More Matplotlib#

Matplotlib is the dominant plotting / visualization package in python. It is important to learn to use it well. In the last lecture, we saw some basic examples in the context of learning numpy. This week, we dive much deeper. The goal is to understand how matplotlib represents figures internally.

from matplotlib import pyplot as plt
%matplotlib inline

Figure and Axes#

The figure is the highest level of organization of matplotlib objects. If we want, we can create a figure explicitly.

fig = plt.figure()

<matplotlib.figure.Figure at 0x116913b38>

fig = plt.figure(figsize=(13, 5))

<matplotlib.figure.Figure at 0x116b31c50>

fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
../../_images/more_matplotlib_5_0.png 
fig = plt.figure()
ax = fig.add_axes([0, 0, 0.5, 1])
../../_images/more_matplotlib_6_0.png 
fig = plt.figure()
ax1 = fig.add_axes([0, 0, 0.5, 1])
ax2 = fig.add_axes([0.6, 0, 0.3, 0.5], facecolor='g')
../../_images/more_matplotlib_7_0.png

Subplots#

Subplot syntax is one way to specify the creation of multiple axes.

fig = plt.figure()
axes = fig.subplots(nrows=2, ncols=3)
../../_images/more_matplotlib_9_0.png 
fig = plt.figure(figsize=(12, 6))
axes = fig.subplots(nrows=2, ncols=3)
../../_images/more_matplotlib_10_0.png 
axes

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11a1972b0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11a3e70f0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11a4f90f0>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a5340f0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11a56f128>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11a310da0>]], dtype=object)

There is a shorthand for doing this all at once.

This is our recommended way to create new figures!

fig, ax = plt.subplots()
../../_images/more_matplotlib_13_0.png 
ax

<matplotlib.axes._subplots.AxesSubplot at 0x116b31be0>

fig, axes = plt.subplots(ncols=2, figsize=(8, 4), subplot_kw={'facecolor': 'g'})
../../_images/more_matplotlib_15_0.png 
axes

array([<matplotlib.axes._subplots.AxesSubplot object at 0x11a476b38>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x11a85bba8>], dtype=object)

Drawing into Axes#

All plots are drawn into axes. It is easiest to understand how matplotlib works if you use the object-oriented style.

# create some data to plot
import numpy as np
x = np.linspace(-np.pi, np.pi, 100)
y = np.cos(x)
z = np.sin(6*x)

fig, ax = plt.subplots()
ax.plot(x, y)

[<matplotlib.lines.Line2D at 0x11a866b70>]
../../_images/more_matplotlib_19_1.png

This does the same thing as

plt.plot(x, y)

[<matplotlib.lines.Line2D at 0x11aa68f98>]
../../_images/more_matplotlib_21_1.png

This starts to matter when we have multiple axes to worry about.

fig, axes = plt.subplots(figsize=(8, 4), ncols=2)
ax0, ax1 = axes
ax0.plot(x, y)
ax1.plot(x, z)

[<matplotlib.lines.Line2D at 0x11a9a3dd8>]
../../_images/more_matplotlib_23_1.png

Labeling Plots#

fig, axes = plt.subplots(figsize=(8, 4), ncols=2)
ax0, ax1 = axes

ax0.plot(x, y)
ax0.set_xlabel('x')
ax0.set_ylabel('y')
ax0.set_title('x vs. y')

ax1.plot(x, z)
ax1.set_xlabel('x')
ax1.set_ylabel('z')
ax1.set_title('x vs. z')

# squeeze everything in
plt.tight_layout()
../../_images/more_matplotlib_25_0.png

Customizing Line Plots#

fig, ax = plt.subplots()
ax.plot(x, y, x, z)

[<matplotlib.lines.Line2D at 0x11abb6c50>,
 <matplotlib.lines.Line2D at 0x11ad442e8>]
../../_images/more_matplotlib_27_1.png

It’s simple to switch axes

fig, ax = plt.subplots()
ax.plot(y, x, z, x)

[<matplotlib.lines.Line2D at 0x11aa86208>,
 <matplotlib.lines.Line2D at 0x11aea6710>]
../../_images/more_matplotlib_29_1.png

A “parametric” graph:

fig, ax = plt.subplots()
ax.plot(y, z)

[<matplotlib.lines.Line2D at 0x11aed2ac8>]
../../_images/more_matplotlib_31_1.png

