Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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723 lines
29 KiB
723 lines
29 KiB
4 years ago
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import builtins
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import numpy as np
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from numpy.testing import suppress_warnings
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from operator import index
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from collections import namedtuple
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__all__ = ['binned_statistic',
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'binned_statistic_2d',
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'binned_statistic_dd']
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BinnedStatisticResult = namedtuple('BinnedStatisticResult',
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('statistic', 'bin_edges', 'binnumber'))
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def binned_statistic(x, values, statistic='mean',
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bins=10, range=None):
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"""
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Compute a binned statistic for one or more sets of data.
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This is a generalization of a histogram function. A histogram divides
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the space into bins, and returns the count of the number of points in
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each bin. This function allows the computation of the sum, mean, median,
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or other statistic of the values (or set of values) within each bin.
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Parameters
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----------
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x : (N,) array_like
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A sequence of values to be binned.
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values : (N,) array_like or list of (N,) array_like
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The data on which the statistic will be computed. This must be
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the same shape as `x`, or a set of sequences - each the same shape as
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`x`. If `values` is a set of sequences, the statistic will be computed
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on each independently.
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statistic : string or callable, optional
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The statistic to compute (default is 'mean').
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The following statistics are available:
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* 'mean' : compute the mean of values for points within each bin.
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Empty bins will be represented by NaN.
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* 'std' : compute the standard deviation within each bin. This
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is implicitly calculated with ddof=0.
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* 'median' : compute the median of values for points within each
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bin. Empty bins will be represented by NaN.
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* 'count' : compute the count of points within each bin. This is
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identical to an unweighted histogram. `values` array is not
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referenced.
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* 'sum' : compute the sum of values for points within each bin.
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This is identical to a weighted histogram.
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* 'min' : compute the minimum of values for points within each bin.
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Empty bins will be represented by NaN.
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* 'max' : compute the maximum of values for point within each bin.
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Empty bins will be represented by NaN.
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* function : a user-defined function which takes a 1D array of
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values, and outputs a single numerical statistic. This function
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will be called on the values in each bin. Empty bins will be
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represented by function([]), or NaN if this returns an error.
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bins : int or sequence of scalars, optional
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If `bins` is an int, it defines the number of equal-width bins in the
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given range (10 by default). If `bins` is a sequence, it defines the
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bin edges, including the rightmost edge, allowing for non-uniform bin
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widths. Values in `x` that are smaller than lowest bin edge are
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assigned to bin number 0, values beyond the highest bin are assigned to
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``bins[-1]``. If the bin edges are specified, the number of bins will
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be, (nx = len(bins)-1).
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range : (float, float) or [(float, float)], optional
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The lower and upper range of the bins. If not provided, range
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is simply ``(x.min(), x.max())``. Values outside the range are
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ignored.
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Returns
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-------
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statistic : array
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The values of the selected statistic in each bin.
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bin_edges : array of dtype float
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Return the bin edges ``(length(statistic)+1)``.
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binnumber: 1-D ndarray of ints
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Indices of the bins (corresponding to `bin_edges`) in which each value
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of `x` belongs. Same length as `values`. A binnumber of `i` means the
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corresponding value is between (bin_edges[i-1], bin_edges[i]).
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See Also
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--------
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numpy.digitize, numpy.histogram, binned_statistic_2d, binned_statistic_dd
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Notes
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-----
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All but the last (righthand-most) bin is half-open. In other words, if
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`bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
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but excluding 2) and the second ``[2, 3)``. The last bin, however, is
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``[3, 4]``, which *includes* 4.
