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hawking 07/11/20 21:15:32 |
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|
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Added: gnuplot-py-1.7-numpy.patch digest-gnuplot-py-1.7-r2 |
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Log: |
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revbump. backported upstream's changes for numpy. numeric dependency changed to numpy. |
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(Portage version: 2.1.3.19) |
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|
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Revision Changes Path |
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1.1 dev-python/gnuplot-py/files/gnuplot-py-1.7-numpy.patch |
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|
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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 |
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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 |
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|
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Index: gnuplot-py-1.7-numpy.patch |
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=================================================================== |
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diff -ur gnuplot-py-1.7/ANNOUNCE.txt gnuplot-py-1.7-numpy/ANNOUNCE.txt |
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--- gnuplot-py-1.7/ANNOUNCE.txt 2003-10-17 18:03:10.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/ANNOUNCE.txt 2007-11-20 22:17:29.000000000 +0200 |
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@@ -9,7 +9,7 @@ |
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|
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Prerequisites (see footnotes): |
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the Python interpreter [1] |
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- the Python Numeric module [3] |
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+ the Python numpy module [3] |
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the gnuplot program [2] |
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|
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or, to use it under Java (experimental): |
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@@ -20,7 +20,7 @@ |
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|
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Some ways this package can be used: |
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|
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-1. Interactive data processing: Use Python's excellent Numeric package |
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+1. Interactive data processing: Use Python's excellent numpy package |
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to create and manipulate arrays of numbers, and use Gnuplot.py to |
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visualize the results. |
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2. Web graphics: write CGI scripts in Python that use gnuplot to |
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diff -ur gnuplot-py-1.7/demo.py gnuplot-py-1.7-numpy/demo.py |
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--- gnuplot-py-1.7/demo.py 2003-10-17 17:28:10.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/demo.py 2007-11-20 22:36:59.000000000 +0200 |
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@@ -16,7 +16,7 @@ |
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__cvs_version__ = '$Revision: 1.1 $' |
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|
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|
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-from Numeric import * |
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+from numpy import * |
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|
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# If the package has been installed correctly, this should work: |
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import Gnuplot, Gnuplot.funcutils |
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@@ -31,7 +31,7 @@ |
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g = Gnuplot.Gnuplot(debug=1) |
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g.title('A simple example') # (optional) |
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g('set data style linespoints') # give gnuplot an arbitrary command |
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- # Plot a list of (x, y) pairs (tuples or a Numeric array would |
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+ # Plot a list of (x, y) pairs (tuples or a numpy array would |
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# also be OK): |
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g.plot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]]) |
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raw_input('Please press return to continue...\n') |
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@@ -39,7 +39,7 @@ |
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g.reset() |
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# Plot one dataset from an array and one via a gnuplot function; |
