diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 37 |
1 files changed, 19 insertions, 18 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 1de4e906c..3a73409fc 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -657,20 +657,20 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, if range is None: if a.size == 0: # handle empty arrays. Can't determine range, so use 0-1. - mn, mx = 0.0, 1.0 + first_edge, last_edge = 0.0, 1.0 else: - mn, mx = a.min() + 0.0, a.max() + 0.0 + first_edge, last_edge = a.min() + 0.0, a.max() + 0.0 else: - mn, mx = [mi + 0.0 for mi in range] - if mn > mx: + first_edge, last_edge = [mi + 0.0 for mi in range] + if first_edge > last_edge: raise ValueError( 'max must be larger than min in range parameter.') - if not np.all(np.isfinite([mn, mx])): + if not np.all(np.isfinite([first_edge, last_edge])): raise ValueError( 'range parameter must be finite.') - if mn == mx: - mn -= 0.5 - mx += 0.5 + if first_edge == last_edge: + first_edge -= 0.5 + last_edge += 0.5 # density overrides the normed keyword if density is not None: @@ -694,8 +694,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, b = a # Update the reference if the range needs truncation if range is not None: - keep = (a >= mn) - keep &= (a <= mx) + keep = (a >= first_edge) + keep &= (a <= last_edge) if not np.logical_and.reduce(keep): b = a[keep] @@ -705,7 +705,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, # Do not call selectors on empty arrays width = _hist_bin_selectors[bin_name](b) if width: - n_equal_bins = int(np.ceil((mx - mn) / 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. @@ -733,7 +733,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, # compute the bins if only the count was specified if n_equal_bins is not None: - bin_edges = linspace(mn, mx, n_equal_bins + 1, endpoint=True) + bin_edges = linspace( + first_edge, last_edge, n_equal_bins + 1, endpoint=True) # Histogram is an integer or a float array depending on the weights. if weights is None: @@ -762,7 +763,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, n = np.zeros(n_equal_bins, ntype) # Pre-compute histogram scaling factor - norm = n_equal_bins / (mx - mn) + 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 @@ -776,18 +777,18 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, tmp_w = weights[i:i + BLOCK] # Only include values in the right range - keep = (tmp_a >= mn) - keep &= (tmp_a <= mx) + 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] tmp_a_data = tmp_a.astype(float) - tmp_a = tmp_a_data - mn + tmp_a = tmp_a_data - first_edge tmp_a *= norm - # Compute the bin indices, and for values that lie exactly on mx we - # need to subtract one + # Compute the bin indices, and for values that lie exactly on + # last_edge we need to subtract one indices = tmp_a.astype(np.intp) indices[indices == n_equal_bins] -= 1 |