221 lines
9.4 KiB
Python
221 lines
9.4 KiB
Python
import logging
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import openpyxl
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from pydantic import ValidationError
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from typing import Union, List, Tuple
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from io import BytesIO
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from app.sample_models import SpreadsheetModel
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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class SpreadsheetImportError(Exception):
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pass
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class SampleSpreadsheetImporter:
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def __init__(self):
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self.filename = None
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self.model = None
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def _clean_value(self, value, expected_type=None):
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"""Clean value by converting it to the expected type and handle edge cases."""
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if value is None:
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return None
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if expected_type == str:
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# Ensure value is converted to string and stripped of whitespace
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return str(value).strip()
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if expected_type in [float, int]:
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try:
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return expected_type(value)
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except (ValueError, TypeError):
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# If conversion fails, return None
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return None
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if isinstance(value, str):
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try:
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# Handle numeric strings
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if '.' in value:
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return float(value)
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else:
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return int(value)
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except ValueError:
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pass
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# In case of failure, return the stripped string
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return value.strip()
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# If no expected type or value type match, return the original value
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return value
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def import_spreadsheet(self, file):
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return self.import_spreadsheet_with_errors(file)
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def get_expected_type(self, col_name):
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type_mapping = {
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'dewarname': str,
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'puckname': str,
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'positioninpuck': int,
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'priority': int,
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'oscillation': float,
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# Add all other mappings based on model requirements
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}
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return type_mapping.get(col_name, str) # Default to `str`
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def import_spreadsheet_with_errors(self, file) -> Tuple[List[SpreadsheetModel], List[dict], List[dict], List[str]]:
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self.model = []
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self.filename = file.filename
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logger.info(f"Importing spreadsheet from .xlsx file: {self.filename}")
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contents = file.file.read()
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file.file.seek(0) # Reset file pointer to the beginning
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if not contents:
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logger.error("The uploaded file is empty.")
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raise SpreadsheetImportError("The uploaded file is empty.")
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try:
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workbook = openpyxl.load_workbook(BytesIO(contents))
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logger.debug("Workbook loaded successfully")
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if "Samples" not in workbook.sheetnames:
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logger.error("The file is missing 'Samples' worksheet.")
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raise SpreadsheetImportError("The file is missing 'Samples' worksheet.")
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sheet = workbook["Samples"]
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except Exception as e:
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logger.error(f"Failed to read the file: {str(e)}")
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raise SpreadsheetImportError(f"Failed to read the file: {str(e)}")
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# Unpack four values from the process_spreadsheet method
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model, errors, raw_data, headers = self.process_spreadsheet(sheet)
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# Now, return the values correctly
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return model, errors, raw_data, headers
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def process_spreadsheet(self, sheet) -> Tuple[List[SpreadsheetModel], List[dict], List[dict], List[str]]:
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model = []
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errors = []
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raw_data = []
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headers = []
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# Skip the first 3 rows
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rows = list(sheet.iter_rows(min_row=4, values_only=True))
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logger.debug(f"Starting to process {len(rows)} rows from the sheet")
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if not rows:
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logger.error("The 'Samples' worksheet is empty.")
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raise SpreadsheetImportError("The 'Samples' worksheet is empty.")
