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"""
Stacked area plot for 1D arrays inspired by Douglas Y'barbo's stackoverflow
answer:
https://stackoverflow.com/q/2225995/
(https://stackoverflow.com/users/66549/doug)
"""
import itertools
import numpy as np
from matplotlib import _api
__all__ = ['stackplot']
def stackplot(axes, x, *args,
labels=(), colors=None, baseline='zero',
**kwargs):
"""
Draw a stacked area plot.
Parameters
----------
x : (N,) array-like
y : (M, N) array-like
The data is assumed to be unstacked. Each of the following
calls is legal::
stackplot(x, y) # where y has shape (M, N)
stackplot(x, y1, y2, y3) # where y1, y2, y3, y4 have length N
baseline : {'zero', 'sym', 'wiggle', 'weighted_wiggle'}
Method used to calculate the baseline:
- ``'zero'``: Constant zero baseline, i.e. a simple stacked plot.
- ``'sym'``: Symmetric around zero and is sometimes called
'ThemeRiver'.
- ``'wiggle'``: Minimizes the sum of the squared slopes.
- ``'weighted_wiggle'``: Does the same but weights to account for
size of each layer. It is also called 'Streamgraph'-layout. More
details can be found at http://leebyron.com/streamgraph/.
labels : list of str, optional
A sequence of labels to assign to each data series. If unspecified,
then no labels will be applied to artists.
colors : list of color, optional
A sequence of colors to be cycled through and used to color the stacked
areas. The sequence need not be exactly the same length as the number
of provided *y*, in which case the colors will repeat from the
beginning.
If not specified, the colors from the Axes property cycle will be used.
data : indexable object, optional
DATA_PARAMETER_PLACEHOLDER
**kwargs
All other keyword arguments are passed to `.Axes.fill_between`.
Returns
-------
list of `.PolyCollection`
A list of `.PolyCollection` instances, one for each element in the
stacked area plot.
"""
y = np.vstack(args)
labels = iter(labels)
if colors is not None:
colors = itertools.cycle(colors)
else:
colors = (axes._get_lines.get_next_color() for _ in y)
# Assume data passed has not been 'stacked', so stack it here.
# We'll need a float buffer for the upcoming calculations.
stack = np.cumsum(y, axis=0, dtype=np.promote_types(y.dtype, np.float32))
_api.check_in_list(['zero', 'sym', 'wiggle', 'weighted_wiggle'],
baseline=baseline)
if baseline == 'zero':
first_line = 0.
elif baseline == 'sym':
first_line = -np.sum(y, 0) * 0.5
stack += first_line[None, :]
elif baseline == 'wiggle':
m = y.shape[0]
first_line = (y * (m - 0.5 - np.arange(m)[:, None])).sum(0)
first_line /= -m
stack += first_line
elif baseline == 'weighted_wiggle':
total = np.sum(y, 0)
# multiply by 1/total (or zero) to avoid infinities in the division:
inv_total = np.zeros_like(total)
mask = total > 0
inv_total[mask] = 1.0 / total[mask]
increase = np.hstack((y[:, 0:1], np.diff(y)))
below_size = total - stack
below_size += 0.5 * y
move_up = below_size * inv_total
move_up[:, 0] = 0.5
center = (move_up - 0.5) * increase
center = np.cumsum(center.sum(0))
first_line = center - 0.5 * total
stack += first_line
# Color between x = 0 and the first array.
coll = axes.fill_between(x, first_line, stack[0, :],
facecolor=next(colors), label=next(labels, None),
**kwargs)
coll.sticky_edges.y[:] = [0]
r = [coll]
# Color between array i-1 and array i
for i in range(len(y) - 1):
r.append(axes.fill_between(x, stack[i, :], stack[i + 1, :],
facecolor=next(colors),
label=next(labels, None),
**kwargs))
return r
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