From: W. Trevor King <wking@drexel.edu>
Date: Fri, 30 Apr 2010 12:10:47 +0000 (-0400)
Subject: Added pyfit script using Python's SciPy module for least-squares fitting
X-Git-Tag: v1.0~391
X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=fe7fe60ef30da6cd919ce183b47668552fb10583;p=thesis.git

Added pyfit script using Python's SciPy module for least-squares fitting
---

diff --git a/tex/src/figures/script/SConscript b/tex/src/figures/script/SConscript
new file mode 100644
index 0000000..41d7526
--- /dev/null
+++ b/tex/src/figures/script/SConscript
@@ -0,0 +1,8 @@
+# Get the passed in environment.
+Import('env')
+
+pyfit = env.File('pyfit')
+env.Alias('pyfit', pyfit)
+
+# Pass back the modified environment.
+Return('env')
diff --git a/tex/src/figures/script/pyfit b/tex/src/figures/script/pyfit
new file mode 100755
index 0000000..e8d6ece
--- /dev/null
+++ b/tex/src/figures/script/pyfit
@@ -0,0 +1,164 @@
+#!/usr/bin/python
+#
+# Copyright (C) 2010  W. Trevor King
+
+from scipy.optimize import leastsq
+from math import sqrt
+
+def _param_char(index):
+    """
+    >>> _param_char(0)
+    'A'
+    >>> _param_char(1)
+    'B'
+    >>> _param_char(25)
+    'Z'
+    """
+    if index < 0 or index > 25 or index != int(index):
+        raise ValueError(index)
+    return chr(ord('A')+index)
+
+def parse_modules(module_strings):
+    """
+    >>> m = parse_modules(['math:sin,cos', 'sys'])
+    >>> import math, sys
+    >>> if m == {'sin':math.sin, 'cos':math.cos, 'sys':sys}:
+    ...     print True
+    ... else:
+    ...     print m
+    True
+    """
+    ms = []
+    for string in module_strings:
+        fields = string.split(':')
+        assert len(fields) in [1, 2], '%d fields in %s: %s' % (len(fields), string, fields)
+        modname = fields[0]
+        objects = []
+        if len(fields) == 2:
+            objects = fields[1].split(',')
+        ms.append((modname, objects))
+
+    _locals = {}
+    for modname,objects in ms:
+        if len(objects) == 0:
+            _locals[modname] = __import__(modname)
+        else:
+            _temp = __import__(modname, globals(), locals(), objects)
+            for objname in objects:
+                _locals[objname] = getattr(_temp, objname)
+    return _locals
+
+def parse_function(function_string):
+    """
+    >>> f = parse_function('A*x+B')
+    >>> f(0, [1,2])
+    2.0
+    >>> f(-1, [1,2])
+    1.0
+    """
+    def f(x, params, _locals={}):
+        _locals['x'] = x
+        for i,p in enumerate(params):
+            _locals[_param_char(i)] = float(p)
+        return eval(function_string, globals(), _locals)
+    return f
+
+def read_data(datalines, xcol=0, ycol=1, wcol=-1):
+    """
+    >>> read_data('1\\t2\\t3\\n4\\t5\\t6\\n'.splitlines())
+    ([1.0, 4.0], [2.0, 5.0], [1, 1])
+    >>> read_data('1\\t2\\t3\\n4\\t5\\t6\\n'.splitlines(), wcol=2)
+    ([1.0, 4.0], [2.0, 5.0], [3.0, 6.0])
+    """
+    xs = []
+    ys = []
+    ws = []
+    for line in datalines:
+        if len(line) == 0 or line.startswith('#'):
+            continue # blank or comment line
+        row = [float(x) for x in line.split('\t')]
+        xs.append(row[xcol])
+        ys.append(row[ycol])
+        if wcol >= 0:
+            ws.append(row[wcol])
+    if wcol < 0:
+        ws = [1]*len(xs)
+    return (xs, ys, ws)
+
+def fit(data, fn, initial_ps, _locals={}, verbose=False):
+    xs,ys,ws = data
+    sqws = [sqrt(w) for w in ws]
+    def residuals(params):
+        return [sqw*(fn(x, params, _locals)-y)
+                for x,y,sqw in zip(xs,ys,sqws)]
+    p,cov,info,mesg,ier = leastsq(residuals, initial_ps,
+                                  full_output=True, maxfev=10000)
+    if verbose == True:
+        print "Fitted params:",p
+        print "Covariance mx:",cov
+        #print "Info:", info  # too much information :p                         
+        print "mesg:", mesg
+        if ier == 1 :
+            print "Solution converged"
+        else :
+            print "Solution did not converge"
+    return p
+
+
+def test():
+    import doctest
+    failures,tests = doctest.testmod()
+    return failures
+
+if __name__ == '__main__':
+    import optparse
+    import sys
+
+    p = optparse.OptionParser(usage='%prog [options] DATAFILE',
+                              epilog="""
+DATAFILE should consist of TAB delimited ASCII values, with each line
+specifying a point.  Lines beginning with a hash character (#) are
+ignored.
