.. currentmodule:: xarray
.. _plotting:

Plotting
========

Introduction
------------

Labeled data enables expressive computations. These same
labels can also be used to easily create informative plots.

Xarray's plotting capabilities are centered around
:py:class:`DataArray` objects.
To plot :py:class:`Dataset` objects
simply access the relevant DataArrays, i.e. ``dset['var1']``.
Dataset specific plotting routines are also available (see :ref:`plot-dataset`).
Here we focus mostly on arrays 2d or larger. If your data fits
nicely into a pandas DataFrame then you're better off using one of the more
developed tools there.

Xarray plotting functionality is a thin wrapper around the popular
`matplotlib <https://matplotlib.org/>`_ library.
Matplotlib syntax and function names were copied as much as possible, which
makes for an easy transition between the two.
Matplotlib must be installed before xarray can plot.

To use xarray's plotting capabilities with time coordinates containing
``cftime.datetime`` objects
`nc-time-axis <https://github.com/SciTools/nc-time-axis>`_ v1.3.0 or later
needs to be installed.

For more extensive plotting applications consider the following projects:

- `Seaborn <https://seaborn.pydata.org/>`_: "provides
  a high-level interface for drawing attractive statistical graphics."
  Integrates well with pandas.

- `HoloViews <https://holoviews.org/>`_
  and `GeoViews <https://geoviews.org/>`_: "Composable, declarative
  data structures for building even complex visualizations easily." Includes
  native support for xarray objects.

- `hvplot <https://hvplot.pyviz.org/>`_: ``hvplot`` makes it very easy to produce
  dynamic plots (backed by ``Holoviews`` or ``Geoviews``) by adding a ``hvplot``
  accessor to DataArrays.

- `Cartopy <https://cartopy.readthedocs.io/stable/>`_: Provides cartographic
  tools.

Imports
~~~~~~~

.. jupyter-execute::
    :hide-code:

    # Use defaults so we don't get gridlines in generated docs
    import matplotlib as mpl

    mpl.rcdefaults()

The following imports are necessary for all of the examples.

.. jupyter-execute::

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import xarray as xr

For these examples we'll use the North American air temperature dataset.

.. jupyter-execute::

    airtemps = xr.tutorial.open_dataset("air_temperature")
    airtemps

.. jupyter-execute::

    # Convert to celsius
    air = airtemps.air - 273.15

    # copy attributes to get nice figure labels and change Kelvin to Celsius
    air.attrs = airtemps.air.attrs
    air.attrs["units"] = "deg C"

.. note::
   Until :issue:`1614` is solved, you might need to copy over the metadata in ``attrs`` to get informative figure labels (as was done above).


DataArrays
----------

One Dimension
~~~~~~~~~~~~~

================
 Simple Example
================

The simplest way to make a plot is to call the :py:func:`DataArray.plot()` method.

.. jupyter-execute::

    air1d = air.isel(lat=10, lon=10)
    air1d.plot();

Xarray uses the coordinate name along with metadata ``attrs.long_name``,
``attrs.standard_name``, ``DataArray.name`` and ``attrs.units`` (if available)
to label the axes.
The names ``long_name``, ``standard_name`` and ``units`` are copied from the
`CF-conventions spec <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch03s03.html>`_.
When choosing names, the order of precedence is ``long_name``, ``standard_name`` and finally ``DataArray.name``.
The y-axis label in the above plot was constructed from the ``long_name`` and ``units`` attributes of ``air1d``.

.. jupyter-execute::

    air1d.attrs

======================
 Additional Arguments
======================

Additional arguments are passed directly to the matplotlib function which
does the work.
For example, :py:func:`xarray.plot.line` calls
matplotlib.pyplot.plot_ passing in the index and the array values as x and y, respectively.
So to make a line plot with blue triangles a matplotlib format string
can be used:

.. _matplotlib.pyplot.plot: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot

.. jupyter-execute::

    air1d[:200].plot.line("b-^");

.. note::
    Not all xarray plotting methods support passing positional arguments
    to the wrapped matplotlib functions, but they do all
    support keyword arguments.

