コード例 #1
0
ファイル: core.py プロジェクト: Charley-fan/metaArray-1
 def gen_i2x(self):
     """
     Regenerate the ijk to xyz space convertion functions
     """
     shape = self.shape = self.data.shape
     x0 = self.info['range']['begin']
     x1 = self.info['range']['end']
     lg = self.info['range']['log']
     # i0 = 0
     # i1 = len(self.data[i])
     if self.debug:
         print "*** starting xyz:", x0, "ending xyz:", x1, "shape:", shape, "ndim:", self.ndim
     lst = []
     for i in range(self.ndim):
         if lg[i] is None or lg[i] == False:
             # Log scale is not applied, everything is linear
             if shape[i] == 1:
                 # Start stop at the same point
                 lst.append(lambda x: x0[i])
             else:
                 lst.append(linearFunc(0, x0[i], shape[i], x1[i]))
         elif isinstance(lg[i], (int, float)):
             # Log scale applied, given log base.
             lst.append(expFunc(x0[i], 0, x1[i], shape[i], base=lg[i]))
         elif lg[i] == True:
             # Log scale applied, use default base
             lst.append(expFunc(x0[i], 0, x1[i], shape[i]))
         else:
             raise ValueError, "Log scale descriptor can only be int,\
                 float, True, False or None, given: " + str(lg[i])
     return lst
コード例 #2
0
ファイル: core.py プロジェクト: Charley-fan/metaArray-1
 def gen_x2i(self):
     """
     Regenerate the xyz to ijk space convertion functions
     """
     shape = self.shape = self.data.shape
     x0 = self.info['range']['begin']
     x1 = self.info['range']['end']
     lg = self.info['range']['log']
     # i0 = 0
     # i1 = len(self.data[i])
     lst = []
     for i in range(self.ndim):
         if lg[i] is None or lg[i] == False:
             # Log scale is not applied, everything is linear
             if x0[i] == x1[i]:
                 # Start stop at the same point
                 lst.append(lambda x: 0)
             else:
                 lst.append(linearFunc(x0[i], 0, x1[i], shape[i]))
         elif isinstance(lg[i], int) or isinstance(lg[i], float):
             # Log scale applied, given log base.
             lst.append(logFunc(x0[i], 0, x1[i], shape[i], base=lg[i]))
         elif lg[i] == True:
             # Log scale applied, use default base
             lst.append(logFunc(x0[i], 0, x1[i], shape[i]))
         else:
             raise ValueError, "Log scale descriptor can only be int,\
                 float, True, False or None, given: " + str(lg[i])
     return lst