Matplotlib - Sample Plots¶

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

# Data for plotting
t = np.arange(0.0, 3.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)

fig, ax = plt.subplots()
ax.plot(t, s)

ax.set(xlabel='time (s)', ylabel='voltage (mV)',
       title='About as simple as it gets, folks')
ax.grid()

fig.savefig("test.png")
plt.show()

import numpy as np
import matplotlib.pyplot as plt


x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)

y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)

plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')

plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')

plt.show()

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator
import numpy as np


# make these smaller to increase the resolution
dx, dy = 0.05, 0.05

# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(1, 5 + dy, dy),
                slice(1, 5 + dx, dx)]

z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)

# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
levels = MaxNLocator(nbins=10).tick_values(z.min(), z.max())


# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.get_cmap('PiYG')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig, (ax0, ax1) = plt.subplots(nrows=2)

im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax0)
ax0.set_title('pcolormesh with levels')


# contours are *point* based plots, so convert our bound into point
# centers
cf = ax1.contourf(x[:-1, :-1] + dx/3.,
                  y[:-1, :-1] + dy/3., z, levels=levels,
                  cmap=cmap)
fig.colorbar(cf, ax=ax1)
ax1.set_title('contourf with levels')

# adjust spacing between subplots so `ax1` title and `ax0` tick labels
# don't overlap
fig.tight_layout()

plt.show()

#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma*np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='pink', alpha=0.75)

# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r--', linewidth=1)

plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)

plt.show()

import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt


fig, ax = plt.subplots()

Path = mpath.Path
path_data = [
    (Path.MOVETO, (1.58, -2.57)),
    (Path.CURVE4, (0.35, -1.1)),
    (Path.CURVE4, (-1.75, 2.0)),
    (Path.CURVE4, (0.375, 2.0)),
    (Path.LINETO, (0.85, 1.15)),
    (Path.CURVE4, (2.2, 3.2)),
    (Path.CURVE4, (3, 0.05)),
    (Path.CURVE4, (2.0, -0.5)),
    (Path.CLOSEPOLY, (1.58, -2.57)),
    ]
codes, verts = zip(*path_data)
path = mpath.Path(verts, codes)
patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
ax.add_patch(patch)

# plot control points and connecting lines
x, y = zip(*path.vertices)
line, = ax.plot(x, y, 'go-')

ax.grid()
ax.axis('equal')
plt.show()

# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import

import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np


fig = plt.figure()
ax = fig.gca(projection='3d')

# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False)

# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=9)

plt.show()

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

w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U*U + V*V)

fig = plt.figure(figsize=(7, 9))
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])

#  Varying density along a streamline
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')

# Varying color along a streamline
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')
fig.colorbar(strm.lines)
ax1.set_title('Varying Color')

plt.tight_layout()
plt.show()

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

w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U*U + V*V)

fig = plt.figure(figsize=(7, 9))
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])

#  Varying density along a streamline
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')

# Varying color along a streamline
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')
fig.colorbar(strm.lines)
ax1.set_title('Varying Color')

#  Varying line width along a streamline
ax2 = fig.add_subplot(gs[1, 0])
lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
ax2.set_title('Varying Line Width')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse

NUM = 250

ells = [Ellipse(xy=np.random.rand(2) * 10,
                width=np.random.rand(), height=np.random.rand(),
                angle=np.random.rand() * 360)
        for i in range(NUM)]

fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
for e in ells:
    ax.add_artist(e)
    e.set_clip_box(ax.bbox)
    e.set_alpha(np.random.rand())
    e.set_facecolor(np.random.rand(3))

ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse

delta = 45.0  # degrees

angles = np.arange(0, 360 + delta, delta)
ells = [Ellipse((1, 1), 4, 2, a) for a in angles]

a = plt.subplot(111, aspect='equal')

for e in ells:
    e.set_clip_box(a.bbox)
    e.set_alpha(0.1)
    a.add_artist(e)

plt.xlim(-2, 4)
plt.ylim(-1, 3)

plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import EllipseCollection

x = np.arange(10)
y = np.arange(15)
X, Y = np.meshgrid(x, y)

