From d94f1dac52b4bda8efc9d8347c1ad6c11b03fe23 Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Mon, 16 Dec 2024 06:31:14 +0100
Subject: [PATCH 1/8] Add `read_netcdf()` to the io-docs

---
 docs/api/io.rst | 24 ++++++++++++++++++++++++
 pyam/netcdf.py  |  7 ++++++-
 2 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/docs/api/io.rst b/docs/api/io.rst
index 3e918536e..644155b77 100644
--- a/docs/api/io.rst
+++ b/docs/api/io.rst
@@ -22,6 +22,30 @@ Exporting to these formats is implemented via the following functions:
 .. automethod:: IamDataFrame.to_csv
    :noindex:
 
+Integration with netcdf files
+-----------------------------
+
+`NetCDF <https://www.unidata.ucar.edu/software/netcdf/>`_ is a powerful file format that
+can efficiently store multiple scientific variables sharing the same dimensions.
+In climate science, data such as temperature, precipitation and radiation can be stored
+in four dimensions: a time dimension and three spatial dimensions (latitude, longitude,
+altitude).
+
+The |pyam| package supports reading and writing to netcdf files that have the following
+structure:
+
+- **Timeseries data** are stored such that each variable (in the sense of the IAMC
+  format) is a separate netcdf-data-variable with the following dimensions *time*,
+  *model*, *scenario* and *region*. The *unit* is given as an attribute of the data
+  variable. The *long_name* attribute is used as the variable name in the
+  :class:`IamDataFrame`. The *time* dimension can be either a datetime format or given
+  as years (integer).
+
+- **Meta indicators** are stored as netcdf-data-variables with the dimensions *model*
+  and *scenario*.
+
+.. autofunction:: read_netcdf
+
 The frictionless Data Package
 -----------------------------
 
diff --git a/pyam/netcdf.py b/pyam/netcdf.py
index cf736ab19..b130bb1d4 100644
--- a/pyam/netcdf.py
+++ b/pyam/netcdf.py
@@ -26,12 +26,17 @@ def read_netcdf(path):
         Scenario data file in netCDF format.
 
     Returns
-    ----------
+    -------
     :class:`IamDataFrame`
 
     See Also
     --------
     pyam.IamDataFrame.to_netcdf
+    Notes
+    -----
+    Read the `pyam-netcdf docs <https://pyam-iamc.readthedocs.io/en/stable/api/io.html>`_
+    for more information on the expected file format structure.
+
     """
     from pyam import IamDataFrame
 

From 4a041d5a9c7942f091c070054a89a1bffda514c0 Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 09:48:08 +0100
Subject: [PATCH 2/8] Add xarray to intersphinx

---
 docs/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/conf.py b/docs/conf.py
index b04c0f60b..74226ade2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -330,6 +330,7 @@
     "nomenclature": ("https://nomenclature-iamc.readthedocs.io/en/stable", None),
     "ixmp4": ("https://docs.ece.iiasa.ac.at/projects/ixmp4/en/stable", None),
     "wbdata": ("https://wbdata.readthedocs.io/en/stable/", None),
+    "xarray": ("https://docs.xarray.dev/en/stable/", None),
 }
 
 # Set up the plotting gallery with plotly scraper

From bc4feb24abed674de38bbfd2bb0dfdb7b1674058 Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 09:48:14 +0100
Subject: [PATCH 3/8] Add empty line

---
 pyam/netcdf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyam/netcdf.py b/pyam/netcdf.py
index b130bb1d4..217828323 100644
--- a/pyam/netcdf.py
+++ b/pyam/netcdf.py
@@ -32,6 +32,7 @@ def read_netcdf(path):
     See Also
     --------
     pyam.IamDataFrame.to_netcdf
+
     Notes
     -----
     Read the `pyam-netcdf docs <https://pyam-iamc.readthedocs.io/en/stable/api/io.html>`_

From 8ff1fa26dacf83e167af323465e637c541bfab35 Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 09:54:06 +0100
Subject: [PATCH 4/8] Add notes

