' class='xr-var-data-in' type='checkbox'><label for='data-102893df-9a13-4a3b-a90e-1148a9222fed' 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><div class='xr-var-data'><pre>array([&#x27;Abitibi-Témiscamingue&#x27;, &#x27;Abitibi-Témiscamingue&#x27;,\n",
+       "       &#x27;Municipalité régionale de comté&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc7f60a1-990b-4a3c-ab0d-f858dc62d4f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc7f60a1-990b-4a3c-ab0d-f858dc62d4f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-111ce39e-9e6d-4313-b21d-831c205f7661' class='xr-var-data-in' type='checkbox'><label for='data-111ce39e-9e6d-4313-b21d-831c205f7661' 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><div class='xr-var-data'><pre>array([&#x27;89&#x27;, &#x27;87&#x27;, &#x27;88&#x27;, &#x27;85&#x27;, &#x27;86&#x27;, &#x27;83&#x27;, &#x27;84&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f706cd17-a315-4dbb-9ea8-41c1a23a9002' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f706cd17-a315-4dbb-9ea8-41c1a23a9002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4ff6549-0e58-414e-8908-eac15b1b0448' class='xr-var-data-in' type='checkbox'><label for='data-f4ff6549-0e58-414e-8908-eac15b1b0448' 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><div class='xr-var-data'><pre>array([&quot;La Vallée-de-l&#x27;Or&quot;, &#x27;Abitibi-Ouest&#x27;, &#x27;Abitibi&#x27;, &#x27;Témiscamingue&#x27;,\n",
+       "       &#x27;Rouyn-Noranda&#x27;, &#x27;La Vallée-de-la-Gatineau&#x27;, &#x27;Pontiac&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc4a749a-e362-4de3-ba25-2e35cb61fdad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc4a749a-e362-4de3-ba25-2e35cb61fdad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd98b608-678a-4d11-ba2d-a7dea83c8edb' class='xr-var-data-in' type='checkbox'><label for='data-bd98b608-678a-4d11-ba2d-a7dea83c8edb' 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><div class='xr-var-data'><pre>array([&#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;07&#x27;, &#x27;07&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c80350d1-2163-497e-828a-2a333e681673' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c80350d1-2163-497e-828a-2a333e681673' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb7f7055-2c03-4579-837f-d6ad0782388c' class='xr-var-data-in' type='checkbox'><label for='data-bb7f7055-2c03-4579-837f-d6ad0782388c' 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><div class='xr-var-data'><pre>array([&#x27;Abitibi-Témiscamingue&#x27;, &#x27;Abitibi-Témiscamingue&#x27;,\n",
        "       &#x27;Abitibi-Témiscamingue&#x27;, &#x27;Abitibi-Témiscamingue&#x27;,\n",
-       "       &#x27;Abitibi-Témiscamingue&#x27;, &#x27;Outaouais&#x27;, &#x27;Outaouais&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REF</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2ba03534-743d-4183-8f85-4de4e4278716' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ba03534-743d-4183-8f85-4de4e4278716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4b3783b-84b7-4f39-a7d8-26acaa8775dc' class='xr-var-data-in' type='checkbox'><label for='data-b4b3783b-84b7-4f39-a7d8-26acaa8775dc' 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><div class='xr-var-data'><pre>array([&#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_VER</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e929b4dc-bb25-4335-9b21-1b10c8f976ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e929b4dc-bb25-4335-9b21-1b10c8f976ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-638740ad-9002-43ac-8cda-20368d43dcbe' class='xr-var-data-in' type='checkbox'><label for='data-638740ad-9002-43ac-8cda-20368d43dcbe' 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><div class='xr-var-data'><pre>array([&#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;,\n",
-       "       &#x27;V2021-02&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c092b68f-312d-4d3d-ac3d-eaca31b77d10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c092b68f-312d-4d3d-ac3d-eaca31b77d10' 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>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b67f1305-f8ff-49b7-991c-6c3b3b62b88b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b67f1305-f8ff-49b7-991c-6c3b3b62b88b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0deca514-711f-4f25-a1f9-df1a769c9c80' class='xr-var-data-in' type='checkbox'><label for='data-0deca514-711f-4f25-a1f9-df1a769c9c80' 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>K</dd><dt><span>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[275.5102 , 275.30804, 275.27954, ..., 276.0464 , 276.98886, 277.89856],\n",
+       "       &#x27;Abitibi-Témiscamingue&#x27;, &#x27;Outaouais&#x27;, &#x27;Outaouais&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REF</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9acd01bb-6cd7-4091-8b37-bed4040d866f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9acd01bb-6cd7-4091-8b37-bed4040d866f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c35374f1-695f-4cb1-a046-5fc1a9b03d12' class='xr-var-data-in' type='checkbox'><label for='data-c35374f1-695f-4cb1-a046-5fc1a9b03d12' 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><div class='xr-var-data'><pre>array([&#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_VER</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02535f40-d97a-469e-af16-6c2055d1f6e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02535f40-d97a-469e-af16-6c2055d1f6e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-584999ee-c4e9-44e8-b49b-0ea367b1900e' class='xr-var-data-in' type='checkbox'><label for='data-584999ee-c4e9-44e8-b49b-0ea367b1900e' 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><div class='xr-var-data'><pre>array([&#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;,\n",
+       "       &#x27;V2021-02&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b3a323a-87b8-49fc-b884-b3223a83b6f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b3a323a-87b8-49fc-b884-b3223a83b6f7' 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>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc678ac9-3ef6-4f6d-bccc-eecc472db6bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc678ac9-3ef6-4f6d-bccc-eecc472db6bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02755bb1-63e5-45f3-9ff6-d4973db4a2a5' class='xr-var-data-in' type='checkbox'><label for='data-02755bb1-63e5-45f3-9ff6-d4973db4a2a5' 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>K</dd><dt><span>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[275.5102 , 275.30804, 275.27954, ..., 276.0464 , 276.98886, 277.89856],\n",
        "       [275.6804 , 275.53677, 275.47397, ..., 276.31674, 277.07428, 277.89096],\n",
        "       [276.14392, 275.95065, 275.94858, ..., 276.68753, 277.50842, 278.35504],\n",
        "       ...,\n",
        "       [276.94772, 276.69022, 276.65686, ..., 277.5143 , 278.46194, 279.40155],\n",
        "       [278.16354, 277.92688, 277.89392, ..., 278.71936, 279.70706, 280.51218],\n",
        "       [277.5508 , 277.4545 , 277.33646, ..., 278.22455, 279.19547, 280.14343]],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d232c78-f1dd-49b7-ba49-419492dca7d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d232c78-f1dd-49b7-ba49-419492dca7d4' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-059d5146-f225-4b9c-938f-514915255c8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-059d5146-f225-4b9c-938f-514915255c8c' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
        "2016-01-19T05:16:03: Interpolate to nrcan_livneh grid.\n",
        "2016-02-10T09:50:15: Bias correction using nrcan_livneh.</dd><dt><span>institution :</span></dt><dd>Norwegian Climate Centre</dd><dt><span>source :</span></dt><dd>NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r112, f19L26);  ocean: MICOM (MICOM-noresm-ver1_cmip5-r112, gx1v6L53);  sea ice: CICE (CICE4-noresm-ver1_cmip5-r112);  land: CLM (CLM4-noresm-ver1_cmip5-r112)\n",
        "30-day moving window 50-bins quantile mapping with detrending.</dd><dt><span>redistribution :</span></dt><dd>Redistribution prohibited. For internal use only.</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
@@ -1937,16 +1939,16 @@
    "id": "db5267fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-06T15:49:06.508123Z",
-     "iopub.status.busy": "2022-07-06T15:49:06.507834Z",
-     "iopub.status.idle": "2022-07-06T15:49:06.512214Z",
-     "shell.execute_reply": "2022-07-06T15:49:06.511506Z"
+     "iopub.execute_input": "2022-10-04T17:33:38.676212Z",
+     "iopub.status.busy": "2022-10-04T17:33:38.675951Z",
+     "iopub.status.idle": "2022-10-04T17:33:38.680579Z",
+     "shell.execute_reply": "2022-10-04T17:33:38.679832Z"
     }
    },
    "outputs": [],
    "source": [
     "tmp_dir.cleanup()\n",
-    "del tmp_dir"
+    "del tmp_dir\n"
    ]
   }
  ],
@@ -1967,7 +1969,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/processes.rst b/docs/source/processes.rst
index a8807a71..b8d6b91e 100644
--- a/docs/source/processes.rst
+++ b/docs/source/processes.rst
@@ -2,835 +2,40 @@
 
 Processes
 =========
+This page documents the processes available with the default configuration of finch. The list of ensemble processes can be different, depending on the configuration. See :ref:`datasetconfig`.
 
 .. contents::
     :local:
     :depth: 1
 
-..
-    from finch.processes import get_processes
-    processes = get_processes(all_processes=True)
-    name = lambda p: p.__class__.__name__
-    ensemble = sorted([p for p in processes if "Ensemble" in name(p)], key=name)
-    indicators = sorted([p for p in processes if name(p).endswith('_Indicator_Process')], key=name)
-    others = sorted([p for p in processes if name(p) not in set(map(name, indicators + ensemble))], key=name)
-    format = lambda p: print(f'.. autoprocess:: finch.processes.xclim.{name(p)}\n')
-    def print_all():
-        print("xclim Indicators")
-        print("----------------")
-        list(map(format, indicators))
-        print("Ensemble Processes")
-        print("------------------")
-        list(map(format, ensemble))
-        print("Other Processes")
-        print("---------------")
-        list(map(format, others))
 
-    print_all()
+.. automodule:: finch.processes.virtual.indicators
+    :members:
+    :imported-members:
+    :undoc-members:
 
 
-xclim Indicators
-----------------
-.. autoprocess:: finch.processes.xclim.base_flow_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.biologically_effective_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.blowing_snow_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.calm_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cdd_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cold_and_dry_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cold_and_wet_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_duration_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_frequency_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_free_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.continuous_snow_cover_end_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.continuous_snow_cover_start_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cool_night_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cooling_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.corn_heat_units_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.cwd_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.days_over_precip_thresh_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.days_with_snow_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.degree_days_exceedance_date_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dlyfrzthw_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.doy_qmax_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.doy_qmin_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dry_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_frequency_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_total_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dtr_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dtrmax_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.dtrvar_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.effective_growing_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.etr_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.fire_season_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_above_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_below_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.first_snowfall_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.fit_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.fraction_over_precip_thresh_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_frequency_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_max_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_mean_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freezing_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freq_analysis_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.freshet_start_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.frost_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_end_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_start_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.frost_season_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.growing_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_end_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_start_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_frequency_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_max_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_total_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.heating_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.high_precip_low_temp_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_frequency_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_max_length_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.huglin_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.humidex_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.hurs_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.hurs_fromdewpoint_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.ice_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.last_snowfall_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.last_spring_frost_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.latitude_temperature_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.liquid_precip_ratio_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.liquidprcptot_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.max_n_day_precipitation_amount_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.max_pr_intensity_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.maximum_consecutive_warm_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.melt_and_precip_max_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.potential_evapotranspiration_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.prcptot_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.prlp_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.prsn_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.rain_frzgr_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.rb_flashiness_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.rx1day_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.sdii_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.sea_ice_area_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.sea_ice_extent_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.snd_max_doy_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.snow_cover_duration_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.snow_depth_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.snow_melt_we_max_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.solidprcptot_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.stats_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg10p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg90p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_above_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_below_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_max_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_mean_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tg_min_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.thawing_degree_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn10p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn90p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_above_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_below_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn_max_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn_mean_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tn_min_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tropical_nights_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx10p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx90p_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_above_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_below_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_max_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_mean_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_min_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.tx_tn_days_above_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.warm_and_dry_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.warm_and_wet_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.warm_spell_duration_index_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.water_budget_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.wet_prcptot_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.wetdays_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.wind_chill_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.wind_speed_from_vector_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.wind_vector_from_speed_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.windy_days_Indicator_Process
-
-.. autoprocess:: finch.processes.xclim.winter_storm_Indicator_Process
-
-Ensemble Processes
-------------------
-.. autoprocess:: finch.processes.xclim.cdd_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cdd_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cdd_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_duration_index_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_duration_index_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_duration_index_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_frequency_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_frequency_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cold_spell_frequency_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_free_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_free_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.consecutive_frost_free_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.cooling_degree_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cooling_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cooling_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.corn_heat_units_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.corn_heat_units_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.corn_heat_units_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.cwd_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.cwd_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.cwd_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.degree_days_exceedance_date_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.degree_days_exceedance_date_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.degree_days_exceedance_date_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dlyfrzthw_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dlyfrzthw_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dlyfrzthw_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dry_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dry_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dry_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_frequency_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_frequency_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_frequency_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_total_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_total_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dry_spell_total_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dtr_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dtr_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dtr_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dtrmax_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dtrmax_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dtrmax_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.dtrvar_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.dtrvar_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.dtrvar_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.effective_growing_degree_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.effective_growing_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.effective_growing_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.etr_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.etr_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.etr_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_above_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_above_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_above_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_below_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_below_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.first_day_below_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_frequency_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_frequency_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_frequency_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_max_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_max_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_max_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_mean_length_Ensemble_Bbox_Process
+.. automodule:: finch.processes.virtual.ensemble
+    :members:
+    :imported-members:
+    :undoc-members:
 
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_mean_length_Ensemble_GridPoint_Process
 
-.. autoprocess:: finch.processes.xclim.freezethaw_spell_mean_length_Ensemble_Polygon_Process
+.. automodule:: finch.processes.virtual.other
+    :members:
+    :imported-members:
+    :undoc-members:
 
-.. autoprocess:: finch.processes.xclim.freezing_degree_days_Ensemble_Bbox_Process
 
-.. autoprocess:: finch.processes.xclim.freezing_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.freezing_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.freshet_start_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.freshet_start_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.freshet_start_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.frost_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.frost_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.frost_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_end_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_end_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_end_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_start_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_start_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.frost_free_season_start_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.frost_season_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.frost_season_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.frost_season_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.growing_degree_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.growing_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.growing_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_end_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_end_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_end_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_start_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_start_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.growing_season_start_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_frequency_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_frequency_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_frequency_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_index_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_index_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_index_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_max_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_max_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_max_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_total_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_total_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.heat_wave_total_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.heating_degree_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.heating_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.heating_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.high_precip_low_temp_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.high_precip_low_temp_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.high_precip_low_temp_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_frequency_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_frequency_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_frequency_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_max_length_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_max_length_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.hot_spell_max_length_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.ice_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.ice_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.ice_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.last_spring_frost_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.last_spring_frost_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.last_spring_frost_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.liquid_precip_ratio_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.liquid_precip_ratio_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.liquid_precip_ratio_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.liquidprcptot_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.liquidprcptot_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.liquidprcptot_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.max_n_day_precipitation_amount_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.max_n_day_precipitation_amount_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.max_n_day_precipitation_amount_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.max_pr_intensity_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.max_pr_intensity_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.max_pr_intensity_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.maximum_consecutive_warm_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.maximum_consecutive_warm_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.maximum_consecutive_warm_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.potential_evapotranspiration_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.potential_evapotranspiration_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.potential_evapotranspiration_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.prcptot_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.prcptot_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.prcptot_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.prlp_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.prlp_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.prlp_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.prsn_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.prsn_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.prsn_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.rain_frzgr_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.rain_frzgr_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.rain_frzgr_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.rx1day_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.rx1day_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.rx1day_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.sdii_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.sdii_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.sdii_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.solidprcptot_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.solidprcptot_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.solidprcptot_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg10p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg10p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg10p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg90p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg90p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg90p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_above_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_above_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_above_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_below_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_below_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_days_below_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_max_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_max_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_max_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_mean_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_mean_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_mean_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tg_min_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tg_min_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tg_min_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.thawing_degree_days_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.thawing_degree_days_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.thawing_degree_days_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn10p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn10p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn10p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn90p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn90p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn90p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_above_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_above_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_above_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_below_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_below_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn_days_below_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn_max_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn_max_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn_max_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn_mean_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn_mean_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn_mean_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tn_min_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tn_min_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tn_min_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tropical_nights_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tropical_nights_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tropical_nights_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx10p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx10p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx10p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx90p_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx90p_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx90p_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_above_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_above_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_above_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_below_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_below_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_days_below_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_max_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_max_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_max_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_mean_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_mean_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_mean_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_min_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_min_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_min_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.tx_tn_days_above_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.tx_tn_days_above_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.tx_tn_days_above_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.warm_spell_duration_index_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.warm_spell_duration_index_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.warm_spell_duration_index_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.water_budget_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.water_budget_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.water_budget_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.wet_prcptot_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.wet_prcptot_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.wet_prcptot_Ensemble_Polygon_Process
-
-.. autoprocess:: finch.processes.xclim.wetdays_Ensemble_Bbox_Process
-
-.. autoprocess:: finch.processes.xclim.wetdays_Ensemble_GridPoint_Process
-
-.. autoprocess:: finch.processes.xclim.wetdays_Ensemble_Polygon_Process
-
-Other Processes
----------------
-.. autoprocess:: finch.processes.xclim.AveragePolygonProcess
-
-.. autoprocess:: finch.processes.xclim.BCCAQV2HeatWave
-
-.. autoprocess:: finch.processes.xclim.EmpiricalQuantileMappingProcess
-
-.. autoprocess:: finch.processes.xclim.GeoseriesToNetcdfProcess
-
-.. autoprocess:: finch.processes.xclim.HourlyToDailyProcess
-
-.. autoprocess:: finch.processes.xclim.SubsetBboxBCCAQV2Process
-
-.. autoprocess:: finch.processes.xclim.SubsetBboxDatasetProcess
-
-.. autoprocess:: finch.processes.xclim.SubsetBboxProcess
-
-.. autoprocess:: finch.processes.xclim.SubsetGridPointBCCAQV2Process
+xclim references
+----------------
 
-.. autoprocess:: finch.processes.xclim.SubsetGridPointDatasetProcess
+.. bibliography::
 
-.. autoprocess:: finch.processes.xclim.SubsetGridPointProcess
+.. There are other categories, but only those with references appearing in the indicator's abstract, title, parameters or returns are needed here. Those in notes don't make it to the process docstring.
+McArthur Forest Fire Danger Indices methods
+*******************************************
 
-.. autoprocess:: finch.processes.xclim.SubsetPolygonProcess
+.. bibliography::
+   :labelprefix: FFDI-
+   :keyprefix: ffdi-
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
index 7fee679c..150f72d1 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,15 +2,15 @@ name: finch
 channels:
   - conda-forge
 dependencies:
-  - python >=3.7
+  - python >=3.8
   - pip
   - jinja2
   - click
   - psutil
   - netcdf4
   - unidecode
-  - dask <= 2022.1.0
-  - distributed <= 2022.1.0
+  - dask
+  - distributed
   - xarray >=0.18.2
   - numpy
   - geopandas
@@ -20,10 +20,11 @@ dependencies:
   - sentry-sdk
   - siphon
   - bottleneck
-  # remember to match xclim version in environment-docs.yml as well
-  - xclim == 0.37
-  - clisops >= 0.8.0
-  - pywps >= 4.5.1
+  # remember to match xclim version in requirements_docs.txt as well
+  - xclim =0.38.*
+  - clisops >=0.9.3
+  - pywps >=4.5.1
   - parse
-  - xesmf >= 0.6.2
+  - xesmf >=0.6.2
   - python-slugify
+  - pyyaml
diff --git a/finch/__init__.py b/finch/__init__.py
index 07130677..89eb1698 100644
--- a/finch/__init__.py
+++ b/finch/__init__.py
@@ -3,5 +3,3 @@
 """Top-level package for Finch."""
 
 from .__version__ import __author__, __email__, __version__  # noqa: F401
-
-from .wsgi import application  # noqa: F401
diff --git a/finch/datasets.yml b/finch/datasets.yml
new file mode 100644
index 00000000..4e320c6d
--- /dev/null
+++ b/finch/datasets.yml
@@ -0,0 +1,95 @@
+candcs-u5:
+  &candcsu5
+  local: false
+  depth: 0
+  path: https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/pcic/BCCAQv2/catalog.xml
+  suffix: "*.nc"
+  pattern: "{variable}_{frequency}_BCCAQv2+ANUSPLIN300_{model}_{scenario}_{realization}_{date_start}-{date_end}.nc"
+  allowed_values:
+    scenario: [rcp26, rcp45, rcp85]
+    variable: [tasmin, tasmax, pr]
+    model:
+      &u5models
+      - BNU-ESM
+      - CCSM4
+      - CESM1-CAM5
+      - CNRM-CM5
+      - CSIRO-Mk3-6-0
+      - CanESM2
+      - FGOALS-g2
+      - GFDL-CM3
+      - GFDL-ESM2G
+      - GFDL-ESM2M
+      - HadGEM2-AO
+      - HadGEM2-ES
+      - IPSL-CM5A-LR
+      - IPSL-CM5A-MR
+      - MIROC-ESM-CHEM
+      - MIROC-ESM
+      - MIROC5
+      - MPI-ESM-LR
+      - MPI-ESM-MR
+      - MRI-CGCM3
+      - NorESM1-M
+      - NorESM1-ME
+      - bcc-csm1-1-m
+      - bcc-csm1-1
+  model_lists:
+    # The absence of realization specification implies r1 is taken.
+    24models: *u5models
+    pcic12:
+    # taken from: https://www.pacificclimate.org/data/statistically-downscaled-climate-scenarios
+      - [ ACCESS1-0 , r1i1p1 ]
+      - [ CCSM4 , r2i1p1 ]
+      - [ CNRM-CM5 , r1i1p1 ]
+      - [ CSIRO-Mk3-6-0 , r1i1p1 ]
+      - [ CanESM2 , r1i1p1 ]
+      - [ GFDL-ESM2G , r1i1p1 ]
+      - [ HadGEM2-CC , r1i1p1 ]
+      - [ HadGEM2-ES , r1i1p1 ]
+      - [ MIROC5 , r3i1p1 ]
+      - [ MPI-ESM-LR , r3i1p1 ]
+      - [ MRI-CGCM3 , r1i1p1 ]
+      - [ inmcm4 , r1i1p1 ]
+
+# This is a copy of the above for backward compatibility reasons.
+bccaqv2: *candcsu5
+
+candcs-u6:
+  local: false
+  depth: 1
+  path: https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/pcic/CanDCS-U6/CMIP6_BCCAQv2/catalog.xml
+  pattern: "{variable}_{frequency}_BCCAQv2+ANUSPLIN300_{model}_{scenario}_{realization}_{:l}_{date_start}-{date_end}.nc"
+  allowed_values:
+    scenario: [ ssp126, ssp245, ssp585 ]
+    variable: [ tasmin, tasmax, pr]
+    model:
+      &u6models
+      - ACCESS-CM2
+      - ACCESS-ESM1-5
+      - BCC-CSM2-MR
+      - CMCC-ESM2
+      - CNRM-CM6-1
+      - CNRM-ESM2-1
+      - CanESM5
+      - EC-Earth3
+      - EC-Earth3-Veg
+      - FGOALS-g3
+      - GFDL-ESM4
+      - HadGEM3-GC31-LL
+      - INM-CM4-8
+      - INM-CM5-0
+      - IPSL-CM6A-LR
+      - KACE-1-0-G
+      - KIOST-ESM
+      - MIROC-ES2L
+      - MIROC6
+      - MPI-ESM1-2-HR
+      - MPI-ESM1-2-LR
+      - MRI-ESM2-0
+      - NorESM2-LM
+      - NorESM2-MM
+      - TaiESM1
+      - UKESM1-0-L
+  model_lists:
+    26models: *u6models
diff --git a/finch/default.cfg b/finch/default.cfg
index 2220d8db..15c09674 100644
--- a/finch/default.cfg
+++ b/finch/default.cfg
@@ -16,9 +16,9 @@ parallelprocesses = 2
 language=en-US,fr
 
 [finch]
-default_dataset = bccaqv2
-dataset_bccaqv2 = https://pavics.ouranos.ca/thredds/catalog/birdhouse/pcic/BCCAQv2/catalog.xml
 subset_threads = 1
+datasets_config = datasets.yml
+default_dataset = candcs-u5
 
 [finch:metadata]
 # All fields here are added as string attributes of computed indices.
diff --git a/finch/processes/__init__.py b/finch/processes/__init__.py
index df9aec92..7d9042a9 100644
--- a/finch/processes/__init__.py
+++ b/finch/processes/__init__.py
@@ -1,11 +1,10 @@
 import logging
 
 from pywps.configuration import get_config_value
-import xclim
-import xclim.indicators.atmos
-from xclim.core.indicator import build_indicator_module
+from xclim.core.indicator import registry as xclim_registry
 
 from .ensemble_utils import uses_accepted_netcdf_variables
+from .utils import get_available_variables, get_datasets_config
 from .wps_base import make_xclim_indicator_process
 from .wps_ensemble_indices_bbox import XclimEnsembleBboxBase
 from .wps_ensemble_indices_point import XclimEnsembleGridPointBase
@@ -30,8 +29,6 @@ def get_indicators(realms=["atmos"], exclude=()):
 
     module : A xclim module.
     """
-    from collections import OrderedDict
-    from xclim.core.indicator import registry
 
     def filter_func(elem):
         name, ind = elem
@@ -42,7 +39,7 @@ def filter_func(elem):
             and ind.identifier.upper() == ind._registry_id  # official indicator
         )
 
-    out = OrderedDict(filter(filter_func, registry.items()))
+    out = dict(filter(filter_func, xclim_registry.items()))
     return [ind.get_instance() for ind in out.values()]
 
 
@@ -56,17 +53,20 @@ def filter_func(elem):
 ]
 
 
-indicators = get_indicators(realms=["atmos", "land", "seaIce"], exclude=not_implemented)
-ensemble_indicators = [i for i in indicators if uses_accepted_netcdf_variables(i)]
-
-
-def get_processes(all_processes=False):
+def get_processes():
     """Get wps processes, using the current global `pywps` configuration"""
-    default_dataset = get_config_value("finch", "default_dataset")
-    datasets_configured = get_config_value("finch", f"dataset_{default_dataset}")
-
-    if not datasets_configured:
-        logger.warning("Datasets not configured. Some processes will not be available.")
+    indicators = get_indicators(realms=["atmos", "land", "seaIce"], exclude=not_implemented)
+
+    dsconf = get_datasets_config()
+    if dsconf:
+        available_variables = get_available_variables()
+        ensemble_indicators = [i for i in indicators if uses_accepted_netcdf_variables(i, available_variables)]
+    else:
+        ensemble_indicators = []
+        logger.warning("No ensemble datasets configured, many processes won't be available.")
+        default_dataset = get_config_value("finch", "default_dataset")
+        if default_dataset not in dsconf:
+            logger.warning("The default dataset is not configured, which is weird.")
 
     processes = []
 
@@ -83,30 +83,30 @@ def get_processes(all_processes=False):
         EmpiricalQuantileMappingProcess()
     ]
 
-    if datasets_configured or all_processes:
-        # ensemble with grid point subset
-        for ind in ensemble_indicators:
-            suffix = "_Ensemble_GridPoint_Process"
-            base_class = XclimEnsembleGridPointBase
-            processes.append(
-                make_xclim_indicator_process(ind, suffix, base_class=base_class)
-            )
-
-        # ensemble with bbox subset
-        for ind in ensemble_indicators:
-            suffix = "_Ensemble_Bbox_Process"
-            base_class = XclimEnsembleBboxBase
-            processes.append(
-                make_xclim_indicator_process(ind, suffix, base_class=base_class)
-            )
-        # ensemble with polygon subset
-        for ind in ensemble_indicators:
-            suffix = "_Ensemble_Polygon_Process"
-            base_class = XclimEnsemblePolygonBase
-            processes.append(
-                make_xclim_indicator_process(ind, suffix, base_class=base_class)
-            )
+    # ensemble with grid point subset
+    for ind in ensemble_indicators:
+        suffix = "_Ensemble_GridPoint_Process"
+        base_class = XclimEnsembleGridPointBase
+        processes.append(
+            make_xclim_indicator_process(ind, suffix, base_class=base_class)
+        )
 
+    # ensemble with bbox subset
+    for ind in ensemble_indicators:
+        suffix = "_Ensemble_Bbox_Process"
+        base_class = XclimEnsembleBboxBase
+        processes.append(
+            make_xclim_indicator_process(ind, suffix, base_class=base_class)
+        )
+    # ensemble with polygon subset
+    for ind in ensemble_indicators:
+        suffix = "_Ensemble_Polygon_Process"
+        base_class = XclimEnsemblePolygonBase
+        processes.append(
+            make_xclim_indicator_process(ind, suffix, base_class=base_class)
+        )
+
+    if ensemble_indicators:
         processes += [
             SubsetGridPointDatasetProcess(),
             SubsetBboxDatasetProcess(),
@@ -123,20 +123,3 @@ def get_processes(all_processes=False):
     ]
 
     return processes
-
-
-# Create virtual module for indicators so Sphinx can find it.
-def _build_xclim():
-    import sys
-
-    processes = get_processes(all_processes=True)
-    objs = {p.__class__.__name__: p.__class__ for p in processes}
-
-    mod = build_indicator_module(
-        "finch.processes.xclim", objs, doc="""XCLIM Processes"""
-    )
-    sys.modules["finch.processes.xclim"] = mod
-    return mod
-
-
-xclim = _build_xclim()  # noqa
diff --git a/finch/processes/constants.py b/finch/processes/constants.py
deleted file mode 100644
index 16529584..00000000
--- a/finch/processes/constants.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from xclim.testing import list_input_variables
-
-BCCAQV2_MODELS = [
-    "BNU-ESM",
-    "CCSM4",
-    "CESM1-CAM5",
-    "CNRM-CM5",
-    "CSIRO-Mk3-6-0",
-    "CanESM2",
-    "FGOALS-g2",
-    "GFDL-CM3",
-    "GFDL-ESM2G",
-    "GFDL-ESM2M",
-    "HadGEM2-AO",
-    "HadGEM2-ES",
-    "IPSL-CM5A-LR",
-    "IPSL-CM5A-MR",
-    "MIROC-ESM-CHEM",
-    "MIROC-ESM",
-    "MIROC5",
-    "MPI-ESM-LR",
-    "MPI-ESM-MR",
-    "MRI-CGCM3",
-    "NorESM1-M",
-    "NorESM1-ME",
-    "bcc-csm1-1-m",
-    "bcc-csm1-1",
-]
-
-# taken from: https://www.pacificclimate.org/data/statistically-downscaled-climate-scenarios
-PCIC_12_MODELS_REALIZATIONS = [
-    ("ACCESS1-0", "r1"),
-    ("CCSM4", "r2"),
-    ("CNRM-CM5", "r1"),
-    ("CSIRO-Mk3-6-0", "r1"),
-    ("CanESM2", "r1"),
-    ("GFDL-ESM2G", "r1"),
-    ("HadGEM2-CC", "r1"),
-    ("HadGEM2-ES", "r1"),
-    ("MIROC5", "r3"),
-    ("MPI-ESM-LR", "r3"),
-    ("MRI-CGCM3", "r1"),
-    ("inmcm4", "r1"),
-]
-
-
-ALL_24_MODELS = "24MODELS"
-PCIC_12 = "PCIC12"
-
-ALLOWED_MODEL_NAMES = [ALL_24_MODELS, PCIC_12] + BCCAQV2_MODELS
-
-bccaq_variables = {"tasmin", "tasmax", "pr"}
-
-xclim_variables = set(list_input_variables(submodules=["atmos", "land", "seaIce"]).keys())
-
-default_percentiles = {
-    'days_over_precip_thresh': {'pr_per': 95},
-    'days_over_precip_doy_thresh': {'pr_per': 95},
-    'fraction_over_precip_doy_thresh': {'pr_per': 95},
-    'fraction_over_precip_thresh': {'pr_per': 95},
-    'cold_and_dry_days': {'pr_per': 25, 'tas_per': 25},
-    'warm_and_dry_days': {'pr_per': 25, 'tas_per': 75},
-    'warm_and_wet_days': {'pr_per': 75, 'tas_per': 75},
-    'cold_and_wet_days': {'pr_per': 75, 'tas_per': 25},
-    'tg90p': {'tas_per': 90},
-    'tg10p': {'tas_per': 10},
-    'tn90p': {'tasmin_per': 90},
-    'tn10p': {'tasmin_per': 10},
-    'tx90p': {'tasmax_per': 90},
-    'tx10p': {'tasmax_per': 10},
-    'cold_spell_duration_index': {'tasmin_per': 10},
-    'warm_spell_duration_index': {'tasmax_per': 90},
-}
diff --git a/finch/processes/ensemble_utils.py b/finch/processes/ensemble_utils.py
index 903427b7..ebf86924 100644
--- a/finch/processes/ensemble_utils.py
+++ b/finch/processes/ensemble_utils.py
@@ -1,14 +1,16 @@
 from collections import deque
 from copy import deepcopy
 from dataclasses import dataclass
-from enum import Enum
+import logging
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Tuple, cast
+import sys
+from typing import Dict, Iterable, List, Optional, Tuple, Union
 import warnings
 
