OwlCyberSecurity - MANAGER

Edit File: histograms.py

""" Histogram-related functions """ import contextlib import functools import operator import warnings import numpy as np from numpy.core import overrides __all__ = ['histogram', 'histogramdd', 'histogram_bin_edges'] array_function_dispatch = functools.partial( overrides.array_function_dispatch, module='numpy') # range is a keyword argument to many functions, so save the builtin so they can # use it. _range = range def _ptp(x): """Peak-to-peak value of x. This implementation avoids the problem of signed integer arrays having a peak-to-peak value that cannot be represented with the array's data type. This function returns an unsigned value for signed integer arrays. """ return _unsigned_subtract(x.max(), x.min()) def _hist_bin_sqrt(x, range): """ Square root histogram bin estimator. Bin width is inversely proportional to the data size. Used by many programs for its simplicity. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused return _ptp(x) / np.sqrt(x.size) def _hist_bin_sturges(x, range): """ Sturges histogram bin estimator. A very simplistic estimator based on the assumption of normality of the data. This estimator has poor performance for non-normal data, which becomes especially obvious for large data sets. The estimate depends only on size of the data. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused return _ptp(x) / (np.log2(x.size) + 1.0) def _hist_bin_rice(x, range): """ Rice histogram bin estimator. Another simple estimator with no normality assumption. It has better performance for large data than Sturges, but tends to overestimate the number of bins. The number of bins is proportional to the cube root of data size (asymptotically optimal). The estimate depends only on size of the data. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused return _ptp(x) / (2.0 * x.size ** (1.0 / 3)) def _hist_bin_scott(x, range): """ Scott histogram bin estimator. The binwidth is proportional to the standard deviation of the data and inversely proportional to the cube root of data size (asymptotically optimal). Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x) def _hist_bin_stone(x, range): """ Histogram bin estimator based on minimizing the estimated integrated squared error (ISE). The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution. The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule. https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule This paper by Stone appears to be the origination of this rule. http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. range : (float, float) The lower and upper range of the bins. Returns ------- h : An estimate of the optimal bin width for the given data. """ n = x.size ptp_x = _ptp(x) if n <= 1 or ptp_x == 0: return 0 def jhat(nbins): hh = ptp_x / nbins p_k = np.histogram(x, bins=nbins, range=range)[0] / n return (2 - (n + 1) * p_k.dot(p_k)) / hh nbins_upper_bound = max(100, int(np.sqrt(n))) nbins = min(_range(1, nbins_upper_bound + 1), key=jhat) if nbins == nbins_upper_bound: warnings.warn("The number of bins estimated may be suboptimal.", RuntimeWarning, stacklevel=3) return ptp_x / nbins def _hist_bin_doane(x, range): """ Doane's histogram bin estimator. Improved version of Sturges' formula which works better for non-normal data. See stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused if x.size > 2: sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3))) sigma = np.std(x) if sigma > 0.0: # These three operations add up to # g1 = np.mean(((x - np.mean(x)) / sigma)**3) # but use only one temp array instead of three temp = x - np.mean(x) np.true_divide(temp, sigma, temp) np.power(temp, 3, temp) g1 = np.mean(temp) return _ptp(x) / (1.0 + np.log2(x.size) + np.log2(1.0 + np.absolute(g1) / sg1)) return 0.0 def _hist_bin_fd(x, range): """ The Freedman-Diaconis histogram bin estimator. The Freedman-Diaconis rule uses interquartile range (IQR) to estimate binwidth. It is considered a variation of the Scott rule with more robustness as the IQR is less affected by outliers than the standard deviation. However, the IQR depends on fewer points than the standard deviation, so it is less accurate, especially for long tailed distributions. If the IQR is 0, this function returns 0 for the bin width. Binwidth is inversely proportional to the cube root of data size (asymptotically optimal). Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. """ del range # unused iqr = np.subtract(*np.percentile(x, [75, 25])) return 2.0 * iqr * x.size ** (-1.0 / 3.0) def _hist_bin_auto(x, range): """ Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero. If the bin width from the FD estimator is 0, the Sturges estimator is used. The FD estimator is usually the most robust method, but its width estimate tends to be too large for small `x` and bad for data with limited variance. The Sturges estimator is quite good for small (<1000) datasets and is the default in the R language. This method gives good off-the-shelf behaviour. .. versionchanged:: 1.15.0 If there is limited variance the IQR can be 0, which results in the FD bin width being 0 too. This is not a valid bin width, so ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal. If the IQR is 0, it's unlikely any variance-based estimators will be of use, so we revert to the Sturges estimator, which only uses the size of the dataset in its calculation. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. See Also -------- _hist_bin_fd, _hist_bin_sturges """ fd_bw = _hist_bin_fd(x, range) sturges_bw = _hist_bin_sturges(x, range) del range # unused if fd_bw: return min(fd_bw, sturges_bw) else: # limited variance, so we return a len dependent bw estimator return sturges_bw # Private dict initialized at module load time _hist_bin_selectors = {'stone': _hist_bin_stone, 'auto': _hist_bin_auto, 'doane': _hist_bin_doane, 'fd': _hist_bin_fd, 'rice': _hist_bin_rice, 'scott': _hist_bin_scott, 'sqrt': _hist_bin_sqrt, 'sturges': _hist_bin_sturges} def _ravel_and_check_weights(a, weights): """ Check a and weights have matching shapes, and ravel both """ a = np.asarray(a) # Ensure that the array is a "subtractable" dtype if a.dtype == np.bool_: warnings.warn("Converting input from {} to {} for compatibility." .format(a.dtype, np.uint8), RuntimeWarning, stacklevel=3) a = a.astype(np.uint8) if weights is not None: weights = np.asarray(weights) if weights.shape != a.shape: raise ValueError( 'weights should have the same shape as a.') weights = weights.ravel() a = a.ravel() return a, weights def _get_outer_edges(a, range): """ Determine the outer bin edges to use, from either the data or the range argument """ if range is not None: first_edge, last_edge = range if first_edge > last_edge: raise ValueError( 'max must be larger than min in range parameter.') if not (np.isfinite(first_edge) and np.isfinite(last_edge)): raise ValueError( "supplied range of [{}, {}] is not finite".format(first_edge, last_edge)) elif a.size == 0: # handle empty arrays. Can't determine range, so use 0-1. first_edge, last_edge = 0, 1 else: first_edge, last_edge = a.min(), a.max() if not (np.isfinite(first_edge) and np.isfinite(last_edge)): raise ValueError( "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge)) # expand empty range to avoid divide by zero if first_edge == last_edge: first_edge = first_edge - 0.5 last_edge = last_edge + 0.5 return first_edge, last_edge def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt) def _get_bin_edges(a, bins, range, weights): """ Computes the bins used internally by `histogram`. Parameters ========== a : ndarray Ravelled data array bins, range Forwarded arguments from `histogram`. weights : ndarray, optional Ravelled weights array, or None Returns ======= bin_edges : ndarray Array of bin edges uniform_bins : (Number, Number, int): The upper bound, lowerbound, and number of bins, used in the optimized implementation of `histogram` that works on uniform bins. """ # parse the overloaded bins argument n_equal_bins = None bin_edges = None if isinstance(bins, str): bin_name = bins # if `bins` is a string for an automatic method, # this will replace it with the number of bins calculated if bin_name not in _hist_bin_selectors: raise ValueError( "{!r} is not a valid estimator for `bins`".format(bin_name)) if weights is not None: raise TypeError("Automated estimation of the number of " "bins is not supported for weighted data") first_edge, last_edge = _get_outer_edges(a, range) # truncate the range if needed if range is not None: keep = (a >= first_edge) keep &= (a <= last_edge) if not np.logical_and.reduce(keep): a = a[keep] if a.size == 0: n_equal_bins = 1 else: # Do not call selectors on empty arrays width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge)) if width: n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width)) else: # Width can be zero for some estimators, e.g. FD when # the IQR of the data is zero. n_equal_bins = 1 elif np.ndim(bins) == 0: try: n_equal_bins = operator.index(bins) except TypeError as e: raise TypeError( '`bins` must be an integer, a string, or an array') from e if n_equal_bins < 1: raise ValueError('`bins` must be positive, when an integer') first_edge, last_edge = _get_outer_edges(a, range) elif np.ndim(bins) == 1: bin_edges = np.asarray(bins) if np.any(bin_edges[:-1] > bin_edges[1:]): raise ValueError( '`bins` must increase monotonically, when an array') else: raise ValueError('`bins` must be 1d, when an array') if n_equal_bins is not None: # gh-10322 means that type resolution rules are dependent on array # shapes. To avoid this causing problems, we pick a type now and stick # with it throughout. bin_type = np.result_type(first_edge, last_edge, a) if np.issubdtype(bin_type, np.integer): bin_type = np.result_type(bin_type, float) # bin edges must be computed bin_edges = np.linspace( first_edge, last_edge, n_equal_bins + 1, endpoint=True, dtype=bin_type) return bin_edges, (first_edge, last_edge, n_equal_bins) else: return bin_edges, None def _search_sorted_inclusive(a, v): """ Like `searchsorted`, but where the last item in `v` is placed on the right. In the context of a histogram, this makes the last bin edge inclusive """ return np.concatenate(( a.searchsorted(v[:-1], 'left'), a.searchsorted(v[-1:], 'right') )) def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None): return (a, bins, weights) @array_function_dispatch(_histogram_bin_edges_dispatcher) def histogram_bin_edges(a, bins=10, range=None, weights=None): r""" Function to calculate only the edges of the bins used by the `histogram` function. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If `bins` is a string from the list below, `histogram_bin_edges` will use the method chosen to calculate the optimal bin width and consequently the number of bins (see `Notes` for more detail on the estimators) from the data that falls within the requested range. While the bin width will be optimal for the actual data in the range, the number of bins will be computed to fill the entire range, including the empty portions. For visualisation, using the 'auto' option is suggested. Weighted data is not supported for automated bin size selection. 'auto' Maximum of the 'sturges' and 'fd' estimators. Provides good all around performance. 'fd' (Freedman Diaconis Estimator) Robust (resilient to outliers) estimator that takes into account data variability and data size. 'doane' An improved version of Sturges' estimator that works better with non-normal datasets. 'scott' Less robust estimator that takes into account data variability and data size. 'stone' Estimator based on leave-one-out cross-validation estimate of the integrated squared error. Can be regarded as a generalization of Scott's rule. 'rice' Estimator does not take variability into account, only data size. Commonly overestimates number of bins required. 'sturges' R's default method, only accounts for data size. Only optimal for gaussian data and underestimates number of bins for large non-gaussian datasets. 'sqrt' Square root (of data size) estimator, used by Excel and other programs for its speed and simplicity. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). This is currently not used by any of the bin estimators, but may be in the future. Returns ------- bin_edges : array of dtype float The edges to pass into `histogram` See Also -------- histogram Notes ----- The methods to estimate the optimal number of bins are well founded in literature, and are inspired by the choices R provides for histogram visualisation. Note that having the number of bins proportional to :math:`n^{1/3}` is asymptotically optimal, which is why it appears in most estimators. These are simply plug-in methods that give good starting points for number of bins. In the equations below, :math:`h` is the binwidth and :math:`n_h` is the number of bins. All estimators that compute bin counts are recast to bin width using the `ptp` of the data. The final bin count is obtained from ``np.round(np.ceil(range / h))``. The final bin width is often less than what is returned by the estimators below. 