numpy.polynomial.hermite.hermadd(c1, c2) [source] Add one Hermite series to another. Returns the sum of two Hermite series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Array representing the Hermite series of their sum. See also hermsub, hermmul, hermdiv,

ndarray.conjugate() Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

numpy.hsplit(ary, indices_or_sections) [source] Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.],

numpy.hstack(tup) [source] Stack arrays in sequence horizontally (column wise). Take a sequence of arrays and stack them horizontally to make a single array. Rebuild arrays divided by hsplit. Parameters: tup : sequence of ndarrays All arrays must have the same shape along all but the second axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. vstack Stack arrays in sequence vertically (row w

numpy.isnan(x[, out]) = Test element-wise for NaN and return result as a boolean array. Parameters: x : array_like Input array. Returns: y : ndarray or bool For scalar input, the result is a new boolean with value True if the input is NaN; otherwise the value is False. For array input, the result is a boolean array of the same dimensions as the input and the values are True if the corresponding element of the input is NaN; otherwise the values are False. See also isinf, isneginf,

matrix.byteswap(inplace) Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Parameters: inplace : bool, optional If True, swap bytes in-place, default is False. Returns: out : ndarray The byteswapped array. If inplace is True, this is a view to self. Examples >>> A = np.array([1, 256, 8755], dtype=np.int16) >>> map(hex, A) ['0x1', '0x100', '0x2233'] >>

numpy.sign(x[, out]) = Returns an element-wise indication of the sign of a number. The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0. nan is returned for nan inputs. For complex inputs, the sign function returns sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j. complex(nan, 0) is returned for complex nan inputs. Parameters: x : array_like Input values. Returns: y : ndarray The sign of x. Notes There is more than one definition of sign in common use for compl

MaskedArray.ids() [source] Return the addresses of the data and mask areas. Parameters: None Examples >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1]) >>> x.ids() (166670640, 166659832) If the array has no mask, the address of nomask is returned. This address is typically not close to the data in memory: >>> x = np.ma.array([1, 2, 3]) >>> x.ids() (166691080, 3083169284L)

numpy.dot(a, b, out=None) Dot product of two arrays. For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of a and the second-to-last of b: dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m]) Parameters: a : array_like First argument. b : array_like Second argument. out : ndarray, optional Output argument. This must have the exact kind that would be ret

numpy.testing.decorators.slow(t) [source] Label a test as ?slow?. The exact definition of a slow test is obviously both subjective and hardware-dependent, but in general any individual test that requires more than a second or two should be labeled as slow (the whole suite consits of thousands of tests, so even a second is significant). Parameters: t : callable The test to label as slow. Returns: t : callable The decorated test t. Examples The numpy.testing module includes import dec