In [1]:
# import
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
A “figure” in matplotlib means the whole window in the user interface. Within this figure there can be “sub-plots”. A figure is the windows in the GUI that has “Figure #” as title. Figures are numbered starting from1 as opposed to the normal Python way starting from 0.
| Argument | Default | Description |
|---|---|---|
| num | 1 | number of figure |
| figsize | figure.figsize | figure size in in inches (width, height) |
| dpi | figure.dpi | resolution in dots per inch |
| facecolor | figure.facecolor | color of the drawing background |
| edgecolor | figure.edgecolor | color of edge around the drawing background |
| frameon | True | draw figure frame or not |
With subplot you can arrange plots in a regular grid. You need to specify the number of rows and columns and the number of the plot.
Axes are very similar to subplots but allow placement of plots at any location in the figure. So if we want to put a smaller plot inside a bigger one we do so with axes.
Well formatted ticks are an important part of publishing-ready figures. Matplotlib provides a totally configurable system for ticks. There are tick locators to specify where ticks should appear and tick formatters to give ticks the appearance you want.
In [2]:
# generating some data points
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)
In [3]:
# creating a figure
fig = plt.figure(figsize=(4,3), dpi=120)
#plotting
plt.plot(X, C, linestyle='--')
plt.plot(X, S)
# plotting
plt.show()
In [4]:
# creating a figure
fig = plt.figure(figsize=(4,3), dpi=120)
#plotting
plt.plot(X, C, linestyle='--')
plt.plot(X, S, linewidth=2)
# adding labels
plt.xlabel("X data")
plt.ylabel("Y data")
plt.title("Plotting")
# adding margins, grid and autoscale
plt.margins(0.15)
plt.autoscale(True)
plt.grid(True)
# plotting
plt.show()
Adding one more sub plot
In [5]:
# creating a figure
fig = plt.figure(figsize=(12,6), dpi=120)
# adding subplot
plt.subplot(1,2,1) # 1 row, 2 columns, 1st plot
plt.plot(X, C, linestyle='--')
plt.xlabel("X data")
plt.ylabel("Y data")
plt.title("Cos Plotting")
plt.grid(True)
plt.margins(0.15)
plt.legend(["Cos"], loc="upper left")
plt.subplot(1,2,2) # 1 row, 2 columns, 2nd plot
plt.plot(X, S, linewidth=2)
plt.xlabel("X data")
plt.ylabel("Y data")
plt.title("Sin Plotting")
plt.legend(["Sin"], loc="upper left")
plt.margins(0.15)
plt.autoscale(True)
plt.grid(True)
# plotting
plt.show()
another way to add subplots
In [6]:
# creating a figure
fig = plt.figure(figsize=(12,6), dpi=120)
# adding subplot
ax1 = fig.add_subplot(1,2,1) # 1 row, 2 columns, 1st plot
ax1.plot(X, C, linestyle='--')
ax1.grid(True)
ax1.margins(0.15)
ax2 = fig.add_subplot(1,2,2) # 1 row, 2 columns, 2nd plot
ax2.plot(X, S, linewidth=2)
ax2.margins(0.2)
ax2.autoscale(True)
ax2.grid(True)
# plotting
plt.xlabel("X data")
plt.ylabel("Y data")
plt.title("Sin Plotting")
plt.legend(["Sin"], loc="upper left")
plt.show()
One more way to add subplots
In [7]:
# creating a figure
plt.figure(figsize=(12,6), dpi=120)
f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)
ax1.plot(X,S, color='green', linestyle='--', linewidth='2')
ax1.grid(True)
ax1.margins(0.2)
ax2.plot(X,C, color='blue', linestyle='-', linewidth='2')
ax2.grid(True)
ax2.margins(0.2)
In [8]:
# creating a figure
plt.figure(figsize=(12,6), dpi=120)
f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)
ax1.plot(X,S, color='green', linestyle='--', linewidth='2', label="Sin wave")
ax1.grid(True)
ax1.margins(0.2)
ax2.plot(X,C, color='blue', linestyle='-', linewidth='2', label="Cos wave")
