matplotlib

import pylab
pylab.plot(range(NUMSTEPS),rabbits,'g',label='Rabbits')
pylab.plot(range(NUMSTEPS),foxes,'r',label='Foxes')
pylab.legend(loc='upper right')
pylab.title('Rabbits vs Foxes')
pylab.xlabel('Time')
pylab.ylabel('Population')
pylab.show()

---

import matplotlib.pyplot as plt
plt.plot([1,2,3,4])  # Take this data as 'y' values
plt.ylabel('some numbers')
plt.show()

plt.plot([1,2,3,4], [1,4,9,16])

---

import matplotlib.pyplot as plt
plt.plot([1,2,3,4], [1,4,9,16], 'ro')   # Take first vector as 'x' values, second as 'y' values
plt.axis([0, 6, 0, 20])                 # Assign axis limits
plt.show()

---

import numpy as np
import matplotlib.pyplot as plt
# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()

---

plt.plot(x, y, linewidth=2.0)

line, = plt.plot(x, y, '-')
line.set_antialiased(False) # turn off antialising

lines = plt.plot(x1, y1, x2, y2)
# use keyword args
plt.setp(lines, color='r', linewidth=2.0)
# or MATLAB style string value pairs
plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

lines = plt.plot([1,2,3])
plt.setp(lines)

The function gca() returns the current axes (a matplotlib.axes.Axes
instance), and gcf() returns the current figure (matplotlib.figure.Figure instance).

---

Below is a script to create two subplots.

import numpy as np
import matplotlib.pyplot as plt
def f(t):
  return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
plt.figure(1)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()

---

# HISTOGRAM
# matplotlib.pyplot.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, 
# histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, 
# hold=None, data=None, **kwargs)

import numpy as np
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='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

t = plt.xlabel('my data', fontsize=14, color='red')

matplotlib accepts TeX equation expressions in any text expression. For example to write the expression
σ i = 15 in the title, you can write a TeX expression surrounded by dollar signs:
plt.title(r'$\sigma_i=15$')

---

import numpy as np
import matplotlib.pyplot as plt
ax = plt.subplot(111)
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s, lw=2)
plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
plt.ylim(-2,2)
plt.show()

import matplotlib.pyplot as plt
plt.style.use('ggplot')

print plt.style.available

---

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
fig = plt.figure()
fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.set_title('axes title')
ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')
ax.text(3, 8, 'boxed italics text in data coords', style='italic',
bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)
ax.text(3, 2, unicode('unicode: Institut f\374r Festk\366rperphysik', 'latin-1'))
ax.text(0.95, 0.01, 'colored text in axes coords',
verticalalignment='bottom', horizontalalignment='right',
transform=ax.transAxes,
color='green', fontsize=15)
ax.plot([2], [1], 'o')
ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
arrowprops=dict(facecolor='black', shrink=0.05))
ax.axis([0, 10, 0, 10])
plt.show()

---

import matplotlib.pyplot as plt
import matplotlib.patches as patches
# build a rectangle in axes coords
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
# axes coordinates are 0,0 is bottom left and 1,1 is upper right
p = patches.Rectangle(
(left, bottom), width, height,
fill=False, transform=ax.transAxes, clip_on=False
)
ax.add_patch(p)
ax.text(left, bottom, 'left top',
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes)
ax.text(left, bottom, 'left bottom',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(right, top, 'right bottom',
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(right, top, 'right top',
horizontalalignment='right',
verticalalignment='top',
transform=ax.transAxes)
ax.text(right, bottom, 'center top',
horizontalalignment='center',
verticalalignment='top',
transform=ax.transAxes)
ax.text(left, 0.5*(bottom+top), 'right center',
horizontalalignment='right',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
ax.text(left, 0.5*(bottom+top), 'left center',
horizontalalignment='left',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',
horizontalalignment='center',
verticalalignment='center',
fontsize=20, color='red',
transform=ax.transAxes)
ax.text(right, 0.5*(bottom+top), 'centered',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
ax.text(left, top, 'rotated\nwith newlines',
horizontalalignment='center',
verticalalignment='center',
rotation=45,
transform=ax.transAxes)
ax.set_axis_off()
plt.show()

---

# plain text
plt.title('alpha > beta')
produces “alpha > beta”.
Whereas this:
# math text
plt.title(r'$\alpha > \beta$')
produces “α > β”.

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To make subscripts and superscripts, use the ’_’ and ’^’ symbols:
r'$\alpha_i > \beta_i$'
α i > β i

---

to write
the sum of x i from 0 to ∞, you could do:
r'$\sum_{i=0}^\infty x_i$'

---

def myplot2():
    plt.plot(upanodes, standard, '-r', label='targeted_order')
    plt.plot(upanodes, fast, '-b', label='fast_targeted_order')
    plt.legend(loc='upper left')
    plt.title('Fast_Targeted_Order vs Targeted_Order in Desktop Python')
    plt.xlabel('Number of Nodes, m=5')
    plt.ylabel('Execution time')
    plt.text(50, .025, r'$UPA\ graphs\ with\ m=5$')
    plt.show()
    
---