102 lines
4.2 KiB
Python
102 lines
4.2 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, StrictBool
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from typing import Any, ClassVar, Dict, List, Optional, Union
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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 PreviewSettings(BaseModel):
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"""
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Settings for JPEG rendering of preview images
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""" # noqa: E501
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saturation: Annotated[int, Field(le=65535, strict=True, ge=0)] = Field(description="Saturation value to set contrast in the preview image")
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show_spots: Optional[StrictBool] = Field(default=True, description="Show spot finding results on the image")
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show_roi: Optional[StrictBool] = Field(default=False, description="Show ROI areas on the image")
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jpeg_quality: Optional[Annotated[int, Field(le=100, strict=True, ge=0)]] = Field(default=100, description="Quality of JPEG image (100 - highest; 0 - lowest)")
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show_indexed: Optional[StrictBool] = Field(default=False, description="Preview indexed images only")
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show_user_mask: Optional[StrictBool] = Field(default=False, description="Show user mask")
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resolution_ring: Optional[Union[Annotated[float, Field(le=100.0, strict=True, ge=0.1)], Annotated[int, Field(le=100, strict=True, ge=1)]]] = 0.1
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__properties: ClassVar[List[str]] = ["saturation", "show_spots", "show_roi", "jpeg_quality", "show_indexed", "show_user_mask", "resolution_ring"]
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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 PreviewSettings 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 PreviewSettings 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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"saturation": obj.get("saturation"),
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"show_spots": obj.get("show_spots") if obj.get("show_spots") is not None else True,
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"show_roi": obj.get("show_roi") if obj.get("show_roi") is not None else False,
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"jpeg_quality": obj.get("jpeg_quality") if obj.get("jpeg_quality") is not None else 100,
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"show_indexed": obj.get("show_indexed") if obj.get("show_indexed") is not None else False,
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"show_user_mask": obj.get("show_user_mask") if obj.get("show_user_mask") is not None else False,
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"resolution_ring": obj.get("resolution_ring") if obj.get("resolution_ring") is not None else 0.1
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})
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return _obj
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