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Improved loading spectra performance #8

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Improved loading spectra performance #8

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bb71fe8
switched to pyproject.toml, and formatted code in tests
mvisani Apr 29, 2026
e45c624
formatted code according to 'ruff format'
mvisani Apr 29, 2026
54c2f10
switch from list to dictionary to search in dataset
mvisani Apr 29, 2026
450ccc1
fixed typo
mvisani Apr 29, 2026
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407 changes: 314 additions & 93 deletions db_prep/__filter.py
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86 changes: 50 additions & 36 deletions db_prep/__parse_formula_db.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,64 +7,76 @@

# load all sdf files in the folder
def load_pubchem_sdf(sdf_file_path: Path, output_path: Path):
for sdf in glob.glob(str(sdf_file_path) + '/*.sdf'):
csv_str = str(sdf).split('Compound_')[1].split('.')[0] + '_formulaCount.csv'
for sdf in glob.glob(str(sdf_file_path) + "/*.sdf"):
csv_str = str(sdf).split("Compound_")[1].split(".")[0] + "_formulaCount.csv"
# if csv file exists, skip
if (output_path / csv_str).exists():
continue

file = PandasTools.LoadSDF(sdf, smilesName='SMILES', molColName='Molecule', includeFingerprints=False)
result_dup = file[['PUBCHEM_MOLECULAR_FORMULA']]
occur = result_dup.groupby(['PUBCHEM_MOLECULAR_FORMULA']).size()
occur_df = pd.DataFrame({'PUBCHEM_MOLECULAR_FORMULA': occur.index, 'PubChem': occur.values})
result_no_dup = result_dup.drop_duplicates(subset=['PUBCHEM_MOLECULAR_FORMULA'])
result = pd.merge(result_no_dup, occur_df, on='PUBCHEM_MOLECULAR_FORMULA')
result = result.rename(columns={'PUBCHEM_MOLECULAR_FORMULA': 'Formula_str'})
file = PandasTools.LoadSDF(
sdf, smilesName="SMILES", molColName="Molecule", includeFingerprints=False
)
result_dup = file[["PUBCHEM_MOLECULAR_FORMULA"]]
occur = result_dup.groupby(["PUBCHEM_MOLECULAR_FORMULA"]).size()
occur_df = pd.DataFrame(
{"PUBCHEM_MOLECULAR_FORMULA": occur.index, "PubChem": occur.values}
)
result_no_dup = result_dup.drop_duplicates(subset=["PUBCHEM_MOLECULAR_FORMULA"])
result = pd.merge(result_no_dup, occur_df, on="PUBCHEM_MOLECULAR_FORMULA")
result = result.rename(columns={"PUBCHEM_MOLECULAR_FORMULA": "Formula_str"})

result.to_csv(output_path / csv_str, index=False)


def merge_pubchem_csv(csv_file_path: Path, output_path: Path):
list_csv = list()
for csv in glob.glob(str(csv_file_path) + '/*formulaCount.csv'):
for csv in glob.glob(str(csv_file_path) + "/*formulaCount.csv"):
file = pd.read_csv(csv)
list_csv.append(file)
merged = pd.concat(list_csv).groupby(['Formula_str']).sum().reset_index()
merged.to_csv(output_path / 'merged_pubchem.csv', index=False)
merged = pd.concat(list_csv).groupby(["Formula_str"]).sum().reset_index()
merged.to_csv(output_path / "merged_pubchem.csv", index=False)


def load_coconut_sdf(input_path: Path, output_path: Path):
coconut = glob.glob(str(input_path) + '/COCONUT_DB.sdf')
file = PandasTools.LoadSDF(coconut[0], smilesName='SMILES', molColName='Molecule', includeFingerprints=False)
result_dup = file[['molecular_formula']]
occur = result_dup.groupby(['molecular_formula']).size()
occur_df = pd.DataFrame({'molecular_formula': occur.index, 'Coconut': occur.values})
result_no_dup = result_dup.drop_duplicates(subset=['molecular_formula'])
result = pd.merge(result_no_dup, occur_df, on='molecular_formula')
result = result.rename(columns={'molecular_formula': 'Formula_str'})
result.to_csv(output_path / 'coconut_formula.csv', index=False)
coconut = glob.glob(str(input_path) + "/COCONUT_DB.sdf")
file = PandasTools.LoadSDF(
coconut[0],
smilesName="SMILES",
molColName="Molecule",
includeFingerprints=False,
)
result_dup = file[["molecular_formula"]]
occur = result_dup.groupby(["molecular_formula"]).size()
occur_df = pd.DataFrame({"molecular_formula": occur.index, "Coconut": occur.values})
result_no_dup = result_dup.drop_duplicates(subset=["molecular_formula"])
result = pd.merge(result_no_dup, occur_df, on="molecular_formula")
result = result.rename(columns={"molecular_formula": "Formula_str"})
result.to_csv(output_path / "coconut_formula.csv", index=False)


def load_npatlas_xlsx(input_path: Path, output_path: Path):
npatlas = glob.glob(str(input_path) + '/NPAtlas_download.xlsx')
npatlas = glob.glob(str(input_path) + "/NPAtlas_download.xlsx")
file = pd.read_excel(npatlas[0])
result_dup = file[['compound_molecular_formula']]
occur = result_dup.groupby(['compound_molecular_formula']).size()
occur_df = pd.DataFrame({'compound_molecular_formula': occur.index, 'npatlas': occur.values})
result_no_dup = result_dup.drop_duplicates(subset=['compound_molecular_formula'])
result = pd.merge(result_no_dup, occur_df, on='compound_molecular_formula')
result = result.rename(columns={'compound_molecular_formula': 'Formula_str'})
result.to_csv(output_path / 'npatlas_formula.csv', index=False)
result_dup = file[["compound_molecular_formula"]]
occur = result_dup.groupby(["compound_molecular_formula"]).size()
occur_df = pd.DataFrame(
{"compound_molecular_formula": occur.index, "npatlas": occur.values}
)
result_no_dup = result_dup.drop_duplicates(subset=["compound_molecular_formula"])
result = pd.merge(result_no_dup, occur_df, on="compound_molecular_formula")
result = result.rename(columns={"compound_molecular_formula": "Formula_str"})
result.to_csv(output_path / "npatlas_formula.csv", index=False)


def get_args():
parser = ArgumentParser(description='Process some integers.')
parser.add_argument('--sdf', type=str, help='sdf file path')
parser.add_argument('--csv', type=str, help='csv file path')
parser.add_argument('--output', type=str, help='output file path')
parser = ArgumentParser(description="Process some integers.")
parser.add_argument("--sdf", type=str, help="sdf file path")
parser.add_argument("--csv", type=str, help="csv file path")
parser.add_argument("--output", type=str, help="output file path")
return parser.parse_args()


if __name__ == '__main__':
if __name__ == "__main__":
# start_time = time.time()
#
# # load_pubchem_sdf(Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"),
Expand All @@ -78,8 +90,10 @@ def get_args():
args = get_args()
# load_coconut_sdf(Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"),
# Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"))
load_npatlas_xlsx(Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"),
Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"))
load_npatlas_xlsx(
Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"),
Path("/Users/tshyun/Desktop/2022.2.10/COOP/workterm1/pyBUDDY/db"),
)

# load_pubchem_sdf(Path(args.sdf), Path(args.output))
# merge_pubchem_csv(Path(args.csv), Path(args.output))
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