605 lines
26 KiB
Python
605 lines
26 KiB
Python
'''
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contains parser object for pulling values from client generated submission sheets.
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'''
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from getpass import getuser
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from pprint import pformat
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from typing import List
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import pandas as pd
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import numpy as np
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from pathlib import Path
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from backend.db.models import *
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from backend.validators import PydSubmission, PydReagent, RSLNamer, PydSample, PydEquipment
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import logging, re
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from collections import OrderedDict
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from datetime import date
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from dateutil.parser import parse, ParserError
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from tools import check_not_nan, convert_nans_to_nones, is_missing, row_map
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logger = logging.getLogger(f"submissions.{__name__}")
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row_keys = {v:k for k,v in row_map.items()}
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class SheetParser(object):
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"""
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object to pull and contain data from excel file
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"""
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def __init__(self, filepath:Path|None = None):
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"""
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Args:
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filepath (Path | None, optional): file path to excel sheet. Defaults to None.
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"""
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logger.debug(f"\n\nParsing {filepath.__str__()}\n\n")
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match filepath:
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case Path():
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self.filepath = filepath
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case str():
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self.filepath = Path(filepath)
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case _:
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logger.error(f"No filepath given.")
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raise ValueError("No filepath given.")
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try:
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self.xl = pd.ExcelFile(filepath)
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except ValueError as e:
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logger.error(f"Incorrect value: {e}")
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raise FileNotFoundError(f"Couldn't parse file {self.filepath}")
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self.sub = OrderedDict()
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# make decision about type of sample we have
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self.sub['submission_type'] = dict(value=RSLNamer.retrieve_submission_type(filename=self.filepath), missing=True)
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# # grab the info map from the submission type in database
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self.parse_info()
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self.import_kit_validation_check()
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self.parse_reagents()
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self.import_reagent_validation_check()
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self.parse_samples()
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self.parse_equipment()
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self.finalize_parse()
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logger.debug(f"Parser.sub after info scrape: {pformat(self.sub)}")
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def parse_info(self):
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"""
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Pulls basic information from the excel sheet
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"""
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parser = InfoParser(xl=self.xl, submission_type=self.sub['submission_type']['value'])
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info = parser.parse_info()
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self.info_map = parser.map
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for k,v in info.items():
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match k:
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case "sample":
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pass
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case _:
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self.sub[k] = v
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def parse_reagents(self, extraction_kit:str|None=None):
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"""
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Pulls reagent info from the excel sheet
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Args:
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extraction_kit (str | None, optional): Relevant extraction kit for reagent map. Defaults to None.
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"""
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if extraction_kit == None:
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extraction_kit = extraction_kit=self.sub['extraction_kit']
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# logger.debug(f"Parsing reagents for {extraction_kit}")
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self.sub['reagents'] = ReagentParser(xl=self.xl, submission_type=self.sub['submission_type'], extraction_kit=extraction_kit).parse_reagents()
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def parse_samples(self):
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"""
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Pulls sample info from the excel sheet
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"""
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parser = SampleParser(xl=self.xl, submission_type=self.sub['submission_type']['value'])
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self.sample_result, self.sub['samples'] = parser.parse_samples()
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self.plate_map = parser.plate_map
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def parse_equipment(self):
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parser = EquipmentParser(xl=self.xl, submission_type=self.sub['submission_type']['value'])
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self.sub['equipment'] = parser.parse_equipment()
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def import_kit_validation_check(self):
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"""
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Enforce that the parser has an extraction kit
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"""
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from frontend.widgets.pop_ups import KitSelector
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if not check_not_nan(self.sub['extraction_kit']['value']):
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dlg = KitSelector(title="Kit Needed", message="At minimum a kit is needed. Please select one.")
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if dlg.exec():
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self.sub['extraction_kit'] = dict(value=dlg.getValues(), missing=True)
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else:
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raise ValueError("Extraction kit needed.")
