1 files changed, 0 insertions, 128 deletions
diff --git a/numpy/oldnumeric/mlab.py b/numpy/oldnumeric/mlab.py
deleted file mode 100644
index 2b357612c..000000000
--- a/numpy/oldnumeric/mlab.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""This module is for compatibility only.  All functions are defined elsewhere.
-
-"""
-from __future__ import division, absolute_import, print_function
-
-__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 *
-
-from . import compat
-from . import precision
-from . import functions
-from . import misc
-from . 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