def __init__(self,
symbol="",
name="",
unit="",
symmetries={},
init_dict=None):
"""Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for SciDataTool type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for SciDataTool Object"""
if init_dict is not None: # Initialisation by dict
check_init_dict(init_dict,
["symbol", "name", "unit", "symmetries"])
# Overwrite default value with init_dict content
if "symbol" in list(init_dict.keys()):
symbol = init_dict["symbol"]
if "name" in list(init_dict.keys()):
name = init_dict["name"]
if "unit" in list(init_dict.keys()):
unit = init_dict["unit"]
if "symmetries" in list(init_dict.keys()):
symmetries = init_dict["symmetries"]
# Initialisation by argument
self.parent = None
self.symbol = symbol
self.name = name
self.unit = unit
self.symmetries = symmetries
# The class is frozen, for now it's impossible to add new properties
self._freeze()
def __init__(
self,
values=[],
symbol="",
name="",
unit="",
symmetries={},
is_components=False,
init_dict=None,
):
"""Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for SciDataTool type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for SciDataTool Object"""
if init_dict is not None: # Initialisation by dict
check_init_dict(
init_dict,
["values", "symbol", "name", "unit", "symmetries", "is_components"],
)
# Overwrite default value with init_dict content
if "values" in list(init_dict.keys()):
values = init_dict["values"]
if "symbol" in list(init_dict.keys()):
symbol = init_dict["symbol"]
if "name" in list(init_dict.keys()):
name = init_dict["name"]
if "unit" in list(init_dict.keys()):
unit = init_dict["unit"]
if "symmetries" in list(init_dict.keys()):
symmetries = init_dict["symmetries"]
if "is_components" in list(init_dict.keys()):
is_components = init_dict["is_components"]
# Initialisation by argument
self.is_components = is_components
# values can be None, a ndarray or a list
set_array(self, "values", squeeze(values))
# Call Data init
super(Data1D, self).__init__(
symbol=symbol, name=name, unit=unit, symmetries=symmetries
)
def __init__(
self,
axes=None,
normalizations={},
FTparameters={},
values=None,
symbol="",
name="",
unit="",
symmetries={},
init_dict=None,
):
"""Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for SciDataTool type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for SciDataTool Object"""
if init_dict is not None: # Initialisation by dict
check_init_dict(
init_dict,
[
"axes",
"normalizations",
"FTparameters",
"values",
"symbol",
"name",
"unit",
"symmetries",
],
)
# Overwrite default value with init_dict content
if "axes" in list(init_dict.keys()):
axes = init_dict["axes"]
if "normalizations" in list(init_dict.keys()):
normalizations = init_dict["normalizations"]
if "FTparameters" in list(init_dict.keys()):
FTparameters = init_dict["FTparameters"]
if "values" in list(init_dict.keys()):
values = init_dict["values"]
if "symbol" in list(init_dict.keys()):
symbol = init_dict["symbol"]
if "name" in list(init_dict.keys()):
name = init_dict["name"]
if "unit" in list(init_dict.keys()):
unit = init_dict["unit"]
if "symmetries" in list(init_dict.keys()):
symmetries = init_dict["symmetries"]
# Initialisation by argument
# Call DataND init
super(DataTime, self).__init__(
axes=axes,
normalizations=normalizations,
FTparameters=FTparameters,
values=values,
symbol=symbol,
name=name,
unit=unit,
symmetries=symmetries,
)
def __init__(
self,
axes=None,
normalizations={},
FTparameters={},
values=None,
symbol="",
name="",
unit="",
symmetries={},
init_dict=None,
):
"""Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for SciDataTool type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for SciDataTool Object"""
if init_dict is not None: # Initialisation by dict
check_init_dict(
init_dict,
[
"axes",
"normalizations",
"FTparameters",
"values",
"symbol",
"name",
"unit",
"symmetries",
],
)
# Overwrite default value with init_dict content
if "axes" in list(init_dict.keys()):
axes = init_dict["axes"]
if "normalizations" in list(init_dict.keys()):
normalizations = init_dict["normalizations"]
if "FTparameters" in list(init_dict.keys()):
FTparameters = init_dict["FTparameters"]
if "values" in list(init_dict.keys()):
values = init_dict["values"]
if "symbol" in list(init_dict.keys()):
symbol = init_dict["symbol"]
if "name" in list(init_dict.keys()):
name = init_dict["name"]
if "unit" in list(init_dict.keys()):
unit = init_dict["unit"]
if "symmetries" in list(init_dict.keys()):
symmetries = init_dict["symmetries"]
# Initialisation by argument
# axes can be None or a list of Data object
self.axes = list()
if type(axes) is list:
for obj in axes:
if obj is None: # Default value
self.axes.append(Data())
elif isinstance(obj, dict):
# Check that the type is correct (including daughter)
class_name = obj.get("__class__")
if class_name not in [
"Data",
"Data1D",
"DataFreq",
"DataLinspace",
"DataND",
"DataTime",
]:
raise InitUnKnowClassError("Unknow class name " +
class_name +
" in init_dict for axes")
# Dynamic import to call the correct constructor
module = __import__("SciDataTool.Classes." + class_name,
fromlist=[class_name])
class_obj = getattr(module, class_name)
self.axes.append(class_obj(init_dict=obj))
else:
self.axes.append(obj)
elif axes is None:
self.axes = list()
else:
self.axes = axes
self.normalizations = normalizations
self.FTparameters = FTparameters
# values can be None, a ndarray or a list
check_dimensions(values, axes)
set_array(self, "values", values)
# Call Data init
super(DataND, self).__init__(symbol=symbol,
name=name,
unit=unit,
symmetries=symmetries)
def __init__(
self,
initial=None,
final=None,
step=None,
number=None,
include_endpoint=False,
symbol="",
name="",
unit="",
symmetries={},
is_components=False,
init_dict=None,
):
"""Constructor of the class. Can be use in two ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for Matrix, None will initialise the property with an empty Matrix
for SciDataTool type, None will call the default constructor
- __init__ (init_dict = d) d must be a dictionnary wiht every properties as keys
ndarray or list can be given for Vector and Matrix
object or dict can be given for SciDataTool Object"""
if init_dict is not None: # Initialisation by dict
check_init_dict(
init_dict,
[
"initial",
"final",
"step",
"number",
"include_endpoint",
"symbol",
"name",
"unit",
"symmetries",
"is_components",
],
)
# Overwrite default value with init_dict content
if "initial" in list(init_dict.keys()):
initial = init_dict["initial"]
if "final" in list(init_dict.keys()):
final = init_dict["final"]
if "step" in list(init_dict.keys()):
step = init_dict["step"]
if "number" in list(init_dict.keys()):
number = init_dict["number"]
if "include_endpoint" in list(init_dict.keys()):
include_endpoint = init_dict["include_endpoint"]
if "symbol" in list(init_dict.keys()):
symbol = init_dict["symbol"]
if "name" in list(init_dict.keys()):
name = init_dict["name"]
if "unit" in list(init_dict.keys()):
unit = init_dict["unit"]
if "symmetries" in list(init_dict.keys()):
symmetries = init_dict["symmetries"]
if "is_components" in list(init_dict.keys()):
is_components = init_dict["is_components"]
# Initialisation by argument
self.initial = initial
self.final = final
self.step = step
self.number = number
self.include_endpoint = include_endpoint
self.is_components = is_components
# Call Data init
super(DataLinspace, self).__init__(symbol=symbol,
name=name,
unit=unit,
symmetries=symmetries)