numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) [source] Returns a boolean array where two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b. Parameters: a, b : array_like Input arrays to compare. rtol : float The relative tolerance parameter (see Notes). atol

numpy.polynomial.hermite_e.hermecompanion(c) [source] Return the scaled companion matrix of c. The basis polynomials are scaled so that the companion matrix is symmetric when c is an HermiteE basis polynomial. This provides better eigenvalue estimates than the unscaled case and for basis polynomials the eigenvalues are guaranteed to be real if numpy.linalg.eigvalsh is used to obtain them. Parameters: c : array_like 1-D array of HermiteE series coefficients ordered from low to high degree.

Polynomial.truncate(size) [source] Truncate series to length size. Reduce the series to length size by discarding the high degree terms. The value of size must be a positive integer. This can be useful in least squares where the coefficients of the high degree terms may be very small. Parameters: size : positive int The series is reduced to length size by discarding the high degree terms. The value of size must be a positive integer. Returns: new_series : series New instance of series

ndarray.ravel([order]) Return a flattened array. Refer to numpy.ravel for full documentation. See also numpy.ravel equivalent function ndarray.flat a flat iterator on the array.

numpy.bitwise_or(x1, x2[, out]) = Compute the bit-wise OR of two arrays element-wise. Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator |. Parameters: x1, x2 : array_like Only integer and boolean types are handled. 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 : array_

MaskedArray.data Return the current data, as a view of the original underlying data.

numpy.mintypecode(typechars, typeset='GDFgdf', default='d') [source] Return the character for the minimum-size type to which given types can be safely cast. The returned type character must represent the smallest size dtype such that an array of the returned type can handle the data from an array of all types in typechars (or if typechars is an array, then its dtype.char). Parameters: typechars : list of str or array_like If a list of strings, each string should represent a dtype. If arra

generic.__array_wrap__() sc.__array_wrap__(obj) return scalar from array

numpy.random.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive) to high (exclusive). Return random integers from the ?discrete uniform? distribution of the specified dtype in the ?half-open? interval [low, high). If high is None (the default), then results are from [0, low). Parameters: low : int Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer). high : int, optiona

class numpy.lib.NumpyVersion(vstring) [source] Parse and compare numpy version strings. Numpy has the following versioning scheme (numbers given are examples; they can be > 9) in principle): Released version: ?1.8.0?, ?1.8.1?, etc. Alpha: ?1.8.0a1?, ?1.8.0a2?, etc. Beta: ?1.8.0b1?, ?1.8.0b2?, etc. Release candidates: ?1.8.0rc1?, ?1.8.0rc2?, etc. Development versions: ?1.8.0.dev-f1234afa? (git commit hash appended) Development versions after a1: ?1.8.0a1.dev-f1234afa?, ?1.8.0b2.dev