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feat: smartspim histology (#12)
* feat: smartspim histology endpt * adds examples * linters
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‎aind-slims-service-server/src/aind_slims_service_server/handlers/histology.py‎

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"""
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Module to handle fetching histology data from slims and parsing it to a model
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"""
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from datetime import datetime, timezone
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from typing import List, Optional, Tuple
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from networkx import DiGraph
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from slims.criteria import is_one_of
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from slims.internal import Record
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from aind_slims_service_server.handlers.table_handler import (
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SlimsTableHandler,
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)
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from aind_slims_service_server.models import (
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HistologyReagentData,
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HistologyWashData,
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SlimsHistologyData,
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)
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class HistologySessionHandler(SlimsTableHandler):
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"""Class to handle getting SPIM Histology Procedures info from SLIMS."""
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def _get_reagent_data(
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self, records: List[Record]
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) -> List[HistologyReagentData]:
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"""
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Get reagent data from records
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Parameters
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----------
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records : List[Record]
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Returns
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-------
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List[HistologyReagentData]
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"""
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reagents = []
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for record in records:
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if record.table_name() == "Content" and self.get_attr_or_none(
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record, "cntn_fk_category", "displayValue"
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) in [
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"Reagents, Externally Manufactured",
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"Reagents, Internally Produced",
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]:
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n_reagent_lot_number = self.get_attr_or_none(
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record, "cntn_cf_lotNumber"
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)
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n_reagent_name = self.get_attr_or_none(
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record, "cntn_cf_fk_reagentCatalogNumber", "displayValue"
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)
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n_reagent_source = self.get_attr_or_none(
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record, "cntn_fk_source", "displayValue"
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)
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reagent_data = HistologyReagentData(
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name=n_reagent_name,
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source=n_reagent_source,
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lot_number=n_reagent_lot_number,
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)
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reagents.append(reagent_data)
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return reagents
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def _get_wash_data(
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self, g: DiGraph, exp_run_step: str, exp_run_step_row: Record
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) -> HistologyWashData:
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"""
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Get wash data from SLIMS records.
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Parameters
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----------
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g : DiGraph
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exp_run_step : str
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Name of the node for the experiment run step
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exp_run_step_row : Record
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The Record attached to the node.
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Returns
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-------
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HistologyWashData
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"""
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wash_data = HistologyWashData()
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wash_data.wash_name = self.get_attr_or_none(
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exp_run_step_row, "xprs_name"
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)
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wash_data.wash_type = self.get_attr_or_none(
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exp_run_step_row, "xprs_cf_spimWashType"
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)
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start_time_ts = self.get_attr_or_none(
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exp_run_step_row, "xprs_cf_startTime"
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)
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wash_data.start_time = (
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None
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if start_time_ts is None
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else datetime.fromtimestamp(start_time_ts / 1000, tz=timezone.utc)
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)
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end_time_ts = self.get_attr_or_none(
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exp_run_step_row, "xprs_cf_endTime"
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)
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wash_data.end_time = (
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None
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if end_time_ts is None
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else datetime.fromtimestamp(end_time_ts / 1000, tz=timezone.utc)
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)
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wash_data.modified_by = self.get_attr_or_none(
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exp_run_step_row, "xprs_modifiedBy"
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)
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wash_data.mass = self.get_attr_or_none(
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exp_run_step_row, "xprs_cf_mass"
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)
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wash_data_successors = g.successors(exp_run_step)
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records = [g.nodes[n]["row"] for n in wash_data_successors]
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reagents = self._get_reagent_data(records)
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wash_data.reagents = reagents
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return wash_data
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def _get_specimen_data(
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self, g: DiGraph, exp_run_step_content: str
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) -> Tuple[Optional[str], Optional[str]]:
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"""
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Get subject_id and specimen_id from Content record.
