matplotlib之pyplot 学习示例

这是一个非常基本的带文字标签的plot()：

import matplotlib.pyplot as plt

import numpy as np

t = np.arange(0.0, 2.0, 0.01)

s = 1 + np.sin(2*np.pi*t)

plt.plot(t, s)

plt.xlabel('time (s)')

plt.ylabel('voltage (mV)')

plt.title('About as simple as it gets, folks')

plt.grid(True)

plt.savefig("test.png")

plt.show()

2、subplot 示例

使用  subplot() 命令创建多个轴（即子图） ：

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()

3、直方图(Histograms)

hist()命令自动生成直方图并返回容器的计数或概率：

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

np.random.seed(0)

mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(437)

num_bins = 50

fig, ax = plt.subplots()

# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, normed=1)

# add a 'best fit' line
y = mlab.normpdf(bins, mu, sigma)
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')

# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
plt.show()

4、路径示例

可以使用  matplotlib.path 模块在matplotlib中添加任意路径 ：

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()

5、3d图（mplot3d）

mplot3d工具包（请参阅mplot3d教程和 mplot3d示例）支持简单的3d图形，包括曲面，线框，散点图和条形图。

from mpl_toolkits.mplot3d import Axes3D
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=5)

plt.show()

6、流线图（Streamplot）

streamplot()函数绘制矢量场的流线。除了简单绘制流线之外，它还允许将流线的颜色和/或线宽映射到单独的参数中，例如矢量场的速度或局部强度。

import numpy as np
import matplotlib.pyplot as plt

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

fig0, ax0 = plt.subplots()
strm = ax0.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
fig0.colorbar(strm.lines)

fig1, (ax1, ax2) = plt.subplots(ncols=2)
ax1.streamplot(X, Y, U, V, density=[0.5, 1])

lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)

plt.show()

为了支持 凤凰号火星飞行任务（使用matplotlib显示航天器的地面跟踪），Michael Droettboom在Charlie Moad的基础上建立了一个非常精确的8次样条逼近椭圆弧（请参考资料 Arc），并对缩放不敏感。

import matplotlib.pyplot as plt
import numpy.random as rnd
from matplotlib.patches import Ellipse

NUM = 250

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

fig = plt.figure(0)
ax = fig.add_subplot(111, aspect='equal')
for e in ells:
ax.add_artist(e)
e.set_clip_box(ax.bbox)
e.set_alpha(rnd.rand())
e.set_facecolor(rnd.rand(3))

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

plt.show()

8、条形图(Bar)

使用 bar() 命令创建条形图是简单的，它包括诸如自定义错误条等。

import numpy as np
import matplotlib.pyplot as plt

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

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

plt.xlabel('Group')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))
plt.legend()

plt.tight_layout()
plt.show()

创建堆积条（bar_stacked.py）或水平条形图（barh_demo.py）也很简单。

9、饼图(Pie)

该 pie()命令允许您轻松创建饼图。可选功能包括自动标记面积百分比，从饼图中心突起一个或多个楔子，以及阴影效果。仔细查看附件中的代码，只需几行代码即可生成此图。

import matplotlib.pyplot as plt

# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
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()

10、表的示例(Table)

   table()命令将一个文本表添加到轴。

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.array([0.0] * 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()

11、散点图(Scatter)

该scatter()命令使用（可选）大小和颜色参数生成散点图。此示例绘制了Google股票价格的变化情况，通过标记：反映交易量的尺寸和随时间变化的颜色。这里，alpha属性用于制作半透明的圆形标记。

import numpy as np
import matplotlib.pyplot as plt

import matplotlib.cbook as cbook

# Load a numpy record array from yahoo csv data with fields date,
# open, close, volume, adj_close from the mpl-data/example directory.
# The record array stores python datetime.date as an object array in
# the date column
datafile = cbook.get_sample_data('goog.npy')
try:
# Python3 cannot load python2 .npy files with datetime(object) arrays
# unless the encoding is set to bytes. However this option was
# not added until numpy 1.10 so this example will only work with
# python 2 or with numpy 1.10 and later
price_data = np.load(datafile, encoding='bytes').view(np.recarray)
except TypeError:
price_data = np.load(datafile).view(np.recarray)
price_data = price_data[-250:] # get the most recent 250 trading days

delta1 = np.diff(price_data.adj_close)/price_data.adj_close[:-1]

# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]

fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)

ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')

ax.grid(True)
fig.tight_layout()

plt.show()

12、滑块示例(Slider)

