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dcheriandcherian
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Merge remote-tracking branch 'upstream/master' into fix/plot-broadcast
* upstream/master: Add drop to api.rst under pending deprecations (pydata#3561) replace duplicate method _from_vars_and_coord_names (pydata#3565) propagate indexes in to_dataset, from_dataset (pydata#3519) Switch examples to notebooks + scipy19 docs improvements (pydata#3557) fix whats-new.rst (pydata#3554)
2 parents 07eddc5 + ff6051d commit 794939e

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‎ci/requirements/doc.yml

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@@ -6,16 +6,23 @@ dependencies:
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- python=3.7
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- bottleneck
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- cartopy
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- eccodes
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- h5netcdf
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- ipykernel
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- ipython
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- iris
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- jupyter_client
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- nbsphinx
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- netcdf4
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- numpy
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- numpydoc
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- pandas<0.25 # Hack around https://github.com/pydata/xarray/issues/3369
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- rasterio
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- seaborn
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- sphinx
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- sphinx-gallery
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- sphinx_rtd_theme
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- zarr
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- pip
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- pip:
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- cfgrib
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‎doc/api.rst

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backends.FileManager
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backends.CachingFileManager
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backends.DummyFileManager
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Deprecated / Pending Deprecation
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================================
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Dataset.drop
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DataArray.drop
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Dataset.apply
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core.groupby.DataArrayGroupBy.apply
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core.groupby.DatasetGroupBy.apply

‎doc/conf.py

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"numpydoc",
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"IPython.sphinxext.ipython_directive",
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"IPython.sphinxext.ipython_console_highlighting",
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"sphinx_gallery.gen_gallery",
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"nbsphinx",
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]
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extlinks = {
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"issue": ("https://github.com/pydata/xarray/issues/%s", "GH"),
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"pull": ("https://github.com/pydata/xarray/pull/%s", "PR"),
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}
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sphinx_gallery_conf = {
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"examples_dirs": "gallery",
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"gallery_dirs": "auto_gallery",
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"backreferences_dir": False,
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"expected_failing_examples": list(allowed_failures),
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}
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nbsphinx_timeout = 600
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nbsphinx_execute = "always"
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nbsphinx_prolog = """
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{% set docname = env.doc2path(env.docname, base=None) %}
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You can run this notebook in a `live session <https://mybinder.org/v2/gh/pydata/xarray/doc/examples/master?urlpath=lab/tree/doc/{{ docname }}>`_ |Binder| or view it `on Github <https://github.com/pydata/xarray/blob/master/doc/{{ docname }}>`_.
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.. |Binder| image:: https://mybinder.org/badge.svg
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:target: https://mybinder.org/v2/gh/pydata/xarray/master?urlpath=lab/tree/doc/{{ docname }}
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"""
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autosummary_generate = True
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autodoc_typehints = "none"
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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exclude_patterns = ["_build"]
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exclude_patterns = ["_build", "**.ipynb_checkpoints"]
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# The reST default role (used for this markup: `text`) to use for all
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# documents.

‎doc/data-structures.rst

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:py:class:`pandas.Index` internally to store their values.
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- **non-dimension coordinates** are variables that contain coordinate
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data, but are not a dimension coordinate. They can be multidimensional
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(see :ref:`examples.multidim`), and there is no relationship between the
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name of a non-dimension coordinate and the name(s) of its dimension(s).
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Non-dimension coordinates can be useful for indexing or plotting; otherwise,
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xarray does not make any direct use of the values associated with them.
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They are not used for alignment or automatic indexing, nor are they required
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to match when doing arithmetic
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(see :ref:`coordinates math`).
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data, but are not a dimension coordinate. They can be multidimensional (see
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:ref:`/examples/multidimensional-coords.ipynb`), and there is no
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relationship between the name of a non-dimension coordinate and the
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name(s) of its dimension(s). Non-dimension coordinates can be
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useful for indexing or plotting; otherwise, xarray does not make any
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direct use of the values associated with them. They are not used
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for alignment or automatic indexing, nor are they required to match
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when doing arithmetic (see :ref:`coordinates math`).
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.. note::
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‎doc/examples.rst

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examples/weather-data
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examples/monthly-means
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examples/multidimensional-coords
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auto_gallery/index
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examples/visualization_gallery
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examples/ROMS_ocean_model
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examples/ERA5-GRIB-example

‎doc/examples/ERA5-GRIB-example.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GRIB Data Example "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To read GRIB data, you can use `xarray.load_dataset`. The only extra code you need is to specify the engine as `cfgrib`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ds = xr.tutorial.load_dataset('era5-2mt-2019-03-uk.grib', engine='cfgrib')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's create a simple plot of 2-m air temperature in degrees Celsius:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ds = ds - 273.15\n",
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"ds.t2m[0].plot(cmap=plt.cm.coolwarm)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With CartoPy, we can create a more detailed plot, using built-in shapefiles to help provide geographic context:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cartopy.crs as ccrs\n",
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"import cartopy\n",
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"fig = plt.figure(figsize=(10,10))\n",
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"ax = plt.axes(projection=ccrs.Robinson())\n",
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"ax.coastlines(resolution='10m')\n",
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"plot = ds.t2m[0].plot(cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink':0.6})\n",
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"plt.title('ERA5 - 2m temperature British Isles March 2019')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, we can also pull out a time series for a given location easily:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ds.t2m.sel(longitude=0,latitude=51.5).plot()\n",
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"plt.title('ERA5 - London 2m temperature March 2019')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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