718.sagews

from pylab import *

x,y = np.random.randn(2,100)
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)
ax1.grid(True)
ax1.axhline(0, color='black', lw=2)

ax2 = fig.add_subplot(212, sharex=ax1)
ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2)
ax2.grid(True)
ax2.axhline(0, color='black', lw=2)

savefig('1.png')

reset()

import numpy as np
import matplotlib.pyplot as plt

plt.clf()

N = 5
menMeans   = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd     = (2, 3, 4, 1, 2)
womenStd   = (3, 5, 2, 3, 3)
ind = np.arange(N)    # the x locations for the groups
width = 0.35       # the width of the bars: can also be len(x) sequence

p1 = plt.bar(ind, menMeans,   width, color='r', yerr=womenStd)
p2 = plt.bar(ind, womenMeans, width, color='y',
             bottom=menMeans, yerr=menStd)

plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind+width/2., ('G1', 'G2', 'G3', 'G4', 'G5') )
plt.yticks(np.arange(0,81,10))
plt.legend( (p1[0], p2[0]), ('Men', 'Women') )

plt.savefig('2.png')

reset()

from pylab import *

t = arange(0.1, 4, 0.1)
s = exp(-t)
e = 0.1*abs(randn(len(s)))
f = 0.1*abs(randn(len(s)))
g = 2*e
h = 2*f

figure()
errorbar(t, s, e, fmt='o')             # vertical symmetric

figure()
errorbar(t, s, None, f, fmt='o')       # horizontal symmetric

figure()
errorbar(t, s, e, f, fmt='o')          # both symmetric

figure()
errorbar(t, s, [e,g], [f,h], fmt='--o')  # both asymmetric

figure()
errorbar(t, s, [e,g], f, fmt='o', ecolor='g')      # both mixed

figure()
errorbar(t, s, e, [f,h], fmt='o')      # both mixed

figure()
errorbar(t, s, [e,g], fmt='o')         # vertical asymmetric

figure()
errorbar(t, s, yerr=e, fmt='o')        # named

figure()
errorbar(t, s, xerr=f, fmt='o')        # named
xlabel('Distance (m)')
ylabel('Height (m)')
title('Mean and standard error as a function of distance')

figure()
ax = subplot(111)
ax.set_yscale('log')
errorbar(t, s+2, e, f, fmt='o')          # both symmetric

savefig('3.png')

reset()

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


fig = plt.figure()
Z = np.arange(10000.0)
Z.shape = 100,100
Z[:,50:] = 1.

im1 = plt.figimage(Z, xo=50, yo=0, cmap=cm.jet, origin='lower')
im2 = plt.figimage(Z, xo=100, yo=100, alpha=.8, cmap=cm.jet, origin='lower')


if 0:
    dpi = 72
    plt.savefig('figimage_%d.png'%dpi, dpi=dpi, facecolor='gray')
    plt.savefig('figimage_%d.pdf'%dpi, dpi=dpi, facecolor='gray')
    plt.savefig('figimage_%d.svg'%dpi, dpi=dpi, facecolor='gray')
    plt.savefig('figimage_%d.eps'%dpi, dpi=dpi, facecolor='gray')


plt.savefig('4.png')

reset()

import numpy as np
import matplotlib.pyplot as plt

plt.clf()

t = np.arange(0.0, 1.01, 0.01)
s = np.sin(2*2*np.pi*t)

plt.fill(t, s*np.exp(-5*t), 'r')
plt.grid(True)

plt.savefig('5.png')

reset()

import matplotlib.mlab as mlab
from matplotlib.pyplot import figure, show
import numpy as np
from matplotlib.pyplot import savefig, clf

clf()

x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)

fig = figure()
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313, sharex=ax1)

ax1.fill_between(x, 0, y1)
ax1.set_ylabel('between y1 and 0')

ax2.fill_between(x, y1, 1)
ax2.set_ylabel('between y1 and 1')

ax3.fill_between(x, y1, y2)
ax3.set_ylabel('between y1 and y2')
ax3.set_xlabel('x')

# now fill between y1 and y2 where a logical condition is met.  Note
# this is different than calling
#   fill_between(x[where], y1[where],y2[where]
# because of edge effects over multiple contiguous regions.
fig = figure()
ax = fig.add_subplot(211)
ax.plot(x, y1, x, y2, color='black')
ax.fill_between(x, y1, y2, where=y2>=y1, facecolor='green')
ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='red')
ax.set_title('fill between where')

# Test support for masked arrays.
y2 = np.ma.masked_greater(y2, 1.0)
ax1 = fig.add_subplot(212, sharex=ax)
ax1.plot(x, y1, x, y2, color='black')
ax1.fill_between(x, y1, y2, where=y2>=y1, facecolor='green')
ax1.fill_between(x, y1, y2, where=y2<=y1, facecolor='red')
ax1.set_title('Now regions with y2>1 are masked')

# This example illustrates a problem; because of the data
# gridding, there are undesired unfilled triangles at the crossover
# points.  A brute-force solution would be to interpolate all
# arrays to a very fine grid before plotting.

# show how to use transforms to create axes spans where a certain condition is satisfied
fig = figure()
ax = fig.add_subplot(111)
y = np.sin(4*np.pi*x)
ax.plot(x, y, color='black')

# use the data coordinates for the x-axis and the axes coordinates for the y-axis
import matplotlib.transforms as mtransforms
trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)
theta = 0.9
ax.axhline(theta, color='green', lw=2, alpha=0.5)
ax.axhline(-theta, color='red', lw=2, alpha=0.5)
ax.fill_between(x, 0, 1, where=y>theta, facecolor='green', alpha=0.5, transform=trans)
ax.fill_between(x, 0, 1, where=y<-theta, facecolor='red', alpha=0.5, transform=trans)

savefig('6.png')