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.. versionadded:: 0.11.0
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Examples
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--------
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>>> from scipy import stats
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>>> import matplotlib.pyplot as plt
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First some basic examples:
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Create two evenly spaced bins in the range of the given sample, and sum the
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corresponding values in each of those bins:
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>>> values = [1.0, 1.0, 2.0, 1.5, 3.0]
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>>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
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BinnedStatisticResult(statistic=array([4. , 4.5]),
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bin_edges=array([1., 4., 7.]), binnumber=array([1, 1, 1, 2, 2]))
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Multiple arrays of values can also be passed. The statistic is calculated
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on each set independently:
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>>> values = [[1.0, 1.0, 2.0, 1.5, 3.0], [2.0, 2.0, 4.0, 3.0, 6.0]]
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>>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
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BinnedStatisticResult(statistic=array([[4. , 4.5],
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[8. , 9. ]]), bin_edges=array([1., 4., 7.]),
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binnumber=array([1, 1, 1, 2, 2]))
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>>> stats.binned_statistic([1, 2, 1, 2, 4], np.arange(5), statistic='mean',
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... bins=3)
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BinnedStatisticResult(statistic=array([1., 2., 4.]),
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bin_edges=array([1., 2., 3., 4.]),
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binnumber=array([1, 2, 1, 2, 3]))
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As a second example, we now generate some random data of sailing boat speed
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as a function of wind speed, and then determine how fast our boat is for
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certain wind speeds:
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>>> windspeed = 8 * np.random.rand(500)
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>>> boatspeed = .3 * windspeed**.5 + .2 * np.random.rand(500)
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>>> bin_means, bin_edges, binnumber = stats.binned_statistic(windspeed,
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... boatspeed, statistic='median', bins=[1,2,3,4,5,6,7])
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>>> plt.figure()
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>>> plt.plot(windspeed, boatspeed, 'b.', label='raw data')
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>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=5,
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... label='binned statistic of data')
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>>> plt.legend()
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Now we can use ``binnumber`` to select all datapoints with a windspeed
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below 1:
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>>> low_boatspeed = boatspeed[binnumber == 0]
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As a final example, we will use ``bin_edges`` and ``binnumber`` to make a
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plot of a distribution that shows the mean and distribution around that
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mean per bin, on top of a regular histogram and the probability
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distribution function:
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>>> x = np.linspace(0, 5, num=500)
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>>> x_pdf = stats.maxwell.pdf(x)
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>>> samples = stats.maxwell.rvs(size=10000)
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>>> bin_means, bin_edges, binnumber = stats.binned_statistic(x, x_pdf,
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... statistic='mean', bins=25)
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>>> bin_width = (bin_edges[1] - bin_edges[0])
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>>> bin_centers = bin_edges[1:] - bin_width/2
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>>> plt.figure()
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>>> plt.hist(samples, bins=50, density=True, histtype='stepfilled',
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... alpha=0.2, label='histogram of data')
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>>> plt.plot(x, x_pdf, 'r-', label='analytical pdf')
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>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=2,
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... label='binned statistic of data')
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>>> plt.plot((binnumber - 0.5) * bin_width, x_pdf, 'g.', alpha=0.5)
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>>> plt.legend(fontsize=10)
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>>> plt.show()
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"""
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try:
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N = len(bins)
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except TypeError:
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N = 1
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if N != 1:
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bins = [np.asarray(bins, float)]
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if range is not None:
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if len(range) == 2:
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range = [range]
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medians, edges, binnumbers = binned_statistic_dd(
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[x], values, statistic, bins, range)
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return BinnedStatisticResult(medians, edges[0], binnumbers)
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BinnedStatistic2dResult = namedtuple('BinnedStatistic2dResult',
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('statistic', 'x_edge', 'y_edge',
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'binnumber'))
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def binned_statistic_2d(x, y, values, statistic='mean',
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bins=10, range=None, expand_binnumbers=False):
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"""
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Compute a bidimensional binned statistic for one or more sets of data.
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This is a generalization of a histogram2d function. A histogram divides
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the space into bins, and returns the count of the number of points in
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each bin. This function allows the computation of the sum, mean, median,
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or other statistic of the values (or set of values) within each bin.
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Parameters
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----------
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x : (N,) array_like
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A sequence of values to be binned along the first dimension.
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y : (N,) array_like
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A sequence of values to be binned along the second dimension.
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values : (N,) array_like or list of (N,) array_like
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The data on which the statistic will be computed. This must be
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the same shape as `x`, or a list of sequences - each with the same
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shape as `x`. If `values` is such a list, the statistic will be
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computed on each independently.
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statistic : string or callable, optional
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The statistic to compute (default is 'mean').
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The following statistics are available:
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* 'mean' : compute the mean of values for points within each bin.
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Empty bins will be represented by NaN.
