# coding: utf-8
from __future__ import annotations
from configparser import ConfigParser
from pydantic import BaseModel, ConfigDict
from ..base.FrmFlatToNestedBase import FrmFlatToNestedBase
from ..base.NestedModelBase import NestedModelBase
from .dark_and_flat_section import DarkAndFlatSection
from .general_section import GeneralSection
from .keys_section import KeysSection
from .sample_section import SampleSection
from .unit_section import UnitSection
from .detector_section import DetectorSection
from ..base.source_section import SourceSection as _SourceSection
from ..base.instrument_section import InstrumentSection
from nxtomomill.utils.io import deprecated_warning
__all__ = ["EDF2nxModel", "generate_default_edf_config"]
class _LegacySourceSection(_SourceSection, InstrumentSection):
"""Historically the instrument name and section was melt with the source section"""
pass
[docs]class EDF2nxModel(
FrmFlatToNestedBase,
GeneralSection,
KeysSection,
DarkAndFlatSection,
SampleSection,
_LegacySourceSection,
DetectorSection,
UnitSection,
):
"""Configuration file to run a conversion from spec-edf to NXtomo format"""
model_config: ConfigDict = ConfigDict(validate_assignment=True)
def to_nested_model(self) -> _NestedTomoEDFConfig:
return _NestedTomoEDFConfig(
general_section=GeneralSection(
output_file=self.output_file,
overwrite=self.overwrite,
log_level=self.log_level,
input_folder=self.input_folder,
file_extension=self.file_extension,
delete_edf_source_files=self.delete_edf_source_files,
output_checks=self.output_checks,
dataset_basename=self.dataset_basename,
dataset_info_file=self.dataset_info_file,
title=self.title,
patterns_to_ignores=self.patterns_to_ignores,
duplicate_data=self.duplicate_data,
external_link_type=self.external_link_type,
),
edf_keys_section=KeysSection(
motor_position_keys=self.motor_position_keys,
motor_mne_keys=self.motor_mne_keys,
rotation_angle_keys=self.rotation_angle_keys,
x_translation_keys=self.x_translation_keys,
y_translation_keys=self.y_translation_keys,
z_translation_keys=self.z_translation_keys,
machine_current_keys=self.machine_current_keys,
),
sample_section=SampleSection(
angle_calculation_endpoint=self.angle_calculation_endpoint,
force_angle_calculation=self.force_angle_calculation,
angle_calculation_rev_neg_scan_range=self.angle_calculation_rev_neg_scan_range,
sample_name=self.sample_name,
),
source_section=_LegacySourceSection(
instrument_name=self.instrument_name,
source_name=self.source_name,
source_type=self.source_type,
source_probe=self.source_probe,
),
detector_section=DetectorSection(
field_of_view=self.field_of_view,
),
unit_section=UnitSection(
pixel_size_unit=self.pixel_size_unit,
sample_detector_distance_unit=self.sample_detector_distance_unit,
energy_unit=self.energy_unit,
machine_current_unit=self.machine_current_unit,
x_translation_unit=self.x_translation_unit,
y_translation_unit=self.y_translation_unit,
z_translation_unit=self.z_translation_unit,
),
dark_and_flat_section=DarkAndFlatSection(
dark_names_prefix=self.dark_names_prefix,
flat_names_prefix=self.flat_names_prefix,
),
)
@classmethod
def from_cfg_file(cls, file_path: str) -> EDF2nxModel:
txt_parser = ConfigParser(allow_no_value=True)
txt_parser.read(file_path)
def get_section(name, default={}):
if txt_parser.has_section(name):
return txt_parser[name]
else:
return default
return _NestedTomoEDFConfig(
general_section=GeneralSection(**get_section("GENERAL_SECTION")),
edf_keys_section=KeysSection(**get_section("EDF_KEYS_SECTION")),
dark_and_flat_section=DarkAndFlatSection(
**get_section("DARK_AND_FLAT_SECTION")
),
sample_section=SampleSection(**get_section("SAMPLE_SECTION")),
unit_section=UnitSection(**get_section("UNIT_SECTION")),
source_section=_LegacySourceSection(**get_section("SOURCE_SECTION")),
detector_section=DetectorSection(**get_section("DETECTOR_SECTION")),
).to_flatten_config()
[docs] @staticmethod
def from_dict(dict_: dict) -> None:
deprecated_warning(
type_="function",
name="from_dict",
reason="replaced by pydantic 'model_dump' function",
replacement="model_dump",
since_version="2.0",
)
dict_ = {key.lower(): value for key, value in dict_.items()}
config = _NestedTomoEDFConfig(**dict_)
return config.to_flatten_config()
class _NestedTomoEDFConfig(
BaseModel,
NestedModelBase,
):
"""
Configuration class to provide to the convert from edf to nx
"""
model_config = ConfigDict(str_to_upper=True)
general_section: GeneralSection = GeneralSection()
edf_keys_section: KeysSection = KeysSection()
dark_and_flat_section: DarkAndFlatSection = DarkAndFlatSection()
sample_section: SampleSection = SampleSection()
source_section: _LegacySourceSection = _LegacySourceSection()
detector_section: DetectorSection = DetectorSection()
unit_section: UnitSection = UnitSection()
def to_flatten_config(self) -> EDF2nxModel:
return EDF2nxModel(
**self.general_section.model_dump(),
**self.edf_keys_section.model_dump(),
**self.dark_and_flat_section.model_dump(),
**self.sample_section.model_dump(),
**self.source_section.model_dump(),
**self.detector_section.model_dump(),
**self.unit_section.model_dump(),
)
[docs]def generate_default_edf_config() -> dict:
"""generate a default configuration for converting spec-edf to NXtomo"""
config = EDF2nxModel()
return {key.upper(): value for key, value in config.model_dump().items()}
