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# This module is for compatibility only. All functions are defined elsewhere.
from numpy.oldnumeric import *
__all__ = numpy.oldnumeric.__all__
__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 linear_algebra as LinearAlgebra
import 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, corrcoef as _Ncorrcoef,\
cov as _Ncov, squeeze, sinc, median, fliplr, mean as _Nmean
from numpy.linalg import eig, svd
from numpy.random import rand, randn
from typeconv import oldtype2dtype as o2d
def eye(N, M=None, k=0, typecode=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = o2d[typecode]
if M is None: M = N
m = nn.equal(nn.subtract.outer(nn.arange(N), nn.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=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 = o2d[typecode]
if M is None: M = N
m = nn.greater_equal(nn.subtract.outer(nn.arange(N), nn.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):
return _Nstd(x, axis)
def mean(x, axis=0):
return _Nmean(x, axis)
def cov(m, y=None, rowvar=0, bias=0):
return _Ncov(m, y, rowvar, bias)
def corrcoef(x, y=None):
return _Ncorrcoef(x,y,0,0)
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