diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 165 |
1 files changed, 78 insertions, 87 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index dea01d12d..4ebca6360 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -604,14 +604,13 @@ def piecewise(x, condlist, funclist, *args, **kw): ) y = zeros(x.shape, x.dtype) - for k in range(n): - item = funclist[k] - if not isinstance(item, collections.abc.Callable): - y[condlist[k]] = item + for cond, func in zip(condlist, funclist): + if not isinstance(func, collections.abc.Callable): + y[cond] = func else: - vals = x[condlist[k]] + vals = x[cond] if vals.size > 0: - y[condlist[k]] = item(vals, *args, **kw) + y[cond] = func(vals, *args, **kw) return y @@ -682,8 +681,7 @@ def select(condlist, choicelist, default=0): choicelist = np.broadcast_arrays(*choicelist) # If cond array is not an ndarray in boolean format or scalar bool, abort. - for i in range(len(condlist)): - cond = condlist[i] + for i, cond in enumerate(condlist): if cond.dtype.type is not np.bool_: raise TypeError( 'invalid entry {} in condlist: should be boolean ndarray'.format(i)) @@ -1332,6 +1330,10 @@ def interp(x, xp, fp, left=None, right=None, period=None): If `xp` or `fp` are not 1-D sequences If `period == 0` + See Also + -------- + scipy.interpolate + Notes ----- The x-coordinate sequence is expected to be increasing, but this is not @@ -3256,7 +3258,6 @@ def kaiser(M, beta): >>> plt.show() """ - from numpy.dual import i0 if M == 1: return np.array([1.]) n = arange(0, M) @@ -3270,10 +3271,17 @@ def _sinc_dispatcher(x): @array_function_dispatch(_sinc_dispatcher) def sinc(x): - """ - Return the sinc function. + r""" + Return the normalized sinc function. - The sinc function is :math:`\\sin(\\pi x)/(\\pi x)`. + The sinc function is :math:`\sin(\pi x)/(\pi x)`. + + .. note:: + + Note the normalization factor of ``pi`` used in the definition. + This is the most commonly used definition in signal processing. + Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function + :math:`\sin(x)/(x)` that is more common in mathematics. Parameters ---------- @@ -3870,15 +3878,20 @@ def _quantile_is_valid(q): return True +def _lerp(a, b, t, out=None): + """ Linearly interpolate from a to b by a factor of t """ + return add(a*(1 - t), b*t, out=out) + + def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): a = asarray(a) - if q.ndim == 0: - # Do not allow 0-d arrays because following code fails for scalar - zerod = True - q = q[None] - else: - zerod = False + + # ufuncs cause 0d array results to decay to scalars (see gh-13105), which + # makes them problematic for __setitem__ and attribute access. As a + # workaround, we call this on the result of every ufunc on a possibly-0d + # array. + not_scalar = np.asanyarray # prepare a for partitioning if overwrite_input: @@ -3895,9 +3908,14 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, if axis is None: axis = 0 - Nx = ap.shape[axis] - indices = q * (Nx - 1) + if q.ndim > 2: + # The code below works fine for nd, but it might not have useful + # semantics. For now, keep the supported dimensions the same as it was + # before. + raise ValueError("q must be a scalar or 1d") + Nx = ap.shape[axis] + indices = not_scalar(q * (Nx - 1)) # round fractional indices according to interpolation method if interpolation == 'lower': indices = floor(indices).astype(intp) @@ -3914,87 +3932,60 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, "interpolation can only be 'linear', 'lower' 'higher', " "'midpoint', or 'nearest'") - n = np.array(False, dtype=bool) # check for nan's flag - if np.issubdtype(indices.dtype, np.integer): # take the points along axis - # Check if the array contains any nan's - if np.issubdtype(a.dtype, np.inexact): - indices = concatenate((indices, [-1])) + # The dimensions of `q` are prepended to the output shape, so we need the + # axis being sampled from `ap` to be first. + ap = np.moveaxis(ap, axis, 0) + del axis - ap.partition(indices, axis=axis) - # ensure axis with q-th is first - ap = np.moveaxis(ap, axis, 0) - axis = 0 + if np.issubdtype(indices.dtype, np.integer): + # take the points along axis - # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): - indices = indices[:-1] - n = np.isnan(ap[-1:, ...]) - - if zerod: - indices = indices[0] - r = take(ap, indices, axis=axis, out=out) - - else: # weight the points above and below the indices - indices_below = floor(indices).astype(intp) - indices_above = indices_below + 1 - indices_above[indices_above > Nx - 1] = Nx - 1 - - # Check if the array contains any nan's - if np.issubdtype(a.dtype, np.inexact): - indices_above = concatenate((indices_above, [-1])) - - weights_above = indices - indices_below - weights_below = 1 - weights_above + # may contain nan, which would sort to the end + ap.partition(concatenate((indices.ravel(), [-1])), axis=0) + n = np.isnan(ap[-1]) + else: + # cannot contain nan + ap.partition(indices.ravel(), axis=0) + n = np.array(False, dtype=bool) - weights_shape = [1, ] * ap.ndim - weights_shape[axis] = len(indices) - weights_below.shape = weights_shape - weights_above.shape = weights_shape + r = take(ap, indices, axis=0, out=out) - ap.partition(concatenate((indices_below, indices_above)), axis=axis) + else: + # weight the points above and below the indices - # ensure axis with q-th is first - ap = np.moveaxis(ap, axis, 0) - weights_below = np.moveaxis(weights_below, axis, 0) - weights_above = np.moveaxis(weights_above, axis, 0) - axis = 0 + indices_below = not_scalar(floor(indices)).astype(intp) + indices_above = not_scalar(indices_below + 1) + indices_above[indices_above > Nx - 1] = Nx - 1 - # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): - indices_above = indices_above[:-1] - n = np.isnan(ap[-1:, ...]) + # may contain nan, which would sort to the end + ap.partition(concatenate(( + indices_below.ravel(), indices_above.ravel(), [-1] + )), axis=0) + n = np.isnan(ap[-1]) + else: + # cannot contain nan + ap.partition(concatenate(( + indices_below.ravel(), indices_above.ravel() + )), axis=0) + n = np.array(False, dtype=bool) - x1 = take(ap, indices_below, axis=axis) * weights_below - x2 = take(ap, indices_above, axis=axis) * weights_above + weights_shape = indices.shape + (1,) * (ap.ndim - 1) + weights_above = not_scalar(indices - indices_below).reshape(weights_shape) - # ensure axis with q-th is first - x1 = np.moveaxis(x1, axis, 0) - x2 = np.moveaxis(x2, axis, 0) + x_below = take(ap, indices_below, axis=0) + x_above = take(ap, indices_above, axis=0) - if zerod: - x1 = x1.squeeze(0) - x2 = x2.squeeze(0) - - if out is not None: - r = add(x1, x2, out=out) - else: - r = add(x1, x2) + r = _lerp(x_below, x_above, weights_above, out=out) + # if any slice contained a nan, then all results on that slice are also nan if np.any(n): - if zerod: - if ap.ndim == 1: - if out is not None: - out[...] = a.dtype.type(np.nan) - r = out - else: - r = a.dtype.type(np.nan) - else: - r[..., n.squeeze(0)] = a.dtype.type(np.nan) + if r.ndim == 0 and out is None: + # can't write to a scalar + r = a.dtype.type(np.nan) else: - if r.ndim == 1: - r[:] = a.dtype.type(np.nan) - else: - r[..., n.repeat(q.size, 0)] = a.dtype.type(np.nan) + r[..., n] = a.dtype.type(np.nan) return r |