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Let your data SPEAK!
Introduction to data visualization
Bartosz Telenczuk
Kiel, 2012
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position
length
angle
area
saturation
brightness
volume
shape
hue
Grouping
containment
connection
similarity
proximity
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Visualization design principles
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Tools
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GET DATA
PARSE IT
PROCESS
VISUALIZE
PUBLISH
urllib2
csv, beautifulsoup
numpy, scipy
matplotlib,
chaco, mayavi2
LaTeX, cherrypy
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John Hunter
1968-2012
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plot scatter bar
polar contour imshow
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import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 2*np.pi, 100) #generate data
y = np.sin(t)
plt.plot(t, y)
plt.xlabel('angle') #add axis labels
plt.ylabel('amplitude')
plt.xlim([0, 2*np.pi]) #set data limits
plt.xticks([0, np.pi, 2*np.pi], #add tick labels
['0', r'$\pi$', r'2$\pi$'])
plt.show() #show plot
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import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
fig = plt.figure(figsize=(5,5)) # create figure container
ax = plt.axes([0,0,1,1], frameon=False) # create axes container
art = mpatches.Circle((0.5, 0.5), 0.5,
ec="none") # create an artist
ax.add_patch(art) # add the artist to the
# container
ax.set_xticks([]) # remove axes ticks
ax.set_yticks([])
plt.show()
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display
transform
data
transform
axes
transform
figure
transform
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
from matplotlib import transforms
fig = plt.figure()
ax = fig.add_subplot(111)
x = 10*np.random.randn(1000)
ax.hist(x, 30)
trans = transforms.blended_transform_factory(
ax.transData, ax.transAxes)
rect = patches.Rectangle((8,0), width=10, height=1,
transform=trans, color='gray',
alpha=0.5)
ax.add_patch(rect)
plt.show()
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Interactivity
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import numpy
from matplotlib.pyplot import figure, show
def onpick(event): # define a handler
i = event.ind # indices of clicked points
ax.plot(xs[i], ys[i], 'ro') # plot the points in red
fig.canvas.draw() # update axes
xs, ys = numpy.random.rand(2,100)
fig = figure()
ax = fig.add_subplot(111)
line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance
fig.canvas.mpl_connect('pick_event', onpick) # connect handler to event
show() # enter the main loop
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points3d( ) contour3d( )
quiver3d( )
plot3d( )
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from enthought.mayavi import mlab
import numpy as np
x, y = np.ogrid[-10:10:100j, -10:10:100j]
r = np.sqrt(x**2 + y**2)
z = np.sin(r)/r
mlab.surf(x,y, 10*z)
mlab.outline()
mlab.colorbar()
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