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* 'std' : compute the standard deviation within each bin. This
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is implicitly calculated with ddof=0.
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* 'median' : compute the median of values for points within each
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bin. Empty bins will be represented by NaN.
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* 'count' : compute the count of points within each bin. This is
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identical to an unweighted histogram. `values` array is not
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referenced.
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* 'sum' : compute the sum of values for points within each bin.
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This is identical to a weighted histogram.
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* 'min' : compute the minimum of values for points within each bin.
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Empty bins will be represented by NaN.
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* 'max' : compute the maximum of values for point within each bin.
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Empty bins will be represented by NaN.
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* function : a user-defined function which takes a 1D array of
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values, and outputs a single numerical statistic. This function
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will be called on the values in each bin. Empty bins will be
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represented by function([]), or NaN if this returns an error.
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bins : int or [int, int] or array_like or [array, array], optional
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The bin specification:
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* the number of bins for the two dimensions (nx = ny = bins),
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* the number of bins in each dimension (nx, ny = bins),
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* the bin edges for the two dimensions (x_edge = y_edge = bins),
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* the bin edges in each dimension (x_edge, y_edge = bins).
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If the bin edges are specified, the number of bins will be,
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(nx = len(x_edge)-1, ny = len(y_edge)-1).
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range : (2,2) array_like, optional
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The leftmost and rightmost edges of the bins along each dimension
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(if not specified explicitly in the `bins` parameters):
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[[xmin, xmax], [ymin, ymax]]. All values outside of this range will be
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considered outliers and not tallied in the histogram.
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expand_binnumbers : bool, optional
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'False' (default): the returned `binnumber` is a shape (N,) array of
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linearized bin indices.
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'True': the returned `binnumber` is 'unraveled' into a shape (2,N)
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ndarray, where each row gives the bin numbers in the corresponding
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dimension.
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See the `binnumber` returned value, and the `Examples` section.
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.. versionadded:: 0.17.0
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Returns
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-------
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statistic : (nx, ny) ndarray
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The values of the selected statistic in each two-dimensional bin.
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x_edge : (nx + 1) ndarray
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The bin edges along the first dimension.
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y_edge : (ny + 1) ndarray
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The bin edges along the second dimension.
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binnumber : (N,) array of ints or (2,N) ndarray of ints
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This assigns to each element of `sample` an integer that represents the
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bin in which this observation falls. The representation depends on the
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`expand_binnumbers` argument. See `Notes` for details.
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See Also
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--------
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numpy.digitize, numpy.histogram2d, binned_statistic, binned_statistic_dd
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Notes
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-----
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Binedges:
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All but the last (righthand-most) bin is half-open. In other words, if
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`bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
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but excluding 2) and the second ``[2, 3)``. The last bin, however, is
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``[3, 4]``, which *includes* 4.
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`binnumber`:
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This returned argument assigns to each element of `sample` an integer that
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represents the bin in which it belongs. The representation depends on the
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`expand_binnumbers` argument. If 'False' (default): The returned
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`binnumber` is a shape (N,) array of linearized indices mapping each
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element of `sample` to its corresponding bin (using row-major ordering).
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If 'True': The returned `binnumber` is a shape (2,N) ndarray where
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each row indicates bin placements for each dimension respectively. In each
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dimension, a binnumber of `i` means the corresponding value is between
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(D_edge[i-1], D_edge[i]), where 'D' is either 'x' or 'y'.
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.. versionadded:: 0.11.0
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Examples
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--------
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>>> from scipy import stats
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Calculate the counts with explicit bin-edges:
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>>> x = [0.1, 0.1, 0.1, 0.6]
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>>> y = [2.1, 2.6, 2.1, 2.1]
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>>> binx = [0.0, 0.5, 1.0]
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>>> biny = [2.0, 2.5, 3.0]
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>>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx, biny])
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>>> ret.statistic
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array([[2., 1.],
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[1., 0.]])
