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
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