A quick introduction to plotting in Python

import matplotlib.pyplot as plt

import numpy as np

# For inline plotting in jupyter notebooks

%matplotlib inline  

# Data #

x = np.linspace(0, 10, num=100)

y1 = np.sin(x)

y2 = np.cos(x)

# Opens a new figure to be plotted

plt.figure()

# plot(x, y, <MATLAB stylestring>)

plt.plot(x, y1, '-')  

plt.plot(x, y2, '--')

# Let's add some more options

# Set figure (whole picture) size to 10 * 10

plt.figure(figsize = (10, 10))

# Add grid

plt.grid() 

# Title

plt.title("Waves")

# Lables for X & Y axes

plt.xlabel("Time")

plt.ylabel("Amplitude")

# 'o-' does not mean orange line rather than circle dots

# '^' means triangle dots

# line labels are also set

plt.plot(x, y1, '^-', label="Line1", color='orange')  

plt.plot(x, y2, 'b--', label="Line2")

# Show the labels

plt.legend(loc='upper left')

# Data #

x = np.linspace(0, 10, 100)

# 4 columns of data = 4 series

# y = sin(x + 0.5k * pi); k = 0, 1, 2, 3

y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))

y.shape

plt.figure()

plt.plot(x, y)

plt.figure()

lines = plt.plot(x, y[:, 0:2])

# Another way to set labels

plt.legend(lines, ['First', 'Second'], loc='upper right')

# Bode plot example

# Define a transfer function

def H(w):

    wc = 4000*np.pi

    return 1.0 / (1.0 + 1j * w / wc)

freq = np.logspace(1,5) # frequencies from 10**1 to 10**5 Hz

plt.figure()

plt.plot(freq, 20*np.log10(abs(H(2*np.pi*freq))))

plt.xscale('log')

plt.xlabel('Frequency (Hz)')

plt.ylabel('dB')

plt.tick_params(

    axis='x',          # changes apply to the x-axis

    which='both',      # both major and minor ticks are affected

    bottom=False,      # ticks along the bottom edge are off

    top=False,         # ticks along the top edge are off

    labelbottom=False) # labels along the bottom edge are off

# MATLAB style

# subplot(rows, columns, panel number)

plt.subplot(2, 1, 1)

plt.plot(x, y1)

# create the second panel and set current axis

plt.subplot(2, 1, 2)

plt.plot(x, y2)

# OO style (recommended)

fig, ax = plt.subplots(2)

# Plot for each axes (an unit in the figure)

ax[0].plot(x, y1)

ax[0].set_title("Upper panel")

ax[1].plot(x, y2)

ax[1].set_title("Lower panel")

# Common title

plt.suptitle("Common title")

# Using plot() function

plt.figure()

x = np.linspace(0, 10)

y1 = np.sin(x)

plt.plot(x, y1, 'o', color='black')

# Same as plt.scatter(x, y1, marker='o', color='black')

# Data #

rng = np.random.RandomState(0)

x = rng.randn(100)

y = rng.randn(100)

colors = rng.rand(100)

sizes = 1000 * rng.rand(100)

# Plot #

plt.figure()

# cmap for color mapping

plt.scatter(x, y, c=colors, s=sizes, alpha=0.3, cmap='viridis')

# show color scale bar

plt.colorbar()

# Data #

x = np.linspace(0, 10, 50)  # Input

dy = 0.8                                  # Uncertainty level

y = np.sin(x) + dy * np.random.randn(50)  # Output with uncertainty

# Plot #

plt.figure()

# xerr or yerr parameter to set error bars

plt.errorbar(x, y, yerr=dy, fmt='.k')

# data #

def f(x, y):

    return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)

x = np.linspace(0, 5, 50)

y = np.linspace(0, 5, 40)

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

Z = f(X, Y)

# plot #

plt.figure()

plt.contour(X, Y, Z)

plt.figure()

# Change color map

plt.contour(X, Y, Z, 20, cmap='RdGy')

plt.figure()

# contourf() for filled countor plot

plt.contourf(X, Y, Z, 20, cmap='RdGy')

plt.colorbar()

plt.figure()

contours = plt.contour(X, Y, Z, 3, colors='black')

# Add labels of levels in the contour plot

plt.clabel(contours, inline=True, fontsize=8)

# Render image on the plot (faster but lower quality)

plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', cmap='RdGy', alpha=0.5)

plt.colorbar()

#### set_clim() to set limits on the values in the color bar

import numpy as np

import matplotlib.pyplot as plt

import matplotlib as mpl

# Data #

x = np.linspace(0, 10, 1000)	            # 1000 * 1

I = np.sin(x) * np.cos(x[:, np.newaxis])  # 1000 * 1000

speckles = (np.random.random(I.shape) < 0.01)