---
 pyam/core.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pyam/core.py b/pyam/core.py
index 7aeeecf0f..03156335f 100755
--- a/pyam/core.py
+++ b/pyam/core.py
@@ -2546,6 +2546,12 @@ def to_netcdf(self, path):
         See Also
         --------
         pyam.read_netcdf
+
+        Notes
+        -----
+        Read the `pyam-netcdf docs <https://pyam-iamc.readthedocs.io/en/stable/api/io.html>`_
+        for more information on the expected file format structure.
+
         """
         self.to_xarray().to_netcdf(path)
 

From bbdaeca5d741bac18e753ca7a036988f13c42b11 Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 09:54:14 +0100
Subject: [PATCH 5/8] Update the tests-readme

---
 tests/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tests/README.md b/tests/README.md
index 99359931d..de1a72067 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -1,7 +1,7 @@
 # Plotting Tests
 
-Plotting tests are used as regression tests for plotting features. They can be
-run locally (see below) and are also run on CI.
+Plotting tests are used as regression tests for plotting features. They can be run
+locally (see below) and are also run on CI.
 
 ## Install Deps
 
@@ -9,9 +9,9 @@ You have to install `pytest-mpl` to run the plotting tests.
 
 ## Tests Failing on CI?
 
-Make sure your local version of `matplotlib` and `seaborn` are the same as on
-CI. `seaborn` can override default `matplotlib` style sheets, and thus both need
-to be the same version.
+Make sure your local version of `matplotlib` and `seaborn` are the same as on CI.
+`seaborn` can override default `matplotlib` style sheets, and thus both need to be the
+same version.
 
 ## Creating Baseline Images
 

From c74b26d91c51f98b475f639f40f655160600422d Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 09:59:48 +0100
Subject: [PATCH 6/8] Remove netcdf tutorial

---
 docs/tutorials/read_netcdf.ipynb | 966 -------------------------------
 1 file changed, 966 deletions(-)
 delete mode 100644 docs/tutorials/read_netcdf.ipynb

