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"""This module is for compatibility only. All functions are defined elsewhere.
"""
from __future__ import division
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
|
