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src/aind_dynamic_foraging_basic_analysis/metrics/session_metrics.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,185 +1,217 @@ | ||
| """ | ||
| Consolidated session metric tool | ||
| df_session = session_metrics(nwb) | ||
| dfs = nwb_utils.create_trials_df(your_nwb) | ||
| """ | ||
|
|
||
| import re | ||
|
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||
| import numpy as np | ||
| import pandas as pd | ||
| import re | ||
|
|
||
| from aind_dynamic_foraging_basic_analysis import compute_foraging_efficiency | ||
|
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||
| # Copied metrics are from process_nwbs.py in bonsai basic | ||
| # nwb.df_trials = nwb_utils.create_trials_df(your_nwb) | ||
| # NOTE: finished_rate_with_autowater is the same as the calculated total response rate | ||
| # NOTE: finished_rate_with_autowater is the same as the calculated total response rate | ||
| LEFT, RIGHT, IGNORE = 0, 1, 2 | ||
|
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|
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| def session_metrics(nwb): | ||
| """ | ||
| """ | ||
| Compute all session level metrics | ||
| Includes session level metadata as a temporary organizer | ||
| Includes session level metadata as a temporary organizer | ||
| Includes majority of metrics from process_nwbs.py | ||
| New addition: chosen_probability - average difference between the chosen probability | ||
| and non-chosen probability / the difference between the largest and smallest probability | ||
| and non-chosen probability / the difference between the largest and smallest probability | ||
| in the session | ||
| """ | ||
|
|
||
| if not hasattr(nwb, 'df_trials'): | ||
| print('You need to compute df_trials: nwb_utils.create_trials_df(nwb)') | ||
| if not hasattr(nwb, "df_trials"): | ||
| print("You need to compute dfs: nwb_utils.create_trials_df(nwb)") | ||
| return | ||
|
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||
| # METADATA PLACEHOLDER | ||
|
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| df = nwb.df_trials.copy() | ||
|
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| # METADATA PLACEHOLDER | ||
| session_start_time = nwb.session_start_time | ||
| session_date = session_start_time.strftime("%Y-%m-%d") | ||
| subject_id_from_meta = nwb.subject.subject_id | ||
|
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||
| # Parse the file name for suffix | ||
| old_re = re.match(r"(?P<subject_id>\d+)_(?P<date>\d{4}-\d{2}-\d{2})(?:_(?P<n>\d+))?\.json", | ||
| nwb.session_id) | ||
|
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| old_re = re.match( | ||
| r"(?P<subject_id>\d+)_(?P<date>\d{4}-\d{2}-\d{2})(?:_(?P<n>\d+))?\.json", nwb.session_id | ||
| ) | ||
|
|
||
| if old_re is not None: | ||
| subject_id, session_date, nwb_suffix = old_re.groups() | ||
| nwb_suffix = int(nwb_suffix) if nwb_suffix is not None else 0 | ||
| else: | ||
| subject_id, session_date, session_json_time = re.match(r"(?P<subject_id>\d+)_(?P<date>\d{4}-\d{2}-\d{2})(?:_(?P<time>.*))\.json", | ||
| nwb.session_id).groups() | ||
| nwb_suffix = int(session_json_time.replace('-', '')) | ||
|
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||
| subject_id, session_date, session_json_time = re.match( | ||
| r"(?P<subject_id>\d+)_(?P<date>\d{4}-\d{2}-\d{2})(?:_(?P<time>.*))\.json", | ||
| nwb.session_id, | ||
| ).groups() | ||
| nwb_suffix = int(session_json_time.replace("-", "")) | ||
|
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||
| # Verify metadata matches | ||
| assert subject_id == subject_id_from_meta, f"Subject name from the metadata ({subject_id_from_meta}) does not match "\ | ||
| f"that from json name ({subject_id})!!" | ||
| assert session_date == session_date, f"Session date from the metadata ({session_date}) does not match "\ | ||
| f"that from json name ({session_date})!!" | ||
|
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| assert subject_id == subject_id_from_meta, ( | ||
| f"Subject name from the metadata ({subject_id_from_meta}) does not match " | ||
| f"that from json name ({subject_id})!!" | ||
| ) | ||
| assert session_date == session_date, ( | ||
| f"Session date from the metadata ({session_date}) does not match " | ||
| f"that from json name ({session_date})!!" | ||
| ) | ||
|
|
||
| # Create session index | ||
| session_index = pd.MultiIndex.from_tuples([(subject_id, session_date, nwb_suffix)], | ||
| names=['subject_id', 'session_date', 'nwb_suffix']) | ||
| session_index = pd.MultiIndex.from_tuples( | ||
| [(subject_id, session_date, nwb_suffix)], names=["subject_id", "session_date", "nwb_suffix"] | ||
| ) | ||
|
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||
| # Get metadata from nwb.scratch | ||
| meta_dict = nwb.scratch['metadata'].to_dataframe().iloc[0].to_dict() | ||
| meta_dict = nwb.scratch["metadata"].to_dataframe().iloc[0].to_dict() | ||
|
|
||
| # Calculate performance metrics | ||
| n_total_trials = len(df_trial) | ||
| n_finished_trials = (df_trial.animal_response != IGNORE).sum() | ||
| n_total_trials = len(df) | ||
| n_finished_trials = (df.animal_response != IGNORE).sum() | ||
|
|
||
| # Actual foraging trials (autowater excluded) | ||
| n_total_trials_non_autowater = df_trial.non_autowater_trial.sum() | ||
| n_finished_trials_non_autowater = df_trial.non_autowater_finished_trial.sum() | ||
|
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||
| n_reward_trials_non_autowater = df_trial.reward_non_autowater.sum() | ||
| reward_rate_non_autowater_finished = n_reward_trials_non_autowater / n_finished_trials_non_autowater if n_finished_trials_non_autowater > 0 else np.nan | ||
|
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||