Matplotlib具有独立于所用图形用户界面的基本GUI控件，允许您编写跨GUI图形的控件。这个示例很有意思，大家多看看，详情请参阅matplotlib.widgets和 widget examples。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons

fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10])

axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)

sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)

def update(val):
amp = samp.val
freq = sfreq.val
l.set_ydata(amp*np.sin(2*np.pi*freq*t))
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)

resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')

def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset)

rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)

def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)

plt.show()

13、填充演示(Fill)

    fill()命令让您绘制填充曲线和多边形：

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1, 500)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)

fig, ax = plt.subplots()

ax.fill(x, y, zorder=10)
ax.grid(True, zorder=5)
plt.show()

14、日期刻度示例（dates模块）

您可以绘制日期数据，包括主要和次要刻度以及两种自定义刻度格式。

import datetime

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook

years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')

datafile = cbook.get_sample_data('goog.npy')
try:
r = np.load(datafile, encoding='bytes').view(np.recarray)
except TypeError:
r = np.load(datafile).view(np.recarray)

fig, ax = plt.subplots()
ax.plot(r.date, r.adj_close)

# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)

datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year + 1, 1, 1)
ax.set_xlim(datemin, datemax)

# format the coords message box
def price(x):
return '$%1.2f' % x
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)

# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()

plt.show()

请参阅matplotlib.ticker并matplotlib.dates了解详细信息和用法。

15、对数图

函数semilogx()， semilogy()和 loglog()简化了对数图的创建。

import numpy as np
import matplotlib.pyplot as plt

plt.subplots_adjust(hspace=0.4)
t = np.arange(0.01, 20.0, 0.01)

# log y axis
plt.subplot(221)
plt.semilogy(t, np.exp(-t/5.0))
plt.title('semilogy')
plt.grid(True)

# log x axis
plt.subplot(222)
plt.semilogx(t, np.sin(2*np.pi*t))
plt.title('semilogx')
plt.grid(True)

# log x and y axis
plt.subplot(223)
plt.loglog(t, 20*np.exp(-t/10.0), basex=2)
plt.grid(True)
plt.title('loglog base 2 on x')

# with errorbars: clip non-positive values
ax = plt.subplot(224)
ax.set_xscale("log", nonposx='clip')
ax.set_yscale("log", nonposy='clip')

x = 10.0**np.linspace(0.0, 2.0, 20)
y = x**2.0
plt.errorbar(x, y, xerr=0.1*x, yerr=5.0 + 0.75*y)
ax.set_ylim(ymin=0.1)
ax.set_title('Errorbars go negative')

plt.show()

16、极图(Polar)

      polar()命令生成极地图。

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) # get radial labels away from plotted line
ax.grid(True)

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

17、自动图例(Legend)

    legend()命令自动生成图例，并带有与MATLAB兼容的图例布局命令。

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('#00FFCC')

plt.show()

18、数学表达式(Mathtext模块）

matplotlib内部mathtext引擎现在支持许多TeX表达式。mathtext模块使用FreeType 和DejaVu，BaKoMa计算机现代版或STIX 字体提供TeX风格的数学表达式。请参阅matplotlib.mathtext模块了解更多详情。

from __future__ import print_function
import matplotlib.pyplot as plt
import os
import sys
import re
import gc

# Selection of features following "Writing mathematical expressions" tutorial
mathtext_titles = {
0: "Header demo",
1: "Subscripts and superscripts",
2: "Fractions, binomials and stacked numbers",
3: "Radicals",
4: "Fonts",
5: "Accents",
6: "Greek, Hebrew",
7: "Delimiters, functions and Symbols"}
n_lines = len(mathtext_titles)

# Randomly picked examples
mathext_demos = {
0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",

1: r"$\alpha_i > \beta_i,\ "
r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
r"\ldots$",

2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",

3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",

4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",

5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
r"\ldots$",

6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",

7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}

def doall():
# Colors used in mpl online documentation.
mpl_blue_rvb = (191./255., 209./256., 212./255.)
mpl_orange_rvb = (202/255., 121/256., 0./255.)
mpl_grey_rvb = (51./255., 51./255., 51./255.)

# Creating figure and axis.
plt.figure(figsize=(6, 7))
plt.axes([0.01, 0.01, 0.98, 0.90], facecolor="white", frameon=True)
plt.gca().set_xlim(0., 1.)
plt.gca().set_ylim(0., 1.)
plt.gca().set_title("Matplotlib's math rendering engine",
color=mpl_grey_rvb, fontsize=14, weight='bold')
plt.gca().set_xticklabels("", visible=False)
plt.gca().set_yticklabels("", visible=False)

# Gap between lines in axes coords
line_axesfrac = (1. / (n_lines))

# Plotting header demonstration formula
full_demo = mathext_demos[0]
plt.annotate(full_demo,
xy=(0.5, 1. - 0.59*line_axesfrac),
xycoords='data', color=mpl_orange_rvb, ha='center',
fontsize=20)

