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DEV: handle cases where bids_string is introduced as args argument
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@jungheejung
jungheejung committed Aug 25, 2024
1 parent 33a2bed commit f894afc
Showing 1 changed file with 89 additions and 43 deletions.
132 changes: 89 additions & 43 deletions spacetop_prep/events/bidsify_social_ENH.py
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Original file line number Diff line number Diff line change
Expand Up @@ -830,52 +830,98 @@ def parse_args():
# 3. load trajectory data and calculate ratings ____________________________
trajectory_glob = glob.glob(join(beh_inputdir, sub_bids, 'task-social', ses_bids, f"{sub_bids}_{ses_bids}_task-social_{run_bids}_runtype-{runtype}_beh-preproc.csv"))

try:
if trajectory_glob:
# try:
# if trajectory_glob:
# trajectory_fname = trajectory_glob[0]
# traj_df = pd.read_csv(trajectory_fname)
# else:
# # If trajectory_glob is empty, raise a custom exception
# raise FileNotFoundError("Trajectory preproc is empty.")

# except FileNotFoundError as e:
# vicarious_logger.warning(str(e))
# continue
# except Exception as e:
# # This catches any other exceptions that might occur
# vicarious_logger.error("An error occurred while processing the trajectory file: %s", str(e))
# continue


# # 3-1. calculate degree based on x, y coordinate
# traj_df['adjusted_expectangle_degrees'] = calc_adjusted_angle_df(
# traj_df, 'expectrating_end_x', 'expectrating_end_y', trajectory_x, trajectory_y)
# traj_df['adjusted_outcomeangle_degrees'] = calc_adjusted_angle_df(
# traj_df, 'outcomerating_end_x', 'outcomerating_end_y', trajectory_x, trajectory_y)



# # 3-3. check if the calculated new degree matches the one in beh_df
# beh_df['event02_expect_fillna'] = beh_df['event02_expect_angle'].round(2)
# beh_df['event02_expect_fillna'].fillna(traj_df['adjusted_expectangle_degrees'].round(2), inplace=True)
# comparison_series = ((beh_df['event02_expect_angle'].round(2)) == (traj_df['adjusted_expectangle_degrees'].round(2)))
# traj_df['comparison_flag'] = ~comparison_series
# expect_overall_flag = traj_df['comparison_flag'].any()
# if expect_overall_flag:
# discrepancy_indices = traj_df[traj_df['comparison_flag']].index
# for idx in discrepancy_indices:
# vicarious_logger.info(f"\tExpect Rating {idx}: (traj_df): {traj_df.loc[idx]['adjusted_expectangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event02_expect_fillna']}")

# beh_df['event04_outcome_fillna'] = beh_df['event04_actual_angle'].round(2)
# beh_df['event04_outcome_fillna'].fillna(traj_df['adjusted_outcomeangle_degrees'].round(2), inplace=True)
# outcome_comparison_mask = ((beh_df['event04_actual_angle'].round(2)) == (traj_df['adjusted_outcomeangle_degrees'].round(2)))
# traj_df['outcome_comparisonflag'] = ~outcome_comparison_mask
# outcome_overall_flag = traj_df['outcome_comparisonflag'].any()

# if outcome_overall_flag:
# discrepancy_indices = traj_df[traj_df['outcome_comparisonflag']].index
# for idx in discrepancy_indices:
# vicarious_logger.info(f"\tOutcome Rating {idx} (traj_df): {traj_df.loc[idx]['adjusted_outcomeangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event04_outcome_fillna']}")

if trajectory_glob:
try:
trajectory_fname = trajectory_glob[0]
traj_df = pd.read_csv(trajectory_fname)
else:
# If trajectory_glob is empty, raise a custom exception
raise FileNotFoundError("Trajectory preproc is empty.")

except FileNotFoundError as e:
vicarious_logger.warning(str(e))
continue
except Exception as e:
# This catches any other exceptions that might occur
vicarious_logger.error("An error occurred while processing the trajectory file: %s", str(e))
continue


# 3-1. calculate degree based on x, y coordinate
traj_df['adjusted_expectangle_degrees'] = calc_adjusted_angle_df(
traj_df, 'expectrating_end_x', 'expectrating_end_y', trajectory_x, trajectory_y)
traj_df['adjusted_outcomeangle_degrees'] = calc_adjusted_angle_df(
traj_df, 'outcomerating_end_x', 'outcomerating_end_y', trajectory_x, trajectory_y)



