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)