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The bin in which each sample is placed is given by the `binnumber`
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returned parameter. By default, these are the linearized bin indices:
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>>> ret.binnumber
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array([5, 6, 5, 9])
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The bin indices can also be expanded into separate entries for each
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dimension using the `expand_binnumbers` parameter:
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>>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx, biny],
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... expand_binnumbers=True)
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>>> ret.binnumber
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array([[1, 1, 1, 2],
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[1, 2, 1, 1]])
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Which shows that the first three elements belong in the xbin 1, and the
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fourth into xbin 2; and so on for y.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
# This code is based on np.histogram2d
|
||
|
|
try:
|
||
|
|
N = len(bins)
|
||
|
|
except TypeError:
|
||
|
|
N = 1
|
||
|
|
|
||
|
|
if N != 1 and N != 2:
|
||
|
|
xedges = yedges = np.asarray(bins, float)
|
||
|
|
bins = [xedges, yedges]
|
||
|
|
|
||
|
|
medians, edges, binnumbers = binned_statistic_dd(
|
||
|
|
[x, y], values, statistic, bins, range,
|
||
|
|
expand_binnumbers=expand_binnumbers)
|
||
|
|
|
||
|
|
return BinnedStatistic2dResult(medians, edges[0], edges[1], binnumbers)
|
||
|
|
|
||
|
|
|
||
|
|
BinnedStatisticddResult = namedtuple('BinnedStatisticddResult',
|
||
|
|
('statistic', 'bin_edges',
|
||
|
|
'binnumber'))
|
||
|
|
|
||
|
|
|
||
|
|
def binned_statistic_dd(sample, values, statistic='mean',
|
||
|
|
bins=10, range=None, expand_binnumbers=False,
|
||
|
|
binned_statistic_result=None):
|
||
|
|
"""
|
||
|
|
Compute a multidimensional binned statistic for a set of data.
|
||
|
|
|
||
|
|
This is a generalization of a histogramdd function. A histogram divides
|
||
|
|
the space into bins, and returns the count of the number of points in
|
||
|
|
each bin. This function allows the computation of the sum, mean, median,
|
||
|
|
or other statistic of the values within each bin.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
sample : array_like
|
||
|
|
Data to histogram passed as a sequence of N arrays of length D, or
|
||
|
|
as an (N,D) array.
|
||
|
|
values : (N,) array_like or list of (N,) array_like
|
||
|
|
The data on which the statistic will be computed. This must be
|
||
|
|
the same shape as `sample`, or a list of sequences - each with the
|
||
|
|
same shape as `sample`. If `values` is such a list, the statistic
|
||
|
|
will be computed on each independently.
|
||
|
|
statistic : string or callable, optional
|
||
|
|
The statistic to compute (default is 'mean').
|
||
|
|
The following statistics are available:
|
||
|
|
|
||
|
|
* 'mean' : compute the mean of values for points within each bin.
|
||
|
|
Empty bins will be represented by NaN.
|
||
|
|
* 'median' : compute the median of values for points within each
|
||
|
|
bin. Empty bins will be represented by NaN.
|
||
|
|
* 'count' : compute the count of points within each bin. This is
|
||
|
|
identical to an unweighted histogram. `values` array is not
|
||
|
|
referenced.
|
||
|
|
* 'sum' : compute the sum of values for points within each bin.
|
||
|
|
This is identical to a weighted histogram.
|
||
|
|
* 'std' : compute the standard deviation within each bin. This
|
||
|
|
is implicitly calculated with ddof=0. If the number of values
|
||
|
|
within a given bin is 0 or 1, the computed standard deviation value
|
||
|
|
will be 0 for the bin.
|
||
|
|
* 'min' : compute the minimum of values for points within each bin.
|
||
|
|
Empty bins will be represented by NaN.
|
||
|
|
* 'max' : compute the maximum of values for point within each bin.
|
||
|
|
Empty bins will be represented by NaN.
|
||
|
|
* function : a user-defined function which takes a 1D array of
|
||
|
|
values, and outputs a single numerical statistic. This function
|
||
|
|
will be called on the values in each bin. Empty bins will be
|
||
|
|
represented by function([]), or NaN if this returns an error.
|
||
|
|
|
||
|
|
bins : sequence or positive int, optional
|
||
|
|
The bin specification must be in one of the following forms:
|
||
|
|
|
||
|
|
* A sequence of arrays describing the bin edges along each dimension.
