fill_between ( t, mu1 + sigma1, mu1 - sigma1, facecolor = 'blue', alpha = 0.5 ) ax. plot ( t, mu2, lw = 2, label = 'mean population 2', color = 'yellow' ) ax. plot ( t, mu1, lw = 2, label = 'mean population 1', color = 'blue' ) ax. std ( axis = 1 ) # plot it! fig, ax = plt. cumsum ( axis = 0 ) # Nsteps length arrays empirical means and standard deviations of both # populations over time mu1 = X1. randn ( Nsteps, Nwalkers ) # an (Nsteps x Nwalkers) array of random walker positions X1 = S1. arange ( Nsteps ) # an (Nsteps x Nwalkers) array of random walk steps S1 = 0.002 + 0.01 * np. Here theĪlpha channel is useful, not just aesthetic. Standard deviation of the mean position of the population. Our next example computes two populations of random walkers with aĭifferent mean and standard deviation of the normal distributions from Limitation, not a matplotlib limitation), so when using alpha save Postscript format does not support alpha (this is a postscript Regions can overlap and alpha allows you to see both. See below, the alpha channel is functionally useful as the shaded The alpha channel is not necessary here, but it can be used to softenĬolors for more visually appealing plots. suptitle ( 'Google (GOOG) daily closing price' ) fig. close, facecolor = 'blue', alpha = 0.5 ) for ax in ax1, ax2 : ax. subplots ( 1, 2, sharex = True, sharey = True ) pricemin = r. recarray )) # create two subplots with the shared x and y axes fig, ( ax1, ax2 ) = plt. get_sample_data ( 'goog.npz', np_load = True ). seed ( 19680801 ) # load up some sample financial data r = ( cbook.

Import matplotlib.pyplot as plt import numpy as np import matplotlib.cbook as cbook # Fixing random state for reproducibility np.

Python panda .cdf files