 from parse import parse
 from pywps import ComplexInput, FORMATS, Process
 from pywps import configuration
+from pywps.exceptions import InvalidParameterValue
 from pywps.app.exceptions import ProcessError
 from siphon.catalog import TDSCatalog
 import xarray as xr
@@ -16,24 +18,18 @@
 from xclim import ensembles
 from xclim.core.calendar import percentile_doy, doy_to_days_since, days_since_to_doy
 from xclim.core.indicator import Indicator
-from xclim.core.utils import InputKind
-
-from .constants import (
-    ALL_24_MODELS,
-    BCCAQV2_MODELS,
-    PCIC_12,
-    PCIC_12_MODELS_REALIZATIONS,
-    bccaq_variables,
-    xclim_variables
-)
+
 from .subset import finch_subset_bbox, finch_subset_gridpoint, finch_subset_shape
 from .utils import (
+    DatasetConfiguration,
     PywpsInput,
     RequestInputs,
     compute_indices,
     dataset_to_dataframe,
     dataset_to_netcdf,
     format_metadata,
+    get_datasets_config,
+    iter_xc_variables,
     log_file_path,
     single_input_or_none,
     valid_filename,
@@ -41,37 +37,7 @@
     zip_files,
 )
 from .wps_base import make_nc_input
-
-
-@dataclass
-class Bccaqv2File:
-    variable: str
-    frequency: str
-    driving_model_id: str
-    driving_experiment_id: str
-    driving_realization: str
-    driving_initialization_method: str
-    driving_physics_version: str
-    date_start: Optional[str] = None
-    date_end: Optional[str] = None
-
-    @classmethod
-    def from_filename(cls, filename):
-        pattern = "_".join(
-            [
-                "{variable}",
-                "{frequency}",
-                "BCCAQv2+ANUSPLIN300",
-                "{driving_model_id}",
-                "{driving_experiment_id}",
-                "r{driving_realization}i{driving_initialization_method}p{driving_physics_version}",
-                "{date_start}-{date_end}.nc",
-            ]
-        )
-        try:
-            return cls(**parse(pattern, filename).named)
-        except AttributeError:
-            return
+LOGGER = logging.getLogger("PYWPS")
 
 
 def _percentile_doy(var: xr.DataArray, perc: int) -> xr.DataArray:
@@ -86,192 +52,144 @@ def _percentile_doy(var: xr.DataArray, perc: int) -> xr.DataArray:
     "pr_per": {"inputs": ["pr"], "args": ["perc_pr"], "function": _percentile_doy}
 }
 
-accepted_variables = bccaq_variables.union(variable_computations)
-not_implemented_variables = xclim_variables - accepted_variables
-
-
-class ParsingMethod(Enum):
-    # parse the filename directly (faster and simpler, more likely to fail)
-    filename = 1
-    # parse each Data Attribute Structure (DAS) by appending .das to the url
-    # One request for each dataset, so lots of small requests to the Thredds server
-    opendap_das = 2
-    # open the dataset using xarray and look at the file attributes
-    # safer, but slower and lots of small requests are made to the Thredds server
-    xarray = 3
-
-
-def get_bccaqv2_local_files_datasets(
-    catalog_url,
-    variables: List[str] = None,
-    rcp: str = None,
-    method: ParsingMethod = ParsingMethod.filename,
-    models=None,
-) -> List[str]:
-    """Get a list of filenames corresponding to variable and rcp on a local filesystem."""
-
-    urls = []
-    for file in Path(catalog_url).glob("*.nc"):
-        if _bccaqv2_filter(
-            method, file.name, str(file), variables=variables, rcp=rcp, models=models
-        ):
-            urls.append(str(file))
-    return urls
 
+@dataclass
+class Dataset:
+    variable: str
+    model: str
+    scenario: str
+    frequency: str = "day"
+    realization: Optional[str] = None
+    date_start: Optional[str] = None
+    date_end: Optional[str] = None
 
-def get_bccaqv2_opendap_datasets(
-    catalog_url,
-    variables: List[str] = None,
-    rcp: str = None,
-    method: ParsingMethod = ParsingMethod.filename,
-    models=None,
-) -> List[str]:
-    """Get a list of urls corresponding to variable and rcp on a Thredds server.
-
-    We assume that the files are named in a certain way on the Thredds server.
-
-    This is the case for pavics.ouranos.ca/thredds
-    For more general use cases, see the `xarray` and `requests` methods below."""
-
-    catalog = TDSCatalog(catalog_url)
-
-    urls = []
-    for dataset in catalog.datasets.values():
-        opendap_url = dataset.access_urls["OPENDAP"]
-        if _bccaqv2_filter(
-            method,
-            dataset.name,
-            opendap_url,
-            variables=variables,
-            rcp=rcp,
-            models=models,
-        ):
-            urls.append(opendap_url)
-    return urls
+    @classmethod
+    def from_filename(cls, filename, pattern):
+        match = parse(pattern, filename)
+        if not match:
+            return None
+        return cls(**match.named)
 
 
-def _bccaqv2_filter(
-    method: ParsingMethod,
-    filename,
-    url,
+def file_is_required(
+    filename: str,
+    pattern: str,
+    model_lists: Optional[Dict[str, List[str]]] = None,
     variables: List[str] = None,
-    rcp: str = None,
-    models=None,
+    scenario: str = None,
+    models: List[Union[str, Tuple[str, int]]] = None,
 ):
-    """Parse metadata and filter BCCAQV2 datasets"""
-
-    if models is None or [m.lower() for m in models] == [ALL_24_MODELS.lower()]:
-        models = BCCAQV2_MODELS
-
-    models = [m.lower() for m in models]
-
-    if method == ParsingMethod.filename:
-        parsed = Bccaqv2File.from_filename(filename)
-        if parsed is None:
-            return False
-
-        if variables and parsed.variable not in variables:
-            return False
-        if rcp and rcp not in parsed.driving_experiment_id:
-            return False
-
-        if models == [PCIC_12.lower()]:
-            for model, realization in PCIC_12_MODELS_REALIZATIONS:
-                model_ok = model.lower() == parsed.driving_model_id.lower()
-                r_ok = realization[1:] == parsed.driving_realization
-                if model_ok and r_ok:
-                    return True
-            return False
-
-        model_ok = parsed.driving_model_id.lower() in models
-        r_ok = parsed.driving_realization == "1"
-        return model_ok and r_ok
-
-    elif method == ParsingMethod.opendap_das:
-
-        raise NotImplementedError("todo: filter models and runs")
-
-        # re_experiment = re.compile(r'String driving_experiment_id "(.+)"')
-        # lines = requests.get(url + ".das").content.decode().split("\n")
-        # variable_ok = variable_ok or any(
-        #     line.startswith(f"    {variable} {{") for line in lines
-        # )
-        # if not rcp_ok:
-        #     for line in lines:
-        #         match = re_experiment.search(line)
-        #         if match and rcp in match.group(1).split(","):
-        #             rcp_ok = True
-
-    elif method == ParsingMethod.xarray:
-
-        raise NotImplementedError("todo: filter models and runs")
-
-        # import xarray as xr
-
-        # ds = xr.open_dataset(url, decode_times=False)
-        # rcps = [
-        #     r
-        #     for r in ds.attrs.get("driving_experiment_id", "").split(",")
-        #     if "rcp" in r
-        # ]
-        # variable_ok = variable_ok or variable in ds.data_vars
-        # rcp_ok = rcp_ok or rcp in rcps
+    """Parse metadata and filter datasets"""
+    file = Dataset.from_filename(filename, pattern)
+    if not file:
+        return False
+
+    if variables and file.variable not in variables:
+        return False
+
+    if scenario and scenario not in file.scenario:
+        return False
+
+    if models is None or models[0].lower() == "all":
+        return True
+
+    if (
+        len(models) == 1
+        and isinstance(models[0], str)
+        and model_lists is not None
+        and models[0].lower() in model_lists
+    ):
+        models = model_lists[models[0]]
+
+    for modelspec in models:
+        if isinstance(modelspec, str):  # case with a single model name
+            if (
+                file.model.lower() == modelspec.lower()
+                and (file.realization is None or file.realization.startswith('r1i'))
+            ):
+                return True
+        else:  # case with a couple model name, realization num.
+            if file.model.lower() == modelspec[0].lower() and file.realization == modelspec[1]:
+                return True
+    return False
+
+
+def iter_remote(cat: TDSCatalog, depth: int = -1):
+    """Generator listing all datasets recursively in a TDSCatalog.
+    The search is limited to a certain depth if `depth` >= 0."""
+    for ds in cat.datasets.values():
+        yield ds.name, ds.access_urls["OPENDAP"]
+
+    if depth != 0:
+        for subcat in cat.catalog_refs.values():
+            yield from iter_remote(subcat.follow(), depth=depth - 1)
+
+
+def iter_local(root: Path, depth: int = -1, pattern: str = '*.nc'):
+    """Generator listing all datasets recursively in a local directory.
+    The search is limited to a certain depth if `depth` >= 0.
+    The path can be given relative to the root finch code repo."""
+    if not root.is_absolute():
+        root = (Path(__file__).parent.parent / root).resolve()
+
+    for file in root.rglob(pattern):
+        yield file.name, file
+
+    if depth != 0:
+        for sub in root.iterdir():
+            if sub.is_dir():
+                yield from iter_local(sub, depth=depth - 1, pattern=pattern)
+
+
+def _make_resource_input(url: str, workdir: str, local: bool):
+    inp = ComplexInput(
+        "resource",
+        "NetCDF resource",
+        max_occurs=1000,
+        supported_formats=[FORMATS.NETCDF, FORMATS.DODS],
+    )
+    inp.workdir = workdir
+    if local:
+        inp.file = url
+    else:
+        inp.url = url
+    return inp
 
 
 def get_datasets(
-    dataset_name: Optional[str],
+    dsconf: DatasetConfiguration,
     workdir: str,
     variables: Optional[List[str]] = None,
-    rcp=None,
+    scenario: Optional[str] = None,
     models: Optional[List[str]] = None,
 ) -> List[PywpsInput]:
-
-    dataset_functions = {"bccaqv2": _get_bccaqv2_inputs}
-
-    if dataset_name is None:
-        dataset_name = configuration.get_config_value("finch", "default_dataset")
-    dataset_name = cast(str, dataset_name)
-    return dataset_functions[dataset_name](
-        workdir=workdir, variables=variables, rcp=rcp, models=models
-    )
-
-
-def _get_bccaqv2_inputs(
-    workdir: str,
-    variables: Optional[List[str]] = None,
-    rcp=None,
-    models=None,
-) -> List[PywpsInput]:
-    """Adds a 'resource' input list with bccaqv2 urls to WPS inputs."""
-    catalog_url = configuration.get_config_value("finch", "dataset_bccaqv2")
+    """Parse a directory to find files and filters the list to return only the needed ones, as resource inputs.
+
+    Parameters
+    ----------
+    dsconf : DatasetConfiguration
+        The dataclass defining a specific ensemble dataset.
+    workdir: str
+        The working directory where files will be downloaded if needed.
+    variables: list of strings
+        A list of the needed variables
+    scenario: str
+        The name of the scenario (experiment), rcps or ssps.
+    models: list of strings
+        A list of the requested models (or name of a models sublist)
+    """
+    if dsconf.local:
+        iterator = iter_local(Path(dsconf.path), dsconf.depth, dsconf.suffix)
+    else:
+        iterator = iter_remote(TDSCatalog(dsconf.path), depth=dsconf.depth)
 
     inputs = []
-
-    def _make_bccaqv2_resource_input():
-        return ComplexInput(
-            "resource",
-            "NetCDF resource",
-            max_occurs=1000,
-            supported_formats=[FORMATS.NETCDF, FORMATS.DODS],
-        )
-
-    if catalog_url.startswith("http"):
-        for url in get_bccaqv2_opendap_datasets(
-            catalog_url, variables=variables, rcp=rcp, models=models
-        ):
-            resource = _make_bccaqv2_resource_input()
-            resource.url = url
-            resource.workdir = workdir
-            inputs.append(resource)
-    else:
-        for file in get_bccaqv2_local_files_datasets(
-            catalog_url, variables=variables, rcp=rcp, models=models
+    for name, url in iterator:
+        if file_is_required(
+            name, dsconf.pattern, dsconf.model_lists,
+            variables=variables, scenario=scenario, models=models
         ):
-            resource = _make_bccaqv2_resource_input()
-            resource.file = file
-            resource.workdir = workdir
-            inputs.append(resource)
-
+            inputs.append(_make_resource_input(url, workdir, dsconf.local))
     return inputs
 
 
@@ -289,13 +207,15 @@ def _formatted_coordinate(value) -> Optional[str]:
     return f"{float(value):.3f}"
 
 
-def make_output_filename(process: Process, inputs: List[PywpsInput], rcp=None):
+def make_output_filename(process: Process, inputs: List[PywpsInput], scenario=None, dataset=None):
     """Returns a filename for the process's output, depending on its inputs.
 
-    The rcp part of the filename can be overriden.
+    The scenario part of the filename can be overriden.
     """
-    if rcp is None:
-        rcp = single_input_or_none(inputs, "rcp")
+    if scenario is None:
+        scenario = single_input_or_none(inputs, "scenario")
+    if dataset is None:
+        dataset = single_input_or_none(inputs, "dataset")
     lat = _formatted_coordinate(single_input_or_none(inputs, "lat"))
     lon = _formatted_coordinate(single_input_or_none(inputs, "lon"))
     lat0 = _formatted_coordinate(single_input_or_none(inputs, "lat0"))
@@ -304,31 +224,38 @@ def make_output_filename(process: Process, inputs: List[PywpsInput], rcp=None):
     lon1 = _formatted_coordinate(single_input_or_none(inputs, "lon1"))
 
     # Get given prefix and if none given, default to the identifier.
-    output_parts = [single_input_or_none(inputs, "output_name") or process.identifier]
+    output_parts = []
+
+    if (prefix := single_input_or_none(inputs, "output_name")):
+        output_parts.append(prefix)
+    else:
+        if dataset:
+            output_parts.append(dataset)
+        output_parts.append(process.identifier)
 
     if lat and lon:
-        output_parts.append(f"{float(lat):.3f}")
-        output_parts.append(f"{float(lon):.3f}")
+        output_parts.extend([lat, lon])
     elif lat0 and lon0:
-        output_parts.append(f"{float(lat0):.3f}")
-        output_parts.append(f"{float(lon0):.3f}")
+        output_parts.extend([lat0, lon0])
 
     if lat1 and lon1:
-        output_parts.append(f"{float(lat1):.3f}")
-        output_parts.append(f"{float(lon1):.3f}")
+        output_parts.extend([lat1, lon1])
 
-    if rcp:
-        output_parts.append(rcp)
+    if isinstance(scenario, str):
+        output_parts.append(scenario)
+    elif scenario:
+        output_parts.extend(scenario)
 
-    return valid_filename("_".join(output_parts))
+    return valid_filename("_".join(output_parts) + '.nc')
 
 
-def uses_accepted_netcdf_variables(indicator: Indicator) -> bool:
-    """Returns True if this indicator uses  netcdf variables in `accepted_variables`."""
-    return not any(
-        n in not_implemented_variables
-        for n, p in indicator.parameters.items()
-        if p.kind in [InputKind.VARIABLE, InputKind.OPTIONAL_VARIABLE]
+def uses_accepted_netcdf_variables(indicator: Indicator, available_variables: set) -> bool:
+    """Returns True if this indicator uses variables available by computation or in any dataset.
+    This mean it may return indicators that are not compatible with all datasets.
+    """
+    return all(
+        n in available_variables or n in variable_computations
+        for n in iter_xc_variables(indicator)
     )
 
 
@@ -337,9 +264,7 @@ def make_indicator_inputs(
 ) -> List[RequestInputs]:
     """From a list of files, make a list of inputs used to call the given xclim indicator."""
 
