diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 200 |
1 files changed, 65 insertions, 135 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 7253740be..92fcf5378 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -38,7 +38,7 @@ def iterable(y): except: return 0 return 1 -def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): +def histogram(a, bins=10, range=None, normed=False, weights=None): """ Compute the histogram of a set of data. @@ -54,9 +54,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): 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. Note that with `new` set to False, values below - the range are ignored, while those above the range are tallied - in the rightmost bin. + ignored. normed : bool, optional If False, the result will contain the number of samples in each bin. If True, the result is the value of the @@ -68,18 +66,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): 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 `normed` is True, the weights are normalized, - so that the integral of the density over the range remains 1. - The `weights` keyword is only available with `new` set to True. - new : {None, True, False}, optional - Whether to use the new semantics for histogram: - * None : the new behaviour is used, no warning is printed. - * True : the new behaviour is used and a warning is raised about - the future removal of the `new` keyword. - * False : the old behaviour is used and a DeprecationWarning - is raised. - As of NumPy 1.3, this keyword should not be used explicitly since it - will disappear in NumPy 1.4. - + so that the integral of the density over the range remains 1 + Returns ------- hist : array @@ -87,7 +75,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): description of the possible semantics. bin_edges : array of dtype float Return the bin edges ``(length(hist)+1)``. - With ``new=False``, return the left bin edges (``length(hist)``). + See Also -------- @@ -123,125 +111,67 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): 1.0 """ - # Old behavior - if new == False: - warnings.warn(""" - The histogram semantics being used is now deprecated and - will disappear in NumPy 1.4. Please update your code to - use the default semantics. - """, DeprecationWarning) - - a = asarray(a).ravel() - - if (range is not None): - mn, mx = range - if (mn > mx): - raise AttributeError, \ - 'max must be larger than min in range parameter.' - - if not iterable(bins): - if range is None: - range = (a.min(), a.max()) - mn, mx = [mi+0.0 for mi in range] - if mn == mx: - mn -= 0.5 - mx += 0.5 - bins = np.linspace(mn, mx, bins, endpoint=False) - else: - if normed: - raise ValueError, 'Use new=True to pass bin edges explicitly.' - raise ValueError, 'Use new=True to pass bin edges explicitly.' - bins = asarray(bins) - if (np.diff(bins) < 0).any(): - raise AttributeError, 'bins must increase monotonically.' - - - if weights is not None: - raise ValueError, 'weights are only available with new=True.' - - # best block size probably depends on processor cache size - block = 65536 - n = sort(a[:block]).searchsorted(bins) - for i in xrange(block, a.size, block): - n += sort(a[i:i+block]).searchsorted(bins) - n = concatenate([n, [len(a)]]) - n = n[1:]-n[:-1] - - if normed: - db = bins[1] - bins[0] - return 1.0/(a.size*db) * n, bins - else: - return n, bins - - - - # New behavior - elif new in [True, None]: - if new is True: - warnings.warn(""" - The new semantics of histogram is now the default and the `new` - keyword will be removed in NumPy 1.4. - """, Warning) - a = asarray(a) - if weights is not None: - weights = asarray(weights) - if np.any(weights.shape != a.shape): - raise ValueError, 'weights should have the same shape as a.' - weights = weights.ravel() - a = a.ravel() - - if (range is not None): - mn, mx = range - if (mn > mx): - raise AttributeError, \ - 'max must be larger than min in range parameter.' - - if not iterable(bins): - if range is None: - range = (a.min(), a.max()) - mn, mx = [mi+0.0 for mi in range] - if mn == mx: - mn -= 0.5 - mx += 0.5 - bins = linspace(mn, mx, bins+1, endpoint=True) - else: - bins = asarray(bins) - if (np.diff(bins) < 0).any(): - raise AttributeError, 'bins must increase monotonically.' + + a = asarray(a) + if weights is not None: + weights = asarray(weights) + if np.any(weights.shape != a.shape): + raise ValueError, 'weights should have the same shape as a.' + weights = weights.ravel() + a = a.ravel() + + if (range is not None): + mn, mx = range + if (mn > mx): + raise AttributeError, \ + 'max must be larger than min in range parameter.' + + if not iterable(bins): + if range is None: + range = (a.min(), a.max()) + mn, mx = [mi+0.0 for mi in range] + if mn == mx: + mn -= 0.5 + mx += 0.5 + bins = linspace(mn, mx, bins+1, endpoint=True) + else: + bins = asarray(bins) + if (np.diff(bins) < 0).any(): + raise AttributeError, 'bins must increase monotonically.' - # Histogram is an integer or a float array depending on the weights. - if weights is None: - ntype = int - else: - ntype = weights.dtype - n = np.zeros(bins.shape, ntype) - - block = 65536 - if weights is None: - for i in arange(0, len(a), block): - sa = sort(a[i:i+block]) - n += np.r_[sa.searchsorted(bins[:-1], 'left'), \ - sa.searchsorted(bins[-1], 'right')] - else: - zero = array(0, dtype=ntype) - for i in 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 = np.r_[sa.searchsorted(bins[:-1], 'left'), \ - sa.searchsorted(bins[-1], 'right')] - n += cw[bin_index] - - n = np.diff(n) - - if normed: - db = array(np.diff(bins), float) - return n/(n*db).sum(), bins - else: - return n, bins + # Histogram is an integer or a float array depending on the weights. + if weights is None: + ntype = int + else: + ntype = weights.dtype + n = np.zeros(bins.shape, ntype) + + block = 65536 + if weights is None: + for i in arange(0, len(a), block): + sa = sort(a[i:i+block]) + n += np.r_[sa.searchsorted(bins[:-1], 'left'), \ + sa.searchsorted(bins[-1], 'right')] + else: + zero = array(0, dtype=ntype) + for i in 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 = np.r_[sa.searchsorted(bins[:-1], 'left'), \ + sa.searchsorted(bins[-1], 'right')] + n += cw[bin_index] + + n = np.diff(n) + + if normed: + db = array(np.diff(bins), float) + return n/(n*db).sum(), bins + else: + return n, bins def histogramdd(sample, bins=10, range=None, normed=False, weights=None): |