ax2.grid(True)
ax2.margins(0.2)
ax1.legend()
ax2.legend()
Out[8]:
In [9]:
# creating a figure
plt.figure(figsize=(12,6), dpi=120)
f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)
ax1.plot(X,S, color='green', linestyle='--', linewidth='2', label="Sin wave")
ax1.grid(True)
ax1.margins(0.2)
ax2.plot(X,C, color='blue', linestyle='-', linewidth='2', label="Cos wave")
ax2.grid(True)
ax2.margins(0.2)
ax1.legend()
ax1.set_xlabel("X data")
ax1.set_ylabel("Sin data")
ax1.set_title("Sin Chart")
ax2.legend()
ax2.set_xlabel("X data")
ax2.set_ylabel("Sin data")
ax2.set_title("Cos Chart")
Out[9]:
Adding some samples plots
In [10]:
fig = plt.figure(figsize=(16,12))
plt.subplots(nrows=3, ncols=4)
plt.show()
In [11]:
fig = plt.figure(figsize=(8,6))
ax1 = plt.subplot(1,2,1)
plt.subplot(3,2,2)
plt.subplot(3,2,4)
plt.subplot(3,2,6)
ax1.plot([1,2,3], [0.5,4,1.5])
plt.show()
In [12]:
X = [ (2,1,1), (2,3,4), (2,3,5), (2,3,6) ]
for nrows, ncols, plot_number in X:
plt.subplot(nrows, ncols, plot_number)
In [13]:
# removing all ticks
X = [ (2,1,1), (2,3,4), (2,3,5), (2,3,6) ]
for nrows, ncols, plot_number in X:
plt.subplot(nrows, ncols, plot_number)
plt.xticks([])
plt.yticks([])
In [14]:
# removing all ticks
X = [ (1,2,1), (3,2,2), (3,2,4), (3,2,6) ]
for nrows, ncols, plot_number in X:
plt.subplot(nrows, ncols, plot_number)
plt.xticks([])
plt.yticks([])
In [15]:
# removing all ticks
X = [ (4,2,1), (4,2,3), (4,2,5), (4,1,4), (4,2,2), (4,2,(4,6)) ]
for nrows, ncols, plot_number in X:
plt.subplot(nrows, ncols, plot_number)
plt.xticks([])
plt.yticks([])
Setting the Plot Range
In [16]:
fig, axes = plt.subplots(1, 3, figsize=(10, 4))
x = np.arange(0, 5, 0.25)
axes[0].plot(x, x**2, x, x**3)
axes[0].set_title("default axes ranges")
axes[1].plot(x, x**2, x, x**3)
axes[1].axis('tight')
axes[1].set_title("tight axes")
axes[2].plot(x, x**2, x, x**3)
axes[2].set_ylim([0, 60])
axes[2].set_xlim([2, 5])
axes[2].set_title("custom axes range");
In [20]:
fig, ax1 = plt.subplots()
x = np.arange(1,7,0.1)
ax1.plot(x, 2 * np.pi * x, lw=2, color="blue")
ax1.set_ylabel(r"Circumference $(cm)$", fontsize=16, color="blue")
for label in ax1.get_yticklabels():
label.set_color("blue")
ax2 = ax1.twinx()
ax2.plot(x, np.pi * x ** 2, lw=2, color="darkgreen")
ax2.set_ylabel(r"area $(cm^2)$", fontsize=16, color="darkgreen")
for label in ax2.get_yticklabels():
label.set_color("darkgreen")
#plt.grid(color='b', alpha=1.5, linestyle='dashed', linewidth=0.5)
In [18]:
fig.savefig("filename.png", dpi=200)
In [24]:
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
def fp(t):
return -2*np.pi * np.exp(-t) * np.sin(2*np.pi*t) - np.e**(-t)*np.cos(2*np.pi*t)
def g(t):
return np.sin(t) * np.cos(1/(t+0.1))
def g(t):
return np.sin(t) * np.cos(1/(t))
python_course_green = "#476042"
fig = plt.figure(figsize=(6, 4))
t = np.arange(-5.0, 1.0, 0.1)
sub1 = fig.add_subplot(221) # instead of plt.subplot(2, 2, 1)
sub1.set_title('The function f') # non OOP: plt.title('The function f')
sub1.plot(t, f(t))
sub2 = fig.add_subplot(222, axisbg="lightgrey")
sub2.set_title('fp, the derivation of f')
sub2.plot(t, fp(t))
t = np.arange(-3.0, 2.0, 0.02)
sub3 = fig.add_subplot(223)
sub3.set_title('The function g')
sub3.plot(t, g(t))
t = np.arange(-0.2, 0.2, 0.001)
sub4 = fig.add_subplot(224, axisbg="lightgrey")
sub4.set_title('A closer look at g')
sub4.set_xticks([-0.2, -0.1, 0, 0.1, 0.2])
sub4.set_yticks([-0.15, -0.1, 0, 0.1, 0.15])
sub4.plot(t, g(t))
plt.plot(t, g(t))
plt.tight_layout()
plt.show()
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