-    arguments = set(indicator.parameters)
-
-    required_netcdf_args = accepted_variables.intersection(arguments)
+    required_netcdf_args = set(iter_xc_variables(indicator))
 
     input_list = []
 
@@ -351,7 +276,7 @@ def make_indicator_inputs(
             inputs[variable_name][0].file = str(path)
             input_list.append(inputs)
     else:
-        for group in make_file_groups(files_list):
+        for group in make_file_groups(files_list, required_netcdf_args):
             inputs = deepcopy(wps_inputs)
             for variable_name, path in group.items():
                 if variable_name not in required_netcdf_args:
@@ -363,8 +288,9 @@ def make_indicator_inputs(
     return input_list
 
 
-def make_file_groups(files_list: List[Path]) -> List[Dict[str, Path]]:
-    """Groups files by filenames, changing only the netcdf variable name."""
+def make_file_groups(files_list: List[Path], variables: set) -> List[Dict[str, Path]]:
+    """Groups files by filenames, changing only the netcdf variable name.
+    The list of variable names to search must be given."""
     groups = []
     filenames = {f.name: f for f in files_list}
 
@@ -372,9 +298,9 @@ def make_file_groups(files_list: List[Path]) -> List[Dict[str, Path]]:
         if file.name not in filenames:
             continue
         group = {}
-        for variable in accepted_variables:
-            if file.name.startswith(f"{variable}_"):
-                for other_var in accepted_variables.difference([variable]):
+        for variable in variables:
+            if file.name.startswith(f"{variable}_") or f'_{variable}_' in file.name:
+                for other_var in variables.difference({variable}):
                     other_filename = file.name.replace(variable, other_var, 1)
                     if other_filename in filenames:
                         group[other_var] = filenames[other_filename]
@@ -421,12 +347,12 @@ def make_ensemble(files: List[Path], percentiles: List[int], average_dims: Optio
 
 
 def compute_intermediate_variables(
-    files_list: List[Path], required_variable_names: Iterable[str], workdir: Path, request_inputs,
+    files_list: List[Path], variables: set, required_variable_names: Iterable[str], workdir: Path, request_inputs,
 ) -> List[Path]:
     """Compute netcdf datasets from a list of required variable names and existing files."""
 
     output_files_list = []
-    file_groups = make_file_groups(files_list)
+    file_groups = make_file_groups(files_list, variables)
     for group in file_groups:
         # add file paths that are required without any computation
         for variable, path in group.items():
@@ -470,19 +396,25 @@ def compute_intermediate_variables(
     return output_files_list
 
 
-def get_sub_inputs(variables):
+def get_input_lists(needed: set, available: set):
     """From a list of dataset variables, get the source variable names to compute them."""
-
-    output_variables = list(variables)
-    while any(v in variable_computations for v in output_variables):
-        new_output_variables = []
-        for variable in output_variables:
-            if variable in variable_computations:
-                new_output_variables += variable_computations[variable]["inputs"]
+    raw = available.intersection(needed)
+    unknown = needed.difference(raw)
+    compute = set()
+    extra = set()
+
+    while unknown:
+        for var in list(unknown):
+            if var in variable_computations:
+                this_needed = set(variable_computations[var]['inputs'])
+                compute.add(var)
+                unknown.remove(var)
+                raw.update(this_needed.intersection(available))
+                unknown.update(this_needed.difference(available))
             else:
-                new_output_variables.append(variable)
-        output_variables = new_output_variables
-    return output_variables
+                unknown.remove(var)
+                extra.add(var)
+    return raw, compute, extra
 
 
 def ensemble_common_handler(process: Process, request, response, subset_function):
@@ -496,11 +428,34 @@ def ensemble_common_handler(process: Process, request, response, subset_function
     request_inputs_not_datasets = {
         k: v for k, v in request.inputs.items() if k in xci_inputs and not k.startswith('perc')
     }
-    percentile_vars = {f"{k.split('_')[1]}_per" for k in xci_inputs if k.startswith('perc')}
-    dataset_input_names = accepted_variables.intersection(percentile_vars.union(xci_inputs))
-    source_variable_names = bccaq_variables.intersection(
-        get_sub_inputs(dataset_input_names)
-    )
+
+    dataset_name = single_input_or_none(request.inputs, "dataset")
+    dataset = get_datasets_config()[dataset_name]
+
+    needed_variables = set(iter_xc_variables(process.xci))
+    avail_variables = set(dataset.allowed_values['variable'])
+    source_variables, computed_variables, extra_variables = get_input_lists(needed_variables, avail_variables)
+
+    scenarios = [r.data.strip() for r in request.inputs["scenario"]]
+    models = [m.data.strip() for m in request.inputs["models"]]
+
+    # Check if arguments are ok for this dataset
+    if not set(dataset.allowed_values['scenario']).issuperset(scenarios):
+        raise InvalidParameterValue(
+            f"Invalid scenarios for dataset {dataset_name}. "
+            f"Should be in {dataset.allowed_values['scenario']}."
+        )
+    if not set(dataset.allowed_values['model']).union(dataset.model_lists.keys()).union({'all'}).issuperset(models):
+        raise InvalidParameterValue(
+            f"Invalid models or model list for dataset {dataset_name}. "
+            f"Should be in {dataset.allowed_values['model']} + {list(dataset.model_lists.keys())}"
+        )
+    if extra_variables:
+        raise InvalidParameterValue(
+            f"The {process.xci.identifier} indicator cannot be used with dataset {dataset_name}"
+            f" because it does not provide the variables {extra_variables} or doesn't provide "
+            "the variable that could be used to compute those."
+        )
 
     convert_to_csv = request.inputs["output_format"][0].data == "csv"
     if not convert_to_csv:
@@ -508,10 +463,7 @@ def ensemble_common_handler(process: Process, request, response, subset_function
     percentiles_string = request.inputs["ensemble_percentiles"][0].data
     ensemble_percentiles = [int(p.strip()) for p in percentiles_string.split(",")]
 
-    rcps = [r.data.strip() for r in request.inputs["rcp"]]
-    write_log(process, f"Processing started ({len(rcps)} rcps)", process_step="start")
-    models = [m.data.strip() for m in request.inputs["models"]]
-    dataset_name = single_input_or_none(request.inputs, "dataset")
+    write_log(process, f"Processing started ({len(scenarios)} scenarios)", process_step="start")
 
     if single_input_or_none(request.inputs, "average"):
         if subset_function == finch_subset_gridpoint:
@@ -524,23 +476,32 @@ def ensemble_common_handler(process: Process, request, response, subset_function
 
     base_work_dir = Path(process.workdir)
     ensembles = []
-    for rcp in rcps:
-        # Ensure no file name conflicts (i.e. if the rcp doesn't appear in the base filename)
-        work_dir = base_work_dir / rcp
+    output_basename = Path(
+        make_output_filename(
+            process, request.inputs, scenario=scenarios, dataset=dataset_name
+        )
+    )
+    for scenario in scenarios:
+        # Ensure no file name conflicts (i.e. if the scenario doesn't appear in the base filename)
+        work_dir = base_work_dir / scenario
         work_dir.mkdir(exist_ok=True)
         process.set_workdir(str(work_dir))
 
-        write_log(process, f"Fetching datasets for rcp={rcp}")
-        output_filename = make_output_filename(process, request.inputs, rcp=rcp)
+        write_log(process, f"Fetching datasets for scenario {scenario}")
         netcdf_inputs = get_datasets(
-            dataset_name,
+            dataset,
             workdir=process.workdir,
-            variables=list(source_variable_names),
-            rcp=rcp,
+            variables=list(source_variables),
+            scenario=scenario,
             models=models,
         )
 
-        write_log(process, f"Running subset rcp={rcp}", process_step="subset")
+        if len(netcdf_inputs) == 0:
+            raise ValueError(
+                f'No netCDF files were selected with filters {scenario=}, {models=} and variables={source_variables}'
+            )
+
+        write_log(process, f"Running subset scen={scenario}", process_step="subset")
         subsetted_files = subset_function(
             process, netcdf_inputs=netcdf_inputs, request_inputs=request.inputs
         )
@@ -549,10 +510,11 @@ def ensemble_common_handler(process: Process, request, response, subset_function
             raise ProcessError(message)
 
         subsetted_intermediate_files = compute_intermediate_variables(
-            subsetted_files, dataset_input_names, process.workdir, request.inputs,
+            subsetted_files, source_variables, needed_variables, process.workdir, request.inputs,
         )
-        write_log(process, f"Computing indices rcp={rcp}", process_step="compute_indices")
+        write_log(process, f"Computing indices scen={scenario}", process_step="compute_indices")
 
+        print(subsetted_intermediate_files, file=sys.stderr)
         input_groups = make_indicator_inputs(
             process.xci, request_inputs_not_datasets, subsetted_intermediate_files
         )
@@ -566,16 +528,13 @@ def ensemble_common_handler(process: Process, request, response, subset_function
         for n, inputs in enumerate(input_groups):
             write_log(
                 process,
-                f"Computing indices for file {n + 1} of {n_groups}, rcp={rcp}",
+                f"Computing indices for file {n + 1} of {n_groups}, scen={scenario}",
                 subtask_percentage=n * 100 // n_groups,
             )
             output_ds = compute_indices(process, process.xci, inputs)
-
-            output_name = f"{output_filename}_{process.identifier}_{n}.nc"
-            for variable in accepted_variables:
-                if variable in inputs:
-                    input_name = Path(inputs.get(variable)[0].file).name
-                    output_name = input_name.replace(variable, process.identifier)
+            for variable in needed_variables:
+                input_name = Path(inputs[variable][0].file).name
+                output_name = input_name.replace(variable, process.identifier)
 
             output_path = Path(process.workdir) / output_name
             dataset_to_netcdf(output_ds, output_path)
@@ -584,14 +543,13 @@ def ensemble_common_handler(process: Process, request, response, subset_function
         warnings.filterwarnings("default", category=FutureWarning)
         warnings.filterwarnings("default", category=UserWarning)
 
-        output_basename = Path(process.workdir) / (output_filename + "_ensemble")
         ensemble = make_ensemble(indices_files, ensemble_percentiles, average_dims)
         ensemble.attrs['source_datasets'] = '\n'.join([dsinp.url for dsinp in netcdf_inputs])
         ensembles.append(ensemble)
 
     process.set_workdir(str(base_work_dir))
 
-    ensemble = xr.concat(ensembles, dim=xr.DataArray(rcps, dims=('rcp',), name='rcp'))
+    ensemble = xr.concat(ensembles, dim=xr.DataArray(scenarios, dims=('scenario',), name='scenario'))
 
     if convert_to_csv:
         ensemble_csv = output_basename.with_suffix(".csv")
@@ -610,17 +568,18 @@ def ensemble_common_handler(process: Process, request, response, subset_function
         df.dropna().to_csv(ensemble_csv)
 
         metadata = format_metadata(ensemble)
-        metadata_file = output_basename.parent / f"{ensemble_csv.stem}_metadata.txt"
+        metadata_file = output_basename.parent / f"{output_basename}_metadata.txt"
         metadata_file.write_text(metadata)
 
-        ensemble_output = Path(process.workdir) / (output_filename + ".zip")
+        ensemble_output = Path(process.workdir) / output_basename.with_suffix(".zip")
         zip_files(ensemble_output, [metadata_file, ensemble_csv])
     else:
+        LOGGER.info(output_basename)
         ensemble_output = output_basename.with_suffix(".nc")
         dataset_to_netcdf(ensemble, ensemble_output)
 
     response.outputs["output"].file = ensemble_output
     response.outputs["output_log"].file = str(log_file_path(process))
 
-    write_log(process, "Processing finished successfully", process_step="done")
+    write_log(process, f"Processing finished successfully : {ensemble_output}", process_step="done")
     return response
diff --git a/finch/processes/utils.py b/finch/processes/utils.py
index 5cb59988..d9d19575 100644
--- a/finch/processes/utils.py
+++ b/finch/processes/utils.py
@@ -1,4 +1,6 @@
+from dataclasses import dataclass, field
 from datetime import timedelta, datetime
+from itertools import chain
 import logging
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
@@ -31,14 +33,18 @@
     Process,
     configuration
 )
+from pywps.configuration import get_config_value
 from pywps.inout.outputs import MetaFile, MetaLink4
 import requests
 from requests.exceptions import ConnectionError, InvalidSchema, MissingSchema
 import sentry_sdk
+from slugify import slugify
 import xarray as xr
 from netCDF4 import num2date
 import xclim
-from slugify import slugify
+from xclim.core.utils import InputKind
+from xclim.testing import list_input_variables
+import yaml
 
 LOGGER = logging.getLogger("PYWPS")
 
@@ -49,6 +55,70 @@
 # These are parameters that set options. They are not `compute` arguments.
 INDICATOR_OPTIONS = ['check_missing', 'missing_options', "cf_compliance", "data_validation"]
 
+# Some other constants
+xclim_variables = set(list_input_variables(submodules=["atmos", "land", "seaIce"]).keys())
+
+
+@dataclass
+class DatasetConfiguration:
+    """
+    Attributes
+    ----------
+    path: str
+        The path (or url) to the root directory where to search for the data.
+    pattern: str
+        The pattern of the filenames. Must include at least : "variable", "scenario" and "model".
+        Patterns must be understandable by :py:func:`parse.parse`.
+    local: bool
+        Whether the path points to a local directory or a remote THREDDS catalog.
+    depth : int
+        The depth to which search for files below the directory. < 0 will search recursively.
+    suffix : str
+        When the files are local, this is the suffix of the files.
+    allowed_values : dict
+        Mapping from field name to a list of allowed values.
+        Must include "scenario", "model" and "variable",
+        the latter defines which variable are available and thus which indicator can be used.
+    model_lists : dict
+        A mapping from list name to a list of model names to provide special sublists.
+        The values can also be a tuple of (model name, realization numer),
+        in which case, pattern must include a "realization" field.
+    """
+    path: str
+    pattern: str
+    local: bool
+    allowed_values: dict
+    depth: int = 0
+    suffix: str = '*nc'
+    model_lists: dict = field(default_factory=dict)
+
+
+def get_datasets_config():
+    p = get_config_value('finch', 'datasets_config')
+    if not p:  # No config given.
+        return {}
+
+    if not Path(p).is_absolute():
+        p = Path(__file__).parent.parent / p
+
+    with open(p) as f:
+        conf = yaml.safe_load(f)
+    return {
+        ds: DatasetConfiguration(**dsconf)
+        for ds, dsconf in conf.items()
+    }
+
+
+def get_available_variables():
+    conf = get_datasets_config()
+    return set(chain(*(d.allowed_values['variable'] for d in conf.values())))
+
+
+def iter_xc_variables(indicator: xclim.core.indicator.Indicator):
+    for n, p in indicator.parameters.items():
+        if p.kind in [InputKind.VARIABLE, InputKind.OPTIONAL_VARIABLE]:
+            yield n
+
 
 def log_file_path(process: Process) -> Path:
     """Returns the filepath to write the process logfile."""
@@ -475,8 +545,9 @@ def get_attrs_fallback(ds, *args):
             df = dataset_to_dataframe(ds)
 
             if calendar not in concat_by_calendar:
-                if "lat" in df.index.names and "lon" in df.index.names:
-                    df = df.reset_index(["lat", "lon"])
+                # TODO: Why was this there? When we have a time axis, this makes the concat fail.
+                # if "lat" in df.index.names and "lon" in df.index.names:
+                #     df = df.reset_index(["lat", "lon"])
                 concat_by_calendar[calendar] = [df]
             else:
                 concat_by_calendar[calendar].append(df[output_variable])
diff --git a/finch/processes/virtual.py b/finch/processes/virtual.py
new file mode 100644
index 00000000..05e68a86
--- /dev/null
+++ b/finch/processes/virtual.py
@@ -0,0 +1,34 @@
+"""
+Processes as a virtual submodule
+--------------------------------
+When imported, this module exposes the default list of processes as virtual modules : indicators, ensemble and other.
+It forces the loading of the "default.cfg" file if no datasets were configured.
+
+It is meant to provide sphinx with something to document, not to be used in reality.
+We needed to have this code within finch, so that the autodoc mock imports are functional.
+"""
+from pathlib import Path
+from types import ModuleType
+from finch.processes import get_processes
+from pywps.configuration import load_configuration, get_config_value
+
+if not get_config_value('finch', 'datasets_config'):
+    load_configuration(Path(__file__).parent.parent / 'default.cfg')
+
+processes = sorted(get_processes(), key=lambda p: p.__class__.__name__)
+
+ens_proc = {p.__class__.__name__: p.__class__
+            for p in processes if "Ensemble" in p.__class__.__name__}
+ind_proc = {p.__class__.__name__: p.__class__
+            for p in processes if p.__class__.__name__.endswith("_Indicator_Process")}
+oth_proc = {p.__class__.__name__: p.__class__
+            for p in processes if p.__class__.__name__ not in (list(ens_proc) + list(ind_proc))}
+
+indicators = ModuleType('indicators', "Indicators Processes\n--------------------")
+indicators.__dict__.update(ind_proc)
+
+ensemble = ModuleType('ensemble', "Ensemble Processes\n------------------")
+ensemble.__dict__.update(ens_proc)
+
+other = ModuleType('other', "Other Processes\n---------------")
+other.__dict__.update(oth_proc)
diff --git a/finch/processes/wps_base.py b/finch/processes/wps_base.py
index 7544b917..8a194508 100644
--- a/finch/processes/wps_base.py
+++ b/finch/processes/wps_base.py
@@ -9,16 +9,35 @@
 from pywps.app.Common import Metadata
 from sentry_sdk import configure_scope
 import xclim
-
 from xclim.core.utils import InputKind
 
-from .constants import default_percentiles
+
 from .utils import PywpsInput
 
 
 LOGGER = logging.getLogger("PYWPS")
 
 
+default_percentiles = {
+    'days_over_precip_thresh': {'pr_per': 95},
+    'days_over_precip_doy_thresh': {'pr_per': 95},
+    'fraction_over_precip_doy_thresh': {'pr_per': 95},
+    'fraction_over_precip_thresh': {'pr_per': 95},
+    'cold_and_dry_days': {'pr_per': 25, 'tas_per': 25},
+    'warm_and_dry_days': {'pr_per': 25, 'tas_per': 75},
+    'warm_and_wet_days': {'pr_per': 75, 'tas_per': 75},
+    'cold_and_wet_days': {'pr_per': 75, 'tas_per': 25},
+    'tg90p': {'tas_per': 90},
+    'tg10p': {'tas_per': 10},
+    'tn90p': {'tasmin_per': 90},
+    'tn10p': {'tasmin_per': 10},
+    'tx90p': {'tasmax_per': 90},
+    'tx10p': {'tasmax_per': 10},
+    'cold_spell_duration_index': {'tasmin_per': 10},
+    'warm_spell_duration_index': {'tasmax_per': 90},
+}
+
+
 class FinchProcess(Process):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -42,7 +61,7 @@ def _handler_wrapper(self, request, response):
             return self.wrapped_handler(request, response)
         except Exception as err:
             LOGGER.exception('FinchProcess handler wrapper failed with:')
-            raise ProcessError(f"Finch failed with {err!r}")
+            raise ProcessError(f"Finch failed with {err!s}")
 
     def sentry_configure_scope(self, request):
         """Add additional data to sentry error messages.
diff --git a/finch/processes/wps_ensemble_indices_bbox.py b/finch/processes/wps_ensemble_indices_bbox.py
index 89266619..9422ac69 100644
--- a/finch/processes/wps_ensemble_indices_bbox.py
+++ b/finch/processes/wps_ensemble_indices_bbox.py
@@ -7,8 +7,7 @@
 from . import wpsio
 from .wps_base import FinchProcess, convert_xclim_inputs_to_pywps
 from .ensemble_utils import ensemble_common_handler
-from .constants import xclim_variables
-
+from .utils import iter_xc_variables
 LOGGER = logging.getLogger("PYWPS")
 
 
@@ -40,14 +39,12 @@ def __init__(self):
             wpsio.end_date,
             wpsio.ensemble_percentiles,
             wpsio.average,
-            wpsio.dataset_name,
-            wpsio.copy_io(wpsio.rcp, min_occurs=1, max_occurs=3),
-            wpsio.models,
+            *wpsio.get_ensemble_inputs(novar=True)
         ]
 
         # all other inputs that are not the xarray data (window, threshold, etc.)
         for i in xci_inputs:
-            if i.identifier not in xclim_variables:
+            if i.identifier not in list(iter_xc_variables(self.xci)):
                 inputs.append(i)
 
         inputs.extend([wpsio.output_prefix, wpsio.output_format_netcdf_csv, wpsio.csv_precision])
diff --git a/finch/processes/wps_ensemble_indices_point.py b/finch/processes/wps_ensemble_indices_point.py
index a6aaa327..02df9566 100644
--- a/finch/processes/wps_ensemble_indices_point.py
+++ b/finch/processes/wps_ensemble_indices_point.py
@@ -7,7 +7,7 @@
 from . import wpsio
 from .wps_base import FinchProcess, convert_xclim_inputs_to_pywps
 from .ensemble_utils import ensemble_common_handler
-from .constants import xclim_variables
+from .utils import iter_xc_variables
 
 LOGGER = logging.getLogger("PYWPS")
 
@@ -38,14 +38,12 @@ def __init__(self):
             wpsio.end_date,
             wpsio.ensemble_percentiles,
             wpsio.average,
-            wpsio.dataset_name,
-            wpsio.copy_io(wpsio.rcp, min_occurs=1, max_occurs=3),
-            wpsio.models,
+            *wpsio.get_ensemble_inputs(novar=True)
         ]
 
         # all other inputs that are not the xarray data (window, threshold, etc.)
         for i in xci_inputs:
-            if i.identifier not in xclim_variables:
+            if i.identifier not in list(iter_xc_variables(self.xci)):
                 inputs.append(i)
 
         inputs.extend([wpsio.output_prefix, wpsio.output_format_netcdf_csv, wpsio.csv_precision])
diff --git a/finch/processes/wps_ensemble_indices_polygon.py b/finch/processes/wps_ensemble_indices_polygon.py
index bfb1deac..eaf493e5 100644
--- a/finch/processes/wps_ensemble_indices_polygon.py
+++ b/finch/processes/wps_ensemble_indices_polygon.py
@@ -5,7 +5,7 @@
 from . import wpsio
 from .wps_base import FinchProcess, convert_xclim_inputs_to_pywps
 from .ensemble_utils import ensemble_common_handler
-from .constants import xclim_variables
+from .utils import iter_xc_variables
 from .subset import finch_subset_shape
 
 LOGGER = logging.getLogger("PYWPS")
@@ -36,14 +36,12 @@ def __init__(self):
             wpsio.end_date,
             wpsio.ensemble_percentiles,
             wpsio.average,
-            wpsio.dataset_name,
-            wpsio.copy_io(wpsio.rcp, min_occurs=1, max_occurs=3),
-            wpsio.models,
+            *wpsio.get_ensemble_inputs(novar=True)
         ]
 
         # all other inputs that are not the xarray data (window, threshold, etc.)
         for i in xci_inputs:
-            if i.identifier not in xclim_variables:
+            if i.identifier not in list(iter_xc_variables(self.xci)):
                 inputs.append(i)
 
         inputs.extend([wpsio.output_prefix, wpsio.output_format_netcdf_csv, wpsio.csv_precision])
diff --git a/finch/processes/wps_sdba.py b/finch/processes/wps_sdba.py
index f8b5c215..2af3205f 100644
--- a/finch/processes/wps_sdba.py
+++ b/finch/processes/wps_sdba.py
@@ -67,7 +67,6 @@
         min_occurs=0,
     )
 )
-variable = wpsio.variable
 
 resources = dict(
     ref=ComplexInput(
@@ -156,7 +155,7 @@ def _log(message, percentage):
         train = {}
         adj = {}
 
-        variable = request.inputs.pop(wpsio.variable.identifier, None)
+        variable = request.inputs.pop(wpsio.variable_any.identifier, None)
 
         for key, input in request.inputs.items():
             if key in resources:
diff --git a/finch/processes/wps_xclim_indices.py b/finch/processes/wps_xclim_indices.py
index bb3ebf0c..956d8cca 100644
--- a/finch/processes/wps_xclim_indices.py
+++ b/finch/processes/wps_xclim_indices.py
@@ -8,7 +8,6 @@
 import xarray as xr
 
 from . import wpsio
-from .constants import xclim_variables
 from .utils import (
     compute_indices,
     dataset_to_dataframe,
@@ -20,6 +19,7 @@
     single_input_or_none,
     valid_filename,
     write_log,
+    xclim_variables,
     zip_files
 )
 from .wps_base import (
diff --git a/finch/processes/wps_xsubset_bbox_dataset.py b/finch/processes/wps_xsubset_bbox_dataset.py
index 6defc421..51518797 100644
--- a/finch/processes/wps_xsubset_bbox_dataset.py
+++ b/finch/processes/wps_xsubset_bbox_dataset.py
@@ -6,7 +6,7 @@
 
 from .ensemble_utils import get_datasets, make_output_filename
 from .subset import finch_subset_bbox
-from .utils import netcdf_file_list_to_csv, single_input_or_none, write_log, zip_files
+from .utils import get_datasets_config, netcdf_file_list_to_csv, single_input_or_none, write_log, zip_files
 from .wps_base import FinchProcess
 from . import wpsio
 
@@ -16,8 +16,7 @@ class SubsetBboxDatasetProcess(FinchProcess):
 
     def __init__(self):
         inputs = [
-            wpsio.variable,
-            wpsio.rcp,
+            *wpsio.get_ensemble_inputs(),
             wpsio.lon0,
             wpsio.lon1,
             wpsio.lat0,
@@ -25,7 +24,6 @@ def __init__(self):
             wpsio.start_date,
             wpsio.end_date,
             wpsio.output_format_netcdf_csv,
-            wpsio.dataset_name,
         ]
 
         outputs = [wpsio.output_netcdf_csv]
@@ -63,20 +61,22 @@ def _handler(self, request: WPSRequest, response: ExecuteResponse):
 
         write_log(self, "Processing started", process_step="start")
 
-        output_filename = make_output_filename(self, request.inputs)
-
         write_log(self, "Fetching datasets")
 
-        variable = request.inputs["variable"][0].data
-        variables = None if variable is None else [variable]
-        rcp = single_input_or_none(request.inputs, "rcp")
+        variables = [v.data for v in request.inputs.get("variable", [])] or None
+        scenario = single_input_or_none(request.inputs, "scenario")
+        models = [m.data.strip() for m in request.inputs["models"]]
 
-        dataset_name = single_input_or_none(request.inputs, "dataset_name")
+        dataset_name = single_input_or_none(request.inputs, "dataset")
+        dataset = get_datasets_config()[dataset_name]
         request.inputs["resource"] = get_datasets(
-            dataset_name, workdir=self.workdir, variables=variables, rcp=rcp
+            dataset, workdir=self.workdir,
+            variables=variables, scenario=scenario, models=models
         )
 
-        write_log(self, "Running subset", process_step="subset")
+        output_filename = Path(make_output_filename(self, request.inputs))
+
+        write_log(self, f"Running subset on {len(request.inputs['resource'])} resources.", process_step="subset")
 
         output_files = finch_subset_bbox(
             self,
@@ -100,7 +100,7 @@ def _handler(self, request: WPSRequest, response: ExecuteResponse):
 
         write_log(self, "Zipping outputs", process_step="zip_outputs")
 
-        output_zip = Path(self.workdir) / (output_filename + ".zip")
+        output_zip = Path(self.workdir) / output_filename.with_suffix(".zip")
 
         def _log(message, percentage):
             write_log(self, message, subtask_percentage=percentage)
diff --git a/finch/processes/wps_xsubset_point_dataset.py b/finch/processes/wps_xsubset_point_dataset.py
index 2d7dd258..2a6c7627 100644
--- a/finch/processes/wps_xsubset_point_dataset.py
+++ b/finch/processes/wps_xsubset_point_dataset.py
@@ -8,7 +8,7 @@
 from .wps_base import FinchProcess
 from .ensemble_utils import get_datasets, make_output_filename
 from .subset import finch_subset_gridpoint
-from .utils import netcdf_file_list_to_csv, single_input_or_none, write_log, zip_files
+from .utils import get_datasets_config, netcdf_file_list_to_csv, single_input_or_none, write_log, zip_files
 from . import wpsio
 
 
@@ -17,8 +17,7 @@ class SubsetGridPointDatasetProcess(FinchProcess):
 
     def __init__(self):
         inputs = [
-            wpsio.variable,
-            wpsio.rcp,
+            *wpsio.get_ensemble_inputs(),
             wpsio.copy_io(wpsio.lat, min_occurs=0),
             wpsio.copy_io(wpsio.lon, min_occurs=0),
             LiteralInput(
@@ -39,7 +38,6 @@ def __init__(self):
             wpsio.end_date,
             wpsio.output_format_netcdf_csv,
             wpsio.csv_precision,
-            wpsio.dataset_name,
         ]
 
         outputs = [wpsio.output_netcdf_csv]
@@ -88,19 +86,21 @@ def _handler(self, request: WPSRequest, response: ExecuteResponse):
         request.inputs.setdefault("lat", request.inputs.get("lat0"))
         # End of 'remove me'
 
-        output_filename = make_output_filename(self, request.inputs)
-
         write_log(self, "Fetching datasets")
 
-        variable = request.inputs["variable"][0].data
-        variables = None if variable is None else [variable]
-        rcp = single_input_or_none(request.inputs, "rcp")
+        variables = [v.data for v in request.inputs.get("variable", [])] or None
+        scenario = single_input_or_none(request.inputs, "scenario")
+        models = [m.data.strip() for m in request.inputs["models"]]
 
-        dataset_name = single_input_or_none(request.inputs, "dataset_name")
+        dataset_name = single_input_or_none(request.inputs, "dataset")
+        dataset = get_datasets_config()[dataset_name]
         request.inputs["resource"] = get_datasets(
-            dataset_name, workdir=self.workdir, variables=variables, rcp=rcp
+            dataset, workdir=self.workdir,
+            variables=variables, scenario=scenario, models=models
         )
 
+        output_filename = Path(make_output_filename(self, request.inputs))
+
         write_log(self, "Running subset", process_step="subset")
 
         output_files = finch_subset_gridpoint(
@@ -126,7 +126,7 @@ def _handler(self, request: WPSRequest, response: ExecuteResponse):
 
         write_log(self, "Zipping outputs", process_step="zip_outputs")
 
-        output_zip = Path(self.workdir) / (output_filename + ".zip")
+        output_zip = Path(self.workdir) / output_filename.with_suffix(".zip")
 
         def _log(message, percentage):
             write_log(self, message, subtask_percentage=percentage)
diff --git a/finch/processes/wpsio.py b/finch/processes/wpsio.py
index 28c56521..edf1f45c 100644
--- a/finch/processes/wpsio.py
+++ b/finch/processes/wpsio.py
@@ -2,9 +2,11 @@
 
 import json
 from copy import deepcopy
+from itertools import chain
 from typing import Union
 
 from pywps import FORMATS, ComplexInput, ComplexOutput, LiteralInput
+from pywps.configuration import get_config_value
 from pywps.inout.literaltypes import AnyValue
 from xclim.core.options import (
     CHECK_MISSING,
@@ -14,9 +16,7 @@
     MISSING_OPTIONS,
     OPTIONS
 )
-
-from .constants import ALL_24_MODELS, ALLOWED_MODEL_NAMES
-from .utils import PywpsInput, PywpsOutput
+from .utils import PywpsInput, PywpsOutput, get_datasets_config
 
 
 def copy_io(
@@ -120,50 +120,74 @@ def copy_io(
     min_occurs=0,
 )
 
-variable = LiteralInput(
+
+variable_any = LiteralInput(
     "variable",
-    "NetCDF Variable",
-    abstract="Name of the variable in the NetCDF file.",
+    "Variable name",
+    abstract="Name of the variable in the input files.",
     data_type="string",
     default=None,
-    min_occurs=0,
-    allowed_values=["tasmin", "tasmax", "pr"],
+    allowed_values=[AnyValue],
+    min_occurs=0
 )
 
-variable_any = copy_io(variable, any_value=True, allowed_values=[AnyValue])
 