diff --git a/docs/tutorials/read_netcdf.ipynb b/docs/tutorials/read_netcdf.ipynb
deleted file mode 100644
index b4da6deda..000000000
--- a/docs/tutorials/read_netcdf.ipynb
+++ /dev/null
@@ -1,966 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Read netCDF file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) is a powerful file format that can store efficiently multiple scientific variables sharing the same multiple dimensions. For example, in climate science, temperature, precipitation, radiation can be stored in four dimensions: one time dimension and three spatial dimensions (longitude, latitude, altitude).\n",
-    "NetCDF format is also versatile as the dimensions can be anything pre-defined and shared between the variables, not necessarily spatial coordinates as in climate science.\n",
-    "For this reason, it is also increasingly used in the energy system modelling community.\n",
-    "\n",
-    "We provide a function in the [pyam](https://github.com/IAMconsortium/pyam) package (pyam.read_netcdf) to read a netCDF file into the IamDataFrame format following the standard by the Integrated Assessment Modeling Consortium ([IAMC](https://www.iamconsortium.org/)). [Read the docs](https://pyam-iamc.readthedocs.io/en/stable/data.html) provides more information on this format.\n",
-    "\n",
-    "To use `pyam.read_netcdf`, the netCDF input file should have the following structure:\n",
-    "- Variables should have four dimensions, namely model, scenario, region, and time.\n",
-    "They are defined in pyam as IAMC indices (`IAMC_IDX`).\n",
-    "- Meta indicators should have two dimensions, namely model and scenario (`META_IDX`)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The package `xarray` provides a nice tool to handle netCDF data with indexing and similar numpy dataframe for each variable "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 128B\n",
-       "Dimensions:              (scenario: 2, time: 2, model: 1, region: 1)\n",
-       "Coordinates:\n",
-       "  * scenario             (scenario) object 16B &#x27;scen_a&#x27; &#x27;scen_b&#x27;\n",
-       "  * time                 (time) int32 8B 2005 2010\n",
-       "  * model                (model) object 8B &#x27;model_a&#x27;\n",
-       "  * region               (region) object 8B &#x27;World&#x27;\n",
-       "Data variables:\n",
-       "    Primary Energy       (time, model, scenario, region) float64 32B ...\n",
-       "    Primary Energy|Coal  (time, model, scenario, region) float64 32B ...\n",
-       "    string               (model, scenario) object 16B ...\n",
-       "    number               (model, scenario) int32 8B ...\n",
-       "Attributes:\n",
-       "    Information:  Created based on test_df from pyam.test_io to test the func...</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-37b63df1-94c4-4b40-b880-b78fdaec913e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37b63df1-94c4-4b40-b880-b78fdaec913e' 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'>scenario</span>: 2</li><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>model</span>: 1</li><li><span class='xr-has-index'>region</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cea4dea9-a2a3-4661-8958-74934e7b0c38' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cea4dea9-a2a3-4661-8958-74934e7b0c38' 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 class='xr-has-index'>scenario</span></div><div class='xr-var-dims'>(scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;scen_a&#x27; &#x27;scen_b&#x27;</div><input id='attrs-a6f7ffc6-a99c-4e03-97c6-bd47fbdc6f60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6f7ffc6-a99c-4e03-97c6-bd47fbdc6f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7708d055-be21-418c-8c2d-1519f7cf700e' class='xr-var-data-in' type='checkbox'><label for='data-7708d055-be21-418c-8c2d-1519f7cf700e' 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;scen_a&#x27;, &#x27;scen_b&#x27;], dtype=object)</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'>int32</div><div class='xr-var-preview xr-preview'>2005 2010</div><input id='attrs-a9234fb9-777a-4bb8-ac72-cd724f13fe27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9234fb9-777a-4bb8-ac72-cd724f13fe27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce43942e-05c1-469d-b465-d7d354142165' class='xr-var-data-in' type='checkbox'><label for='data-ce43942e-05c1-469d-b465-d7d354142165' 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([2005, 2010], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;model_a&#x27;</div><input id='attrs-e41c432a-4ac2-4dfc-855a-323d7b05522b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e41c432a-4ac2-4dfc-855a-323d7b05522b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbf680ce-acd8-46f6-a82d-48132583787d' class='xr-var-data-in' type='checkbox'><label for='data-cbf680ce-acd8-46f6-a82d-48132583787d' 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;model_a&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-cf63666d-cad9-4917-8eca-464d9c348e3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf63666d-cad9-4917-8eca-464d9c348e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc966621-8c54-4bdd-abf4-c61b5ebbd511' class='xr-var-data-in' type='checkbox'><label for='data-dc966621-8c54-4bdd-abf4-c61b5ebbd511' 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;World&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aefea888-ffe1-4eef-9cba-239a1d845028' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aefea888-ffe1-4eef-9cba-239a1d845028' 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>Primary