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To: gentoo-commits@g.o
From: "Ali Polatel (hawking)" <hawking@g.o>
Subject: gentoo-x86 commit in dev-python/gnuplot-py/files: gnuplot-py-1.7-numpy.patch digest-gnuplot-py-1.7-r2
Date: Tue, 20 Nov 2007 21:15:32 +0000
hawking     07/11/20 21:15:32

  Added:                gnuplot-py-1.7-numpy.patch digest-gnuplot-py-1.7-r2
  Log:
  revbump. backported upstream's changes for numpy. numeric dependency changed to numpy.
  (Portage version: 2.1.3.19)

Revision  Changes    Path
1.1                  dev-python/gnuplot-py/files/gnuplot-py-1.7-numpy.patch

file : http://sources.gentoo.org/viewcvs.py/gentoo-x86/dev-python/gnuplot-py/files/gnuplot-py-1.7-numpy.patch?rev=1.1&view=markup
plain: http://sources.gentoo.org/viewcvs.py/gentoo-x86/dev-python/gnuplot-py/files/gnuplot-py-1.7-numpy.patch?rev=1.1&content-type=text/plain

Index: gnuplot-py-1.7-numpy.patch
===================================================================
diff -ur gnuplot-py-1.7/ANNOUNCE.txt gnuplot-py-1.7-numpy/ANNOUNCE.txt
--- gnuplot-py-1.7/ANNOUNCE.txt	2003-10-17 18:03:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/ANNOUNCE.txt	2007-11-20 22:17:29.000000000 +0200
@@ -9,7 +9,7 @@
 
 Prerequisites (see footnotes):
     the Python interpreter [1]
-    the Python Numeric module [3]
+    the Python numpy module [3]
     the gnuplot program [2]
 
 or, to use it under Java (experimental):
@@ -20,7 +20,7 @@
 
 Some ways this package can be used:
 
-1. Interactive data processing: Use Python's excellent Numeric package
+1. Interactive data processing: Use Python's excellent numpy package
    to create and manipulate arrays of numbers, and use Gnuplot.py to
    visualize the results.
 2. Web graphics: write CGI scripts in Python that use gnuplot to
diff -ur gnuplot-py-1.7/demo.py gnuplot-py-1.7-numpy/demo.py
--- gnuplot-py-1.7/demo.py	2003-10-17 17:28:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/demo.py	2007-11-20 22:36:59.000000000 +0200
@@ -16,7 +16,7 @@
 __cvs_version__ = '$Revision: 1.1 $'
 
 
-from Numeric import *
+from numpy import *
 
 # If the package has been installed correctly, this should work:
 import Gnuplot, Gnuplot.funcutils
@@ -31,7 +31,7 @@
     g = Gnuplot.Gnuplot(debug=1)
     g.title('A simple example') # (optional)
     g('set data style linespoints') # give gnuplot an arbitrary command
-    # Plot a list of (x, y) pairs (tuples or a Numeric array would
+    # Plot a list of (x, y) pairs (tuples or a numpy array would
     # also be OK):
     g.plot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]])
     raw_input('Please press return to continue...\n')
@@ -39,7 +39,7 @@
     g.reset()
     # Plot one dataset from an array and one via a gnuplot function;
     # also demonstrate the use of item-specific options:
-    x = arange(10, typecode=Float)
+    x = arange(10, dtype='float_')
     y1 = x**2
     # Notice how this plotitem is created here but used later?  This
     # is convenient if the same dataset has to be plotted multiple
@@ -74,8 +74,8 @@
     # Make a 2-d array containing a function of x and y.  First create
     # xm and ym which contain the x and y values in a matrix form that
     # can be `broadcast' into a matrix of the appropriate shape:
-    xm = x[:,NewAxis]
-    ym = y[NewAxis,:]
+    xm = x[:,newaxis]
+    ym = y[newaxis,:]
     m = (sin(xm) + 0.1*xm) - ym**2
     g('set parametric')
     g('set data style lines')
diff -ur gnuplot-py-1.7/FAQ.txt gnuplot-py-1.7-numpy/FAQ.txt
--- gnuplot-py-1.7/FAQ.txt	2003-10-17 17:28:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/FAQ.txt	2007-11-20 22:17:50.000000000 +0200
@@ -17,7 +17,7 @@
 #! /usr/bin/python2
 
