ndarray.__setslice__ x.__setslice__(i, j, y) <==> x[i:j]=y Use of negative indices is not supported.

numpy.asmatrix(data, dtype=None) [source] Interpret the input as a matrix. Unlike matrix, asmatrix does not make a copy if the input is already a matrix or an ndarray. Equivalent to matrix(data, copy=False). Parameters: data : array_like Input data. dtype : data-type Data-type of the output matrix. Returns: mat : matrix data interpreted as a matrix. Examples >>> x = np.array([[1, 2], [3, 4]]) >>> m = np.asmatrix(x) >>> x[0,0] = 5 >>> m matrix

numpy.ma.dot(a, b, strict=False, out=None) [source] Return the dot product of two arrays. This function is the equivalent of numpy.dot that takes masked values into account. Note that strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. Note Works only with 2-D arrays at the moment. Parameters: a,

chararray.title() [source] For each element in self, return a titlecased version of the string: words start with uppercase characters, all remaining cased characters are lowercase. See also char.title

numpy.around(a, decimals=0, out=None) [source] Evenly round to the given number of decimals. Parameters: a : array_like Input data. decimals : int, optional Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if nece

record.all() 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

ndarray.__eq__ x.__eq__(y) <==> x==y

numpy.random.multivariate_normal(mean, cov[, size]) Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. Such a distribution is specified by its mean and covariance matrix. These parameters are analogous to the mean (average or ?center?) and variance (standard deviation, or ?width,? squared) of the one-dimensional normal distribution. Par

numpy.right_shift(x1, x2[, out]) = Shift the bits of an integer to the right. Bits are shifted to the right x2. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing x1 by 2**x2. Parameters: x1 : array_like, int Input values. x2 : array_like, int Number of bits to remove at the right of x1. Returns: out : ndarray, int Return x1 with bits shifted x2 times to the right. See also left_shift Shift the bits of an integer to th

Common test support for all numpy test scripts. This single module should provide all the common functionality for numpy tests in a single location, so that test scripts can just import it and work right away. Asserts assert_almost_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to desired precision. assert_approx_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to significant digits. assert_array_almost_equal(x, y[,