XY = np.column_stack((X.ravel(), Y.ravel()))

ww = X / 10.0
hh = Y / 15.0
aa = X * 9


fig, ax = plt.subplots()

ec = EllipseCollection(ww, hh, aa, units='x', offsets=XY,
                       transOffset=ax.transData)
ec.set_array((X + Y).ravel())
ax.add_collection(ec)
ax.autoscale_view()
ax.set_xlabel('X')
ax.set_ylabel('y')
cbar = plt.colorbar(ec)
cbar.set_label('X+Y')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple


n_groups = 5

means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)

means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)

fig, ax = plt.subplots()

index = np.arange(n_groups)
bar_width = 0.35

opacity = 0.4
error_config = {'ecolor': '0.3'}

rects1 = ax.bar(index, means_men, bar_width,
                alpha=opacity, color='b',
                yerr=std_men, error_kw=error_config,
                label='Men')

rects2 = ax.bar(index + bar_width, means_women, bar_width,
                alpha=opacity, color='r',
                yerr=std_women, error_kw=error_config,
                label='Women')

ax.set_xlabel('Group')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(('A', 'B', 'C', 'D', 'E'))
ax.legend()

fig.tight_layout()
plt.show()

Student = namedtuple('Student', ['name', 'grade', 'gender'])
Score = namedtuple('Score', ['score', 'percentile'])

# GLOBAL CONSTANTS
testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
             'Push Ups']
testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', '']))


def attach_ordinal(num):
    """helper function to add ordinal string to integers

    1 -> 1st
    56 -> 56th
    """
    suffixes = {str(i): v
                for i, v in enumerate(['th', 'st', 'nd', 'rd', 'th',
                                       'th', 'th', 'th', 'th', 'th'])}

    v = str(num)
    # special case early teens
    if v in {'11', '12', '13'}:
        return v + 'th'
    return v + suffixes[v[-1]]


def format_score(scr, test):
    """
    Build up the score labels for the right Y-axis by first
    appending a carriage return to each string and then tacking on
    the appropriate meta information (i.e., 'laps' vs 'seconds'). We
    want the labels centered on the ticks, so if there is no meta
    info (like for pushups) then don't add the carriage return to
    the string
    """
    md = testMeta[test]
    if md:
        return '{0}\n{1}'.format(scr, md)
    else:
        return scr


def format_ycursor(y):
    y = int(y)
    if y < 0 or y >= len(testNames):
        return ''
    else:
        return testNames[y]


def plot_student_results(student, scores, cohort_size):
    #  create the figure
    fig, ax1 = plt.subplots(figsize=(9, 7))
    fig.subplots_adjust(left=0.115, right=0.88)
    fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')

    pos = np.arange(len(testNames))

    rects = ax1.barh(pos, [scores[k].percentile for k in testNames],
                     align='center',
                     height=0.5, color='m',
                     tick_label=testNames)

    ax1.set_title(student.name)

    ax1.set_xlim([0, 100])
    ax1.xaxis.set_major_locator(MaxNLocator(11))
    ax1.xaxis.grid(True, linestyle='--', which='major',
                   color='grey', alpha=.25)

    # Plot a solid vertical gridline to highlight the median position
    ax1.axvline(50, color='grey', alpha=0.25)
    # set X-axis tick marks at the deciles
    cohort_label = ax1.text(.5, -.07, 'Cohort Size: {0}'.format(cohort_size),
                            horizontalalignment='center', size='small',
                            transform=ax1.transAxes)

    # Set the right-hand Y-axis ticks and labels
    ax2 = ax1.twinx()

    scoreLabels = [format_score(scores[k].score, k) for k in testNames]

    # set the tick locations
    ax2.set_yticks(pos)
    # make sure that the limits are set equally on both yaxis so the
    # ticks line up
    ax2.set_ylim(ax1.get_ylim())

    # set the tick labels
    ax2.set_yticklabels(scoreLabels)

    ax2.set_ylabel('Test Scores')

    ax2.set_xlabel(('Percentile Ranking Across '
                    '{grade} Grade {gender}s').format(
                        grade=attach_ordinal(student.grade),
                        gender=student.gender.title()))

    rect_labels = []
    # Lastly, write in the ranking inside each bar to aid in interpretation
    for rect in rects:
        # Rectangle widths are already integer-valued but are floating
        # type, so it helps to remove the trailing decimal point and 0 by
        # converting width to int type
        width = int(rect.get_width())

        rankStr = attach_ordinal(width)
        # The bars aren't wide enough to print the ranking inside
        if width < 5:
            # Shift the text to the right side of the right edge
            xloc = width + 1
            # Black against white background
            clr = 'black'
            align = 'left'
        else:
            # Shift the text to the left side of the right edge
            xloc = 0.98*width
            # White on magenta
            clr = 'white'
            align = 'right'

        # Center the text vertically in the bar
        yloc = rect.get_y() + rect.get_height()/2.0
        label = ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
                         verticalalignment='center', color=clr, weight='bold',
                         clip_on=True)
        rect_labels.append(label)

