update to datacollection parameters model
This commit is contained in:
GotthardG
+28
-214
@@ -3,7 +3,7 @@ import re
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from enum import Enum
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from typing import Annotated, Literal, Tuple, List, Optional
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from dataclasses import dataclass
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from pydantic import BaseModel, Field, field_validator, AfterValidator, ConfigDict
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from pydantic import BaseModel, Field, field_validator, AfterValidator, ConfigDict, AliasChoices
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from aare.common.coordinate import Coordinate, positive_coords
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from aare.common.diffraction_geometry import DiffractionGeometry
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@@ -54,22 +54,32 @@ class PuckLoadedInfo(BaseModel):
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class DataCollectionParameters(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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model_config = ConfigDict(from_attributes=True, populate_by_name=True)
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directory: Optional[str] = None
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oscillation: Optional[float] = None # Only accept positive float
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exposure: Optional[float] = None # Only accept positive floats between 0 and 1
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totalrange: Optional[int] = None # Only accept positive integers between 0 and 360
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totalangle: Optional[int] = Field( # was totalrange
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default=None,
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validation_alias=AliasChoices('totalangle', 'totalrange')
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) # Only accept positive integers between 0 and 360
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transmission: Optional[int] = None # Only accept positive integers between 0 and 100
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targetresolution: Optional[float] = None # Only accept positive float
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beamsize: Optional[str] = None
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aperture: Optional[int] = None # Optional string field
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datacollectiontype: Optional[str] = None # Only accept "standard", other types might be added later
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processingpipeline: Optional[str] = "" # Only accept "gopy", "autoproc", "xia2dials"
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spacegroupnumber: Optional[int] = None # Only accept positive integers between 1 and 230
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cellparameters: Optional[str] = None # Must be a set of six positive floats or integers
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unitcell: Optional[str] = Field( # was cellparameters
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default=None,
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validation_alias=AliasChoices('unitcell', 'cellparameters')
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) # Must be a set of six positive floats or integers
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rescutkey: Optional[str] = None # Only accept "is" or "cchalf"
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rescutvalue: Optional[float] = None # Must be a positive float if rescutkey is provided
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userresolution: Optional[float] = None
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processingresolution: Optional[float] = Field( # was userresolution
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default=None,
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validation_alias=AliasChoices('processingresolution', 'userresolution')
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)
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pdbid: Optional[str] = "" # Accepts either the format of the protein data bank code or {provided}
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autoprocfull: Optional[bool] = None
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procfull: Optional[bool] = None
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@@ -83,10 +93,6 @@ class DataCollectionParameters(BaseModel):
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cloud: bool = True
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pdbmodel: Optional[str] = None
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def to_dict(self):
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"""Convert the model instance to a dictionary."""
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return self.model_dump(exclude_unset=True)
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@field_validator("directory", mode="after")
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@classmethod
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def directory_characters(cls, v):
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@@ -105,13 +111,16 @@ class DataCollectionParameters(BaseModel):
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# Validate directory pattern with macros and allowed characters
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valid_macros = [
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"{date}",
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"{prefix}",
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"{sgPuck}",
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"{sgPosition}",
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# Current macros
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"{puck}",
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"{position}",
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"{prefix}",
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"{date}",
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"{run}",
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"{beamline}",
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# Legacy macros — accepted for back-compat, no longer documented
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"{sgPuck}",
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"{sgPosition}",
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"{sgPrefix}",
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"{sgPriority}",
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"{protein}",
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@@ -134,220 +143,25 @@ class DataCollectionParameters(BaseModel):
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)
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return v
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@field_validator("aperture", mode="before")
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@field_validator("unitcell", mode="before")
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@classmethod
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def aperture_selection(cls, v):
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if v is not None:
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try:
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v = int(float(v))
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if v not in {1, 2, 3}:
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raise ValueError(f" '{v}' is not valid. Value must be 1, 2, or 3.")
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid. Value must be 1, 2, or 3."
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) from e
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return v
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@field_validator("oscillation", mode="before")
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@classmethod
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def positive_float_validator(cls, v):
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if v is None:
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return None
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try:
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v = float(v)
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if v <= 0:
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raise ValueError(f"'{v}' is not valid. Value must be a positive float.")
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except (ValueError, TypeError) as e:
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raise ValueError(
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f"'{v}' is not valid. Value must be a positive float."
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) from e
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return v
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@field_validator("exposure", mode="before")
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@classmethod
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def exposure_in_range(cls, v):
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if v is not None:
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try:
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v = float(v)
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if not (0 <= v <= 1):
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raise ValueError(
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f" '{v}' is not valid. Value must be a float between 0 and 1."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid. Value must be a float between 0 and 1."
