def calc_csv(dataframe: object,
save_folder: str,
aa_column: str = 'Info_window_seq',
Ncores: int = 1,
chunksize: int = None):
"""
Calculates conjoint triads features chunk by chunk from the inputted 'dataframe'.
It saves each processed chunk as a CSV(s).
Results appended as a new column named feat_CT_{subsequence} e.g. feat_CT_305 etc.
This is a Ram efficient way of calculating the Features as the features are calculated on a single chunk of the dataframe (of
chunksize number of rows) at a time and when a chunk has been been processed and saved as a CSV, then the chunk
is deleted freeing up RAM.
:param dataframe: A pandas DataFrame that contains a column/feature that is composed of purely Amino-Acid sequences (pepides).
:param save_folder: Path to folder for saving the output.
:param aa_column: Name of column in dataframe consisting of Amino-Acid sequences to process. Default='Info_window_seq'
:param Ncores: Number of cores to use. default=1
:param chunksize: Number of rows to be processed at a time. default=None (Where a 'None' object denotes no chunks but the entire dataframe to be processed)
"""
_utils.multiprocessing_export_csv(dataframe=dataframe,
function=_algorithm,
Ncores=Ncores,
chunksize=chunksize,
save_folder=save_folder,
aa_column=aa_column)
"""