diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 170 |
1 files changed, 83 insertions, 87 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 32c999dfc..905e60512 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1,7 +1,6 @@ from __future__ import division, absolute_import, print_function import collections -import operator import re import sys import warnings @@ -16,7 +15,7 @@ from numpy.core.numeric import ( ) from numpy.core.umath import ( pi, multiply, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin, - mod, exp, log10 + mod, exp, log10, not_equal, subtract ) from numpy.core.fromnumeric import ( ravel, nonzero, sort, partition, mean, any, sum @@ -57,8 +56,6 @@ def rot90(m, k=1, axes=(0,1)): Rotation direction is from the first towards the second axis. - .. versionadded:: 1.12.0 - Parameters ---------- m : array_like @@ -69,6 +66,8 @@ def rot90(m, k=1, axes=(0,1)): The array is rotated in the plane defined by the axes. Axes must be different. + .. versionadded:: 1.12.0 + Returns ------- y : ndarray @@ -627,7 +626,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, 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)) + >>> np.sum(hist * np.diff(bin_edges)) 1.0 .. versionadded:: 1.11.0 @@ -718,7 +717,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, # At this point, if the weights are not integer, floating point, or # complex, we have to use the slow algorithm. if weights is not None and not (np.can_cast(weights.dtype, np.double) or - np.can_cast(weights.dtype, np.complex)): + np.can_cast(weights.dtype, complex)): bins = linspace(mn, mx, bins + 1, endpoint=True) if not iterable(bins): @@ -974,7 +973,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): on_edge = (around(sample[:, i], decimal) == around(edges[i][-1], decimal)) # Shift these points one bin to the left. - Ncount[i][where(on_edge & not_smaller_than_edge)[0]] -= 1 + Ncount[i][nonzero(on_edge & not_smaller_than_edge)[0]] -= 1 # Flattened histogram matrix (1D) # Reshape is used so that overlarge arrays @@ -1321,16 +1320,8 @@ def piecewise(x, condlist, funclist, *args, **kw): x = x[None] zerod = True if n == n2 - 1: # compute the "otherwise" condition. - totlist = np.logical_or.reduce(condlist, axis=0) - # Only able to stack vertically if the array is 1d or less - if x.ndim <= 1: - condlist = np.vstack([condlist, ~totlist]) - else: - condlist = [asarray(c, dtype=bool) for c in condlist] - totlist = condlist[0] - for k in range(1, n): - totlist |= condlist[k] - condlist.append(~totlist) + condelse = ~np.any(condlist, axis=0, keepdims=True) + condlist = np.concatenate([condlist, condelse], axis=0) n += 1 y = zeros(x.shape, x.dtype) @@ -1550,7 +1541,7 @@ def gradient(f, *varargs, **kwargs): Examples -------- - >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=np.float) + >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=float) >>> np.gradient(f) array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ]) >>> np.gradient(f, 2) @@ -1566,7 +1557,7 @@ def gradient(f, *varargs, **kwargs): Or a non uniform one: - >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=np.float) + >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=float) >>> np.gradient(f, x) array([ 1. , 3. , 3.5, 6.7, 6.9, 2.5]) @@ -1574,7 +1565,7 @@ def gradient(f, *varargs, **kwargs): axis. In this example the first array stands for the gradient in rows and the second one in columns direction: - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float)) [array([[ 2., 2., -1.], [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ], [ 1. , 1. , 1. ]])] @@ -1584,7 +1575,7 @@ def gradient(f, *varargs, **kwargs): >>> dx = 2. >>> y = [1., 1.5, 3.5] - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), dx, y) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), dx, y) [array([[ 1. , 1. , -0.5], [ 1. , 1. , -0.5]]), array([[ 2. , 2. , 2. ], [ 2. , 1.7, 0.5]])] @@ -1601,7 +1592,7 @@ def gradient(f, *varargs, **kwargs): The `axis` keyword can be used to specify a subset of axes of which the gradient is calculated - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), axis=0) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), axis=0) array([[ 2., 2., -1.], [ 2., 2., -1.]]) @@ -1728,33 +1719,27 @@ def gradient(f, *varargs, **kwargs): slice3 = [slice(None)]*N slice4 = [slice(None)]*N - otype = f.dtype.char - if otype not in ['f', 'd', 'F', 'D', 'm', 'M']: - otype = 'd' - - # Difference of datetime64 elements results in timedelta64 - if otype == 'M': - # Need to use the full dtype name because it contains unit information - otype = f.dtype.name.replace('datetime', 'timedelta') - elif otype == 'm': - # Needs to keep the specific units, can't be a general unit - otype = f.dtype - - # Convert datetime64 data into ints. Make dummy variable `y` - # that is a view