Energy</span></div><div class='xr-var-dims'>(time, model, scenario, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1e2855d-3f17-470f-ae4a-a7dacaf6756b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e2855d-3f17-470f-ae4a-a7dacaf6756b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30272efb-9a44-4c34-ba1a-36788d7af4c7' class='xr-var-data-in' type='checkbox'><label for='data-30272efb-9a44-4c34-ba1a-36788d7af4c7' 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>unit :</span></dt><dd>EJ/yr</dd><dt><span>long_name :</span></dt><dd>Primary Energy</dd></dl></div><div class='xr-var-data'><pre>[4 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Primary Energy|Coal</span></div><div class='xr-var-dims'>(time, model, scenario, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e8f3f96-0d49-4a06-8174-0f68207de739' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e8f3f96-0d49-4a06-8174-0f68207de739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1360617b-bb89-42b4-b870-3200f314cfb6' class='xr-var-data-in' type='checkbox'><label for='data-1360617b-bb89-42b4-b870-3200f314cfb6' 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>unit :</span></dt><dd>EJ/yr</dd><dt><span>long_name :</span></dt><dd>Primary Energy|Coal</dd></dl></div><div class='xr-var-data'><pre>[4 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>string</span></div><div class='xr-var-dims'>(model, scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ccbb3274-9939-447c-85be-cd1c4826266e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccbb3274-9939-447c-85be-cd1c4826266e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9a069e8-3d3a-4fde-9af0-359cd9ae1e76' class='xr-var-data-in' type='checkbox'><label for='data-f9a069e8-3d3a-4fde-9af0-359cd9ae1e76' 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>[2 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>(model, scenario)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5f675005-5b5a-41a8-870c-5d46690e7587' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f675005-5b5a-41a8-870c-5d46690e7587' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42dcfae8-98eb-4efd-9de1-d89f46c7f4a3' class='xr-var-data-in' type='checkbox'><label for='data-42dcfae8-98eb-4efd-9de1-d89f46c7f4a3' 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>[2 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4cc0edb8-2752-4c5b-9330-e48cc672edbf' class='xr-section-summary-in' type='checkbox'  ><label for='section-4cc0edb8-2752-4c5b-9330-e48cc672edbf' class='xr-section-summary' >Indexes: <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-index-name'><div>scenario</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7b3eafa-deb4-44e2-b528-f9e0ba92e5fe' class='xr-index-data-in' type='checkbox'/><label for='index-b7b3eafa-deb4-44e2-b528-f9e0ba92e5fe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;scen_a&#x27;, &#x27;scen_b&#x27;], dtype=&#x27;object&#x27;, name=&#x27;scenario&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5f3405b-20ed-4ef8-81c3-93307abfd2e6' class='xr-index-data-in' type='checkbox'/><label for='index-d5f3405b-20ed-4ef8-81c3-93307abfd2e6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2005, 2010], dtype=&#x27;int32&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e7ecb43-ed01-454b-a00e-7ba66969a434' class='xr-index-data-in' type='checkbox'/><label for='index-5e7ecb43-ed01-454b-a00e-7ba66969a434' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;model_a&#x27;], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a892e7f2-f158-4b19-ae10-5f1ed9eba2a5' class='xr-index-data-in' type='checkbox'/><label for='index-a892e7f2-f158-4b19-ae10-5f1ed9eba2a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;World&#x27;], dtype=&#x27;object&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db1f5fb4-3ba7-4596-a1cb-6e32578dd197' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db1f5fb4-3ba7-4596-a1cb-6e32578dd197' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Information :</span></dt><dd>Created based on test_df from pyam.test_io to test the function pyam.read_netcdf</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 128B\n",
-       "Dimensions:              (scenario: 2, time: 2, model: 1, region: 1)\n",
-       "Coordinates:\n",
-       "  * scenario             (scenario) object 16B 'scen_a' 'scen_b'\n",
-       "  * time                 (time) int32 8B 2005 2010\n",
-       "  * model                (model) object 8B 'model_a'\n",
-       "  * region               (region) object 8B 'World'\n",
-       "Data variables:\n",
-       "    Primary Energy       (time, model, scenario, region) float64 32B ...\n",
-       "    Primary Energy|Coal  (time, model, scenario, region) float64 32B ...\n",
-       "    string               (model, scenario) object 16B ...\n",
-       "    number               (model, scenario) int32 8B ...\n",
-       "Attributes:\n",
-       "    Information:  Created based on test_df from pyam.test_io to test the func..."
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "\n",
-    "ds = xr.open_dataset(\"test_df.nc\")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " Additionally, variables have two attributes, namely `long name` and `unit`. The `long name` of each variable in netCDF file will be used as its variable name in IamDataFrame. This is to bridge the differentce in naming convention of variables between netCDF file with underscores, e.g., `Primary_Energy__Coal`, and the IAMC naming convention with spaces and `|`, e.g., `Primary Energy|Coal`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  display: none;\n",
