plotpy.pyplot

The pyplot module provides an interactive plotting interface similar to Matplotlib‘s, i.e. with MATLAB-like syntax.

The plotpy.pyplot module was designed to be as close as possible to the matplotlib.pyplot module, so that one could easily switch between these two modules by simply changing the import statement. Basically, if plotpy does support the plotting commands called in your script, replacing import matplotlib.pyplot by import plotpy.pyplot should suffice, as shown in the following example:

• Simple example using matplotlib:

  import matplotlib.pyplot as plt
  import numpy as np
  x = np.linspace(-10, 10)
  plt.plot(x, x**2, 'r+')
  plt.show()
  

• Switching from matplotlib to plotpy is trivial:

  import plotpy.pyplot as plt # only this line has changed!
  import numpy as np
  x = np.linspace(-10, 10)
  plt.plot(x, x**2, 'r+')
  plt.show()
  

Examples

>>> import numpy as np
>>> from plotpy.pyplot import * # ugly but acceptable in an interactive session
>>> ion() # switching to interactive mode
>>> x = np.linspace(-5, 5, 1000)
>>> figure(1)
>>> subplot(2, 1, 1)
>>> plot(x, np.sin(x), "r+")
>>> plot(x, np.cos(x), "g-")
>>> errorbar(x, -1+x**2/20+.2*np.random.rand(len(x)), x/20)
>>> xlabel("Axe x")
>>> ylabel("Axe y")
>>> subplot(2, 1, 2)
>>> img = np.fromfunction(lambda x, y: np.sin((x/200.)*(y/200.)**2), (1000, 1000))
>>> xlabel("pixels")
>>> ylabel("pixels")
>>> zlabel("intensity")
>>> gray()
>>> imshow(img)
>>> figure("plotyy")
>>> plotyy(x, np.sin(x), x, np.cos(x))
>>> ylabel("sinus", "cosinus")
>>> show()

Reference

plotpy.pyplot.interactive(state)

Toggle interactive mode

plotpy.pyplot.ion()

Turn interactive mode on

plotpy.pyplot.ioff()

Turn interactive mode off

plotpy.pyplot.figure(N=None)

Create a new figure

plotpy.pyplot.gcf()

Get current figure

plotpy.pyplot.gca()

Get current axes

plotpy.pyplot.show(mainloop=True)

Show all figures and enter Qt event loop This should be the last line of your script

plotpy.pyplot.subplot(n, m, k)

Create a subplot command

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
figure(1)
subplot(2, 1, 1)
plot(x, np.sin(x), "r+")
subplot(2, 1, 2)
plot(x, np.cos(x), "g-")
show()

plotpy.pyplot.close(N=None, all=False)

Close figure

plotpy.pyplot.title(text)

Set current figure title

plotpy.pyplot.xlabel(bottom='', top='')

Set current x-axis label

plotpy.pyplot.ylabel(left='', right='')

Set current y-axis label

plotpy.pyplot.zlabel(label)

Set current z-axis label

plotpy.pyplot.yreverse(reverse)

Set y-axis direction of increasing values

reverse = False (default)

y-axis values increase from bottom to top

reverse = True

y-axis values increase from top to bottom

plotpy.pyplot.grid(act)

Toggle grid visibility

plotpy.pyplot.legend(pos='TR')

Add legend to current axes (pos=’TR’, ‘TL’, ‘BR’, ...)

plotpy.pyplot.colormap(name)

Set color map to name

plotpy.pyplot.savefig(fname, format=None, draft=False)

Save figure

Currently supports PDF and PNG formats only

plotpy.pyplot.plot(*args, **kwargs)

Plot curves

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
plot(x, np.sin(x), "r+")
plot(x, np.cos(x), "g-")
show()

plotpy.pyplot.plotyy(x1, y1, x2, y2)

Plot curves with two different y axes

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
plotyy(x, np.sin(x), x, np.cos(x))
ylabel("sinus", "cosinus")
show()

plotpy.pyplot.semilogx(*args, **kwargs)

Plot curves with logarithmic x-axis scale

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
semilogx(x, np.sin(12*x), "g-")
show()

plotpy.pyplot.semilogy(*args, **kwargs)

Plot curves with logarithmic y-axis scale

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
semilogy(x, np.sin(12*x), "g-")
show()

plotpy.pyplot.loglog(*args, **kwargs)

Plot curves with logarithmic x-axis and y-axis scales

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
loglog(x, np.sin(12*x), "g-")
show()

plotpy.pyplot.errorbar(*args, **kwargs)

Plot curves with error bars

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
errorbar(x, -1+x**2/20+.2*np.random.rand(len(x)), x/20)
show()

plotpy.pyplot.hist(data, bins=None, logscale=None, title=None, color=None)

Plot 1-D histogram

Example:

from numpy.random import normal
data = normal(0, 1, (2000, ))
hist(data)
show()

plotpy.pyplot.imshow(data, interpolation=None, mask=None)

Display the image in data to current axes interpolation: ‘nearest’, ‘linear’ (default), ‘antialiasing’

Example:

import numpy as np
x = np.linspace(-5, 5, 1000)
img = np.fromfunction(lambda x, y: np.sin((x/200.)*(y/200.)**2), (1000, 1000))
gray()
imshow(img)
show()

plotpy.pyplot.pcolor(*args)

Create a pseudocolor plot of a 2-D array

Example:

import numpy as np
r = np.linspace(1., 16, 100)
th = np.linspace(0., np.pi, 100)
R, TH = np.meshgrid(r, th)
X = R*np.cos(TH)
Y = R*np.sin(TH)
Z = 4*TH+R
pcolor(X, Y, Z)
show()