In [1]:
#!pip install matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [2]:
#shows graphic without 'plt.show()'
%matplotlib inline
In [3]:
# creating two variables to represent
x = np.linspace(0, 5, 11)
y = x ** 2
display(x)
display(y)
Creating figure¶
Matplotlib allows the aspect ratio, DPI and figure size to be specified when the Figure object is created. You can use the figsize and dpi keyword arguments.
figsizeis a tuple of the width and height of the figure in inchesdpiis the dots-per-inch (pixel per inch).
In [4]:
#Create figure
fig = plt.figure()
In [5]:
#Figure size and DPI
fig = plt.figure(figsize=(8,4), dpi=100)
In [6]:
#Subplots layout
fig, axes = plt.subplots(nrows=1, ncols=2)
In [7]:
#Figure size in subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,3))
In [8]:
# Multiple plots in the same canvas
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes
Adding and ploting axes¶
In [9]:
# Create Figure (empty canvas)
fig = plt.figure()
# Add set of axes to figure
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)
# Plot on that set of axes
axes.plot(x, y, 'b')
Out[9]:
In [10]:
fig, ax = plt.subplots()
ax.plot(x, x+2, color="blue") # by colour name
ax.plot(x, x+3, color="#FF8C00") # RGB hex code
ax.plot(x, x+1, color="blue", alpha=0.5) # half-transparant
Out[10]:
Line and marker styles¶
To change the line width, we can use the linewidth or lw keyword argument. The line style can be selected using the linestyle or ls keyword arguments:
In [11]:
#Line width
fig, ax = plt.subplots(figsize=(12,6))
ax.plot(x, x+1, color="red", linewidth=0.25)
ax.plot(x, x+2, color="red", linewidth=0.50)
ax.plot(x, x+3, color="red", linewidth=1.00)
ax.plot(x, x+4, color="red", linewidth=2.00)
Out[11]:
In [12]:
#Line type
fig, ax = plt.subplots(figsize=(12,6))
# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’
ax.plot(x, x+5, color="green", lw=3, linestyle='-')
ax.plot(x, x+6, color="green", lw=3, ls='-.')
ax.plot(x, x+7, color="green", lw=3, ls=':')
#custom dash
line, = ax.plot(x, x+8, color="black", lw=1.50)
line.set_dashes([5, 10, 15, 10])
In [13]:
#Marker symbols
fig, ax = plt.subplots(figsize=(12,6))
ax.plot(x, x+ 9, color="blue", lw=3, ls='-', marker='+')
ax.plot(x, x+10, color="blue", lw=3, ls='--', marker='o')
ax.plot(x, x+11, color="blue", lw=3, ls='-', marker='s')
ax.plot(x, x+12, color="blue", lw=3, ls='--', marker='1')
Out[13]:
In [14]:
# marker size and color
fig, ax = plt.subplots(figsize=(12,6))
ax.plot(x, x+13, color="purple", lw=1, ls='-', marker='o', markersize=2)
ax.plot(x, x+14, color="purple", lw=1, ls='-', marker='o', markersize=4)
ax.plot(x, x+15, color="purple", lw=1, ls='-', marker='o', markersize=8, markerfacecolor="red")
ax.plot(x, x+16, color="purple", lw=1, ls='-', marker='s', markersize=8,
markerfacecolor="yellow", markeredgewidth=3, markeredgecolor="green");
Ploting title and axes labels¶
A title can be added to each axis instance in a figure. To set the title, use the set_title method.