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) from e
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return v
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@field_validator("totalrange", mode="before")
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@classmethod
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def totalrange_in_range(cls, v):
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if v is not None:
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try:
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v = int(v)
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if not (0 <= v <= 360):
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 0 and 360."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 0 and 360."
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) from e
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return v
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@field_validator("transmission", mode="before")
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@classmethod
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def transmission_fraction(cls, v):
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if v is not None:
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try:
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v = int(v)
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if not (0 <= v <= 100):
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 0 and 100."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 0 and 100."
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) from e
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return v
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@field_validator("datacollectiontype", mode="before")
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@classmethod
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def datacollectiontype_allowed(cls, v):
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allowed = {"standard"} # Other types of data collection might be added later
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if v and v.lower() not in allowed:
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raise ValueError(f" '{v}' is not valid." f"Value must be one of {allowed}.")
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return v
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@field_validator("processingpipeline", mode="before")
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@classmethod
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def processingpipeline_allowed(cls, v):
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allowed = {"aareproc", "autoproc"}
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if v and v.lower() not in allowed:
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raise ValueError(f" '{v}' is not valid." f"Value must be one of {allowed}.")
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return v
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@field_validator("spacegroupnumber", mode="before")
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@classmethod
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def spacegroupnumber_allowed(cls, v):
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if v is not None:
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try:
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v = int(v)
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if not (1 <= v <= 230):
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 1 and 230."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be an integer between 1 and 230."
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) from e
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return v
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@field_validator("cellparameters", mode="before")
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@classmethod
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def cellparameters_format(cls, v):
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def unitcell_format(cls, v):
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if v:
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# Replace commas with spaces, then split on whitespace
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tokens = v.replace(",", " ").split()
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try:
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values = [float(i) for i in tokens]
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except ValueError:
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raise ValueError(
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f" '{v}' is not valid."
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" Value must be a set of six positive floats"
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" or integers (separated by space or comma)."
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f"'{v}' is not valid. "
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"Value must be six positive floats or integers (space or comma separated)."
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)
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if len(values) != 6 or any(val <= 0 for val in values):
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raise ValueError(
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f" '{v}' is not valid."
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" Value must be a set of six positive floats"
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" or integers (separated by space or comma)."
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f"'{v}' is not valid. "
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"Value must be six positive floats or integers (space or comma separated)."
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)
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return v
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# @field_validator("rescutkey", "rescutvalue", mode="before")
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# @classmethod
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# def rescutkey_value_pair(cls, values):
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# rescutkey = values.get("rescutkey")
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# rescutvalue = values.get("rescutvalue")
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# if rescutkey and rescutvalue:
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# if rescutkey not in {"is", "cchalf"}:
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# raise ValueError("Rescutkey must be either 'is' or 'cchalf'")
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# if not isinstance(rescutvalue, float) or rescutvalue <= 0:
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# raise ValueError(
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# "Rescutvalue must be a positive float if rescutkey is provided"
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# )
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# return values
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@field_validator("trustedhigh", mode="before")
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@classmethod
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def trustedhigh_allowed(cls, v):
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if v is not None:
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try:
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v = float(v)
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if not (0 <= v <= 2.0):
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be a float between 0 and 2.0."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid." f"Value must be a float between 0 and 2.0."
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) from e
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return v
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@field_validator("chiphiangles", mode="before")
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@classmethod
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def chiphiangles_allowed(cls, v):
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if v is not None:
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try:
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v = float(v)
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if not (0 <= v <= 30):
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raise ValueError(
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f" '{v}' is not valid."
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f"Value must be a float between 0 and 30."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid. Value must be a float between 0 and 30."
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) from e
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return v
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@field_validator("dose", mode="before")
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@classmethod
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def dose_positive(cls, v):
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if v is not None:
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try:
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v = float(v)
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if v <= 0:
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raise ValueError(
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f" '{v}' is not valid. Value must be a positive float."
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)
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except (ValueError, TypeError) as e:
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raise ValueError(
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f" '{v}' is not valid. Value must be a positive float."
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) from e
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return v
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@field_validator("pdbmodel", mode="after")
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@classmethod
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def validate_filepath(cls, v):
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if v is None:
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return v
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v_str = str(v) # Ensure v is a string for further checks
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if any(c in v_str for c in '<>:"|?*'):
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raise ValueError("File path contains invalid characters.")
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path = Path(v_str)
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if not path.parts:
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raise ValueError("Not a valid path.")
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return v_str # Return as string for JSON serialization
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@field_validator("cloud", mode="before")
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@classmethod
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def coerce_cloud_default(cls, v):
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