def do_pack(D):
for v in D.itervalues():
# type checking
if isinstance(v, OrderedDict):
do_pack(v) # recursion!
continue
elif not isinstance(v, valid_types):
continue
elif np.rank(v) > 2:
continue
# make column vectors
v = atleast_2d_col(v)
# handle output type
if vector:
# unravel into 1d vector
v = v.ravel(order='F')
else:
# check array size
size[0] = size[0] or v.shape[
0] # updates size once on first array
if v.shape[0] != size[0]:
#warn ('array size mismatch, skipping. all values in data must have same number of rows for array packing',RuntimeWarning)
continue
# dump to list
M.append(v)
def function(self,x):
x = self.variables.scaled.unpack_array(x)
func = self.evaluator.function
tag = self.tag
scl = self.scale
result = func(x)[tag]
result = atleast_2d_col(result)
result = result / scl
return result
def function(self, x):
x = self.variables.scaled.unpack_array(x)
func = self.evaluator.function
tag = self.tag
scl = self.scale
result = func(x)[tag]
result = atleast_2d_col(result)
result = result / scl
return result
def function(self, x):
x = self.variables.scaled.unpack_array(x)
func = self.evaluator.function
tag = self.tag
scl = self.scale
if x.keys() == ['vector']:
x = x['vector']
result = func(x)
if isinstance(result, dict):
result = result[tag]
result = atleast_2d_col(result)
result = result / scl
return result
def function(self,x):
x = self.variables.scaled.unpack_array(x)
func = self.evaluator.function
tag = self.tag
scl = self.scale
if x.keys() == ['vector']:
x = x['vector']
result = func(x)
if isinstance(result,dict):
result = result[tag]
result = atleast_2d_col(result)
result = result / scl
return result
def do_pack(D):
for v in D.itervalues():
# type checking
if isinstance( v, OrderedDict ):
do_pack(v) # recursion!
continue
elif not isinstance( v, valid_types ): continue
elif np.ndim(v) > 2: continue
# make column vectors
v = atleast_2d_col(v)
# handle output type
if vector:
# unravel into 1d vector
v = v.ravel(order='F')
else:
# check array size
size[0] = size[0] or v.shape[0] # updates size once on first array
if v.shape[0] != size[0]:
#warn ('array size mismatch, skipping. all values in data must have same number of rows for array packing',RuntimeWarning)
continue
# dump to list
M.append(v)