Keyword arguments work the same way, and are more explicit.

.. jupyter-execute::

    air1d[:200].plot.line(color="purple", marker="o");

=========================
 Adding to Existing Axis
=========================

To add the plot to an existing axis pass in the axis as a keyword argument
``ax``. This works for all xarray plotting methods.
In this example ``axs`` is an array consisting of the left and right
axes created by ``plt.subplots``.

.. jupyter-execute::

    fig, axs = plt.subplots(ncols=2)

    print(axs)

    air1d.plot(ax=axs[0])
    air1d.plot.hist(ax=axs[1]);

On the right is a histogram created by :py:func:`xarray.plot.hist`.

.. _plotting.figsize:

=============================
 Controlling the figure size
=============================

You can pass a ``figsize`` argument to all xarray's plotting methods to
control the figure size. For convenience, xarray's plotting methods also
support the ``aspect`` and ``size`` arguments which control the size of the
resulting image via the formula ``figsize = (aspect * size, size)``:

.. jupyter-execute::

    air1d.plot(aspect=2, size=3);

This feature also works with :ref:`plotting.faceting`. For facet plots,
``size`` and ``aspect`` refer to a single panel (so that ``aspect * size``
gives the width of each facet in inches), while ``figsize`` refers to the
entire figure (as for matplotlib's ``figsize`` argument).

.. note::

    If ``figsize`` or ``size`` are used, a new figure is created,
    so this is mutually exclusive with the ``ax`` argument.

.. note::

    The convention used by xarray (``figsize = (aspect * size, size)``) is
    borrowed from seaborn: it is therefore `not equivalent to matplotlib's`_.

.. _not equivalent to matplotlib's: https://github.com/mwaskom/seaborn/issues/746


.. _plotting.multiplelines:

=========================
 Determine x-axis values
=========================

Per default dimension coordinates are used for the x-axis (here the time coordinates).
However, you can also use non-dimension coordinates, MultiIndex levels, and dimensions
without coordinates along the x-axis. To illustrate this, let's calculate a 'decimal day' (epoch)
from the time and assign it as a non-dimension coordinate:

.. jupyter-execute::

    decimal_day = (air1d.time - air1d.time[0]) / pd.Timedelta("1d")
    air1d_multi = air1d.assign_coords(decimal_day=("time", decimal_day.data))
    air1d_multi

To use ``'decimal_day'`` as x coordinate it must be explicitly specified:

.. jupyter-execute::

    air1d_multi.plot(x="decimal_day");

Creating a new MultiIndex named ``'date'`` from ``'time'`` and ``'decimal_day'``,
it is also possible to use a MultiIndex level as x-axis:

.. jupyter-execute::

    air1d_multi = air1d_multi.set_index(date=("time", "decimal_day"))
    air1d_multi.plot(x="decimal_day");

Finally, if a dataset does not have any coordinates it enumerates all data points:

.. jupyter-execute::

    air1d_multi = air1d_multi.drop_vars(["date", "time", "decimal_day"])
    air1d_multi.plot();

The same applies to 2D plots below.

====================================================
 Multiple lines showing variation along a dimension
====================================================

It is possible to make line plots of two-dimensional data by calling :py:func:`xarray.plot.line`
with appropriate arguments. Consider the 3D variable ``air`` defined above. We can use line
plots to check the variation of air temperature at three different latitudes along a longitude line:

.. jupyter-execute::

    air.isel(lon=10, lat=[19, 21, 22]).plot.line(x="time");

It is required to explicitly specify either

1. ``x``: the dimension to be used for the x-axis, or
2. ``hue``: the dimension you want to represent by multiple lines.