Line Styles#

fig, axes = plt.subplots(figsize=(16, 5), ncols=3)
axes[0].plot(x, y, linestyle='dashed')
axes[0].plot(x, z, linestyle='--')

axes[1].plot(x, y, linestyle='dotted')
axes[1].plot(x, z, linestyle=':')

axes[2].plot(x, y, linestyle='dashdot', linewidth=5)
axes[2].plot(x, z, linestyle='-.', linewidth=0.5)

[<matplotlib.lines.Line2D at 0x11b0bd978>]
../../_images/more_matplotlib_33_1.png

Colors#

As described in the colors documentation, there are some special codes for commonly used colors:

  • b: blue

  • g: green

  • r: red

  • c: cyan

  • m: magenta

  • y: yellow

  • k: black

  • w: white

fig, ax = plt.subplots()
ax.plot(x, y, color='k')
ax.plot(x, z, color='r')

[<matplotlib.lines.Line2D at 0x11a3c1240>]
../../_images/more_matplotlib_35_1.png

Other ways to specify colors:

fig, axes = plt.subplots(figsize=(16, 5), ncols=3)

# grayscale
axes[0].plot(x, y, color='0.8')
axes[0].plot(x, z, color='0.2')

# RGB tuple
axes[1].plot(x, y, color=(1, 0, 0.7))
axes[1].plot(x, z, color=(0, 0.4, 0.3))

# HTML hex code
axes[2].plot(x, y, color='#00dcba')
axes[2].plot(x, z, color='#b029ee')

[<matplotlib.lines.Line2D at 0x11a3850f0>]
../../_images/more_matplotlib_37_1.png

There is a default color cycle built into matplotlib.

plt.rcParams['axes.prop_cycle']
'color'
'#1f77b4'
'#ff7f0e'
'#2ca02c'
'#d62728'
'#9467bd'
'#8c564b'
'#e377c2'
'#7f7f7f'
'#bcbd22'
'#17becf'
fig, ax = plt.subplots(figsize=(12, 10))
for factor in np.linspace(0.2, 1, 11):
    ax.plot(x, factor*y)
../../_images/more_matplotlib_40_0.png

Markers#

There are lots of different markers availabile in matplotlib!

fig, axes = plt.subplots(figsize=(12, 5), ncols=2)

axes[0].plot(x[:20], y[:20], marker='.')
axes[0].plot(x[:20], z[:20], marker='o')

axes[1].plot(x[:20], z[:20], marker='^',
             markersize=10, markerfacecolor='r',
             markeredgecolor='k')

[<matplotlib.lines.Line2D at 0x11a2de048>]
../../_images/more_matplotlib_42_1.png

Label, Ticks, and Gridlines#

fig, ax = plt.subplots(figsize=(12, 7))
ax.plot(x, y)

ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title(r'A complicated math function: $f(x) = \cos(x)$')

ax.set_xticks(np.pi * np.array([-1, 0, 1]))
ax.set_xticklabels([r'$-\pi$', '0', r'$\pi$'])
ax.set_yticks([-1, 0, 1])

ax.set_yticks(np.arange(-1, 1.1, 0.2), minor=True)
#ax.set_xticks(np.arange(-3, 3.1, 0.2), minor=True)

ax.grid(which='minor', linestyle='--')
ax.grid(which='major', linewidth=2)
../../_images/more_matplotlib_44_0.png

Axis Limits#

fig, ax = plt.subplots()
ax.plot(x, y, x, z)
ax.set_xlim(-5, 5)
ax.set_ylim(-3, 3)

(-3, 3)
../../_images/more_matplotlib_46_1.png

Text Annotations#

fig, ax = plt.subplots()
ax.plot(x, y)
ax.text(-3, 0.3, 'hello world')
ax.annotate('the maximum', xy=(0, 1),
             xytext=(0, 0), arrowprops={'facecolor': 'k'})

Text(0,0,'the maximum')
../../_images/more_matplotlib_48_1.png

Other 1D Plots#

Scatter Plots#

fig, ax = plt.subplots()

splot = ax.scatter(y, z, c=x, s=(100*z**2 + 5))
fig.colorbar(splot)

<matplotlib.colorbar.Colorbar at 0x11b909d30>
../../_images/more_matplotlib_50_1.png

