source: releases/mystic-0.2a1-patch1.diff @ 246

mystic-0.2a1/mystic/tools.py

@@ -132 +132 @@
         self._y = []   
 
     def __call__(self, x, y):
+        from numpy import ndarray
+        if isinstance(x,ndarray): x = list(x)
         self._x.append(x)
         self._y.append(y)
      
@@ -153 +155 @@
     """
     import numpy
     def __init__(self, interval = 10, xinterval = numpy.inf):
-        Sow.__init__(self)
+       #Sow.__init__(self)
+        super(VerboseSow,self).__init__()
         self._step = 0
         self._yinterval = interval
         self._xinterval = xinterval
         return
     def __call__(self, x, y):
         from numpy import ndarray
-        Sow.__call__(self, x, y)
+       #Sow.__call__(self, x, y)
+        super(VerboseSow,self).__call__(x, y)
         if isinstance(y,(list,ndarray)):
             y = y[0] #XXX: get the "best" fit... which should be in y[0]
         if isinstance(x[0],(list,ndarray)): #XXX: x should always be iterable
@@ -182 +186 @@
     import numpy
     def __init__(self, interval=1, filename='log.txt', new=False):
         import datetime
-        Sow.__init__(self)
+       #Sow.__init__(self)
+        super(LoggingSow,self).__init__()
         self._filename = filename
         self._step = 0
         self._yinterval = interval
@@ -197 +202 @@
     def __call__(self, x, y):
         self._file = open(self._filename,'a')
         from numpy import ndarray
-        Sow.__call__(self, x, y)
+       #Sow.__call__(self, x, y)
+        super(LoggingSow,self).__call__(x, y)
         if isinstance(y,(list,ndarray)):
             y = y[0] #XXX: get the "best" fit... which should be in y[0]
         if isinstance(x[0],(list,ndarray)): #XXX: x should always be iterable

mystic-0.2a1/examples/test_mogi.py

@@ -114 +114 @@
     #
     desol, dstepmon = de_solve()
     print "desol: ", desol
-    print "dstepmon 10: ", dstepmon.x[50]
-    print "dstepmon 20: ", dstepmon.x[100]
+    print "dstepmon 50: ", dstepmon.x[50]
+    print "dstepmon 100: ", dstepmon.x[100]
     #
     # this will try to use nelder_mean from a relatively "near by" point (very sensitive)
     point = [1234., -500., 10., 0.001] # both cg and nm does fine