# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Elmer/ElmerResults.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Elmer/ElmerResults
"""
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 .Elmer import Elmer
# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
from ..Methods.Elmer.ElmerResults.load_data import load_data
except ImportError as error:
load_data = error
try:
from ..Methods.Elmer.ElmerResults.load_columns import load_columns
except ImportError as error:
load_columns = error
try:
from ..Methods.Elmer.ElmerResults.get_data import get_data
except ImportError as error:
get_data = error
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class ElmerResults(Elmer):
"""Class to get 'SaveScalars' and 'SaveLine' data"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Elmer.ElmerResults.load_data
if isinstance(load_data, ImportError):
load_data = property(
fget=lambda x: raise_(
ImportError(
"Can't use ElmerResults method load_data: " + str(load_data)
)
)
)
else:
load_data = load_data
# cf Methods.Elmer.ElmerResults.load_columns
if isinstance(load_columns, ImportError):
load_columns = property(
fget=lambda x: raise_(
ImportError(
"Can't use ElmerResults method load_columns: " + str(load_columns)
)
)
)
else:
load_columns = load_columns
# cf Methods.Elmer.ElmerResults.get_data
if isinstance(get_data, ImportError):
get_data = property(
fget=lambda x: raise_(
ImportError("Can't use ElmerResults method get_data: " + str(get_data))
)
)
else:
get_data = get_data
# 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,
data=-1,
file="",
usecols=-1,
columns=-1,
is_scalars=False,
logger_name="Pyleecan.Elmer",
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 "data" in list(init_dict.keys()):
data = init_dict["data"]
if "file" in list(init_dict.keys()):
file = init_dict["file"]
if "usecols" in list(init_dict.keys()):
usecols = init_dict["usecols"]
if "columns" in list(init_dict.keys()):
columns = init_dict["columns"]
if "is_scalars" in list(init_dict.keys()):
is_scalars = init_dict["is_scalars"]
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.data = data
self.file = file
self.usecols = usecols
self.columns = columns
self.is_scalars = is_scalars
# Call Elmer init
super(ElmerResults, self).__init__(logger_name=logger_name)
# The class is frozen (in Elmer init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
ElmerResults_str = ""
# Get the properties inherited from Elmer
ElmerResults_str += super(ElmerResults, self).__str__()
ElmerResults_str += "data = " + str(self.data) + linesep
ElmerResults_str += 'file = "' + str(self.file) + '"' + linesep
ElmerResults_str += (
"usecols = "
+ linesep
+ str(self.usecols).replace(linesep, linesep + "\t")
+ linesep
)
ElmerResults_str += (
"columns = "
+ linesep
+ str(self.columns).replace(linesep, linesep + "\t")
+ linesep
)
ElmerResults_str += "is_scalars = " + str(self.is_scalars) + linesep
return ElmerResults_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from Elmer
if not super(ElmerResults, self).__eq__(other):
return False
if other.data != self.data:
return False
if other.file != self.file:
return False
if other.usecols != self.usecols:
return False
if other.columns != self.columns:
return False
if other.is_scalars != self.is_scalars:
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 Elmer
diff_list.extend(
super(ElmerResults, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if other._data != self._data:
if is_add_value:
val_str = (
" (self=" + str(self._data) + ", other=" + str(other._data) + ")"
)
diff_list.append(name + ".data" + val_str)
else:
diff_list.append(name + ".data")
if other._file != self._file:
if is_add_value:
val_str = (
" (self=" + str(self._file) + ", other=" + str(other._file) + ")"
)
diff_list.append(name + ".file" + val_str)
else:
diff_list.append(name + ".file")
if other._usecols != self._usecols:
if is_add_value:
val_str = (
" (self="
+ str(self._usecols)
+ ", other="
+ str(other._usecols)
+ ")"
)
diff_list.append(name + ".usecols" + val_str)
else:
diff_list.append(name + ".usecols")
