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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<title>api example code: filled_step.py — Matplotlib 2.0.2 documentation</title>

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<h1>api example code: filled_step.py<a class="headerlink" href="#api-example-code-filled-step-py" title="Permalink to this headline">¶</a></h1>

(<a class="reference external" href="../../mpl_examples/api/filled_step.py">Source code</a>)

(<a class="reference external" href="../../mpl_examples/api/filled_step_00.png">png</a>, <a class="reference external" href="../../mpl_examples/api/filled_step_00.pdf">pdf</a>)

</div>

(<a class="reference external" href="../../mpl_examples/api/filled_step_01.png">png</a>, <a class="reference external" href="../../mpl_examples/api/filled_step_01.pdf">pdf</a>)

</div>

<div class="highlight-default"><div class="highlight"><pre>"""

=========================

Hatch-filled histograms

=========================

This example showcases the hatching capabilities of matplotlib by plotting

various histograms.

"""

import itertools

from collections import OrderedDict

from functools import partial

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.ticker as mticker

from cycler import cycler

from six.moves import zip

def filled_hist(ax, edges, values, bottoms=None, orientation='v',

**kwargs):

"""

Draw a histogram as a stepped patch.

Extra kwargs are passed through to `fill_between`

Parameters

----------

ax : Axes

The axes to plot to

edges : array

A length n+1 array giving the left edges of each bin and the

right edge of the last bin.

values : array

A length n array of bin counts or values

bottoms : scalar or array, optional

A length n array of the bottom of the bars. If None, zero is used.

orientation : {'v', 'h'}

Orientation of the histogram. 'v' (default) has

the bars increasing in the positive y-direction.

Returns

-------

ret : PolyCollection

Artist added to the Axes

"""

print(orientation)

if orientation not in set('hv'):

raise ValueError("orientation must be in {{'h', 'v'}} "

"not {o}".format(o=orientation))

kwargs.setdefault('step', 'post')

edges = np.asarray(edges)

values = np.asarray(values)

if len(edges) - 1 != len(values):

raise ValueError('Must provide one more bin edge than value not: '

'len(edges): {lb} len(values): {lv}'.format(

lb=len(edges), lv=len(values)))

if bottoms is None:

bottoms = np.zeros_like(values)

if np.isscalar(bottoms):

bottoms = np.ones_like(values) * bottoms

values = np.r_[values, values[-1]]

bottoms = np.r_[bottoms, bottoms[-1]]

if orientation == 'h':

return ax.fill_betweenx(edges, values, bottoms,

**kwargs)

elif orientation == 'v':

return ax.fill_between(edges, values, bottoms,

**kwargs)

else:

raise AssertionError("you should never be here")

def stack_hist(ax, stacked_data, sty_cycle, bottoms=None,

hist_func=None, labels=None,

plot_func=None, plot_kwargs=None):

"""

ax : axes.Axes

The axes to add artists too

stacked_data : array or Mapping

A (N, M) shaped array. The first dimension will be iterated over to

compute histograms row-wise

sty_cycle : Cycler or operable of dict

Style to apply to each set

bottoms : array, optional

The initial positions of the bottoms, defaults to 0

hist_func : callable, optional

Must have signature `bin_vals, bin_edges = f(data)`.

`bin_edges` expected to be one longer than `bin_vals`

labels : list of str, optional

The label for each set.

If not given and stacked data is an array defaults to 'default set {n}'

If stacked_data is a mapping, and labels is None, default to the keys

(which may come out in a random order).

If stacked_data is a mapping and labels is given then only

the columns listed by be plotted.

plot_func : callable, optional

Function to call to draw the histogram must have signature:

ret = plot_func(ax, edges, top, bottoms=bottoms,

label=label, **kwargs)

plot_kwargs : dict, optional

Any extra kwargs to pass through to the plotting function. This

will be the same for all calls to the plotting function and will

over-ride the values in cycle.

Returns

-------

arts : dict

Dictionary of artists keyed on their labels

"""

# deal with default binning function

if hist_func is None:

hist_func = np.histogram

# deal with default plotting function

if plot_func is None:

plot_func = filled_hist

# deal with default

if plot_kwargs is None:

plot_kwargs = {}

print(plot_kwargs)

try:

l_keys = stacked_data.keys()

label_data = True

if labels is None:

labels = l_keys

except AttributeError:

label_data = False

if labels is None:

labels = itertools.repeat(None)

if label_data:

loop_iter = enumerate((stacked_data[lab], lab, s) for lab, s in

zip(labels, sty_cycle))

else:

loop_iter = enumerate(zip(stacked_data, labels, sty_cycle))

arts = {}

for j, (data, label, sty) in loop_iter:

if label is None:

label = 'dflt set {n}'.format(n=j)

label = sty.pop('label', label)

vals, edges = hist_func(data)

if bottoms is None:

top = bottoms + vals

print(sty)

sty.update(plot_kwargs)

print(sty)

ret = plot_func(ax, edges, top, bottoms=bottoms,

label=label, **sty)

bottoms = top

arts[label] = ret

ax.legend(fontsize=10)

return arts

# set up histogram function to fixed bins

edges = np.linspace(-3, 3, 20, endpoint=True)

hist_func = partial(np.histogram, bins=edges)

# set up style cycles

color_cycle = cycler(facecolor=plt.rcParams['axes.prop_cycle'][:4])

label_cycle = cycler('label', ['set {n}'.format(n=n) for n in range(4)])

hatch_cycle = cycler('hatch', ['/', '*', '+', '|'])

# make some synthetic data

np.random.seed(0)

stack_data = np.random.randn(4, 12250)

dict_data = OrderedDict(zip((c['label'] for c in label_cycle), stack_data))

# work with plain arrays

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4.5), tight_layout=True)

arts = stack_hist(ax1, stack_data, color_cycle + label_cycle + hatch_cycle,

hist_func=hist_func)

arts = stack_hist(ax2, stack_data, color_cycle,

hist_func=hist_func,

plot_kwargs=dict(edgecolor='w', orientation='h'))

ax1.set_ylabel('counts')

ax1.set_xlabel('x')

ax2.set_xlabel('counts')

ax2.set_ylabel('x')

# work with labeled data

tight_layout=True, sharey=True)

arts = stack_hist(ax1, dict_data, color_cycle + hatch_cycle,

hist_func=hist_func)

arts = stack_hist(ax2, dict_data, color_cycle + hatch_cycle,

hist_func=hist_func, labels=['set 0', 'set 3'])

ax1.xaxis.set_major_locator(mticker.MaxNLocator(5))

ax1.set_xlabel('counts')

ax1.set_ylabel('x')

ax2.set_ylabel('x')

plt.show()

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