Source code for pyleecan.Classes.OutLoss

# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Output/OutLoss.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Output/OutLoss
"""

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

# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
    from ..Methods.Output.OutLoss.get_loss_density_ag import get_loss_density_ag
except ImportError as error:
    get_loss_density_ag = error

try:
    from ..Methods.Output.OutLoss.get_loss_overall import get_loss_overall
except ImportError as error:
    get_loss_overall = error

try:
    from ..Methods.Output.OutLoss.get_power_dict import get_power_dict
except ImportError as error:
    get_power_dict = error

try:
    from ..Methods.Output.OutLoss.plot_losses import plot_losses
except ImportError as error:
    plot_losses = error

try:
    from ..Methods.Output.OutLoss.__getitem__ import __getitem__
except ImportError as error:
    __getitem__ = error

try:
    from ..Methods.Output.OutLoss.__len__ import __len__
except ImportError as error:
    __len__ = error


from numpy import isnan
from ._check import InitUnKnowClassError


[docs]class OutLoss(FrozenClass): """Gather the loss module outputs""" VERSION = 1 # Check ImportError to remove unnecessary dependencies in unused method # cf Methods.Output.OutLoss.get_loss_density_ag if isinstance(get_loss_density_ag, ImportError): get_loss_density_ag = property( fget=lambda x: raise_( ImportError( "Can't use OutLoss method get_loss_density_ag: " + str(get_loss_density_ag) ) ) ) else: get_loss_density_ag = get_loss_density_ag # cf Methods.Output.OutLoss.get_loss_overall if isinstance(get_loss_overall, ImportError): get_loss_overall = property( fget=lambda x: raise_( ImportError( "Can't use OutLoss method get_loss_overall: " + str(get_loss_overall) ) ) ) else: get_loss_overall = get_loss_overall # cf Methods.Output.OutLoss.get_power_dict if isinstance(get_power_dict, ImportError): get_power_dict = property( fget=lambda x: raise_( ImportError( "Can't use OutLoss method get_power_dict: " + str(get_power_dict) ) ) ) else: get_power_dict = get_power_dict # cf Methods.Output.OutLoss.plot_losses if isinstance(plot_losses, ImportError): plot_losses = property( fget=lambda x: raise_( ImportError("Can't use OutLoss method plot_losses: " + str(plot_losses)) ) ) else: plot_losses = plot_losses # cf Methods.Output.OutLoss.__getitem__ if isinstance(__getitem__, ImportError): __getitem__ = property( fget=lambda x: raise_( ImportError("Can't use OutLoss method __getitem__: " + str(__getitem__)) ) ) else: __getitem__ = __getitem__ # cf Methods.Output.OutLoss.__len__ if isinstance(__len__, ImportError): __len__ = property( fget=lambda x: raise_( ImportError("Can't use OutLoss method __len__: " + str(__len__)) ) ) else: __len__ = __len__ # 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, axes_dict=None, loss_dict=-1, logger_name="Pyleecan.Loss", 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 "axes_dict" in list(init_dict.keys()): axes_dict = init_dict["axes_dict"] if "loss_dict" in list(init_dict.keys()): loss_dict = init_dict["loss_dict"] if "logger_name" in list(init_dict.keys()): logger_name = init_dict["logger_name"] # Set the properties (value check and convertion are done in setter) self.parent = None self.axes_dict = axes_dict self.loss_dict = loss_dict self.logger_name = logger_name # 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)""" OutLoss_str = "" if self.parent is None: OutLoss_str += "parent = None " + linesep else: OutLoss_str += "parent = " + str(type(self.parent)) + " object" + linesep OutLoss_str += "axes_dict = " + str(self.axes_dict) + linesep + linesep if len(self.loss_dict) == 0: OutLoss_str += "loss_dict = dict()" + linesep for key, obj in self.loss_dict.items(): tmp = ( self.loss_dict[key].__str__().replace(linesep, linesep + "\t") + linesep ) OutLoss_str += "loss_dict[" + key + "] =" + tmp + linesep + linesep OutLoss_str += 'logger_name = "' + str(self.logger_name) + '"' + linesep return OutLoss_str def __eq__(self, other): """Compare two objects (skip parent)""" if type(other) != type(self): return False if other.axes_dict != self.axes_dict: return False if other.loss_dict != self.loss_dict: return False if other.logger_name != self.logger_name: 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.axes_dict is None and self.axes_dict is not None) or ( other.axes_dict is not None and self.axes_dict is None ): diff_list.append(name + ".axes_dict None mismatch") elif self.axes_dict is None: pass elif len(other.axes_dict) != len(self.axes_dict): diff_list.append("len(" + name + "axes_dict)") else: for key in self.axes_dict: diff_list.extend( self.axes_dict[key].compare( other.axes_dict[key], name=name + ".axes_dict[" + str(key) + "]", ignore_list=ignore_list, is_add_value=is_add_value, ) ) if (other.loss_dict is None and self.loss_dict is not None) or ( other.loss_dict is not None and self.loss_dict is None ): diff_list.append(name + ".loss_dict None mismatch") elif self.loss_dict is None: pass elif len(other.loss_dict) != len(self.loss_dict): diff_list.append("len(" + name + "loss_dict)") else: for key in self.loss_dict: diff_list.extend( self.loss_dict[key].compare( other.loss_dict[key], name=name + ".loss_dict[" + str(key) + "]", ignore_list=ignore_list, is_add_value=is_add_value, ) ) if other._logger_name != self._logger_name: if is_add_value: val_str = ( " (self=" + str(self._logger_name) + ", other=" + str(other._logger_name) + ")" ) diff_list.append(name + ".logger_name" + val_str) else: diff_list.append(name + ".logger_name") # 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 if self.axes_dict is not None: for key, value in self.axes_dict.items(): S += getsizeof(value) + getsizeof(key) if self.loss_dict is not None: for key, value in self.loss_dict.items(): S += getsizeof(value) + getsizeof(key) S += getsizeof(self.logger_name) 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. """ OutLoss_dict = dict() if self.axes_dict is None: OutLoss_dict["axes_dict"] = None else: OutLoss_dict["axes_dict"] = dict() for key, obj in self.axes_dict.items(): if obj is not None: OutLoss_dict["axes_dict"][key] = obj.as_dict( type_handle_ndarray=type_handle_ndarray, keep_function=keep_function, **kwargs ) else: OutLoss_dict["axes_dict"][key] = None if self.loss_dict is None: OutLoss_dict["loss_dict"] = None else: OutLoss_dict["loss_dict"] = dict() for key, obj in self.loss_dict.items(): if obj is not None: OutLoss_dict["loss_dict"][key] = obj.as_dict( type_handle_ndarray=type_handle_ndarray, keep_function=keep_function, **kwargs ) else: OutLoss_dict["loss_dict"][key] = None OutLoss_dict["logger_name"] = self.logger_name # The class name is added to the dict for deserialisation purpose OutLoss_dict["__class__"] = "OutLoss" return OutLoss_dict
[docs] def copy(self): """Creates a deepcopy of the object""" # Handle deepcopy of all the properties if self.axes_dict is None: axes_dict_val = None else: axes_dict_val = dict() for key, obj in self.axes_dict.items(): axes_dict_val[key] = obj.copy() if self.loss_dict is None: loss_dict_val = None else: loss_dict_val = dict() for key, obj in self.loss_dict.items(): loss_dict_val[key] = obj.copy() logger_name_val = self.logger_name # Creates new object of the same type with the copied properties obj_copy = type(self)( axes_dict=axes_dict_val, loss_dict=loss_dict_val, logger_name=logger_name_val, ) return obj_copy
def _set_None(self): """Set all the properties to None (except pyleecan object)""" self.axes_dict = None self.loss_dict = None self.logger_name = None def _get_axes_dict(self): """getter of axes_dict""" if self._axes_dict is not None: for key, obj in self._axes_dict.items(): if obj is not None: obj.parent = self return self._axes_dict def _set_axes_dict(self, value): """setter of axes_dict""" if type(value) is dict: for key, obj in value.items(): if isinstance(obj, str): # Load from file try: obj = load_init_dict(obj)[1] except Exception as e: self.get_logger().error( "Error while loading " + obj + ", setting None instead" ) obj = None value[key] = None if type(obj) is dict: class_obj = import_class( "SciDataTool.Classes", obj.get("__class__"), "axes_dict" ) value[key] = class_obj(init_dict=obj) if type(value) is int and value == -1: value = dict() check_var("axes_dict", value, "{Data}") self._axes_dict = value axes_dict = property( fget=_get_axes_dict, fset=_set_axes_dict, doc=u"""Dict containing axes data used for Magnetics :Type: {SciDataTool.Classes.DataND.Data} """, ) def _get_loss_dict(self): """getter of loss_dict""" if self._loss_dict is not None: for key, obj in self._loss_dict.items(): if obj is not None: obj.parent = self return self._loss_dict def _set_loss_dict(self, value): """setter of loss_dict""" if type(value) is dict: for key, obj in value.items(): if isinstance(obj, str): # Load from file try: obj = load_init_dict(obj)[1] except Exception as e: self.get_logger().error( "Error while loading " + obj + ", setting None instead" ) obj = None value[key] = None if type(obj) is dict: class_obj = import_class( "pyleecan.Classes", obj.get("__class__"), "loss_dict" ) value[key] = class_obj(init_dict=obj) if type(value) is int and value == -1: value = dict() check_var("loss_dict", value, "{OutLossModel}") self._loss_dict = value loss_dict = property( fget=_get_loss_dict, fset=_set_loss_dict, doc=u"""Dict containing OutLossModel obects for each type of loss :Type: {OutLossModel} """, ) def _get_logger_name(self): """getter of logger_name""" return self._logger_name def _set_logger_name(self, value): """setter of logger_name""" check_var("logger_name", value, "str") self._logger_name = value logger_name = property( fget=_get_logger_name, fset=_set_logger_name, doc=u"""Name of the logger to use :Type: str """, )