Updated numerix/scipy to new package structure.

teoliphant · teoliphant · commit b4b9432e495a · 2005-12-28T03:46:54.000Z
svn path=/trunk/matplotlib/; revision=1929
diff --git a/CHANGELOG b/CHANGELOG
@@ -1,3 +1,6 @@
+2005-12-27 Altered numerix/scipy to support new scipy package
+           structure - TEO
+
 2005-12-20 Fixed Jame's Boyles date tick reversal problem - JDH
 
 2005-12-20 Added Jouni's rc patch to support lists of keys to set on -
diff --git a/lib/matplotlib/numerix/_sp_imports.py b/lib/matplotlib/numerix/_sp_imports.py
@@ -1,15 +1,11 @@
 # Not sure why all the equivalences don't exist ...
-from scipy import Int8, UnsignedInt8, \
-     Int16, UnsignedInt16, \
-     Int32, uint32, \
+from scipy import Int8, UInt8, \
+     Int16, UInt16, \
+     Int32, UInt32, \
      Float32, Float64, \
      Complex32, Complex64, \
      Float, Int, Complex
 
-UInt8 = UnsignedInt8
-UInt16 = UnsignedInt16
-UInt32 = uint32
-
 class _TypeNamespace:
     """Numeric compatible type aliases for use with extension functions."""
     Int8          = Int8
diff --git a/lib/matplotlib/numerix/fft/__init__.py b/lib/matplotlib/numerix/fft/__init__.py
@@ -5,6 +5,6 @@
 elif which[0] == "numeric":
     from FFT import *
 elif which[0] == "scipy":
-    from scipy.basic.fft import *
+    from scipy.corefft import *
 else:
     raise RuntimeError("invalid numerix selector")
diff --git a/lib/matplotlib/numerix/linear_algebra/__init__.py b/lib/matplotlib/numerix/linear_algebra/__init__.py
@@ -5,6 +5,6 @@
 elif which[0] == "numeric":
     from LinearAlgebra import *
 elif which[0] == "scipy":
-    from scipy.basic.linalg import *
+    from scipy.corelinalg import *
 else:
     raise RuntimeError("invalid numerix selector")
diff --git a/lib/matplotlib/numerix/mlab/__init__.py b/lib/matplotlib/numerix/mlab/__init__.py
@@ -5,110 +5,9 @@
 elif which[0] == "numeric":
     from MLab import *
 elif which[0] == "scipy":
-    from scipy import *
-    from scipy.basic.linalg import svd, eig
-    try:
-        from scipy.integrate import trapz
-        from scipy.signal.signaltools import \
-            hanning, kaiser, blackman, bartlett, \
-            hamming
-        from scipy.special import sinc
-    except ImportError:
-        pass
-
-    def mean(m,axis=0):
-        """mean(m,axis=0) returns the mean of m along the given dimension.
-           If m is of integer type, returns a floating point answer.
-        """
-        m = asarray(m)
-        return add.reduce(m,axis)/float(m.shape[axis])
-
-
-    def std(m,axis=0):
-        """std(m,axis=0) returns the standard deviation along the given
-        dimension of m.  The result is unbiased with division by N-1.
-        If m is of integer type returns a floating point answer.
-        """
-        x = asarray(m)
-        n = float(x.shape[axis])
-        mx = asarray(mean(x,axis))
-        if axis < 0:
-            axis = len(x.shape) + axis
-        mx.shape = mx.shape[:axis] + (1,) + mx.shape[axis:]
-        x = x - mx
-        return sqrt(add.reduce(x*x,axis)/(n-1.0))
-
-    def cov(m,y=None, rowvar=0, bias=0):
-        """Estimate the covariance matrix.
-
-        If m is a vector, return the variance.  For matrices where each row
-        is an observation, and each column a variable, return the covariance
-        matrix.  Note that in this case diag(cov(m)) is a vector of
-        variances for each column.
-
-        cov(m) is the same as cov(m, m)
-
-        Normalization is by (N-1) where N is the number of observations
-        (unbiased estimate).  If bias is 1 then normalization is by N.
-
-        If rowvar is zero, then each row is a variable with
-        observations in the columns.
-        """
-        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 - mean(m,axis=0)
-        y = y - mean(y,axis=0)
-        if bias:
-            fact = N*1.0
-        else:
-            fact = N-1.0
-        #
-        val = squeeze(dot(transpose(m),conjugate(y)) / fact)
-        return val
-
-    def bartlett(M):
-        """bartlett(M) returns the M-point Bartlett window.
-        """
-        n = arange(0,M)
-        return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))
-
-    def hanning(M):
-        """hanning(M) returns the M-point Hanning window.
-        """
-        n = arange(0,M)
-        return 0.5-0.5*cos(2.0*pi*n/(M-1))
-
-    def hamming(M):
-        """hamming(M) returns the M-point Hamming window.
-        """
-        n = arange(0,M)
-        return 0.54-0.46*cos(2.0*pi*n/(M-1))
-
-    def sinc(x):
-        """sinc(x) returns sin(pi*x)/(pi*x) at all points of array x.
-        """
-        y = pi* where(x == 0, 1.0e-20, x)
-        return sin(y)/y
-
-    def msort(a):
-        return sort(a, axis=0)
+    from scipy.base.mlab import *
 else:
     raise RuntimeError("invalid numerix selector")
 
-if which[0] != "scipy":
-    # for easy access to these functions w/o clobbering builtins;
-    # scipy already has amin, amax
-    amin = min
-    amax = max
+amin = min
+amax = max
diff --git a/lib/matplotlib/numerix/random_array/__init__.py b/lib/matplotlib/numerix/random_array/__init__.py
@@ -5,6 +5,6 @@
 elif which[0] == "numeric":
     from RandomArray import *
 elif which[0] == "scipy":
-    from scipy.basic.random import *
+    from scipy.random import *
 else:
     raise RuntimeError("invalid numerix selector")