|
||
|
|
* The number of bins for each dimension (nx, ny, ... = bins).
|
||
|
|
* The number of bins for all dimensions (nx = ny = ... = bins).
|
||
|
|
range : sequence, optional
|
||
|
|
A sequence of lower and upper bin edges to be used if the edges are
|
||
|
|
not given explicitly in `bins`. Defaults to the minimum and maximum
|
||
|
|
values along each dimension.
|
||
|
|
expand_binnumbers : bool, optional
|
||
|
|
'False' (default): the returned `binnumber` is a shape (N,) array of
|
||
|
|
linearized bin indices.
|
||
|
|
'True': the returned `binnumber` is 'unraveled' into a shape (D,N)
|
||
|
|
ndarray, where each row gives the bin numbers in the corresponding
|
||
|
|
dimension.
|
||
|
|
See the `binnumber` returned value, and the `Examples` section of
|
||
|
|
`binned_statistic_2d`.
|
||
|
|
binned_statistic_result : binnedStatisticddResult
|
||
|
|
Result of a previous call to the function in order to reuse bin edges
|
||
|
|
and bin numbers with new values and/or a different statistic.
|
||
|
|
To reuse bin numbers, `expand_binnumbers` must have been set to False
|
||
|
|
(the default)
|
||
|
|
|
||
|
|
.. versionadded:: 0.17.0
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
statistic : ndarray, shape(nx1, nx2, nx3,...)
|
||
|
|
The values of the selected statistic in each two-dimensional bin.
|
||
|
|
bin_edges : list of ndarrays
|
||
|
|
A list of D arrays describing the (nxi + 1) bin edges for each
|
||
|
|
dimension.
|
||
|
|
binnumber : (N,) array of ints or (D,N) ndarray of ints
|
||
|
|
This assigns to each element of `sample` an integer that represents the
|
||
|
|
bin in which this observation falls. The representation depends on the
|
||
|
|
`expand_binnumbers` argument. See `Notes` for details.
|
||
|
|
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
numpy.digitize, numpy.histogramdd, binned_statistic, binned_statistic_2d
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Binedges:
|
||
|
|
All but the last (righthand-most) bin is half-open in each dimension. In
|
||
|
|
other words, if `bins` is ``[1, 2, 3, 4]``, then the first bin is
|
||
|
|
``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The
|
||
|
|
last bin, however, is ``[3, 4]``, which *includes* 4.
|
||
|
|
|
||
|
|
`binnumber`:
|
||
|
|
This returned argument assigns to each element of `sample` an integer that
|
||
|
|
represents the bin in which it belongs. The representation depends on the
|
||
|
|
`expand_binnumbers` argument. If 'False' (default): The returned
|
||
|
|
`binnumber` is a shape (N,) array of linearized indices mapping each
|
||
|
|
element of `sample` to its corresponding bin (using row-major ordering).
|
||
|
|
If 'True': The returned `binnumber` is a shape (D,N) ndarray where
|
||
|
|
each row indicates bin placements for each dimension respectively. In each
|
||
|
|
dimension, a binnumber of `i` means the corresponding value is between
|
||
|
|
(bin_edges[D][i-1], bin_edges[D][i]), for each dimension 'D'.
|
||
|
|
|
||
|
|
.. versionadded:: 0.11.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy import stats
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from mpl_toolkits.mplot3d import Axes3D
|
||
|
|
|
||
|
|
Take an array of 600 (x, y) coordinates as an example.
|
||
|
|
`binned_statistic_dd` can handle arrays of higher dimension `D`. But a plot
|
||
|
|
of dimension `D+1` is required.
|
||
|
|
|
||
|
|
>>> mu = np.array([0., 1.])
