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Commit 146a417

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phobsonphobson
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DOC: fix types in stats docstrings
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‎examples/statistics/boxplot_demo.py

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mean is the only value not shown by default). The second
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figure demonstrates how the styles of the artists of can
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be customized. It also demonstrates how to set the limit
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if the whiskers to specific percentiles (lower right axes)
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of the whiskers to specific percentiles (lower right axes)
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"""
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import numpy as np

‎examples/statistics/bxp_demo.py

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statistics to the box plot drawer. The first figure demostrates
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how to remove and add individual components (note that the
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mean is the only value not shown by default). The second
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figure demonstrates how the styles of the artists of can
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figure demonstrates how the styles of the artists can
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be customized.
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"""
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‎examples/statistics/customized_violin_demo.py

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The first plot shows the default style by providing only
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the data. The second plot first limits what matplotlib draws
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with additional kwargs. Then a simplified representation of
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a box plot in drawn on top. Lastly, the styles of arists
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a box plot in drawn on top. Lastly, the styles of the artists
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of the violins are modified.
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"""
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@@ -91,9 +91,9 @@ def adjacent_values(vals):
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for ax in [ax1, ax2]:
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ax.get_xaxis().set_tick_params(direction='out')
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ax.xaxis.set_ticks_position('bottom')
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ax.set_xticks([x + 1 for x in range(len(lab))])
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ax.set_xticks([x for x in np.arange(1, len(lab) + 1)])
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ax.set_xticklabels(lab)
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ax.set_xlim(0.25, len(lab)+0.75)
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ax.set_xlim(0.25, len(lab) + 0.75)
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ax.set_xlabel('Sample name')
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plt.subplots_adjust(bottom=0.15, wspace=0.05)

‎examples/statistics/errorbar_demo.py

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Demo of the errorbar function
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=============================
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This exhibits the most basic use of of the error bar method.
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In this case, constant values are provided for both the error
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in the x- and y-directions.
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This exhibits the most basic use of the error bar method.
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In this case, constant values are provided for the error
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in both the x- and y-directions.
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"""
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import numpy as np

‎examples/statistics/errorbar_demo_features.py

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===================================================
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Errors can be specified as a constant value (as shown in
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`errorbar_demo.py`), or as demonstrated in this example, they can be
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specified by an N x 1 or 2 x N, where N is the number of data points.
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`errorbar_demo.py`). However, this example demonstrates
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how they vary by specifying arrays of error values.
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N x 1:
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If the raw `x` and `y` data have length N, there are two options:
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Array of shape (N,):
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Error varies for each point, but the error values are
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symmetric (i.e. the lower and upper values are equal).
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2 x N:
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Array of shape (2, N):
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Error varies for each point, and the lower and upper limits
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(in that order) are different (asymmetric case)
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‎examples/statistics/errorbar_limits.py

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In matplotlib, errors bars can have "limits". Applying limits to the
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error bars essentially makes the error unidirectional. Because of that,
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upper and lower limits can be applied in both the y- and x-directions
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via the `uplims`, `lolims`, `xuplims`, and `xlolims` parameters,
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respectively. This parameters can be scalar or boolean arrays.
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via the ``uplims``, ``lolims``, ``xuplims``, and ``xlolims`` parameters,
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respectively. These parameters can be scalar or boolean arrays.
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For example, if `xlolims` is `True`, the x-error bars will only extend
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from the data towards increasing values. If `uplims` is an array filled
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with `False` except for the 3rd and 5th values, all of the y-error bars
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will be bi-directional, except the 3rd and 5th bars, which will extend
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from the data towards decreasing y-values.
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For example, if ``xlolims`` is ``True``, the x-error bars will only
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extend from the data towards increasing values. If ``uplims`` is an
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array filled with `False` except for the 4th and 7th values, all of the
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y-error bars will be bidirectional, except the 4th and 7th bars, which
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will extend from the data towards decreasing y-values.
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"""
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import numpy as np

‎examples/statistics/errorbars_and_boxes.py

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In this example, we snazz up a pretty standard error bar plot by adding
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a rectangle patch defined by the limits of the bars in both the x- and
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y- directions. Do this, we have to write our own custom function called
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`make_error_boxes`. Close inspection of this function will reveal the
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preferred pattern in writting functions for matplotlib:
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y- directions. To do this, we have to write our own custom function
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called `make_error_boxes`. Close inspection of this function will reveal
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the preferred pattern in writing functions for matplotlib:
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1. an `Axes` object is passed directly to the function
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2. the function operates on the `Axes` methods directly, not through

