In [4]:
y = randn(1000)
plot(y)
Out[4]:
y = randn(1000)
plot(y)
y = randn(1000)
plot(y, 'g^')
y1 = randn(1000)
y2 = randn(1000)
plot(y1, 'r,', y2, 'b,')
y1 = randn(1000)
y2 = randn(1000)
plot(y1, 'r-', y2, 'b-.')
x = randn(10000)
hist(x, 100)
"""
Show how to make date plots in matplotlib using date tick locators and
formatters. See major_minor_demo1.py for more information on
controlling major and minor ticks
All matplotlib date plotting is done by converting date instances into
days since the 0001-01-01 UTC. The conversion, tick locating and
formatting is done behind the scenes so this is most transparent to
you. The dates module provides several converter functions date2num
and num2date
"""
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')
# 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')
r = np.load(datafile).view(np.recarray)
fig = plt.figure()
ax = fig.add_subplot(111)
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()
import pandas as pd
%cd 'data'
filename = 'worldcitiespop.txt'
data = pd.read_csv(filename)
plot(data.Longitude, data.Latitude, 'r,')
%cd..
# Take from http://stackoverflow.com/questions/2369492/generate-a-heatmap-in-matplotlib-using-a-scatter-data-set
# This makes a 50x50 heatmap.
# If you want, say, 512x384, you can put bins=(512, 384) in the call to histogram2d.
import matplotlib.pyplot as plt
# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)
heatmap, xedges, yedges = np.histogram2d(x, y, bins=(50, 50))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.clf()
plt.imshow(heatmap, extent=extent)
plt.show()
import matplotlib.pyplot as plt
# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)
heatmap, xedges, yedges = np.histogram2d(x, y, bins=(512, 384))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.clf()
plt.imshow(heatmap, extent=extent)
plt.show()
x = linspace(0, 2 * pi, 1000)
y = 1 + 2 * cos(5 * x)
subplot(1,2,1)
plot(x,y)
subplot(1,2,2,polar=True)
polar(x,y)
import urllib2
from PIL import Image
png= urllib2.urlopen('http://ipython.rossant.net/squirrel.png')
im = imread(png)
im.shape
img = Image.fromarray((im * 255).astype('uint8'))
im = array(img)
imshow(im)
from scipy.cluster.vq import *
M = im[:,:,0].ravel()
centroids, _ = kmeans(M, 4)
qnt, _ = vq(M, centroids)
clustered = centroids[reshape(qnt, (300, 300))]
cmap = matplotlib.colors.ListedColormap([(0,0.2,0.3),(0.85,0.1,0.13),(0.44,0.6,0.6),(1.0,0.9,0.65)])
imshow(clustered, cmap=cmap)
import pandas as pd
%cd 'data'
filename = 'worldcitiespop.txt'
data = pd.read_csv(filename)
plot(data.Longitude, data.Latitude, 'r,')
%cd..
locations = data[['Longitude','Latitude']].as_matrix()
locations
population = data.Population
population
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='mill', llcrnrlat=-65, urcrnrlat=85, llcrnrlon=-180, urcrnrlon=180)
x,y = m(locations[:,0],locations[:,1])
x,y
x0, y0 = m(-180, -65)
x1, y1 = m(180, 85)
x1, y1
weights = population.copy()
weights[isnan(weights)] = 1000
h, _, _ = histogram2d(x, y, weights = weights, bins=(linspace(x0, x1, 500), linspace(y0, y1, 500)))
h
import scipy.ndimage.filters
h[h==0] = 1
z = scipy.ndimage.filters.gaussian_filter(log(h.T), 1)
m.drawcoastlines()
m.imshow(z, origin='lower', extent=[x0,x1,y0,y1], cmap=get_cmap('Reds'))
The %load magic lets you load code from URLs or local files:
%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py
#!/usr/bin/env python
# implement the example graphs/integral from pyx
from pylab import *
from matplotlib.patches import Polygon
def func(x):
return (x-3)*(x-5)*(x-7)+85
ax = subplot(111)
a, b = 2, 9 # integral area
x = arange(0, 10, 0.01)
y = func(x)
plot(x, y, linewidth=1)
# make the shaded region
ix = arange(a, b, 0.01)
iy = func(ix)
verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]
poly = Polygon(verts, facecolor='0.8', edgecolor='k')
ax.add_patch(poly)
text(0.5 * (a + b), 30,
r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center',
fontsize=20)
axis([0,10, 0, 180])
figtext(0.9, 0.05, 'x')
figtext(0.1, 0.9, 'y')
ax.set_xticks((a,b))
ax.set_xticklabels(('a','b'))
ax.set_yticks([])
show()
