# Matplotlib

Matplotlib Cheat Sheet for Quick reference

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats.

Installation : https://pypi.org/project/matplotlib/    : pip install matplotlib

Prepare The Data
import numpy as np
x = np.linspace(0, 10, 100)
y = np.cos(x)
z = np.sin(x)

2D Data or Images
data = 2 * np.random.random((10, 10))
data2 = 3 * np.random.random((10, 10))
Y, X = np.mgrid[-3:3:100j, -3:3:100j]
U = -1 – X**2 + Y
V = 1 + X – Y**2
from matplotlib.cbook import get_sample_data

Create Plot
import matplotlib.pyplot as plt
fig = plt.figure()
fig2 = plt.figure(figsize=plt.figaspect(2.0))

Axes
fig3, axes = plt.subplots(nrows=2,ncols=2)
fig4, axes2 = plt.subplots(ncols=3)

Plotting Routines
1D Data
Draw points with lines or markers connecting them : lines = ax.plot(x,y)
Draw unconnected points, scaled or colored : ax.scatter(x,y)
Plot vertical rectangles (constant width) : axes[0,0].bar([1,2,3],[3,4,5])
Plot horiontal rectangles (constant height) : axes[1,0].barh([0.5,1,2.5],[0,1,2])
Draw a horizontal line across axes : axes[1,1].axhline(0.45)
Draw a vertical line across axes : axes[0,1].axvline(0.65)
Draw filled polygons : ax.fill(x,y,color=’blue’)
Fill between y-values and 0 : ax.fill_between(x,y,color=’yellow’)

2D Data or Images
fig, ax = plt.subplots()
im = ax.imshow(img, cmap=’gist_earth’, interpolation=’nearest’, vmin=-2, vmax=2)

Vector Fields
Add an arrow to the axes : axes[0,1].arrow(0,0,0.5,0.5)
Plot a 2D field of arrows : axes[1,1].quiver(y,z)
Plot 2D vector fields : axes[0,1].streamplot(X,Y,U,V)

Data Distributions
Plot a histogram : ax1.hist(y)
Make a box and whisker plot : ax3.boxplot(y)
Make a violin plot : ax3.violinplot(z)

Pseudocolor plot of 2D array : axes2[0].pcolor(data2)
Pseudocolor plot of 2D array : axes2[0].pcolormesh(data)
Plot contours : CS = plt.contour(Y,X,U)
Plot filled contours : axes2[2].contourf(data1)
Label a contour plot : axes2[2]= ax.clabel(CS)

Customize Plot
Colors, Color Bars & Color Maps
plt.plot(x, x, x, x**2, x, x**3)
ax.plot(x, y, alpha = 0.4)
ax.plot(x, y, c=’k’)
fig.colorbar(im, orientation=’horizontal’)
im = ax.imshow(img, cmap=’seismic’)

Markers
fig, ax = plt.subplots()
ax.scatter(x,y,marker=”.”)
ax.plot(x,y,marker=”o”)

Linestyles
plt.plot(x,y,linewidth=4.0)
plt.plot(x,y,ls=’solid’)
plt.plot(x,y,ls=’–‘)
plt.plot(x,y,’–‘,x**2,y**2,’-.’)
plt.setp(lines,color=’r’,linewidth=4.0)

Text & Annotations
ax.text(1, -2.1, ‘Example Graph’, style=’italic’)
ax.annotate(“Sine”, xy=(8, 0), xycoords=’data’, xytext=(10.5, 0), textcoords=’data’, arrowprops=dict(arrowstyle=”->”, connectionstyle=”arc3″),)

Mathtext
plt.title(r’\$sigma_i=15\$’, fontsize=20)

Limits, Legends & Layouts
Limits & Autoscaling
Set the aspect ratio of the plot to 1 : ax.axis(‘equal’)
Set limits for x-and y-axis : ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])
Set limits for x-axis : ax.set_xlim(0,10.5)

Legends : Set a title and x-and y-axis labels
ax.set(title=’An Example Axes’, ylabel=’Y-Axis’, xlabel=’X-Axis’)
ax.legend(loc=’best’)

Ticks
Manually set x-ticks
ax.xaxis.set(ticks=range(1,5), ticklabels=[3,100,-12,”foo”])
Make y-ticks longer and go in and out
ax.tick_params(axis=’y’, direction=’inout’, length=10)

Subplot Spacing
fig.tight_layout() Fit subplot(s) in to the figure area

Axis Spines
Make the top axis line for a plot invisible
ax1.spines[‘top’].set_visible(False)
Move the bottom axis line outward
ax1.spines[‘bottom’].set_position((‘outward’,10))

Save Plot
plt.savefig(‘foo.png’)

Save figures
Save transparent figures
plt.savefig(‘foo.png’, transparent=True)

Show Plot
plt.show()

Close & Clear
plt.cla() Clear an axis
plt.clf() Clear the entire figure
plt.close() Close a window

Axis functions
Set the limits for the x axis : plt.xl­im(­xmin, xmax)
Set the limits for the y axis : plt.yl­im(­ymin, ymax)
Set scale for the x axis (ex. “­log­”) : plt.xs­cal­e(“scale type”)
Set scale for the y axis (ex. “­log­”) : plt.ys­cal­e(“scale type”)
Add a second y axis : plt.tw­inx()
Do not show the axes : plt.ax­is(­”­off­”)
Invert the x axis : plt.gc­a().in­vert_ xaxis()
Invert the y axis : plt.gc­a().in­vert_ yaxis()

Labeling functions