Introduction to Matplotlib and alternatives

Todo

• Based on https://matplotlib.org/, in particular https://matplotlib.org/stable/users/explain/quick_start.html and https://matplotlib.org/stable/gallery/index.html.

• Mention Vega-Altair

(gallery)

The default library to plot data is Matplotlib. It allows one the creation of graphs that are ready for publications with the same functionality than Matlab.

# these IPython commands load special backend for notebooks
# %matplotlib widget
%matplotlib inline

When running code using matplotlib, it is highly recommended to start IPython with the option --matplotlib (or to use the magic IPython command %matplotlib).

import numpy as np
import matplotlib.pyplot as plt

You can plot any kind of numerical data.

y = [1, 2, 10, 0, 5, 2]
plt.plot(y)

[<matplotlib.lines.Line2D at 0x7f41c04ee990>]

In scripts, the plt.show method needs to be invoked at the end of the script.

We can plot data by giving specific coordinates.

x = np.linspace(0, 2, 20)
y = x**2

plt.figure()
plt.plot(x, y, label="Square function")
plt.xlabel("x")
plt.ylabel("y")
plt.legend()

<matplotlib.legend.Legend at 0x7f41c0392ba0>

We can associate the plot with an object figure. This object will allow us to add labels, subplot, modify the axis or save it as an image.

fig, ax = plt.subplots()
lines = ax.plot(
    x,
    y,
    color="red",
    linestyle="dashed",
    linewidth=3,
    marker="o",
    markersize=5,
)

ax.set_xlabel("$Re$")
ax.set_ylabel(r"$\Pi / \epsilon$")

Text(0, 0.5, '$\\Pi / \\epsilon$')

We can also recover the plotted matplotlib object to get info on it.

line_object = lines[0]
type(line_object)

matplotlib.lines.Line2D

print("Color of the line is", line_object.get_color())
print("X data of the plot:", line_object.get_xdata())

Color of the line is red
X data of the plot: [0.         0.10526316 0.21052632 0.31578947 0.42105263 0.52631579
 0.63157895 0.73684211 0.84210526 0.94736842 1.05263158 1.15789474
 1.26315789 1.36842105 1.47368421 1.57894737 1.68421053 1.78947368
 1.89473684 2.        ]

Example of multiple subplots

fig, axes = plt.subplots(nrows=2, ncols=1)
ax1, ax2 = axes
X = np.arange(0, 2 * np.pi, 0.1)
ax1.plot(X, np.cos(2 * X), color="red")
ax2.plot(X, np.sin(2 * X), color="magenta")
ax2.set_xlabel("Angle (rad)")
fig.tight_layout()

Titles, labels and legends

Titles can be added to figures and subplots. Labels can be added when plotting to generate a legend afterwards.

x = np.arange(0, 2, 0.01)

fig, ax = plt.subplots()
ax.plot(x, x**2, label="$x^2$")
ax.plot(x, x**3, label="$x^3$")
ax.plot(x, np.exp(x) - 1, label="$e^{x} - 1$")
ax.set_title("ax title")
ax.legend()
fig.suptitle("FIG TITLE", fontweight="bold")

Text(0.5, 0.98, 'FIG TITLE')

Note that legends are attached to subplots. Note also the difference between the subplot title and the title of the figure.

Saving the figure

Figures can be saved by calling savefig on the Figure object

fig.savefig("/tmp/my_figure.png")

Anatomy of a Matplotlib figure

For consistent figure changes, define your own stylesheets that are basically a list of parameters to tune the aspect of the figure elements. See https://matplotlib.org/tutorials/introductory/customizing.html for more info.

2D plots

There are two main methods:

• imshow: for square grids. X, Y are the center of pixels and (0,0) is top-left by default.

• pcolormesh (or pcolor): for non-regular rectangular grids. X, Y are the corners of pixels and (0,0) is bottom-left by default.

noise = np.random.random((10, 10))

fig, axes = plt.subplots(1, 2)
axes[0].imshow(noise)
axes[1].pcolormesh(noise)

<matplotlib.collections.QuadMesh at 0x7f41c027a3c0>

We can also add a colorbar and adjust the colormap.

fig, ax = plt.subplots()
im = ax.imshow(noise, cmap=plt.cm.gray)
plt.colorbar(im)

<matplotlib.colorbar.Colorbar at 0x7f41c027a510>

Meshgrid

When in need of plotting a 2D function, it is useful to use meshgrid that wil generate a 2D mesh from the values of abscissas and ordinates

x = np.linspace(-2 * np.pi, 2 * np.pi, 200)
y = x

mesh_x, mesh_y = np.meshgrid(x, y)
Z = np.cos(2 * mesh_x) + np.cos(4 * mesh_y)

fig, ax = plt.subplots()
pcmesh = ax.pcolormesh(mesh_x, mesh_y, Z, cmap="RdBu")
fig.colorbar(pcmesh)

<matplotlib.colorbar.Colorbar at 0x7f41bdbc7250>

Choose your colormaps wisely !

When doing such colorplots, it is easy to lose the interesting features by setting a colormap that is not adapted to the data.

As a rule of thumb:

• use sequential colormaps for data varying continuously from a value to another (ex: x**2 for positive values).

• use divergent colormaps for data varying around a mean value (ex: cos(x)).

Also, when producing scientific figures, think about how will your plot will look like to colorblind people or in greyscales (as it can happen in printed articles…).

See the interesting discussion on matplotlib website: https://matplotlib.org/users/colormaps.html.

And this very important article on the scientific (mis)use of colour: https://www.nature.com/articles/s41467-020-19160-7

Other plot types

Matplotlib also allows to plot:

• Histograms

• Plots with error bars

• Box plots

• Contours

• in 3D

• …

See the gallery to see what suits you the most.

# 3D example
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")

ax.plot_surface(mesh_x, mesh_y, np.exp(-(mesh_x**2 + mesh_y**2)), cmap="viridis")

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f41c027b770>