一句代码！将气象数据可视化为生动的GIF动画

!pip install geogif -i https://pypi.mirrors.ustc.edu.cn/simple/

Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Collecting geogif
  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/0f/49/dfe35363818fd47372897929828f3cc47e951046582171caa78a3a3ef5ad/geogif-0.2-py3-none-any.whl (8.8 kB)
Requirement already satisfied: xarray>=0.18 in /opt/conda/lib/python3.9/site-packages (from geogif) (2024.2.0)
Collecting Pillow<11.0,>=10.1
  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/bc/a8/8655557c9c7202b8abbd001f61ff36711cefaf750debcaa1c24d154ef602/pillow-10.4.0-cp39-cp39-manylinux_2_28_x86_64.whl (4.5 MB)
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Installing collected packages: Pillow, geogif
  Attempting uninstall: Pillow
    Found existing installation: Pillow 9.4.0
    Uninstalling Pillow-9.4.0:
      Successfully uninstalled Pillow-9.4.0
[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
s2cloudless 1.7.2 requires opencv-python-headless, which is not installed.
eo-learn 1.5.3 requires opencv-python-headless, which is not installed.
eo-learn-mask 1.4.2 requires opencv-python-headless, which is not installed.
pyninjotiff 0.4.0 requires dask[array]<2022.0.0,>=2021.9.1, but you have dask 2024.2.0 which is incompatible.
pyninjotiff 0.4.0 requires xarray<0.20.0,>=0.19.0, but you have xarray 2024.2.0 which is incompatible.
pycaret 2.2.3 requires scikit-learn==0.23.2, but you have scikit-learn 0.24.2 which is incompatible.
eo-learn 1.5.3 requires rasterio>=1.3.8, but you have rasterio 1.3.6 which is incompatible.
basemap 1.3.6 requires matplotlib<3.7,>=1.5; python_version >= "3.5", but you have matplotlib 3.8.3 which is incompatible.
basemap 1.3.6 requires numpy<1.25,>=1.22; python_version >= "3.8", but you have numpy 1.26.4 which is incompatible.[0m[31m
[0mSuccessfully installed Pillow-10.4.0 geogif-0.2

import xarray as xr
ds = xr.open_dataset('/home/mw/input/cru3546/cru_ts4.07.2021.2022.pre.dat.nc')

<xarray.Dataset> Size: 75MB
Dimensions:  (lon: 720, lat: 360, time: 24)
Coordinates:
• lon      (lon) float32 3kB -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8• lat      (lat) float32 1kB -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75• time     (time) datetime64[ns] 192B 2021-01-16 2021-02-15 ... 2022-12-16
Data variables:
pre      (time, lat, lon) float32 25MB ...
stn      (time, lat, lon) float64 50MB ...
Attributes:
Conventions:  CF-1.4
title:        CRU TS4.07 Precipitation
institution:  Data held at British Atmospheric Data Centre, RAL, UK.
source:       Run ID = 2304141047. Data generated from:pre.2304141039.dtb
history:      Fri 14 Apr 12:49:03 BST 2023 : User f098 : Program makegrid...
references:   Information on the data is available at http://badc.nerc.ac...
comment:      Access to these data is available to any registered CEDA user.
contact:      support@ceda.ac.uk

p = ds.pre
p.plot.imshow(col="time", col_wrap=5, robust=True)

<xarray.plot.facetgrid.FacetGrid at 0x7f103a57b0d0>

help(gif)

Help on function gif in module geogif.gif:

gif(arr: 'xr.DataArray', *, to: 'str | Path | BinaryIO | None' = None, fps: 'int' = 16, robust: 'bool' = True, vmin: 'float | None' = None, vmax: 'float | None' = None, cmap: 'str | matplotlib.colors.Colormap | None' = None, date_format: 'str | None' = '%Y-%m-%d', date_position: "Literal['ul', 'ur', 'll', 'lr']" = 'ul', date_color: 'tuple[int, int, int]' = (255, 255, 255), date_bg: 'tuple[int, int, int] | None' = (0, 0, 0), date_size: 'int | float' = 0.15) -> 'IPython.display.Image | None'
    Render a `~xarray.DataArray` timestack (``time``, ``band``, ``y``, ``x``) into a GIF.
    
    If the `~xarray.DataArray` contains a `dask.array.Array`, use `dgif` (delayed-GIF) instead.
    
    The `~xarray.DataArray` must have 1 or 3 bands.
    
    Unless ``date_format=None``, a small timestamp will be printed onto each frame of the animation.
    You can control the position and styling of this with the ``date_position``, ``date_color``, and
    ``date_bg`` arguments.
    
    Parameters
    ----------
    arr:
        Time-stacked array to animate. Must have 3 or 4 dimensions, which are assumed to be
        in the order ``time``, [optional ``band``], ``y``, ``x``.
    to:
        Where to write the GIF. If None (default), an `IPython.display.Image` is returned,
        which will display the GIF in your Jupyter notebook.
    fps:
        Frames per second
    robust:
        Calculate ``vmin`` and ``vmax`` from the 2nd and 98th percentiles of the data
        (default True)
    vmin:
        Value in the data to map to 0 (black). If None (default), it's calculated
        from the minimum value of the data or the 2nd percentile, depending on ``robust``.
    vmax:
        Value in the data to map to 255 (white). If None (default), it's calculated
        from the maximum value of the data or the 98nd percentile, depending on ``robust``.
    cmap:
        Colormap to use for single-band data. Can be a
        :doc:`matplotlib colormap name <gallery/color/colormap_reference>` as a string,
        or a `~matplotlib.colors.Colormap` object for custom colormapping.
    
        If None (default), the default matplotlib colormap (usually ``viridis``) will automatically
        be used for 1-band data. Setting a colormap for multi-band data is an error.
    date_format:
        Date format string (like ``"%Y-%m-%d"``, the default) used to format the timestamps
        written onto each frame of the animation. If the coordinates for axis 0 of the
        `~xarray.DataArray` are not timestamps or timedeltas, you must explicitly pass
        ``date_format=None``.
    
        See the `Python string format doc
        <https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior>`__
        for details.
    date_position:
        Where to print the timestamp on each frame.
        One of ``"ul"`` (upper-left), ``"ur"`` (upper-right), ``"ll"`` (lower-left),
        ``"lr"`` (lower-right), default ``"ul"``.
    date_color:
        Color for the timestamp font, as an RGB 3-tuple. Default: ``(255, 255, 255)``
        (white).
    date_bg:
        Fill color to draw behind the timestamp (for legibility), as an RGB 3-tuple.
        Default: ``(0, 0, 0)`` (black). Set to None to disable.
    date_size:
        If a float, make the label this fraction of the width of the image.
        If an int, use this absolute font size for the label.
        Default: 0.15 (so the label is 15% of the image width).
    
        Note that if Pillow does not have FreeType support, the font size
        cannot be adjusted, and the text will be whatever size Pillow's
        default basic font is (usually rather small).
    
    Returns
    -------
    IPython.display.Image or None
        If ``to`` is None, returns an `IPython.display.Image`, which will display the
        GIF in a Jupyter Notebook. (You can also get the GIF data as bytes from the Image's
        ``.data`` attribute.)
    
        Otherwise, returns None, and the GIF data is written to ``to``.
    
    Example
    -------
    >>> # Generate a GIF and show it in your notebook:
    >>> stackstac.gif(arr, date_format="Year: %Y")
    
    >>> # Write the GIF to a file, with no timestamp printed:
    >>> stackstac.gif(arr, to="animation.gif", fps=24, date_format=None)
    
    >>> # Show a colormapped GIF of single-band data in your notebook,
    >>> # with the timestamp font in black and no background behind it:
    >>> stackstac.gif(
    ...     arr.sel(band="ndvi"), cmap="YlGn", date_color=(0, 0, 0), date_bg=None
    ... )

from geogif import gif, dgif
gif(p)

pd = p.isel(lat=slice(None, None, -1))
pd

<xarray.DataArray 'pre' (time: 24, lat: 360, lon: 720)> Size: 25MB
array([[[nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan],
        ...,
        [nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan]],

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]],

   ...,

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]],

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:

pd1 = p[:, ::-1, :]
pd1

<xarray.DataArray 'pre' (time: 24, lat: 360, lon: 720)> Size: 25MB
array([[[nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan],
        ...,
        [nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan]],

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]],

   ...,

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]],

   [[nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan],
    ...,
    [nan, nan, ..., nan, nan],
    [nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:

from geogif import gif, dgif
gif(pd,date_format=False)

gif(pd1,fps=5)

gif(pd,fps=5, cmap="Greens")