numpy.frombuffer(buffer, dtype=float, count=-1, offset=0) Interpret a buffer as a 1-dimensional array. Parameters: buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. -1 means all data in the buffer. offset : int, optional Start reading the buffer from this offset; default: 0. Notes If the buffer has data that is not in machine byte-order, this

numpy.frexp(x[, out1, out2]) = Decompose the elements of x into mantissa and twos exponent. Returns (mantissa, exponent), where x = mantissa * 2**exponent`. The mantissa is lies in the open interval(-1, 1), while the twos exponent is a signed integer. Parameters: x : array_like Array of numbers to be decomposed. out1 : ndarray, optional Output array for the mantissa. Must have the same shape as x. out2 : ndarray, optional Output array for the exponent. Must have the same shape as x.

class numpy.format_parser(formats, names, titles, aligned=False, byteorder=None) [source] Class to convert formats, names, titles description to a dtype. After constructing the format_parser object, the dtype attribute is the converted data-type: dtype = format_parser(formats, names, titles).dtype Parameters: formats : str or list of str The format description, either specified as a string with comma-separated format descriptions in the form 'f8, i4, a5', or a list of format description s

numpy.fmod(x1, x2[, out]) = Return the element-wise remainder of division. This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend x1. It is equivalent to the Matlab(TM) rem function and should not be confused with the Python modulus operator x1 % x2. Parameters: x1 : array_like Dividend. x2 : array_like Divisor. Returns: y : array_like The remainder of the division of x1 by x2. See also remainder Equivalent to the Pyth

numpy.fmin(x1, x2[, out]) = Element-wise minimum of array elements. Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters: x1, x2

numpy.fmax(x1, x2[, out]) = Element-wise maximum of array elements. Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters: x1, x2

numpy.floor_divide(x1, x2[, out]) = Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python // operator and pairs with the Python % (remainder), function so that b = a % b + b * (a // b) up to roundoff. Parameters: x1 : array_like Numerator. x2 : array_like Denominator. Returns: y : ndarray y = floor(x1/x2) See also remainder Remainder complementary to floor_divide. divide Standard division. floor Round a number to the n

numpy.floor(x[, out]) = Return the floor of the input, element-wise. The floor of the scalar x is the largest integer i, such that i <= x. It is often denoted as . Parameters: x : array_like Input data. Returns: y : ndarray or scalar The floor of each element in x. See also ceil, trunc, rint Notes Some spreadsheet programs calculate the ?floor-towards-zero?, in other words floor(-2.5) == -2. NumPy instead uses the definition of floor where floor(-2.5) == -3. Examples >>&

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.fliplr(m) [source] Flip array in the left/right direction. Flip the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before. Parameters: m : array_like Input array, must be at least 2-D. Returns: f : ndarray A view of m with the columns reversed. Since a view is returned, this operation is . See also flipud Flip array in the up/down direction. rot90 Rotate array counterclockwise. Notes Equivalent to A[:,::-1].