pytutorial/matplotlib/subplots

subplots

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Questions to David Rotermund

Example 1 (2d regular grid)

import numpy as np
import matplotlib.pylab as plt

rng = np.random.default_rng()

a = rng.random((100, 100))

fig, axs = plt.subplots(nrows=3, ncols=3)
print(axs.shape)  # -> (3, 3)

for x in range(0, axs.shape[0]):
    for y in range(0, axs.shape[1]):
        im = axs[x, y].imshow(a, cmap="hot")
        axs[x, y].set_axis_off()
        fig.colorbar(im, ax=axs[x, y], orientation="vertical")
plt.show()

Example 2 (2d)

## Example 1 (2d regular grid)

import numpy as np
import matplotlib.pylab as plt
import matplotlib.gridspec as gridspec

rng = np.random.default_rng()

a = rng.random((100, 200))
b = rng.random((300, 100))
c = rng.random((200, 200))

fig = plt.figure()
gs = gridspec.GridSpec(3, 3)


ax1 = plt.subplot(gs[0, 0:2])
im = ax1.imshow(a, cmap="hot", aspect="auto")
ax1.set_axis_off()
fig.colorbar(im, ax=ax1, orientation="vertical")

ax2 = plt.subplot(gs[0:3, 2])
im = ax2.imshow(b, cmap="hot", aspect="auto")
ax2.set_axis_off()
fig.colorbar(im, ax=ax2, orientation="vertical")

ax3 = plt.subplot(gs[1:3, 0:2])
im = ax3.imshow(b, cmap="hot", aspect="auto")
ax3.set_axis_off()
fig.colorbar(im, ax=ax3, orientation="vertical")

plt.show()

matplotlib.pyplot.subplots

matplotlib.pyplot.subplots(nrows=1, ncols=1, *, sharex=False, sharey=False, squeeze=True, width_ratios=None, height_ratios=None, subplot_kw=None, gridspec_kw=None, **fig_kw)

Create a figure and a set of subplots.

This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.

nrows, ncols : int, default: 1

Number of rows/columns of the subplot grid.

matplotlib.axes.Axes.imshow

Axes.imshow(X, cmap=None, norm=None, *, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, interpolation_stage=None, filternorm=True, filterrad=4.0, resample=None, url=None, data=None, **kwargs)[source]

Display data as an image, i.e., on a 2D regular raster.

The input may either be actual RGB(A) data, or 2D scalar data, which will be rendered as a pseudocolor image. For displaying a grayscale image, set up the colormapping using the parameters cmap='gray', vmin=0, vmax=255.

The number of pixels used to render an image is set by the Axes size and the figure dpi. This can lead to aliasing artifacts when the image is resampled, because the displayed image size will usually not match the size of X (see Image antialiasing). The resampling can be controlled via the interpolation parameter and/or rcParams["image.interpolation"] (default: 'antialiased').

matplotlib.axes.Axes.set_axis_off

Axes.set_axis_off()

Hide all visual components of the x- and y-axis.

This sets a flag to suppress drawing of all axis decorations, i.e. axis labels, axis spines, and the axis tick component (tick markers, tick labels, and grid lines). Individual visibility settings of these components are ignored as long as set_axis_off() is in effect.

matplotlib.axes.Axes.set_axis_on

Axes.set_axis_on()

Do not hide all visual components of the x- and y-axis.

This reverts the effect of a prior set_axis_off() call. Whether the individual axis decorations are drawn is controlled by their respective visibility settings.

This is on by default.

matplotlib.axes.Axes.set_xlim

Axes.set_xlim(left=None, right=None, *, emit=True, auto=False, xmin=None, xmax=None)

Set the y-axis view limits.

matplotlib.axes.Axes.set_ylim

Axes.set_ylim(bottom=None, top=None, *, emit=True, auto=False, ymin=None, ymax=None)

Set the y-axis view limits.

matplotlib.axes.Axes.set_xlabel

Axes.set_xlabel(xlabel, fontdict=None, labelpad=None, *, loc=None, **kwargs)

Set the label for the x-axis.

matplotlib.axes.Axes.set_ylabel

Axes.set_ylabel(ylabel, fontdict=None, labelpad=None, *, loc=None, **kwargs)

Set the label for the y-axis.

matplotlib.axes.Axes.set_title

Axes.set_title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)[source]

Set a title for the Axes.

Set one of the three available Axes titles. The available titles are positioned above the Axes in the center, flush with the left edge, and flush with the right edge.

matplotlib.axes.Axes.legend

Axes.legend(*args, **kwargs)

Place a legend on the Axes.

matplotlib.axes.Axes.plot

Axes.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

Plot y versus x as lines and/or markers.

matplotlib.axes.Axes.loglog

Axes.loglog(*args, **kwargs)

Make a plot with log scaling on both the x- and y-axis.

matplotlib.axes.Axes.semilogx

Axes.semilogx(*args, **kwargs)

Make a plot with log scaling on the x-axis.

matplotlib.axes.Axes.semilogy

Axes.semilogy(*args, **kwargs)

Make a plot with log scaling on the y-axis.