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un codespell throughout fixing few left typos automagically #122

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un codespell throughout fixing few left typos automagically #122

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2 changes: 1 addition & 1 deletion CODE_OF_CONDUCT.md
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Expand Up @@ -5,7 +5,7 @@
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
identity and expression, level of experience, education, socioeconomic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.

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2 changes: 1 addition & 1 deletion spacetop_prep/datalad/remove_longerevents.py
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Expand Up @@ -7,7 +7,7 @@
Examples: participant squeeze emergency ball towards the end of the run, reporting equipment failure.
Scan was aborted 41 seconds earlier than expected. Stimulu PC behavioral data was collected entirely

This code will remove the extra rows from the events.tsv file , which will match the legnth of the bOLD data
This code will remove the extra rows from the events.tsv file , which will match the length of the bOLD data
and resolve the bids-validator errors of "SUSPICIOUSLY_LONG_EVENT_DESIGN"
"""

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4 changes: 2 additions & 2 deletions spacetop_prep/events/DEP_bidsify_cue_pain.py
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Expand Up @@ -319,7 +319,7 @@ def is_equivalent(val1, val2, tolerance=1):
stim['onset_ttl4'] = (stim['onset_ttl3'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampdown'])).round(2)

stim['stim_file'] = beh_df['event03_stimulus_type'].map(temperature_map)
stim['stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")


# outcome __________________________________________________________________
Expand All @@ -343,7 +343,7 @@ def is_equivalent(val1, val2, tolerance=1):
outcome['onset_ttl3'] = "n/a"
outcome['onset_ttl4'] = "n/a"
outcome['stim_file'] = 'task-pain_scale.png'
outcome['stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")

events = pd.concat([cue, expect, stim, outcome], ignore_index=True)
events_sorted = events.sort_values(by='onset')
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4 changes: 2 additions & 2 deletions spacetop_prep/events/DEP_bidsify_dryrun_social.py
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Expand Up @@ -709,7 +709,7 @@ def is_equivalent(val1, val2, tolerance=1):
stim['pain_onset_ttl2'] = (stim['pain_onset_ttl1'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampup'])).round(2)
stim['pain_onset_ttl3'] = (stim['pain_onset_ttl2'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['plateau'])).round(2)
stim['pain_onset_ttl4'] = (stim['pain_onset_ttl3'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampdown'])).round(2)
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['cognitive_correct_response'] = "n/a"
stim['cognitive_participant_response'] = "n/a"
stim['cognitive_response_accuracy'] = "n/a"
Expand Down Expand Up @@ -737,7 +737,7 @@ def is_equivalent(val1, val2, tolerance=1):
outcome['pain_onset_ttl2'] = "n/a"
outcome['pain_onset_ttl3'] = "n/a"
outcome['pain_onset_ttl4'] = "n/a"
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['cognitive_correct_response'] = "n/a"
outcome['cognitive_participant_response'] = "n/a"
outcome['cognitive_response_accuracy'] = "n/a"
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4 changes: 2 additions & 2 deletions spacetop_prep/events/bidsify_social.py
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Expand Up @@ -685,7 +685,7 @@ def is_equivalent(val1, val2, tolerance=1):
stim['pain_onset_ttl2'] = (stim['pain_onset_ttl1'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampup'])).round(2)
stim['pain_onset_ttl3'] = (stim['pain_onset_ttl2'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['plateau'])).round(2)
stim['pain_onset_ttl4'] = (stim['pain_onset_ttl3'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampdown'])).round(2)
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['cognitive_correct_response'] = "n/a"
stim['cognitive_participant_response'] = "n/a"
stim['cognitive_response_accuracy'] = "n/a"
Expand Down Expand Up @@ -713,7 +713,7 @@ def is_equivalent(val1, val2, tolerance=1):
outcome['pain_onset_ttl2'] = "n/a"
outcome['pain_onset_ttl3'] = "n/a"
outcome['pain_onset_ttl4'] = "n/a"
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['cognitive_correct_response'] = "n/a"
outcome['cognitive_participant_response'] = "n/a"
outcome['cognitive_response_accuracy'] = "n/a"
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4 changes: 2 additions & 2 deletions spacetop_prep/events/bidsify_social_ENH.py
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Expand Up @@ -757,7 +757,7 @@ def parse_args():
stim['pain_onset_ttl2'] = (stim['pain_onset_ttl1'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampup'])).round(2)
stim['pain_onset_ttl3'] = (stim['pain_onset_ttl2'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['plateau'])).round(2)
stim['pain_onset_ttl4'] = (stim['pain_onset_ttl3'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampdown'])).round(2)
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['cognitive_correct_response'] = "n/a"
stim['cognitive_participant_response'] = "n/a"
stim['cognitive_response_accuracy'] = "n/a"
Expand All @@ -783,7 +783,7 @@ def parse_args():
outcome['pain_onset_ttl2'] = "n/a"
outcome['pain_onset_ttl3'] = "n/a"
outcome['pain_onset_ttl4'] = "n/a"
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['cognitive_correct_response'] = "n/a"
outcome['cognitive_participant_response'] = "n/a"
outcome['cognitive_response_accuracy'] = "n/a"
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4 changes: 2 additions & 2 deletions spacetop_prep/events/bidsify_social_factorize_ENH.py
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Expand Up @@ -359,7 +359,7 @@ def process_behavioral_data(cue, expect, stim, outcome, beh_df, traj_df, trigger
stim['pain_onset_ttl2'] = (stim['pain_onset_ttl1'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampup'])).round(2)
stim['pain_onset_ttl3'] = (stim['pain_onset_ttl2'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['plateau'])).round(2)
stim['pain_onset_ttl4'] = (stim['pain_onset_ttl3'] + beh_df['event03_stimulus_type'].apply(lambda x: stimulus_times[x]['rampdown'])).round(2)
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
stim['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")

if task_name == 'cognitive':
stim['cognitive_correct_response'] = beh_df['event03_C_stim_match']
Expand Down Expand Up @@ -390,7 +390,7 @@ def process_behavioral_data(cue, expect, stim, outcome, beh_df, traj_df, trigger
outcome['stimulusintensity'] = beh_df['event03_stimulus_type']
outcome['stim_file'] = f'task-social/outcomerating/task-{task_name}_scale.png'
if task_name == "pain":
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Recieved: TRIGGER_AND_Response: RESULT_OK" else "fail")
outcome['pain_stimulus_delivery_success'] = beh_df['event03_stimulus_P_trigger'].apply(lambda x: "success" if x == "Command Received: TRIGGER_AND_Response: RESULT_OK" else "fail")
else:
outcome['pain_stimulus_delivery_success'] = "n/a"

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