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else:
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if isinstance(self.sub['extraction_kit'], str):
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self.sub['extraction_kit'] = dict(value=self.sub['extraction_kit'], missing=True)
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def import_reagent_validation_check(self):
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"""
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Enforce that only allowed reagents get into the Pydantic Model
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"""
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kit = KitType.query(name=self.sub['extraction_kit']['value'])
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allowed_reagents = [item.name for item in kit.get_reagents()]
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# logger.debug(f"List of reagents for comparison with allowed_reagents: {pformat(self.sub['reagents'])}")
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self.sub['reagents'] = [reagent for reagent in self.sub['reagents'] if reagent.type in allowed_reagents]
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def finalize_parse(self):
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"""
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Run custom final validations of data for submission subclasses.
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"""
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finisher = BasicSubmission.find_polymorphic_subclass(polymorphic_identity=self.sub['submission_type']).finalize_parse
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self.sub = finisher(input_dict=self.sub, xl=self.xl, info_map=self.info_map, plate_map=self.plate_map)
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def to_pydantic(self) -> PydSubmission:
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"""
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Generates a pydantic model of scraped data for validation
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Returns:
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PydSubmission: output pydantic model
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"""
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# logger.debug(f"Submission dictionary coming into 'to_pydantic':\n{pformat(self.sub)}")
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logger.debug(f"Equipment: {self.sub['equipment']}")
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if len(self.sub['equipment']) == 0:
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self.sub['equipment'] = None
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psm = PydSubmission(filepath=self.filepath, **self.sub)
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return psm
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class InfoParser(object):
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def __init__(self, xl:pd.ExcelFile, submission_type:str):
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logger.info(f"\n\Hello from InfoParser!\n\n")
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self.map = self.fetch_submission_info_map(submission_type=submission_type)
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self.xl = xl
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logger.debug(f"Info map for InfoParser: {pformat(self.map)}")
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def fetch_submission_info_map(self, submission_type:str|dict) -> dict:
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"""
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Gets location of basic info from the submission_type object in the database.
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Args:
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submission_type (str|dict): name of the submission type or parsed object with value=submission_type
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Returns:
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dict: Location map of all info for this submission type
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"""
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if isinstance(submission_type, str):
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submission_type = dict(value=submission_type, missing=True)
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logger.debug(f"Looking up submission type: {submission_type['value']}")
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submission_type = SubmissionType.query(name=submission_type['value'])
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info_map = submission_type.info_map
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# Get the parse_info method from the submission type specified
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self.custom_parser = BasicSubmission.find_polymorphic_subclass(polymorphic_identity=submission_type.name).parse_info
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return info_map
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def parse_info(self) -> dict:
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"""
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Pulls basic info from the excel sheet.
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Returns:
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dict: key:value of basic info
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"""
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dicto = {}
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for sheet in self.xl.sheet_names:
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df = self.xl.parse(sheet, header=None)
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relevant = {}
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for k, v in self.map.items():
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if isinstance(v, str):
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dicto[k] = dict(value=v, missing=False)
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continue
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if k in ["samples", "all_sheets"]:
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continue
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if sheet in self.map[k]['sheets']:
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relevant[k] = v
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logger.debug(f"relevant map for {sheet}: {pformat(relevant)}")
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if relevant == {}:
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continue
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for item in relevant:
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value = df.iat[relevant[item]['row']-1, relevant[item]['column']-1]
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match item:
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case "submission_type":
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value, missing = is_missing(value)
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value = value.title()
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case _:
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value, missing = is_missing(value)
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logger.debug(f"Setting {item} on {sheet} to {value}")
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try:
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dicto[item] = dict(value=value, missing=missing)
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except (KeyError, IndexError):
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continue
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return self.custom_parser(input_dict=dicto, xl=self.xl)
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class ReagentParser(object):
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def __init__(self, xl:pd.ExcelFile, submission_type:str, extraction_kit:str):
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logger.debug("\n\nHello from ReagentParser!\n\n")
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self.map = self.fetch_kit_info_map(extraction_kit=extraction_kit, submission_type=submission_type)
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logger.debug(f"Reagent Parser map: {self.map}")
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self.xl = xl
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def fetch_kit_info_map(self, extraction_kit:dict, submission_type:str) -> dict:
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"""
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Gets location of kit reagents from database
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Args:
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extraction_kit (dict): Relevant kit information.
