Animations with xmovie

This tutorial demonstrates how to make an animation with Cartopy and xmovie. See https://github.com/jbusecke/xmovie for more details on xmovie package.

[1]:

import intake
import xarray as xr
import cmocean as cm
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from xmovie import Movie

import warnings
warnings.filterwarnings(
    action="ignore",
    category=UserWarning,
    message=r"No `(vmin|vmax)` provided. Data limits are calculated from input. Depending on the input this can take long. Pass `\1` to avoid this step"
)

%matplotlib inline

[2]:

from dask.distributed import Client
client = Client(threads_per_worker = 1)
client

[2]:

Client

Client-2f1b04dc-68cb-11ef-8403-000007dafe80

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: /proxy/8787/status

Cluster Info

Load the ACCESS-NRI catalog

[3]:

catalog = intake.cat.access_nri

We use the sea-surface temperature (SST) from a 0.25°-resolution experiment as our sample data. These maps should work with any 2D data.

[4]:

experiment = "025deg_jra55_iaf_omip2_cycle6"
ds = catalog[experiment].search(variable="sst", frequency="1mon").to_dask()

# convert from degrees K to degrees C
sst = ds.sst - 273.15

# slice just few months for the tutorial
sst = sst.isel(time=slice(0, 11))
sst

[4]:

<xarray.DataArray 'sst' (time: 11, yt_ocean: 1080, xt_ocean: 1440)> Size: 68MB
dask.array<getitem, shape=(11, 1080, 1440), dtype=float32, chunksize=(1, 216, 240), chunktype=numpy.ndarray>
Coordinates:
  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88
  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95
  * time      (time) datetime64[ns] 88B 1958-01-14T12:00:00 ... 1958-11-14

We load the unmasked coordinates and assign them to the dataset; see the tutorial on maps with Cartopy for more details.

[5]:

# these lon/lat arrays are NOT masked.
# NB. This is a hack. We would like to improve this.
geolon_t = xr.open_dataset("/g/data/ik11/grids/ocean_grid_025.nc").geolon_t
geolat_t = xr.open_dataset("/g/data/ik11/grids/ocean_grid_025.nc").geolat_t

sst = sst.assign_coords({"geolat_t": geolat_t, "geolon_t": geolon_t})

We create a custom function that plots every frame. See https://xmovie.readthedocs.io/en/latest/examples/quickstart.html#Modifying-plots for more details on the argument structure requirements of this plotting function that we will provide to xmovie.Movie method.

[14]:

def plot_sst(da, fig, timestamp, *args, **kwargs):

    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson(central_longitude=-100))

    da.isel(time=timestamp).plot.contourf(
        ax=ax,
        x="geolon_t",
        y="geolat_t",
        levels=33,
        vmin=-2,
        vmax=30,
        extend="both",
        cmap=cm.cm.thermal,
        transform=ccrs.PlateCarree(),
        cbar_kwargs={"label": "SST (°C)", "fraction": 0.03, "aspect": 15, "shrink": 0.7},
    )

    ax.coastlines()
    ax.set_title(da['time'].dt.strftime('%d-%m-%Y')[timestamp].item())

    return ax, None

Now we create a figure and the movie using xmovie.Movie method.

[17]:

fig = plt.figure(figsize=(8, 6))

mov = Movie(sst, plot_sst);

<Figure size 800x600 with 0 Axes>

Let’s preview how one of the frames look; choose here frame 2.

[16]:

mov.preview(2)
../_images/Tutorials_Animations_with_xmovie_14_0.png

And now make the movie. We can save as .gif or .mp4.

[18]:

mov.save('movie_sst.gif', progress=True)

Movie created at movie_sst.mp4
GIF created at movie_sst.gif
movie_sst.gif