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ophth_algo_types

This module contains the dataclasses and constants used in the Ophthalmology.

Classes​

ColumnFilter​

class ColumnFilter(    column: str,    operator: str,    value: Union[str, int, float],    how: PartialMatchingType = 'all',):

Dataclass for column filtering.

Arguments

  • column: The column name on which the filter will be applied. The filtering ignores capitalization or spaces for the column name.
  • operator: The operator for the filtering operation. E.g., "less than", ">=", "not equal", "==".
  • value: The value for the filter. This is allowed to be a string only for equals or not equal operators, and needs to be a float or integer for all other operations.

Raises

  • ValueError: If an inequality comparison operation is given with a value which cannot be converted to a float.

Variables​

  • static column : str
  • static how : Literal['any', 'all']
  • static operator : str
  • static value : Union[str, int, float]

GAMetrics​

class GAMetrics(*args, **kwargs):

Output of the GA calculation algorithm.

Attributes

  • total_ga_area: Total area of GA in the image in mm^2.
  • smallest_lesion_size: Size of the smallest lesion in the image in mm^2.
  • largest_lesion_size: Size of the largest lesion in the image in mm^2.
  • num_bscans_with_ga: Number of B-scans with GA in the image.
  • num_ga_lesions: Number of GA lesions in the image.
  • distance_from_image_centre: Distance from the image centre to the nearest lesion in mm. Image centre is used as a proxy for the fovea.
  • max_cnv_probability: Maximum probability of CNV across all B-scans in the image. This value will be between 0 and 1.
  • max_ga_bscan_index: Index of the B-scan with the largest GA lesion if there is GA, otherwise None.
  • segmentation_areas: A dictionary containing the area of each segmentation class in the image in mm^2.

Ancestors​

  • builtins.dict

Variables​

  • static distance_from_image_centre : float
  • static largest_lesion_size : float
  • static max_cnv_probability : float
  • static max_ga_bscan_index : Optional[int]
  • static num_bscans_with_ga : int
  • static num_ga_lesions : int
  • static segmentation_areas : dict
  • static smallest_lesion_size : float
  • static total_ga_area : float

ImageFieldType​

class ImageFieldType(column: str):

Stores information for an image field.

Variables​

  • static column : str

OCTImageMetadataColumns​

class OCTImageMetadataColumns(    height_mm_column: str = 'dimensions_mm_height',    width_mm_column: str = 'dimensions_mm_width',    depth_mm_column: str = 'dimensions_mm_depth',):

Dataclass for storing columns related to the OCT dimensions.

Arguments

  • height_mm_column: The name of the column for where the height in mm of the OCT image. Defaults to dimensions_mm_height.
  • width_mm_column: The name of the column for where the width in mm of the OCT image. Defaults to dimensions_mm_width.
  • depth_mm_column: The name of the column for where the depth in mm of the OCT image. Defaults to dimensions_mm_depth.

Variables​

  • static depth_mm_column : str
  • static height_mm_column : str
  • static width_mm_column : str

ReportMetadata​

class ReportMetadata(    text_fields: list[TextFieldType],    heading: Optional[str] = None,    image_field: Optional[ImageFieldType] = None,):

Dataclass for storing pdf report metadata fields.

Variables​

  • static heading : Optional[str]
  • static text_fields : list

SLOImageMetadataColumns​

class SLOImageMetadataColumns(    height_mm_column: str = 'slo_dimensions_mm_height',    width_mm_column: str = 'slo_dimensions_mm_width',):

Dataclass for storing columns related to the SLO dimensions.

Arguments

  • height_mm_column: The name of the column for where the height in mm of the SLO image. Defaults to slo_dimensions_mm_height.
  • width_mm_column: The name of the column for where the width in mm of the SLO image. Defaults to slo_dimensions_mm_width.

Variables​

  • static height_mm_column : str
  • static width_mm_column : str

SLOSegmentationLocationPrefix​

class SLOSegmentationLocationPrefix(    start_x_image: str = 'loc_start_x_image_',    start_y_image: str = 'loc_start_y_image_',    end_x_image: str = 'loc_end_x_image_',    end_y_image: str = 'loc_end_y_image_',):

Dataclass for location columns prefixes for the OCT images on the SLO.

Arguments

  • start_x_image: Column name prefix where the start x-axis pixel location of the first OCT image is on SLO. Defaults to loc_start_x_image_.
  • start_y_image: Column name prefix where the start y-axis pixel location of the first OCT image is on SLO. Defaults to loc_start_y_image_.
  • end_x_image: Column name prefix where the end x-axis pixel location of the first OCT image is on SLO. Defaults to loc_end_x_image_.
  • end_y_image: Column name prefix where the end y-axis pixel location of the first OCT image is on SLO. Defaults to loc_end_y_image_.

Variables​

  • static end_x_image : str
  • static end_y_image : str
  • static start_x_image : str
  • static start_y_image : str

TextFieldType​

class TextFieldType(    heading: str,    column: Optional[str] = None,    value: Optional[str] = None,    datetime_format: Optional[str] = None,):

Stores information for a text field.

Variables​

  • static column : Optional[str]
  • static datetime_format : Optional[str]
  • static heading : str
  • static value : Optional[str]

TrialNotesCSVArgs​

class TrialNotesCSVArgs(    columns_for_csv: list[str],    columns_from_data: Optional[dict[str, str]] = None,    columns_to_populate_with_static_values: Optional[dict[str, str]] = None,    eligible_only: bool = True,):

Dataclass for storing the arguments for the trial notes CSV.

Arguments

  • columns_for_csv: The columns to include in the trial notes CSV.
  • columns_from_data: The columns to include from the data. Defaults to None.
  • columns_to_populate_with_static_values: The columns to populate with static values. Defaults to None

Variables​

  • static columns_for_csv : list
  • static columns_from_data : Optional[dict]
  • static columns_to_populate_with_static_values : Optional[dict]
  • static eligible_only : bool