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submission_type (str): Name of submission type.
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Returns:
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dict: locations of reagent info for the kit.
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"""
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if isinstance(extraction_kit, dict):
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extraction_kit = extraction_kit['value']
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kit = KitType.query(name=extraction_kit)
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if isinstance(submission_type, dict):
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submission_type = submission_type['value']
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reagent_map = kit.construct_xl_map_for_use(submission_type.title())
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del reagent_map['info']
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return reagent_map
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def parse_reagents(self) -> List[PydReagent]:
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"""
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Extracts reagent information from the excel form.
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Returns:
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List[PydReagent]: List of parsed reagents.
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"""
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listo = []
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for sheet in self.xl.sheet_names:
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df = self.xl.parse(sheet, header=None, dtype=object)
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df.replace({np.nan: None}, inplace = True)
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relevant = {k.strip():v for k,v in self.map.items() if sheet in self.map[k]['sheet']}
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logger.debug(f"relevant map for {sheet}: {pformat(relevant)}")
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if relevant == {}:
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continue
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for item in relevant:
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logger.debug(f"Attempting to scrape: {item}")
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try:
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name = df.iat[relevant[item]['name']['row']-1, relevant[item]['name']['column']-1]
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lot = df.iat[relevant[item]['lot']['row']-1, relevant[item]['lot']['column']-1]
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expiry = df.iat[relevant[item]['expiry']['row']-1, relevant[item]['expiry']['column']-1]
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if 'comment' in relevant[item].keys():
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comment = df.iat[relevant[item]['comment']['row']-1, relevant[item]['comment']['column']-1]
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else:
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comment = ""
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except (KeyError, IndexError):
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listo.append(PydReagent(type=item.strip(), lot=None, expiry=None, name=None, comment="", missing=True))
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continue
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# If the cell is blank tell the PydReagent
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if check_not_nan(lot):
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missing = False
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else:
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missing = True
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# logger.debug(f"Got lot for {item}-{name}: {lot} as {type(lot)}")
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lot = str(lot)
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logger.debug(f"Going into pydantic: name: {name}, lot: {lot}, expiry: {expiry}, type: {item.strip()}, comment: {comment}")
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listo.append(PydReagent(type=item.strip(), lot=lot, expiry=expiry, name=name, comment=comment, missing=missing))
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return listo
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class SampleParser(object):
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"""
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object to pull data for samples in excel sheet and construct individual sample objects
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"""
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def __init__(self, xl:pd.ExcelFile, submission_type:str) -> None:
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"""
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convert sample sub-dataframe to dictionary of records
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Args:
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df (pd.DataFrame): input sample dataframe
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elution_map (pd.DataFrame | None, optional): optional map of elution plate. Defaults to None.
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"""
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logger.debug("\n\nHello from SampleParser!\n\n")
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self.samples = []
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self.xl = xl
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self.submission_type = submission_type
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sample_info_map = self.fetch_sample_info_map(submission_type=submission_type)
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logger.debug(f"sample_info_map: {sample_info_map}")
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self.plate_map = self.construct_plate_map(plate_map_location=sample_info_map['plate_map'])
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logger.debug(f"plate_map: {self.plate_map}")
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self.lookup_table = self.construct_lookup_table(lookup_table_location=sample_info_map['lookup_table'])
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if "plates" in sample_info_map:
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self.plates = sample_info_map['plates']
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self.excel_to_db_map = sample_info_map['xl_db_translation']
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self.create_basic_dictionaries_from_plate_map()
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if isinstance(self.lookup_table, pd.DataFrame):
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self.parse_lookup_table()
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def fetch_sample_info_map(self, submission_type:str) -> dict:
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"""
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Gets info locations in excel book for submission type.
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Args:
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submission_type (str): submission type
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Returns:
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dict: Info locations.