Bar Plots#

labels = ['first', 'second', 'third']
values = [10, 5, 30]

fig, axes = plt.subplots(figsize=(10, 5), ncols=2)
axes[0].bar(labels, values)
axes[1].barh(labels, values)

<Container object of 3 artists>
../../_images/more_matplotlib_52_1.png

2D Plotting Methods#

imshow#

x1d = np.linspace(-2*np.pi, 2*np.pi, 100)
y1d = np.linspace(-np.pi, np.pi, 50)
xx, yy = np.meshgrid(x1d, y1d)
f = np.cos(xx) * np.sin(yy)
print(f.shape)

(50, 100)

fig, ax = plt.subplots(figsize=(12,4), ncols=2)
ax[0].imshow(f)
ax[1].imshow(f, origin='bottom')

<matplotlib.image.AxesImage at 0x11bd28748>
../../_images/more_matplotlib_55_1.png

pcolormesh#

fig, ax = plt.subplots(ncols=2, figsize=(12, 5))
pc0 = ax[0].pcolormesh(x1d, y1d, f)
pc1 = ax[1].pcolormesh(xx, yy, f)
fig.colorbar(pc0, ax=ax[0])
fig.colorbar(pc1, ax=ax[1])

<matplotlib.colorbar.Colorbar at 0x11bee30f0>
../../_images/more_matplotlib_57_1.png 
x_sm, y_sm, f_sm = xx[:10, :10], yy[:10, :10], f[:10, :10]

fig, ax = plt.subplots(figsize=(12,5), ncols=2)

# last row and column ignored!
ax[0].pcolormesh(x_sm, y_sm, f_sm, edgecolors='k')

# same!
ax[1].pcolormesh(x_sm, y_sm, f_sm[:-1, :-1], edgecolors='k')

<matplotlib.collections.QuadMesh at 0x11bfb4240>
../../_images/more_matplotlib_58_1.png 
y_distorted = y_sm*(1 + 0.1*np.cos(6*x_sm))

plt.figure(figsize=(12,6))
plt.pcolormesh(x_sm, y_distorted, f_sm[:-1, :-1], edgecolors='w')
plt.scatter(x_sm, y_distorted, c='k')

<matplotlib.collections.PathCollection at 0x11c02c438>
../../_images/more_matplotlib_59_1.png

contour / contourf#

fig, ax = plt.subplots(figsize=(12, 5), ncols=2)

# same thing!
ax[0].contour(x1d, y1d, f)
ax[1].contour(xx, yy, f)

<matplotlib.contour.QuadContourSet at 0x11bee8358>
../../_images/more_matplotlib_61_1.png 
fig, ax = plt.subplots(figsize=(12, 5), ncols=2)

c0 = ax[0].contour(xx, yy, f, 5)
c1 = ax[1].contour(xx, yy, f, 20)

plt.clabel(c0, fmt='%2.1f')
plt.colorbar(c1, ax=ax[1])

<matplotlib.colorbar.Colorbar at 0x11c42b198>
../../_images/more_matplotlib_62_1.png 
fig, ax = plt.subplots(figsize=(12, 5), ncols=2)

clevels = np.arange(-1, 1, 0.2) + 0.1

cf0 = ax[0].contourf(xx, yy, f, clevels, cmap='RdBu_r', extend='both')
cf1 = ax[1].contourf(xx, yy, f, clevels, cmap='inferno', extend='both')

fig.colorbar(cf0, ax=ax[0])
fig.colorbar(cf1, ax=ax[1])

<matplotlib.colorbar.Colorbar at 0x11c7da1d0>
../../_images/more_matplotlib_63_1.png

quiver#

u = -np.cos(xx) * np.cos(yy)
v = -np.sin(xx) * np.sin(yy)

fig, ax = plt.subplots(figsize=(12, 7))
ax.contour(xx, yy, f, clevels, cmap='RdBu_r', extend='both', zorder=0)
ax.quiver(xx[::4, ::4], yy[::4, ::4],
           u[::4, ::4], v[::4, ::4], zorder=1)

<matplotlib.quiver.Quiver at 0x11bd07b70>
../../_images/more_matplotlib_65_1.png

streamplot#

fig, ax = plt.subplots(figsize=(12, 7))
ax.streamplot(xx, yy, u, v, density=2, color=(u**2 + v**2))

<matplotlib.streamplot.StreamplotSet at 0x11cd34a58>
../../_images/more_matplotlib_67_1.png