'auto' (maximum of the 'sturges' and 'fd' estimators) A compromise to get a good value. For small datasets the Sturges value will usually be chosen, while larger datasets will usually default to FD. Avoids the overly conservative behaviour of FD and Sturges for small and large datasets respectively. Switchover point is usually :math:`a.size \approx 1000`. 'fd' (Freedman Diaconis Estimator) .. math:: h = 2 \frac{IQR}{n^{1/3}} The binwidth is proportional to the interquartile range (IQR) and inversely proportional to cube root of a.size. Can be too conservative for small datasets, but is quite good for large datasets. The IQR is very robust to outliers. 'scott' .. math:: h = \sigma \sqrt[3]{\frac{24 \sqrt{\pi}}{n}} The binwidth is proportional to the standard deviation of the data and inversely proportional to cube root of ``x.size``. Can be too conservative for small datasets, but is quite good for large datasets. The standard deviation is not very robust to outliers. Values are very similar to the Freedman-Diaconis estimator in the absence of outliers. 'rice' .. math:: n_h = 2n^{1/3} The number of bins is only proportional to cube root of ``a.size``. It tends to overestimate the number of bins and it does not take into account data variability. 'sturges' .. math:: n_h = \log _{2}(n) + 1 The number of bins is the base 2 log of ``a.size``. This estimator assumes normality of data and is too conservative for larger, non-normal datasets. This is the default method in R's ``hist`` method. 'doane' .. math:: n_h = 1 + \log_{2}(n) + \log_{2}\left(1 + \frac{|g_1|}{\sigma_{g_1}}\right) g_1 = mean\left[\left(\frac{x - \mu}{\sigma}\right)^3\right] \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}} An improved version of Sturges' formula that produces better estimates for non-normal datasets. This estimator attempts to account for the skew of the data. 'sqrt' .. math:: n_h = \sqrt n The simplest and fastest estimator. Only takes into account the data size. Examples -------- >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5]) >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1)) array([0. , 0.25, 0.5 , 0.75, 1. ]) >>> np.histogram_bin_edges(arr, bins=2) array([0. , 2.5, 5. ]) For consistency with histogram, an array of pre-computed bins is passed through unmodified: >>> np.histogram_bin_edges(arr, [1, 2]) array([1, 2]) This function allows one set of bins to be computed, and reused across multiple histograms: >>> shared_bins = np.histogram_bin_edges(arr, bins='auto') >>> shared_bins array([0., 1., 2., 3., 4., 5.]) >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1]) >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins) >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins) >>> hist_0; hist_1 array([1, 1, 0, 1, 0]) array([2, 0, 1, 1, 2]) Which gives more easily comparable results than using separate bins for each histogram: >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto') >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto') >>> hist_0; hist_1 array([1, 1, 1]) array([2, 1, 1, 2]) >>> bins_0; bins_1 array([0., 1., 2., 3.]) array([0. , 1.25, 2.5 , 3.75, 5. ]) """ a, weights = _ravel_and_check_weights(a, weights) bin_edges, _ = _get_bin_edges(a, bins, range, weights) return bin_edges def _histogram_dispatcher( a, bins=None, range=None, density=None, weights=None): return (a, bins, weights) @array_function_dispatch(_histogram_dispatcher) def histogram(a, bins=10, range=None, density=None, weights=None): r""" Compute the histogram of a dataset. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths. .. versionadded:: 1.11.0 If `bins` is a string, it defines the method used to calculate the optimal bin width, as defined by `histogram_bin_edges`. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). If `density` is True, the weights are normalized, so that the integral of the density over the range remains 1. density : bool, optional If ``False``, the result will contain the number of samples in each bin. If ``True``, the result is the value of the probability *density* function at the bin, normalized such that the *integral* over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability *mass* function. Returns ------- hist : array The values of the histogram. See `density` and `weights` for a description of the possible semantics. bin_edges : array of dtype float Return the bin edges ``(length(hist)+1)``. See Also -------- histogramdd, bincount, searchsorted, digitize, histogram_bin_edges Notes ----- All but the last (righthand-most) bin is half-open. 