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# also demonstrate the use of item-specific options: |
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- x = arange(10, typecode=Float) |
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+ x = arange(10, dtype='float_') |
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y1 = x**2 |
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# Notice how this plotitem is created here but used later? This |
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# is convenient if the same dataset has to be plotted multiple |
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@@ -74,8 +74,8 @@ |
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# Make a 2-d array containing a function of x and y. First create |
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# xm and ym which contain the x and y values in a matrix form that |
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# can be `broadcast' into a matrix of the appropriate shape: |
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- xm = x[:,NewAxis] |
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- ym = y[NewAxis,:] |
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+ xm = x[:,newaxis] |
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+ ym = y[newaxis,:] |
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m = (sin(xm) + 0.1*xm) - ym**2 |
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g('set parametric') |
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g('set data style lines') |
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diff -ur gnuplot-py-1.7/FAQ.txt gnuplot-py-1.7-numpy/FAQ.txt |
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--- gnuplot-py-1.7/FAQ.txt 2003-10-17 17:28:10.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/FAQ.txt 2007-11-20 22:17:50.000000000 +0200 |
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@@ -17,7 +17,7 @@ |
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#! /usr/bin/python2 |
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|
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import Gnuplot, Gnuplot.funcutils |
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-from Numeric import * |
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+from numpy import * |
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|
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g = Gnuplot.Gnuplot() |
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g.plot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]]) |
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diff -ur gnuplot-py-1.7/funcutils.py gnuplot-py-1.7-numpy/funcutils.py |
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--- gnuplot-py-1.7/funcutils.py 2003-10-17 17:28:10.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/funcutils.py 2007-11-20 22:25:24.000000000 +0200 |
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@@ -16,19 +16,19 @@ |
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|
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__cvs_version__ = '$Revision: 1.1 $' |
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|
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-import Numeric |
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+import numpy |
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|
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import Gnuplot, utils |
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|
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|
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-def tabulate_function(f, xvals, yvals=None, typecode=None, ufunc=0): |
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+def tabulate_function(f, xvals, yvals=None, dtype=None, ufunc=0): |
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"""Evaluate and tabulate a function on a 1- or 2-D grid of points. |
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|
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f should be a function taking one or two floating-point |
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parameters. |
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|
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If f takes one parameter, then xvals should be a 1-D array and |
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- yvals should be None. The return value is a Numeric array |
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+ yvals should be None. The return value is a numpy array |
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'[f(x[0]), f(x[1]), ..., f(x[-1])]'. |
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|
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If f takes two parameters, then 'xvals' and 'yvals' should each be |
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@@ -39,7 +39,7 @@ |
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|
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If 'ufunc=0', then 'f' is evaluated at each point using a Python |
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loop. This can be slow if the number of points is large. If |
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- speed is an issue, you should write 'f' in terms of Numeric ufuncs |