 import Gnuplot, Gnuplot.funcutils
-from Numeric import *
+from numpy import *
 
 g = Gnuplot.Gnuplot()
 g.plot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]])
diff -ur gnuplot-py-1.7/funcutils.py gnuplot-py-1.7-numpy/funcutils.py
--- gnuplot-py-1.7/funcutils.py	2003-10-17 17:28:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/funcutils.py	2007-11-20 22:25:24.000000000 +0200
@@ -16,19 +16,19 @@
 
 __cvs_version__ = '$Revision: 1.1 $'
 
-import Numeric
+import numpy
 
 import Gnuplot, utils
 
 
-def tabulate_function(f, xvals, yvals=None, typecode=None, ufunc=0):
+def tabulate_function(f, xvals, yvals=None, dtype=None, ufunc=0):
     """Evaluate and tabulate a function on a 1- or 2-D grid of points.
 
     f should be a function taking one or two floating-point
     parameters.
 
     If f takes one parameter, then xvals should be a 1-D array and
-    yvals should be None.  The return value is a Numeric array
+    yvals should be None.  The return value is a numpy array
     '[f(x[0]), f(x[1]), ..., f(x[-1])]'.
 
     If f takes two parameters, then 'xvals' and 'yvals' should each be
@@ -39,7 +39,7 @@
 
     If 'ufunc=0', then 'f' is evaluated at each point using a Python
     loop.  This can be slow if the number of points is large.  If
-    speed is an issue, you should write 'f' in terms of Numeric ufuncs
+    speed is an issue, you should write 'f' in terms of numpy ufuncs
     and use the 'ufunc=1' feature described next.
 
     If called with 'ufunc=1', then 'f' should be a function that is
@@ -51,34 +51,33 @@
 
     if yvals is None:
         # f is a function of only one variable:
-        xvals = Numeric.asarray(xvals, typecode)
+        xvals = numpy.asarray(xvals, dtype)
 
         if ufunc:
             return f(xvals)
         else:
-            if typecode is None:
-                typecode = xvals.typecode()
+            if dtype is None:
+                dtype = xvals.dtype.char
 
-            m = Numeric.zeros((len(xvals),), typecode)
+            m = numpy.zeros((len(xvals),), dtype)
             for xi in range(len(xvals)):
                 x = xvals[xi]
                 m[xi] = f(x)
             return m
     else:
         # f is a function of two variables:
-        xvals = Numeric.asarray(xvals, typecode)
-        yvals = Numeric.asarray(yvals, typecode)
+        xvals = numpy.asarray(xvals, dtype)
+        yvals = numpy.asarray(yvals, dtype)
 
         if ufunc:
-            return f(xvals[:,Numeric.NewAxis], yvals[Numeric.NewAxis,:])
+            return f(xvals[:,numpy.newaxis], yvals[numpy.newaxis,:])
         else:
-            if typecode is None:
+            if dtype is None:
                 # choose a result typecode based on what '+' would return
                 # (yecch!):
-                typecode = (Numeric.zeros((1,), xvals.typecode()) +
-                            Numeric.zeros((1,), yvals.typecode())).typecode()
-
-            m = Numeric.zeros((len(xvals), len(yvals)), typecode)
+                dtype = (numpy.zeros((1,), xvals.dtype.char) +
+                        numpy.zeros((1,), yvals.dtype.char)).dtype.char
+            m = numpy.zeros((len(xvals), len(yvals)), dtype)
             for xi in range(len(xvals)):
                 x = xvals[xi]
                 for yi in range(len(yvals)):
diff -ur gnuplot-py-1.7/_Gnuplot.py gnuplot-py-1.7-numpy/_Gnuplot.py
--- gnuplot-py-1.7/_Gnuplot.py	2003-10-17 17:28:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/_Gnuplot.py	2007-11-20 22:37:26.000000000 +0200
@@ -228,8 +228,8 @@
 
