96 lines
3.5 KiB
Python
96 lines
3.5 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, Field
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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 StandardDetectorGeometry(BaseModel):
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"""
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Regular rectangular geometry, first module is in the bottom left corner of the detector
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""" # noqa: E501
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nmodules: Annotated[int, Field(strict=True, ge=1)] = Field(description="Number of modules in the detector")
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gap_x: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=8, description="Gap size in X direction [pixels]")
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gap_y: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=36, description="Gap size in Y direction [pixels]")
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modules_in_row: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=1, description="Number of modules in one row")
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__properties: ClassVar[List[str]] = ["nmodules", "gap_x", "gap_y", "modules_in_row"]
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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 StandardDetectorGeometry 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 StandardDetectorGeometry 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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"nmodules": obj.get("nmodules"),
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"gap_x": obj.get("gap_x") if obj.get("gap_x") is not None else 8,
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"gap_y": obj.get("gap_y") if obj.get("gap_y") is not None else 36,
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"modules_in_row": obj.get("modules_in_row") if obj.get("modules_in_row") is not None else 1
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})
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return _obj
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