mkt.databases.io_utils

File I/O helpers for CSV/dataframe round-tripping, tar creation, and kinase-dict loading.

Includes helpers to load and save dataframes, concatenate CSVs by glob, parse iterables into dataframes, create metadata-free tar archives, and load the packaged kinase dictionary.

Functions

`check_outdir_exists`()	Check if OUTPUT_DIR in environmental variables and create directory if doesn't exist.
`concatenate_csv_files_with_glob`(str_find[, ...])	Use glob to find csv files to concatenate.
`convert_str2list`(input_str)	Convert a string to a list.
`create_tar_without_metadata`(path_source, ...)	Create a tar file without metadata.
`get_repo_root`()	Get the root of the git repository.
`load_csv_to_dataframe`(filename)	Load a CSV file as a dataframe
`parse_iterabc2dataframe`(input_object[, ...])	Parse an iterable containing Abstract Base Classes into a dataframe.
`return_kinase_dict`([bool_hgnc])	Return a dictionary of kinase objects.
`save_dataframe_to_csv`(df, filename)	Save a dataframe to a CSV file.

mkt.databases.io_utils.check_outdir_exists() → str[source]

Check if OUTPUT_DIR in environmental variables and create directory if doesn’t exist.

Returns:: Path to OUTPUT_DIR
Return type:: str | None

mkt.databases.io_utils.concatenate_csv_files_with_glob(str_find: str, str_remove: str = 'transformed_mutations.csv') → DataFrame[source]

Use glob to find csv files to concatenate.

Parameters:: str_find (str) – String to use to find files containing csv files of interest
Returns:: Concatenated dataframe
Return type:: pd.DataFrame

mkt.databases.io_utils.convert_str2list(input_str: str) → list[str][source]

Convert a string to a list.

Parameters:: str (str) – String to convert to list
Returns:: List of strings
Return type:: list[str]

mkt.databases.io_utils.create_tar_without_metadata(path_source: str, filename_tar: str) → None[source]

Create a tar file without metadata.

Parameters:

path_source (str) – Path to the source directory to be tarred
filename_tar (str) – Path and filename to the save the tar file

Return type:

None

mkt.databases.io_utils.get_repo_root()[source]

Get the root of the git repository.

Returns:: Path to the root of the git repository; if not found, return current directory
Return type:: str

mkt.databases.io_utils.load_csv_to_dataframe(filename: str) → None[source]

Load a CSV file as a dataframe

Parameters:: filename (str) – Filename to load (either with or without “csv” suffix)
Returns:: df – Dataframe loaded from CSV file
Return type:: pd.DataFrame

mkt.databases.io_utils.parse_iterabc2dataframe(input_object: iter, str_prefix: str | None = None, verbose: bool = True) → DataFrame[source]

Parse an iterable containing Abstract Base Classes into a dataframe.

Parameters:

input_object (iter) – Iterable of Abstract Base Classes objects
str_prefix (str | None, optional) – Prefix to add to the column names, by default None

Returns:

Dataframe for the input list of Abstract Base Classes objects

Return type:

pd.DataFrame

mkt.databases.io_utils.return_kinase_dict(bool_hgnc: bool = True) → dict[str, object][source]

Return a dictionary of kinase objects.

Parameters:: bool_hgnc (bool, optional) – If True, return a dictionary of kinase objects with HGNC IDs, by default True
Returns:: Dictionary of kinase objects with HGNC IDs as keys if bool_hgnc is True, otherwise with UniProt IDs as keys
Return type:: dict[str, object]

mkt.databases.io_utils.save_dataframe_to_csv(df: DataFrame, filename: str) → None[source]

Save a dataframe to a CSV file.

Parameters:

df (pd.DataFrame) – Dataframe to save
filename (str) – Filename to save (either with or without “csv” suffix)

Return type:

None