if other._columns != self._columns:
if is_add_value:
val_str = (
" (self="
+ str(self._columns)
+ ", other="
+ str(other._columns)
+ ")"
)
diff_list.append(name + ".columns" + val_str)
else:
diff_list.append(name + ".columns")
if other._is_scalars != self._is_scalars:
if is_add_value:
val_str = (
" (self="
+ str(self._is_scalars)
+ ", other="
+ str(other._is_scalars)
+ ")"
)
diff_list.append(name + ".is_scalars" + val_str)
else:
diff_list.append(name + ".is_scalars")
# 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 Elmer
S += super(ElmerResults, self).__sizeof__()
if self.data is not None:
for key, value in self.data.items():
S += getsizeof(value) + getsizeof(key)
S += getsizeof(self.file)
if self.usecols is not None:
for value in self.usecols:
S += getsizeof(value)
if self.columns is not None:
for value in self.columns:
S += getsizeof(value)
S += getsizeof(self.is_scalars)
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 Elmer
ElmerResults_dict = super(ElmerResults, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
ElmerResults_dict["data"] = self.data.copy() if self.data is not None else None
ElmerResults_dict["file"] = self.file
ElmerResults_dict["usecols"] = (
self.usecols.copy() if self.usecols is not None else None
)
ElmerResults_dict["columns"] = (
self.columns.copy() if self.columns is not None else None
)
ElmerResults_dict["is_scalars"] = self.is_scalars
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
ElmerResults_dict["__class__"] = "ElmerResults"
return ElmerResults_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
if self.data is None:
data_val = None
else:
data_val = self.data.copy()
file_val = self.file
if self.usecols is None:
usecols_val = None
else:
usecols_val = self.usecols.copy()
if self.columns is None:
columns_val = None
else:
columns_val = self.columns.copy()
is_scalars_val = self.is_scalars
logger_name_val = self.logger_name
# Creates new object of the same type with the copied properties
obj_copy = type(self)(
data=data_val,
file=file_val,
usecols=usecols_val,
columns=columns_val,
is_scalars=is_scalars_val,
logger_name=logger_name_val,
)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.data = None
self.file = None
self.usecols = None
self.columns = None
self.is_scalars = None
# Set to None the properties inherited from Elmer
super(ElmerResults, self)._set_None()
def _get_data(self):
"""getter of data"""
return self._data
def _set_data(self, value):
"""setter of data"""
if type(value) is int and value == -1:
value = dict()
check_var("data", value, "dict")
self._data = value
data = property(
fget=_get_data,
fset=_set_data,
doc=u"""Dict with simulation results
:Type: dict
""",
)
def _get_file(self):
"""getter of file"""
return self._file
def _set_file(self, value):
"""setter of file"""
check_var("file", value, "str")
self._file = value
file = property(
fget=_get_file,
fset=_set_file,
doc=u"""Filename of the results data file
:Type: str
""",
)
def _get_usecols(self):
"""getter of usecols"""
return self._usecols
def _set_usecols(self, value):
"""setter of usecols"""
if type(value) is int and value == -1:
value = list()
check_var("usecols", value, "list")
self._usecols = value
usecols = property(
fget=_get_usecols,
fset=_set_usecols,
doc=u"""List integers (starting with 1) of columns to load. If usecols is not set all columns are loaded.
:Type: list
""",
)
def _get_columns(self):
"""getter of columns"""
return self._columns
def _set_columns(self, value):
"""setter of columns"""
if type(value) is int and value == -1:
value = list()
check_var("columns", value, "list")
self._columns = value
columns = property(
fget=_get_columns,
fset=_set_columns,
doc=u"""List of columns data names
:Type: list
""",
)
def _get_is_scalars(self):
"""getter of is_scalars"""
return self._is_scalars
def _set_is_scalars(self, value):
"""setter of is_scalars"""
check_var("is_scalars", value, "bool")
self._is_scalars = value
is_scalars = property(
fget=_get_is_scalars,
fset=_set_is_scalars,
doc=u"""Determin if data are 'SaveScalars' data, else 'SaveLine' data are assumed
:Type: bool
""",
)