         'items' is a sequence of items, each of which should be a
         'PlotItem' of some kind, a string (interpreted as a function
-        string for gnuplot to evaluate), or a Numeric array (or
-        something that can be converted to a Numeric array).
+        string for gnuplot to evaluate), or a numpy array (or
+        something that can be converted to a numpy array).
 
         """
 
diff -ur gnuplot-py-1.7/__init__.py gnuplot-py-1.7-numpy/__init__.py
--- gnuplot-py-1.7/__init__.py	2003-10-17 18:04:29.000000000 +0300
+++ gnuplot-py-1.7-numpy/__init__.py	2007-11-20 22:19:00.000000000 +0200
@@ -128,9 +128,9 @@
 
 Restrictions:
 
- -  Relies on the Numeric Python extension.  This can be obtained from
-    "SourceForge", http://sourceforge.net/projects/numpy/.  If you're
-    interested in gnuplot, you would probably also want NumPy anyway.
+ -  Relies on the numpy Python extension.  This can be obtained from
+    the Scipy group at <http://www.scipy.org/Download>..  If you're
+    interested in gnuplot, you would probably also want numpy anyway.
 
  -  Only a small fraction of gnuplot functionality is implemented as
     explicit method functions.  However, you can give arbitrary
diff -ur gnuplot-py-1.7/NEWS.txt gnuplot-py-1.7-numpy/NEWS.txt
--- gnuplot-py-1.7/NEWS.txt	2003-10-17 18:04:29.000000000 +0300
+++ gnuplot-py-1.7-numpy/NEWS.txt	2007-11-20 22:22:08.000000000 +0200
@@ -57,7 +57,7 @@
   equivalent.)  If I find the time I might try to produce a version
   that doesn't require Numeric at all, under either Python or Jython.
 
-* Removed the oldplot.py module: (1) I doubt anybody is still using
+ Removed the oldplot.py module: (1) I doubt anybody is still using
   it. (2) It seems to be broken anyway. (3) I don't have the energy to
   fix or maintain it.  Let me know if I'm wrong about point 1.
 
@@ -222,10 +222,10 @@
   dataset; e.g., what used to be written as
 
       g = Gnuplot.Gnuplot()
-      x = Numeric.arange(100)/10.0
+      x = numpy.arange(100)/10.0
       y = x**2
       # Create an array of (x,y) pairs:
-      g.plot(Gnuplot.Data(Numeric.transpose((x, y))))
+      g.plot(Gnuplot.Data(numpy.transpose((x, y))))
 
   can now be shortened to
 
diff -ur gnuplot-py-1.7/PlotItems.py gnuplot-py-1.7-numpy/PlotItems.py
--- gnuplot-py-1.7/PlotItems.py	2003-10-17 17:39:03.000000000 +0300
+++ gnuplot-py-1.7-numpy/PlotItems.py	2007-11-20 22:34:49.000000000 +0200
@@ -23,7 +23,7 @@
 except ImportError:
     from StringIO import StringIO
 
-import Numeric
+import numpy
 
 import gp, utils, Errors
 
@@ -471,12 +471,12 @@
     return apply(_FileItem, (filename,), keyw)
 
 
-def Data(*set, **keyw):
-    """Create and return a _FileItem representing the data from *set.
+def Data(*data, **keyw):
+    """Create and return a _FileItem representing the data from *data.
 
