Source code for pyleecan.Classes.LossModelProximity

# -*- 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 """, )