In [15]:
ax.set_title("title");
Similarly, with the methods set_xlabel and set_ylabel, we can set the labels of the X and Y axes:
In [16]:
ax.set_xlabel("x")
ax.set_ylabel("y");
In [17]:
# Create Figure (empty canvas)
fig = plt.figure()
# Add set of axes to figure
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)
# Plot on that set of axes
axes.plot(x, y, 'b')
#Plot axis labels and title
axes.set_title('Set Title')
axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods
axes.set_ylabel('Set y Label')
Out[17]:
Plotting legend¶
You can use the label="label text" keyword argument when plots or other objects are added to the figure, and then using the legend method without arguments to add the legend to the figure:
In [18]:
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(x, x**2, label="x**2")
ax.plot(x, x**3, label="x**3")
ax.legend()
Out[18]:
In [19]:
# Legend position
ax.legend(loc=0) # optimal location
ax.legend(loc=1) # upper right corner
ax.legend(loc=2) # upper left corner
ax.legend(loc=3) # lower left corner
ax.legend(loc=4) # lower right corner
Out[19]:
In [20]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, x**2, x, x**3)
axes.set_ylim([0, 60])
axes.set_xlim([2, 5])
axes.set_title("custom axes range");
In [21]:
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
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")
Out[21]:
Logarithmic scale¶
It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact only one application of a more general transformation system in Matplotlib. Each of the axes' scales are set seperately using set_xscale and set_yscale methods which accept one parameter (with the value "log" in this case):
In [22]:
fig, axes = plt.subplots(1, 2, figsize=(10,4))
axes[0].plot(x, x**2, x, np.exp(x))
axes[0].set_title("Normal scale")
axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)");
Ticks and custom tick labels¶
We can explicitly determine where we want the axis ticks with set_xticks and set_yticks, which both take a list of values for where on the axis the ticks are to be placed. We can also use the set_xticklabels and set_yticklabels methods to provide a list of custom text labels for each tick location:
In [23]:
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.plot(x, x**2, x, x**3, lw=2)
ax.set_xticks([1, 2, 3, 4, 5])
ax.set_xticklabels([r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$', r'$\epsilon$'], fontsize=18)
yticks = [0, 50, 100, 150]
ax.set_yticks(yticks)
ax.set_yticklabels(["$%.1f$" % y for y in yticks], fontsize=18); # use LaTeX formatted labels
Scientific notation¶
With large numbers on axes, it is often better use scientific notation:
In [24]:
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.plot(x, x**2, x, np.exp(x))
ax.set_title("scientific notation")
ax.set_yticks([0, 50, 100, 150])
from matplotlib import ticker
formatter = ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-1,1))
ax.yaxis.set_major_formatter(formatter)
Axis grid¶
With the grid method in the axis object, we can turn on and off grid lines. We can also customize the appearance of the grid lines using the same keyword arguments as the plot function:
In [25]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# default grid appearance
axes.plot(x, x**2, x, x**3, lw=2)
axes.grid(True)
In [26]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# custom grid appearance
axes.plot(x, x**2, x, x**3, lw=2)
axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
Axis spines¶
In [27]:
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('blue')
ax.spines['left'].set_color('red')
ax.spines['left'].set_linewidth(2)
# turn off axis spine to the right
ax.spines['right'].set_color("none")
ax.yaxis.tick_left() # only ticks on the left side
Twin axes¶
Sometimes it is useful to have dual x or y axes in a figure; for example, when plotting curves with different units together. Matplotlib supports this with the twinx and twiny functions:
In [28]:
fig, ax1 = plt.subplots()
ax1.plot(x, x**2, lw=2, color="blue")
ax1.set_ylabel(r"area $(m^2)$", fontsize=18, color="blue")
for label in ax1.get_yticklabels():
label.set_color("blue")
ax2 = ax1.twinx()
ax2.plot(x, x**3, lw=2, color="red")
ax2.set_ylabel(r"volume $(m^3)$", fontsize=18, color="red")
for label in ax2.get_yticklabels():
label.set_color("red")
Axes where x and y is zero¶
In [29]:
fig, ax = plt.subplots()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0)) # set position of x spine to x=0
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0)) # set position of y spine to y=0
xx = np.linspace(-0.75, 1., 100)
ax.plot(xx, xx**3);
Text annotation¶
In [30]:
fig, ax = plt.subplots()
ax.plot(xx, xx**2, xx, xx**3)
ax.text(0.15, 0.2, r"$y=x^2$", fontsize=20, color="blue")
ax.text(0.65, 0.1, r"$y=x^3$", fontsize=20, color="darkorange");
Multiplots¶
In [31]:
# Creates blank canvas
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes
# Larger Figure Axes 1
axes1.plot(x, y, 'b')
axes1.set_xlabel('X_label_axes2')
axes1.set_ylabel('Y_label_axes2')
axes1.set_title('Axes 2 Title')
# Insert Figure Axes 2
axes2.plot(y, x, 'r')
axes2.set_xlabel('X_label_axes2')
axes2.set_ylabel('Y_label_axes2')
axes2.set_title('Axes 2 Title');
In [32]:
fig, axes = plt.subplots(nrows=1, ncols=2)
for ax in axes:
ax.plot(x, y, 'g')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title')
fig
plt.tight_layout() #automatically adjusts so that there is no overlapping content
Saving figures¶
Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF.