     Create a '_FileItem' object (which is a type of 'PlotItem') out of
-    one or more Float Python Numeric arrays (or objects that can be
-    converted to a Float Numeric array).  If the routine is passed a
+    one or more Float Python numpy arrays (or objects that can be
+    converted to a float numpy array).  If the routine is passed a
     single with multiple dimensions, then the last index ranges over
     the values comprising a single data point (e.g., [<x>, <y>,
     <sigma>]) and the rest of the indices select the data point.  If
@@ -508,29 +508,29 @@
 
     """
 
-    if len(set) == 1:
-        # set was passed as a single structure
-        set = utils.float_array(set[0])
+    if len(data) == 1:
+        # data was passed as a single structure
+        data = utils.float_array(data[0])
 
         # As a special case, if passed a single 1-D array, then it is
         # treated as one value per point (by default, plotted against
         # its index):
-        if len(set.shape) == 1:
-            set = set[:,Numeric.NewAxis]
+        if len(data.shape) == 1:
+            data = data[:,numpy.newaxis]
     else:
-        # set was passed column by column (for example,
+        # data was passed column by column (for example,
         # Data(x,y)); pack it into one big array (this will test
         # that sizes are all the same):
-        set = utils.float_array(set)
-        dims = len(set.shape)
+        data = utils.float_array(data)
+        dims = len(data.shape)
         # transpose so that the last index selects x vs. y:
-        set = Numeric.transpose(set, (dims-1,) + tuple(range(dims-1)))
+        data = numpy.transpose(data, (dims-1,) + tuple(range(dims-1)))
     if keyw.has_key('cols'):
         cols = keyw['cols']
         del keyw['cols']
-        if type(cols) is types.IntType:
+        if isinstance(cols, types.IntType):
             cols = (cols,)
-        set = Numeric.take(set, cols, -1)
+        data = numpy.take(data, cols, -1)
 
     if keyw.has_key('inline'):
         inline = keyw['inline']
@@ -540,7 +540,7 @@
 
     # Output the content into a string:
     f = StringIO()
-    utils.write_array(f, set)
+    utils.write_array(f, data)
     content = f.getvalue()
     if inline:
         return apply(_InlineFileItem, (content,), keyw)
@@ -610,7 +610,7 @@
         raise Errors.DataError('data array must be two-dimensional')
 
     if xvals is None:
-        xvals = Numeric.arange(numx)
+        xvals = numpy.arange(numx)
     else:
         xvals = utils.float_array(xvals)
         if xvals.shape != (numx,):
@@ -619,7 +619,7 @@
                 'the first dimension of the data array')
 
     if yvals is None:
-        yvals = Numeric.arange(numy)
+        yvals = numpy.arange(numy)
     else:
         yvals = utils.float_array(yvals)
         if yvals.shape != (numy,):
@@ -647,17 +647,17 @@
         # documentation has the roles of x and y exchanged.  We ignore
         # the documentation and go with the code.
 
-        mout = Numeric.zeros((numy + 1, numx + 1), Numeric.Float32)
+        mout = numpy.zeros((numy + 1, numx + 1), numpy.float32)
         mout[0,0] = numx
-        mout[0,1:] = xvals.astype(Numeric.Float32)
-        mout[1:,0] = yvals.astype(Numeric.Float32)
+        mout[0,1:] = xvals.astype(numpy.float32)
+        mout[1:,0] = yvals.astype(numpy.float32)
         try:
             # try copying without the additional copy implied by astype():
-            mout[1:,1:] = Numeric.transpose(data)
+            mout[1:,1:] = numpy.transpose(data)
         except:
             # if that didn't work then downcasting from double
             # must be necessary:
-            mout[1:,1:] = Numeric.transpose(data.astype(Numeric.Float32))
+            mout[1:,1:] = numpy.transpose(data.astype(numpy.float32))
 
         content = mout.tostring()
         if gp.GnuplotOpts.prefer_fifo_data:
@@ -668,10 +668,10 @@
         # output data to file as "x y f(x)" triplets.  This
         # requires numy copies of each x value and numx copies of
         # each y value.  First reformat the data:
-        set = Numeric.transpose(
-            Numeric.array(
-                (Numeric.transpose(Numeric.resize(xvals, (numy, numx))),
-                 Numeric.resize(yvals, (numx, numy)),
+        set = numpy.transpose(
+            numpy.array(
+                (numpy.transpose(numpy.resize(xvals, (numy, numx))),
+                 numpy.resize(yvals, (numx, numy)),
                  data)), (1,2,0))
 
