# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Loss/LossModelBertotti.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/LossModelBertotti
"""
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 .LossModel import LossModel
# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
from ..Methods.Loss.LossModelBertotti.comp_coeff import comp_coeff
except ImportError as error:
comp_coeff = error
try:
from ..Methods.Loss.LossModelBertotti.comp_loss import comp_loss
except ImportError as error:
comp_loss = error
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class LossModelBertotti(LossModel):
"""Bertotti Loss Model Class"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Loss.LossModelBertotti.comp_coeff
if isinstance(comp_coeff, ImportError):
comp_coeff = property(
fget=lambda x: raise_(
ImportError(
"Can't use LossModelBertotti method comp_coeff: " + str(comp_coeff)
)
)
)
else:
comp_coeff = comp_coeff
# cf Methods.Loss.LossModelBertotti.comp_loss
if isinstance(comp_loss, ImportError):
comp_loss = property(
fget=lambda x: raise_(
ImportError(
"Can't use LossModelBertotti method comp_loss: " + str(comp_loss)
)
)
)
else:
comp_loss = comp_loss
# 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,
k_hy=None,
k_ed=None,
k_ex=None,
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 "k_hy" in list(init_dict.keys()):
k_hy = init_dict["k_hy"]
if "k_ed" in list(init_dict.keys()):
k_ed = init_dict["k_ed"]
if "k_ex" in list(init_dict.keys()):
k_ex = init_dict["k_ex"]
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.k_hy = k_hy
self.k_ed = k_ed
self.k_ex = k_ex
# Call LossModel init
super(LossModelBertotti, self).__init__(
name=name, group=group, is_show_fig=is_show_fig, coeff_dict=coeff_dict
)
# The class is frozen (in LossModel init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
LossModelBertotti_str = ""
# Get the properties inherited from LossModel
LossModelBertotti_str += super(LossModelBertotti, self).__str__()
LossModelBertotti_str += "k_hy = " + str(self.k_hy) + linesep
LossModelBertotti_str += "k_ed = " + str(self.k_ed) + linesep
LossModelBertotti_str += "k_ex = " + str(self.k_ex) + linesep
return LossModelBertotti_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from LossModel
if not super(LossModelBertotti, self).__eq__(other):
return False
if other.k_hy != self.k_hy:
return False
if other.k_ed != self.k_ed:
return False
if other.k_ex != self.k_ex:
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 LossModel
diff_list.extend(
super(LossModelBertotti, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if (
other._k_hy is not None
and self._k_hy is not None
and isnan(other._k_hy)
and isnan(self._k_hy)
):
pass
elif other._k_hy != self._k_hy:
if is_add_value:
val_str = (
" (self=" + str(self._k_hy) + ", other=" + str(other._k_hy) + ")"
)
diff_list.append(name + ".k_hy" + val_str)
else:
diff_list.append(name + ".k_hy")
if (
other._k_ed is not None
and self._k_ed is not None
and isnan(other._k_ed)
and isnan(self._k_ed)
):
pass
elif other._k_ed != self._k_ed:
if is_add_value:
val_str = (
" (self=" + str(self._k_ed) + ", other=" + str(other._k_ed) + ")"
)
diff_list.append(name + ".k_ed" + val_str)
else:
diff_list.append(name + ".k_ed")
if (
other._k_ex is not None
and self._k_ex is not None
and isnan(other._k_ex)
and isnan(self._k_ex)
):
pass
elif other._k_ex != self._k_ex:
if is_add_value:
val_str = (
" (self=" + str(self._k_ex) + ", other=" + str(other._k_ex) + ")"
)
diff_list.append(name + ".k_ex" + val_str)
else:
diff_list.append(name + ".k_ex")
# 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 LossModel
S += super(LossModelBertotti, self).__sizeof__()
S += getsizeof(self.k_hy)
S += getsizeof(self.k_ed)
S += getsizeof(self.k_ex)
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 LossModel
LossModelBertotti_dict = super(LossModelBertotti, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
LossModelBertotti_dict["k_hy"] = self.k_hy
LossModelBertotti_dict["k_ed"] = self.k_ed
LossModelBertotti_dict["k_ex"] = self.k_ex
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
LossModelBertotti_dict["__class__"] = "LossModelBertotti"
return LossModelBertotti_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
k_hy_val = self.k_hy
k_ed_val = self.k_ed
k_ex_val = self.k_ex
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)(
k_hy=k_hy_val,
k_ed=k_ed_val,
k_ex=k_ex_val,
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.k_hy = None
self.k_ed = None
self.k_ex = None
# Set to None the properties inherited from LossModel
super(LossModelBertotti, self)._set_None()
def _get_k_hy(self):
"""getter of k_hy"""
return self._k_hy
def _set_k_hy(self, value):
"""setter of k_hy"""
check_var("k_hy", value, "float")
self._k_hy = value
k_hy = property(
fget=_get_k_hy,
fset=_set_k_hy,
doc=u"""Hysteresis loss coefficient
:Type: float
""",
)
def _get_k_ed(self):
"""getter of k_ed"""
return self._k_ed
def _set_k_ed(self, value):
"""setter of k_ed"""
check_var("k_ed", value, "float")
self._k_ed = value
k_ed = property(
fget=_get_k_ed,
fset=_set_k_ed,
doc=u"""Eddy current loss coefficient
:Type: float
""",
)
def _get_k_ex(self):
"""getter of k_ex"""
return self._k_ex
def _set_k_ex(self, value):
"""setter of k_ex"""
check_var("k_ex", value, "float")
self._k_ex = value
k_ex = property(
fget=_get_k_ex,
fset=_set_k_ex,
doc=u"""Excess loss coefficient
:Type: float
""",
)
