Fix formatting with black
This commit is contained in:
GotthardG
@ -34,7 +34,7 @@ class SampleSpreadsheetImporter:
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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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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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@ -50,16 +50,18 @@ class SampleSpreadsheetImporter:
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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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"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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def import_spreadsheet_with_errors(
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self, file
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) -> 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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@ -88,7 +90,9 @@ class SampleSpreadsheetImporter:
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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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def process_spreadsheet(
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self, sheet
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) -> 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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@ -106,12 +110,38 @@ class SampleSpreadsheetImporter:
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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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"dewarname",
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"puckname",
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"pucktype",
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"crystalname",
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"positioninpuck",
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"priority",
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"comments",
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"directory",
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"proteinname",
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"oscillation",
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"aperture",
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"exposure",
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"totalrange",
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"transmission",
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"dose",
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"targetresolution",
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"datacollectiontype",
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"processingpipeline",
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"spacegroupnumber",
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"cellparameters",
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"rescutkey",
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"rescutvalue",
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"userresolution",
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"pdbid",
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"autoprocfull",
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"procfull",
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"adpenabled",
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"noano",
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"ffcscampaign",
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"trustedhigh",
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"autoprocextraparams",
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"chiphiangles",
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]
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for index, row in enumerate(rows):
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@ -128,38 +158,38 @@ class SampleSpreadsheetImporter:
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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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"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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@ -169,52 +199,54 @@ class SampleSpreadsheetImporter:
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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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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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"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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"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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logger.info(
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f"Finished processing {len(model)} records with {len(errors)} errors"
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)
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return self.model, errors, raw_data, headers # Include headers in the response
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