ewoksid02.tasks.normalizationtask.NormalizationTask#

class ewoksid02.tasks.normalizationtask.NormalizationTask(inputs=None, varinfo=None, node_id=None, node_attrs=None, execinfo=None, profile_directory=None)[source]#

Bases: ID02ProcessingTask

The NormalizationTask class is responsible for normalizing datasets in the ID02 SAXS pipeline. It extends the ID02ProcessingTask class and provides additional functionality to apply a standard pyFAI normalization: - Methods to read monitor values from the metadata file or from blissdata - Methods to read normalization parameters from the metadata file or from the headers - Methods to cache pyFAI azimuthal integrator and apply normalization - Applies corrections such as masking, dark frame subtraction, flat field correction, and polarization adjustments.

Optional Inputs:

  • filename_mask_normalization (str): Path to the mask file for correcting detector gaps or bad pixels.

  • filename_dark_normalization (str): Path to the file for dark current correction.

  • filename_flat_normalization (str): Path to the file for flat field correction.

  • Dummy (float): Value to replace invalid pixels in the dataset.

  • DDummy (float): Tolerance for dummy pixel replacement.

  • NormalizationFactor (float): Factor for normalizing the dataset.

  • polarization_factor (float): Factor for polarization correction.

  • polarization_axis_offset (float): Axis for polarization correction.

  • Center_1 (float): Beam center in the first dimension.

  • Center_2 (float): Beam center in the second dimension.

  • PSize_1 (float): Pixel size 1.

  • PSize_2 (float): Pixel size 2.

  • BSize_1 (float): Pixel binning 1.

  • BSize_2 (float): Pixel binning 2.

  • SampleDistance (float): Sample to detector distance in meters.

  • WaveLength (float): Wavelength of beam in meters.

  • DetectorRotation_1 (float): rot2 of pyFAI.

  • DetectorRotation_2 (float): rot1 of pyFAI.

  • DetectorRotation_3 (float): rot3 of pyFAI.

  • pin_monitor (str): Pin to the monitor stream.

  • header_pin_monitor (str): Header key used to monitor values.

  • variance_formula (str): Formula for calculating variance in the dataset.

  • algorithm_normalization (str): Implementation to perform the normalization (cython or cupy).

  • dark_filter (str): Filter to use in case the dark-current file is a multi-frame scan file.

  • dark_filter_quantil_lower (str): In case the dark_filter is quantil, lower limit.

  • dark_filter_quantil_upper (str): In case the dark_filter is quantil, upper limit.

Parameters:

  • inputs (Optional[Mapping])

  • varinfo (Optional[dict])

  • node_id (Union[str, int, tuple, None])

  • node_attrs (Optional[dict])

  • execinfo (Optional[dict])

  • profile_directory (Optional[dict])

MISSING_DATA = <MISSING_DATA>#
assert_ready_to_execute()#
cancel()#

Function called when a task is cancelled. To be implemented by the derived classes

property cancelled: bool#

Return True if the task has been cancelled by the user

classmethod class_nonce()#
classmethod class_nonce_data()#
classmethod class_registry_name()#
Return type:

Optional[str]

cleanup_references()#

Removes all references to the inputs. Side effect: fixes the uhash of the task and outputs

create_id02_processed_file(filename)#
Parameters:

filename (str)

property done: bool#

Completed (with or without exception)

property exception: Exception | None#
execute(force_rerun=False, raise_on_error=True, cleanup_references=False)#
Parameters:

  • force_rerun (Optional[bool])

  • raise_on_error (Optional[bool])

  • cleanup_references (Optional[bool])

property failed: bool#

Completed with exception

fix_uhash()#

Fix the uhash when it is derived from the uhash data.

get_HS32C_array()#
get_HS32V_array()#
get_exposuretime_values()#
Return type:

tuple

get_input_uhashes()#
get_input_value(key, default=<MISSING_DATA>)#
Parameters:

default (Any)

Return type:

Any

get_input_values()#
get_memory_info()#
get_monitor_1_values()[source]#

Generic method to read monitor_values online (from blissdata streams) or offline (from a group in the scalers file)

get_named_input_values()#
get_output_transfer_data()#

The values are either DataUri or Variable

get_output_uhashes()#
get_output_value(key, default=<MISSING_DATA>)#
Parameters:

default (Any)

