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)