"""Tools to collate chemical data files."""
from typing import Callable, Dict, Iterable, List, Optional, Tuple, TextIO, Union
from collections import Counter
from functools import partial
from glob import glob
import os
from carabiner.pd import read_table, resolve_delim
from carabiner import print_err
import numpy as np
from pandas import DataFrame, concat
from .converting import convert_string_representation, _FROM_FUNCTIONS
from .io import FILE_READERS
GROUPING_COLUMNS = ("filename", "file_format", "library_name", "string_representation")
ESSENTIAL_COLUMNS = GROUPING_COLUMNS + ("compound_collection", "plate_id", "well_id")
def _column_mapper(df: DataFrame,
cols: Iterable[str]) -> Tuple[Callable, Dict]:
basic_map = {column: df[column].tolist()[0] for column in cols}
inv_basic_map = {value: key for key, value in basic_map.items()}
def column_mapper(x: DataFrame) -> DataFrame:
new_df = DataFrame()
for new_col, old_col in basic_map.items():
# old_col = str(old_col)
if old_col is None or str(old_col) in ('None', 'nan', 'NA'):
new_df[new_col] = None
elif '+' in old_col:
splits = old_col.split('+')
new_df[new_col] = x[splits[0]].str.cat([x[s].astype(str)
for s in splits[1:]])
elif ';' in old_col:
col, char, index = old_col.split(';')
index = [int(i) for i in index.split(':')]
if len(index) == 1:
index = slice(index[0], index[0] + 1)
else:
index = slice(*index)
try:
new_df[new_col] = (x[col]
.str.split(char)
.map(lambda y: char.join(y[index] if y is not np.nan else []))
.str.strip())
except TypeError as e:
print_err(x[col].str.split(char))
raise e
else:
try:
new_df[new_col] = x[old_col].copy()
except KeyError:
raise KeyError(f"Column {old_col} mapped to {new_col} is not in the input data: " + ", ".join(x.columns))
return new_df
return column_mapper, inv_basic_map
def _check_catalog(catalog: DataFrame,
catalog_smiles_column: str = 'input_smiles') -> None:
essential_columns = (catalog_smiles_column, ) + ESSENTIAL_COLUMNS
missing_essential_cols = [col for col in essential_columns
if col not in catalog]
if len(missing_essential_cols) > 0:
print_err(catalog.columns.tolist())
raise KeyError("Missing required columns from catalog: " +
", ".join(missing_essential_cols))
return None
[docs]
def collate_inventory(catalog: DataFrame,
root_dir: Optional[str] = None,
drop_invalid: bool = True,
drop_unmapped: bool = False,
catalog_smiles_column: str = 'input_smiles',
id_column_name: Optional[str] = None,
id_n_digits: int = 8,
id_prefix: str = '') -> DataFrame:
f"""Process a catalog of files containing chemical libraries into a uniform dataframe.
The catalog table needs to have columns {', '.join(ESSENTIAL_COLUMNS)}:
- filename is a glob pattern of files to collate
- file_format is one of {', '.join(FILE_READERS.keys())}
- smiles_column contains smiles strings
Other columns are optional and can have any name, but must contain the name or a pattern
matching a column (for tabular data) or field (for SDF data) in the files
of the `filename` column. In the output DataFrame, the named column data will be mapped.
Optional column contents can be either concatenated or split using the following
pattern:
- col1+col2: concatenates the contents of `col1` and `col2`
- col1;-;1:2 : splits the contents of `col1` on the `-` character, and takes splits 1-2 (0-indexed)
Parameters
----------
catalog : pd.DataFrame
Table cataloging locations and format of data. Requires
columns {', '.join(ESSENTIAL_COLUMNS)}.
root_dir : str, optional
Path to look for data files. Default: current directory.
drop_invalid : bool, optional
Whether to drop rows containing invalid SMILES.
Returns
-------
pd.DataFrame
Collated chemical data.
"""
root_dir = root_dir or '.'