-dataset_name = LiteralInput(
-    "dataset_name",
-    "Dataset name",
-    abstract="Name of the dataset from which to get netcdf files for inputs.",
-    data_type="string",
-    default=None,
-    min_occurs=0,
-    allowed_values=["bccaqv2"],
-)
+def get_ensemble_inputs(novar=False):
+    datasets_config = get_datasets_config()
+    default_dataset = get_config_value('finch', 'default_dataset')
+
+    dataset = LiteralInput(
+        "dataset",
+        "Dataset name",
+        abstract="Name of the dataset from which to get netcdf files for inputs.",
+        data_type="string",
+        default=default_dataset,
+        min_occurs=0,
+        allowed_values=list(datasets_config.keys()),
+    )
+
+    scenario = LiteralInput(
+        "scenario",
+        "Emission Scenario",
+        abstract="Emission scenario (RCPs or SSPs, depending on the dataset)",
+        data_type="string",
+        default=None,
+        min_occurs=0,
+        max_occurs=3,
+        allowed_values=list(set(chain(*[d.allowed_values['scenario'] for d in datasets_config.values()]))),
+    )
+
+    models = LiteralInput(
+        "models",
+        "Models to include in ensemble",
+        abstract=(
+            "When calculating the ensemble, include only these models. Allowed values depend on the dataset chosen. "
+            "By default, all models are used ('all'), taking the first realization of each. "
+            "Special sublists are also available :"
+        ) + ", ".join([f"{dsid}: {list((d.model_lists or {}).keys())}" for dsid, d in datasets_config.items()]),
+        data_type="string",
+        default="all",
+        min_occurs=0,
+        max_occurs=1000,
+        allowed_values=["all"] + (
+            list(chain(*[d.allowed_values['model'] for d in datasets_config.values()]))
+            + list(chain(*[d.model_lists.keys() for d in datasets_config.values()]))
+        )
+    )
+    if novar:
+        return dataset, scenario, models
+
+    variable = LiteralInput(
+        "variable",
+        "NetCDF Variable",
+        abstract="Name of the variable in the NetCDF file. Allowed values depend on the dataset.",
+        data_type="string",
+        default=None,
+        min_occurs=0,
+        allowed_values=list(chain(*[d.allowed_values['variable'] for d in datasets_config.values()])),
+    )
+    return dataset, scenario, models, variable
 
-rcp = LiteralInput(
-    "rcp",
-    "RCP Scenario",
-    abstract="Representative Concentration Pathway (RCP)",
-    data_type="string",
-    default=None,
-    min_occurs=0,
-    allowed_values=["rcp26", "rcp45", "rcp85"],
-)
-
-models = LiteralInput(
-    "models",
-    "Models to include in ensemble",
-    abstract=(
-        "When calculating the ensemble, include only these models. By default, all 24 models are used."
-    ),
-    data_type="string",
-    default=ALL_24_MODELS,
-    min_occurs=0,
-    max_occurs=1000,
-    allowed_values=ALLOWED_MODEL_NAMES,
-)
 
 shape = ComplexInput(
     "shape",
@@ -301,7 +325,7 @@ def copy_io(
 
 output_prefix = copy_io(
     output_name,
-    abstract="Prefix of the output filename, defaults to the identifier of the process."
+    abstract="Prefix of the output filename, defaults to the dataset name and the identifier of the process."
 )
 
 csv_precision = LiteralInput(
diff --git a/finch/wsgi.py b/finch/wsgi.py
index a47f32c9..4f16f090 100644
--- a/finch/wsgi.py
+++ b/finch/wsgi.py
@@ -2,9 +2,9 @@
 
 import sentry_sdk
 from pywps.app.Service import Service
-
 from .processes import get_processes
 
+
 if os.environ.get("SENTRY_DSN"):
     sentry_sdk.init(os.environ["SENTRY_DSN"])
 
@@ -19,11 +19,8 @@ def create_app(cfgfiles=None):
         config_files.append(os.environ["PYWPS_CFG"])
     service = Service(cfgfiles=config_files)
 
-    # delay the call to get_processes() so that the configuration is loaded
+    # delay the call of get_processes() so that the configuration is loaded
     # when instantiating the service
     service.processes = {p.identifier: p for p in get_processes()}
 
     return service
-
-
-application = create_app()
diff --git a/requirements.txt b/requirements.txt
index 5bd8a129..0c11f2d2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,11 @@
 cftime
 pywps>=4.5.1
-xclim==0.37
+xclim==0.38
+pint<=0.19.2  # xclim < 0.39 breaks with pint 0.20
 xarray>=0.18.2
 pandas
 xesmf>=0.6.2
-clisops>=0.8.0
+clisops>=0.9.3
 geopandas
 jinja2
 click
@@ -20,3 +21,4 @@ sentry-sdk
 siphon
 parse
 python-slugify
+pyyaml
diff --git a/requirements_dev.txt b/requirements_dev.txt
index aba71dc8..bfd22674 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -14,4 +14,5 @@ jupyter_client
 # Changing dependencies above this comment will create merge conflicts when updating the cookiecutter template with cruft. Add extra requirements below this line. 
 birdhouse-birdy>=0.8.1
 geojson
+h5netcdf
 matplotlib
diff --git a/requirements_docs.txt b/requirements_docs.txt
index 8f6ad4b8..84dcdba7 100644
--- a/requirements_docs.txt
+++ b/requirements_docs.txt
@@ -2,6 +2,9 @@ pywps >= 4.5.1
 sphinx >= 4.0
 nbsphinx
 ipython
-xclim~=0.37
+xclim==0.38
+pint<=0.19.2  # xclim < 0.39 breaks with pint 0.20
 matplotlib
 birdhouse-birdy>=0.8.1
+unidecode
+sphinxcontrib-bibtex


Test patch:
diff --git a/tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_subset.nc b/tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp26_subset.nc
similarity index 100%
rename from tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_subset.nc
rename to tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp26_subset.nc
diff --git a/tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc b/tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc
new file mode 100644
index 00000000..b8129c58
Binary files /dev/null and b/tests/data/bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc differ
diff --git a/tests/data/bccaqv2_subset_sample/tasmax_inmcm4_subset.nc b/tests/data/bccaqv2_subset_sample/tasmax_inmcm4_rcp26_subset.nc
similarity index 100%
rename from tests/data/bccaqv2_subset_sample/tasmax_inmcm4_subset.nc
rename to tests/data/bccaqv2_subset_sample/tasmax_inmcm4_rcp26_subset.nc
diff --git a/tests/data/bccaqv2_subset_sample/tasmax_inmcm4_rcp45_subset.nc b/tests/data/bccaqv2_subset_sample/tasmax_inmcm4_rcp45_subset.nc
new file mode 100644
index 00000000..d660e256
Binary files /dev/null and b/tests/data/bccaqv2_subset_sample/tasmax_inmcm4_rcp45_subset.nc differ
diff --git a/tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_subset.nc b/tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_rcp26_subset.nc
similarity index 100%
rename from tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_subset.nc
rename to tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_rcp26_subset.nc
diff --git a/tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_rcp45_subset.nc b/tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_rcp45_subset.nc
new file mode 100644
index 00000000..1b5de011
Binary files /dev/null and b/tests/data/bccaqv2_subset_sample/tasmin_bcc-csm1-1_rcp45_subset.nc differ
diff --git a/tests/data/bccaqv2_subset_sample/tasmin_inmcm4_subset.nc b/tests/data/bccaqv2_subset_sample/tasmin_inmcm4_rcp26_subset.nc
similarity index 100%
rename from tests/data/bccaqv2_subset_sample/tasmin_inmcm4_subset.nc
rename to tests/data/bccaqv2_subset_sample/tasmin_inmcm4_rcp26_subset.nc
diff --git a/tests/data/bccaqv2_subset_sample/tasmin_inmcm4_rcp45_subset.nc b/tests/data/bccaqv2_subset_sample/tasmin_inmcm4_rcp45_subset.nc
new file mode 100644
index 00000000..10217a67
Binary files /dev/null and b/tests/data/bccaqv2_subset_sample/tasmin_inmcm4_rcp45_subset.nc differ
diff --git a/tests/test.cfg b/tests/test.cfg
index 63d31a29..f5e95ecb 100644
--- a/tests/test.cfg
+++ b/tests/test.cfg
@@ -2,9 +2,16 @@
 allowedinputpaths=/
 
 [finch]
-default_dataset = bccaqv2
-dataset_bccaqv2 = https://pavics.ouranos.ca/thredds/catalog/birdhouse/pcic/BCCAQv2/catalog.xml
+default_dataset = test_single_cell
+datasets_config = ../tests/test_data.yml
 subset_threads = 1
 
+[finch:metadata]
+contact = Canadian Centre for Climate Services
+institution = Canadian Centre for Climate Services (CCCS)
+institute_id = CCCS
+
 [logging]
-level = DEBUG
+level = INFO
+file = finch_tests.log
+format = [%(asctime)s] [%(levelname)s] line=%(lineno)s module=%(module)s %(message)s
diff --git a/tests/test_data.yml b/tests/test_data.yml
new file mode 100644
index 00000000..44a125a6
--- /dev/null
+++ b/tests/test_data.yml
@@ -0,0 +1,66 @@
+test_single_cell:
+  local: true
+  depth: 0
+  path: ../tests/data/bccaqv2_single_cell/
+  suffix: "*.nc"
+  pattern: "{variable}_{frequency}_BCCAQv2+ANUSPLIN300_{model}_{scenario}_{realization}_{date_start}-{date_end}.nc"
+  allowed_values:
+    scenario: [rcp26, rcp45, rcp85]
+    variable: [tasmin, tasmax, pr]
+    model:
+      &u5models
+      - BNU-ESM
+      - CCSM4
+      - CESM1-CAM5
+      - CNRM-CM5
+      - CSIRO-Mk3-6-0
+      - CanESM2
+      - FGOALS-g2
+      - GFDL-CM3
+      - GFDL-ESM2G
+      - GFDL-ESM2M
+      - HadGEM2-AO
+      - HadGEM2-ES
+      - IPSL-CM5A-LR
+      - IPSL-CM5A-MR
+      - MIROC-ESM-CHEM
+      - MIROC-ESM
+      - MIROC5
+      - MPI-ESM-LR
+      - MPI-ESM-MR
+      - MRI-CGCM3
+      - NorESM1-M
+      - NorESM1-ME
+      - bcc-csm1-1-m
+      - bcc-csm1-1
+  model_lists:
+    # The absence of realization specification implies r1 is taken.
+    24models: *u5models
+    pcic12:
+    # taken from: https://www.pacificclimate.org/data/statistically-downscaled-climate-scenarios
+      - [ ACCESS1-0 , r1i1p1 ]
+      - [ CCSM4 , r2i1p1 ]
+      - [ CNRM-CM5 , r1i1p1 ]
+      - [ CSIRO-Mk3-6-0 , r1i1p1 ]
+      - [ CanESM2 , r1i1p1 ]
+      - [ GFDL-ESM2G , r1i1p1 ]
+      - [ HadGEM2-CC , r1i1p1 ]
+      - [ HadGEM2-ES , r1i1p1 ]
+      - [ MIROC5 , r3i1p1 ]
+      - [ MPI-ESM-LR , r3i1p1 ]
+      - [ MRI-CGCM3 , r1i1p1 ]
+      - [ inmcm4 , r1i1p1 ]
+
+test_subset:
+  local: true
+  depth: 0
+  path: ../tests/data/bccaqv2_subset_sample/
+  suffix: "*.nc"
+  pattern: "{variable}_{model}_{scenario}_subset.nc"
+  allowed_values:
+    scenario: [rcp26, rcp45]
+    variable: [tasmin, tasmax]
+    model:
+      - inmcm4
+      - bcc-csm1-1
+  model_lists: {}
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 5e42213b..4c9ed77a 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,20 +1,13 @@
-from finch.processes.constants import ALL_24_MODELS, PCIC_12
 from pathlib import Path
 import shutil
-from unittest import mock
 import zipfile
 
 import numpy as np
 import pandas as pd
 import pytest
-from pywps import configuration
 import xarray as xr
 
 from finch.processes import ensemble_utils
-from finch.processes.ensemble_utils import (
-    get_bccaqv2_opendap_datasets,
-    get_bccaqv2_local_files_datasets,
-)
 from finch.processes.utils import (
     drs_filename,
     is_opendap_url,
@@ -26,59 +19,6 @@
 test_data = Path(__file__).parent / "data"
 
 
-@mock.patch("finch.processes.ensemble_utils.Path")
-def test_get_local_datasets_bccaqv2(mock_path):
-    names = [
-        "/mock_path/tasmin_day_BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp85_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmin_day_BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmin_day_BCCAQv2+ANUSPLIN300_CanESM2_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmax_day_BCCAQv2+ANUSPLIN300_CanESM2_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-M_historical+rcp26_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-ME_historical+rcp85_r1i1p1_19500101-21001231.nc",
-        "/mock_path/tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-ME_historical+rcp45_r1i1p1_19500101-21001231.nc",
-    ]
-    catalog_url = "/mock_path"
-    variable = "tasmin"
-    rcp = "rcp45"
-
-    mock_path_instance = mock.MagicMock()
-    mock_path.return_value = mock_path_instance
-    mock_path_instance.glob.return_value = [Path(n) for n in names]
-
-    files = get_bccaqv2_local_files_datasets(catalog_url, [variable], rcp)
-    assert len(files) == 2
-
-
-@mock.patch("finch.processes.ensemble_utils.TDSCatalog")
-def test_get_opendap_datasets_bccaqv2(mock_tdscatalog):
-    names = [
-        "tasmin_day_BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp85_r1i1p1_19500101-21001231.nc",
-        "tasmin_day_BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "tasmin_day_BCCAQv2+ANUSPLIN300_CanESM2_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "tasmax_day_BCCAQv2+ANUSPLIN300_CanESM2_historical+rcp45_r1i1p1_19500101-21001231.nc",
-        "tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-M_historical+rcp26_r1i1p1_19500101-21001231.nc",
-        "tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-ME_historical+rcp85_r1i1p1_19500101-21001231.nc",
-        "tasmax_day_BCCAQv2+ANUSPLIN300_NorESM1-ME_historical+rcp45_r1i1p1_19500101-21001231.nc",
-    ]
-    catalog_url = configuration.get_config_value("finch", "dataset_bccaqv2")
-    variable = "tasmin"
-    rcp = "rcp45"
-
-    mock_catalog = mock.MagicMock()
-    mock_tdscatalog.return_value = mock_catalog
-
-    def make_dataset(name):
-        dataset = mock.MagicMock()
-        dataset.access_urls = {"OPENDAP": "url"}
-        dataset.name = name
-        return dataset
-
-    mock_catalog.datasets = {name: make_dataset(name) for name in names}
-
-    urls = get_bccaqv2_opendap_datasets(catalog_url, [variable], rcp)
-    assert len(urls) == 2
-
-
 def test_netcdf_file_list_to_csv_to_zip():
     here = Path(__file__).parent
     folder = here / "data" / "bccaqv2_single_cell"
@@ -180,10 +120,10 @@ def test_is_opendap_url():
     assert not is_opendap_url(url)
 
 
-def test_bccaqv2_make_file_groups():
+def test_make_file_groups():
     folder = Path(__file__).parent / "data" / "bccaqv2_single_cell"
     files_list = list(folder.glob("*.nc"))
-    groups = ensemble_utils.make_file_groups(files_list)
+    groups = ensemble_utils.make_file_groups(files_list, {'tasmin', 'tasmax', 'pr'})
 
     assert len(groups) == 85
     assert all(len(g) == 3 for g in groups)
@@ -191,7 +131,7 @@ def test_bccaqv2_make_file_groups():
 
 def test_drs_filename():
     ds = xr.open_dataset(
-        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_subset.nc"
+        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc"
     )
     filename = drs_filename(ds)
     assert filename == "tasmax_bcc-csm1-1_historical+rcp85_r1i1p1_19500101-19500410.nc"
@@ -199,7 +139,7 @@ def test_drs_filename():
 
 def test_drs_filename_unknown_project():
     ds = xr.open_dataset(
-        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_subset.nc"
+        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc"
     )
     ds.attrs["project_id"] = "unknown"
     filename = drs_filename(ds)
@@ -208,7 +148,7 @@ def test_drs_filename_unknown_project():
 
 def test_drs_filename_no_spaces():
     ds = xr.open_dataset(
-        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_subset.nc"
+        test_data / "bccaqv2_subset_sample/tasmax_bcc-csm1-1_rcp45_subset.nc"
     )
     ds.attrs["driving_model_id"] = "bcc csm1 1"
     filename = drs_filename(ds)
@@ -222,101 +162,6 @@ def test_drs_filename_cordex():
     assert filename == expected
 
