# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Loss/LossModel.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Loss/LossModel
"""
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 ..Functions.save import save
from ..Functions.load import load_init_dict
from ..Functions.Load.import_class import import_class
from copy import deepcopy
from ._frozen import FrozenClass
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class LossModel(FrozenClass):
"""Abstract Loss Model Class"""
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,
name="",
group="",
is_show_fig=False,
coeff_dict=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 "name" in list(init_dict.keys()):
name = init_dict["name"]
if "group" in list(init_dict.keys()):
group = init_dict["group"]
if "is_show_fig" in list(init_dict.keys()):
is_show_fig = init_dict["is_show_fig"]
if "coeff_dict" in list(init_dict.keys()):
coeff_dict = init_dict["coeff_dict"]
# Set the properties (value check and convertion are done in setter)
self.parent = None
self.name = name
self.group = group
self.is_show_fig = is_show_fig
self.coeff_dict = coeff_dict
# The class is frozen, for now it's impossible to add new properties
self._freeze()
def __str__(self):
"""Convert this object in a readeable string (for print)"""
LossModel_str = ""
if self.parent is None:
LossModel_str += "parent = None " + linesep
else:
LossModel_str += "parent = " + str(type(self.parent)) + " object" + linesep
LossModel_str += 'name = "' + str(self.name) + '"' + linesep
LossModel_str += 'group = "' + str(self.group) + '"' + linesep
LossModel_str += "is_show_fig = " + str(self.is_show_fig) + linesep
LossModel_str += "coeff_dict = " + str(self.coeff_dict) + linesep
return LossModel_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
if other.name != self.name:
return False
if other.group != self.group:
return False
if other.is_show_fig != self.is_show_fig:
return False
if other.coeff_dict != self.coeff_dict:
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()
if other._name != self._name:
if is_add_value:
val_str = (
" (self=" + str(self._name) + ", other=" + str(other._name) + ")"
)
diff_list.append(name + ".name" + val_str)
else:
diff_list.append(name + ".name")
if other._group != self._group:
if is_add_value:
val_str = (
" (self=" + str(self._group) + ", other=" + str(other._group) + ")"
)
diff_list.append(name + ".group" + val_str)
else:
diff_list.append(name + ".group")
if other._is_show_fig != self._is_show_fig:
if is_add_value:
val_str = (
" (self="
+ str(self._is_show_fig)
+ ", other="
+ str(other._is_show_fig)
+ ")"
)
diff_list.append(name + ".is_show_fig" + val_str)
else:
diff_list.append(name + ".is_show_fig")
if other._coeff_dict != self._coeff_dict:
if is_add_value:
val_str = (
" (self="
+ str(self._coeff_dict)
+ ", other="
+ str(other._coeff_dict)
+ ")"
)
diff_list.append(name + ".coeff_dict" + val_str)
else:
diff_list.append(name + ".coeff_dict")
# 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
S += getsizeof(self.name)
S += getsizeof(self.group)
S += getsizeof(self.is_show_fig)
if self.coeff_dict is not None:
for key, value in self.coeff_dict.items():
S += getsizeof(value) + getsizeof(key)
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.
"""
LossModel_dict = dict()
LossModel_dict["name"] = self.name
LossModel_dict["group"] = self.group
LossModel_dict["is_show_fig"] = self.is_show_fig
LossModel_dict["coeff_dict"] = (
self.coeff_dict.copy() if self.coeff_dict is not None else None
)
# The class name is added to the dict for deserialisation purpose
LossModel_dict["__class__"] = "LossModel"
return LossModel_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
name_val = self.name
group_val = self.group
is_show_fig_val = self.is_show_fig
if self.coeff_dict is None:
coeff_dict_val = None
else:
coeff_dict_val = self.coeff_dict.copy()
# Creates new object of the same type with the copied properties
obj_copy = type(self)(
name=name_val,
group=group_val,
is_show_fig=is_show_fig_val,
coeff_dict=coeff_dict_val,
)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.name = None
self.group = None
self.is_show_fig = None
self.coeff_dict = None
def _get_name(self):
"""getter of name"""
return self._name
def _set_name(self, value):
"""setter of name"""
check_var("name", value, "str")
self._name = value
name = property(
fget=_get_name,
fset=_set_name,
doc=u"""Name of the loss simulation (has to be unique)
:Type: str
""",
)
def _get_group(self):
"""getter of group"""
return self._group
def _set_group(self, value):
"""setter of group"""
check_var("group", value, "str")
self._group = value
group = property(
fget=_get_group,
fset=_set_group,
doc=u"""Group in which the loss will be computed
:Type: str
""",
)
def _get_is_show_fig(self):
"""getter of is_show_fig"""
return self._is_show_fig
def _set_is_show_fig(self, value):
"""setter of is_show_fig"""
check_var("is_show_fig", value, "bool")
self._is_show_fig = value
is_show_fig = property(
fget=_get_is_show_fig,
fset=_set_is_show_fig,
doc=u"""True to show the plot of the curve fitting
:Type: bool
""",
)
def _get_coeff_dict(self):
"""getter of coeff_dict"""
return self._coeff_dict
def _set_coeff_dict(self, value):
"""setter of coeff_dict"""
if type(value) is int and value == -1:
value = dict()
check_var("coeff_dict", value, "dict")
self._coeff_dict = value
coeff_dict = property(
fget=_get_coeff_dict,
fset=_set_coeff_dict,
doc=u"""dict of coefficients to compute losses with respect to frequency
:Type: dict
""",
)
