1 files changed, 122 insertions, 0 deletions
diff --git a/numpy/oldnumeric/mlab.py b/numpy/oldnumeric/mlab.py
new file mode 100644
index 000000000..47be89e1b
--- /dev/null
+++ b/numpy/oldnumeric/mlab.py
@@ -0,0 +1,122 @@
+# This module is for compatibility only.  All functions are defined elsewhere.
+
+__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 nn
+
+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 = 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, 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 = 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):
+    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