‎examples/statistics/histogram_demo_cumulative.py

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==========================================================
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This shows how to plot a cumulative, normalized histogram as a
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step function as means of visualization the empirical cumulative
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step function in order to visualize the empirical cumulative
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distribution function (CDF) of a sample. We also use the `mlab`
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module to show the theoretical CDF.
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A couple of other options to the `hist` function are demostrated.
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Namely, we use the `normed` parameter to normalize the histogram and
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A couple of other options to the ``hist`` function are demonstrated.
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Namely, we use the ``normed`` parameter to normalize the histogram and
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a couple of different options to the `cumulative` parameter. Normalizing
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a histogram means that the counts within each bin are scaled such that
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the total height of each bin sum up to 1. Since we're showing the
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a histogram means that the heights bins are scaled such that
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the total area is 1. Since we're showing a normalized and
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cumulative histogram, the max value at the end of the series is 1.
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The `normed` parameter takes a boolean value.
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The ``normed`` parameter takes a boolean value.
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The `cumulative` kwarg is a little more nuanced. Like `normed`, you can
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pass it True or False, but you can also pass it -1 and that will
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reverse the distribution. In engineering, CDFs where `cumulative` is
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simply True are sometimes "non-excedance" curves. In other words, you
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can look at the y-value to set the probability of excedance. For example
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the value of 225 on the x-axis corresponse to about 0.85 on the y-axis,
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so there's an 85% chance that an observation in the sames does not
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exceed 225.
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The ``cumulative`` kwarg is a little more nuanced. Like ``normed``, you
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can pass it True or False, but you can also pass it -1 and that will
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reverse the distribution. In engineering, CDFs where ``cumulative`` is
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simply True are sometimes "non-exceedance" curves. In other words, you
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can look at the y-value to set the probability of exceedance. For
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example the value of 225 on the x-axis corresponds to about 0.85 on the
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y-axis, so there's an 85% chance that an observation in the sample does
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not exceed 225.
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Conversely, setting, `cumulative` to -1 as is done in the last series
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for this example, creates a "excedance" curve.
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Conversely, setting, ``cumulative`` to -1 as is done in the last series
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for this example, creates a "exceedance" curve.
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"""
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mu = 200
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sigma = 25
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n_bins = 50
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x = mu + sigma*np.random.randn(100)
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x = np.random.normal(mu, sigma, size=100)
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fig, ax = plt.subplots(figsize=(8, 4))
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ax.legend(loc='right')
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ax.set_title('Cumulative step histograms')
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ax.set_xlabel('Annual rainfall (mm)')
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ax.set_ylabel('Likelihood of occurance')
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ax.set_ylabel('Likelihood of occurrence')
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plt.show()

‎examples/statistics/histogram_demo_multihist.py

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ax1.set_title('stacked bar')
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ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
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ax2.set_title('step (unfilled)')
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ax2.set_title('stack step (unfilled)')
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# Make a multiple-histogram of data-sets with different length.
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x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]

‎examples/statistics/multiple_histograms_side_by_side.py

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Demo of how to produce multiple histograms side by side
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=======================================================
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This example plots horizonal histograms of different samples along
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This example plots horizontal histograms of different samples along
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a categorical x-axis. Additionally, the histograms are plotted to
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be symmetrical about their x-position, thus making them very similar
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to violin plots.

‎examples/statistics/violinplot_demo.py

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Violin plots are similar to histograms and box plots in that they show
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an abstract representation of the probability distribution of the
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sample. Rather than showing counts of data points that fall into bins
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or order statistics, violin plots use kernal density estimation (KDE) to
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compute an emperical distribution of the sample. That computation
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or order statistics, violin plots use kernel density estimation (KDE) to
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compute an empirical distribution of the sample. That computation
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is controlled by several parameters. This example demostrates how to
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modify the number of points at the KDE is evaluated (``points``) and
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how to modify the band-width of the kde (``bw_method``).
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modify the number of points at which the KDE is evaluated (``points``)
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and how to modify the band-width of the KDE (``bw_method``).
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For more information on violin plots and KDE, the scikit-learn docs
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have a great section: http://scikit-learn.org/stable/modules/density.html

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