of ints if the data is datetime64, otherwise - # just set y equal to the array `f`. - if f.dtype.char in ["M", "m"]: - y = f.view('int64') + otype = f.dtype + if otype.type is np.datetime64: + # the timedelta dtype with the same unit information + otype = np.dtype(otype.name.replace('datetime', 'timedelta')) + # view as timedelta to allow addition + f = f.view(otype) + elif otype.type is np.timedelta64: + pass + elif np.issubdtype(otype, np.inexact): + pass else: - y = f + # all other types convert to floating point + otype = np.double for i, axis in enumerate(axes): - if y.shape[axis] < edge_order + 1: + if f.shape[axis] < edge_order + 1: raise ValueError( "Shape of array too small to calculate a numerical gradient, " "at least (edge_order + 1) elements are required.") # result allocation - out = np.empty_like(y, dtype=otype) + out = np.empty_like(f, dtype=otype) uniform_spacing = np.ndim(dx[i]) == 0 @@ -1785,15 +1770,15 @@ def gradient(f, *varargs, **kwargs): slice2[axis] = 1 slice3[axis] = 0 dx_0 = dx[i] if uniform_spacing else dx[i][0] - # 1D equivalent -- out[0] = (y[1] - y[0]) / (x[1] - x[0]) - out[slice1] = (y[slice2] - y[slice3]) / dx_0 + # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0]) + out[slice1] = (f[slice2] - f[slice3]) / dx_0 slice1[axis] = -1 slice2[axis] = -1 slice3[axis] = -2 dx_n = dx[i] if uniform_spacing else dx[i][-1] - # 1D equivalent -- out[-1] = (y[-1] - y[-2]) / (x[-1] - x[-2]) - out[slice1] = (y[slice2] - y[slice3]) / dx_n + # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2]) + out[slice1] = (f[slice2] - f[slice3]) / dx_n # Numerical differentiation: 2nd order edges else: @@ -1811,8 +1796,8 @@ def gradient(f, *varargs, **kwargs): a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2)) b = (dx1 + dx2) / (dx1 * dx2) c = - dx1 / (dx2 * (dx1 + dx2)) - # 1D equivalent -- out[0] = a * y[0] + b * y[1] + c * y[2] - out[slice1] = a * y[slice2] + b * y[slice3] + c * y[slice4] + # 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2] + out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4] slice1[axis] = -1 slice2[axis] = -3 @@ -1829,7 +1814,7 @@ def gradient(f, *varargs, **kwargs): b = - (dx2 + dx1) / (dx1 * dx2) c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2)) # 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1] - out[slice1] = a * y[slice2] + b * y[slice3] + c * y[slice4] + out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4] outvals.append(out) @@ -1847,7 +1832,7 @@ def gradient(f, *varargs, **kwargs): def diff(a, n=1, axis=-1): """ - Calculate the n-th discrete difference along given axis. + Calculate the n-th discrete difference along the given axis. The first difference is given by ``out[n] = a[n+1] - a[n]`` along the given axis, higher differences are calculated by using `diff` @@ -1858,16 +1843,21 @@ def diff(a, n=1, axis=-1): a : array_like Input array n : int, optional - The number of times values are differenced. + The number of times values are differenced. If zero, the input + is returned as-is. axis : int, optional - The axis along which the difference is taken, default is the last axis. + The axis along which the difference is taken, default is the + last axis. Returns ------- diff : ndarray The n-th differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. The - type of the output is the same as that of the input. + type of the output is the same as the type of the difference + between any two elements of `a`. This is the same as the type of + `a` in most cases. A notable exception is `datetime64`, which + results in a `timedelta64` output array. See Also -------- @@ -1875,13 +1865,13 @@ def diff(a, n=1, axis=-1): Notes ----- - For boolean arrays, the preservation of type means that the result - will contain `False` when consecutive elements are the same and - `True` when they differ. + Type is preserved for boolean arrays, so the result will contain + `False` when consecutive elements are the same and `True` when they + differ. - For unsigned integer arrays, the results will also be unsigned. This should - not be surprising, as the result is consistent with calculating the - difference directly: + For unsigned integer arrays, the results will also be unsigned. This + should not be surprising, as the result is consistent with + calculating the difference directly: >>> u8_arr = np.array([1, 0], dtype=np.uint8) >>> np.diff(u8_arr) @@ -1889,8 +1879,8 @@ def diff(a, n=1, axis=-1): >>> u8_arr[1,...] - u8_arr[0,...] array(255, np.uint8) - If this is not desirable, then the array should be cast to a larger integer - type first: + If this is not desirable, then the array should be cast to a larger + integer type first: >>> i16_arr = u8_arr.astype(np.int16) >>> np.diff(i16_arr) @@ -1911,24 +1901,33 @@ def diff(a, n=1, axis=-1): >>> np.diff(x, axis=0) array([[-1, 