+""".strip())
+    p.add_option('-f', '--model-function', dest='function', default='A*x+B',
+                 metavar='STRING',
+                 help='Define the model function used for fitting.  Use capital letters as parameters for the function (%default)')
+    p.add_option('-p', '--initial-params', dest='params', default='1,1',
+                 metavar='FLOAT[,...]',
+                 help='Set initial model parameters for fitting (%default)')
+    p.add_option('-m', '--modules', dest='modules', default=[],
+                 action='append', metavar='MODULE[:OBJ[,...]]',
+                 help='Add a module necessary for your model function.')
+    p.add_option('-x', '--x-column', dest='xcol', default=0, type='int',
+                 help='Select the column used for x values (%default)')
+    p.add_option('-y', '--y-column', dest='ycol', default=1, type='int',
+                 help='Select the column used for y values (%default)')
+    p.add_option('-w', '--weight-column', dest='wcol', default=-1, type='int',
+                 help='Select the column used for weighting points.  Set to -1 to use uniform weighting (%default)')
+    p.add_option('-v', '--verbose', dest='verbose', default=False,
+                 action='store_true', help='Print technical fitting details.')
+    p.add_option('-t', '--test', dest='test', default=False,
+                 action='store_true', help='Run internal tests and exit')
+
+    options,args = p.parse_args()
+
+    if options.test == True:
+        n = test()
+        sys.exit(min(127, n))
+
+    assert len(args) == 1, len(args)
+    datafile = args[0]
+
+    mod_includes = parse_modules(options.modules)
+    fn = parse_function(options.function)
+    data = read_data(file(datafile, 'r'),
+                     options.xcol, options.ycol, options.wcol)
+    initial_ps = [float(x) for x in options.params.split(',')]
+
+    assert len(initial_ps) < 26, ('More parameters than letters! (%d)'
+                                  % len(params))
+    params = fit(data, fn, initial_ps, mod_includes, verbose=options.verbose)
+    assert len(params) == len(initial_ps)
+
+    for i,p in enumerate(params):
+        print '%s: %g' % (_param_char(i), p)
diff --git a/tex/src/figures/scripts/SConscript b/tex/src/figures/scripts/SConscript
new file mode 100644
index 0000000..41d7526
--- /dev/null
+++ b/tex/src/figures/scripts/SConscript
@@ -0,0 +1,8 @@
+# Get the passed in environment.
+Import('env')
+
+pyfit = env.File('pyfit')
+env.Alias('pyfit', pyfit)
+
+# Pass back the modified environment.
+Return('env')
diff --git a/tex/src/figures/scripts/pyfit b/tex/src/figures/scripts/pyfit
new file mode 100755
index 0000000..e8d6ece
--- /dev/null
+++ b/tex/src/figures/scripts/pyfit
@@ -0,0 +1,164 @@
+#!/usr/bin/python
+#
+# Copyright (C) 2010  W. Trevor King
+
+from scipy.optimize import leastsq
+from math import sqrt
+
+def _param_char(index):
+    """
+    >>> _param_char(0)
+    'A'
+    >>> _param_char(1)
+    'B'
+    >>> _param_char(25)
+    'Z'
+    """
+    if index < 0 or index > 25 or index != int(index):
+        raise ValueError(index)
+    return chr(ord('A')+index)
+
+def parse_modules(module_strings):
+    """
+    >>> m = parse_modules(['math:sin,cos', 'sys'])
+    >>> import math, sys
+    >>> if m == {'sin':math.sin, 'cos':math.cos, 'sys':sys}:
+    ...     print True
+    ... else:
+    ...     print m
+    True
+    """
+    ms = []
+    for string in module_strings:
+        fields = string.split(':')
+        assert len(fields) in [1, 2], '%d fields in %s: %s' % (len(fields), string, fields)
+        modname = fields[0]
+        objects = []
+        if len(fields) == 2:
+            objects = fields[1].split(',')
+        ms.append((modname, objects))
+
+    _locals = {}
+    for modname,objects in ms:
+        if len(objects) == 0:
+            _locals[modname] = __import__(modname)
+        else:
+            _temp = __import__(modname, globals(), locals(), objects)
+            for objname in objects:
+                _locals[objname] = getattr(_temp, objname)
+    return _locals
+
+def parse_function(function_string):
+    """
+    >>> f = parse_function('A*x+B')
+    >>> f(0, [1,2])
+    2.0
+    >>> f(-1, [1,2])
+    1.0
+    """
