Customizing plots

Load libraries:

import xarray
import pyku
import pyku.analyse as analyse
import matplotlib.pyplot as plt

import test data, make it smaller by using a lower resolution and compute to load into memory for speed

ds = pyku.resources.get_test_data('hourly-tas').pyku.project('HYR-GER-LAEA-12.5').compute()

/home/runner/work/pyku/pyku/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

pyku provides default plots, but these are customizable. You can pass the parameters directly in the plot functions. Titles can be given by setting the attribute label.

Cartopy

cartopy gridline options can be passed directly:

analyse.n_maps(
    ds.isel(time=0).assign_attrs({'label': 'First time step'}),
    ds.isel(time=0).assign_attrs({'label': 'Second time step'}),
    var='tas',
    crs='HYR-GER-LAEA-5',
    size_inches=(10, 7),
   xlabel_style = {'size': 15, 'color': 'red'}
)

<Figure size 640x480 with 0 Axes>

You can always get the documentation inline in jupyter notebooks or ipython with analyse.n_maps?. The full list of cartopy gridline parameters that can be passed can be found in the geoaxes.GeoAxes.gridlines documentation

Matplotlib

With matplotlib parameters, these can be directly passed to the function, or set afterward like so:

analyse.diurnal_cycle(
    ds.assign_attrs({'label': 'Your title'}),
    var='tas'
)
plt.xticks(fontsize=14, color='red', rotation=45)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23]),
 [Text(0, 0, '0'),
  Text(1, 0, '1'),
  Text(2, 0, '2'),
  Text(3, 0, '3'),
  Text(4, 0, '4'),
  Text(5, 0, '5'),
  Text(6, 0, '6'),
  Text(7, 0, '7'),
  Text(8, 0, '8'),
  Text(9, 0, '9'),
  Text(10, 0, '10'),
  Text(11, 0, '11'),
  Text(12, 0, '12'),
  Text(13, 0, '13'),
  Text(14, 0, '14'),
  Text(15, 0, '15'),
  Text(16, 0, '16'),
  Text(17, 0, '17'),
  Text(18, 0, '18'),
  Text(19, 0, '19'),
  Text(20, 0, '20'),
  Text(21, 0, '21'),
  Text(22, 0, '22'),
  Text(23, 0, '23')])

Note that in jupyter, these options have to be part of the same cell. This is due to the beahavior of jupyter notebooks that reset all plots after each cell has run.

Composites

Compsite plots can also be produced like so:

fig, axs = plt.subplots(2, 2)
ds = ds.assign_attrs({'label': 'Here is the tile'})
analyse.pdf(ds, var='tas', ax=axs[0,0])
analyse.diurnal_cycle(ds, var='tas', ax=axs[0,1])
analyse.monthly_variability(ds, var='tas', ax=axs[1,1])
fig.set_size_inches(10, 10)

Matplotlib style sheet

It is also possible to configure a matplotlib style sheet that will be then used automatically for all plots:

https://matplotlib.org/stable/users/explain/customizing.html