ibllib.pipes.training_status

Functions

add_training_lines
check_up_to_date	Check which sessions on local file system are missing from the computed training table
compute_session_duration_delay_location	Get meta information about task.
compute_training_status	Compute the training status for compute date based on training from that session and two previous days
find_earliest_recompute_date	Find the earliest date that we need to compute the training status from.
get_latest_training_information	Extracts the latest training status
get_training_info_for_session	Extract the training information needed for plots for each session
get_trials_task
load_combined_trials	Load and concatenate trials for multiple sessions.
load_existing_dataframe	Load training dataframe from disk, if dataframe doesn't exist returns None
load_trials	Load trials data for session.
make_plots
pass_through_training_hierachy	Makes sure that the new training status is not less than the one from the previous day.
plot_fit_params
plot_heatmap_performance_over_days
plot_over_days
plot_performance_easy_median_reaction_time
plot_psychometric_curve
plot_trial_count_and_session_duration
save_dataframe	Save training dataframe to disk
save_path

get_trials_task(session_path, one)

save_path(subj_path)

save_dataframe(df, subj_path)

Save training dataframe to disk

Parameters

• df – dataframe to save

• subj_path – path to subject folder

Returns

load_existing_dataframe(subj_path)

Load training dataframe from disk, if dataframe doesn’t exist returns None

Parameters

subj_path – path to subject folder

Returns

load_trials(sess_path, one)

Load trials data for session. First attempts to load from local session path, if this fails will attempt to download via ONE, if this also fails, will then attempt to re-extraxt locally

Parameters

• sess_path – session path

• one – ONE instance

Returns

load_combined_trials(sess_paths, one)

Load and concatenate trials for multiple sessions. Used when we want to concatenate trials for two sessions on the same day

Parameters

• sess_paths – list of paths to sessions

• one – ONE instance

Returns

get_latest_training_information(sess_path, one)

Extracts the latest training status

Parameters

• sess_path –

• one –

Returns

find_earliest_recompute_date(df)

Find the earliest date that we need to compute the training status from. Training status depends on previous sessions so if a session was missing and now has been added we need to recompute everything from that date onwards

Parameters

df –

Returns

compute_training_status(df, compute_date, one)

Compute the training status for compute date based on training from that session and two previous days

Parameters

• df – training dataframe

• compute_date – date to compute training on

• one – ONE instance

Returns

pass_through_training_hierachy(status_new, status_old)

Makes sure that the new training status is not less than the one from the previous day. e.g Subject cannot regress in performance

Parameters

• status_new – latest training status

• status_old – previous training status

Returns

compute_session_duration_delay_location(sess_path)

Get meta information about task. Extracts session duration, delay before session start and location of session

Parameters

sess_path – session path

Returns

get_training_info_for_session(session_paths, one)

Extract the training information needed for plots for each session

Parameters

• session_paths – list of session paths on same date

• one – ONE instance

Returns

check_up_to_date(subj_path, df)

Check which sessions on local file system are missing from the computed training table

Parameters

subj_path –

Returns

plot_trial_count_and_session_duration(df, subject)

plot_performance_easy_median_reaction_time(df, subject)

plot_fit_params(df, subject)

plot_psychometric_curve(df, subject, one)

plot_over_days(df, subject, y1, y2=None, ax=None, legend=True, title=True, training_lines=True)

add_training_lines(df, ax)

plot_heatmap_performance_over_days(df, subject)

make_plots(session_path, one, df=None, save=False, upload=False)