Thus, we could have made the previous plot by specifying ``hue='lat'`` instead of ``x='time'``.
If required, the automatic legend can be turned off using ``add_legend=False``. Alternatively,
``hue`` can be passed directly to :py:func:`xarray.plot.line` as ``air.isel(lon=10, lat=[19,21,22]).plot.line(hue='lat')``.


========================
 Dimension along y-axis
========================

It is also possible to make line plots such that the data are on the x-axis and a dimension is on the y-axis. This can be done by specifying the appropriate ``y`` keyword argument.

.. jupyter-execute::

    air.isel(time=10, lon=[10, 11]).plot(y="lat", hue="lon");

============
 Step plots
============

As an alternative, also a step plot similar to matplotlib's ``plt.step`` can be
made using 1D data.

.. jupyter-execute::

    air1d[:20].plot.step(where="mid");

The argument ``where`` defines where the steps should be placed, options are
``'pre'`` (default), ``'post'``, and ``'mid'``. This is particularly handy
when plotting data grouped with :py:meth:`Dataset.groupby_bins`.

.. jupyter-execute::

    air_grp = air.mean(["time", "lon"]).groupby_bins("lat", [0, 23.5, 66.5, 90])
    air_mean = air_grp.mean()
    air_std = air_grp.std()
    air_mean.plot.step()
    (air_mean + air_std).plot.step(ls=":")
    (air_mean - air_std).plot.step(ls=":")
    plt.ylim(-20, 30)
    plt.title("Zonal mean temperature");

In this case, the actual boundaries of the bins are used and the ``where`` argument
is ignored.


Other axes kwargs
~~~~~~~~~~~~~~~~~


The keyword arguments ``xincrease`` and ``yincrease`` let you control the axes direction.

.. jupyter-execute::

    air.isel(time=10, lon=[10, 11]).plot.line(
        y="lat", hue="lon", xincrease=False, yincrease=False
    );

In addition, one can use ``xscale, yscale`` to set axes scaling;
``xticks, yticks`` to set axes ticks and ``xlim, ylim`` to set axes limits.
These accept the same values as the matplotlib methods ``ax.set_(x,y)scale()``,
``ax.set_(x,y)ticks()``, ``ax.set_(x,y)lim()``, respectively.


Two Dimensions
~~~~~~~~~~~~~~

================
 Simple Example
================

The default method :py:meth:`DataArray.plot` calls :py:func:`xarray.plot.pcolormesh`
by default when the data is two-dimensional.

.. jupyter-execute::

    air2d = air.isel(time=500)
    air2d.plot();

All 2d plots in xarray allow the use of the keyword arguments ``yincrease``
and ``xincrease``.

.. jupyter-execute::

    air2d.plot(yincrease=False);

.. note::

    We use :py:func:`xarray.plot.pcolormesh` as the default two-dimensional plot
    method because it is more flexible than :py:func:`xarray.plot.imshow`.
    However, for large arrays, ``imshow`` can be much faster than ``pcolormesh``.
    If speed is important to you and you are plotting a regular mesh, consider
    using ``imshow``.

================
 Missing Values
================

Xarray plots data with :ref:`missing_values`.

.. jupyter-execute::

    bad_air2d = air2d.copy()
    bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan
    bad_air2d.plot();

========================
 Nonuniform Coordinates
========================

It's not necessary for the coordinates to be evenly spaced. Both
:py:func:`xarray.plot.pcolormesh` (default) and :py:func:`xarray.plot.contourf` can
produce plots with nonuniform coordinates.

.. jupyter-execute::

    b = air2d.copy()
    # Apply a nonlinear transformation to one of the coords
    b.coords["lat"] = np.log(b.coords["lat"])

    b.plot();

====================
 Other types of plot
====================

There are several other options for plotting 2D data.