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"""
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logger.debug(f"Looking up submission type: {submission_type}")
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submission_type = SubmissionType.query(name=submission_type)
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logger.debug(f"info_map: {pformat(submission_type.info_map)}")
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sample_info_map = submission_type.info_map['samples']
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self.custom_sub_parser = BasicSubmission.find_polymorphic_subclass(polymorphic_identity=submission_type.name).parse_samples
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self.custom_sample_parser = BasicSample.find_polymorphic_subclass(polymorphic_identity=f"{submission_type.name} Sample").parse_sample
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return sample_info_map
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def construct_plate_map(self, plate_map_location:dict) -> pd.DataFrame:
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"""
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Gets location of samples from plate map grid in excel sheet.
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Args:
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plate_map_location (dict): sheet name, start/end row/column
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Returns:
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pd.DataFrame: Plate map grid
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"""
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logger.debug(f"Plate map location: {plate_map_location}")
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df = self.xl.parse(plate_map_location['sheet'], header=None, dtype=object)
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df = df.iloc[plate_map_location['start_row']-1:plate_map_location['end_row'], plate_map_location['start_column']-1:plate_map_location['end_column']]
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df = pd.DataFrame(df.values[1:], columns=df.iloc[0])
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df = df.set_index(df.columns[0])
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logger.debug(f"Vanilla platemap: {df}")
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custom_mapper = BasicSubmission.find_polymorphic_subclass(polymorphic_identity=self.submission_type)
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df = custom_mapper.custom_platemap(self.xl, df)
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logger.debug(f"Custom platemap:\n{df}")
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return df
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def construct_lookup_table(self, lookup_table_location:dict) -> pd.DataFrame:
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"""
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Gets table of misc information from excel book
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Args:
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lookup_table_location (dict): sheet name, start/end row
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Returns:
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pd.DataFrame: _description_
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"""
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try:
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df = self.xl.parse(lookup_table_location['sheet'], header=None, dtype=object)
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except KeyError:
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return None
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df = df.iloc[lookup_table_location['start_row']-1:lookup_table_location['end_row']]
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df = pd.DataFrame(df.values[1:], columns=df.iloc[0])
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df = df.reset_index(drop=True)
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return df
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def create_basic_dictionaries_from_plate_map(self):
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"""
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Parse sample location/name from plate map
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"""
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invalids = [0, "0", "EMPTY"]
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new_df = self.plate_map.dropna(axis=1, how='all')
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columns = new_df.columns.tolist()
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for _, iii in new_df.iterrows():
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for c in columns:
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if check_not_nan(iii[c]):
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if iii[c] in invalids:
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logger.debug(f"Invalid sample name: {iii[c]}, skipping.")
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continue
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id = iii[c]
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logger.debug(f"Adding sample {iii[c]}")
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try:
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c = self.plate_map.columns.get_loc(c) + 1
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except Exception as e:
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logger.error(f"Unable to get column index of {c} due to {e}")
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self.samples.append(dict(submitter_id=id, row=row_keys[iii._name], column=c))
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def parse_lookup_table(self):
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"""
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Parse misc info from lookup table.
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"""
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def determine_if_date(input_str) -> str|date:
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regex = re.compile(r"^\d{4}-?\d{2}-?\d{2}")
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if bool(regex.search(input_str)):
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logger.warning(f"{input_str} is a date!")
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try:
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return parse(input_str)
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except ParserError:
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return None
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else:
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return input_str
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for sample in self.samples:
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addition = self.lookup_table[self.lookup_table.isin([sample['submitter_id']]).any(axis=1)].squeeze()
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# logger.debug(addition)
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if isinstance(addition, pd.DataFrame) and not addition.empty:
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addition = addition.iloc[0]
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# logger.debug(f"Lookuptable info: {addition.to_dict()}")
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for k,v in addition.to_dict().items():
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# logger.debug(f"Checking {k} in lookup table.")
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if check_not_nan(k) and isinstance(k, str):
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if k.lower() not in sample:
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k = k.replace(" ", "_").replace("#","num").lower()
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# logger.debug(f"Adding {type(v)} - {k}, {v} to the lookuptable output dict")
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match v:
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case pd.Timestamp():
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sample[k] = v.date()
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case str():
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sample[k] = determine_if_date(v)
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case _:
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sample[k] = v
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# Set row in lookup table to blank values to prevent multipe lookups.
