5 Matplotlib Methods That You Never Knew Existed

1. quiver

1. quiver

The quiver method is used to plot vector fields in matplotlib.

A vector field is a mathematical model of the magnitude and direction of different vectors in a 2D or 3D space.

These are used to represent different physical quantities like:

• Electromagnetic fields
• Fluid dynamics, and more

How To Use?

quiver([X, Y], U, V, [C], **kwargs)

The parameters passed to this method are as follows:

• X, Y: define the x and y coordinates of the arrow locations
• U, V: define the x and y direction components of the arrow vectors/directions
• C: sets the colour of the arrows

Example Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2*np.pi, 20)
y = np.linspace(0, 2*np.pi, 20)

# Starting positions of the vectors in the vector field
X, Y = np.meshgrid(x, y)

# Horizontal component of the vectors 
U = np.cos(X)

# Vertical component of the vectors
V = np.sin(Y)

plt.quiver(X, Y, U, V)
plt.show()

Documentation

2. contour / contourf

contour method is used to create a Contour plot that visualises 3D data in two dimensions using contour lines.

How To Use?

contour([X, Y,] Z, [levels], **kwargs) 

The parameters passed to this method are as follows:

• X, Y: define the coordinates of the values in Z
• Z: define the height values over which the contour is drawn
• levels: defines the number and positions of the contour lines/regions

Example Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)

X, Y = np.meshgrid(x, y)

Z = np.sin(np.sqrt(X**2 + Y**2))

plt.contour(X, Y, Z)
plt.show()

The contourf method creates filled contour plots that provide a visual representation of continuous data.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)

X, Y = np.meshgrid(x, y)

#Z is a Gaussian function
Z = np.exp(-(X**2 + Y**2))

plt.contourf(X, Y, Z, levels = 20)

plt.colorbar()
plt.show()

Documentation

3. cohere

The cohere method is used to plot the coherence/ correlation between two signals in the frequency domain.

Mathematically, Coherence is the normalized cross-spectral density as shown below:

How To Use?

plt.cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None)

The parameters passed to this method are as follows:

• x, y: Input signals to be analyzed
• NFFT: The number of data points used in each block for the FFT (Fast Fourier Transform)
• Fs: The sampling frequency of the input signals
• Fc: The centre frequency of x
• detrend: The function applied to each segment before FFT
• window and noverlap control the computation specifics for FFT

Example Plot

import matplotlib.pyplot as plt
import numpy as np

# Generate two sample signals
t = np.arange(0.0, 5.0, 0.01)
s1 = np.sin(2 * np.pi * t)
s2 = np.sin(2 * np.pi * t + np.pi / 2)

plt.cohere(s1, s2, NFFT=64, Fs=1. / 0.01)

plt.xlabel('Frequency')
plt.ylabel('Coherence')
plt.show()

Documentation

4. triplot

The triplot method is used to create a Triangular grid or Triangulations that are subdivisions of a geometric object into triangles.

How To Use?

triplot(triangulation, ...)

#or

triplot(x, y, [triangles], *, [mask=mask], ...)

The parameter to the methods can either be passed a Triangulation object or can be given the points to define a triangular grid (x, y, triangles and a mask).

Example Plot

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.tri as tri

points = np.random.rand(20, 2)

# Delaunay triangulation on the random points
triangulation = tri.Triangulation(points[:, 0], points[:, 1])

plt.triplot(triangulation, 'bo-')

plt.show()

Documentation

5. matshow

The matshow method is used to display matrices (2D arrays) as colour-coded grids.

How To Use?

matshow(A, fignum=None, **kwargs)

The parameters passed to the method are as follows:

• A: the matrix to be displayed
• fignum: specifies the figure that should be used for the plot

Example Plot

import matplotlib.pyplot as plt
import numpy as np

# Sample matrix
data = np.random.rand(10, 10)

plt.matshow(data)
plt.colorbar() 
plt.show()

Documentation

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