2, 0, -2]]) + >>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) + >>> np.diff(x) + array([1, 1], dtype='timedelta64[D]') + """ if n == 0: return a if n < 0: raise ValueError( "order must be non-negative but got " + repr(n)) + a = asanyarray(a) nd = a.ndim - slice1 = [slice(None)]*nd - slice2 = [slice(None)]*nd + axis = normalize_axis_index(axis, nd) + + slice1 = [slice(None)] * nd + slice2 = [slice(None)] * nd slice1[axis] = slice(1, None) slice2[axis] = slice(None, -1) slice1 = tuple(slice1) slice2 = tuple(slice2) - if n > 1: - return diff(a[slice1]-a[slice2], n-1, axis=axis) - else: - return a[slice1]-a[slice2] + + op = not_equal if a.dtype == np.bool_ else subtract + for _ in range(n): + a = op(a[slice1], a[slice2]) + + return a def interp(x, xp, fp, left=None, right=None, period=None): @@ -2074,6 +2073,7 @@ def interp(x, xp, fp, left=None, right=None, period=None): else: return interp_func(x, xp, fp, left, right).item() + def angle(z, deg=0): """ Return the angle of the complex argument. @@ -2096,8 +2096,6 @@ def angle(z, deg=0): arctan2 absolute - - Examples -------- >>> np.angle([1.0, 1.0j, 1+1j]) # in radians @@ -2607,7 +2605,7 @@ class vectorize(object): >>> out = vfunc([1, 2, 3, 4], 2) >>> type(out[0]) <type 'numpy.int32'> - >>> vfunc = np.vectorize(myfunc, otypes=[np.float]) + >>> vfunc = np.vectorize(myfunc, otypes=[float]) >>> out = vfunc([1, 2, 3, 4], 2) >>> type(out[0]) <type 'numpy.float64'> @@ -2987,7 +2985,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, >>> x = [-2.1, -1, 4.3] >>> y = [3, 1.1, 0.12] - >>> X = np.vstack((x,y)) + >>> X = np.stack((x, y), axis=0) >>> print(np.cov(X)) [[ 11.71 -4.286 ] [ -4.286 2.14413333]] @@ -3025,7 +3023,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, y = array(y, copy=False, ndmin=2, dtype=dtype) if not rowvar and y.shape[0] != 1: y = y.T - X = np.vstack((X, y)) + X = np.concatenate((X, y), axis=0) if ddof is None: if bias == 0: @@ -3036,7 +3034,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, # Get the product of frequencies and weights w = None if fweights is not None: - fweights = np.asarray(fweights, dtype=np.float) + fweights = np.asarray(fweights, dtype=float) if not np.all(fweights == np.around(fweights)): raise TypeError( "fweights must be integer") @@ -3051,7 +3049,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, "fweights cannot be negative") w = fweights if aweights is not None: - aweights = np.asarray(aweights, dtype=np.float) + aweights = np.asarray(aweights, dtype=float) if aweights.ndim > 1: raise RuntimeError( "cannot handle multidimensional aweights") @@ -4010,8 +4008,9 @@ def _ureduce(a, func, **kwargs): # merge reduced axis a = a.reshape(a.shape[:nkeep] + (-1,)) kwargs['axis'] = -1 + keepdim = tuple(keepdim) else: - keepdim = [1] * a.ndim + keepdim = (1,) * a.ndim r = func(a, **kwargs) return r, keepdim @@ -4273,10 +4272,7 @@ def percentile(a, q, axis=None, out=None, overwrite_input=overwrite_input, interpolation=interpolation) if keepdims: - if q.ndim == 0: - return r.reshape(k) - else: - return r.reshape([len(q)] + k) + return r.reshape(q.shape + k) else: return r @@ -4345,7 +4341,7 @@ def _percentile(a, q, axis=None, out=None, ap.partition(indices, axis=axis) # ensure axis with qth is first - ap = np.rollaxis(ap, axis, 0) + ap = np.moveaxis(ap, axis, 0) axis = 0 # Check if the array contains any nan's @@ -4378,9 +4374,9 @@ def _percentile(a, q, axis=None, out=None, ap.partition(concatenate((indices_below, indices_above)), axis=axis) # ensure axis with qth is first - ap = np.rollaxis(ap, axis, 0) - weights_below = np.rollaxis(weights_below, axis, 0) - weights_above = np.rollaxis(weights_above, axis, 0) + ap = np.moveaxis(ap, axis, 0) + weights_below = np.moveaxis(weights_below, axis, 0) + weights_above = np.moveaxis(weights_above, axis, 0) axis = 0 # Check if the array contains any nan's @@ -4392,8 +4388,8 @@ def _percentile(a, q, axis=None, out=None, x2 = take(ap, indices_above, axis=axis) * weights_above # ensure axis with qth is first - x1 = np.rollaxis(x1, axis, 0) - x2 = np.rollaxis(x2, axis, 0) + x1 = np.moveaxis(x1, axis, 0) + x2 = np.moveaxis(x2, axis, 0) if zerod: x1 = x1.squeeze(0) @@ -4546,7 +4542,7 @@ def add_newdoc(place, obj, doc): elif isinstance(doc, list): for val in doc: add_docstring(getattr(new, val[0]), val[1].strip()) - except: + except Exception: pass @@ -5049,7 +5045,7 @@ def insert(arr, obj, values, axis=None): # broadcasting is very different here, since a[:,0,:] = ... behaves # very different from a[:,[0],:] = ...! This changes values so that # it works likes the second case. (here a[:,0:1,:]) - values = np.rollaxis(values, 0, (axis % values.ndim) + 1) + values = np.moveaxis(values, 0, axis) numnew = values.shape[axis] newshape[axis] += numnew new = empty(newshape, arr.dtype, arrorder) |