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Parameters
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----------
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g : DiGraph
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exp_run_step_content : str
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Name of the node for the experiment run step content
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Returns
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-------
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tuple
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(subject_id, specimen_id)
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"""
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content_nodes = g.successors(exp_run_step_content)
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records = [g.nodes[c]["row"] for c in content_nodes]
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specimen_id = None
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subject_id = None
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for record in records:
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n_subject_id = self.get_attr_or_none(record, "cntn_id")
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if n_subject_id is not None:
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subject_id = n_subject_id
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n_specimen_id = self.get_attr_or_none(record, "cntn_barCode")
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if n_specimen_id is not None:
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specimen_id = n_specimen_id
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return subject_id, specimen_id
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def _parse_graph(
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self, g: DiGraph, root_nodes: List[str], subject_id: Optional[str]
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) -> List[SlimsHistologyData]:
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"""
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Parses the graph object into a list of pydantic models.
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Parameters
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----------
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g : DiGraph
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Graph of the SLIMS records.
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root_nodes : List[str]
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List of root nodes to pull descendants from.
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subject_id : str | None
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Labtracks ID of mouse to filter records by.
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Returns
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-------
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List[SlimsHistologyData]
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"""
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histology_data_list = []
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for node in root_nodes:
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histology_data = SlimsHistologyData()
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washes = []
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experiment_run_created_on_ts = self.get_attr_or_none(
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g.nodes[node]["row"], "xprn_createdOn"
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)
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histology_data.experiment_run_created_on = (
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None
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if experiment_run_created_on_ts is None
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else datetime.fromtimestamp(
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experiment_run_created_on_ts / 1000, tz=timezone.utc
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)
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)
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exp_run_name = self.get_attr_or_none(
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g.nodes[node]["row"], "xptm_name"
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)
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histology_data.procedure_name = exp_run_name
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exp_run_steps = g.successors(node)
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for exp_run_step in exp_run_steps:
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exp_run_step_row = g.nodes[exp_run_step]["row"]
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exp_run_step_name = self.get_attr_or_none(
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exp_run_step_row, "xprs_name"
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)
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if exp_run_step_name in [
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"Wash 1",
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"Wash 2",
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"Wash 3",
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"Wash 4",
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"Refractive Index Matching Wash",
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"Primary Antibody Wash",
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"Secondary Antibody Wash",
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"MBS Wash",
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"Gelation PBS Wash",
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"Stock X + VA-044 Equilibration",
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"Gelation + ProK RT",
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"Gelation + Add'l ProK 37C",
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"Final PBS Wash",
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]:
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wash_data = self._get_wash_data(
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g,
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exp_run_step=exp_run_step,
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exp_run_step_row=exp_run_step_row,
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)
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washes.append(wash_data)
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exp_run_step_children = g.successors(exp_run_step)
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for exp_run_step_child in exp_run_step_children:
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table_name = g.nodes[exp_run_step_child]["table_name"]
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row = g.nodes[exp_run_step_child]["row"]
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if table_name == "SOP":
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stop_link = self.get_attr_or_none(row, "stop_link")
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stop_name = self.get_attr_or_none(row, "stop_name")
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histology_data.protocol_id = stop_link
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histology_data.protocol_name = stop_name
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if table_name == "ExperimentRunStepContent":
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n_subject_id, n_specimen_id = self._get_specimen_data(
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g=g, exp_run_step_content=exp_run_step_child
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)
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if n_subject_id is not None:
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histology_data.subject_id = n_subject_id
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if n_specimen_id is not None:
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histology_data.specimen_id = n_specimen_id
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histology_data.washes = washes
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if subject_id is None or subject_id == histology_data.subject_id:
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histology_data_list.append(histology_data)
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return histology_data_list
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def _get_graph(
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self,
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start_date_greater_than_or_equal: Optional[datetime] = None,
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end_date_less_than_or_equal: Optional[datetime] = None,
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) -> Tuple[DiGraph, List[str]]:
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"""
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Generate a Graph of the records from SLIMS for histology.
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Parameters
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----------
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start_date_greater_than_or_equal : datetime | None
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Filter experiment runs that were created on or after this datetime.
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end_date_less_than_or_equal : datetime | None
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Filter experiment runs that were created on or before this datetime.
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Returns
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-------
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Tuple[DiGraph, List[str]]
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A directed graph of the SLIMS records and a list of the root nodes.
