Commit 7c7c3ca4 by Dan Lipsa

### Add missing numpy functions previously available in ParaView 4.1.

```Functions added: negative, reciprocal, square, rint, divide, multiply,
add, subtract, mod, remainder, power, hypot.

Change-Id: I0ecb7d08c3cc32b7917f3a77194507ae02de6d83```
parent f54b5b9f
 ... ... @@ -245,7 +245,7 @@ def max(array, axis=None, controller=None): """Returns the max of all values along a particular axis (dimension). Given an array of m tuples and n components: * Default is to return the max of all values in an array. * axis=0: Return the max values of all components and return a * axis=0: Return the max values of all tuples and return a one tuple, n-component array. * axis=1: Return the max values of all components of each tuple and return an m-tuple, 1-component array. ... ... @@ -284,7 +284,7 @@ def min(array, axis=None, controller=None): """Returns the min of all values along a particular axis (dimension). Given an array of m tuples and n components: * Default is to return the min of all values in an array. * axis=0: Return the min values of all components and return a one * axis=0: Return the min values of all tuples and return a one tuple, n-component array. * axis=1: Return the min values of all components of each tuple and return an m-tuple, 1-component array. ... ... @@ -909,6 +909,15 @@ def unstructured_from_composite_arrays(points, arrays, controller=None): sqrt = _make_ufunc(numpy.sqrt) sqrt.__doc__ = "Computes square root." negative = _make_ufunc(numpy.negative) negative.__doc__ = "Numerical negative, element-wise." reciprocal = _make_ufunc(numpy.reciprocal) reciprocal.__doc__ = "Return the reciprocal (1/x) of the argument, element-wise." square = _make_ufunc(numpy.square) square.__doc__ = "Return the element-wise square of the input." exp = _make_ufunc(numpy.exp) exp.__doc__ = "The exponential function." ... ... @@ -918,8 +927,8 @@ floor.__doc__ = "Returns the floor of floating point values." ceil = _make_ufunc(numpy.ceil) ceil.__doc__ = "Returns the ceiling of floating point values." round = _make_ufunc(numpy.round) round.__doc__ = "Rounds floating points values to integers." rint = _make_ufunc(numpy.rint) rint.__doc__ = "Round elements of the array to the nearest integer." sin = _make_ufunc(numpy.sin) sin.__doc__ = "Computes sine of values in radians." ... ... @@ -991,7 +1000,7 @@ expand_dims = _make_dfunc(numpy.expand_dims) expand_dims.__doc__ = """Insert a new dimension, corresponding to a given position in the array shape. In VTK, this function's main use is to enable an operator to work on a vector and a scalar field. For example, say you want to devide each component of a vector by the magnitude of say you want to divide each component of a vector by the magnitude of that vector. You might try this: >>> v ... ... @@ -1121,4 +1130,28 @@ vertex_normal = _make_dsfunc2(algs.vertex_normal) vertex_normal.__doc__ = "Returns the normal at each vertex of a dataset, which is defined as the average of the cell normals of all cells containing that vertex." logical_not = _make_ufunc(numpy.logical_not) logical_not.__doc__ = "Computes the truth value of NOT x element-wise" logical_not.__doc__ = "Computes the truth value of NOT x element-wise." divide = _make_dfunc(numpy.divide) divide.__doc__ = "Element by element division. Both elements can be single values or arrays. Same as /." multiply = _make_dfunc(numpy.multiply) multiply.__doc__ = "Element by element multiplication. Both elements can be single values or arrays. Same as *." add = _make_dfunc(numpy.add) add.__doc__ = "Element by element addition. Both elements can be single values or arrays. Same as +." subtract = _make_dfunc(numpy.subtract) subtract.__doc__ = "Returns the difference of two values element-wise. Same as x - y." mod = _make_dfunc(numpy.mod) mod.__doc__ = "Computes x1 - floor(x1 / x2) * x2, the result has the same sign as the divisor x2. It is equivalent to the Python modulus operator x1 % x2. Same as remainder." remainder = _make_dfunc(numpy.remainder) remainder.__doc__ = "Computes x1 - floor(x1 / x2) * x2, the result has the same sign as the divisor x2. It is equivalent to the Python modulus operator x1 % x2. Same as mod." power = _make_dfunc(numpy.power) power.__doc__ = "First array elements raised to powers from second array, element-wise." hypot = _make_dfunc(numpy.hypot) hypot.__doc__ = "Given the 'legs' of a right triangle, return its hypotenuse."
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