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-       ".xr-header {\n",
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-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
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-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       "\n",
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-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
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-       "  display: grid;\n",
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-       "\n",
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-       "  display: contents;\n",
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-       "\n",
-       ".xr-section-item input {\n",
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-       "  padding-bottom: 4px;\n",
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-       "\n",
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-       "  margin-bottom: 5px;\n",
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-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-dim-list {\n",
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-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
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-       "  content: '(';\n",
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-       "\n",
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-       "\n",
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-       "  padding-right: 5px;\n",
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-       "\n",
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-       "  font-weight: bold;\n",
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-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
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-       "\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
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-       "\n",
-       ".xr-var-preview {\n",
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-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
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-       ".xr-var-name:hover,\n",
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-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
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-       "\n",
-       ".xr-var-data > table {\n",
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-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
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-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
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-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Primary Energy&#x27; (time: 2, model: 1, scenario: 2, region: 1)&gt; Size: 32B\n",
-       "[4 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * scenario  (scenario) object 16B &#x27;scen_a&#x27; &#x27;scen_b&#x27;\n",
-       "  * time      (time) int32 8B 2005 2010\n",
-       "  * model     (model) object 8B &#x27;model_a&#x27;\n",
-       "  * region    (region) object 8B &#x27;World&#x27;\n",
-       "Attributes:\n",
-       "    unit:       EJ/yr\n",
-       "    long_name:  Primary Energy</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'>'Primary Energy'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>model</span>: 1</li><li><span class='xr-has-index'>scenario</span>: 2</li><li><span class='xr-has-index'>region</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd835f8c-10ab-4dbb-abe6-2e348259270c' class='xr-array-in' type='checkbox' checked><label for='section-bd835f8c-10ab-4dbb-abe6-2e348259270c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[4 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-643c4a42-a475-4935-b96a-c8d5f46d57f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-643c4a42-a475-4935-b96a-c8d5f46d57f8' 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 class='xr-has-index'>scenario</span></div><div class='xr-var-dims'>(scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;scen_a&#x27; &#x27;scen_b&#x27;</div><input id='attrs-dcbfeda3-e52d-4828-99d8-ce33feb2080f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcbfeda3-e52d-4828-99d8-ce33feb2080f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db854fc-8a0b-4373-979d-ddf987c4a650' class='xr-var-data-in' type='checkbox'><label for='data-3db854fc-8a0b-4373-979d-ddf987c4a650' 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;scen_a&#x27;, &#x27;scen_b&#x27;], dtype=object)</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'>int32</div><div class='xr-var-preview xr-preview'>2005 2010</div><input id='attrs-21ac7373-ce49-469f-8320-d930fda1acd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21ac7373-ce49-469f-8320-d930fda1acd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-282d72d5-9fa8-46c0-b473-71dcc6fd438b' class='xr-var-data-in' type='checkbox'><label for='data-282d72d5-9fa8-46c0-b473-71dcc6fd438b' 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([2005, 2010], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;model_a&#x27;</div><input id='attrs-e966b6e6-b2e4-4e26-b082-5112e38a2292' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e966b6e6-b2e4-4e26-b082-5112e38a2292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83237d96-2e30-4934-a808-2079f683c90b' class='xr-var-data-in' type='checkbox'><label for='data-83237d96-2e30-4934-a808-2079f683c90b' 