Source code for pyleecan.Classes.PostFunction

# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Post/PostFunction.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at

from os import linesep
from sys import getsizeof
from logging import getLogger
from ._check import check_var, raise_
from ..Functions.get_logger import get_logger
from import save
from ..Functions.load import load_init_dict
from ..Functions.Load.import_class import import_class
from copy import deepcopy
from .Post import Post

from ntpath import basename
from os.path import isfile
from ._check import CheckTypeError
import numpy as np
import random
from numpy import isnan
from ._check import InitUnKnowClassError

[docs]class PostFunction(Post): """Post-processing from a user-defined function""" VERSION = 1 # generic save method is available in all object save = save # get_logger method is available in all object get_logger = get_logger def __init__(self, run=None, init_dict=None, init_str=None): """Constructor of the class. Can be use in three ways : - __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values for pyleecan type, -1 will call the default constructor - __init__ (init_dict = d) d must be a dictionary with property names as keys - __init__ (init_str = s) s must be a string s is the file path to load ndarray or list can be given for Vector and Matrix object or dict can be given for pyleecan Object""" if init_str is not None: # Load from a file init_dict = load_init_dict(init_str)[1] if init_dict is not None: # Initialisation by dict assert type(init_dict) is dict # Overwrite default value with init_dict content if "run" in list(init_dict.keys()): run = init_dict["run"] # Set the properties (value check and convertion are done in setter) = run # Call Post init super(PostFunction, self).__init__() # The class is frozen (in Post init), for now it's impossible to # add new properties def __str__(self): """Convert this object in a readeable string (for print)""" PostFunction_str = "" # Get the properties inherited from Post PostFunction_str += super(PostFunction, self).__str__() if self._run_str is not None: PostFunction_str += "run = " + self._run_str + linesep elif self._run_func is not None: PostFunction_str += "run = " + str(self._run_func) + linesep else: PostFunction_str += "run = None" + linesep + linesep return PostFunction_str def __eq__(self, other): """Compare two objects (skip parent)""" if type(other) != type(self): return False # Check the properties inherited from Post if not super(PostFunction, self).__eq__(other): return False if other._run_str != self._run_str: return False return True
[docs] def compare(self, other, name="self", ignore_list=None, is_add_value=False): """Compare two objects and return list of differences""" if ignore_list is None: ignore_list = list() if type(other) != type(self): return ["type(" + name + ")"] diff_list = list() # Check the properties inherited from Post diff_list.extend( super(PostFunction, self).compare( other, name=name, ignore_list=ignore_list, is_add_value=is_add_value ) ) if other._run_str != self._run_str: diff_list.append(name + ".run") # Filter ignore differences diff_list = list(filter(lambda x: x not in ignore_list, diff_list)) return diff_list
def __sizeof__(self): """Return the size in memory of the object (including all subobject)""" S = 0 # Full size of the object # Get size of the properties inherited from Post S += super(PostFunction, self).__sizeof__() S += getsizeof(self._run_str) return S
[docs] def as_dict(self, type_handle_ndarray=0, keep_function=False, **kwargs): """ Convert this object in a json serializable dict (can be use in __init__). type_handle_ndarray: int How to handle ndarray (0: tolist, 1: copy, 2: nothing) keep_function : bool True to keep the function object, else return str Optional keyword input parameter is for internal use only and may prevent json serializability. """ # Get the properties inherited from Post PostFunction_dict = super(PostFunction, self).as_dict( type_handle_ndarray=type_handle_ndarray, keep_function=keep_function, **kwargs, ) if self._run_str is not None: PostFunction_dict["run"] = self._run_str elif keep_function: PostFunction_dict["run"] = else: PostFunction_dict["run"] = None if is not None: self.get_logger().warning( "PostFunction.as_dict(): " + f"Function {} is not serializable " + "and will be converted to None." ) # The class name is added to the dict for deserialisation purpose # Overwrite the mother class name PostFunction_dict["__class__"] = "PostFunction" return PostFunction_dict
[docs] def copy(self): """Creates a deepcopy of the object""" # Handle deepcopy of all the properties if self._run_str is not None: run_val = self._run_str else: run_val = self._run_func # Creates new object of the same type with the copied properties obj_copy = type(self)(run=run_val) return obj_copy
def _set_None(self): """Set all the properties to None (except pyleecan object)""" = None # Set to None the properties inherited from Post super(PostFunction, self)._set_None() def _get_run(self): """getter of run""" return self._run_func def _set_run(self, value): """setter of run""" if value is None: self._run_str = None self._run_func = None elif isinstance(value, str) and "lambda" in value: self._run_str = value self._run_func = eval(value) elif isinstance(value, str) and isfile(value) and value[-3:] == ".py": self._run_str = value f = open(value, "r") exec(, globals()) self._run_func = eval(basename(value[:-3])) elif callable(value): self._run_str = None self._run_func = value else: raise CheckTypeError( "For property run Expected function or str (path to python file or lambda), got: " + str(type(value)) ) run = property( fget=_get_run, fset=_set_run, doc="""Post-processing that takes an output in argument :Type: function """, )