Rewrapped __all__ definition to conform to PEP8.

Standardize NumPy import as "import numpy as np".
Removed unused imports.
Fixed undefined reference to ndarray (should be np.ndarray).
Fixed undefined references to exp (should be math.exp).

4 files changed, 29 insertions, 24 deletions

diff --git a/numpy/oldnumeric/arrayfns.py b/numpy/oldnumeric/arrayfns.py
index 4c31a6827..dbb910770 100644
--- a/numpy/oldnumeric/arrayfns.py
+++ b/numpy/oldnumeric/arrayfns.py
@@ -1,10 +1,11 @@
 """Backward compatible with arrayfns from Numeric
 """
 
-__all__ = ['array_set', 'construct3', 'digitize', 'error', 'find_mask', 'histogram', 'index_sort',
-           'interp', 'nz', 'reverse', 'span', 'to_corners', 'zmin_zmax']
+__all__ = ['array_set', 'construct3', 'digitize', 'error', 'find_mask', 
+           'histogram', 'index_sort', 'interp', 'nz', 'reverse', 'span', 
+           'to_corners', 'zmin_zmax']
 
-import numpy as nx
+import numpy as np
 from numpy import asarray
 
 class error(Exception):
@@ -14,7 +15,7 @@ def array_set(vals1, indices, vals2):
     indices = asarray(indices)
     if indices.ndim != 1:
         raise ValueError, "index array must be 1-d"
-    if not isinstance(vals1, ndarray):
+    if not isinstance(vals1, np.ndarray):
         raise TypeError, "vals1 must be an ndarray"
     vals1 = asarray(vals1)
     vals2 = asarray(vals2)
@@ -31,7 +32,7 @@ def index_sort(arr):
 def interp(y, x, z, typ=None):
     """y(z) interpolated by treating y(x) as piecewise function
     """
-    res = numpy.interp(z, x, y)
+    res = np.interp(z, x, y)
     if typ is None or typ == 'd':
         return res
     if typ == 'f':
@@ -40,17 +41,17 @@ def interp(y, x, z, typ=None):
     raise error, "incompatible typecode"
 
 def nz(x):
-    x = asarray(x,dtype=nx.ubyte)
+    x = asarray(x,dtype=np.ubyte)
     if x.ndim != 1:
         raise TypeError, "intput must have 1 dimension."
-    indxs = nx.flatnonzero(x != 0)
+    indxs = np.flatnonzero(x != 0)
     return indxs[-1].item()+1
 
 def reverse(x, n):
     x = asarray(x,dtype='d')
     if x.ndim != 2:
         raise ValueError, "input must be 2-d"
-    y = nx.empty_like(x)
+    y = np.empty_like(x)
     if n == 0:
         y[...] = x[::-1,:]
     elif n == 1:
@@ -58,11 +59,11 @@ def reverse(x, n):
     return y
 
 def span(lo, hi, num, d2=0):
-    x = linspace(lo, hi, num)
+    x = np.linspace(lo, hi, num)
     if d2 <= 0:
         return x
     else:
-        ret = empty((d2,num),x.dtype)
+        ret = np.empty((d2,num),x.dtype)
         ret[...] = x
         return ret
 
@@ -71,15 +72,15 @@ def zmin_zmax(z, ireg):
     ireg = asarray(ireg, dtype=int)
     if z.shape != ireg.shape or z.ndim != 2:
         raise ValueError, "z and ireg must be the same shape and 2-d"
-    ix, iy = nx.nonzero(ireg)
+    ix, iy = np.nonzero(ireg)
     # Now, add more indices
     x1m = ix - 1
     y1m = iy-1
     i1 = x1m>=0
     i2 = y1m>=0
     i3 = i1 & i2
-    nix = nx.r_[ix, x1m[i1], x1m[i1], ix[i2] ]
-    niy = nx.r_[iy, iy[i1],  y1m[i3], y1m[i2]]
+    nix = np.r_[ix, x1m[i1], x1m[i1], ix[i2] ]
+    niy = np.r_[iy, iy[i1],  y1m[i3], y1m[i2]]
     # remove any negative indices
     zres = z[nix,niy]
     return zres.min().item(), zres.max().item()
diff --git a/numpy/oldnumeric/compat.py b/numpy/oldnumeric/compat.py
index 7f123fa69..3e1d53d0e 100644
--- a/numpy/oldnumeric/compat.py
+++ b/numpy/oldnumeric/compat.py
@@ -12,7 +12,7 @@ __all__ = ['NewAxis',
 