Return type:

Any

get_output_values()#
get_parameter(key, to_integer=False, default=None)#
Parameters:

  • key (str)

  • to_integer (bool)

get_positional_input_values()#
get_processing_parameters()[source]#
Return type:

dict

get_stream(index_pin=None, name=None, header_key_pin=None, prefix='', suffix='', subscan_2=False)#
Parameters:

  • index_pin (int)

  • name (str)

  • header_key_pin (str)

  • prefix (str)

  • suffix (str)

  • subscan_2 (bool)

Return type:

tuple

get_stream_deltatime()#
Return type:

tuple

get_stream_exposuretime()#

In blissdata, this stream is (normally) called scalers:time (in subscan1) In the header, there is key “HSTime” pointing to the string ‘time’

Return type:

tuple

get_stream_exposuretime_raw()#

In blissdata, this stream is (normally) called mcs:time_raw (in subscan1) In the header, there is key “HSTime” pointing to the string ‘time’

Return type:

tuple

get_stream_fast_timer()#
Return type:

tuple

get_stream_monitor_0()#
Return type:

tuple

get_stream_monitor_1()#
Return type:

tuple

get_stream_slow_timer()#
Return type:

tuple

get_streams_HS32_raw()#
Return type:

list

get_streams_HS32_scalers()#
Return type:

list

classmethod get_subclass(registry_name, _second_attempt=False)#

Retrieving a derived class

classmethod get_subclass_names()#
Return type:

List[str]

classmethod get_subclasses()#
get_uhash_init(serialize=False)#
classmethod input_model()#
Return type:

Optional[Type[BaseInputModel]]

classmethod input_names()#
Return type:

Set[str]

property input_uhashes#
property input_values#

DEPRECATED

property input_variables: VariableContainer#
property inputs: ReadOnlyVariableContainerNamespace#
instance_nonce()#
classmethod instantiate(registry_name, **kw)#

Factory method for instantiating a derived class.

Parameters:

  • registry_name (str) – for example “tasklib.tasks.MyTask” or “MyTask”

  • **kw – Task constructor arguments

  • registry_name

Returns Task:

is_first_loop()#
is_new_cycle()#
is_new_workflow()#
property is_ready_to_execute#
property job_id: str | None#
property label: str#
load_scan()#
log_allocated_memory()#
log_debug(msg)#
log_error(msg)#
log_info(msg)#
log_warning(msg)#
property missing_inputs: VariableContainerMissingNamespace#
property missing_outputs: VariableContainerMissingNamespace#
property n_positional_inputs: int#
classmethod n_required_positional_inputs()#
Return type:

int

property named_input_values#

DEPRECATED

property node_id: str | int | tuple#
property npositional_inputs#

DEPRECATED

open_id02_file(filename, mode='r')#
Parameters:

  • filename (str)

  • mode (str)

classmethod optional_input_names()#
Return type:

Set[str]

property output_metadata: dict | None#
classmethod output_model()#
Return type:

Optional[Type[BaseOutputModel]]

classmethod output_names()#
Return type:

Set[str]

property output_transfer_data#

DEPRECATED

property output_uhashes#

DEPRECATED

property output_values#

DEPRECATED

property output_variables: VariableContainer#
property outputs: VariableContainerNamespace#
property positional_input_values#

DEPRECATED

process()[source]#
Return type:

None

read_from_stream_interpolate(stream_sliceable, stream_slice=None, datatype='float32')#

stream_object is a sliceable object (numpy.ndarray or blissdata stream object) Valid for all streams, from subscan1 and subscan2

Parameters:

datatype (str)

Return type:

ndarray

classmethod required_input_names()#
Return type:

Set[str]

reset_state()#
run()#

To be implemented by the derived classes

save()#
set_uhash_init(pre_uhash=None, instance_nonce=None)#
Parameters:

  • pre_uhash (Union[str, bytes, UniversalHash, HasUhash, None])

  • instance_nonce (Optional[Any])

property succeeded: bool#

Completed without exception and with output values

property task_identifier: str#
property uhash: UniversalHash | None#
uhash_randomize()#
undo_fix_uhash()#
undo_randomize()#
property workflow_id: str | None#
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