Coverage for local/lib/python2.7/site-packages/sage/modules/vector_real_double_dense.pyx : 85%

r""" 

Dense real double vectors using a NumPy backend. 

EXAMPLES: 

sage: v = vector(RDF,[1, pi, sqrt(2)]) 

sage: v 

(1.0, 3.141592653589793, 1.414213562373095) 

sage: type(v) 

<type 'sage.modules.vector_real_double_dense.Vector_real_double_dense'> 

sage: parent(v) 

Vector space of dimension 3 over Real Double Field 

sage: v[0] = 5 

sage: v 

(5.0, 3.141592653589793, 1.414213562373095) 

sage: loads(dumps(v)) == v 

True 

AUTHORS: 

-- Jason Grout, Oct 2008: switch to numpy backend, factored out 

Vector_double_dense class 

""" 

#***************************************************************************** 

# Copyright (C) 2008 Jason Grout <jason-sage@creativetrax.com> 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 2 of the License, or 

# (at your option) any later version. 

# http://www.gnu.org/licenses/ 

#***************************************************************************** 

from __future__ import absolute_import 

from sage.rings.real_double import RDF 

cimport numpy 

cdef class Vector_real_double_dense(Vector_double_dense): 

""" 

Vectors over the Real Double Field. These are supposed to be fast 

vector operations using C doubles. Most operations are implemented 

using numpy which will call the underlying BLAS, if needed, on the 

system. 

EXAMPLES: 

sage: v = vector(RDF, [1,2,3,4]); v 

(1.0, 2.0, 3.0, 4.0) 

sage: v*v 

30.0 

""" 

def __cinit__(self, parent, entries, coerce=True, copy=True): 

self._numpy_dtype = numpy.dtype('float64') 

self._numpy_dtypeint = numpy.NPY_DOUBLE 

self._python_dtype = float 

# TODO: Make RealDoubleElement instead of RDF for speed 

self._sage_dtype = RDF 

self.__create_vector__() 

return 

def stats_skew(self): 

""" 

Computes the skewness of a data set. 

For normally distributed data, the skewness should be about 

0. A skewness value > 0 means that there is more weight in the 

left tail of the distribution. (Paragraph from the scipy.stats 

docstring.) 

EXAMPLES: 

sage: v = vector(RDF, range(9)) 

sage: v.stats_skew() 

0.0 

""" 

import scipy.stats 

return self._sage_dtype(scipy.stats.skew(self._vector_numpy)) 

def __reduce__(self): 

""" 

Pickling 

EXAMPLES: 

sage: a = vector(RDF, range(9)) 

sage: loads(dumps(a)) == a 

True 

""" 

return (unpickle_v1, (self._parent, self.list(), self._degree, self._is_mutable)) 

# For backwards compatibility, we must keep the function unpickle_v0 

def unpickle_v0(parent, entries, degree): 

""" 

Create a real double vector containing the entries. 

EXAMPLES: 

sage: v = vector(RDF, [1,2,3]) 

sage: w = sage.modules.vector_real_double_dense.unpickle_v0(v.parent(), list(v), v.degree()) 

sage: v == w 

True 

""" 

return unpickle_v1(parent, entries, degree) 

def unpickle_v1(parent, entries, degree, is_mutable=None): 

""" 

Create a real double vector with the given parent, entries, 

degree, and mutability. 

EXAMPLES: 

sage: v = vector(RDF, [1,2,3]) 

sage: w = sage.modules.vector_real_double_dense.unpickle_v1(v.parent(), list(v), v.degree(), v.is_mutable()) 

sage: v == w 

True 

""" 

cdef Vector_real_double_dense v = Vector_real_double_dense(parent, entries) 

if is_mutable is not None: 

v._is_mutable = is_mutable 

return v