Contour plot using :py:meth:`DataArray.plot.contour()`

.. jupyter-execute::

    air2d.plot.contour();

Filled contour plot using :py:meth:`DataArray.plot.contourf()`

.. jupyter-execute::

    air2d.plot.contourf();

Surface plot using :py:meth:`DataArray.plot.surface()`

.. jupyter-execute::

    # transpose just to make the example look a bit nicer
    air2d.T.plot.surface();

====================
 Calling Matplotlib
====================

Since this is a thin wrapper around matplotlib, all the functionality of
matplotlib is available.

.. jupyter-execute::

    air2d.plot(cmap=plt.cm.Blues)
    plt.title("These colors prove North America\nhas fallen in the ocean")
    plt.ylabel("latitude")
    plt.xlabel("longitude");

.. note::

    Xarray methods update label information and generally play around with the
    axes. So any kind of updates to the plot
    should be done *after* the call to the xarray's plot.
    In the example below, ``plt.xlabel`` effectively does nothing, since
    ``d_ylog.plot()`` updates the xlabel.

    .. jupyter-execute::

        plt.xlabel("Never gonna see this.")
        air2d.plot();

===========
 Colormaps
===========

Xarray borrows logic from Seaborn to infer what kind of color map to use. For
example, consider the original data in Kelvins rather than Celsius:

.. jupyter-execute::

    airtemps.air.isel(time=0).plot();

The Celsius data contain 0, so a diverging color map was used. The
Kelvins do not have 0, so the default color map was used.

.. _robust-plotting:

========
 Robust
========

Outliers often have an extreme effect on the output of the plot.
Here we add two bad data points. This affects the color scale,
washing out the plot.

.. jupyter-execute::

    air_outliers = airtemps.air.isel(time=0).copy()
    air_outliers[0, 0] = 100
    air_outliers[-1, -1] = 400

    air_outliers.plot();

This plot shows that we have outliers. The easy way to visualize
the data without the outliers is to pass the parameter
``robust=True``.
This will use the 2nd and 98th
percentiles of the data to compute the color limits.

.. jupyter-execute::

    air_outliers.plot(robust=True);

Observe that the ranges of the color bar have changed. The arrows on the
color bar indicate
that the colors include data points outside the bounds.

====================
 Discrete Colormaps
====================

It is often useful, when visualizing 2d data, to use a discrete colormap,
rather than the default continuous colormaps that matplotlib uses. The
``levels`` keyword argument can be used to generate plots with discrete
colormaps. For example, to make a plot with 8 discrete color intervals:

.. jupyter-execute::

    air2d.plot(levels=8);

It is also possible to use a list of levels to specify the boundaries of the
discrete colormap:

.. jupyter-execute::

    air2d.plot(levels=[0, 12, 18, 30]);

You can also specify a list of discrete colors through the ``colors`` argument:

.. jupyter-execute::

    flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
    air2d.plot(levels=[0, 12, 18, 30], colors=flatui);

Finally, if you have `Seaborn <https://seaborn.pydata.org/>`_
installed, you can also specify a seaborn color palette to the ``cmap``
argument. Note that ``levels`` *must* be specified with seaborn color palettes
if using ``imshow`` or ``pcolormesh`` (but not with ``contour`` or ``contourf``,
since levels are chosen automatically).

.. jupyter-execute::

    air2d.plot(levels=10, cmap="husl");

.. _plotting.faceting:

