116 lines
5.9 KiB
Python
116 lines
5.9 KiB
Python
# coding: utf-8
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"""
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Jungfraujoch
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Jungfraujoch Broker Web API
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The version of the OpenAPI document: 1.0.0-rc.15
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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, Field, StrictBool, StrictInt
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from typing import Any, ClassVar, Dict, List, Optional
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from typing_extensions import Annotated
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from typing import Optional, Set
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from typing_extensions import Self
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class DetectorSettings(BaseModel):
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"""
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DetectorSettings
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""" # noqa: E501
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frame_time_us: Annotated[int, Field(strict=True, ge=450)] = Field(description="Interval between consecutive frames.")
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count_time_us: Optional[StrictInt] = Field(default=None, description="Integration time of the detector. If not provided count time will be set to maximum value for a given frame time.")
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storage_cell_count: Annotated[int, Field(le=16, strict=True, ge=1)]
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internal_frame_generator: StrictBool = Field(description="Use internal frame generator in FPGA instead of getting data from a real detector")
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internal_frame_generator_images: Annotated[int, Field(le=128, strict=True, ge=1)]
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pedestal_g0_frames: Annotated[int, Field(strict=True, ge=0)]
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pedestal_g1_frames: Annotated[int, Field(strict=True, ge=0)]
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pedestal_g2_frames: Annotated[int, Field(strict=True, ge=0)]
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pedestal_g0_rms_limit: Annotated[int, Field(strict=True, ge=0)] = Field(description="Pixels with pedestal G0 RMS above the threshold are marked as masked pixels")
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pedestal_min_image_count: Annotated[int, Field(strict=True, ge=32)] = Field(description="Minimum number of collected images for pedestal to consider it viable")
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storage_cell_delay_ns: Annotated[int, Field(strict=True, ge=2100)] = Field(description="Delay between two storage cells [ns]")
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detector_trigger_delay_ns: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=0, description="Delay between TTL trigger and acquisition start [ns]")
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fixed_gain_g1: Optional[StrictBool] = Field(default=False, description="Fix gain to G1 (can be useful for storage cells)")
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use_gain_hg0: Optional[StrictBool] = Field(default=False, description="Use high G0 (for low energy applications)")
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__properties: ClassVar[List[str]] = ["frame_time_us", "count_time_us", "storage_cell_count", "internal_frame_generator", "internal_frame_generator_images", "pedestal_g0_frames", "pedestal_g1_frames", "pedestal_g2_frames", "pedestal_g0_rms_limit", "pedestal_min_image_count", "storage_cell_delay_ns", "detector_trigger_delay_ns", "fixed_gain_g1", "use_gain_hg0"]
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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 DetectorSettings 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 DetectorSettings 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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"frame_time_us": obj.get("frame_time_us"),
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"count_time_us": obj.get("count_time_us"),
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"storage_cell_count": obj.get("storage_cell_count") if obj.get("storage_cell_count") is not None else 1,
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"internal_frame_generator": obj.get("internal_frame_generator") if obj.get("internal_frame_generator") is not None else False,
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"internal_frame_generator_images": obj.get("internal_frame_generator_images") if obj.get("internal_frame_generator_images") is not None else 1,
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"pedestal_g0_frames": obj.get("pedestal_g0_frames") if obj.get("pedestal_g0_frames") is not None else 2000,
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"pedestal_g1_frames": obj.get("pedestal_g1_frames") if obj.get("pedestal_g1_frames") is not None else 300,
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"pedestal_g2_frames": obj.get("pedestal_g2_frames") if obj.get("pedestal_g2_frames") is not None else 300,
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"pedestal_g0_rms_limit": obj.get("pedestal_g0_rms_limit") if obj.get("pedestal_g0_rms_limit") is not None else 100,
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"pedestal_min_image_count": obj.get("pedestal_min_image_count") if obj.get("pedestal_min_image_count") is not None else 128,
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"storage_cell_delay_ns": obj.get("storage_cell_delay_ns") if obj.get("storage_cell_delay_ns") is not None else 5000,
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"detector_trigger_delay_ns": obj.get("detector_trigger_delay_ns") if obj.get("detector_trigger_delay_ns") is not None else 0,
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"fixed_gain_g1": obj.get("fixed_gain_g1") if obj.get("fixed_gain_g1") is not None else False,
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"use_gain_hg0": obj.get("use_gain_hg0") if obj.get("use_gain_hg0") is not None else False
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})
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return _obj
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