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DEV: factorize code and capture task differences
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@jungheejung
jungheejung committed Aug 25, 2024
1 parent 48ff92f commit d88f96a
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Showing 2 changed files with 685 additions and 146 deletions.
154 changes: 8 additions & 146 deletions spacetop_prep/events/bidsify_social_ENH.py
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Original file line number Diff line number Diff line change
Expand Up @@ -285,7 +285,7 @@ def parse_args():


# %% ---------------------------------------------------------------------------
# 3. Cognitive BIDSify
# 1. Cognitive BIDSify
# ------------------------------------------------------------------------------

task_name = 'cognitive'
Expand All @@ -309,6 +309,8 @@ def parse_args():
print(f'No behavior data file found for {sub}, {ses}, {run}. Checked both standard and temporary filenames.')
filtered_cognitive_flist = []
cognitive_logger.error(f"An error occurred while processing the trajectory file: {sub}, {ses}, {run}")
else:
continue # continue to next task
else:
cognitive_flist = glob.glob(join(beh_inputdir,'sub-*', '**','task-social', '**', f'*{task_name}*.csv'), recursive=True)
filtered_cognitive_flist = [file for file in cognitive_flist if "sub-0001" not in file]
Expand Down Expand Up @@ -350,55 +352,7 @@ 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:
# 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 DOES NOT exist")

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


# # 3-1. calculate degree based on x, y coordinate


# # 3-2. Calculate the angle in radians and then convert to degrees
# 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_fillna'].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:
# cognitive_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:
# cognitive_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]
Expand Down Expand Up @@ -465,8 +419,9 @@ def parse_args():
cognitive_logger.error(f"4-1. cue parameter does not match")
continue
cue['stimulusintensity'] = "n/a"
# cue['stim_file'] = beh_df["event01_cue_filename"]

cue['stim_file'] = beh_df["event01_cue_filename"].apply(
lambda x: f'task-social/cue/runtype-{task_name}/{"sch/" if x.startswith("h") else "scl/"}' + x
)

cue['pain_onset_ttl1'] = "n/a"
cue['pain_onset_ttl2'] = "n/a"
Expand Down Expand Up @@ -569,7 +524,7 @@ def parse_args():


# %% ---------------------------------------------------------------------------
# 3. Pain BIDSify
# 2. Pain BIDSify
# ------------------------------------------------------------------------------
task_name = 'pain'

Expand Down Expand Up @@ -636,52 +591,6 @@ 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:
# 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 DOES NOT EXIST")

# except FileNotFoundError as e:
# pain_warning_logger.warning(str(e))
# pain_warning_logger.warning("Trajectory preproc DOES NOT EXIST")
# continue
# except Exception as e:
# # This catches any other exceptions that might occur
# pain_warning_logger.error("An error occurred while processing the trajectory file: %s", str(e))
# continue


# # 3-1. calculate degree based on x, y coordinate
# # 3-2. Calculate the angle in radians and then convert to degrees
# 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_fillna'].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:
# pain_info_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:
# pain_info_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']}")

try:
# Check if trajectory file exists
Expand Down Expand Up @@ -948,53 +857,6 @@ 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:
# 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]
Expand Down
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