Matplotlib
Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
1) Prepare The Data
1D 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
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))
2) Create Plot
>>> import matplotlib.pyplot as plt
Figure
>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0)
Axes
All plotting is done with respect to an Axes. In most cases, a subplot will fit your needs. A subplot is an axes on a grid system.
>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) # row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)
3) Plotting Routines
1D Data
>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) Draw points with lines or markers connecting them
>>> ax.scatter(x,y) Draw unconnected points, scaled or colored >>> axes[0,0].bar([1,2,3],[3,4,5]) Plot vertical rectangles (constant width) >>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') Fill between y-values and 0
Vector Fields
>>> axes[0,1].arrow(0,0,0.5,0.5) Add an arrow to the axes
>>> axes[1,1].quiver(y,z) Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) Plot a 2D field of arrows
Data Distributions
>>> ax1.hist(y) Plot a histogram
>>> ax3.boxplot(y) Make a box and whisker plot
>>> ax3.violinplot(z) Make a violin plot
2D Data or Images
>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img,
cmap='gist_earth', Colormapped or RGB arrays
interpolation='nearest',
vmin=-2,
vmax=2)
>>> axes2[0].pcolor(data2) Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) Plot contours
>>> axes2[2].contourf(data1) Plot filled contours
>>> axes2[2]= ax.clabel(CS) Label a contour plot
4) 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
>>> ax.margins(x=0.0,y=0.1) Add padding to a plot
>>> ax.axis('equal') Set the aspect ratio of the plot to 1 >>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) Set limits for x-axis
Legends
>>> ax.set(title='An Example Axes',
ylabel='Y-Axis', Set a title and x-and y-axis labels
xlabel='X-Axis')
>>> ax.legend(loc='best') No overlapping plot elements
Ticks
>>> ax.xaxis.set(ticks=range(1,5), Manually set x-ticks
ticklabels=[3,100,-12,"foo"])
>>> ax.tick_params(axis='y', Make y-ticks longer and go in and out
direction='inout',
length=10)
Subplot Spacing
>>> fig3.subplots_adjust(wspace=0.5, Adjust the spacing between subplots hspace=0.3,
left=0.125,
right=0.9,
top=0.9,
bottom=0.1)
>>> fig.tight_layout() Fit subplot(s) in to the figure area Axis Spines
>>> ax1.spines['top'].set_visible(False) Make the top axis line for a plot invisible
>>> ax1.spines['bottom'].set_position(('outward',10)) Move the bottom axis line outward
5) Save Plot
Save figures
>>> plt.savefig('foo.png') Save transparent figures
>>> plt.savefig('foo.png', transparent=True)
6) Show Plot
>>> plt.show()
Close & Clear
>>> plt.cla() Clear an axis
>>> plt.clf() Clear the entire figure
>>> plt.close() Close a window
