# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Output/OutLossModel.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/OutLossModel
"""
from os import linesep
from sys import getsizeof
from logging import getLogger
from ._check import set_array, 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.OutLossModel.get_mesh_solution import get_mesh_solution
except ImportError as error:
get_mesh_solution = error
try:
from ..Methods.Output.OutLossModel.get_loss_scalar import get_loss_scalar
except ImportError as error:
get_loss_scalar = error
try:
from ..Methods.Output.OutLossModel.__add__ import __add__
except ImportError as error:
__add__ = error
try:
from ..Methods.Output.OutLossModel.__radd__ import __radd__
except ImportError as error:
__radd__ = error
try:
from ..Methods.Output.OutLossModel.__sub__ import __sub__
except ImportError as error:
__sub__ = error
try:
from ..Methods.Output.OutLossModel.__rsub__ import __rsub__
except ImportError as error:
__rsub__ = error
try:
from ..Methods.Output.OutLossModel.plot_mesh import plot_mesh
except ImportError as error:
plot_mesh = error
from numpy import array, array_equal
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class OutLossModel(FrozenClass):
"""Gather the loss module outputs"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Output.OutLossModel.get_mesh_solution
if isinstance(get_mesh_solution, ImportError):
get_mesh_solution = property(
fget=lambda x: raise_(
ImportError(
"Can't use OutLossModel method get_mesh_solution: "
+ str(get_mesh_solution)
)
)
)
else:
get_mesh_solution = get_mesh_solution
# cf Methods.Output.OutLossModel.get_loss_scalar
if isinstance(get_loss_scalar, ImportError):
get_loss_scalar = property(
fget=lambda x: raise_(
ImportError(
"Can't use OutLossModel method get_loss_scalar: "
+ str(get_loss_scalar)
)
)
)
else:
get_loss_scalar = get_loss_scalar
# cf Methods.Output.OutLossModel.__add__
if isinstance(__add__, ImportError):
__add__ = property(
fget=lambda x: raise_(
ImportError("Can't use OutLossModel method __add__: " + str(__add__))
)
)
else:
__add__ = __add__
# cf Methods.Output.OutLossModel.__radd__
if isinstance(__radd__, ImportError):
__radd__ = property(
fget=lambda x: raise_(
ImportError("Can't use OutLossModel method __radd__: " + str(__radd__))
)
)
else:
__radd__ = __radd__
# cf Methods.Output.OutLossModel.__sub__
if isinstance(__sub__, ImportError):
__sub__ = property(
fget=lambda x: raise_(
ImportError("Can't use OutLossModel method __sub__: " + str(__sub__))
)
)
else:
__sub__ = __sub__
# cf Methods.Output.OutLossModel.__rsub__
if isinstance(__rsub__, ImportError):
__rsub__ = property(
fget=lambda x: raise_(
ImportError("Can't use OutLossModel method __rsub__: " + str(__rsub__))
)
)
else:
__rsub__ = __rsub__
# cf Methods.Output.OutLossModel.plot_mesh
if isinstance(plot_mesh, ImportError):
plot_mesh = property(
fget=lambda x: raise_(
ImportError(
"Can't use OutLossModel method plot_mesh: " + str(plot_mesh)
)
)
)
else:
plot_mesh = plot_mesh
# 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,
name="",
loss_density=None,
coeff_dict=None,
group=None,
loss_model=None,
scalar_value=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 "name" in list(init_dict.keys()):
name = init_dict["name"]
if "loss_density" in list(init_dict.keys()):
loss_density = init_dict["loss_density"]
if "coeff_dict" in list(init_dict.keys()):
coeff_dict = init_dict["coeff_dict"]
if "group" in list(init_dict.keys()):
group = init_dict["group"]
if "loss_model" in list(init_dict.keys()):
loss_model = init_dict["loss_model"]
if "scalar_value" in list(init_dict.keys()):
scalar_value = init_dict["scalar_value"]
# Set the properties (value check and convertion are done in setter)
self.parent = None
self.name = name
self.loss_density = loss_density
self.coeff_dict = coeff_dict
self.group = group
self.loss_model = loss_model
self.scalar_value = scalar_value
# 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)"""
OutLossModel_str = ""
if self.parent is None:
OutLossModel_str += "parent = None " + linesep
else:
OutLossModel_str += (
"parent = " + str(type(self.parent)) + " object" + linesep
)
OutLossModel_str += 'name = "' + str(self.name) + '"' + linesep
OutLossModel_str += (
"loss_density = "
+ linesep
+ str(self.loss_density).replace(linesep, linesep + "\t")
+ linesep
+ linesep
)
OutLossModel_str += "coeff_dict = " + str(self.coeff_dict) + linesep
OutLossModel_str += 'group = "' + str(self.group) + '"' + linesep
OutLossModel_str += 'loss_model = "' + str(self.loss_model) + '"' + linesep
OutLossModel_str += "scalar_value = " + str(self.scalar_value) + linesep
return OutLossModel_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
if other.name != self.name:
return False
if not array_equal(other.loss_density, self.loss_density):
return False
if other.coeff_dict != self.coeff_dict:
return False
if other.group != self.group:
return False
if other.loss_model != self.loss_model:
return False
if other.scalar_value != self.scalar_value:
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._name != self._name:
if is_add_value:
val_str = (
" (self=" + str(self._name) + ", other=" + str(other._name) + ")"
)
diff_list.append(name + ".name" + val_str)
else:
diff_list.append(name + ".name")
if not array_equal(other.loss_density, self.loss_density):
diff_list.append(name + ".loss_density")
if other._coeff_dict != self._coeff_dict:
if is_add_value:
val_str = (
" (self="
+ str(self._coeff_dict)
+ ", other="
+ str(other._coeff_dict)
+ ")"
)
diff_list.append(name + ".coeff_dict" + val_str)
else:
diff_list.append(name + ".coeff_dict")
if other._group != self._group:
if is_add_value:
val_str = (
" (self=" + str(self._group) + ", other=" + str(other._group) + ")"
)
diff_list.append(name + ".group" + val_str)
else:
diff_list.append(name + ".group")
if other._loss_model != self._loss_model:
if is_add_value:
val_str = (
" (self="
+ str(self._loss_model)
+ ", other="
+ str(other._loss_model)
+ ")"
)
diff_list.append(name + ".loss_model" + val_str)
else:
diff_list.append(name + ".loss_model")
if (
other._scalar_value is not None
and self._scalar_value is not None
and isnan(other._scalar_value)
and isnan(self._scalar_value)
):
pass
elif other._scalar_value != self._scalar_value:
if is_add_value:
val_str = (
" (self="
+ str(self._scalar_value)
+ ", other="
+ str(other._scalar_value)
+ ")"
)
diff_list.append(name + ".scalar_value" + val_str)
else:
diff_list.append(name + ".scalar_value")
# 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
S += getsizeof(self.name)
S += getsizeof(self.loss_density)
if self.coeff_dict is not None:
for key, value in self.coeff_dict.items():
S += getsizeof(value) + getsizeof(key)
S += getsizeof(self.group)
S += getsizeof(self.loss_model)
S += getsizeof(self.scalar_value)
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.
"""
OutLossModel_dict = dict()
OutLossModel_dict["name"] = self.name
if self.loss_density is None:
OutLossModel_dict["loss_density"] = None
else:
if type_handle_ndarray == 0:
OutLossModel_dict["loss_density"] = self.loss_density.tolist()
elif type_handle_ndarray == 1:
OutLossModel_dict["loss_density"] = self.loss_density.copy()
elif type_handle_ndarray == 2:
OutLossModel_dict["loss_density"] = self.loss_density
else:
raise Exception(
"Unknown type_handle_ndarray: " + str(type_handle_ndarray)
)
OutLossModel_dict["coeff_dict"] = (
self.coeff_dict.copy() if self.coeff_dict is not None else None
)
OutLossModel_dict["group"] = self.group
OutLossModel_dict["loss_model"] = self.loss_model
OutLossModel_dict["scalar_value"] = self.scalar_value
# The class name is added to the dict for deserialisation purpose
OutLossModel_dict["__class__"] = "OutLossModel"
return OutLossModel_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
name_val = self.name
if self.loss_density is None:
loss_density_val = None
else:
loss_density_val = self.loss_density.copy()
if self.coeff_dict is None:
coeff_dict_val = None
else:
coeff_dict_val = self.coeff_dict.copy()
group_val = self.group
loss_model_val = self.loss_model
scalar_value_val = self.scalar_value
# Creates new object of the same type with the copied properties
obj_copy = type(self)(
name=name_val,
loss_density=loss_density_val,
coeff_dict=coeff_dict_val,
group=group_val,
loss_model=loss_model_val,
scalar_value=scalar_value_val,
)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.name = None
self.loss_density = None
self.coeff_dict = None
self.group = None
self.loss_model = None
self.scalar_value = None
def _get_name(self):
"""getter of name"""
return self._name
def _set_name(self, value):
"""setter of name"""
check_var("name", value, "str")
self._name = value
name = property(
fget=_get_name,
fset=_set_name,
doc=u"""Name of the loss
:Type: str
""",
)
def _get_loss_density(self):
"""getter of loss_density"""
return self._loss_density
def _set_loss_density(self, value):
"""setter of loss_density"""
if type(value) is int and value == -1:
value = array([])
elif type(value) is list:
try:
value = array(value)
except:
pass
check_var("loss_density", value, "ndarray")
self._loss_density = value
loss_density = property(
fget=_get_loss_density,
fset=_set_loss_density,
doc=u"""Loss density
:Type: ndarray
""",
)
def _get_coeff_dict(self):
"""getter of coeff_dict"""
return self._coeff_dict
def _set_coeff_dict(self, value):
"""setter of coeff_dict"""
if type(value) is int and value == -1:
value = dict()
check_var("coeff_dict", value, "dict")
self._coeff_dict = value
coeff_dict = property(
fget=_get_coeff_dict,
fset=_set_coeff_dict,
doc=u"""dict of coefficients to compute the scalar value with respcet to frequency
:Type: dict
""",
)
def _get_group(self):
"""getter of group"""
return self._group
def _set_group(self, value):
"""setter of group"""
check_var("group", value, "str")
self._group = value
group = property(
fget=_get_group,
fset=_set_group,
doc=u"""group to which the loss applies
:Type: str
""",
)
def _get_loss_model(self):
"""getter of loss_model"""
return self._loss_model
def _set_loss_model(self, value):
"""setter of loss_model"""
check_var("loss_model", value, "str")
self._loss_model = value
loss_model = property(
fget=_get_loss_model,
fset=_set_loss_model,
doc=u"""The name of the loss model used to compute the loss stored in this output
:Type: str
""",
)
def _get_scalar_value(self):
"""getter of scalar_value"""
return self._scalar_value
def _set_scalar_value(self, value):
"""setter of scalar_value"""
check_var("scalar_value", value, "float")
self._scalar_value = value
scalar_value = property(
fget=_get_scalar_value,
fset=_set_scalar_value,
doc=u"""To store the value of get_loss_scalar (for scalar losses or with coeff_dict cleaned)
:Type: float
""",
)