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expected_columns = 32 # Number of columns expected based on the model
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# Add the headers (the first row in the spreadsheet or map them explicitly)
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headers = [
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'dewarname', 'puckname', 'pucktype', 'crystalname', 'positioninpuck', 'priority',
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'comments', 'directory', 'proteinname', 'oscillation', 'aperture', 'exposure',
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'totalrange', 'transmission', 'dose', 'targetresolution', 'datacollectiontype',
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'processingpipeline', 'spacegroupnumber', 'cellparameters', 'rescutkey', 'rescutvalue',
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'userresolution', 'pdbid', 'autoprocfull', 'procfull', 'adpenabled', 'noano',
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'ffcscampaign', 'trustedhigh', 'autoprocextraparams', 'chiphiangles'
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]
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for index, row in enumerate(rows):
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if not any(row):
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logger.debug(f"Skipping empty row at index {index}")
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continue
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# Record raw data for later use
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raw_data.append({"row_num": index + 4, "data": row})
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# Pad the row to ensure it has the expected number of columns
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if len(row) < expected_columns:
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row = list(row) + [None] * (expected_columns - len(row))
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# Prepare the record with the cleaned values
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record = {
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'dewarname': self._clean_value(row[0], str),
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'puckname': self._clean_value(row[1], str),
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'pucktype': self._clean_value(row[2], str),
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'crystalname': self._clean_value(row[3], str),
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'positioninpuck': self._clean_value(row[4], int),
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'priority': self._clean_value(row[5], int),
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'comments': self._clean_value(row[6], str),
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'directory': self._clean_value(row[7], str),
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'proteinname': self._clean_value(row[8], str),
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'oscillation': self._clean_value(row[9], float),
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'aperture': self._clean_value(row[10], str),
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'exposure': self._clean_value(row[11], float),
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'totalrange': self._clean_value(row[12], float),
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'transmission': self._clean_value(row[13], int),
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'dose': self._clean_value(row[14], float),
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'targetresolution': self._clean_value(row[15], float),
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'datacollectiontype': self._clean_value(row[16], str),
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'processingpipeline': self._clean_value(row[17], str),
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'spacegroupnumber': self._clean_value(row[18], int),
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'cellparameters': self._clean_value(row[19], str),
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'rescutkey': self._clean_value(row[20], str),
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'rescutvalue': self._clean_value(row[21], str),
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'userresolution': self._clean_value(row[22], str),
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'pdbid': self._clean_value(row[23], str),
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'autoprocfull': self._clean_value(row[24], str),
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'procfull': self._clean_value(row[25], str),
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'adpenabled': self._clean_value(row[26], str),
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'noano': self._clean_value(row[27], str),
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'ffcscampaign': self._clean_value(row[28], str),
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'trustedhigh': self._clean_value(row[29], str),
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'autoprocextraparams': self._clean_value(row[30], str),
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'chiphiangles': self._clean_value(row[31], str)
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}
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try:
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validated_record = SpreadsheetModel(**record)
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model.append(validated_record)
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logger.debug(f"Row {index + 4} processed and validated successfully")
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except ValidationError as e:
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logger.error(f"Validation error in row {index + 4}: {e}")
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for error in e.errors():
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field = error['loc'][0]
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msg = error['msg']
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# Map field name (which is the key in `record`) to its index in the row
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field_to_col = {
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'dewarname': 0,
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'puckname': 1,
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'pucktype': 2,
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'crystalname': 3,
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'positioninpuck': 4,
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'priority': 5,
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'comments': 6,
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'directory': 7,
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'proteinname': 8,
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'oscillation': 9,
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'aperture': 10,
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'exposure': 11,
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'totalrange': 12,
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'transmission': 13,
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'dose': 14,
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'targetresolution': 15,
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'datacollectiontype': 16,
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'processingpipeline': 17,
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'spacegroupnumber': 18,
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'cellparameters': 19,
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'rescutkey': 20,
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'rescutvalue': 21,
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'userresolution': 22,
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'pdbid': 23,
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'autoprocfull': 24,
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'procfull': 25,
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'adpenabled': 26,
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'noano': 27,
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'ffcscampaign': 28,
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'trustedhigh': 29,
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'autoprocextraparams': 30,
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'chiphiangles': 31
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}
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column_index = field_to_col[field]
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error_info = {
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'row': index + 4,
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'cell': column_index,
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'value': row[column_index], # Value that caused the error
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'message': msg
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}
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errors.append(error_info)
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self.model = model
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logger.info(f"Finished processing {len(model)} records with {len(errors)} errors")
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return self.model, errors, raw_data, headers # Include headers in the response
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