|
||
|
|
>>> sigma = np.array([[1., -0.5],[-0.5, 1.5]])
|
||
|
|
>>> multinormal = stats.multivariate_normal(mu, sigma)
|
||
|
|
>>> data = multinormal.rvs(size=600, random_state=235412)
|
||
|
|
>>> data.shape
|
||
|
|
(600, 2)
|
||
|
|
|
||
|
|
Create bins and count how many arrays fall in each bin:
|
||
|
|
|
||
|
|
>>> N = 60
|
||
|
|
>>> x = np.linspace(-3, 3, N)
|
||
|
|
>>> y = np.linspace(-3, 4, N)
|
||
|
|
>>> ret = stats.binned_statistic_dd(data, np.arange(600), bins=[x, y],
|
||
|
|
... statistic='count')
|
||
|
|
>>> bincounts = ret.statistic
|
||
|
|
|
||
|
|
Set the volume and the location of bars:
|
||
|
|
|
||
|
|
>>> dx = x[1] - x[0]
|
||
|
|
>>> dy = y[1] - y[0]
|
||
|
|
>>> x, y = np.meshgrid(x[:-1]+dx/2, y[:-1]+dy/2)
|
||
|
|
>>> z = 0
|
||
|
|
|
||
|
|
>>> bincounts = bincounts.ravel()
|
||
|
|
>>> x = x.ravel()
|
||
|
|
>>> y = y.ravel()
|
||
|
|
|
||
|
|
>>> fig = plt.figure()
|
||
|
|
>>> ax = fig.add_subplot(111, projection='3d')
|
||
|
|
>>> with np.errstate(divide='ignore'): # silence random axes3d warning
|
||
|
|
... ax.bar3d(x, y, z, dx, dy, bincounts)
|
||
|
|
|
||
|
|
Reuse bin numbers and bin edges with new values:
|
||
|
|
|
||
|
|
>>> ret2 = stats.binned_statistic_dd(data, -np.arange(600),
|
||
|
|
... binned_statistic_result=ret,
|
||
|
|
... statistic='mean')
|
||
|
|
"""
|
||
|
|
known_stats = ['mean', 'median', 'count', 'sum', 'std', 'min', 'max']
|
||
|
|
if not callable(statistic) and statistic not in known_stats:
|
||
|
|
raise ValueError('invalid statistic %r' % (statistic,))
|
||
|
|
|
||
|
|
try:
|
||
|
|
bins = index(bins)
|
||
|
|
except TypeError:
|
||
|
|
# bins is not an integer
|
||
|
|
pass
|
||
|
|
# If bins was an integer-like object, now it is an actual Python int.
|
||
|
|
|
||
|
|
# NOTE: for _bin_edges(), see e.g. gh-11365
|
||
|
|
if isinstance(bins, int) and not np.isfinite(sample).all():
|
||
|
|
raise ValueError('%r contains non-finite values.' % (sample,))
|
||
|
|
|
||
|
|
# `Ndim` is the number of dimensions (e.g. `2` for `binned_statistic_2d`)
|
||
|
|
# `Dlen` is the length of elements along each dimension.
|
||
|
|
# This code is based on np.histogramdd
|
||
|
|
try:
|
||
|
|
# `sample` is an ND-array.
|
||
|
|
Dlen, Ndim = sample.shape
|
||
|
|
except (AttributeError, ValueError):
|
||
|
|
# `sample` is a sequence of 1D arrays.
|
||
|
|
sample = np.atleast_2d(sample).T
|
||
|
|
Dlen, Ndim = sample.shape
|
||
|
|
|
||
|
|
# Store initial shape of `values` to preserve it in the output
|
||
|
|
values = np.asarray(values)
|
||
|
|
input_shape = list(values.shape)
|
||
|
|
# Make sure that `values` is 2D to iterate over rows
|
||
|
|
values = np.atleast_2d(values)
|
||
|
|
Vdim, Vlen = values.shape
|
||
|
|
|
||
|
|
# Make sure `values` match `sample`
|
||
|
|
if(statistic != 'count' and Vlen != Dlen):
|
||
|
|
raise AttributeError('The number of `values` elements must match the '
|
||
|
|
'length of each `sample` dimension.')
|
||
|
|
|
||
|
|
try:
|
||
|
|
M = len(bins)
|
||
|
|
if M != Ndim:
|
||
|
|
raise AttributeError('The dimension of bins must be equal '
|
||
|
|
'to the dimension of the sample x.')