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+ speed is an issue, you should write 'f' in terms of numpy ufuncs |
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and use the 'ufunc=1' feature described next. |
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|
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If called with 'ufunc=1', then 'f' should be a function that is |
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@@ -51,34 +51,33 @@ |
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|
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if yvals is None: |
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# f is a function of only one variable: |
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- xvals = Numeric.asarray(xvals, typecode) |
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+ xvals = numpy.asarray(xvals, dtype) |
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|
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if ufunc: |
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return f(xvals) |
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else: |
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- if typecode is None: |
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- typecode = xvals.typecode() |
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+ if dtype is None: |
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+ dtype = xvals.dtype.char |
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|
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- m = Numeric.zeros((len(xvals),), typecode) |
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+ m = numpy.zeros((len(xvals),), dtype) |
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for xi in range(len(xvals)): |
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x = xvals[xi] |
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m[xi] = f(x) |
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return m |
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else: |
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# f is a function of two variables: |
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- xvals = Numeric.asarray(xvals, typecode) |
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- yvals = Numeric.asarray(yvals, typecode) |
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+ xvals = numpy.asarray(xvals, dtype) |
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+ yvals = numpy.asarray(yvals, dtype) |
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|
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if ufunc: |
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- return f(xvals[:,Numeric.NewAxis], yvals[Numeric.NewAxis,:]) |
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+ return f(xvals[:,numpy.newaxis], yvals[numpy.newaxis,:]) |
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else: |
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- if typecode is None: |
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+ if dtype is None: |
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# choose a result typecode based on what '+' would return |
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# (yecch!): |
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- typecode = (Numeric.zeros((1,), xvals.typecode()) + |
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- Numeric.zeros((1,), yvals.typecode())).typecode() |
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- |
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- m = Numeric.zeros((len(xvals), len(yvals)), typecode) |
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+ dtype = (numpy.zeros((1,), xvals.dtype.char) + |
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+ numpy.zeros((1,), yvals.dtype.char)).dtype.char |
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+ m = numpy.zeros((len(xvals), len(yvals)), dtype) |
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for xi in range(len(xvals)): |
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x = xvals[xi] |
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for yi in range(len(yvals)): |
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diff -ur gnuplot-py-1.7/_Gnuplot.py gnuplot-py-1.7-numpy/_Gnuplot.py |
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--- gnuplot-py-1.7/_Gnuplot.py 2003-10-17 17:28:10.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/_Gnuplot.py 2007-11-20 22:37:26.000000000 +0200 |
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@@ -228,8 +228,8 @@ |
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|
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'items' is a sequence of items, each of which should be a |
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'PlotItem' of some kind, a string (interpreted as a function |
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- string for gnuplot to evaluate), or a Numeric array (or |
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- something that can be converted to a Numeric array). |
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+ string for gnuplot to evaluate), or a numpy array (or |
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+ something that can be converted to a numpy array). |
