105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
# coding: utf-8
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"""
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Jungfraujoch
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API to control Jungfraujoch developed by the Paul Scherrer Institute (Switzerland). Jungfraujoch is a data acquisition and analysis system for pixel array detectors, primarly PSI JUNGFRAU. Jungfraujoch uses FPGA boards to acquire data at high data rates.
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The version of the OpenAPI document: 1.0.0-rc.23
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Contact: filip.leonarski@psi.ch
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Generated by OpenAPI Generator (https://openapi-generator.tech)
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Do not edit the class manually.
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""" # noqa: E501
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from __future__ import annotations
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import pprint
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import re # noqa: F401
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import json
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from pydantic import BaseModel, ConfigDict, StrictFloat, StrictInt
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from typing import Any, ClassVar, Dict, List, Union
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from typing import Optional, Set
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from typing_extensions import Self
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class CalibrationStatisticsInner(BaseModel):
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"""
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CalibrationStatisticsInner
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""" # noqa: E501
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module_number: StrictInt
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storage_cell_number: StrictInt
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pedestal_g0_mean: Union[StrictFloat, StrictInt]
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pedestal_g1_mean: Union[StrictFloat, StrictInt]
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pedestal_g2_mean: Union[StrictFloat, StrictInt]
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gain_g0_mean: Union[StrictFloat, StrictInt]
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gain_g1_mean: Union[StrictFloat, StrictInt]
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gain_g2_mean: Union[StrictFloat, StrictInt]
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masked_pixels: StrictInt
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__properties: ClassVar[List[str]] = ["module_number", "storage_cell_number", "pedestal_g0_mean", "pedestal_g1_mean", "pedestal_g2_mean", "gain_g0_mean", "gain_g1_mean", "gain_g2_mean", "masked_pixels"]
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model_config = ConfigDict(
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populate_by_name=True,
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validate_assignment=True,
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protected_namespaces=(),
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)
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def to_str(self) -> str:
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"""Returns the string representation of the model using alias"""
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return pprint.pformat(self.model_dump(by_alias=True))
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def to_json(self) -> str:
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"""Returns the JSON representation of the model using alias"""
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# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
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return json.dumps(self.to_dict())
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@classmethod
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def from_json(cls, json_str: str) -> Optional[Self]:
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"""Create an instance of CalibrationStatisticsInner from a JSON string"""
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return cls.from_dict(json.loads(json_str))
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def to_dict(self) -> Dict[str, Any]:
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"""Return the dictionary representation of the model using alias.
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This has the following differences from calling pydantic's
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`self.model_dump(by_alias=True)`:
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* `None` is only added to the output dict for nullable fields that
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were set at model initialization. Other fields with value `None`
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are ignored.
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"""
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excluded_fields: Set[str] = set([
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])
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_dict = self.model_dump(
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by_alias=True,
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exclude=excluded_fields,
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exclude_none=True,
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)
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return _dict
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@classmethod
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def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
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"""Create an instance of CalibrationStatisticsInner from a dict"""
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if obj is None:
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return None
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if not isinstance(obj, dict):
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return cls.model_validate(obj)
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_obj = cls.model_validate({
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"module_number": obj.get("module_number"),
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"storage_cell_number": obj.get("storage_cell_number"),
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"pedestal_g0_mean": obj.get("pedestal_g0_mean"),
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"pedestal_g1_mean": obj.get("pedestal_g1_mean"),
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"pedestal_g2_mean": obj.get("pedestal_g2_mean"),
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"gain_g0_mean": obj.get("gain_g0_mean"),
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"gain_g1_mean": obj.get("gain_g1_mean"),
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"gain_g2_mean": obj.get("gain_g2_mean"),
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"masked_pixels": obj.get("masked_pixels")
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})
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return _obj
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