numpy.core.records.fromfile(fd, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None) [source] Create an array from binary file data If file is a string then that file is opened, else it is assumed to be a file object. >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.newbyteorder('<') >>&

chararray.reshape(shape, order='C') Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape equivalent function

chararray.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function

numpy.tensordot(a, b, axes=2) [source] Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one), a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a?s and b?s elements (components) over the axes specified by a_axes and b_axes. The third argument can be a single non-negative integer_like scalar, N; if it is such, then the last N dimensions of a and the fi

numpy.ma.argmax(a, axis=None, fill_value=None) [source] Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. Parameters: axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : {var}, optional Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out : {None, array}, optional Array into w

numpy.matlib.randn(*args) [source] Return a random matrix with data from the ?standard normal? distribution. randn generates a matrix filled with random floats sampled from a univariate ?normal? (Gaussian) distribution of mean 0 and variance 1. Parameters: *args : Arguments Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns: Z : matrix of floats A matrix of floating-point samp

numpy.tril_indices_from(arr, k=0) [source] Return the indices for the lower-triangle of arr. See tril_indices for full details. Parameters: arr : array_like The indices will be valid for square arrays whose dimensions are the same as arr. k : int, optional Diagonal offset (see tril for details). See also tril_indices, tril Notes New in version 1.4.0.

numpy.fill_diagonal(a, val, wrap=False) [source] Fill the main diagonal of the given array of any dimensionality. For an array a with a.ndim > 2, the diagonal is the list of locations with indices a[i, i, ..., i] all identical. This function modifies the input array in-place, it does not return a value. Parameters: a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar Value to be written on the diagonal, its type must be compatible wit

Laguerre.linspace(n=100, domain=None) [source] Return x, y values at equally spaced points in domain. Returns the x, y values at n linearly spaced points across the domain. Here y is the value of the polynomial at the points x. By default the domain is the same as that of the series instance. This method is intended mostly as a plotting aid. New in version 1.5.0. Parameters: n : int, optional Number of point pairs to return. The default value is 100. domain : {None, array_like}, option

record.tofile() 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