MaskedArray.argmin(axis=None, fill_value=None, out=None) [source] Return array of indices to the minimum values along the given axis. 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 minimum_fill_value(self._data) is used instead. out : {None, array}, optional Array into which the result can be placed. Its type is preserved a

MaskedArray.argmax(axis=None, fill_value=None, out=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 Ar

MaskedArray.any(axis=None, out=None) [source] Check if any of the elements of a are true. Performs a logical_or over the given axis and returns the result. Masked values are considered as False during computation. Parameters: axis : {None, integer} Axis to perform the operation over. If None, perform over flattened array and return a scalar. out : {None, array}, optional Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output

MaskedArray.anom(axis=None, dtype=None) [source] Compute the anomalies (deviations from the arithmetic mean) along the given axis. Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis. Parameters: axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type

MaskedArray.all(axis=None, out=None) [source] Check if all of the elements of a are true. Performs a logical_and over the given axis and returns the result. Masked values are considered as True during computation. For convenience, the output array is masked where ALL the values along the current axis are masked: if the output would have been a scalar and that all the values are masked, then the output is masked. Parameters: axis : {None, integer} Axis to perform the operation over. If Non

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks. The numpy.ma moduleRationale What is a masked array? The numpy.ma module Using numpy.maConstructing masked arrays Accessing the data Accessing the mask Accessing only the valid entries Modifying the mask Indexing and slicing Operations on masked arrays ExamplesData with a given value representing missing data Filli

Constants ma.MaskType alias of bool_ Creation From existing data ma.masked_array alias of MaskedArray ma.array(data[, dtype, copy, order, mask, ...]) An array class with possibly masked values. ma.copy(self, *args, **params) a.copy(order=) Return a copy of the array. ma.frombuffer(buffer[, dtype, count, offset]) Interpret a buffer as a 1-dimensional array. ma.fromfunction(function, shape, **kwargs) Construct an array by executing a function over each coordinate. ma.MaskedArray.copy(

Truth value testing all(a[, axis, out, keepdims]) Test whether all array elements along a given axis evaluate to True. any(a[, axis, out, keepdims]) Test whether any array element along a given axis evaluates to True. Array contents isfinite(x[, out]) Test element-wise for finiteness (not infinity or not Not a Number). isinf(x[, out]) Test element-wise for positive or negative infinity. isnan(x[, out]) Test element-wise for NaN and return result as a boolean array. isneginf(x[, y]) Te

Matrix and vector products dot(a, b[, out]) Dot product of two arrays. vdot(a, b) Return the dot product of two vectors. inner(a, b) Inner product of two arrays. outer(a, b[, out]) Compute the outer product of two vectors. matmul(a, b[, out]) Matrix product of two arrays. tensordot(a, b[, axes]) Compute tensor dot product along specified axes for arrays >= 1-D. einsum(subscripts, *operands[, out, dtype, ...]) Evaluates the Einstein summation convention on the operands. linalg.matri

Legendre.__call__(arg) [source]