134 lines
6.1 KiB
Python
134 lines
6.1 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, StrictFloat, StrictInt, StrictStr, field_validator
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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 MeasurementStatistics(BaseModel):
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"""
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MeasurementStatistics
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""" # noqa: E501
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file_prefix: Optional[StrictStr] = None
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run_number: Optional[StrictInt] = Field(default=None, description="Number of data collection run. This can be either automatically incremented or provided externally for each data collection. ")
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experiment_group: Optional[StrictStr] = Field(default=None, description="Name of group owning the data (e.g. p-group or proposal number). ")
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images_expected: Optional[StrictInt] = None
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images_collected: Optional[StrictInt] = Field(default=None, description="Images collected by the receiver. This number will be lower than images expected if there were issues with data collection performance. ")
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images_sent: Optional[StrictInt] = Field(default=None, description="Images sent to the writer. The value does not include images discarded by lossy compression filter and images not forwarded due to full ZeroMQ queue. ")
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images_discarded_lossy_compression: Optional[StrictInt] = Field(default=None, description="Images discarded by the lossy compression filter")
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max_image_number_sent: Optional[StrictInt] = None
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collection_efficiency: Optional[Union[Annotated[float, Field(le=1.0, strict=True, ge=0.0)], Annotated[int, Field(le=1, strict=True, ge=0)]]] = None
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compression_ratio: Optional[Union[Annotated[float, Field(strict=True, ge=0.0)], Annotated[int, Field(strict=True, ge=0)]]] = None
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cancelled: Optional[StrictBool] = None
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max_receiver_delay: Optional[StrictInt] = None
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indexing_rate: Optional[Union[StrictFloat, StrictInt]] = None
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detector_width: Optional[StrictInt] = None
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detector_height: Optional[StrictInt] = None
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detector_pixel_depth: Optional[StrictInt] = None
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bkg_estimate: Optional[Union[StrictFloat, StrictInt]] = None
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unit_cell: Optional[StrictStr] = None
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__properties: ClassVar[List[str]] = ["file_prefix", "run_number", "experiment_group", "images_expected", "images_collected", "images_sent", "images_discarded_lossy_compression", "max_image_number_sent", "collection_efficiency", "compression_ratio", "cancelled", "max_receiver_delay", "indexing_rate", "detector_width", "detector_height", "detector_pixel_depth", "bkg_estimate", "unit_cell"]
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@field_validator('detector_pixel_depth')
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def detector_pixel_depth_validate_enum(cls, value):
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"""Validates the enum"""
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if value is None:
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return value
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if value not in set([2, 4]):
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raise ValueError("must be one of enum values (2, 4)")
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return value
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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 MeasurementStatistics 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 MeasurementStatistics 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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"file_prefix": obj.get("file_prefix"),
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"run_number": obj.get("run_number"),
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"experiment_group": obj.get("experiment_group"),
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"images_expected": obj.get("images_expected"),
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"images_collected": obj.get("images_collected"),
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"images_sent": obj.get("images_sent"),
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"images_discarded_lossy_compression": obj.get("images_discarded_lossy_compression"),
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"max_image_number_sent": obj.get("max_image_number_sent"),
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"collection_efficiency": obj.get("collection_efficiency"),
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"compression_ratio": obj.get("compression_ratio"),
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"cancelled": obj.get("cancelled"),
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"max_receiver_delay": obj.get("max_receiver_delay"),
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"indexing_rate": obj.get("indexing_rate"),
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"detector_width": obj.get("detector_width"),
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"detector_height": obj.get("detector_height"),
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"detector_pixel_depth": obj.get("detector_pixel_depth"),
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"bkg_estimate": obj.get("bkg_estimate"),
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"unit_cell": obj.get("unit_cell")
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})
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return _obj
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