# 3-3. check if the calculated new degree matches the one in beh_df
beh_df['event02_expect_fillna'] = beh_df['event02_expect_angle'].round(2)
beh_df['event02_expect_fillna'].fillna(traj_df['adjusted_expectangle_degrees'].round(2), inplace=True)
comparison_series = ((beh_df['event02_expect_angle'].round(2)) == (traj_df['adjusted_expectangle_degrees'].round(2)))
traj_df['comparison_flag'] = ~comparison_series
expect_overall_flag = traj_df['comparison_flag'].any()
if expect_overall_flag:
discrepancy_indices = traj_df[traj_df['comparison_flag']].index
for idx in discrepancy_indices:
vicarious_logger.info(f"\tExpect Rating {idx}: (traj_df): {traj_df.loc[idx]['adjusted_expectangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event02_expect_fillna']}")

beh_df['event04_outcome_fillna'] = beh_df['event04_actual_angle'].round(2)
beh_df['event04_outcome_fillna'].fillna(traj_df['adjusted_outcomeangle_degrees'].round(2), inplace=True)
outcome_comparison_mask = ((beh_df['event04_actual_angle'].round(2)) == (traj_df['adjusted_outcomeangle_degrees'].round(2)))
traj_df['outcome_comparisonflag'] = ~outcome_comparison_mask
outcome_overall_flag = traj_df['outcome_comparisonflag'].any()

if outcome_overall_flag:
discrepancy_indices = traj_df[traj_df['outcome_comparisonflag']].index
for idx in discrepancy_indices:
vicarious_logger.info(f"\tOutcome Rating {idx} (traj_df): {traj_df.loc[idx]['adjusted_outcomeangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event04_outcome_fillna']}")
# Step 2: Calculate degrees based on x, y coordinates
traj_df['adjusted_expectangle_degrees'] = calc_adjusted_angle_df(
traj_df, 'expectrating_end_x', 'expectrating_end_y', trajectory_x, trajectory_y)
traj_df['adjusted_outcomeangle_degrees'] = calc_adjusted_angle_df(
traj_df, 'outcomerating_end_x', 'outcomerating_end_y', trajectory_x, trajectory_y)

# Step 3: Check if the calculated new degree matches the one in beh_df
beh_df['event02_expect_fillna'] = beh_df['event02_expect_angle'].round(2)
beh_df['event02_expect_fillna'].fillna(traj_df['adjusted_expectangle_degrees'].round(2), inplace=True)
comparison_series = ((beh_df['event02_expect_angle'].round(2)) == (traj_df['adjusted_expectangle_degrees'].round(2)))
traj_df['comparison_flag'] = ~comparison_series
expect_overall_flag = traj_df['comparison_flag'].any()

if expect_overall_flag:
discrepancy_indices = traj_df[traj_df['comparison_flag']].index
for idx in discrepancy_indices:
vicarious_logger.info(f"\tExpect Rating {idx}: (traj_df): {traj_df.loc[idx]['adjusted_expectangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event02_expect_fillna']}")

beh_df['event04_outcome_fillna'] = beh_df['event04_actual_angle'].round(2)
beh_df['event04_outcome_fillna'].fillna(traj_df['adjusted_outcomeangle_degrees'].round(2), inplace=True)
outcome_comparison_mask = ((beh_df['event04_actual_angle'].round(2)) == (traj_df['adjusted_outcomeangle_degrees'].round(2)))
traj_df['outcome_comparisonflag'] = ~outcome_comparison_mask
outcome_overall_flag = traj_df['outcome_comparisonflag'].any()

if outcome_overall_flag:
discrepancy_indices = traj_df[traj_df['outcome_comparisonflag']].index
for idx in discrepancy_indices:
vicarious_logger.info(f"\tOutcome Rating {idx} (traj_df): {traj_df.loc[idx]['adjusted_outcomeangle_degrees'].round(2)} \t(beh_df): {beh_df.loc[idx]['event04_outcome_fillna']}")

except Exception as e:
vicarious_logger.error("An error occurred while processing the trajectory file: %s", str(e))
else:
if args.bids_string:
# Step 2: Handle case where trajectory data is missing but bids_string is provided
beh_df['event02_expect_fillna'] = 'n/a'
beh_df['event04_outcome_fillna'] = 'n/a'
vicarious_logger.info(f"Skipping trajectory processing for {args.bids_string} as no trajectory file exists.")
else:
# Step 3: Flag the issue if neither trajectory data exists nor bids_string is provided
vicarious_logger.warning(f"No trajectory data found for {sub_bids}, {ses_bids}, {run_bids}. No BIDS string provided.")


# grab the intersection raise warning if dont match
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