         # Now output the data with the usual routine.  This will
diff -ur gnuplot-py-1.7/README.txt gnuplot-py-1.7-numpy/README.txt
--- gnuplot-py-1.7/README.txt	2003-10-19 17:52:35.000000000 +0300
+++ gnuplot-py-1.7-numpy/README.txt	2007-11-20 22:35:30.000000000 +0200
@@ -65,8 +65,8 @@
 
 Obviously, you must have the gnuplot program if Gnuplot.py is to be of
 any use to you.  Gnuplot can be obtained via
-<http://www.gnuplot.info>.  You also need Python's Numerical
-extension, which is available from <http://numpy.sourceforge.net>.
+<http://www.gnuplot.info>.  You also need a copy of the numpy package, which
+is available from the Scipy group at <http://www.scipy.org/Download>.
 
 Gnuplot.py uses Python distutils
 <http://www.python.org/doc/current/inst/inst.html> and can be
diff -ur gnuplot-py-1.7/setup.py gnuplot-py-1.7-numpy/setup.py
--- gnuplot-py-1.7/setup.py	2003-10-17 17:52:28.000000000 +0300
+++ gnuplot-py-1.7-numpy/setup.py	2007-11-20 22:19:20.000000000 +0200
@@ -31,7 +31,7 @@
     author_email='mhagger@...',
     url='http://gnuplot-py.sourceforge.net',
     license='LGPL',
-    licence='LGPL', # Spelling error in distutils
+    #licence='LGPL', # Spelling error in distutils
 
     # Description of the package in the distribution
     package_dir={'Gnuplot' : '.'},
diff -ur gnuplot-py-1.7/test.py gnuplot-py-1.7-numpy/test.py
--- gnuplot-py-1.7/test.py	2003-10-17 17:28:10.000000000 +0300
+++ gnuplot-py-1.7-numpy/test.py	2007-11-20 22:43:26.000000000 +0200
@@ -17,8 +17,7 @@
 __cvs_version__ = '$Revision: 1.1 $'
 
 import os, time, math, tempfile
-import Numeric
-from Numeric import NewAxis
+import numpy
 
 try:
     import Gnuplot, Gnuplot.PlotItems, Gnuplot.funcutils
@@ -55,7 +54,7 @@
     filename1 = tempfile.mktemp()
     f = open(filename1, 'w')
     try:
-        for x in Numeric.arange(100)/5. - 10.:
+        for x in numpy.arange(100.)/5. - 10.:
             f.write('%s %s %s\n' % (x, math.cos(x), math.sin(x)))
         f.close()
 
@@ -137,10 +136,10 @@
         g.plot(f)
 
         print '############### test Data ###################################'
-        x = Numeric.arange(100)/5. - 10.
-        y1 = Numeric.cos(x)
-        y2 = Numeric.sin(x)
-        d = Numeric.transpose((x,y1,y2))
+        x = numpy.arange(100)/5. - 10.
+        y1 = numpy.cos(x)
+        y2 = numpy.sin(x)
+        d = numpy.transpose((x,y1,y2))
 
         wait('Plot Data against its index')
         g.plot(Gnuplot.Data(y2, inline=0))
@@ -173,7 +172,7 @@
         g.plot(Gnuplot.Data(d, title='Cosine of x'))
 
         print '############### test compute_Data ###########################'
-        x = Numeric.arange(100)/5. - 10.
+        x = numpy.arange(100)/5. - 10.
 