Faceting
~~~~~~~~

Faceting here refers to splitting an array along one or two dimensions and
plotting each group.
Xarray's basic plotting is useful for plotting two dimensional arrays. What
about three or four dimensional arrays? That's where facets become helpful.
The general approach to plotting here is called “small multiples”, where the
same kind of plot is repeated multiple times, and the specific use of small
multiples to display the same relationship conditioned on one or more other
variables is often called a “trellis plot”.

Consider the temperature data set. There are 4 observations per day for two
years which makes for 2920 values along the time dimension.
One way to visualize this data is to make a
separate plot for each time period.

The faceted dimension should not have too many values;
faceting on the time dimension will produce 2920 plots. That's
too much to be helpful. To handle this situation try performing
an operation that reduces the size of the data in some way. For example, we
could compute the average air temperature for each month and reduce the
size of this dimension from 2920 -> 12. A simpler way is
to just take a slice on that dimension.
So let's use a slice to pick 6 times throughout the first year.

.. jupyter-execute::

    t = air.isel(time=slice(0, 365 * 4, 250))
    t.coords

================
 Simple Example
================

The easiest way to create faceted plots is to pass in ``row`` or ``col``
arguments to the xarray plotting methods/functions. This returns a
:py:class:`xarray.plot.FacetGrid` object.

.. jupyter-execute::

    g_simple = t.plot(x="lon", y="lat", col="time", col_wrap=3);

Faceting also works for line plots.

.. jupyter-execute::

    g_simple_line = t.isel(lat=slice(0, None, 4)).plot(
        x="lon", hue="lat", col="time", col_wrap=3
    );

===============
 4 dimensional
===============

For 4 dimensional arrays we can use the rows and columns of the grids.
Here we create a 4 dimensional array by taking the original data and adding
a fixed amount. Now we can see how the temperature maps would compare if
one were much hotter.

.. jupyter-execute::

    t2 = t.isel(time=slice(0, 2))
    t4d = xr.concat([t2, t2 + 40], pd.Index(["normal", "hot"], name="fourth_dim"))
    # This is a 4d array
    t4d.coords

    t4d.plot(x="lon", y="lat", col="time", row="fourth_dim");

================
 Other features
================

Faceted plotting supports other arguments common to xarray 2d plots.

.. jupyter-execute::

    hasoutliers = t.isel(time=slice(0, 5)).copy()
    hasoutliers[0, 0, 0] = -100
    hasoutliers[-1, -1, -1] = 400

    g = hasoutliers.plot.pcolormesh(
        x="lon",
        y="lat",
        col="time",
        col_wrap=3,
        robust=True,
        cmap="viridis",
        cbar_kwargs={"label": "this has outliers"},
    )

===================
 FacetGrid Objects
===================

The object returned, ``g`` in the above examples, is a :py:class:`~xarray.plot.FacetGrid` object
that links a :py:class:`DataArray` to a matplotlib figure with a particular structure.
This object can be used to control the behavior of the multiple plots.
It borrows an API and code from `Seaborn's FacetGrid
<https://seaborn.pydata.org/tutorial/axis_grids.html>`_.
The structure is contained within the ``axs`` and ``name_dicts``
attributes, both 2d NumPy object arrays.

.. jupyter-execute::

    g.axs

.. jupyter-execute::

    g.name_dicts

It's possible to select the :py:class:`xarray.DataArray` or
:py:class:`xarray.Dataset` corresponding to the FacetGrid through the
``name_dicts``.

.. jupyter-execute::

    g.data.loc[g.name_dicts[0, 0]]

Here is an example of using the lower level API and then modifying the axes after
they have been plotted.

.. jupyter-execute::


    g = t.plot.imshow(x="lon", y="lat", col="time", col_wrap=3, robust=True)

    for i, ax in enumerate(g.axs.flat):
        ax.set_title("Air Temperature %d" % i)

    bottomright = g.axs[-1, -1]
    bottomright.annotate("bottom right", (240, 40));


:py:class:`~xarray.plot.FacetGrid` objects have methods that let you customize the automatically generated
axis labels, axis ticks and plot titles. See :py:meth:`~xarray.plot.FacetGrid.set_titles`,
:py:meth:`~xarray.plot.FacetGrid.set_xlabels`, :py:meth:`~xarray.plot.FacetGrid.set_ylabels` and
:py:meth:`~xarray.plot.FacetGrid.set_ticks` for more information.
Plotting functions can be applied to each subset of the data by calling
:py:meth:`~xarray.plot.FacetGrid.map_dataarray` or to each subplot by calling :py:meth:`~xarray.plot.FacetGrid.map`.

TODO: add an example of using the ``map`` method to plot dataset variables
(e.g., with ``plt.quiver``).

.. _plot-dataset:

Datasets
--------

Xarray has limited support for plotting Dataset variables against each other.
Consider this dataset

.. jupyter-execute::

    ds = xr.tutorial.scatter_example_dataset(seed=42)
    ds


Scatter
~~~~~~~

Let's plot the ``A`` DataArray as a function of the ``y`` coord

.. jupyter-execute::

    with xr.set_options(display_expand_data=False):
        display(ds.A)

.. jupyter-execute::

    ds.A.plot.scatter(x="y");

Same plot can be displayed using the dataset:

.. jupyter-execute::

    ds.plot.scatter(x="y", y="A");

Now suppose we want to scatter the ``A`` DataArray against the ``B`` DataArray

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B");

The ``hue`` kwarg lets you vary the color by variable value

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", hue="w");

You can force a legend instead of a colorbar by setting ``add_legend=True, add_colorbar=False``.

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", hue="w", add_legend=True, add_colorbar=False);

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", hue="w", add_legend=False, add_colorbar=True);

The ``markersize`` kwarg lets you vary the point's size by variable value.
You can additionally pass ``size_norm`` to control how the variable's values are mapped to point sizes.

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", hue="y", markersize="z");

The ``z`` kwarg lets you plot the data along the z-axis as well.

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x");

Faceting is also possible

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", hue="y", markersize="x", row="x", col="w");

And adding the z-axis

.. jupyter-execute::

    ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x", row="x", col="w");

For more advanced scatter plots, we recommend converting the relevant data variables
to a pandas DataFrame and using the extensive plotting capabilities of ``seaborn``.

Quiver
~~~~~~

Visualizing vector fields is supported with quiver plots:

.. jupyter-execute::

    ds.isel(w=1, z=1).plot.quiver(x="x", y="y", u="A", v="B");


where ``u`` and ``v`` denote the x and y direction components of the arrow vectors. Again, faceting is also possible:

.. jupyter-execute::

    ds.plot.quiver(x="x", y="y", u="A", v="B", col="w", row="z", scale=4);

``scale`` is required for faceted quiver plots.
The scale determines the number of data units per arrow length unit, i.e. a smaller scale parameter makes the arrow longer.

Streamplot
~~~~~~~~~~

Visualizing vector fields is also supported with streamline plots:

.. jupyter-execute::

    ds.isel(w=1, z=1).plot.streamplot(x="x", y="y", u="A", v="B");


where ``u`` and ``v`` denote the x and y direction components of the vectors tangent to the streamlines.
Again, faceting is also possible:

.. jupyter-execute::

    ds.plot.streamplot(x="x", y="y", u="A", v="B", col="w", row="z");

.. _plot-maps:

Maps
----

To follow this section you'll need to have Cartopy installed and working.

This script will plot the air temperature on a map.

.. jupyter-execute::
    :stderr:

    air = xr.tutorial.open_dataset("air_temperature").air

    p = air.isel(time=0).plot(
        subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor="gray"),
        transform=ccrs.PlateCarree(),
    )
    p.axes.set_global()

    p.axes.coastlines();

When faceting on maps, the projection can be transferred to the ``plot``
function using the ``subplot_kws`` keyword. The axes for the subplots created
by faceting are accessible in the object returned by ``plot``:

.. jupyter-execute::

    p = air.isel(time=[0, 4]).plot(
        transform=ccrs.PlateCarree(),
        col="time",
        subplot_kws={"projection": ccrs.Orthographic(-80, 35)},
    )
    for ax in p.axs.flat:
        ax.coastlines()
        ax.gridlines()


