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usdm running now #1759

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139 changes: 69 additions & 70 deletions cdisc_rules_engine/services/csv_metadata_reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ def __init__(
):
self.file_path = file_path
self.file_name = file_name
self.dataset = Path(file_name).stem
self.encoding = encoding
self.variables_csv_path = (
Path(variables_csv_path)
Expand All @@ -32,59 +33,87 @@ def __init__(
)

def read(self) -> dict:
dataset_name = Path(self.file_name).stem.lower()
metadata = {}
metadata.update(self.__dataset_metadata())
metadata.update(
{
"dataset_modification_date": datetime.fromtimestamp(
Path(self.file_path).stat().st_mtime
).isoformat(),
"adam_info": {
"categorization_scheme": {},
"w_indexes": {},
"period": {},
"selection_algorithm": {},
},
}
)
metadata.update(self.__variable_metadata())
metadata.update(self.__data_metadata())
return metadata

if not self.variables_csv_path.exists():
logger = logging.getLogger("validator")
logger.info("No variables file found for %s", dataset_name)
variables_meta = {}
else:
variables_meta = self.__get_variable_metadata(
dataset_name, self.variables_csv_path
def __dataset_metadata(self) -> dict:
logger = logging.getLogger("validator")

if not self.datasets_csv_path.exists():
logger.info("No datasets file found for %s", self.dataset)
return {}

try:
datasets_df = pd.read_csv(self.datasets_csv_path, encoding=self.encoding)
except (UnicodeDecodeError, UnicodeError) as e:
logger.error(
f"\n Error reading CSV from: {self.file_path}"
f"\n Failed to decode with {self.encoding} encoding: {e}"
f"\n Please specify the correct encoding using the -e flag."
)
return {}
except Exception as e:
logger.error("Error reading CSV file %s. %s", self.file_path, e)
return {}

metadata = {
"dataset_name": dataset_name.upper(),
"dataset_modification_date": datetime.fromtimestamp(
Path(self.file_path).stat().st_mtime
).isoformat(),
"adam_info": {
"categorization_scheme": {},
"w_indexes": {},
"period": {},
"selection_algorithm": {},
},
if "Filename" not in datasets_df.columns:
return {}

match = datasets_df[datasets_df["Filename"] == self.dataset]

if match.empty or len(match) > 1:
return {}

single_match = match.iloc[0]

return {
"dataset_name": (
single_match["Dataset Name"]
if "Dataset Name" in datasets_df.columns
else str(single_match["Filename"]).upper()
),
"dataset_label": str(single_match["Label"]),
}
metadata.update(variables_meta)
metadata.update(self.__data_meta())
metadata.update(self.__dataset_label())
return metadata

def __get_variable_metadata(
self, dataset_name: str, variables_file_path: Path
def __variable_metadata(
self,
) -> dict:
logger = logging.getLogger("validator")
if not self.variables_csv_path.exists():
logger.info("No variables file found for %s", self.dataset)
return {}
try:
meta_df = pd.read_csv(variables_file_path, encoding=self.encoding)
meta_df = pd.read_csv(self.variables_csv_path, encoding=self.encoding)
except (UnicodeDecodeError, UnicodeError) as e:
logger.error(
f"Could not decode CSV file {variables_file_path} with {self.encoding} encoding: {e}. "
f"Could not decode CSV file {self.variables_csv_path} with {self.encoding} encoding: {e}. "
f"Please specify the correct encoding using the -e flag."
)
return {}
except Exception as e:
logger.error("Error reading CSV file %s. %s", self.file_path, e)
return {}

meta_df["dataset"] = meta_df["dataset"].apply(
lambda x: Path(str(x)).stem.lower()
)

dataset_meta_df = meta_df[meta_df["dataset"] == dataset_name]
dataset_meta_df = meta_df[meta_df["dataset"] == self.dataset]

if dataset_meta_df.empty:
logger = logging.getLogger("validator")
logger.info("No dataset metadata found for %s", dataset_name)
logger.info("No dataset metadata found for %s", self.dataset)
return {}

variable_names = dataset_meta_df["variable"].tolist()
Expand All @@ -95,7 +124,11 @@ def __get_variable_metadata(
zip(variable_names, dataset_meta_df["type"])
)
variable_name_to_size_map = {
var: (int(length) if pd.notna(length) else None)
var: (
int(length)
if pd.notna(length) and (isinstance(length, int) or length.isdigit())
else None
)
for var, length in zip(variable_names, dataset_meta_df["length"])
}
return {
Expand All @@ -108,41 +141,7 @@ def __get_variable_metadata(
"number_of_variables": len(variable_names),
}

def __dataset_label(self) -> dict:
logger = logging.getLogger("validator")

if not self.datasets_csv_path.exists():
return {}

try:
datasets_df = pd.read_csv(self.datasets_csv_path, encoding=self.encoding)
except (UnicodeDecodeError, UnicodeError) as e:
logger.error(
f"\n Error reading CSV from: {self.file_path}"
f"\n Failed to decode with {self.encoding} encoding: {e}"
f"\n Please specify the correct encoding using the -e flag."
)
return {}
except Exception as e:
logger.error("Error reading CSV file %s. %s", self.file_path, e)
return {}

if "Filename" not in datasets_df.columns or "Label" not in datasets_df.columns:
return {}

datasets_df["dataset"] = datasets_df["Filename"].apply(
lambda x: Path(str(x)).stem.lower()
)

current_dataset = Path(self.file_name).stem.lower()
match = datasets_df[datasets_df["dataset"] == current_dataset]

if match.empty:
return {}

return {"dataset_label": str(match.iloc[0]["Label"])}

def __data_meta(self):
def __data_metadata(self):
logger = logging.getLogger("validator")
result = {
"dataset_length": 0,
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