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;model_a&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-3730a776-7142-454a-a985-367b675606f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3730a776-7142-454a-a985-367b675606f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b1ca23-b233-4370-9a8b-9bbd6d16add7' class='xr-var-data-in' type='checkbox'><label for='data-06b1ca23-b233-4370-9a8b-9bbd6d16add7' 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;World&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2cd878ce-8d63-4114-b383-a024e247fa41' class='xr-section-summary-in' type='checkbox'  ><label for='section-2cd878ce-8d63-4114-b383-a024e247fa41' class='xr-section-summary' >Indexes: <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-index-name'><div>scenario</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0fb181a-9474-48c0-94f9-d3ec734b6018' class='xr-index-data-in' type='checkbox'/><label for='index-c0fb181a-9474-48c0-94f9-d3ec734b6018' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;scen_a&#x27;, &#x27;scen_b&#x27;], dtype=&#x27;object&#x27;, name=&#x27;scenario&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-29ef3fc8-6e92-438c-b6ee-73ab9e4f64f4' class='xr-index-data-in' type='checkbox'/><label for='index-29ef3fc8-6e92-438c-b6ee-73ab9e4f64f4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2005, 2010], dtype=&#x27;int32&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bacba867-f283-4ab4-936d-53dc070cb328' class='xr-index-data-in' type='checkbox'/><label for='index-bacba867-f283-4ab4-936d-53dc070cb328' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;model_a&#x27;], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a7a2ad8f-6700-429e-add1-db0ccceab684' class='xr-index-data-in' type='checkbox'/><label for='index-a7a2ad8f-6700-429e-add1-db0ccceab684' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;World&#x27;], dtype=&#x27;object&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c46e32e1-2256-411e-b10d-65389454bf69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c46e32e1-2256-411e-b10d-65389454bf69' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>EJ/yr</dd><dt><span>long_name :</span></dt><dd>Primary Energy</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'Primary Energy' (time: 2, model: 1, scenario: 2, region: 1)> Size: 32B\n",
-       "[4 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * scenario  (scenario) object 16B 'scen_a' 'scen_b'\n",
-       "  * time      (time) int32 8B 2005 2010\n",
-       "  * model     (model) object 8B 'model_a'\n",
-       "  * region    (region) object 8B 'World'\n",
-       "Attributes:\n",
-       "    unit:       EJ/yr\n",
-       "    long_name:  Primary Energy"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds[\"Primary Energy\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function pyam.read_netcdf can read data in both year-based and timeseries format. \n",
-    "The output would have a year or time coordinate respectively.\n",
-    "\n",
-    "If the file contains metadata, they should also be store as variables but with two dimensions\n",
-    "model and scenario. These two dimensions are definded as meta indices META_IDX in pyam.\n",
-    "In the output IamDataFrame, they are read separately and called Meta indicators\n",
-    "\n",
-    "If there are any other variables contain different dimensions in the above structure, the function will throw an error.\n",
-    "\n",
-    "The output of the pyam.read_netcdf function has the IamDataFrame format. It has two indices model and scenario, four coordinates region, variable, unit, year or time depending on the input netCDF file, and meta indicators if available. More information about IamDataFrame and meta indicators in the pyam package can be found in the [pyam software tool article](https://open-research-europe.ec.europa.eu/articles/1-74)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'pyam.core.IamDataFrame'>\n",
-       "Index:\n",
-       " * model    : model_a (1)\n",
-       " * scenario : scen_a, scen_b (2)\n",
-       "Timeseries data coordinates:\n",
-       "   region   : World (1)\n",
-       "   variable : Primary Energy, Primary Energy|Coal (2)\n",
-       "   unit     : EJ/yr (1)\n",
-       "   year     : 2005, 2010 (2)\n",
-       "Meta indicators:\n",
-       "   string (object) foo, nan (2)\n",
-       "   number (int32) 1, 2 (2)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pyam\n",
-    "\n",
-    "df = pyam.read_netcdf(\"test_df.nc\")\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This output dataframe has all the functions as in the IamDataFrame class from pyam. For example, select one variable, aggregated with all the regions then plot with coloured by scenario.\n",
-    "See more tutorials for pyam IamDataFrame in [Read the docs](https://pyam-iamc.readthedocs.io/en/stable/tutorials/pyam_first_steps.html).\n",
-    "\n",
-    "**Note** </br>\n",
-    "The following netCDF file is an output from the model [Calliope](https://calliope.readthedocs.io/) for illustration. The data are not save with the Jupyter notebook and only the plot is saved directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pyam.read_netcdf(\"iamc_year_based.nc\")\n",
-    "\n",
-    "ax = df.aggregate_region(\"Emissions|CO2\", components=True).plot(\n",
-    "    color=\"scenario\", legend=dict(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py_pyam",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 043022e0a07edffbcb3dc4fca493196993afd09b Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 10:46:00 +0100
Subject: [PATCH 7/8] Update the netcdf description