    # make the interactive mouse over give the bar title
    ax2.fmt_ydata = format_ycursor
    # return all of the artists created
    return {'fig': fig,
            'ax': ax1,
            'ax_right': ax2,
            'bars': rects,
            'perc_labels': rect_labels,
            'cohort_label': cohort_label}

student = Student('Johnny Doe', 2, 'boy')
scores = dict(zip(testNames,
                  (Score(v, p) for v, p in
                   zip(['7', '48', '12:52', '17', '14'],
                       np.round(np.random.uniform(0, 1,
                                                  len(testNames))*100, 0)))))
cohort_size = 62  # The number of other 2nd grade boys

arts = plot_student_results(student, scores, cohort_size)
plt.show()

import matplotlib.pyplot as plt

# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Ice-Cream', 'Pizza', 'HotDogs', 'Burger'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)
ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()

import numpy as np
import matplotlib.pyplot as plt


data = [[ 66386, 174296,  75131, 577908,  32015],
        [ 58230, 381139,  78045,  99308, 160454],
        [ 89135,  80552, 152558, 497981, 603535],
        [ 78415,  81858, 150656, 193263,  69638],
        [139361, 331509, 343164, 781380,  52269]]

columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]

values = np.arange(0, 2500, 500)
value_increment = 1000

# Get some pastel shades for the colors
colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))
n_rows = len(data)

index = np.arange(len(columns)) + 0.3
bar_width = 0.4

# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.zeros(len(columns))

# Plot bars and create text labels for the table
cell_text = []
for row in range(n_rows):
    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])
    y_offset = y_offset + data[row]
    cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset])
# Reverse colors and text labels to display the last value at the top.
colors = colors[::-1]
cell_text.reverse()

# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text,
                      rowLabels=rows,
                      rowColours=colors,
                      colLabels=columns,
                      loc='bottom')

# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.2, bottom=0.2)

plt.ylabel("Loss in ${0}'s".format(value_increment))
plt.yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt.title('Loss by Disaster')

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Fixing random state for reproducibility
np.random.seed(19680801)


N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii

plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(19680801)
data = np.random.randn(2, 100)

fig, axs = plt.subplots(2, 2, figsize=(5, 5))
axs[0, 0].hist(data[0])
axs[1, 0].scatter(data[0], data[1])
axs[0, 1].plot(data[0], data[1])
axs[1, 1].hist2d(data[0], data[1])

plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = [0, 1, 2, 1]
y = [1, 2, 1, 0]

fig, ax = plt.subplots()
ax.fill(x, y)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1.5 * np.pi, 500)
y1 = np.sin(x)
y2 = np.sin(3 * x)

fig, ax = plt.subplots()

ax.fill(x, y1, 'b', x, y2, 'r', alpha=0.3)

# Outline of the region we've filled in
ax.plot(x, y1, c='b', alpha=0.8)
ax.plot(x, y2, c='r', alpha=0.8)
ax.plot([x[0], x[-1]], [y1[0], y1[-1]], c='b', alpha=0.8)
ax.plot([x[0], x[-1]], [y2[0], y2[-1]], c='r', alpha=0.8)

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Data for plotting
t = np.arange(0.01, 20.0, 0.01)

# Create figure
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

# log y axis
ax1.semilogy(t, np.exp(-t / 5.0))
ax1.set(title='semilogy')
ax1.grid()

# log x axis
ax2.semilogx(t, np.sin(2 * np.pi * t))
ax2.set(title='semilogx')
ax2.grid()

# log x and y axis
ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)
ax3.set(title='loglog base 2 on x')
ax3.grid()

# With errorbars: clip non-positive values
# Use new data for plotting
x = 10.0**np.linspace(0.0, 2.0, 20)
y = x**2.0

ax4.set_xscale("log", nonposx='clip')
ax4.set_yscale("log", nonposy='clip')
ax4.set(title='Errorbars go negative')
ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
# ylim must be set after errorbar to allow errorbar to autoscale limits
ax4.set_ylim(bottom=0.1)

fig.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt


r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r

ax = plt.subplot(111, projection='polar')
ax.plot(theta, r)
ax.set_rmax(2)
ax.set_rticks([0.5, 1, 1.5, 2])  # Less radial ticks
ax.set_rlabel_position(-22.5)  # Move radial labels away from plotted line
ax.grid(True)

ax.set_title("A line plot on a polar axis", va='bottom')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Make some fake data.
a = b = np.arange(0, 3, .02)
c = np.exp(a)
d = c[::-1]

# Create plots with pre-defined labels.
fig, ax = plt.subplots()
ax.plot(a, c, 'k--', label='Model length')
ax.plot(a, d, 'k:', label='Data length')
ax.plot(a, c + d, 'k', label='Total message length')

legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')

# Put a nicer background color on the legend.
legend.get_frame().set_facecolor('C0')

plt.show()