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"""Functions that should behave the same as Numeric and need changing
"""
from __future__ import division, absolute_import, print_function
import numpy as np
import numpy.core.multiarray as mu
import numpy.core.numeric as nn
from .typeconv import convtypecode, convtypecode2
__all__ = ['take', 'repeat', 'sum', 'product', 'sometrue', 'alltrue',
'cumsum', 'cumproduct', 'compress', 'fromfunction',
'ones', 'empty', 'identity', 'zeros', 'array', 'asarray',
'nonzero', 'reshape', 'arange', 'fromstring', 'ravel', 'trace',
'indices', 'where', 'sarray', 'cross_product', 'argmax', 'argmin',
'average']
def take(a, indicies, axis=0):
return np.take(a, indicies, axis)
def repeat(a, repeats, axis=0):
return np.repeat(a, repeats, axis)
def sum(x, axis=0):
return np.sum(x, axis)
def product(x, axis=0):
return np.product(x, axis)
def sometrue(x, axis=0):
return np.sometrue(x, axis)
def alltrue(x, axis=0):
return np.alltrue(x, axis)
def cumsum(x, axis=0):
return np.cumsum(x, axis)
def cumproduct(x, axis=0):
return np.cumproduct(x, axis)
def argmax(x, axis=-1):
return np.argmax(x, axis)
def argmin(x, axis=-1):
return np.argmin(x, axis)
def compress(condition, m, axis=-1):
return np.compress(condition, m, axis)
def fromfunction(args, dimensions):
return np.fromfunction(args, dimensions, dtype=int)
def ones(shape, typecode='l', savespace=0, dtype=None):
"""ones(shape, dtype=int) returns an array of the given
dimensions which is initialized to all ones.
"""
dtype = convtypecode(typecode, dtype)
a = mu.empty(shape, dtype)
a.fill(1)
return a
def zeros(shape, typecode='l', savespace=0, dtype=None):
"""zeros(shape, dtype=int) returns an array of the given
dimensions which is initialized to all zeros
"""
dtype = convtypecode(typecode, dtype)
return mu.zeros(shape, dtype)
def identity(n,typecode='l', dtype=None):
"""identity(n) returns the identity 2-d array of shape n x n.
"""
dtype = convtypecode(typecode, dtype)
return nn.identity(n, dtype)
def empty(shape, typecode='l', dtype=None):
dtype = convtypecode(typecode, dtype)
return mu.empty(shape, dtype)
def array(sequence, typecode=None, copy=1, savespace=0, dtype=None):
dtype = convtypecode2(typecode, dtype)
return mu.array(sequence, dtype, copy=copy)
def sarray(a, typecode=None, copy=False, dtype=None):
dtype = convtypecode2(typecode, dtype)
return mu.array(a, dtype, copy)
def asarray(a, typecode=None, dtype=None):
dtype = convtypecode2(typecode, dtype)
return mu.array(a, dtype, copy=0)
def nonzero(a):
res = np.nonzero(a)
if len(res) == 1:
return res[0]
else:
raise ValueError("Input argument must be 1d")
def reshape(a, shape):
return np.reshape(a, shape)
def arange(start, stop=None, step=1, typecode=None, dtype=None):
dtype = convtypecode2(typecode, dtype)
return mu.arange(start, stop, step, dtype)
def fromstring(string, typecode='l', count=-1, dtype=None):
dtype = convtypecode(typecode, dtype)
return mu.fromstring(string, dtype, count=count)
def ravel(m):
return np.ravel(m)
def trace(a, offset=0, axis1=0, axis2=1):
return np.trace(a, offset=0, axis1=0, axis2=1)
def indices(dimensions, typecode=None, dtype=None):
dtype = convtypecode(typecode, dtype)
return np.indices(dimensions, dtype)
def where(condition, x, y):
return np.where(condition, x, y)
def cross_product(a, b, axis1=-1, axis2=-1):
return np.cross(a, b, axis1, axis2)
def average(a, axis=0, weights=None, returned=False):
return np.average(a, axis, weights, returned)
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