diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 69 |
1 files changed, 33 insertions, 36 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 6ea9cc4de..c7ddbdb8d 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -433,7 +433,7 @@ def asarray_chkfinite(a, dtype=None, order=None): By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. - 'C' row-major (C-style), + 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order @@ -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 @@ -1625,6 +1625,7 @@ def trim_zeros(filt, trim='fb'): [1, 2] """ + first = 0 trim = trim.upper() if 'F' in trim: @@ -1934,8 +1935,8 @@ class vectorize: .. versionadded:: 1.7.0 cache : bool, optional - If `True`, then cache the first function call that determines the number - of outputs if `otypes` is not provided. + If `True`, then cache the first function call that determines the number + of outputs if `otypes` is not provided. .. versionadded:: 1.7.0 @@ -2546,11 +2547,11 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): for backwards compatibility with previous versions of this function. These arguments had no effect on the return values of the function and can be safely ignored in this and previous versions of numpy. - + Examples - -------- + -------- In this example we generate two random arrays, ``xarr`` and ``yarr``, and - compute the row-wise and column-wise Pearson correlation coefficients, + compute the row-wise and column-wise Pearson correlation coefficients, ``R``. Since ``rowvar`` is true by default, we first find the row-wise Pearson correlation coefficients between the variables of ``xarr``. @@ -2566,11 +2567,11 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): array([[ 1. , 0.99256089, -0.68080986], [ 0.99256089, 1. , -0.76492172], [-0.68080986, -0.76492172, 1. ]]) - - If we add another set of variables and observations ``yarr``, we can + + If we add another set of variables and observations ``yarr``, we can compute the row-wise Pearson correlation coefficients between the variables in ``xarr`` and ``yarr``. - + >>> yarr = rng.random((3, 3)) >>> yarr array([[0.45038594, 0.37079802, 0.92676499], @@ -2592,7 +2593,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): 1. ]]) Finally if we use the option ``rowvar=False``, the columns are now - being treated as the variables and we will find the column-wise Pearson + being treated as the variables and we will find the column-wise Pearson correlation coefficients between variables in ``xarr`` and ``yarr``. >>> R3 = np.corrcoef(xarr, yarr, rowvar=False) @@ -2732,8 +2733,8 @@ 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') @@ -2841,8 +2842,8 @@ 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') @@ -2945,8 +2946,8 @@ 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') @@ -3045,8 +3046,8 @@ 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 @@ -3142,25 +3143,18 @@ def i0(x): """ Modified Bessel function of the first kind, order 0. - Usually denoted :math:`I_0`. This function does broadcast, but will *not* - "up-cast" int dtype arguments unless accompanied by at least one float or - complex dtype argument (see Raises below). + Usually denoted :math:`I_0`. Parameters ---------- - x : array_like, dtype float or complex + x : array_like of float Argument of the Bessel function. Returns ------- - out : ndarray, shape = x.shape, dtype = x.dtype + out : ndarray, shape = x.shape, dtype = float The modified Bessel function evaluated at each of the elements of `x`. - Raises - ------ - TypeError: array cannot be safely cast to required type - If argument consists exclusively of int dtypes. - See Also -------- scipy.special.i0, scipy.special.iv, scipy.special.ive @@ -3190,12 +3184,16 @@ def i0(x): Examples -------- >>> np.i0(0.) - array(1.0) # may vary - >>> np.i0([0., 1. + 2j]) - array([ 1.00000000+0.j , 0.18785373+0.64616944j]) # may vary + array(1.0) + >>> np.i0([0, 1, 2, 3]) + array([1. , 1.26606588, 2.2795853 , 4.88079259]) """ x = np.asanyarray(x) + if x.dtype.kind == 'c': + raise TypeError("i0 not supported for complex values") + if x.dtype.kind != 'f': + x = x.astype(float) x = np.abs(x) return piecewise(x, [x <= 8.0], [_i0_1, _i0_2]) @@ -4231,10 +4229,9 @@ 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 -------- |