---
 docs/api/io.rst | 28 ++++++++++++++++++----------
 docs/data.rst   |  2 ++
 2 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/docs/api/io.rst b/docs/api/io.rst
index 644155b77..caeb4a3f6 100644
--- a/docs/api/io.rst
+++ b/docs/api/io.rst
@@ -6,10 +6,10 @@ Input/output file formats
 DataFrames and xlsx/csv files
 -----------------------------
 
-A :class:`pandas.DataFrame` or a path to an :code:`xlsx` or :code:`csv`
-with data in the required structure (i.e., index/columns) can be imported
-directly by initializing an :class:`IamDataFrame` - see
-`this tutorial <../tutorials/data_table_formats.html>`_ for more information.
+A :class:`pandas.DataFrame` or a path to an *xlsx* or *csv* with data in the required
+structure (i.e., index/columns) can be imported directly by initializing an
+:class:`IamDataFrame` - see `this tutorial <../tutorials/data_table_formats.html>`_ for
+more information.
 
 Exporting to these formats is implemented via the following functions:
 
@@ -34,18 +34,26 @@ altitude).
 The |pyam| package supports reading and writing to netcdf files that have the following
 structure:
 
-- **Timeseries data** are stored such that each variable (in the sense of the IAMC
-  format) is a separate netcdf-data-variable with the following dimensions *time*,
-  *model*, *scenario* and *region*. The *unit* is given as an attribute of the data
-  variable. The *long_name* attribute is used as the variable name in the
-  :class:`IamDataFrame`. The *time* dimension can be either a datetime format or given
-  as years (integer).
+- **Timeseries data** are stored such that each *variable* (in the sense of the IAMC
+  format) is a separate netcdf-data-variable with the dimensions *time*, *model*,
+  *scenario* and *region*. The *unit* is given as an attribute of the data variable.
+  The *long_name* attribute is used as the *variable* in the :class:`IamDataFrame`.
+  The *time* dimension can be either a datetime format or given as years (integer).
 
 - **Meta indicators** are stored as netcdf-data-variables with the dimensions *model*
   and *scenario*.
 
+Read more about :ref:`pyam_data_model`. The :attr:`exclude <pyam.IamDataFrame.exclude>`
+attribute is not written to netcdf files.
+
 .. autofunction:: read_netcdf
 
+.. automethod:: IamDataFrame.to_netcdf
+   :noindex:
+
+.. automethod:: IamDataFrame.to_xarray
+   :noindex:
+
 The frictionless Data Package
 -----------------------------
 
diff --git a/docs/data.rst b/docs/data.rst
index 6295395fb..4b0bdeb45 100644
--- a/docs/data.rst
+++ b/docs/data.rst
@@ -41,6 +41,8 @@ using the IAMC data format.
 
 .. _`CD-LINKS`: https://www.cd-links.org
 
+.. _pyam_data_model:
+
 The pyam data model
 -------------------
 

From a0fb0a5dc0dfdd50f9a350233bf5b3efce16edfe Mon Sep 17 00:00:00 2001
From: Daniel Huppmann <dh@dergelbesalon.at>
Date: Thu, 19 Dec 2024 10:46:28 +0100
Subject: [PATCH 8/8] Bump the docs to show support for Python 3.13

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index fe01187eb..09f8c95c6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -24,7 +24,7 @@ Release v\ |version|.
 .. |ruff| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
 
-.. |python| image:: https://img.shields.io/badge/python-≥3.10,<3.13-blue?logo=python&logoColor=white
+.. |python| image:: https://img.shields.io/badge/python-≥3.10,<3.14-blue?logo=python&logoColor=white
    :target: https://github.com/IAMconsortium/pyam
 
 .. |pytest| image:: https://img.shields.io/github/actions/workflow/status/iamconsortium/pyam/pytest.yml?logo=GitHub&label=pytest