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|
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""" |
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|
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diff -ur gnuplot-py-1.7/__init__.py gnuplot-py-1.7-numpy/__init__.py |
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--- gnuplot-py-1.7/__init__.py 2003-10-17 18:04:29.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/__init__.py 2007-11-20 22:19:00.000000000 +0200 |
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@@ -128,9 +128,9 @@ |
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|
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Restrictions: |
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|
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- - Relies on the Numeric Python extension. This can be obtained from |
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- "SourceForge", http://sourceforge.net/projects/numpy/. If you're |
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- interested in gnuplot, you would probably also want NumPy anyway. |
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+ - Relies on the numpy Python extension. This can be obtained from |
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+ the Scipy group at <http://www.scipy.org/Download>.. If you're |
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+ interested in gnuplot, you would probably also want numpy anyway. |
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|
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- Only a small fraction of gnuplot functionality is implemented as |
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explicit method functions. However, you can give arbitrary |
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diff -ur gnuplot-py-1.7/NEWS.txt gnuplot-py-1.7-numpy/NEWS.txt |
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--- gnuplot-py-1.7/NEWS.txt 2003-10-17 18:04:29.000000000 +0300 |
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+++ gnuplot-py-1.7-numpy/NEWS.txt 2007-11-20 22:22:08.000000000 +0200 |
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@@ -57,7 +57,7 @@ |
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equivalent.) If I find the time I might try to produce a version |
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that doesn't require Numeric at all, under either Python or Jython. |
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|
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-* Removed the oldplot.py module: (1) I doubt anybody is still using |
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+ Removed the oldplot.py module: (1) I doubt anybody is still using |
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it. (2) It seems to be broken anyway. (3) I don't have the energy to |
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fix or maintain it. Let me know if I'm wrong about point 1. |
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|
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@@ -222,10 +222,10 @@ |
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dataset; e.g., what used to be written as |
215 |
|
216 |
g = Gnuplot.Gnuplot() |
217 |
- x = Numeric.arange(100)/10.0 |
218 |
+ x = numpy.arange(100)/10.0 |
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y = x**2 |
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# Create an array of (x,y) pairs: |
221 |
- g.plot(Gnuplot.Data(Numeric.transpose((x, y)))) |
222 |
+ g.plot(Gnuplot.Data(numpy.transpose((x, y)))) |
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|
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can now be shortened to |
225 |
|
226 |
diff -ur gnuplot-py-1.7/PlotItems.py gnuplot-py-1.7-numpy/PlotItems.py |
227 |
--- gnuplot-py-1.7/PlotItems.py 2003-10-17 17:39:03.000000000 +0300 |
228 |
+++ gnuplot-py-1.7-numpy/PlotItems.py 2007-11-20 22:34:49.000000000 +0200 |
229 |
@@ -23,7 +23,7 @@ |
230 |
except ImportError: |
231 |
from StringIO import StringIO |
232 |
|
233 |
-import Numeric |
234 |
+import numpy |
235 |
|
236 |
import gp, utils, Errors |
237 |
|
238 |
@@ -471,12 +471,12 @@ |
239 |
return apply(_FileItem, (filename,), keyw) |
240 |
|
241 |
|
242 |
-def Data(*set, **keyw): |
243 |
- """Create and return a _FileItem representing the data from *set. |
244 |
+def Data(*data, **keyw): |
245 |
+ """Create and return a _FileItem representing the data from *data. |
246 |
|
247 |
Create a '_FileItem' object (which is a type of 'PlotItem') out of |
248 |
- one or more Float Python Numeric arrays (or objects that can be |
249 |
- converted to a Float Numeric array). If the routine is passed a |
250 |
+ one or more Float Python numpy arrays (or objects that can be |
251 |
+ converted to a float numpy array). If the routine is passed a |
252 |
single with multiple dimensions, then the last index ranges over |
253 |
the values comprising a single data point (e.g., [<x>, <y>, |
254 |
<sigma>]) and the rest of the indices select the data point. If |
255 |
@@ -508,29 +508,29 @@ |
256 |
|
257 |
""" |
258 |
|
259 |
- if len(set) == 1: |
260 |
- # set was passed as a single structure |
261 |
- set = utils.float_array(set[0]) |
262 |
+ if len(data) == 1: |
263 |
