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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 1.5.3 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.hires.png">hires.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.hires.png">hires.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>import itertools

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', 'rgbm')

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

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

# make some synthetic data

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

dict_data = {lab: d for lab, d in zip(list(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')

</pre></div>

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