# -*- 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
""",
)