# Plotting features demonstration formulae
for i_line in range(1, n_lines):
baseline = 1. - (i_line)*line_axesfrac
baseline_next = baseline - line_axesfrac*1.
title = mathtext_titles[i_line] + ":"
fill_color = ['white', mpl_blue_rvb][i_line % 2]
plt.fill_between([0., 1.], [baseline, baseline],
[baseline_next, baseline_next],
color=fill_color, alpha=0.5)
plt.annotate(title,
xy=(0.07, baseline - 0.3*line_axesfrac),
xycoords='data', color=mpl_grey_rvb, weight='bold')
demo = mathext_demos[i_line]
plt.annotate(demo,
xy=(0.05, baseline - 0.75*line_axesfrac),
xycoords='data', color=mpl_grey_rvb,
fontsize=16)

for i in range(n_lines):
s = mathext_demos[i]
print(i, s)
plt.show()

if '--latex' in sys.argv:
# Run: python mathtext_examples.py --latex
# Need amsmath and amssymb packages.
fd = open("mathtext_examples.ltx", "w")
fd.write("\\documentclass{article}\n")
fd.write("\\usepackage{amsmath, amssymb}\n")
fd.write("\\begin{document}\n")
fd.write("\\begin{enumerate}\n")

for i in range(n_lines):
s = mathext_demos[i]
s = re.sub(r"(?<!\\)\$", "$$", s)
fd.write("\\item %s\n" % s)

fd.write("\\end{enumerate}\n")
fd.write("\\end{document}\n")
fd.close()

os.system("pdflatex mathtext_examples.ltx")
else:
doall()

Matplotlib的mathtext基础架构是一个独立的实现，不需要在您的计算机上安装TeX或任何外部软件包。请参阅编写数学表达式的教程。

19、数学文本的TeX渲染（usetex）

尽管matplotlib的内部数学渲染引擎非常强大，但有时您需要TeX。Matplotlib使用usetex选项支持对字符串进行外部TeX呈现。

import numpy as np
import matplotlib.pyplot as plt

# Example data
t = np.arange(0.0, 1.0 + 0.01, 0.01)
s = np.cos(4 * np.pi * t) + 2

plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.plot(t, s)

plt.xlabel(r'\textbf{time} (s)')
plt.ylabel(r'\textit{voltage} (mV)',fontsize=16)
plt.title(r"\TeX\ is Number "
r"$\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!",
fontsize=16, color='gray')
# Make room for the ridiculously large title.
plt.subplots_adjust(top=0.8)

plt.savefig('tex_demo')
plt.show()

20、EEG 演示(pbrain)

您可以将matplotlib嵌入到pygtk，wx，Tk或Qt应用程序中。这是一个名为pbrain的EEG查看器屏幕截图。

较低的轴用specgram() 绘制其中一个EEG通道的光谱图。

有关如何在不同工具包中嵌入matplotlib的示例，请参阅：

• user_interfaces示例代码：embedding_in_gtk2.py
• user_interfaces示例代码：embedding_in_wx2.py
• user_interfaces示例代码：mpl_with_glade.py
• user_interfaces示例代码：embedding_in_qt4.py
• user_interfaces示例代码：embedding_in_tk.py

21、XKCD-风格草图

matplotlib支持绘制 xkcd 风格。

import numpy as np
import matplotlib.pyplot as plt

with plt.xkcd():
# Based on "Stove Ownership" from XKCD by Randall Monroe
# http://xkcd.com/418/

fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xticks([])
plt.yticks([])
ax.set_ylim([-30, 10])

data = np.ones(100)
data[70:] -= np.arange(30)

plt.annotate(
'THE DAY I REALIZED\nI COULD COOK BACON\nWHENEVER I WANTED',
xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))

plt.plot(data)

plt.xlabel('time')
plt.ylabel('my overall health')
fig.text(
0.5, 0.05,
'"Stove Ownership" from xkcd by Randall Monroe',
ha='center')

# Based on "The Data So Far" from XKCD by Randall Monroe
# http://xkcd.com/373/

fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.bar([0, 1], [0, 100], 0.25)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks([0, 1])
ax.set_xlim([-0.5, 1.5])
ax.set_ylim([0, 110])
ax.set_xticklabels(['CONFIRMED BY\nEXPERIMENT', 'REFUTED BY\nEXPERIMENT'])
plt.yticks([])

plt.title("CLAIMS OF SUPERNATURAL POWERS")

fig.text(
0.5, 0.05,
'"The Data So Far" from xkcd by Randall Monroe',
ha='center')

plt.show()