 
-def test_bccaqv2file():
-    filename = "tasmin_day_BCCAQv2+ANUSPLIN300_inmcm4_historical+rcp85_r1i1p1_19500101-21001231.nc"
-    file = ensemble_utils.Bccaqv2File.from_filename(filename)
-    expected = ensemble_utils.Bccaqv2File(
-        variable="tasmin",
-        frequency="day",
-        driving_model_id="inmcm4",
-        driving_experiment_id="historical+rcp85",
-        driving_realization="1",
-        driving_initialization_method="1",
-        driving_physics_version="1",
-        date_start="19500101",
-        date_end="21001231",
-    )
-    assert expected == file
-
-
-@pytest.mark.parametrize(
-    "filename,variable,rcp,models,expected",
-    [
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_canesm2_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            None,
-            True,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_canesm2_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            [ALL_24_MODELS],
-            True,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_canesm2_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmax",
-            "rcp85",
-            [ALL_24_MODELS],
-            False,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_canesm2_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp45",
-            [ALL_24_MODELS],
-            False,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_canesm2_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            ["HadGEM2-ES"],
-            False,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_HadGEM2-ES_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            ["HadGEM2-ES"],
-            True,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_MPI-ESM-LR_historical+rcp85_r3i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            [PCIC_12],
-            True,
-        ),
-        (
-            "tasmin_day_BCCAQv2+ANUSPLIN300_MPI-ESM-LR_historical+rcp85_r1i1p1_19500101-21001231.nc",
-            "tasmin",
-            "rcp85",
-            [PCIC_12],
-            False,
-        ),
-        ("tasmin_not_proper_filename.nc", "tasmin", None, None, False),
-    ],
-)
-def test_bccaqv2_filter(filename, variable, rcp, models, expected):
-    method = ensemble_utils.ParsingMethod.filename
-    url = None
-    variables = [variable]
-
-    result = ensemble_utils._bccaqv2_filter(
-        method=method,
-        filename=filename,
-        url=url,
-        variables=variables,
-        rcp=rcp,
-        models=models,
-    )
-    assert result == expected
-
-
 def test_invalid_filename():
     with pytest.raises(ValueError):
         valid_filename("./..")
diff --git a/tests/test_wps_caps.py b/tests/test_wps_caps.py
index 98474982..eb75052a 100644
--- a/tests/test_wps_caps.py
+++ b/tests/test_wps_caps.py
@@ -2,8 +2,8 @@
 import pywps.configuration
 import pytest
 
-import finch.processes
-from finch.processes import indicators, get_processes
+from finch.processes import get_indicators, get_processes, not_implemented
+import finch.processes.utils
 from .common import client_for, CFG_FILE
 
 
@@ -26,24 +26,15 @@ def test_wps_caps(client):
     assert len(names) == len(pnames)
 
 
-@pytest.fixture
-def monkeypatch_config(request):
-    previous = pywps.configuration.CONFIG.get("finch", "dataset_bccaqv2")
-    pywps.configuration.CONFIG.set("finch", "dataset_bccaqv2", "")
-    request.addfinalizer(
-        lambda: pywps.configuration.CONFIG.set("finch", "dataset_bccaqv2", previous)
-    )
-
-
 def test_wps_caps_no_datasets(client, monkeypatch):
     """Check that when no default dataset is configured, we get a lot less indicators. """
     def mock_config_get(*args, **kwargs):
-        if args[:2] == ("finch", "dataset_bccaqv2"):
+        if args[:2] == ("finch", "datasets_config"):
             return ""
         return old_get_config_value(*args, **kwargs)
 
     old_get_config_value = pywps.configuration.get_config_value
-    monkeypatch.setattr(finch.processes, "get_config_value", mock_config_get)
+    monkeypatch.setattr(finch.processes.utils, "get_config_value", mock_config_get)
 
     client = client_for(create_app(cfgfiles=CFG_FILE))
 
@@ -52,6 +43,7 @@ def mock_config_get(*args, **kwargs):
         "/wps:Capabilities/wps:ProcessOfferings/wps:Process/ows:Identifier"
     ).split()
 
+    indicators = get_indicators(realms=["atmos", "land", "seaIce"], exclude=not_implemented)
     subset_processes_count = 4
     sdba_processes_count = 1
     others = 2
diff --git a/tests/test_wps_ensemble.py b/tests/test_wps_ensemble.py
index d1fe8e46..9091b021 100644
--- a/tests/test_wps_ensemble.py
+++ b/tests/test_wps_ensemble.py
@@ -1,22 +1,21 @@
 from collections import namedtuple
 from pathlib import Path
-from unittest import mock
 import zipfile
 
 import numpy as np
 import geojson
-from xarray import open_dataset
 import pytest
+from xarray import open_dataset
 
 from finch.processes import ensemble_utils
-from finch.processes.constants import PCIC_12
-from tests.utils import execute_process, mock_local_datasets, wps_literal_input
+from pywps.app.exceptions import ProcessError
+from tests.utils import execute_process, wps_literal_input
 
 mock_filenames = [
-    "tasmax_bcc-csm1-1_subset.nc",
-    "tasmax_inmcm4_subset.nc",
-    "tasmin_bcc-csm1-1_subset.nc",
-    "tasmin_inmcm4_subset.nc",
+    "tasmax_bcc-csm1-1_rcp45_subset.nc",
+    "tasmax_inmcm4_rcp45_subset.nc",
+    "tasmin_bcc-csm1-1_rcp45_subset.nc",
+    "tasmin_inmcm4_rcp45_subset.nc",
 ]
 
 poly = {
@@ -29,18 +28,14 @@
 }
 
 
-@pytest.fixture
-def mock_datasets(monkeypatch):
-    mock_local_datasets(monkeypatch, filenames=mock_filenames)
-
-
-def test_ensemble_heatwave_frequency_grid_point(mock_datasets, client):
+def test_ensemble_heatwave_frequency_grid_point(client):
     # --- given ---
     identifier = "ensemble_grid_point_heat_wave_frequency"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -55,13 +50,13 @@ def test_ensemble_heatwave_frequency_grid_point(mock_datasets, client):
 
     # --- then ---
     assert len(outputs) == 1
-    assert Path(outputs[0]).stem.startswith('testens')
+    assert Path(outputs[0]).stem.startswith('testens_46_000_72_800_rcp45')
     ds = open_dataset(outputs[0])
     dims = dict(ds.dims)
     assert dims == {
         "region": 1,
         "time": 4,  # there are roughly 4 months in the test datasets
-        "rcp": 1
+        "scenario": 1
     }
 
     ensemble_variables = {k: v for k, v in ds.data_vars.items()}
@@ -70,19 +65,20 @@ def test_ensemble_heatwave_frequency_grid_point(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = {d: s for d, s in zip(var.dims, var.shape)}
-        assert variable_dims == {"region": 1, "time": 4, "rcp": 1}
+        assert variable_dims == {"region": 1, "time": 4, "scenario": 1}
 
     assert len(ds.attrs['source_datasets'].split('\n')) == 4
 
 
-def test_ensemble_dded_grid_point_multircp(mock_datasets, client):
+def test_ensemble_dded_grid_point_multiscenario(client):
     # --- given ---
     identifier = "ensemble_grid_point_degree_days_exceedance_date"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
-        wps_literal_input("rcp", "rcp45"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh", "-5 degC"),
         wps_literal_input("sum_thresh", "30 K days"),
         wps_literal_input("op", ">"),
@@ -101,7 +97,7 @@ def test_ensemble_dded_grid_point_multircp(mock_datasets, client):
     assert dims == {
         "region": 1,
         "time": 4,  # there are roughly 4 months in the test datasets
-        "rcp": 2,
+        "scenario": 2,
     }
 
     ensemble_variables = {k: v for k, v in ds.data_vars.items()}
@@ -110,10 +106,10 @@ def test_ensemble_dded_grid_point_multircp(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"region": 1, "time": 4, "rcp": 2}
+        assert variable_dims == {"region": 1, "time": 4, "scenario": 2}
 
 
-def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
+def test_ensemble_heatwave_frequency_bbox(client):
     # --- given ---
     identifier = "ensemble_bbox_heat_wave_frequency"
     inputs = [
@@ -121,7 +117,8 @@ def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
         wps_literal_input("lat1", "46.2"),
         wps_literal_input("lon0", "-73.0"),
         wps_literal_input("lon1", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -141,7 +138,7 @@ def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
         "lat": 2,
         "lon": 2,
         "time": 4,  # there are roughly 4 months in the test datasets
-        "rcp": 1
+        "scenario": 1
     }
 
     ensemble_variables = {k: v for k, v in ds.data_vars.items()}
@@ -150,7 +147,7 @@ def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"time": 4, "lat": 2, "lon": 2, "rcp": 1}
+        assert variable_dims == {"time": 4, "lat": 2, "lon": 2, "scenario": 1}
 
     inputs.append(wps_literal_input("average", "True"))
     outputs = execute_process(client, identifier, inputs)
@@ -158,7 +155,7 @@ def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
     assert len(outputs) == 1
     ds = open_dataset(outputs[0])
     dims = dict(ds.dims)
-    assert dims == {"time": 4, "rcp": 1}  # Spatial average has been taken.
+    assert dims == {"time": 4, "scenario": 1}  # Spatial average has been taken.
 
     ensemble_variables = {k: v for k, v in ds.data_vars.items()}
     assert sorted(ensemble_variables) == [
@@ -166,16 +163,17 @@ def test_ensemble_heatwave_frequency_bbox(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"time": 4, "rcp": 1}
+        assert variable_dims == {"time": 4, "scenario": 1}
 
 
-def test_ensemble_heatwave_frequency_grid_point_csv(mock_datasets, client):
+def test_ensemble_heatwave_frequency_grid_point_csv(client):
     # --- given ---
     identifier = "ensemble_grid_point_heat_wave_frequency"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -194,12 +192,12 @@ def test_ensemble_heatwave_frequency_grid_point_csv(mock_datasets, client):
     data_filename = [n for n in zf.namelist() if "metadata" not in n]
     csv = zf.read(data_filename[0]).decode()
     lines = csv.split("\n")
-    assert lines[0].startswith("lat,lon,time,rcp")
+    assert lines[0].startswith("lat,lon,time,scenario")
     n_data_rows = len(lines) - 2
     assert n_data_rows == 3  # time=3 (last month is NaN)
 
 
-def test_ensemble_heatwave_frequency_bbox_csv(mock_datasets, client):
+def test_ensemble_heatwave_frequency_bbox_csv(client):
     # --- given ---
     identifier = "ensemble_bbox_heat_wave_frequency"
     inputs = [
@@ -207,7 +205,8 @@ def test_ensemble_heatwave_frequency_bbox_csv(mock_datasets, client):
         wps_literal_input("lat1", "46.2"),
         wps_literal_input("lon0", "-73.0"),
         wps_literal_input("lon1", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -231,13 +230,14 @@ def test_ensemble_heatwave_frequency_bbox_csv(mock_datasets, client):
     assert n_data_rows == 2 * 2 * 3  # lat=2, lon=2, time=3 (last month is NaN)
 
 
-def test_ensemble_heatwave_frequency_grid_point_dates(mock_datasets, client):
+def test_ensemble_heatwave_frequency_grid_point_dates(client):
     # --- given ---
     identifier = "ensemble_grid_point_heat_wave_frequency"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("start_date", "1950"),
         wps_literal_input("end_date", "1950-03-31"),
         wps_literal_input("freq", "MS"),
@@ -254,7 +254,7 @@ def test_ensemble_heatwave_frequency_grid_point_dates(mock_datasets, client):
     assert dims == {
         "region": 1,
         "time": 3,
-        "rcp": 1
+        "scenario": 1
     }
 
     ensemble_variables = dict(ds.data_vars)
@@ -264,67 +264,40 @@ def test_ensemble_heatwave_frequency_grid_point_dates(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"region": 1, "time": 3, "rcp": 1}
+        assert variable_dims == {"region": 1, "time": 3, "scenario": 1}
 
 
-def test_ensemble_heatwave_frequency_grid_point_models(mock_datasets, client):
+def test_ensemble_heatwave_frequency_grid_point_models(client):
     # --- given ---
     identifier = "ensemble_grid_point_heat_wave_frequency"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_single_cell"),
         wps_literal_input("freq", "MS"),
         wps_literal_input("models", "CSIRO-Mk3-6-0"),
         wps_literal_input("models", "HadGEM2-ES"),
         wps_literal_input("models", "MRI-CGCM3"),
         wps_literal_input("output_format", "netcdf"),
     ]
-    from pywps.configuration import CONFIG
-
-    CONFIG.set("finch", "dataset_bccaqv2", "/mock_local/path")
-    subset_sample = Path(__file__).parent / "data" / "bccaqv2_subset_sample"
 
-    # --- when ---
-    with mock.patch(
-        "finch.processes.ensemble_utils.get_bccaqv2_local_files_datasets"
-    ) as mock_datasets:
-        mock_datasets.return_value = [str(subset_sample / f) for f in mock_filenames]
-        execute_process(client, identifier, inputs)
-
-    # --- then ---
-    assert mock_datasets.call_args[1]["models"] == [
-        "CSIRO-Mk3-6-0",
-        "HadGEM2-ES",
-        "MRI-CGCM3",
-    ]
+    execute_process(client, identifier, inputs)
 
 
-def test_ensemble_heatwave_frequency_grid_point_models_pcic(mock_datasets, client):
+def test_ensemble_heatwave_frequency_grid_point_models_pcic(client):
     # --- given ---
     identifier = "ensemble_grid_point_heat_wave_frequency"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_single_cell"),
         wps_literal_input("freq", "MS"),
-        wps_literal_input("models", PCIC_12),
+        wps_literal_input("models", "pcic12"),
         wps_literal_input("output_format", "netcdf"),
     ]
-    from pywps.configuration import CONFIG
-
-    CONFIG.set("finch", "dataset_bccaqv2", "/mock_local/path")
-    subset_sample = Path(__file__).parent / "data" / "bccaqv2_subset_sample"
-
-    # --- when ---
-    with mock.patch(
-        "finch.processes.ensemble_utils.get_bccaqv2_local_files_datasets"
-    ) as mock_datasets:
-        mock_datasets.return_value = [str(subset_sample / f) for f in mock_filenames]
-        execute_process(client, identifier, inputs)
-
-    # --- then ---
-    assert mock_datasets.call_args[1]["models"] == [PCIC_12]
+    execute_process(client, identifier, inputs)
 
 
 def test_compute_intermediate_variables(monkeypatch):
@@ -338,14 +311,14 @@ def test_compute_intermediate_variables(monkeypatch):
     literal_input = namedtuple('LiteralInput', ['data', 'identifier'])
     # --- when ---
     files_outputs = ensemble_utils.compute_intermediate_variables(
-        mock_paths, required_variables, workdir,
+        mock_paths, {'tasmin', 'tasmax'}, required_variables, workdir,
         {'perc_tasmin': [literal_input(10, 'perc_tasmin')]}
     )
 
     # --- then ---
     datasets = [
-        "bcc-csm1-1_subset.nc",
-        "inmcm4_subset.nc",
+        "bcc-csm1-1_rcp45_subset.nc",
+        "inmcm4_rcp45_subset.nc",
     ]
     expected = [
         workdir / f"{v}_{dataset}" for v in required_variables for dataset in datasets
@@ -355,14 +328,15 @@ def test_compute_intermediate_variables(monkeypatch):
 
 
 def test_ensemble_compute_intermediate_cold_spell_duration_index_grid_point(
-    mock_datasets, client
+    client
 ):
     # --- given ---
     identifier = "ensemble_grid_point_cold_spell_duration_index"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("window", "6"),
         wps_literal_input("freq", "YS"),
         wps_literal_input("ensemble_percentiles", "20, 50, 80"),
@@ -380,25 +354,26 @@ def test_ensemble_compute_intermediate_cold_spell_duration_index_grid_point(
     assert dims == {
         "region": 1,
         "time": 1,
-        "rcp": 1
+        "scenario": 1
     }
 
     ensemble_variables = dict(ds.data_vars)
     assert sorted(ensemble_variables) == [f"csdi_6_p{p}" for p in (20, 50, 80)]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"region": 1, "time": 1, "rcp": 1}
+        assert variable_dims == {"region": 1, "time": 1, "scenario": 1}
 
 
 def test_ensemble_compute_intermediate_growing_degree_days_grid_point(
-    mock_datasets, client
+    client
 ):
     # --- given ---
     identifier = "ensemble_grid_point_growing_degree_days"
     inputs = [
         wps_literal_input("lat", "46"),
         wps_literal_input("lon", "-72.8"),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("ensemble_percentiles", "20, 50, 80"),
         wps_literal_input("output_format", "netcdf"),
     ]
@@ -413,7 +388,7 @@ def test_ensemble_compute_intermediate_growing_degree_days_grid_point(
     assert dims == {
         "region": 1,
         "time": 1,
-        "rcp": 1
+        "scenario": 1
     }
 
     ensemble_variables = dict(ds.data_vars)
@@ -422,15 +397,16 @@ def test_ensemble_compute_intermediate_growing_degree_days_grid_point(
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"region": 1, "time": 1, "rcp": 1}
+        assert variable_dims == {"region": 1, "time": 1, "scenario": 1}
 
