""" Histogram-related functions """ from __future__ import division, absolute_import, print_function import operator import numpy as np from numpy.compat.py3k import basestring __all__ = ['histogram', 'histogramdd'] def _hist_bin_sqrt(x): """ 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. """ return x.ptp() / np.sqrt(x.size) def _hist_bin_sturges(x): """ 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. """ return x.ptp() / (np.log2(x.size) + 1.0) def _hist_bin_rice(x): """ 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. """ return x.ptp() / (2.0 * x.size ** (1.0 / 3)) def _hist_bin_scott(x): """ 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. """ return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x) def _hist_bin_doane(x): """ 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. """ 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 x.ptp() / (1.0 + np.log2(x.size) + np.log2(1.0 + np.absolute(g1) / sg1)) return 0.0 def _hist_bin_fd(x): """ 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 1 for the number of bins. 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. """ iqr = np.subtract(*np.percentile(x, [75, 25])) return 2.0 * iqr * x.size ** (-1.0 / 3.0) def _hist_bin_auto(x): """ Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators. The FD estimator is usually the most robust method, but its width estimate tends to be too large for small `x`. 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. 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 """ # There is no need to check for zero here. If ptp is, so is IQR and # vice versa. Either both are zero or neither one is. return min(_hist_bin_fd(x), _hist_bin_sturges(x)) # Private dict initialized at module load time _hist_bin_selectors = {'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) 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 _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, basestring): 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) if width: n_equal_bins = int(np.ceil((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: raise TypeError( '`bins` must be an integer, a string, or an array') 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(a, bins=10, range=None, normed=False, weights=None, density=None): r""" Compute the histogram of a set of data. 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. .. versionadded:: 1.11.0 If `bins` is a string from the list below, `histogram` 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 that takes into account data variability and data size. '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. normed : bool, optional This keyword is deprecated in NumPy 1.6.0 due to confusing/buggy behavior. It will be removed in NumPy 2.0.0. Use the ``density`` keyword instead. 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 this latter behavior is known to be buggy with unequal bin widths; use ``density`` instead. 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. Overrides the ``normed`` keyword if given. 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 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. .. versionadded:: 1.11.0 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))`. '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}(1 + \frac{|g_1|}{\sigma_{g_1}}) g_1 = mean[(\frac{x - \mu}{\sigma})^3] \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 -------- >>> 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") >>> 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 / (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 np.arange(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 = (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 np.arange(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 np.arange(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) # density overrides the normed keyword if density is not None: normed = False if density: db = np.array(np.diff(bin_edges), float) return n/db/n.sum(), bin_edges elif normed: # deprecated, buggy behavior. Remove for NumPy 2.0.0 db = np.array(np.diff(bin_edges), float) return n/(n*db).sum(), bin_edges else: return n, bin_edges def histogramdd(sample, bins=10, range=None, normed=False, weights=None): """ Compute the multidimensional histogram of some data. Parameters ---------- sample : array_like The data to be histogrammed. It must be an (N,D) array or data that can be converted to such. The rows of the resulting array are the coordinates of points in a D dimensional polytope. bins : sequence or int, optional The bin specification: * 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. normed : bool, optional If False, returns the number of samples in each bin. If True, returns the bin density ``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 normed is True. If normed 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 normed 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, int) 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] # Select range for each dimension # Used only if number of bins is given. if range is None: # Handle empty input. Range can't be determined in that case, use 0-1. if N == 0: smin = np.zeros(D) smax = np.ones(D) else: smin = np.atleast_1d(np.array(sample.min(0), float)) smax = np.atleast_1d(np.array(sample.max(0), float)) else: if not np.all(np.isfinite(range)): raise ValueError( 'range parameter must be finite.') smin = np.zeros(D) smax = np.zeros(D) for i in np.arange(D): smin[i], smax[i] = range[i] # Make sure the bins have a finite width. for i in np.arange(len(smin)): if smin[i] == smax[i]: smin[i] = smin[i] - .5 smax[i] = smax[i] + .5 # avoid rounding issues for comparisons when dealing with inexact types if np.issubdtype(sample.dtype, np.inexact): edge_dt = sample.dtype else: edge_dt = float # Create edge arrays for i in np.arange(D): if np.isscalar(bins[i]): if bins[i] < 1: raise ValueError( "Element at index %s in `bins` should be a positive " "integer." % i) nbin[i] = bins[i] + 2 # +2 for outlier bins edges[i] = np.linspace(smin[i], smax[i], nbin[i]-1, dtype=edge_dt) else: edges[i] = np.asarray(bins[i], edge_dt) nbin[i] = len(edges[i]) + 1 # +1 for outlier bins dedges[i] = np.diff(edges[i]) if np.any(np.asarray(dedges[i]) <= 0): raise ValueError( "Found bin edge of size <= 0. Did you specify `bins` with" "non-monotonic sequence?") nbin = np.asarray(nbin) # Handle empty input. if N == 0: return np.zeros(nbin-2), edges # Compute the bin number each sample falls into. Ncount = {} for i in np.arange(D): Ncount[i] = np.digitize(sample[:, i], edges[i]) # 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 np.arange(D): # Rounding precision mindiff = dedges[i].min() if not np.isinf(mindiff): decimal = int(-np.log10(mindiff)) + 6 # Find which points are on the rightmost edge. not_smaller_than_edge = (sample[:, i] >= edges[i][-1]) on_edge = (np.around(sample[:, i], decimal) == np.around(edges[i][-1], decimal)) # Shift these points one bin to the left. Ncount[i][np.nonzero(on_edge & not_smaller_than_edge)[0]] -= 1 # Flattened histogram matrix (1D) # Reshape is used so that overlarge arrays # will raise an error. hist = np.zeros(nbin, float).reshape(-1) # Compute the sample indices in the flattened histogram matrix. ni = nbin.argsort() xy = np.zeros(N, int) for i in np.arange(0, D-1): xy += Ncount[ni[i]] * nbin[ni[i+1:]].prod() xy += Ncount[ni[-1]] # Compute the number of repetitions in xy and assign it to the # flattened histmat. if len(xy) == 0: return np.zeros(nbin-2, int), edges flatcount = np.bincount(xy, weights) a = np.arange(len(flatcount)) hist[a] = flatcount # Shape into a proper matrix hist = hist.reshape(np.sort(nbin)) for i in np.arange(nbin.size): j = ni.argsort()[i] hist = hist.swapaxes(i, j) ni[i], ni[j] = ni[j], ni[i] # Remove outliers (indices 0 and -1 for each dimension). core = D*(slice(1, -1),) hist = hist[core] # Normalize if normed is True if normed: s = hist.sum() for i in np.arange(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