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try:
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self.lookup_table.loc[self.lookup_table['Sample #']==addition['Sample #']] = np.nan
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except (ValueError, KeyError):
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pass
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try:
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self.lookup_table.loc[self.lookup_table['Well']==addition['Well']] = np.nan
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except (ValueError, KeyError):
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pass
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# logger.debug(f"Output sample dict: {sample}")
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logger.debug(f"Final lookup_table: \n\n {self.lookup_table}")
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def parse_samples(self) -> List[dict]|List[BasicSample]:
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"""
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Parse merged platemap\lookup info into dicts/samples
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Returns:
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List[dict]|List[models.BasicSample]: List of samples
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"""
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result = None
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new_samples = []
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logger.debug(f"Starting samples: {pformat(self.samples)}")
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for sample in self.samples:
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translated_dict = {}
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for k, v in sample.items():
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match v:
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case dict():
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v = None
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case float():
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v = convert_nans_to_nones(v)
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case _:
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v = v
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try:
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translated_dict[self.excel_to_db_map[k]] = convert_nans_to_nones(v)
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except KeyError:
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translated_dict[k] = convert_nans_to_nones(v)
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translated_dict['sample_type'] = f"{self.submission_type} Sample"
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translated_dict = self.custom_sub_parser(translated_dict)
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translated_dict = self.custom_sample_parser(translated_dict)
|
|
# logger.debug(f"Here is the output of the custom parser:\n{translated_dict}")
|
|
new_samples.append(PydSample(**translated_dict))
|
|
return result, new_samples
|
|
|
|
def grab_plates(self) -> List[str]:
|
|
"""
|
|
Parse plate names from
|
|
|
|
Returns:
|
|
List[str]: list of plate names.
|
|
"""
|
|
plates = []
|
|
for plate in self.plates:
|
|
df = self.xl.parse(plate['sheet'], header=None)
|
|
if isinstance(df.iat[plate['row']-1, plate['column']-1], str):
|
|
output = RSLNamer.retrieve_rsl_number(filename=df.iat[plate['row']-1, plate['column']-1])
|
|
else:
|
|
continue
|
|
plates.append(output)
|
|
return plates
|
|
|
|
class EquipmentParser(object):
|
|
|
|
def __init__(self, xl:pd.ExcelFile, submission_type:str) -> None:
|
|
self.submission_type = submission_type
|
|
self.xl = xl
|
|
self.map = self.fetch_equipment_map()
|
|
|
|
def fetch_equipment_map(self) -> List[dict]:
|
|
"""
|
|
Gets the map of equipment locations in the submission type's spreadsheet
|
|
|
|
Returns:
|
|
List[dict]: List of locations
|
|
"""
|
|
submission_type = SubmissionType.query(name=self.submission_type)
|
|
return submission_type.construct_equipment_map()
|
|
|
|
def get_asset_number(self, input:str) -> str:
|
|
"""
|
|
Pulls asset number from string.
|
|
|
|
Args:
|
|
input (str): String to be scraped
|
|
|
|
Returns:
|
|
str: asset number
|
|
"""
|
|
regex = Equipment.get_regex()
|
|
logger.debug(f"Using equipment regex: {regex} on {input}")
|
|
try:
|
|
return regex.search(input).group().strip("-")
|
|
except AttributeError:
|
|
return input
|
|
|
|
def parse_equipment(self) -> List[PydEquipment]:
|
|
"""
|
|
Scrapes equipment from xl sheet
|
|
|
|
Returns:
|
|
List[PydEquipment]: list of equipment
|
|
"""
|
|
logger.debug(f"Equipment parser going into parsing: {pformat(self.__dict__)}")
|
|
output = []
|
|
# logger.debug(f"Sheets: {sheets}")
|
|
for sheet in self.xl.sheet_names:
|
|
df = self.xl.parse(sheet, header=None, dtype=object)
|
|
try:
|
|
relevant = [item for item in self.map if item['sheet']==sheet]
|
|
except (TypeError, KeyError):
|
|
continue
|
|
# logger.debug(f"Relevant equipment: {pformat(relevant)}")
|
|
previous_asset = ""
|
|
for equipment in relevant:
|
|
asset = df.iat[equipment['name']['row']-1, equipment['name']['column']-1]
|
|
if not check_not_nan(asset):
|
|
asset = previous_asset
|
|
else:
|
|
previous_asset = asset
|
|
asset = self.get_asset_number(input=asset)
|
|
eq = Equipment.query(asset_number=asset)
|
|
process = df.iat[equipment['process']['row']-1, equipment['process']['column']-1]
|
|
try:
|
|
output.append(PydEquipment(name=eq.name, processes=[process], role=equipment['role'], asset_number=asset, nickname=eq.nickname))
|
|
except AttributeError:
|
|
logger.error(f"Unable to add {eq} to PydEquipment list.")