 
-def test_ensemble_heatwave_frequency_polygon(mock_datasets, client):
+def test_ensemble_heatwave_frequency_polygon(client):
     # --- given ---
     identifier = "ensemble_polygon_heat_wave_frequency"
     inputs = [
         wps_literal_input("shape", geojson.dumps(poly)),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -450,9 +426,9 @@ def test_ensemble_heatwave_frequency_polygon(mock_datasets, client):
         "lat": 11,
         "lon": 11,
         "time": 4,  # there are roughly 4 months in the test datasets
-        "rcp": 1
+        "scenario": 1
     }
-    data = ds["heat_wave_frequency_p20"].isel(rcp=0, time=1).data
+    data = ds["heat_wave_frequency_p20"].isel(scenario=0, time=1).data
     assert np.isnan(data).sum() == 55
     assert (~np.isnan(data)).sum() == 66
 
@@ -462,7 +438,7 @@ def test_ensemble_heatwave_frequency_polygon(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"lat": 11, "lon": 11, "time": 4, "rcp": 1}
+        assert variable_dims == {"lat": 11, "lon": 11, "time": 4, "scenario": 1}
 
     inputs.append(wps_literal_input("average", "True"))
     outputs = execute_process(client, identifier, inputs)
@@ -471,7 +447,7 @@ def test_ensemble_heatwave_frequency_polygon(mock_datasets, client):
     assert len(outputs) == 1
     ds = open_dataset(outputs[0])
     dims = dict(ds.dims)
-    assert dims == {"time": 4, "rcp": 1}
+    assert dims == {"time": 4, "scenario": 1}
 
     ensemble_variables = dict(ds.data_vars)
     assert sorted(ensemble_variables) == [
@@ -479,15 +455,16 @@ def test_ensemble_heatwave_frequency_polygon(mock_datasets, client):
     ]
     for var in ensemble_variables.values():
         variable_dims = dict(zip(var.dims, var.shape))
-        assert variable_dims == {"time": 4, "rcp": 1}
+        assert variable_dims == {"time": 4, "scenario": 1}
 
 
-def test_ensemble_heatwave_frequency_polygon_csv(mock_datasets, client):
+def test_ensemble_heatwave_frequency_polygon_csv(client):
     # --- given ---
     identifier = "ensemble_polygon_heat_wave_frequency"
     inputs = [
         wps_literal_input("shape", geojson.dumps(poly)),
-        wps_literal_input("rcp", "rcp26"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("dataset", "test_subset"),
         wps_literal_input("thresh_tasmin", "22.0 degC"),
         wps_literal_input("thresh_tasmax", "30 degC"),
         wps_literal_input("window", "3"),
@@ -528,3 +505,35 @@ def test_ensemble_heatwave_frequency_polygon_csv(mock_datasets, client):
     n_data_rows = len(lines) - 2  # header + ending line
     # after spatial average, time=3 (last month is NaN)
     assert n_data_rows == 3
+
+
+def test_ensemble_invalid_parameters(client):
+    identifier = "ensemble_grid_point_heat_wave_frequency"
+    inputs = [
+        wps_literal_input("lat", "46"),
+        wps_literal_input("lon", "-72.8"),
+        wps_literal_input("scenario", "rcp85"),
+        wps_literal_input("dataset", "test_subset"),
+        wps_literal_input("thresh_tasmin", "22.0 degC"),
+        wps_literal_input("thresh_tasmax", "30 degC"),
+        wps_literal_input("window", "3"),
+        wps_literal_input("freq", "MS"),
+        wps_literal_input("ensemble_percentiles", "20, 50, 80"),
+        wps_literal_input("output_format", "netcdf"),
+        wps_literal_input("output_name", "testens")
+    ]
+
+    # --- when ---
+    # scenario not in this dataset
+    with pytest.raises(ProcessError, match="InvalidParameterValue"):
+        execute_process(client, identifier, inputs)
+
+    inputs[2] = wps_literal_input("scenario", "rcp45")
+    inputs.append(wps_literal_input("models", "MIROC5"))  # model not in this dataset
+    with pytest.raises(ProcessError, match="InvalidParameterValue"):
+        execute_process(client, identifier, inputs)
+
+    inputs.pop(-1)
+    identifier = "ensemble_grid_point_cwd"  # dataset doesnt have PR
+    with pytest.raises(ProcessError, match="InvalidParameterValue"):
+        execute_process(client, identifier, inputs)
diff --git a/tests/test_wps_xclim_indices.py b/tests/test_wps_xclim_indices.py
index 7352afaf..2871617b 100644
--- a/tests/test_wps_xclim_indices.py
+++ b/tests/test_wps_xclim_indices.py
@@ -5,22 +5,19 @@
 import xarray as xr
 import pandas as pd
 from zipfile import ZipFile
-import warnings
 import finch
-import finch.processes
+from finch.processes import get_indicators, not_implemented
 from finch.processes.wps_xclim_indices import XclimIndicatorBase
 from finch.processes.wps_base import make_xclim_indicator_process
-from . utils import execute_process, wps_input_file, wps_literal_input
+from .utils import execute_process, wps_input_file, wps_literal_input
 from pathlib import Path
 from pywps.app.exceptions import ProcessError
-from pywps import configuration
 from unittest import mock
 from numpy.testing import assert_equal
 from xclim.testing import open_dataset
 
 
 K2C = 273.16
-configuration.CONFIG['finch:metadata']['testing_session'] = "True"
 
 
 def _get_output_standard_name(process_identifier):
@@ -29,7 +26,10 @@ def _get_output_standard_name(process_identifier):
             return p.xci.standard_name
 
 
-@pytest.mark.parametrize("indicator", finch.processes.indicators)
+@pytest.mark.parametrize(
+    "indicator",
+    get_indicators(realms=["atmos", "land", "seaIce"], exclude=not_implemented)
+)
 def test_indicators_processes_discovery(indicator):
     process = make_xclim_indicator_process(indicator, "Process", XclimIndicatorBase)
     assert indicator.identifier == process.identifier
@@ -56,7 +56,7 @@ def test_indicators_processes_discovery(indicator):
 def test_processes(client, netcdf_datasets):
     """Run a dummy calculation for every process, keeping some default parameters."""
     # indicators = finch.processes.indicators
-    processes = filter(lambda x: isinstance(x, XclimIndicatorBase), finch.processes.xclim.__dict__.values())
+    processes = filter(lambda x: isinstance(x, XclimIndicatorBase), client.application.processes.values())
     literal_inputs = {
         "freq": "MS",
         "window": "3",
@@ -78,30 +78,31 @@ def test_processes(client, netcdf_datasets):
             elif name in keep_defaults:
                 pass
             else:
-                raise NotImplementedError
-        outputs = execute_process(client, process.identifier, inputs)
-        ds = xr.open_dataset(outputs[0])
-        output_variable = list(ds.data_vars)[0]
-
-        assert getattr(ds, output_variable).standard_name == process.xci.standard_name
-        assert ds.attrs['testing_session']
-
-        model = attrs["driving_model_id"]
-        experiment = attrs["driving_experiment_id"].replace(",", "+")
-        ensemble = (
-            f"r{attrs['driving_realization']}"
-            f"i{attrs['driving_initialization_method']}"
-            f"p{attrs['driving_physics_version']}"
-        )
-        date_start = pd.to_datetime(str(ds.time[0].values))
-        date_end = pd.to_datetime(str(ds.time[-1].values))
-
-        expected = (
-            f"{output_variable.replace('_', '-')}_"
-            f"{model}_{experiment}_{ensemble}_"
-            f"{date_start:%Y%m%d}-{date_end:%Y%m%d}.nc"
-        )
-        assert Path(outputs[0]).name == expected
+                break
+        else:
+            outputs = execute_process(client, process.identifier, inputs)
+            ds = xr.open_dataset(outputs[0])
+            output_variable = list(ds.data_vars)[0]
+
+            assert getattr(ds, output_variable).standard_name == process.xci.standard_name
+            assert ds.attrs['testing_session']
+
+            model = attrs["driving_model_id"]
+            experiment = attrs["driving_experiment_id"].replace(",", "+")
+            ensemble = (
+                f"r{attrs['driving_realization']}"
+                f"i{attrs['driving_initialization_method']}"
+                f"p{attrs['driving_physics_version']}"
+            )
+            date_start = pd.to_datetime(str(ds.time[0].values))
+            date_end = pd.to_datetime(str(ds.time[-1].values))
+
+            expected = (
+                f"{output_variable.replace('_', '-')}_"
+                f"{model}_{experiment}_{ensemble}_"
+                f"{date_start:%Y%m%d}-{date_end:%Y%m%d}.nc"
+            )
+            assert Path(outputs[0]).name == expected
 
 
 def test_wps_daily_temperature_range_multiple(client, netcdf_datasets):
diff --git a/tests/test_wps_xsubset_bbox.py b/tests/test_wps_xsubset_bbox.py
index 556bc314..d2f8b733 100644
--- a/tests/test_wps_xsubset_bbox.py
+++ b/tests/test_wps_xsubset_bbox.py
@@ -1,11 +1,14 @@
+from pathlib import Path
+import pytest
 import numpy as np
 import xarray as xr
 from pywps import Service
 from pywps.tests import assert_response_success, client_for
-
+import zipfile
 from finch.processes import SubsetBboxProcess
 
 from .common import CFG_FILE, get_output
+from .utils import execute_process, wps_literal_input
 
 
 def test_wps_subsetbbox(netcdf_datasets):
@@ -29,3 +32,41 @@ def test_wps_subsetbbox(netcdf_datasets):
     ds = xr.open_dataset(out["output"][7:])
     np.testing.assert_array_equal(ds.lat, [2, 3, 4])
     np.testing.assert_array_equal(ds.lon, [3, 4])
+
+
+@pytest.mark.parametrize("outfmt", ['netcdf', 'csv'])
+def test_wps_subsetbbox_dataset(client, outfmt):
+    # --- given ---
+    identifier = "subset_bbox_dataset"
+    inputs = [
+        wps_literal_input("lat0", "45"),
+        wps_literal_input("lon0", "-74"),
+        wps_literal_input("lat1", "46"),
+        wps_literal_input("lon1", "-73"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
+        wps_literal_input("output_format", outfmt),
+    ]
+
+    # --- when ---
+    outputs = execute_process(client, identifier, inputs)
+
+    # --- then ---
+    assert len(outputs) == 1
+    assert Path(outputs[0]).stem == 'test_subset_subset_bbox_dataset_45_000_74_000_46_000_73_000_rcp45'
+
+    zf = zipfile.ZipFile(outputs[0])
+    assert len(zf.namelist()) == (4 if outfmt == 'netcdf' else 5)
+
+    if outfmt == 'netcdf':
+        data_filenames = [n for n in zf.namelist() if "metadata" not in n]
+
+        with zf.open(data_filenames[0]) as f:
+            ds = xr.open_dataset(f)
+
+            dims = dict(ds.dims)
+            assert dims == {
+                "lon": 6,
+                "lat": 6,
+                "time": 100,
+            }
diff --git a/tests/test_wps_xsubset_point.py b/tests/test_wps_xsubset_point.py
index f975c84f..460e5465 100644
--- a/tests/test_wps_xsubset_point.py
+++ b/tests/test_wps_xsubset_point.py
@@ -1,12 +1,15 @@
+from pathlib import Path
 import pytest
 import xarray as xr
 from numpy.testing import assert_array_equal
 from pywps import Service
 from pywps.tests import assert_response_success, client_for
+import zipfile
 
 from finch.processes import SubsetGridPointProcess
 
 from .common import CFG_FILE, get_metalinks, get_output
+from .utils import execute_process, wps_literal_input
 
 
 def test_wps_xsubsetpoint(netcdf_datasets):
@@ -63,12 +66,12 @@ def test_thredds():
         Service(processes=[SubsetGridPointProcess()], cfgfiles=CFG_FILE)
     )
     fn1 = (
-        "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/cmip5/MRI/rcp85/fx/atmos/r0i0p0/sftlf/"
-        "sftlf_fx_MRI-CGCM3_rcp85_r0i0p0.nc"
+        "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/"
+        "birdhouse/disk2/cmip5/MRI/rcp85/fx/atmos/r0i0p0/sftlf/sftlf_fx_MRI-CGCM3_rcp85_r0i0p0.nc"
     )
     fn2 = (
-        "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/cmip5/MRI/rcp85/fx/atmos/r0i0p0/orog/"
-        "orog_fx_MRI-CGCM3_rcp85_r0i0p0.nc"
+        "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/"
+        "birdhouse/disk2/cmip5/MRI/rcp85/fx/atmos/r0i0p0/orog/orog_fx_MRI-CGCM3_rcp85_r0i0p0.nc"
     )
 
     datainputs = (
@@ -93,7 +96,7 @@ def test_bad_link_on_thredds():
     client = client_for(
         Service(processes=[SubsetGridPointProcess()], cfgfiles=CFG_FILE)
     )
-    fn = "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/cmip5/bad_link.nc"
+    fn = "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/disk2/cmip5/bad_link.nc"
     datainputs = (
         f"resource=files@xlink:href={fn};"
         "lat=45.0;"
@@ -121,3 +124,38 @@ def test_bad_link_on_fs():
     )
 
     assert "No such file or directory" in resp.response[0].decode()
+
+
+@pytest.mark.parametrize("outfmt", ['netcdf', 'csv'])
+def test_wps_subsetpoint_dataset(client, outfmt):
+    # --- given ---
+    identifier = "subset_grid_point_dataset"
+    inputs = [
+        wps_literal_input("lat", "45"),
+        wps_literal_input("lon", "-74"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
+        wps_literal_input("output_format", outfmt),
+    ]
+
+    # --- when ---
+    outputs = execute_process(client, identifier, inputs)
+
+    # --- then ---
+    assert len(outputs) == 1
+    assert Path(outputs[0]).stem == 'test_subset_subset_grid_point_dataset_45_000_74_000_rcp45'
+
+    zf = zipfile.ZipFile(outputs[0])
+    assert len(zf.namelist()) == (4 if outfmt == 'netcdf' else 5)
+
+    if outfmt == 'netcdf':
+        data_filenames = [n for n in zf.namelist() if "metadata" not in n]
+
+        with zf.open(data_filenames[0]) as f:
+            ds = xr.open_dataset(f)
+
+            dims = dict(ds.dims)
+            assert dims == {
+                "region": 1,
+                "time": 100,
+            }
diff --git a/tests/utils.py b/tests/utils.py
index ab6cc30a..9e3e01ee 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -15,40 +15,6 @@ def shapefile_zip():
     return (Path(__file__).parent / "data" / "shapefile.zip").as_posix()
 
 
-def mock_local_datasets(monkeypatch, filenames=None):
-    """Mock the get_bccaqv2_local_files_datasets function
-
-    Datasets are of the following format:
-
-    >>> ds
-    <xarray.Dataset>
-    Dimensions:  (lat: 12, lon: 12, time: 100)
-    Coordinates:
-    * lon      (lon) float64 -73.46 -73.38 -73.29 -73.21 ... -72.71 -72.63 -72.54
-    * lat      (lat) float64 45.54 45.62 45.71 45.79 ... 46.21 46.29 46.37 46.46
-    * time     (time) object 1950-01-01 12:00:00 ... 1950-04-10 12:00:00
-    Data variables:
-        tasmax   (time, lat, lon) float32 ...
-    """
-    from pywps.configuration import CONFIG
-    from finch.processes import ensemble_utils
-
-    CONFIG.set("finch", "dataset_bccaqv2", "/mock_local/path")
-
-    subset_sample = Path(__file__).parent / "data" / "bccaqv2_subset_sample"
-
-    if filenames is None:
-        filenames = ["tasmin_subset.nc", "tasmax_subset.nc"]
-
-    test_data = [subset_sample / f for f in filenames]
-
-    monkeypatch.setattr(
-        ensemble_utils,
-        "get_bccaqv2_local_files_datasets",
-        lambda *args, **kwargs: [str(f) for f in test_data],
-    )
-
-
 def wps_input_file(identifier, filename):
     return WPS.Input(
         OWS.Identifier(identifier),