CODEWITHSUNDEEP
pythonmatplotlibsubplot
Antoine Gautier
Antoine Gautier

Reputation: 653

How to add specific axes to matplotlib subplot?

I am trying to make a matrix plot with matplotlib.

The individual plots are made with a specific module windrose which subclasses PolarAxes. However there does not seem to be any projection defined in the module to be called as subplot kwargs. The standard polar projection does not work since some of the subclass arguments are missing.

I have tested several approaches without success (even with seaborn map considering this post: https://stackoverflow.com/a/25702476/3416205). Hereunder is the closest I have tried. Is there any way to do what I want without properly creating a new matplotlib projection associated with the specific WindroseAxes?

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from windrose import WindroseAxes

df = pd.read_csv('https://raw.githubusercontent.com/AntoineGautier/Data/master/tmp.csv')
fig = plt.figure()
gs = gridspec.GridSpec(4, 2)

def wind_plot(x, y, title=None, axes=None, fig=None):
    ax = WindroseAxes.from_ax()
    ax.set_position(axes.get_position(fig))
    ax.bar(x, y, normed=True, opening=0.8, edgecolor='white', bins=[0, 2.5, 5, 7.5, 10])
    ax.set_title(title)

for (id_s, s) in enumerate(pd.unique(df.saison)):
    for (id_jn, jn) in enumerate(pd.unique(df.jn)):
        tmp = df.query('saison==@s & jn==@jn')
        _ = plt.subplot(gs[id_s, id_jn], polar=True)
        wind_plot(tmp.wd, tmp.ws, title=s + ' - ' + jn, axes=_, fig=fig)

plt.show()

Upvotes: 0

Views: 1881

Answers (1)

Antoine Gautier
Antoine Gautier

Reputation: 653

The github page from the windrose module actually provides an example of subplots: https://github.com/scls19fr/windrose/blob/master/samples/example_subplots.py.

The following works.

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from windrose import WindroseAxes

df = pd.read_csv('https://raw.githubusercontent.com/AntoineGautier/Data/master/tmp.csv')

fig = plt.figure(figsize=(20, 10))
gs = gridspec.GridSpec(2, 4)
gp = gs.get_grid_positions(fig)  # [bottom, top, left, right]

def wind_plot(x, y, title, fig, rect):
    ax = WindroseAxes(fig, rect)
    fig.add_axes(ax)
    ax.bar(x, y, normed=True, opening=0.8, edgecolor='white', bins=[0, 2.5, 5, 7.5, 10])
    ax.set_title(title, position=(0.5, 1.1))

for (id_s, s) in enumerate(pd.unique(df.saison)):
    for (id_jn, jn) in enumerate(pd.unique(df.jn)):
        tmp = df.query('saison==@s & jn==@jn')
        rect = [gp[2][id_s], gp[0][id_jn],
                gp[3][id_s]-gp[2][id_s],
                gp[1][id_jn]-gp[0][id_jn]]  # [left, bottom, width, height]
        wind_plot(tmp.wd, tmp.ws, s + ' | ' + jn, fig, rect)

plt.show()

Upvotes: 1

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