Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D plots from data in arrays. Matplotlib is written in Python and makes use of NumPy, the numerical mathematics extension of Python. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter. It can be used in Python and IPython shells, Jupyter notebook and web application servers also. Matplotlib has a procedural interface named the Pylab, which is designed to resemble MATLAB, a proprietary programming language developed by MathWorks. Matplotlib along with NumPy can be considered as the open source equivalent of MATLAB. Matplotlib was originally written by John D. Hunter in 2003. The current stable version is 2.2.0 released in January 2018.
Ideally, the system package manager or pip should be used to install matplotlib, either by installing the python-matplotlib package or by running
pip install matplotlib
Debian/Ubuntu: sudo apt-get install python-matplotlib
Fedora/Red Hat: sudo yum install python-matplotlib
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from matplotlib import pyplot as plt import numpy as np import math #needed for definition of pi x=np.arange(0, math.pi*2, 0.05) y=np.sin(x) plt.plot(x,y) plt.xlabel("angle") plt.ylabel("sine") plt.title('sine wave') plt.show() |
# Customize the plot ax.grid(1, ls='--', color='#777777', alpha=0.5, lw=1) ax.tick_params(labelsize=12, length=0) ax.set_axis_bgcolor('w') # add a legend leg = plt.legend( ['text'], loc=1 ) fr = leg.get_frame() fr.set_facecolor('w') fr.set_alpha(.7) plt.draw() |
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import numpy as np from matplotlib.pyplot import imshow, show, colorbar image = np.random.rand(4,4) imshow(image) colorbar() show() |
import matplotlib.pyplot as plt plt.plot([1,2,3]) plt.subplot(211) plt.plot(range(12)) plt.subplot(212, facecolor='y') # creates 2nd subplot with a yellow background plt.plot(range(12)) |
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import matplotlib.pyplot as plt a1= plt.subplot2grid((3,3),(0,0),colspan=2) a2=plt.subplot2grid((3,3),(0,2), rowspan=3) a3=plt.subplot2grid((3,3),(1,0),rowspan=2, colspan=2) import numpy as np x=np.arange(1,10) a2.plot(x, x*x) a2.set_title('square') a1.plot(x, np.exp(x)) a1.set_title('exp') a3.plot(x, np.log(x)) a3.set_title('log') plt.tight_layout() plt.show() |
import matplotlib.pyplot as plt fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.spines['bottom'].set_color('blue') ax.spines['left'].set_color('red') ax.spines['left'].set_linewidth(2) ax.spines['right'].set_color(None) ax.spines['top'].set_color(None) ax.plot([1,2,3,4,5]) plt.show()
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import matplotlib.pyplot as plt import numpy as np fig=plt.figure() a1=fig.add_axes([0,0,1,1]) x=np.arange(1,11) a1.plot(x,np.exp(x)) a1.set_ylabel('exp') a2=a1.twinx() a2.plot(x, np.log(x),'ro-') a2.set_ylabel('log') fig.legend(labels=('exp','log'),loc='upper left') plt.show() |
import numpy as np import matplotlib.pyplot as plt xlist = np.linspace(-3.0, 3.0, 100) ylist = np.linspace(-3.0, 3.0, 100) X, Y = np.meshgrid(xlist, ylist) Z = np.sqrt(X**2 + Y**2) fig,ax=plt.subplots(1,1) cp = ax.contourf(X, Y, Z) fig.colorbar(cp) # Add a colorbar to a plot ax.set_title('Filled Contours Plot') ax.set_ylabel('y (cm)') plt.show() |
Three-dimensional plots
X = np.arange(0, 6, 0.25)
Y = np.arange(0, 6, 0.25)
X, Y = np.meshgrid(X, Y)
Z1 = np.empty_like(X)
Z2 = np.empty_like(X)
C1 = np.empty_like(X, dtype=object)
C2 = np.empty_like(X, dtype=object)
for i in range(len(X)):
for j in range(len(X[0])):
z1 = 0.5*(erf((X[i,j]+Y[i,j]-4.5)*0.5)+1)
z2 = 0.5*(erf((-X[i,j]-Y[i,j]+4.5)*0.5)+1)
Z1[i,j] = z1
Z2[i,j] = z2
# If you want to grab a colour from a matplotlib cmap function,
# you need to give it a number between 0 and 1. z1 and z2 are
# already in this range, so it just works as is.
C1[i,j] = plt.get_cmap("Oranges")(z1)
C2[i,j] = plt.get_cmap("Blues")(z2)
# Create a transparent bridge region
X_bridge = np.vstack([X[-1,:],X[-1,:]])
Y_bridge = np.vstack([Y[-1,:],Y[-1,:]])
Z_bridge = np.vstack([Z1[-1,:],Z2[-1,:]])
color_bridge = np.empty_like(Z_bridge, dtype=object)
color_bridge.fill((1,1,1,0)) # RGBA colour, onlt the last component matters -
# Join the two surfaces flipping one of them (using also the bridge)
X_full = np.vstack([X, X_bridge, np.flipud(X)])
Y_full = np.vstack([Y, Y_bridge, np.flipud(Y)])
Z_full = np.vstack([Z1, Z_bridge, np.flipud(Z2)])
color_full = np.vstack([C1, color_bridge, np.flipud(C2)])
surf_full = ax.plot_surface(X_full, Y_full, Z_full, rstride=1, cstride=1, facecolors=color_full, linewidth=0, antialiased=False)
plt.show()
The image module in Matplotlib package provides functionalities required for loading, rescaling and displaying image.
Loading image data is supported by the Pillow library. Natively, Matplotlib only supports PNG images. The commands shown below fall back on Pillow if the native read fails. The image used in this example is a PNG file, but keep that Pillow requirement in mind for your own data. The imread() function is used to read image data in an ndarray object of float32 dtype.
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
img=mpimg.imread('mtplogo.png')