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. Examples -------- >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3]) (array([0, 2, 1]), array([0, 1, 2, 3])) >>> np.histogram(np.arange(4), bins=np.arange(5), density=True) (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4])) >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]) (array([1, 4, 1]), array([0, 1, 2, 3])) >>> a = np.arange(5) >>> hist, bin_edges = np.histogram(a, density=True) >>> hist array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]) >>> hist.sum() 2.4999999999999996 >>> np.sum(hist * np.diff(bin_edges)) 1.0 .. versionadded:: 1.11.0 Automated Bin Selection Methods example, using 2 peak random data with 2000 points: >>> import matplotlib.pyplot as plt >>> rng = np.random.RandomState(10) # deterministic random data >>> a = np.hstack((rng.normal(size=1000), ... rng.normal(loc=5, scale=2, size=1000))) >>> _ = plt.hist(a, bins='auto') # arguments are passed to np.histogram >>> plt.title("Histogram with 'auto' bins") Text(0.5, 1.0, "Histogram with 'auto' bins") >>> plt.show() """ a, weights = _ravel_and_check_weights(a, weights) bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights) # Histogram is an integer or a float array depending on the weights. if weights is None: ntype = np.dtype(np.intp) else: ntype = weights.dtype # We set a block size, as this allows us to iterate over chunks when # computing histograms, to minimize memory usage. BLOCK = 65536 # The fast path uses bincount, but that only works for certain types # of weight simple_weights = ( weights is None or np.can_cast(weights.dtype, np.double) or np.can_cast(weights.dtype, complex) ) if uniform_bins is not None and simple_weights: # Fast algorithm for equal bins # We now convert values of a to bin indices, under the assumption of # equal bin widths (which is valid here). first_edge, last_edge, n_equal_bins = uniform_bins # Initialize empty histogram n = np.zeros(n_equal_bins, ntype) # Pre-compute histogram scaling factor norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge) # We iterate over blocks here for two reasons: the first is that for # large arrays, it is actually faster (for example for a 10^8 array it # is 2x as fast) and it results in a memory footprint 3x lower in the # limit of large arrays. for i in _range(0, len(a), BLOCK): tmp_a = a[i:i+BLOCK] if weights is None: tmp_w = None else: tmp_w = weights[i:i + BLOCK] # Only include values in the right range keep = (tmp_a >= first_edge) keep &= (tmp_a <= last_edge) if not np.logical_and.reduce(keep): tmp_a = tmp_a[keep] if tmp_w is not None: tmp_w = tmp_w[keep] # This cast ensures no type promotions occur below, which gh-10322 # make unpredictable. Getting it wrong leads to precision errors # like gh-8123. tmp_a = tmp_a.astype(bin_edges.dtype, copy=False) # Compute the bin indices, and for values that lie exactly on # last_edge we need to subtract one f_indices = _unsigned_subtract(tmp_a, first_edge) * norm indices = f_indices.astype(np.intp) indices[indices == n_equal_bins] -= 1 # The index computation is not guaranteed to give exactly # consistent results within ~1 ULP of the bin edges. decrement = tmp_a < bin_edges[indices] indices[decrement] -= 1 # The last bin includes the right edge. The other bins do not. increment = ((tmp_a >= bin_edges[indices + 1]) & (indices != n_equal_bins - 1)) indices[increment] += 1 # We now compute the histogram using bincount if ntype.kind == 'c': n.real += np.bincount(indices, weights=tmp_w.real, minlength=n_equal_bins) n.imag += np.bincount(indices, weights=tmp_w.imag, minlength=n_equal_bins) else: n += np.bincount(indices, weights=tmp_w, minlength=n_equal_bins).astype(ntype) else: # Compute via cumulative histogram cum_n = np.zeros(bin_edges.shape, ntype) if weights is None: for i in _range(0, len(a), BLOCK): sa = np.sort(a[i:i+BLOCK]) cum_n += _search_sorted_inclusive(sa, bin_edges) else: zero = np.zeros(1, dtype=ntype) for i in _range(0, len(a), BLOCK): tmp_a = a[i:i+BLOCK] tmp_w = weights[i:i+BLOCK] sorting_index = np.argsort(tmp_a) sa = tmp_a[sorting_index] sw = tmp_w[sorting_index] cw = np.concatenate((zero, sw.cumsum())) bin_index = _search_sorted_inclusive(sa, bin_edges) cum_n += cw[bin_index] n = np.diff(cum_n) if density: db = np.array(np.diff(bin_edges), float) return n/db/n.sum(), bin_edges return n, bin_edges def _histogramdd_dispatcher(sample, bins=None, range=None, density=None, weights=None): if hasattr(sample, 'shape'): # same