diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 120 |
1 files changed, 39 insertions, 81 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 0db00a0f2..696fe617b 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1290,7 +1290,7 @@ def _interp_dispatcher(x, xp, fp, left=None, right=None, period=None): @array_function_dispatch(_interp_dispatcher) def interp(x, xp, fp, left=None, right=None, period=None): """ - One-dimensional linear interpolation. + One-dimensional linear interpolation for monotonically increasing sample points. Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (`xp`, `fp`), evaluated at `x`. @@ -1337,8 +1337,8 @@ def interp(x, xp, fp, left=None, right=None, period=None): -------- scipy.interpolate - Notes - ----- + Warnings + -------- The x-coordinate sequence is expected to be increasing, but this is not explicitly enforced. However, if the sequence `xp` is non-increasing, interpolation results are meaningless. @@ -1450,7 +1450,7 @@ def angle(z, deg=False): The counterclockwise angle from the positive real axis on the complex plane in the range ``(-pi, pi]``, with dtype as numpy.float64. - ..versionchanged:: 1.16.0 + .. versionchanged:: 1.16.0 This function works on subclasses of ndarray like `ma.array`. See Also @@ -1625,63 +1625,7 @@ def trim_zeros(filt, trim='fb'): [1, 2] """ - try: - return _trim_zeros_new(filt, trim) - except Exception as ex: - # Numpy 1.20.0, 2020-07-31 - warning = DeprecationWarning( - "in the future trim_zeros will require a 1-D array as input " - "that supports elementwise comparisons with zero" - ) - warning.__cause__ = ex - - # Fall back to the old implementation if an exception is encountered - # Note that the same exception may or may not be raised here as well - ret = _trim_zeros_old(filt, trim) - warnings.warn(warning, stacklevel=3) - return ret - - -def _trim_zeros_new(filt, trim='fb'): - """Newer optimized implementation of ``trim_zeros()``.""" - arr_any = np.asanyarray(filt) - arr = arr_any != 0 if arr_any.dtype != bool else arr_any - - if arr is False: - # not all dtypes support elementwise comparisons with `0` (e.g. str); - # they will return `False` instead - raise TypeError('elementwise comparison failed; unsupported data type') - elif arr.ndim != 1: - raise ValueError('trim_zeros requires an array of exactly one dimension') - elif not len(arr): - return filt - - trim_upper = trim.upper() - first = last = None - - if 'F' in trim_upper: - first = arr.argmax() - # If `arr[first] is False` then so are all other elements - if not arr[first]: - return filt[:0] - - if 'B' in trim_upper: - last = len(arr) - arr[::-1].argmax() - # If `arr[last - 1] is False` then so are all other elements - if not arr[last - 1]: - return filt[:0] - - return filt[first:last] - - -def _trim_zeros_old(filt, trim='fb'): - """ - Older unoptimized implementation of ``trim_zeros()``. - Used as fallback in case an exception is encountered - in ``_trim_zeros_new()``. - - """ first = 0 trim = trim.upper() if 'F' in trim: @@ -2324,13 +2268,13 @@ class vectorize: def _cov_dispatcher(m, y=None, rowvar=None, bias=None, ddof=None, - fweights=None, aweights=None): + fweights=None, aweights=None, *, dtype=None): return (m, y, fweights, aweights) @array_function_dispatch(_cov_dispatcher) def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, - aweights=None): + aweights=None, *, dtype=None): """ Estimate a covariance matrix, given data and weights. @@ -2381,6 +2325,11 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, weights can be used to assign probabilities to observation vectors. .. versionadded:: 1.10 + dtype : data-type, optional + Data-type of the result. By default, the return data-type will have + at least `numpy.float64` precision. + + .. versionadded:: 1.20 Returns ------- @@ -2456,13 +2405,16 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, if m.ndim > 2: raise ValueError("m has more than 2 dimensions") - if y is None: - dtype = np.result_type(m, np.float64) - else: + if y is not None: y = np.asarray(y) if y.ndim > 2: raise ValueError("y has more than 2 dimensions") - dtype = np.result_type(m, y, np.float64) + + if dtype is None: + if y is None: + dtype = np.result_type(m, np.float64) + else: + dtype = np.result_type(m, y, np.float64) X = array(m, ndmin=2, dtype=dtype) if not rowvar and X.shape[0] != 1: @@ -2542,12 +2494,14 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, return c.squeeze() -def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None): +def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None, *, + dtype=None): return (x, y) @array_function_dispatch(_corrcoef_dispatcher) -def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): +def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *, + dtype=None): """ Return Pearson product-moment correlation coefficients. @@ -2581,6 +2535,11 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): Has no effect, do not use. .. deprecated:: 1.10.0 + dtype : data-type, optional + Data-type of the result. By default, the return data-type will have + at least `numpy.float64` precision. + + .. versionadded:: 1.20 Returns ------- @@ -2672,7 +2631,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): # 2015-03-15, 1.10 warnings.warn('bias and ddof have no effect and are deprecated', DeprecationWarning, stacklevel=3) - c = cov(x, y, rowvar) + c = cov(x, y, rowvar, dtype=dtype) try: d = diag(c) except ValueError: @@ -2789,8 +2748,8 @@ def blackman(M): return array([]) if M == 1: return ones(1, float) - n = arange(0, M) - return 0.42 - 0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1)) + n = arange(1-M, M, 2) + return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1)) @set_module('numpy') @@ -2898,8 +2857,8 @@ def bartlett(M): return array([]) if M == 1: return ones(1, float) - n = arange(0, M) - return where(less_equal(n, (M-1)/2.0), 2.0*n/(M-1), 2.0 - 2.0*n/(M-1)) + n = arange(1-M, M, 2) + return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1)) @set_module('numpy') @@ -3002,8 +2961,8 @@ def hanning(M): return array([]) if M == 1: return ones(1, float) - n = arange(0, M) - return 0.5 - 0.5*cos(2.0*pi*n/(M-1)) + n = arange(1-M, M, 2) + return 0.5 + 0.5*cos(pi*n/(M-1)) @set_module('numpy') @@ -3102,8 +3061,8 @@ def hamming(M): return array([]) if M == 1: return ones(1, float) - n = arange(0, M) - return 0.54 - 0.46*cos(2.0*pi*n/(M-1)) + n = arange(1-M, M, 2) + return 0.54 + 0.46*cos(pi*n/(M-1)) ## Code from cephes for i0 @@ -4285,10 +4244,9 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): See Also -------- - index_tricks.mgrid : Construct a multi-dimensional "meshgrid" - using indexing notation. - index_tricks.ogrid : Construct an open multi-dimensional "meshgrid" - using indexing notation. + mgrid : Construct a multi-dimensional "meshgrid" using indexing notation. + ogrid : Construct an open multi-dimensional "meshgrid" using indexing + notation. Examples -------- |