|
||
|
|
except TypeError:
|
||
|
|
bins = Ndim * [bins]
|
||
|
|
|
||
|
|
if binned_statistic_result is None:
|
||
|
|
nbin, edges, dedges = _bin_edges(sample, bins, range)
|
||
|
|
binnumbers = _bin_numbers(sample, nbin, edges, dedges)
|
||
|
|
else:
|
||
|
|
edges = binned_statistic_result.bin_edges
|
||
|
|
nbin = np.array([len(edges[i]) + 1 for i in builtins.range(Ndim)])
|
||
|
|
# +1 for outlier bins
|
||
|
|
dedges = [np.diff(edges[i]) for i in builtins.range(Ndim)]
|
||
|
|
binnumbers = binned_statistic_result.binnumber
|
||
|
|
|
||
|
|
result = np.empty([Vdim, nbin.prod()], float)
|
||
|
|
|
||
|
|
if statistic == 'mean':
|
||
|
|
result.fill(np.nan)
|
||
|
|
flatcount = np.bincount(binnumbers, None)
|
||
|
|
a = flatcount.nonzero()
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
flatsum = np.bincount(binnumbers, values[vv])
|
||
|
|
result[vv, a] = flatsum[a] / flatcount[a]
|
||
|
|
elif statistic == 'std':
|
||
|
|
result.fill(0)
|
||
|
|
flatcount = np.bincount(binnumbers, None)
|
||
|
|
a = flatcount.nonzero()
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
for i in np.unique(binnumbers):
|
||
|
|
# NOTE: take std dev by bin, np.std() is 2-pass and stable
|
||
|
|
binned_data = values[vv, binnumbers == i]
|
||
|
|
# calc std only when binned data is 2 or more for speed up.
|
||
|
|
if len(binned_data) >= 2:
|
||
|
|
result[vv, i] = np.std(binned_data)
|
||
|
|
elif statistic == 'count':
|
||
|
|
result.fill(0)
|
||
|
|
flatcount = np.bincount(binnumbers, None)
|
||
|
|
a = np.arange(len(flatcount))
|
||
|
|
result[:, a] = flatcount[np.newaxis, :]
|
||
|
|
elif statistic == 'sum':
|
||
|
|
result.fill(0)
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
flatsum = np.bincount(binnumbers, values[vv])
|
||
|
|
a = np.arange(len(flatsum))
|
||
|
|
result[vv, a] = flatsum
|
||
|
|
elif statistic == 'median':
|
||
|
|
result.fill(np.nan)
|
||
|
|
for i in np.unique(binnumbers):
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
result[vv, i] = np.median(values[vv, binnumbers == i])
|
||
|
|
elif statistic == 'min':
|
||
|
|
result.fill(np.nan)
|
||
|
|
for i in np.unique(binnumbers):
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
result[vv, i] = np.min(values[vv, binnumbers == i])
|
||
|
|
elif statistic == 'max':
|
||
|
|
result.fill(np.nan)
|
||
|
|
for i in np.unique(binnumbers):
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
result[vv, i] = np.max(values[vv, binnumbers == i])
|
||
|
|
elif callable(statistic):
|
||
|
|
with np.errstate(invalid='ignore'), suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning)
|
||
|
|
try:
|
||
|
|
null = statistic([])
|
||
|
|
except Exception:
|
||
|
|
null = np.nan
|
||
|
|
result.fill(null)
|
||
|
|
for i in np.unique(binnumbers):
|
||
|
|
for vv in builtins.range(Vdim):
|
||
|
|
result[vv, i] = statistic(values[vv, binnumbers == i])
|
||
|
|
|
||
|
|
# Shape into a proper matrix
|
||
|
|
result = result.reshape(np.append(Vdim, nbin))