         wait('Plot Data, computed by Gnuplot.py')
         g.plot(Gnuplot.funcutils.compute_Data(x, lambda x: math.cos(x), inline=0))
@@ -235,14 +234,14 @@
 
         print '############### test GridData and compute_GridData ##########'
         # set up x and y values at which the function will be tabulated:
-        x = Numeric.arange(35)/2.0
-        y = Numeric.arange(30)/10.0 - 1.5
+        x = numpy.arange(35)/2.0
+        y = numpy.arange(30)/10.0 - 1.5
         # Make a 2-d array containing a function of x and y.  First create
         # xm and ym which contain the x and y values in a matrix form that
         # can be `broadcast' into a matrix of the appropriate shape:
-        xm = x[:,NewAxis]
-        ym = y[NewAxis,:]
-        m = (Numeric.sin(xm) + 0.1*xm) - ym**2
+        xm = x[:,numpy.newaxis]
+        ym = y[numpy.newaxis,:]
+        m = (numpy.sin(xm) + 0.1*xm) - ym**2
         wait('a function of two variables from a GridData file')
         g('set parametric')
         g('set data style lines')
@@ -264,7 +263,7 @@
 
         wait('Use compute_GridData in ufunc and binary mode')
         g.splot(Gnuplot.funcutils.compute_GridData(
-            x,y, lambda x,y: Numeric.sin(x) + 0.1*x - y**2,
+            x,y, lambda x,y: numpy.sin(x) + 0.1*x - y**2,
             ufunc=1, binary=1,
             ))
 
diff -ur gnuplot-py-1.7/utils.py gnuplot-py-1.7-numpy/utils.py
--- gnuplot-py-1.7/utils.py	2003-10-17 17:38:44.000000000 +0300
+++ gnuplot-py-1.7-numpy/utils.py	2007-11-20 22:21:24.000000000 +0200
@@ -17,28 +17,32 @@
 __cvs_version__ = '$Revision: 1.1 $'
 
 import string
-import Numeric
+import numpy
 
 
 def float_array(m):
-    """Return the argument as a Numeric array of type at least 'Float32'.
+    """Return the argument as a numpy array of type at least 'Float32'.
 
     Leave 'Float64' unchanged, but upcast all other types to
     'Float32'.  Allow also for the possibility that the argument is a
-    python native type that can be converted to a Numeric array using
-    'Numeric.asarray()', but in that case don't worry about
+    python native type that can be converted to a numpy array using
+    'numpy.asarray()', but in that case don't worry about
     downcasting to single-precision float.
 
     """
 
     try:
         # Try Float32 (this will refuse to downcast)
-        return Numeric.asarray(m, Numeric.Float32)
+        return numpy.asarray(m, numpy.float32)
     except TypeError:
         # That failure might have been because the input array was
-        # of a wider data type than Float32; try to convert to the
+        # of a wider data type than float32; try to convert to the
         # largest floating-point type available:
-        return Numeric.asarray(m, Numeric.Float)
+        try:
+            return numpy.asarray(m, numpy.float_)
+        except TypeError:
+            print "Fatal: array dimensions not equal!"
+            return None
 
 
 def write_array(f, set,



1.1                  dev-python/gnuplot-py/files/digest-gnuplot-py-1.7-r2

file : http://sources.gentoo.org/viewcvs.py/gentoo-x86/dev-python/gnuplot-py/files/digest-gnuplot-py-1.7-r2?rev=1.1&view=markup
plain: http://sources.gentoo.org/viewcvs.py/gentoo-x86/dev-python/gnuplot-py/files/digest-gnuplot-py-1.7-r2?rev=1.1&content-type=text/plain

Index: digest-gnuplot-py-1.7-r2
===================================================================
MD5 724f9eee164d6ff763777b22a5851572 gnuplot-py-1.7.tar.gz 107278
RMD160 0d0f465f0dad0e3ff35f6bdea5fc6ea9ab1b245f gnuplot-py-1.7.tar.gz 107278
SHA256 78e8716324b654337801fd68212cc2184a81313421086df301718c19bb49e216 gnuplot-py-1.7.tar.gz 107278



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