+    def f(x, params, _locals={}):
+        _locals['x'] = x
+        for i,p in enumerate(params):
+            _locals[_param_char(i)] = float(p)
+        return eval(function_string, globals(), _locals)
+    return f
+
+def read_data(datalines, xcol=0, ycol=1, wcol=-1):
+    """
+    >>> read_data('1\\t2\\t3\\n4\\t5\\t6\\n'.splitlines())
+    ([1.0, 4.0], [2.0, 5.0], [1, 1])
+    >>> read_data('1\\t2\\t3\\n4\\t5\\t6\\n'.splitlines(), wcol=2)
+    ([1.0, 4.0], [2.0, 5.0], [3.0, 6.0])
+    """
+    xs = []
+    ys = []
+    ws = []
+    for line in datalines:
+        if len(line) == 0 or line.startswith('#'):
+            continue # blank or comment line
+        row = [float(x) for x in line.split('\t')]
+        xs.append(row[xcol])
+        ys.append(row[ycol])
+        if wcol >= 0:
+            ws.append(row[wcol])
+    if wcol < 0:
+        ws = [1]*len(xs)
+    return (xs, ys, ws)
+
+def fit(data, fn, initial_ps, _locals={}, verbose=False):
+    xs,ys,ws = data
+    sqws = [sqrt(w) for w in ws]
+    def residuals(params):
+        return [sqw*(fn(x, params, _locals)-y)
+                for x,y,sqw in zip(xs,ys,sqws)]
+    p,cov,info,mesg,ier = leastsq(residuals, initial_ps,
+                                  full_output=True, maxfev=10000)
+    if verbose == True:
+        print "Fitted params:",p
+        print "Covariance mx:",cov
+        #print "Info:", info  # too much information :p                         
+        print "mesg:", mesg
+        if ier == 1 :
+            print "Solution converged"
+        else :
+            print "Solution did not converge"
+    return p
+
+
+def test():
+    import doctest
+    failures,tests = doctest.testmod()
+    return failures
+
+if __name__ == '__main__':
+    import optparse
+    import sys
+
+    p = optparse.OptionParser(usage='%prog [options] DATAFILE',
+                              epilog="""
+DATAFILE should consist of TAB delimited ASCII values, with each line
+specifying a point.  Lines beginning with a hash character (#) are
+ignored.
+""".strip())
+    p.add_option('-f', '--model-function', dest='function', default='A*x+B',
+                 metavar='STRING',
+                 help='Define the model function used for fitting.  Use capital letters as parameters for the function (%default)')
+    p.add_option('-p', '--initial-params', dest='params', default='1,1',
+                 metavar='FLOAT[,...]',
+                 help='Set initial model parameters for fitting (%default)')
+    p.add_option('-m', '--modules', dest='modules', default=[],
+                 action='append', metavar='MODULE[:OBJ[,...]]',
+                 help='Add a module necessary for your model function.')
+    p.add_option('-x', '--x-column', dest='xcol', default=0, type='int',
+                 help='Select the column used for x values (%default)')
+    p.add_option('-y', '--y-column', dest='ycol', default=1, type='int',
+                 help='Select the column used for y values (%default)')
+    p.add_option('-w', '--weight-column', dest='wcol', default=-1, type='int',
+                 help='Select the column used for weighting points.  Set to -1 to use uniform weighting (%default)')
+    p.add_option('-v', '--verbose', dest='verbose', default=False,
+                 action='store_true', help='Print technical fitting details.')
+    p.add_option('-t', '--test', dest='test', default=False,
+                 action='store_true', help='Run internal tests and exit')
+
+    options,args = p.parse_args()
+
+    if options.test == True:
+        n = test()
+        sys.exit(min(127, n))
+
+    assert len(args) == 1, len(args)
+    datafile = args[0]
+
+    mod_includes = parse_modules(options.modules)
+    fn = parse_function(options.function)
+    data = read_data(file(datafile, 'r'),
+                     options.xcol, options.ycol, options.wcol)
+    initial_ps = [float(x) for x in options.params.split(',')]
+
+    assert len(initial_ps) < 26, ('More parameters than letters! (%d)'
+                                  % len(params))
+    params = fit(data, fn, initial_ps, mod_includes, verbose=options.verbose)
+    assert len(params) == len(initial_ps)
+
+    for i,p in enumerate(params):
+        print '%s: %g' % (_param_char(i), p)