Stacked plotting in matplotlib with equal x data is as easy as
from matplotlib import pyplot as plt
x0 = [0.0, 0.5, 2.0]
y0 = [1.0, 1.5, 1.0]
# x1 = [0.0, 1.5, 2.0]
y1 = [1.0, 1.5, 1.0]
plt.stackplot(x0, (y0, y1))
plt.show()
Is it possible to stack two plots with different x data too?
It does not seem to be possible. If you look at the code for Matplotlib's stackplot, then this is the part that draws the stacked plot itself:
# Color between array i-1 and array i
for i in xrange(len(y) - 1):
color = axes._get_lines.get_next_color()
r.append(axes.fill_between(x, stack[i, :], stack[i + 1, :],
facecolor=color,
label= six.next(labels, None),
**kwargs))
So it will always use the same x for all stacks.
You could on the other hand create a new x array for the stacked plot, and include all values from all the different x arrays you have, and then calculate the missing y stack values using linear interpolation.
A possible solution using interpolation could look like this:
from matplotlib import pyplot as plt
def interp_nans(x, y):
is_nan = np.isnan(y)
res = y * 1.0
res[is_nan] = np.interp(x[is_nan], x[-is_nan], y[-is_nan])
return res
x = np.array([0.0, 0.5, 1.5, 2.0])
y0 = np.array([1.0, 1.5, np.nan, 1.0])
y1 = np.array([1.0, np.nan, 1.5, 1.0])
plt.stackplot(x, (interp_nans(x, y0), interp_nans(x, y1)))
plt.show()
But if interpolation can not be used in this case, then it would not work.
