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import numbers
import numpy as np
from ._histogram_core import (
_histogram1d,
_histogram1d_weighted,
_histogram2d,
_histogram2d_weighted,
_histogramdd,
_histogramdd_weighted,
)
NUMERICAL_TYPES = {"f", "i", "u"}
__all__ = ["histogram1d", "histogram2d", "histogramdd"]
def histogram1d(x, bins, range, weights=None):
"""
Compute a 1D histogram assuming equally spaced bins.
Parameters
----------
x : `~numpy.ndarray`
The position of the points to bin in the 1D histogram
bins : int
The number of bins
range : iterable
The range as a tuple of (xmin, xmax)
weights : `~numpy.ndarray`
The weights of the points in the 1D histogram
Returns
-------
array : `~numpy.ndarray`
The 1D histogram array
"""
nx = bins
if not np.isscalar(bins):
raise TypeError("bins should be an integer")
xmin, xmax = range
if not np.isfinite(xmin):
raise ValueError("xmin should be finite")
if not np.isfinite(xmax):
raise ValueError("xmax should be finite")
if xmax <= xmin:
raise ValueError("xmax should be greater than xmin")
if nx <= 0:
raise ValueError("nx should be strictly positive")
x = np.atleast_1d(x)
if x.dtype.kind not in NUMERICAL_TYPES:
raise TypeError("x is not or cannot be converted to a numerical array")
if weights is None:
return _histogram1d(x, nx, xmin, xmax)
else:
weights = np.atleast_1d(weights)
if weights.dtype.kind not in NUMERICAL_TYPES:
raise TypeError(
"weights is not or cannot be converted to a numerical array"
)
return _histogram1d_weighted(x, weights, nx, xmin, xmax)
def histogram2d(x, y, bins, range, weights=None):
"""
Compute a 2D histogram assuming equally spaced bins.
Parameters
----------
x, y : `~numpy.ndarray`
The position of the points to bin in the 2D histogram
bins : int or iterable
The number of bins in each dimension. If given as an integer, the same
number of bins is used for each dimension.
range : iterable
The range to use in each dimention, as an iterable of value pairs, i.e.
[(xmin, xmax), (ymin, ymax)]
weights : `~numpy.ndarray`
The weights of the points in the 1D histogram
Returns
-------
array : `~numpy.ndarray`
The 2D histogram array
"""
if isinstance(bins, numbers.Integral):
nx = ny = bins
else:
nx, ny = bins
if not np.isscalar(nx) or not np.isscalar(ny):
raise TypeError("bins should be an iterable of two integers")
(xmin, xmax), (ymin, ymax) = range
if not np.isfinite(xmin):
raise ValueError("xmin should be finite")
if not np.isfinite(xmax):
raise ValueError("xmax should be finite")
if not np.isfinite(ymin):
raise ValueError("ymin should be finite")
if not np.isfinite(ymax):
raise ValueError("ymax should be finite")
if xmax <= xmin:
raise ValueError("xmax should be greater than xmin")
if ymax <= ymin:
raise ValueError("xmax should be greater than xmin")
if nx <= 0:
raise ValueError("nx should be strictly positive")
if ny <= 0:
raise ValueError("ny should be strictly positive")
x = np.atleast_1d(x)
y = np.atleast_1d(y)
if x.dtype.kind not in NUMERICAL_TYPES:
raise TypeError("x is not or cannot be converted to a numerical array")
if y.dtype.kind not in NUMERICAL_TYPES:
raise TypeError("y is not or cannot be converted to a numerical array")
if weights is None:
return _histogram2d(x, y, nx, xmin, xmax, ny, ymin, ymax)
else:
weights = np.atleast_1d(weights)
if weights.dtype.kind not in NUMERICAL_TYPES:
raise TypeError(
"weights is not or cannot be converted to a numerical array"
)
return _histogram2d_weighted(x, y, weights, nx, xmin, xmax, ny, ymin, ymax)
def histogramdd(sample, bins, range, weights=None):
"""
Compute a histogram of N samples in D dimensions.
Parameters
----------
sample : (N, D) `~numpy.ndarray`, or (D, N) array_like
The data to be histogrammed.
* When an array_like, each element is the list of values for single
coordinate - such as ``histogramdd((X, Y, Z), bins, range)``.
* When a `~numpy.ndarray`, each row is a coordinate in a D-dimensional space -
such as ``histogramdd(np.array([p1, p2, p3]), bins, range)``.
* In the special case of D = 1, it is allowed to pass an array or array_like
with length N.
The second form is converted internally into the first form, thus the first form
is preferred.
bins : int or iterable
The number of bins in each dimension. If given as an integer, the same
number of bins is used for every dimension.
range : iterable
The range to use in each dimention, as an iterable of D value pairs, i.e.
[(xmin, xmax), (ymin, ymax)]
weights : `~numpy.ndarray`
The weights of the points in `sample`.
Returns
-------
array : `~numpy.ndarray`
The ND histogram array
"""
if isinstance(sample, np.ndarray):
_sample = tuple(np.atleast_2d(sample.T))
else:
# handle special case in 1D
if isinstance(sample[0], numbers.Real):
_sample = (np.atleast_1d(sample),)
else:
_sample = tuple([np.atleast_1d(x) for x in sample])
for x in _sample:
if x.dtype.kind not in NUMERICAL_TYPES:
raise TypeError("input is not or cannot be converted to a numerical array")
ndim = len(_sample)
if isinstance(bins, numbers.Integral):
_bins = bins * np.ones(ndim, dtype=np.intp)
else:
_bins = np.array(bins, dtype=np.intp)
if len(_bins) != ndim:
raise ValueError("number of bin counts does not match number of dimensions")
if np.any(_bins <= 0):
raise ValueError("all bin numbers should be strictly positive")
_range = np.zeros((ndim, 2), dtype=np.double)
if not len(range) == ndim:
raise ValueError("number of ranges does not equal number of dimensions")
for i, r in enumerate(range):
if not len(r) == 2:
raise ValueError(
"should pass a minimum and maximum value for each dimension"
)
if r[0] >= r[1]:
raise ValueError("each range should be strictly increasing")
_range[i][0] = r[0]
_range[i][1] = r[1]
if weights is None:
return _histogramdd(_sample, _bins, _range)
else:
weights = np.atleast_1d(weights)
if weights.dtype.kind not in NUMERICAL_TYPES:
raise TypeError(
"weights is not or cannot be converted to a numerical array"
)
return _histogramdd_weighted(_sample, _bins, _range, weights)
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