numpy.exp(x[, out]) = Calculate the exponential of all elements in the input array. Parameters: x : array_like Input values. Returns: out : ndarray Output array, element-wise exponential of x. See also expm1 Calculate exp(x) - 1 for all elements in the array. exp2 Calculate 2**x for all elements in the array. Notes The irrational number e is also known as Euler?s number. It is approximately 2.718281, and is the base of the natural logarithm, ln (this means that, if , then .

class numpy.errstate(**kwargs) [source] Context manager for floating-point error handling. Using an instance of errstate as a context manager allows statements in that context to execute with a known error handling behavior. Upon entering the context the error handling is set with seterr and seterrcall, and upon exiting it is reset to what it was before. Parameters: kwargs : {divide, over, under, invalid} Keyword arguments. The valid keywords are the possible floating-point exceptions. Ea

numpy.equal(x1, x2[, out]) = Return (x1 == x2) element-wise. Parameters: x1, x2 : array_like Input arrays of the same shape. Returns: out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See also not_equal, greater_equal, less_equal, greater, less Examples >>> np.equal([0, 1, 3], np.arange(3)) array([ True, True, False], dtype=bool) What is compared are values, not types. So an int (1) and an array of length one can evaluate as True:

numpy.empty_like(a, dtype=None, order='K', subok=True) Return a new array with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. New in version 1.6.0. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C?

numpy.empty(shape, dtype=float, order='C') Return a new array of given shape and type, without initializing entries. Parameters: shape : int or tuple of int Shape of the empty array dtype : data-type, optional Desired output data-type. order : {?C?, ?F?}, optional Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Returns: out : ndarray Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object

numpy.einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe') Evaluates the Einstein summation convention on the operands. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way to compute such summations. The best way to understand this function is to try the examples below, which show how many common NumPy functions can be implemented as calls to einsum. Parameters:

numpy.ediff1d(ary, to_end=None, to_begin=None) [source] The differences between consecutive elements of an array. Parameters: ary : array_like If necessary, will be flattened before the differences are taken. to_end : array_like, optional Number(s) to append at the end of the returned differences. to_begin : array_like, optional Number(s) to prepend at the beginning of the returned differences. Returns: ediff1d : ndarray The differences. Loosely, this is ary.flat[1:] - ary.flat[:-

class numpy.dtype [source] Create a data type object. A numpy array is homogeneous, and contains elements described by a dtype object. A dtype object can be constructed from different combinations of fundamental numeric types. Parameters: obj Object to be converted to a data type object. align : bool, optional Add padding to the fields to match what a C compiler would output for a similar C-struct. Can be True only if obj is a dictionary or a comma-separated string. If a struct dtype is

numpy.dstack(tup) [source] Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns: stacked : ndarray The array formed by stacking the given arrays.

numpy.dsplit(ary, indices_or_sections) [source] Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the split documentation. dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.],