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"""
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experiment_template_rows = self.session.fetch(
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table="ExperimentTemplate",
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criteria=is_one_of(
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"xptm_name",
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[
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"SmartSPIM Labeling",
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"SmartSPIM Delipidation",
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"SmartSPIM Refractive Index Matching",
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],
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),
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)
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date_criteria = self._get_date_criteria(
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start_date=start_date_greater_than_or_equal,
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end_date=end_date_less_than_or_equal,
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field_name="xprn_createdOn",
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)
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exp_run_rows = self.get_rows_from_foreign_table(
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input_table="ExperimentTemplate",
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input_rows=experiment_template_rows,
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input_table_cols=["xptm_pk"],
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foreign_table="ExperimentRun",
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foreign_table_col="xprn_fk_experimentTemplate",
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extra_criteria=date_criteria,
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)
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G = DiGraph()
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root_nodes = []
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for row in exp_run_rows:
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G.add_node(
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f"{row.table_name()}.{row.pk()}",
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row=row,
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pk=row.pk(),
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table_name=row.table_name(),
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)
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root_nodes.append(f"{row.table_name()}.{row.pk()}")
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exp_run_step_rows = self.get_rows_from_foreign_table(
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input_table="ExperimentRun",
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input_rows=exp_run_rows,
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input_table_cols=["xprn_pk"],
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foreign_table="ExperimentRunStep",
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foreign_table_col="xprs_fk_experimentRun",
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graph=G,
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)
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_ = self.get_rows_from_foreign_table(
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input_table="ExperimentRunStep",
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input_rows=exp_run_step_rows,
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input_table_cols=["xprs_cf_fk_protocol"],
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foreign_table="SOP",
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foreign_table_col="stop_pk",
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graph=G,
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)
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exp_run_step_content_rows = self.get_rows_from_foreign_table(
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input_table="ExperimentRunStep",
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input_rows=exp_run_step_rows,
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input_table_cols=["xprs_pk"],
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foreign_table="ExperimentRunStepContent",
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foreign_table_col="xrsc_fk_experimentRunStep",
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graph=G,
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)
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_ = self.get_rows_from_foreign_table(
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input_table="ExperimentRunStepContent",
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input_rows=exp_run_step_content_rows,
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input_table_cols=["xrsc_fk_content"],
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foreign_table="Content",
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foreign_table_col="cntn_pk",
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graph=G,
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)
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reagent_content_rows = self.get_rows_from_foreign_table(
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input_table="ExperimentRunStep",
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input_rows=exp_run_step_rows,
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input_table_cols=["xprs_cf_fk_reagent"],
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foreign_table="Content",
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foreign_table_col="cntn_pk",
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graph=G,
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)
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_ = self.get_rows_from_foreign_table(
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input_table="Content",
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input_rows=reagent_content_rows,
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input_table_cols=["cntn_cf_fk_reagentCatalogNumber"],
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foreign_table="ReferenceDataRecord",
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foreign_table_col="rdrc_pk",
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graph=G,
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)
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return G, root_nodes
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def get_histology_data_from_slims(
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self,
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subject_id: Optional[str] = None,
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start_date_greater_than_or_equal: Optional[str] = None,
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end_date_less_than_or_equal: Optional[str] = None,
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) -> List[SlimsHistologyData]:
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"""
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Get Histology data from SLIMS.
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Parameters
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----------
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subject_id : str | None
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Labtracks ID of mouse. If None, then no filter will be performed.
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start_date_greater_than_or_equal : str | None
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Filter experiment runs that were created on or after this datetime.
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end_date_less_than_or_equal : str | None
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Filter experiment runs that were created on or before this datetime.
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Returns
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-------
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List[SlimsHistologyData]
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Raises
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------
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ValueError
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The subject_id cannot be an empty string.
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"""
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if subject_id is not None and len(subject_id) == 0:
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raise ValueError("subject_id must not be empty!")
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G, root_nodes = self._get_graph(
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start_date_greater_than_or_equal=self.parse_date(
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start_date_greater_than_or_equal
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),
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end_date_less_than_or_equal=self.parse_date(
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end_date_less_than_or_equal
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),
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)
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hist_data = self._parse_graph(
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g=G, root_nodes=root_nodes, subject_id=subject_id
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)
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return hist_data

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