numpy.ravel_multi_index(multi_index, dims, mode='raise', order='C') Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index. Parameters: multi_index : tuple of array_like A tuple of integer arrays, one array for each dimension. dims : tuple of ints The shape of array into which the indices from multi_index apply. mode : {?raise?, ?wrap?, ?clip?}, optional Specifies how out-of-bounds indices are handled. Can specify either one mode or

apply_along_axis(func1d, axis, arr, *args, ...) Apply a function to 1-D slices along the given axis. apply_over_axes(func, a, axes) Apply a function repeatedly over multiple axes. vectorize(pyfunc[, otypes, doc, excluded, cache]) Generalized function class. frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. piecewise(x, condlist, funclist, *args, **kw) Evaluate a piecewise-defined function.

record.mean() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

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

numpy.ma.soften_mask(self) = Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

numpy.core.defchararray.istitle(a) [source] Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. Call str.istitle element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like of str or unicode Returns: out : ndarray Output array of bools See also str.istitle

numpy.ptp(a, axis=None, out=None) [source] Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ?peak to peak?. Parameters: a : array_like Input values. axis : int, optional Axis along which to find the peaks. By default, flatten the array. out : array_like Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type of the output values will be cast if necess

class numpy.iinfo(type) [source] Machine limits for integer types. Parameters: int_type : integer type, dtype, or instance The kind of integer data type to get information about. See also finfo The equivalent for floating point data types. Examples With types: >>> ii16 = np.iinfo(np.int16) >>> ii16.min -32768 >>> ii16.max 32767 >>> ii32 = np.iinfo(np.int32) >>> ii32.min -2147483648 >>> ii32.max 2147483647 With instances: >&g

Legendre.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.

numpy.arcsinh(x[, out]) = Inverse hyperbolic sine element-wise. Parameters: x : array_like Input array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: out : ndarray Array of of the same shape as x. Notes arcsinh is a multivalued function: for each x there are infinitely many numbers z such that sinh(z) = x. The convention is to return the z whose imaginary part lie