numpy.array_str(a, max_line_width=None, precision=None, suppress_small=None) [source] Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to array_repr, the difference being that array_repr also returns information on the kind of array and its data type. Parameters: a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than max_line_width. The default is, indirectly, 7

numpy.polynomial.legendre.poly2leg(pol) [source] Convert a polynomial to a Legendre series. Convert an array representing the coefficients of a polynomial (relative to the ?standard? basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Legendre series, ordered from lowest to highest degree. Parameters: pol : array_like 1-D array containing the polynomial coefficients Returns: c : ndarray 1-D array containing the coefficients of the equivalent

numpy.polynomial.hermite_e.hermezero = array([0])

recarray.itemset(*args) Insert scalar into an array (scalar is cast to array?s dtype, if possible) There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a. Parameters: *args : Arguments If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a

numpy.getbufsize() [source] Return the size of the buffer used in ufuncs. Returns: getbufsize : int Size of ufunc buffer in bytes.

numpy.ma.transpose(a, axes=None) [source] Permute the dimensions of an array. This function is exactly equivalent to numpy.transpose. See also numpy.transpose Equivalent function in top-level NumPy module. Examples >>> import numpy.ma as ma >>> x = ma.arange(4).reshape((2,2)) >>> x[1, 1] = ma.masked >>>> x masked_array(data = [[0 1] [2 --]], mask = [[False False] [False True]], fill_value = 999999) >>> ma.transpos

numpy.seterrobj(errobj) Set the object that defines floating-point error handling. The error object contains all information that defines the error handling behavior in Numpy. seterrobj is used internally by the other functions that set error handling behavior (seterr, seterrcall). Parameters: errobj : list The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function]. The error mask is a single integer that holds the treatment infor

numpy.polynomial.legendre.legpow(c, pow, maxpower=16) [source] Raise a Legendre series to a power. Returns the Legendre series c raised to the power pow. The arguement c is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series P_0 + 2*P_1 + 3*P_2. Parameters: c : array_like 1-D array of Legendre series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is main

numpy.flipud(m) [source] Flip array in the up/down direction. Flip the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before. Parameters: m : array_like Input array. Returns: out : array_like A view of m with the rows reversed. Since a view is returned, this operation is . See also fliplr Flip array in the left/right direction. rot90 Rotate array counterclockwise. Notes Equivalent to A[::-1,...]. Does not require the

numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False) [source] Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates c