parameters: (self, stage_, field_)
return_statement: return out
docstring: Get numerical data from the dynamics module of the solver:
  
              :param stage: integer corresponding to shooting node
              :param field: string, e.g. 'A'
docstring_summary: Get numerical data from the dynamics module of the solver:
docstring_tokens: Get numerical data from the dynamics module of the solver :
function: def dynamics_get(self, stage_, field_):
          """
          Get numerical data from the dynamics module of the solver:
  
              :param stage: integer corresponding to shooting node
              :param field: string, e.g. 'A'
          """
  
          field = field_
          field = field.encode('utf-8')
          stage = c_int(stage_)
  
          # get dims
          self.shared_lib.ocp_nlp_dynamics_dims_get_from_attr.argtypes = \
              [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]
          self.shared_lib.ocp_nlp_dynamics_dims_get_from_attr.restype = c_int
  
          dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)
          dims_data = cast(dims.ctypes.data, POINTER(c_int))
  
          self.shared_lib.ocp_nlp_dynamics_dims_get_from_attr(self.nlp_config, \
              self.nlp_dims, self.nlp_out, stage_, field, dims_data)
  
          # create output data
          out = np.ascontiguousarray(np.zeros((np.prod(dims),)), dtype=np.float64)
          out = out.reshape(dims[0], dims[1], order='F')
  
          out_data = cast(out.ctypes.data, POINTER(c_double))
          out_data_p = cast((out_data), c_void_p)
  
          # call getter
          self.shared_lib.ocp_nlp_get_at_stage.argtypes = \
              [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
          self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \
              self.nlp_dims, self.nlp_solver, stage, field, out_data_p)
  
          return out
function_tokens: def dynamics_get ( self , stage_ , field_ ) : field = field_ field = field . encode ( 'utf-8' ) stage = c_int ( stage_ ) # get dims self . shared_lib . ocp_nlp_dynamics_dims_get_from_attr . argtypes = [ c_void_p , c_void_p , c_void_p , c_int , c_char_p , POINTER ( c_int ) ] self . shared_lib . ocp_nlp_dynamics_dims_get_from_attr . restype = c_int dims = np . ascontiguousarray ( np . zeros ( ( 2 , ) ) , dtype = np . intc ) dims_data = cast ( dims . ctypes . data , POINTER ( c_int ) ) self . shared_lib . ocp_nlp_dynamics_dims_get_from_attr ( self . nlp_config , self . nlp_dims , self . nlp_out , stage_ , field , dims_data ) # create output data out = np . ascontiguousarray ( np . zeros ( ( np . prod ( dims ) , ) ) , dtype = np . float64 ) out = out . reshape ( dims [ 0 ] , dims [ 1 ] , order = 'F' ) out_data = cast ( out . ctypes . data , POINTER ( c_double ) ) out_data_p = cast ( ( out_data ) , c_void_p ) # call getter self . shared_lib . ocp_nlp_get_at_stage . argtypes = [ c_void_p , c_void_p , c_void_p , c_int , c_char_p , c_void_p ] self . shared_lib . ocp_nlp_get_at_stage ( self . nlp_config , self . nlp_dims , self . nlp_solver , stage , field , out_data_p ) return out