|
|
# logger.debug(f"Here is the output so far: {pformat(output)}")
|
|
return output
|
|
|
|
class PCRParser(object):
|
|
"""
|
|
Object to pull data from Design and Analysis PCR export file.
|
|
"""
|
|
def __init__(self, filepath:Path|None = None) -> None:
|
|
"""
|
|
Initializes object.
|
|
|
|
Args:
|
|
filepath (Path | None, optional): file to parse. Defaults to None.
|
|
"""
|
|
logger.debug(f"Parsing {filepath.__str__()}")
|
|
if filepath == None:
|
|
logger.error(f"No filepath given.")
|
|
self.xl = None
|
|
else:
|
|
try:
|
|
self.xl = pd.ExcelFile(filepath.__str__())
|
|
except ValueError as e:
|
|
logger.error(f"Incorrect value: {e}")
|
|
self.xl = None
|
|
except PermissionError:
|
|
logger.error(f"Couldn't get permissions for {filepath.__str__()}. Operation might have been cancelled.")
|
|
return
|
|
self.parse_general(sheet_name="Results")
|
|
namer = RSLNamer(filename=filepath.__str__())
|
|
self.plate_num = namer.parsed_name
|
|
self.submission_type = namer.submission_type
|
|
logger.debug(f"Set plate number to {self.plate_num} and type to {self.submission_type}")
|
|
parser = BasicSubmission.find_polymorphic_subclass(self.submission_type)
|
|
self.samples = parser.parse_pcr(xl=self.xl, rsl_number=self.plate_num)
|
|
|
|
def parse_general(self, sheet_name:str):
|
|
"""
|
|
Parse general info rows for all types of PCR results
|
|
|
|
Args:
|
|
sheet_name (str): Name of sheet in excel workbook that holds info.
|
|
"""
|
|
self.pcr = {}
|
|
df = self.xl.parse(sheet_name=sheet_name, dtype=object).fillna("")
|
|
self.pcr['comment'] = df.iloc[0][1]
|
|
self.pcr['operator'] = df.iloc[1][1]
|
|
self.pcr['barcode'] = df.iloc[2][1]
|
|
self.pcr['instrument'] = df.iloc[3][1]
|
|
self.pcr['block_type'] = df.iloc[4][1]
|
|
self.pcr['instrument_name'] = df.iloc[5][1]
|
|
self.pcr['instrument_serial'] = df.iloc[6][1]
|
|
self.pcr['heated_cover_serial'] = df.iloc[7][1]
|
|
self.pcr['block_serial'] = df.iloc[8][1]
|
|
self.pcr['run-start'] = df.iloc[9][1]
|
|
self.pcr['run_end'] = df.iloc[10][1]
|
|
self.pcr['run_duration'] = df.iloc[11][1]
|
|
self.pcr['sample_volume'] = df.iloc[12][1]
|
|
self.pcr['cover_temp'] = df.iloc[13][1]
|
|
self.pcr['passive_ref'] = df.iloc[14][1]
|
|
self.pcr['pcr_step'] = df.iloc[15][1]
|
|
self.pcr['quant_cycle_method'] = df.iloc[16][1]
|
|
self.pcr['analysis_time'] = df.iloc[17][1]
|
|
self.pcr['software'] = df.iloc[18][1]
|
|
self.pcr['plugin'] = df.iloc[19][1]
|
|
self.pcr['exported_on'] = df.iloc[20][1]
|
|
self.pcr['imported_by'] = getuser()
|
|
|
|
|