Remove obsolete comments.

imputation_and_cleaning
Primoz 2022-10-27 14:12:56 +00:00
parent 6b487fcf7b
commit 3c0585a566
2 changed files with 2 additions and 5 deletions

View File

@ -14,7 +14,6 @@ from src.features import empatica_data_yield as edy
pd.set_option('display.max_columns', 20)
def straw_cleaning(sensor_data_files, provider):
# TODO (maybe): reorganize the script based on the overall
features = pd.read_csv(sensor_data_files["sensor_data"][0])

View File

@ -23,7 +23,7 @@ def format_timestamp(x):
return tstring
def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spremeni naziv segmenta
def extract_ers_from_file(esm_df, device_id):
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
@ -60,9 +60,7 @@ def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spre
return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
# TODO: potrebno preveriti kako se izvaja iskanje prek device_id -> na tem temelji tudi proces ekstrahiranja ERS
if snakemake.params["stage"] == "extract": # TODO: najprej preveri ustreznost umeščenosti v RAPIDS pipelineu
if snakemake.params["stage"] == "extract":
esm_df = pd.read_csv(input_data_files['esm_raw_input'])
with open(input_data_files['pid_file'], 'r') as stream: