# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Loss/LossModelProximity.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/LossModelProximity
"""
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.LossModelProximity.comp_coeff import comp_coeff
except ImportError as error:
comp_coeff = error
try:
from ..Methods.Loss.LossModelProximity.comp_loss import comp_loss
except ImportError as error:
comp_loss = error
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class LossModelProximity(LossModel):
"""Proximity Loss Model Class"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Loss.LossModelProximity.comp_coeff
if isinstance(comp_coeff, ImportError):
comp_coeff = property(
fget=lambda x: raise_(
ImportError(
"Can't use LossModelProximity method comp_coeff: " + str(comp_coeff)
)
)
)
else:
comp_coeff = comp_coeff
# cf Methods.Loss.LossModelProximity.comp_loss
if isinstance(comp_loss, ImportError):
comp_loss = property(
fget=lambda x: raise_(
ImportError(
"Can't use LossModelProximity 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_p=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_p" in list(init_dict.keys()):
k_p = init_dict["k_p"]
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_p = k_p
# Call LossModel init
super(LossModelProximity, 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)"""
LossModelProximity_str = ""
# Get the properties inherited from LossModel
LossModelProximity_str += super(LossModelProximity, self).__str__()
LossModelProximity_str += "k_p = " + str(self.k_p) + linesep
return LossModelProximity_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(LossModelProximity, self).__eq__(other):
return False
if other.k_p != self.k_p:
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(LossModelProximity, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if (
other._k_p is not None
and self._k_p is not None
and isnan(other._k_p)
and isnan(self._k_p)
):
pass
elif other._k_p != self._k_p:
if is_add_value:
val_str = (
" (self=" + str(self._k_p) + ", other=" + str(other._k_p) + ")"
)
diff_list.append(name + ".k_p" + val_str)
else:
diff_list.append(name + ".k_p")
# 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(LossModelProximity, self).__sizeof__()
S += getsizeof(self.k_p)
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
LossModelProximity_dict = super(LossModelProximity, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
LossModelProximity_dict["k_p"] = self.k_p
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
LossModelProximity_dict["__class__"] = "LossModelProximity"
return LossModelProximity_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
k_p_val = self.k_p
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_p=k_p_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_p = None
# Set to None the properties inherited from LossModel
super(LossModelProximity, self)._set_None()
def _get_k_p(self):
"""getter of k_p"""
return self._k_p
def _set_k_p(self, value):
"""setter of k_p"""
check_var("k_p", value, "float")
self._k_p = value
k_p = property(
fget=_get_k_p,
fset=_set_k_p,
doc=u"""Proximity loss coefficient
:Type: float
""",
)