I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles))

# Figure #

fig, axs = plt.subplots(ncols=2, figsize=(10, 5))

# Left subplot

axs[0].set_title('Without limit')

im0 = axs[0].imshow(I, cmap='RdBu')

cb0 = plt.colorbar(im0, ax=axs[0], orientation='horizontal')

# Right subplot

axs[1].set_title('With limit')

im1 = axs[1].imshow(I, cmap='RdBu')

im1.set_clim(-1, 1)

cb1 = plt.colorbar(im1, ax=axs[1], extend='both', orientation='horizontal')

import sys

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.patches import Circle

# Mean magnitude of the Earth's magnetic field at the equator in T

B0 = 3.12e-5

# Radius of Earth, Mm (10^6 m: mega-metres!)

RE = 6.370

# Deviation of magnetic pole from axis

alpha = np.radians(9.6)

def B(r, theta):

    """Return the magnetic field vector at (r, theta)."""

    fac = B0 * (RE / r)**3

    return -2 * fac * np.cos(theta + alpha), -fac * np.sin(theta + alpha)

# Grid of x, y points on a Cartesian grid

nx, ny = 64, 64

XMAX, YMAX = 40, 40

x = np.linspace(-XMAX, XMAX, nx)

y = np.linspace(-YMAX, YMAX, ny)

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

r, theta = np.hypot(X, Y), np.arctan2(Y, X)

# Magnetic field vector, B = (Ex, Ey), as separate components

Br, Btheta = B(r, theta)

# Transform to Cartesian coordinates: NB make North point up, not to the right.

c, s = np.cos(np.pi/2 + theta), np.sin(np.pi/2 + theta)

Bx = -Btheta * s + Br * c

By = Btheta * c + Br * s

fig, ax = plt.subplots()

# Plot the streamlines with an appropriate colormap and arrow style

color = 2 * np.log(np.hypot(Bx, By))

ax.streamplot(x, y, Bx, By, color=color, linewidth=1, cmap=plt.cm.inferno,

              density=2, arrowstyle='->', arrowsize=1.5)

# Add a filled circle for the Earth; make sure it's on top of the streamlines.

ax.add_patch(Circle((0,0), RE, color='b', zorder=100))

ax.set_xlabel('$x$')

ax.set_ylabel('$y$')

ax.set_xlim(-XMAX, XMAX)

ax.set_ylim(-YMAX, YMAX)

ax.set_aspect('equal')

# Another vector field example

import numpy as np

import matplotlib.pyplot as plt

# Set limits and number of points in grid

y, x = np.mgrid[10:-10:100j, 10:-10:100j]

x_obstacle, y_obstacle = 0.0, 0.0

alpha_obstacle, a_obstacle, b_obstacle = 1.0, 1e3, 2e3

p = -alpha_obstacle * np.exp(-((x - x_obstacle)**2 / a_obstacle + (y - y_obstacle)**2 / b_obstacle))

# For the absolute values of "dx" and "dy" to mean anything, we'll need to

# specify the "cellsize" of our grid.  For purely visual purposes, though,

# we could get away with just "dy, dx = np.gradient(p)".

dy, dx = np.gradient(p)

fig, ax = plt.subplots()

ax.quiver(x, y, dx, dy, p)

ax.set(aspect=1, title='Quiver Plot')

# Quiver plot with less density (draw every 3rd point)

skip = (slice(None, None, 3), slice(None, None, 3))

fig, ax = plt.subplots()

ax.quiver(x[skip], y[skip], dx[skip], dy[skip], p[skip])

ax.set(aspect=1, title='Quiver Plot (3rd points)')

# Streamplot with contour plot

fig, ax = plt.subplots()

ax.streamplot(x, y, dx, dy, color=p, density=0.5, cmap='gist_earth')

cont = ax.contour(x, y, p, cmap='gist_earth')

ax.clabel(cont)

ax.set(aspect=1, title='Streamplot with contours')

# Streamplot with stroke + contour plot

from matplotlib.patheffects import withStroke

fig, ax = plt.subplots()

ax.streamplot(x, y, dx, dy, linewidth=500*np.hypot(dx, dy),color=p, density=1.2, cmap='gist_earth')

cont = ax.contour(x, y, p, cmap='gist_earth', vmin=p.min(), vmax=p.max())

labels = ax.clabel(cont)

plt.setp(labels, path_effects=[withStroke(linewidth=8, foreground='w')])

ax.set(aspect=1, title='Streamplot with contours and strokes')