Source code for pyleecan.Classes.ModelBH_exponential
# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Material/ModelBH_exponential.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Material/ModelBH_exponential
"""
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 ModelBH_exponential(FrozenClass):
"""Abstract class for BH curve model """
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, Bs=None, mu_a=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 "Bs" in list(init_dict.keys()):
Bs = init_dict["Bs"]
if "mu_a" in list(init_dict.keys()):
mu_a = init_dict["mu_a"]
# Set the properties (value check and convertion are done in setter)
self.parent = None
self.Bs = Bs
self.mu_a = mu_a
# 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)"""
ModelBH_exponential_str = ""
if self.parent is None:
ModelBH_exponential_str += "parent = None " + linesep
else:
ModelBH_exponential_str += (
"parent = " + str(type(self.parent)) + " object" + linesep
)
ModelBH_exponential_str += "Bs = " + str(self.Bs) + linesep
ModelBH_exponential_str += "mu_a = " + str(self.mu_a) + linesep
return ModelBH_exponential_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
if other.Bs != self.Bs:
return False
if other.mu_a != self.mu_a:
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._Bs is not None
and self._Bs is not None
and isnan(other._Bs)
and isnan(self._Bs)
):
pass
elif other._Bs != self._Bs:
if is_add_value:
val_str = " (self=" + str(self._Bs) + ", other=" + str(other._Bs) + ")"
diff_list.append(name + ".Bs" + val_str)
else:
diff_list.append(name + ".Bs")
if (
other._mu_a is not None
and self._mu_a is not None
and isnan(other._mu_a)
and isnan(self._mu_a)
):
pass
elif other._mu_a != self._mu_a:
if is_add_value:
val_str = (
" (self=" + str(self._mu_a) + ", other=" + str(other._mu_a) + ")"
)
diff_list.append(name + ".mu_a" + val_str)
else:
diff_list.append(name + ".mu_a")
# 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.Bs)
S += getsizeof(self.mu_a)
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.
"""
ModelBH_exponential_dict = dict()
ModelBH_exponential_dict["Bs"] = self.Bs
ModelBH_exponential_dict["mu_a"] = self.mu_a
# The class name is added to the dict for deserialisation purpose
ModelBH_exponential_dict["__class__"] = "ModelBH_exponential"
return ModelBH_exponential_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
Bs_val = self.Bs
mu_a_val = self.mu_a
# Creates new object of the same type with the copied properties
obj_copy = type(self)(Bs=Bs_val, mu_a=mu_a_val)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.Bs = None
self.mu_a = None
def _get_Bs(self):
"""getter of Bs"""
return self._Bs
def _set_Bs(self, value):
"""setter of Bs"""
check_var("Bs", value, "float")
self._Bs = value
Bs = property(
fget=_get_Bs,
fset=_set_Bs,
doc=u"""BH curve parameter
:Type: float
""",
)
def _get_mu_a(self):
"""getter of mu_a"""
return self._mu_a
def _set_mu_a(self, value):
"""setter of mu_a"""
check_var("mu_a", value, "float")
self._mu_a = value
mu_a = property(
fget=_get_mu_a,
fset=_set_mu_a,
doc=u"""Saturation permeability parameter
:Type: float
""",
)