Details
-------

Ways to Use
~~~~~~~~~~~

There are three ways to use the xarray plotting functionality:

1. Use ``plot`` as a convenience method for a DataArray.

2. Access a specific plotting method from the ``plot`` attribute of a
   DataArray.

3. Directly from the xarray plot submodule.

These are provided for user convenience; they all call the same code.

.. jupyter-execute::

    da = xr.DataArray(range(5))
    fig, axs = plt.subplots(ncols=2, nrows=2)
    da.plot(ax=axs[0, 0])
    da.plot.line(ax=axs[0, 1])
    xr.plot.plot(da, ax=axs[1, 0])
    xr.plot.line(da, ax=axs[1, 1]);

Here the output is the same. Since the data is 1 dimensional the line plot
was used.

The convenience method :py:meth:`xarray.DataArray.plot` dispatches to an appropriate
plotting function based on the dimensions of the ``DataArray`` and whether
the coordinates are sorted and uniformly spaced. This table
describes what gets plotted:

=============== ===========================
Dimensions      Plotting function
--------------- ---------------------------
1               :py:func:`xarray.plot.line`
2               :py:func:`xarray.plot.pcolormesh`
Anything else   :py:func:`xarray.plot.hist`
=============== ===========================

Coordinates
~~~~~~~~~~~

If you'd like to find out what's really going on in the coordinate system,
read on.

.. jupyter-execute::

    a0 = xr.DataArray(np.zeros((4, 3, 2)), dims=("y", "x", "z"), name="temperature")
    a0[0, 0, 0] = 1
    a = a0.isel(z=0)
    a

The plot will produce an image corresponding to the values of the array.
Hence the top left pixel will be a different color than the others.
Before reading on, you may want to look at the coordinates and
think carefully about what the limits, labels, and orientation for
each of the axes should be.

.. jupyter-execute::

    a.plot();

It may seem strange that
the values on the y axis are decreasing with -0.5 on the top. This is because
the pixels are centered over their coordinates, and the
axis labels and ranges correspond to the values of the
coordinates.

Multidimensional coordinates
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

See also: :ref:`/examples/multidimensional-coords.ipynb`.

You can plot irregular grids defined by multidimensional coordinates with
xarray, but you'll have to tell the plot function to use these coordinates
instead of the default ones:

.. jupyter-execute::

    lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4))
    lon += lat / 10
    lat += lon / 10
    da = xr.DataArray(
        np.arange(20).reshape(4, 5),
        dims=["y", "x"],
        coords={"lat": (("y", "x"), lat), "lon": (("y", "x"), lon)},
    )

    da.plot.pcolormesh(x="lon", y="lat");

Note that in this case, xarray still follows the pixel centered convention.
This might be undesirable in some cases, for example when your data is defined
on a polar projection (:issue:`781`). This is why the default is to not follow
this convention when plotting on a map:

.. jupyter-execute::
    :stderr:

    ax = plt.subplot(projection=ccrs.PlateCarree())
    da.plot.pcolormesh(x="lon", y="lat", ax=ax)
    ax.scatter(lon, lat, transform=ccrs.PlateCarree())
    ax.coastlines()
    ax.gridlines(draw_labels=True);

You can however decide to infer the cell boundaries and use the
``infer_intervals`` keyword:

.. jupyter-execute::

    ax = plt.subplot(projection=ccrs.PlateCarree())
    da.plot.pcolormesh(x="lon", y="lat", ax=ax, infer_intervals=True)
    ax.scatter(lon, lat, transform=ccrs.PlateCarree())
    ax.coastlines()
    ax.gridlines(draw_labels=True);

.. note::
    The data model of xarray does not support datasets with `cell boundaries`_
    yet. If you want to use these coordinates, you'll have to make the plots
    outside the xarray framework.

.. _cell boundaries: https://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#cell-boundaries

One can also make line plots with multidimensional coordinates. In this case, ``hue`` must be a dimension name, not a coordinate name.

.. jupyter-execute::

    f, ax = plt.subplots(2, 1)
    da.plot.line(x="lon", hue="y", ax=ax[0])
    da.plot.line(x="lon", hue="x", ax=ax[1]);
