Source code for pyleecan.Classes.NotchEvenDist

# -*- 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

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 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
    from ..Methods.Machine.NotchEvenDist.comp_surface import comp_surface
except ImportError as error:
    comp_surface = error

    from ..Methods.Machine.NotchEvenDist.comp_periodicity_spatial import (
except ImportError as error:
    comp_periodicity_spatial = error

    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( 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 """, )