|
||
|
|
|
||
|
|
# Remove outliers (indices 0 and -1 for each bin-dimension).
|
||
|
|
core = tuple([slice(None)] + Ndim * [slice(1, -1)])
|
||
|
|
result = result[core]
|
||
|
|
|
||
|
|
# Unravel binnumbers into an ndarray, each row the bins for each dimension
|
||
|
|
if(expand_binnumbers and Ndim > 1):
|
||
|
|
binnumbers = np.asarray(np.unravel_index(binnumbers, nbin))
|
||
|
|
|
||
|
|
if np.any(result.shape[1:] != nbin - 2):
|
||
|
|
raise RuntimeError('Internal Shape Error')
|
||
|
|
|
||
|
|
# Reshape to have output (`result`) match input (`values`) shape
|
||
|
|
result = result.reshape(input_shape[:-1] + list(nbin-2))
|
||
|
|
|
||
|
|
return BinnedStatisticddResult(result, edges, binnumbers)
|
||
|
|
|
||
|
|
|
||
|
|
def _bin_edges(sample, bins=None, range=None):
|
||
|
|
""" Create edge arrays
|
||
|
|
"""
|
||
|
|
Dlen, Ndim = sample.shape
|
||
|
|
|
||
|
|
nbin = np.empty(Ndim, int) # Number of bins in each dimension
|
||
|
|
edges = Ndim * [None] # Bin edges for each dim (will be 2D array)
|
||
|
|
dedges = Ndim * [None] # Spacing between edges (will be 2D array)
|
||
|
|
|
||
|
|
# Select range for each dimension
|
||
|
|
# Used only if number of bins is given.
|
||
|
|
if range is None:
|
||
|
|
smin = np.atleast_1d(np.array(sample.min(axis=0), float))
|
||
|
|
smax = np.atleast_1d(np.array(sample.max(axis=0), float))
|
||
|
|
else:
|
||
|
|
smin = np.zeros(Ndim)
|
||
|
|
smax = np.zeros(Ndim)
|
||
|
|
for i in builtins.range(Ndim):
|
||
|
|
smin[i], smax[i] = range[i]
|
||
|
|
|
||
|
|
# Make sure the bins have a finite width.
|
||
|
|
for i in builtins.range(len(smin)):
|
||
|
|
if smin[i] == smax[i]:
|
||
|
|
smin[i] = smin[i] - .5
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||
|
|
smax[i] = smax[i] + .5
|
||
|
|
|
||
|
|
# Create edge arrays
|
||
|
|
for i in builtins.range(Ndim):
|
||
|
|
if np.isscalar(bins[i]):
|
||
|
|
nbin[i] = bins[i] + 2 # +2 for outlier bins
|
||
|
|
edges[i] = np.linspace(smin[i], smax[i], nbin[i] - 1)
|
||
|
|
else:
|
||
|
|
edges[i] = np.asarray(bins[i], float)
|
||
|
|
nbin[i] = len(edges[i]) + 1 # +1 for outlier bins
|
||
|
|
dedges[i] = np.diff(edges[i])
|
||
|
|
|
||
|
|
nbin = np.asarray(nbin)
|
||
|
|
|
||
|
|
return nbin, edges, dedges
|
||
|
|
|
||
|
|
|
||
|
|
def _bin_numbers(sample, nbin, edges, dedges):
|
||
|
|
"""Compute the bin number each sample falls into, in each dimension
|
||
|
|
"""
|
||
|
|
Dlen, Ndim = sample.shape
|
||
|
|
|
||
|
|
sampBin = [
|
||
|
|
np.digitize(sample[:, i], edges[i])
|
||
|
|
for i in range(Ndim)
|
||
|
|
]
|
||
|
|
|
||
|
|
# Using `digitize`, values that fall on an edge are put in the right bin.
|
||
|
|
# For the rightmost bin, we want values equal to the right
|
||
|
|
# edge to be counted in the last bin, and not as an outlier.
|
||
|
|
for i in range(Ndim):
|
||
|
|
# Find the rounding precision
|
||
|
|
dedges_min = dedges[i].min()
|
||
|
|
if dedges_min == 0:
|
||
|
|
raise ValueError('The smallest edge difference is numerically 0.')
|
||
|
|
decimal = int(-np.log10(dedges_min)) + 6
|
||
|
|
# Find which points are on the rightmost edge.
|
||
|
|
on_edge = np.where(np.around(sample[:, i], decimal) ==
|
||
|
|
np.around(edges[i][-1], decimal))[0]
|
||
|
|
# Shift these points one bin to the left.
|
||
|
|
sampBin[i][on_edge] -= 1
|
||
|
|
|
||
|
|
# Compute the sample indices in the flattened statistic matrix.
|
||
|
|
binnumbers = np.ravel_multi_index(sampBin, nbin)
|
||
|
|
|
||
|
|
return binnumbers
|