+ # data was passed as a single structure |
264 |
+ data = utils.float_array(data[0]) |
265 |
|
266 |
# As a special case, if passed a single 1-D array, then it is |
267 |
# treated as one value per point (by default, plotted against |
268 |
# its index): |
269 |
- if len(set.shape) == 1: |
270 |
- set = set[:,Numeric.NewAxis] |
271 |
+ if len(data.shape) == 1: |
272 |
+ data = data[:,numpy.newaxis] |
273 |
else: |
274 |
- # set was passed column by column (for example, |
275 |
+ # data was passed column by column (for example, |
276 |
# Data(x,y)); pack it into one big array (this will test |
277 |
# that sizes are all the same): |
278 |
- set = utils.float_array(set) |
279 |
- dims = len(set.shape) |
280 |
+ data = utils.float_array(data) |
281 |
+ dims = len(data.shape) |
282 |
# transpose so that the last index selects x vs. y: |
283 |
- set = Numeric.transpose(set, (dims-1,) + tuple(range(dims-1))) |
284 |
+ data = numpy.transpose(data, (dims-1,) + tuple(range(dims-1))) |
285 |
if keyw.has_key('cols'): |
286 |
cols = keyw['cols'] |
287 |
del keyw['cols'] |
288 |
- if type(cols) is types.IntType: |
289 |
+ if isinstance(cols, types.IntType): |
290 |
cols = (cols,) |
291 |
- set = Numeric.take(set, cols, -1) |
292 |
+ data = numpy.take(data, cols, -1) |
293 |
|
294 |
if keyw.has_key('inline'): |
295 |
inline = keyw['inline'] |
296 |
@@ -540,7 +540,7 @@ |
297 |
|
298 |
# Output the content into a string: |
299 |
f = StringIO() |
300 |
- utils.write_array(f, set) |
301 |
+ utils.write_array(f, data) |
302 |
content = f.getvalue() |
303 |
if inline: |
304 |
return apply(_InlineFileItem, (content,), keyw) |
305 |
@@ -610,7 +610,7 @@ |
306 |
raise Errors.DataError('data array must be two-dimensional') |
307 |
|
308 |
if xvals is None: |
309 |
- xvals = Numeric.arange(numx) |
310 |
+ xvals = numpy.arange(numx) |
311 |
else: |
312 |
xvals = utils.float_array(xvals) |
313 |
if xvals.shape != (numx,): |
314 |
@@ -619,7 +619,7 @@ |
315 |
'the first dimension of the data array') |
316 |
|
317 |
if yvals is None: |
318 |
- yvals = Numeric.arange(numy) |
319 |
+ yvals = numpy.arange(numy) |
320 |
else: |
321 |
yvals = utils.float_array(yvals) |
322 |
if yvals.shape != (numy,): |
323 |
@@ -647,17 +647,17 @@ |
324 |
# documentation has the roles of x and y exchanged. We ignore |
325 |
# the documentation and go with the code. |
326 |
|
327 |
- mout = Numeric.zeros((numy + 1, numx + 1), Numeric.Float32) |
328 |
+ mout = numpy.zeros((numy + 1, numx + 1), numpy.float32) |
329 |
mout[0,0] = numx |
330 |
- mout[0,1:] = xvals.astype(Numeric.Float32) |
331 |
- mout[1:,0] = yvals.astype(Numeric.Float32) |
332 |
+ mout[0,1:] = xvals.astype(numpy.float32) |
333 |
+ mout[1:,0] = yvals.astype(numpy.float32) |
334 |
try: |
335 |
# try copying without the additional copy implied by astype(): |
336 |
- mout[1:,1:] = Numeric.transpose(data) |
337 |
+ mout[1:,1:] = numpy.transpose(data) |
338 |
except: |
339 |
# if that didn't work then downcasting from double |
340 |
# must be necessary: |
341 |
- mout[1:,1:] = Numeric.transpose(data.astype(Numeric.Float32)) |
342 |
+ mout[1:,1:] = numpy.transpose(data.astype(numpy.float32)) |
343 |
|
344 |
content = mout.tostring() |
345 |
if gp.GnuplotOpts.prefer_fifo_data: |
346 |
@@ -668,10 +668,10 @@ |
347 |
# output data to file as "x y f(x)" triplets. This |
348 |
# requires numy copies of each x value and numx copies of |
349 |
# each y value. First reformat the data: |
350 |
- set = Numeric.transpose( |
351 |
- Numeric.array( |
352 |
- (Numeric.transpose(Numeric.resize(xvals, (numy, numx))), |
353 |
- Numeric.resize(yvals, (numx, numy)), |
354 |
+ set = numpy.transpose( |
355 |
+ numpy.array( |
356 |
+ (numpy.transpose(numpy.resize(xvals, (numy, numx))), |
357 |
+ numpy.resize(yvals, (numx, numy)), |
358 |
data)), (1,2,0)) |
359 |
|
360 |
# Now output the data with the usual routine. This will |
361 |
diff -ur gnuplot-py-1.7/README.txt gnuplot-py-1.7-numpy/README.txt |
362 |
--- gnuplot-py-1.7/README.txt 2003-10-19 17:52:35.000000000 +0300 |
363 |
+++ gnuplot-py-1.7-numpy/README.txt 2007-11-20 22:35:30.000000000 +0200 |
364 |
@@ -65,8 +65,8 @@ |
365 |
|
366 |
Obviously, you must have the gnuplot program if Gnuplot.py is to be of |
367 |
any use to you. Gnuplot can be obtained via |
368 |
-<http://www.gnuplot.info>. You also need Python's Numerical |
369 |
-extension, which is available from <http://numpy.sourceforge.net>. |
370 |
+<http://www.gnuplot.info>. You also need a copy of the numpy package, which |
371 |
+is available from the Scipy group at <http://www.scipy.org/Download>. |
372 |
|
373 |
Gnuplot.py uses Python distutils |
374 |
<http://www.python.org/doc/current/inst/inst.html> and can be |
375 |
diff -ur gnuplot-py-1.7/setup.py gnuplot-py-1.7-numpy/setup.py |
376 |
--- gnuplot-py-1.7/setup.py 2003-10-17 17:52:28.000000000 +0300 |
377 |
+++ gnuplot-py-1.7-numpy/setup.py 2007-11-20 22:19:20.000000000 +0200 |
378 |
@@ -31,7 +31,7 @@ |
379 |
author_email='mhagger@××××××××.edu', |
380 |
url='http://gnuplot-py.sourceforge.net', |
381 |
license='LGPL', |
382 |
- licence='LGPL', # Spelling error in distutils |
383 |
+ #licence='LGPL', # Spelling error in distutils |
384 |
|
385 |
# Description of the package in the distribution |
386 |
package_dir={'Gnuplot' : '.'}, |
387 |
diff -ur gnuplot-py-1.7/test.py gnuplot-py-1.7-numpy/test.py |
388 |