_check_catalog(catalog, catalog_smiles_column)
nongroup_columns = [col for col in catalog
if col not in GROUPING_COLUMNS]
loaded_dataframes = []
report = Counter({"invalid SMILES": 0,
"rows processed": 0})
grouped_catalog = catalog.groupby(list(GROUPING_COLUMNS))
for (this_glob, this_filetype,
this_library_name, this_representation), filename_df in grouped_catalog:
print_err(f'\nProcessing {this_glob}:')
this_glob = glob(os.path.join(root_dir, this_glob))
these_filenames = sorted(f for f in this_glob
if not os.path.basename(f).startswith('~$'))
print_err('\t- ' + '\n\t- '.join(these_filenames))
column_mapper, mapped_cols = _column_mapper(filename_df,
nongroup_columns)
reader = FILE_READERS.get(this_filetype, read_table)
for filename in these_filenames:
this_data0 = reader(filename)
if not drop_unmapped:
unmapped_cols = {col: 'x_' + col.casefold().replace(' ', '_')
for col in this_data0 if col not in mapped_cols}
this_data = this_data0[list(unmapped_cols)].rename(columns=unmapped_cols)
this_data = concat([column_mapper(this_data0), this_data],
axis=1)
else:
this_data = column_mapper(this_data0)
if this_representation.casefold() not in _FROM_FUNCTIONS:
raise TypeError(' or '.join(list(set(this_representation, this_representation.casefold()))) +
"not a supported string representation. Try one of " + ", ".join(_FROM_FUNCTIONS))
this_converter = partial(convert_string_representation,
input_representation=this_representation.casefold())
this_data = (this_data
.query('compound_collection != "NA"')
.assign(library_name=this_library_name,
input_file_format=this_filetype,
input_string_representation=this_representation,
plate_id=lambda x: x['plate_id'].astype(str),
plate_loc=lambda x: x['library_name'].str.cat([x['compound_collection'], x['plate_id'].astype(str), x['well_id'].astype(str)], sep=':'),
canonical_smiles=lambda x: list(this_converter(x[catalog_smiles_column])),
is_valid_smiles=lambda x: [s is not None for s in x['canonical_smiles']]))
report.update({"invalid SMILES": (~this_data['is_valid_smiles']).sum(),
"rows processed": this_data.shape[0]})
if drop_invalid:
this_data = this_data.query('is_valid_smiles')
if id_column_name is not None:
this_converter = partial(convert_string_representation,
output_representation='id',
options=dict(n=id_n_digits,
prefix=id_prefix))
this_data = this_data.assign(**{id_column_name: lambda x: list(this_converter(x['canonical_smiles']))})
loaded_dataframes.append(this_data)
collated_df = concat(loaded_dataframes, axis=0)
return report, collated_df
[docs]
def collate_inventory_from_file(catalog_path: Union[str, TextIO],
root_dir: Optional[str] = None,
format: Optional[str] = None,
*args, **kwargs) -> DataFrame:
f"""Process a catalog of files containing chemical libraries into a uniform dataframe.
The catalog table needs to have columns {', '.join(ESSENTIAL_COLUMNS)}:
- filename is a glob pattern of files to collate
- file_format is one of {', '.join(FILE_READERS.keys())}
- smiles_column contains smiles strings
Other columns are optional and can have any name, but must contain the name or a pattern
matching a column (for tabular data) or field (for SDF data) in the files
of the `filename` column. In the output DataFrame, the named column data will be mapped.
Optional column contents can be either concatenated or split using the following
pattern:
- col1+col2: concatenates the contents of `col1` and `col2`
- col1;-;1:2 : splits the contents of `col1` on the `-` character, and takes splits 1-2 (0-indexed)
Parameters
----------
catalog_path : str
Path to catalog file in XLSX, TSV or CSV format. Requires
columns {', '.join(ESSENTIAL_COLUMNS)}.
format : str, optional
Format of catalog file. Default: infer from file extension.
root_dir : str, optional
Path to look for data files. Default: use directory containing
the catalog.
Returns
-------
pd.DataFrame
Collated chemical data.
"""
root_dir = root_dir or os.path.dirname(catalog_path)
data_catalog = read_table(catalog_path, format=format)
return collate_inventory(catalog=data_catalog,
root_dir=root_dir,
*args, **kwargs)
[docs]
def deduplicate(df: DataFrame,
column: str = 'smiles',
input_representation: str = 'smiles',
index_columns: Optional[List[str]] = None,
drop_inchikey: bool = False) -> DataFrame:
index_columns = index_columns or []
inchikey_converter = partial(convert_string_representation,
input_representation=input_representation,
output_representation='inchikey')
df = df.assign(inchikey=lambda x: inchikey_converter(x[column]))
structure_columns = [column, 'inchikey']
df_unique = []
for (string_rep, inchikey), structure_df in df.groupby(structure_columns):
collapsed_indexes = {col: [';'.join(sorted(map(str, set(structure_df[col].tolist()))))]
for col in structure_df if col in index_columns}
collapsed_indexes.update({column: [string_rep],
'inchikey': [inchikey],
'instance_count': [structure_df.shape[0]]})
df_unique.append(DataFrame(collapsed_indexes))
df_unique = concat(df_unique, axis=0)
if drop_inchikey:
df_unique = df_unique.drop(columns=['inchikey'])
report = {'starting rows:': df.shape[0],
'ending_rows': df_unique.shape[0]}
return report, df_unique
[docs]
def deduplicate_file(filename: Union[str, TextIO],
format: Optional[str] = None,
*args, **kwargs) -> DataFrame:
table = read_table(filename)
return deduplicate(table, *args, **kwargs)