condition as used in histogramdd yield sample else: yield from sample with contextlib.suppress(TypeError): yield from bins yield weights @array_function_dispatch(_histogramdd_dispatcher) def histogramdd(sample, bins=10, range=None, density=None, weights=None): """ Compute the multidimensional histogram of some data. Parameters ---------- sample : (N, D) array, or (N, D) array_like The data to be histogrammed. Note the unusual interpretation of sample when an array_like: * When an array, each row is a coordinate in a D-dimensional space - such as ``histogramdd(np.array([p1, p2, p3]))``. * When an array_like, each element is the list of values for single coordinate - such as ``histogramdd((X, Y, Z))``. The first form should be preferred. bins : sequence or int, optional The bin specification: * A sequence of arrays describing the monotonically increasing 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 length D, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in `bins`. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of D None values. density : bool, optional If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. weights : (N,) array_like, optional An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. Weights are normalized to 1 if density is True. If density is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. Returns ------- H : ndarray The multidimensional histogram of sample x. See density and weights for the different possible semantics. edges : list A list of D arrays describing the bin edges for each dimension. See Also -------- histogram: 1-D histogram histogram2d: 2-D histogram Examples -------- >>> r = np.random.randn(100,3) >>> H, edges = np.histogramdd(r, bins = (5, 8, 4)) >>> H.shape, edges[0].size, edges[1].size, edges[2].size ((5, 8, 4), 6, 9, 5) """ try: # Sample is an ND-array. N, D = sample.shape except (AttributeError, ValueError): # Sample is a sequence of 1D arrays. sample = np.atleast_2d(sample).T N, D = sample.shape nbin = np.empty(D, np.intp) edges = D*[None] dedges = D*[None] if weights is not None: weights = np.asarray(weights) try: M = len(bins) if M != D: raise ValueError( 'The dimension of bins must be equal to the dimension of the ' 'sample x.') except TypeError: # bins is an integer bins = D*[bins] # normalize the range argument if range is None: range = (None,) * D elif len(range) != D: raise ValueError('range argument must have one entry per dimension') # Create edge arrays for i in _range(D): if np.ndim(bins[i]) == 0: if bins[i] < 1: raise ValueError( '`bins[{}]` must be positive, when an integer'.format(i)) smin, smax = _get_outer_edges(sample[:,i], range[i]) try: n = operator.index(bins[i]) except TypeError as e: raise TypeError( "`bins[{}]` must be an integer, when a scalar".format(i) ) from e edges[i] = np.linspace(smin, smax, n + 1) elif np.ndim(bins[i]) == 1: edges[i] = np.asarray(bins[i]) if np.any(edges[i][:-1] > edges[i][1:]): raise ValueError( '`bins[{}]` must be monotonically increasing, when an array' .format(i)) else: raise ValueError( '`bins[{}]` must be a scalar or 1d array'.format(i)) nbin[i] = len(edges[i]) + 1 # includes an outlier on each end dedges[i] = np.diff(edges[i]) # Compute the bin number each sample falls into. Ncount = tuple( # avoid np.digitize to work around gh-11022 np.searchsorted(edges[i], sample[:, i], side='right') for i in _range(D) ) # 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(D): # Find which points are on the rightmost edge. on_edge = (sample[:, i] == edges[i][-1]) # Shift these points one bin to the left. Ncount[i][on_edge] -= 1 # Compute the sample indices in the flattened histogram matrix. # This raises an error if the array is too large. xy = np.ravel_multi_index(Ncount, nbin) # Compute the number of repetitions in xy and assign it to the # flattened histmat. hist = np.bincount(xy, weights, minlength=nbin.prod()) # Shape into a proper matrix hist = hist.reshape(nbin) # This preserves the (bad) behavior observed in gh-7845, for now. hist = hist.astype(float, casting='safe') # Remove outliers (indices 0 and -1 for each dimension). core = D*(slice(1, -1),) hist = hist[core] if density: # calculate the probability density function s = hist.sum() for i in _range(D): shape = np.ones(D, int) shape[i] = nbin[i] - 2 hist = hist / dedges[i].reshape(shape) hist /= s if (hist.shape != nbin - 2).any(): raise RuntimeError( "Internal Shape Error") return hist, edges