# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Machine/NotchEvenDist.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Machine/NotchEvenDist
"""
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 .Notch import Notch
# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
from ..Methods.Machine.NotchEvenDist.comp_surface import comp_surface
except ImportError as error:
comp_surface = error
try:
from ..Methods.Machine.NotchEvenDist.comp_periodicity_spatial import (
comp_periodicity_spatial,
)
except ImportError as error:
comp_periodicity_spatial = error
try:
from ..Methods.Machine.NotchEvenDist.get_notch_desc_list import get_notch_desc_list
except ImportError as error:
get_notch_desc_list = error
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class NotchEvenDist(Notch):
"""Class for evenly distributed notches (according to Zs)"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Machine.NotchEvenDist.comp_surface
if isinstance(comp_surface, ImportError):
comp_surface = property(
fget=lambda x: raise_(
ImportError(
"Can't use NotchEvenDist method comp_surface: " + str(comp_surface)
)
)
)
else:
comp_surface = comp_surface
# cf Methods.Machine.NotchEvenDist.comp_periodicity_spatial
if isinstance(comp_periodicity_spatial, ImportError):
comp_periodicity_spatial = property(
fget=lambda x: raise_(
ImportError(
"Can't use NotchEvenDist method comp_periodicity_spatial: "
+ str(comp_periodicity_spatial)
)
)
)
else:
comp_periodicity_spatial = comp_periodicity_spatial
# cf Methods.Machine.NotchEvenDist.get_notch_desc_list
if isinstance(get_notch_desc_list, ImportError):
get_notch_desc_list = property(
fget=lambda x: raise_(
ImportError(
"Can't use NotchEvenDist method get_notch_desc_list: "
+ str(get_notch_desc_list)
)
)
)
else:
get_notch_desc_list = get_notch_desc_list
# 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, alpha=0, notch_shape=-1, 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 "alpha" in list(init_dict.keys()):
alpha = init_dict["alpha"]
if "notch_shape" in list(init_dict.keys()):
notch_shape = init_dict["notch_shape"]
# Set the properties (value check and convertion are done in setter)
self.alpha = alpha
self.notch_shape = notch_shape
# Call Notch init
super(NotchEvenDist, self).__init__()
# The class is frozen (in Notch init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
NotchEvenDist_str = ""
# Get the properties inherited from Notch
NotchEvenDist_str += super(NotchEvenDist, self).__str__()
NotchEvenDist_str += "alpha = " + str(self.alpha) + linesep
if self.notch_shape is not None:
tmp = (
self.notch_shape.__str__().replace(linesep, linesep + "\t").rstrip("\t")
)
NotchEvenDist_str += "notch_shape = " + tmp
else:
NotchEvenDist_str += "notch_shape = None" + linesep + linesep
return NotchEvenDist_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from Notch
if not super(NotchEvenDist, self).__eq__(other):
return False
if other.alpha != self.alpha:
return False
if other.notch_shape != self.notch_shape:
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 Notch
diff_list.extend(
super(NotchEvenDist, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if (
other._alpha is not None
and self._alpha is not None
and isnan(other._alpha)
and isnan(self._alpha)
):
pass
elif other._alpha != self._alpha:
if is_add_value:
val_str = (
" (self=" + str(self._alpha) + ", other=" + str(other._alpha) + ")"
)
diff_list.append(name + ".alpha" + val_str)
else:
diff_list.append(name + ".alpha")
if (other.notch_shape is None and self.notch_shape is not None) or (
other.notch_shape is not None and self.notch_shape is None
):
diff_list.append(name + ".notch_shape None mismatch")
elif self.notch_shape is not None:
diff_list.extend(
self.notch_shape.compare(
other.notch_shape,
name=name + ".notch_shape",
ignore_list=ignore_list,
is_add_value=is_add_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
# Get size of the properties inherited from Notch
S += super(NotchEvenDist, self).__sizeof__()
S += getsizeof(self.alpha)
S += getsizeof(self.notch_shape)
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 Notch
NotchEvenDist_dict = super(NotchEvenDist, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
NotchEvenDist_dict["alpha"] = self.alpha
if self.notch_shape is None:
NotchEvenDist_dict["notch_shape"] = None
else:
NotchEvenDist_dict["notch_shape"] = self.notch_shape.as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
NotchEvenDist_dict["__class__"] = "NotchEvenDist"
return NotchEvenDist_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
alpha_val = self.alpha
if self.notch_shape is None:
notch_shape_val = None
else:
notch_shape_val = self.notch_shape.copy()
# Creates new object of the same type with the copied properties
obj_copy = type(self)(alpha=alpha_val, notch_shape=notch_shape_val)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.alpha = None
if self.notch_shape is not None:
self.notch_shape._set_None()
# Set to None the properties inherited from Notch
super(NotchEvenDist, self)._set_None()
def _get_alpha(self):
"""getter of alpha"""
return self._alpha
def _set_alpha(self, value):
"""setter of alpha"""
check_var("alpha", value, "float")
self._alpha = value
alpha = property(
fget=_get_alpha,
fset=_set_alpha,
doc=u"""angular positon of the first notch (0 is middle of first tooth)
:Type: float
""",
)
def _get_notch_shape(self):
"""getter of notch_shape"""
return self._notch_shape
def _set_notch_shape(self, value):
"""setter of notch_shape"""
if isinstance(value, str): # Load from file
try:
value = load_init_dict(value)[1]
except Exception as e:
self.get_logger().error(
"Error while loading " + value + ", setting None instead"
)
value = None
if isinstance(value, dict) and "__class__" in value:
class_obj = import_class(
"pyleecan.Classes", value.get("__class__"), "notch_shape"
)
value = class_obj(init_dict=value)
elif type(value) is int and value == -1: # Default constructor
Slot = import_class("pyleecan.Classes", "Slot", "notch_shape")
value = Slot()
check_var("notch_shape", value, "Slot")
self._notch_shape = value
if self._notch_shape is not None:
self._notch_shape.parent = self
notch_shape = property(
fget=_get_notch_shape,
fset=_set_notch_shape,
doc=u"""Shape of the Notch
:Type: Slot
""",
)