To save a figure to a file we can use the savefig method in the Figure class:
In [33]:
fig.savefig("filename.png")
fig.savefig("filename.png", dpi=200) #specify DPI
Other Plot Types¶
There are many specialized plots we can create, such as barplots, histograms, scatter plots, and much more.
In [34]:
n = np.array([0,1,2,3,4,5])
Scatter plot¶
In [35]:
plt.scatter(x,y)
Out[35]:
In [36]:
fig1 = plt.figure()
axes = fig1.add_axes([0.1, 0.1, 0.8, 0.8])
axes.scatter(xx, xx + 0.25*np.random.randn(len(xx)))
Out[36]:
Histogram¶
In [37]:
from random import sample
data = sample(range(1, 1000), 100)
plt.hist(data)
Out[37]:
Bar plot¶
In [38]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.bar(n, n**2, align="center", width=0.5, alpha=0.5)
Out[38]:
In [39]:
## Box plot
In [40]:
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
# rectangular box plot
plt.boxplot(data,vert=True,patch_artist=True);
Steps¶
In [41]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.step(n, n**2, lw=2)
Out[41]:
Fill between¶
In [42]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.fill_between(x, x**2, x**3, color="green", alpha=0.5);
Color map¶
In [43]:
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)
In [44]:
phi_m = np.linspace(0, 2*np.pi, 100)
phi_p = np.linspace(0, 2*np.pi, 100)
X,Y = np.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
In [45]:
# pcolor
fig, ax = plt.subplots()
p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=plt.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)
In [46]:
#imshow
fig, ax = plt.subplots()
im = ax.imshow(Z, cmap=plt.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
im.set_interpolation('bilinear')
cb = fig.colorbar(im, ax=ax)
In [47]:
#contour
fig, ax = plt.subplots()
cnt = ax.contour(Z, cmap=plt.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
Surface plot¶
In [48]:
from mpl_toolkits.mplot3d.axes3d import Axes3D
fig = plt.figure(figsize=(14,6))
# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)
# surface_plot with color grading and color bar
ax = fig.add_subplot(1, 2, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.coolwarm, linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5)
Wire frame plot¶
In [49]:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1, 1, 1, projection='3d')
p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)
Coutor plot with projections¶
In [50]:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1,1,1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
cset = ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=plt.cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=plt.cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=3*np.pi, cmap=plt.cm.coolwarm)
ax.set_xlim3d(-np.pi, 2*np.pi);
ax.set_ylim3d(0, 3*np.pi);
ax.set_zlim3d(-np.pi, 2*np.pi);
In [51]:
x = np.linspace(0, 5, 11)
y = x ** 2
plt.plot(x, y)
Out[51]:
In [52]:
plt.style.use('default')
plt.plot(x, y)
Out[52]:
In [53]:
plt.style.use('classic')
plt.plot(x, y)
Out[53]:
In [54]:
plt.style.use('bmh')
plt.plot(x, y)
Out[54]:
In [55]:
plt.style.use('fivethirtyeight')
plt.plot(x, y)
Out[55]:
In [56]:
plt.style.use('ggplot')
plt.plot(x, y)
Out[56]:
In [57]:
plt.style.use('grayscale')
plt.plot(x, y)
Out[57]:
In [58]:
plt.style.use('dark_background')
plt.plot(x, y)
Out[58]:
In [59]:
plt.style.use('seaborn')
plt.plot(x, y)
Out[59]:
Color¶