--- gnuplot-py-1.7/test.py 2003-10-17 17:28:10.000000000 +0300 |
389 |
+++ gnuplot-py-1.7-numpy/test.py 2007-11-20 22:43:26.000000000 +0200 |
390 |
@@ -17,8 +17,7 @@ |
391 |
__cvs_version__ = '$Revision: 1.1 $' |
392 |
|
393 |
import os, time, math, tempfile |
394 |
-import Numeric |
395 |
-from Numeric import NewAxis |
396 |
+import numpy |
397 |
|
398 |
try: |
399 |
import Gnuplot, Gnuplot.PlotItems, Gnuplot.funcutils |
400 |
@@ -55,7 +54,7 @@ |
401 |
filename1 = tempfile.mktemp() |
402 |
f = open(filename1, 'w') |
403 |
try: |
404 |
- for x in Numeric.arange(100)/5. - 10.: |
405 |
+ for x in numpy.arange(100.)/5. - 10.: |
406 |
f.write('%s %s %s\n' % (x, math.cos(x), math.sin(x))) |
407 |
f.close() |
408 |
|
409 |
@@ -137,10 +136,10 @@ |
410 |
g.plot(f) |
411 |
|
412 |
print '############### test Data ###################################' |
413 |
- x = Numeric.arange(100)/5. - 10. |
414 |
- y1 = Numeric.cos(x) |
415 |
- y2 = Numeric.sin(x) |
416 |
- d = Numeric.transpose((x,y1,y2)) |
417 |
+ x = numpy.arange(100)/5. - 10. |
418 |
+ y1 = numpy.cos(x) |
419 |
+ y2 = numpy.sin(x) |
420 |
+ d = numpy.transpose((x,y1,y2)) |
421 |
|
422 |
wait('Plot Data against its index') |
423 |
g.plot(Gnuplot.Data(y2, inline=0)) |
424 |
@@ -173,7 +172,7 @@ |
425 |
g.plot(Gnuplot.Data(d, title='Cosine of x')) |
426 |
|
427 |
print '############### test compute_Data ###########################' |
428 |
- x = Numeric.arange(100)/5. - 10. |
429 |
+ x = numpy.arange(100)/5. - 10. |
430 |
|
431 |
wait('Plot Data, computed by Gnuplot.py') |
432 |
g.plot(Gnuplot.funcutils.compute_Data(x, lambda x: math.cos(x), inline=0)) |
433 |
@@ -235,14 +234,14 @@ |
434 |
|
435 |
print '############### test GridData and compute_GridData ##########' |
436 |
# set up x and y values at which the function will be tabulated: |
437 |
- x = Numeric.arange(35)/2.0 |
438 |
- y = Numeric.arange(30)/10.0 - 1.5 |
439 |
+ x = numpy.arange(35)/2.0 |
440 |
+ y = numpy.arange(30)/10.0 - 1.5 |
441 |
# Make a 2-d array containing a function of x and y. First create |
442 |
# xm and ym which contain the x and y values in a matrix form that |
443 |
# can be `broadcast' into a matrix of the appropriate shape: |
444 |
- xm = x[:,NewAxis] |
445 |
- ym = y[NewAxis,:] |
446 |
- m = (Numeric.sin(xm) + 0.1*xm) - ym**2 |
447 |
+ xm = x[:,numpy.newaxis] |
448 |
+ ym = y[numpy.newaxis,:] |
449 |
+ m = (numpy.sin(xm) + 0.1*xm) - ym**2 |
450 |
wait('a function of two variables from a GridData file') |
451 |
g('set parametric') |
452 |
g('set data style lines') |
453 |
@@ -264,7 +263,7 @@ |
454 |
|
455 |
wait('Use compute_GridData in ufunc and binary mode') |
456 |
g.splot(Gnuplot.funcutils.compute_GridData( |
457 |
- x,y, lambda x,y: Numeric.sin(x) + 0.1*x - y**2, |
458 |
+ x,y, lambda x,y: numpy.sin(x) + 0.1*x - y**2, |
459 |
ufunc=1, binary=1, |
460 |
)) |
461 |
|
462 |
diff -ur gnuplot-py-1.7/utils.py gnuplot-py-1.7-numpy/utils.py |
463 |
--- gnuplot-py-1.7/utils.py 2003-10-17 17:38:44.000000000 +0300 |
464 |
+++ gnuplot-py-1.7-numpy/utils.py 2007-11-20 22:21:24.000000000 +0200 |
465 |
@@ -17,28 +17,32 @@ |
466 |
__cvs_version__ = '$Revision: 1.1 $' |
467 |
|
468 |
import string |
469 |
-import Numeric |
470 |
+import numpy |
471 |
|
472 |
|
473 |
def float_array(m): |
474 |
- """Return the argument as a Numeric array of type at least 'Float32'. |
475 |
+ """Return the argument as a numpy array of type at least 'Float32'. |
476 |
|
477 |
Leave 'Float64' unchanged, but upcast all other types to |
478 |
'Float32'. Allow also for the possibility that the argument is a |
479 |
- python native type that can be converted to a Numeric array using |
480 |
- 'Numeric.asarray()', but in that case don't worry about |
481 |
+ python native type that can be converted to a numpy array using |
482 |
+ 'numpy.asarray()', but in that case don't worry about |
483 |
downcasting to single-precision float. |
484 |
|
485 |
""" |
486 |
|
487 |
try: |
488 |
# Try Float32 (this will refuse to downcast) |
489 |
- return Numeric.asarray(m, Numeric.Float32) |
490 |
+ return numpy.asarray(m, numpy.float32) |
491 |
except TypeError: |
492 |
# That failure might have been because the input array was |
493 |
- # of a wider data type than Float32; try to convert to the |
494 |
+ # of a wider data type than float32; try to convert to the |
495 |
# largest floating-point type available: |
496 |
- return Numeric.asarray(m, Numeric.Float) |
497 |
+ try: |
498 |
+ return numpy.asarray(m, numpy.float_) |
499 |
+ except TypeError: |
500 |
+ print "Fatal: array dimensions not equal!" |
501 |
+ return None |
502 |
|
503 |
|
504 |
def write_array(f, set, |
505 |
|
506 |
|
507 |
|
508 |
1.1 dev-python/gnuplot-py/files/digest-gnuplot-py-1.7-r2 |
509 |
|
510 |
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 |
511 |
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 |
512 |
|
513 |
Index: digest-gnuplot-py-1.7-r2 |
514 |
=================================================================== |
515 |
MD5 724f9eee164d6ff763777b22a5851572 gnuplot-py-1.7.tar.gz 107278 |
516 |
RMD160 0d0f465f0dad0e3ff35f6bdea5fc6ea9ab1b245f gnuplot-py-1.7.tar.gz 107278 |
517 |
SHA256 78e8716324b654337801fd68212cc2184a81313421086df301718c19bb49e216 gnuplot-py-1.7.tar.gz 107278 |
518 |
|
519 |
|
520 |
|
521 |
-- |
522 |
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