 import numpy.core.multiarray as multiarray
 import numpy.core.umath as um
-from numpy.core.numeric import array, correlate
+from numpy.core.numeric import array
 import functions
 import sys
 
diff --git a/numpy/oldnumeric/mlab.py b/numpy/oldnumeric/mlab.py
index 47be89e1b..c11e34c1f 100644
--- a/numpy/oldnumeric/mlab.py
+++ b/numpy/oldnumeric/mlab.py
@@ -1,6 +1,10 @@
 # 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']
+__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
@@ -12,7 +16,7 @@ from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
 
 from numpy.linalg import eig, svd
 from numpy.random import rand, randn
-import numpy as nn
+import numpy as np
 
 from typeconv import convtypecode
 
@@ -22,7 +26,7 @@ def eye(N, M=None, k=0, typecode=None, dtype=None):
     """
     dtype = convtypecode(typecode, dtype)
     if M is None: M = N
-    m = nn.equal(nn.subtract.outer(nn.arange(N), nn.arange(M)),-k)
+    m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
     if m.dtype != dtype:
         return m.astype(dtype)
 
@@ -32,7 +36,7 @@ def tri(N, M=None, k=0, typecode=None, dtype=None):
     """
     dtype = convtypecode(typecode, dtype)
     if M is None: M = N
-    m = nn.greater_equal(nn.subtract.outer(nn.arange(N), nn.arange(M)),-k)
+    m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
     if m.dtype != dtype:
         return m.astype(dtype)
 
diff --git a/numpy/oldnumeric/rng.py b/numpy/oldnumeric/rng.py
index fcf08bb37..b4c72a68c 100644
--- a/numpy/oldnumeric/rng.py
+++ b/numpy/oldnumeric/rng.py
@@ -4,9 +4,9 @@
 # It is for backwards compatibility only.
 
 
-__all__ = ['CreateGenerator','ExponentialDistribution','LogNormalDistribution','NormalDistribution',
-           'UniformDistribution', 'error', 'default_distribution', 'random_sample', 'ranf',
-           'standard_generator']
+__all__ = ['CreateGenerator','ExponentialDistribution','LogNormalDistribution',
+           'NormalDistribution', 'UniformDistribution', 'error', 'ranf', 
+           'default_distribution', 'random_sample', 'standard_generator']
 
 import numpy.random.mtrand as mt
 import math
@@ -44,7 +44,7 @@ class ExponentialDistribution(Distribution):
             return 0.0
         else:
             lambda_ = self._args[0]
-            return lambda_*exp(-lambda_*x)
+            return lambda_*math.exp(-lambda_*x)
 
 class LogNormalDistribution(Distribution):
     def __init__(self, m, s):
@@ -61,7 +61,7 @@ class LogNormalDistribution(Distribution):
     def density(x):
         m,s = self._args
         y = (math.log(x)-self._mn)/self._sn
-        return self._fac*exp(-0.5*y*y)/x
+        return self._fac*math.exp(-0.5*y*y)/x
 
 
 class NormalDistribution(Distribution):
@@ -76,7 +76,7 @@ class NormalDistribution(Distribution):
     def density(x):
         m,s = self._args
         y = (x-m)/s
-        return self._fac*exp(-0.5*y*y)
+        return self._fac*math.exp(-0.5*y*y)
 
 class UniformDistribution(Distribution):
     def __init__(self, a, b):