Lunch Time Python¶
Lunch 7: matplotlib¶
matplotlib is a plotting library for Python and the NumPy library. It is easy to use and can be used to generate scatter or bar plots, density maps and even 3D plots in publication quality.
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Lunch Time Python, Scientific Software Center, Heidelberg University
Advantages of matplotlib:¶
- matplotlib is mostly used in conjunction with pyplot - a matplotlib module - that provides an easy-to-use interface
- Very versatile, works with many types of data
- Directly plot NumPy functions and arrays
- Many export options
- Customizable
- Can be used with additional toolkits that extend the functionality, like seaborn
matplotlib installation¶
Available via pip:
python -m pip install -U matplotlib
Or install via conda:
conda install matplotlib
Basic plots¶
Plot the sin and cos over a range of angles. For this, we also need numpy.
import numpy as np
import matplotlib.pyplot as plt
xvals = np.arange(0, 2 * np.pi, 0.1)
plt.plot(xvals, np.sin(xvals))
plt.plot(xvals, np.cos(xvals))
[<matplotlib.lines.Line2D at 0x7fd3e4558990>]
The default settions already look quite nice!
Running inside a script¶
Or more generally, if you want to suppress the output (return) of the plt()
function.
plt.plot(xvals, np.sin(xvals))
plt.plot(xvals, np.cos(xvals))
plt.show()
plt.show()
closes the plot; if you plot using a script and not a notebook, place one plt.show()
command at the end of your script.
Running inside a notebook: static images¶
You can plot static images inside your notebook using the %matplotlib inline
magic: You only need to run this once. It is not always necessary to put this, but it makes it clear which Matplotlib backend should be used.
%matplotlib inline
plt.plot(xvals, np.sin(xvals))
plt.plot(xvals, np.cos(xvals))
plt.show()
Scatter plot¶
xvals = np.random.randint(10, size=10)
yvals = np.random.randint(10, size=10)
plt.scatter(xvals, yvals)
plt.show()
Bar plot¶
xvals = np.linspace(1, 10, 10)
yvals = np.random.randint(10, size=10)
plt.bar(xvals, yvals)
plt.show()
Customizing plots¶
plt.bar(xvals, yvals, label="Random series")
plt.legend()
plt.show()
Customizing plots¶
plt.bar(xvals, yvals, label="Random series")
plt.legend(fontsize=16, loc="upper right")
plt.xlabel("Integer", fontsize=18)
plt.ylabel("Magnitude", fontsize=22, color="red")
plt.title("My custom plot", fontsize=22)
plt.show()
Customizing plots¶
xvals = np.arange(0, 2 * np.pi, 0.1)
plt.plot(xvals, np.sin(xvals), marker="x", markevery=10, color="blue")
plt.plot(xvals, np.cos(xvals), marker="<", color="black", alpha=0.5)
plt.show()
Figure class: Advanced plots with the artist layer¶
A Figure
in matplotlib is the whole plot (or window in the user interface) and can contain multiple plots. By accessing the Artist layer ("object-based plotting"), you can access more customizing options than with the basic plt.xxx
Scripting layer ("procedural plotting") (see https://matplotlib.org/1.5.1/faq/usage_faq.html#parts-of-a-figure).
This also allows you to include multiple plots in one Figure.
fig = plt.figure(figsize=(12, 10)) # width = 12 inches and height = 10 inches
# create one axes object
ax1 = fig.add_subplot(211) # (2, 1, 1) no of rows, no of columns, no of plots
ax1.plot(xvals, np.sin(xvals), marker="x", color="blue")
plt.show()
It is also possible to use add_axes()
instead of add_subplot()
, but not recommended as with the latter, matplotlib takes care of the exact position of the axes in the figure.
Several subplots¶
Using add_subplot()
, we can add one axes
object at a time:
fig = plt.figure(figsize=(12, 10))
ax1 = fig.add_subplot(221)
ax1.plot(xvals, np.sin(xvals), marker="x", color="blue")
ax2 = fig.add_subplot(222)
ax2.plot(xvals, np.cos(xvals), marker="x", color="blue")
ax3 = fig.add_subplot(223)
ax3.plot(xvals, np.tan(xvals), marker="x", color="blue")
ax4 = fig.add_subplot(224)
ax4.plot(xvals, np.tanh(xvals), marker="x", color="blue")
plt.show()
A more practical way to add multiple subplots¶
fig, ax = plt.subplots(figsize=(12, 10), nrows=2, ncols=2)
ax[0, 0].plot(xvals, np.sin(xvals), marker="x", color="blue")
ax[0, 1].plot(xvals, np.cos(xvals), marker="x", color="blue")
ax[1, 0].plot(xvals, np.tan(xvals), marker="x", color="blue")
ax[1, 1].plot(xvals, np.tanh(xvals), marker="x", color="blue")
plt.savefig("my_figure.pdf", bbox_inches="tight")
plt.savefig("my_figure.jpg", dpi=300, bbox_inches="tight")
plt.show()
2D plots¶
def myfunc(x, y):
return np.sin(np.sqrt(5) + x) * y
mf = 16
# the x and y values need to be spanned on a 2D mesh
num1 = np.arange(-5, 5, 0.1)
num2 = np.arange(-5, 5, 0.1)
X, Y = np.meshgrid(num1, num2)
print(X[0])
print(Y[0])
[-5.00000000e+00 -4.90000000e+00 -4.80000000e+00 -4.70000000e+00 -4.60000000e+00 -4.50000000e+00 -4.40000000e+00 -4.30000000e+00 -4.20000000e+00 -4.10000000e+00 -4.00000000e+00 -3.90000000e+00 -3.80000000e+00 -3.70000000e+00 -3.60000000e+00 -3.50000000e+00 -3.40000000e+00 -3.30000000e+00 -3.20000000e+00 -3.10000000e+00 -3.00000000e+00 -2.90000000e+00 -2.80000000e+00 -2.70000000e+00 -2.60000000e+00 -2.50000000e+00 -2.40000000e+00 -2.30000000e+00 -2.20000000e+00 -2.10000000e+00 -2.00000000e+00 -1.90000000e+00 -1.80000000e+00 -1.70000000e+00 -1.60000000e+00 -1.50000000e+00 -1.40000000e+00 -1.30000000e+00 -1.20000000e+00 -1.10000000e+00 -1.00000000e+00 -9.00000000e-01 -8.00000000e-01 -7.00000000e-01 -6.00000000e-01 -5.00000000e-01 -4.00000000e-01 -3.00000000e-01 -2.00000000e-01 -1.00000000e-01 -1.77635684e-14 1.00000000e-01 2.00000000e-01 3.00000000e-01 4.00000000e-01 5.00000000e-01 6.00000000e-01 7.00000000e-01 8.00000000e-01 9.00000000e-01 1.00000000e+00 1.10000000e+00 1.20000000e+00 1.30000000e+00 1.40000000e+00 1.50000000e+00 1.60000000e+00 1.70000000e+00 1.80000000e+00 1.90000000e+00 2.00000000e+00 2.10000000e+00 2.20000000e+00 2.30000000e+00 2.40000000e+00 2.50000000e+00 2.60000000e+00 2.70000000e+00 2.80000000e+00 2.90000000e+00 3.00000000e+00 3.10000000e+00 3.20000000e+00 3.30000000e+00 3.40000000e+00 3.50000000e+00 3.60000000e+00 3.70000000e+00 3.80000000e+00 3.90000000e+00 4.00000000e+00 4.10000000e+00 4.20000000e+00 4.30000000e+00 4.40000000e+00 4.50000000e+00 4.60000000e+00 4.70000000e+00 4.80000000e+00 4.90000000e+00] [-5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5. -5.]
fig, ax = plt.subplots(figsize=(15, 5), nrows=1, ncols=3)
ax[0].pcolor(X, Y, myfunc(X, Y), cmap="viridis")
ax[0].set_title("pcolor", fontsize=mf)
plt.show()
pcolor
is the slowest plotting method of the three, but is more flexible in terms of the data mesh.
fig, ax = plt.subplots(figsize=(15, 5), nrows=1, ncols=3)
ax[0].pcolor(X, Y, myfunc(X, Y), cmap="viridis")
ax[0].set_title("pcolor", fontsize=mf)
ax[1].pcolormesh(X, Y, myfunc(X, Y), cmap="viridis")
ax[1].set_title("pcolormesh", fontsize=mf)
plt.show()
pcolormesh
is basically identical to pcolor
but faster.
fig, ax = plt.subplots(figsize=(15, 5), nrows=1, ncols=3)
ax[0].pcolor(X, Y, myfunc(X, Y), cmap="viridis")
ax[0].set_title("pcolor", fontsize=mf)
ax[1].pcolormesh(X, Y, myfunc(X, Y), cmap="viridis")
ax[1].set_title("pcolormesh", fontsize=mf)
ax[2].imshow(myfunc(X, Y))
ax[2].set_title("imshow", fontsize=mf)
plt.show()
imshow
is the fastest of the three methods. Note that the image is flipped compared to pcolor
and pcolormesh
and that the axis range is specified differently.
fig, ax = plt.subplots(figsize=(15, 5), nrows=1, ncols=3)
ax[0].pcolor(X, Y, myfunc(X, Y), cmap="viridis")
ax[0].set_title("pcolor", fontsize=mf)
ax[1].pcolormesh(X, Y, myfunc(X, Y), cmap="viridis")
ax[1].set_title("pcolormesh", fontsize=mf)
ax[2].imshow(myfunc(X, Y), extent=[-5, 5, -5, 5], origin="lower", aspect="auto")
ax[2].set_title("imshow", fontsize=mf)
plt.show()
Here, we have repositioned the origin of imshow
to match pcolor
and pcolormesh
, and further adjusted the aspect ratio and tick labels.
Imshow with contour bar¶
fig, ax = plt.subplots(figsize=(5, 5))
c = ax.imshow(myfunc(X, Y), extent=[-5, 5, -5, 5], origin="lower", aspect="auto")
cbar = plt.colorbar(c)
cbar.set_ticks([-5, -2.5, 0, 2.5, 5])
cbar.set_label("my colors", rotation=90, fontsize=mf)
ax.set_title("imshow", fontsize=mf)
plt.tight_layout()
plt.show()