| n_total_trials_non_autowater = df.non_autowater_trial.sum() | ||
| n_finished_trials_non_autowater = df.non_autowater_finished_trial.sum() | ||
|
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||
| n_reward_trials_non_autowater = df.reward_non_autowater.sum() | ||
| reward_rate_non_autowater_finished = ( | ||
| n_reward_trials_non_autowater / n_finished_trials_non_autowater | ||
| if n_finished_trials_non_autowater > 0 | ||
| else np.nan | ||
| ) | ||
|
|
||
| # Calculate foraging efficiency | ||
| foraging_eff, foraging_eff_random_seed = compute_foraging_efficiency( | ||
| baited='without bait' not in nwb.protocol.lower(), | ||
| choice_history=df_trial.animal_response.map({0: 0, 1: 1, 2: np.nan}).values, | ||
| reward_history=df_trial.rewarded_historyL | df_trial.rewarded_historyR, | ||
| baited="without bait" not in nwb.protocol.lower(), | ||
| choice_history=df.animal_response.map({0: 0, 1: 1, 2: np.nan}).values, | ||
| reward_history=df.rewarded_historyL | df.rewarded_historyR, | ||
| p_reward=[ | ||
| df_trial.reward_probabilityL.values, | ||
| df_trial.reward_probabilityR.values, | ||
| df.reward_probabilityL.values, | ||
| df.reward_probabilityR.values, | ||
| ], | ||
| random_number=[ | ||
| df_trial.reward_random_number_left.values, | ||
| df_trial.reward_random_number_right.values, | ||
| df.reward_random_number_left.values, | ||
| df.reward_random_number_right.values, | ||
| ], | ||
| autowater_offered=(df_trial.auto_waterL == 1) | (df_trial.auto_waterR == 1), | ||
| autowater_offered=(df.auto_waterL == 1) | (df.auto_waterR == 1), | ||
| ) | ||
|
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||
| # Override foraging_eff_random_seed for old bpod sessions | ||
| if 'bpod' in nwb.session_description: | ||
| foraging_eff_random_seed = nwb.get_scratch('metadata')['foraging_efficiency_with_actual_random_seed'].values[0] | ||
|
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| finished_rate = n_finished_trials_non_autowater / n_total_trials_non_autowater if n_total_trials_non_autowater > 0 else np.nan | ||
|
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||
| if "bpod" in nwb.session_description: | ||
| foraging_eff_random_seed = nwb.get_scratch("metadata")[ | ||
| "foraging_efficiency_with_actual_random_seed" | ||
| ].values[0] | ||
|
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||
| finished_rate = ( | ||
| n_finished_trials_non_autowater / n_total_trials_non_autowater | ||
| if n_total_trials_non_autowater > 0 | ||
| else np.nan | ||
| ) | ||
|
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||
| # New Metrics | ||
|
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| # Probability chosen calculation | ||
| # Probability chosen calculation | ||
| probability_chosen = [] | ||
| probability_not_chosen = [] | ||
|
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||
| for _, row in df_trial.iterrows(): | ||
| for _, row in df.iterrows(): | ||
| if row.animal_response == 2: | ||
| probability_chosen.append(np.nan) | ||
| probability_not_chosen.append(np.nan) | ||
| elif row.animal_response == 0: # Chosen = left choice left probability, not chosen = left choice right probability | ||
| elif ( | ||
| row.animal_response == 0 | ||
| ): # Chosen = left choice left probability, not chosen = left choice right probability | ||
| probability_chosen.append(row.reward_probabilityL) | ||
| probability_not_chosen.append(row.reward_probabilityR) | ||
| else: # Chosen = right choice right probability, not chosen = right choice left probability | ||
| else: # Chosen = right choice right probability, not chosen = right choice left probability | ||
| probability_chosen.append(row.reward_probabilityR) | ||
| probability_not_chosen.append(row.reward_probabilityL) | ||
|
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||
| df_trial['probability_chosen'] = probability_chosen | ||
| df_trial['probability_not_chosen'] = probability_not_chosen | ||
| df["probability_chosen"] = probability_chosen | ||
| df["probability_not_chosen"] = probability_not_chosen | ||
|
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| # Calculate the chosen probability | ||
| average = (df_trial['probability_chosen'] - df_trial['probability_not_chosen']) | ||
|
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| p_larger_global = max(df_trial['probability_chosen'].max(), df_trial['probability_not_chosen'].max()) | ||
|
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| p_smaller_global = min(df_trial['probability_chosen'].min(), df_trial['probability_not_chosen'].min()) | ||
|
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| average = df["probability_chosen"] - df["probability_not_chosen"] | ||
|
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| p_larger_global = max( | ||
| df["probability_chosen"].max(), df["probability_not_chosen"].max() | ||
| ) | ||
|
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||
| p_smaller_global = min( | ||
| df["probability_chosen"].min(), df["probability_not_chosen"].min() | ||
| ) | ||
|
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| mean_difference = average.mean() | ||
| chosen_probability = mean_difference / (p_larger_global - p_smaller_global) | ||
|
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|
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| # Pack all data | ||
| dict_meta = { | ||
| # Basic metadata | ||
| 'rig': meta_dict['box'], | ||
| 'user_name': nwb.experimenter[0], | ||
| 'task': nwb.protocol, | ||
|
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| "rig": meta_dict["box"], | ||
| "user_name": nwb.experimenter[0], | ||
| "task": nwb.protocol, | ||
| # New metric | ||
| 'chosen_probability': chosen_probability, | ||
|
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| "chosen_probability": chosen_probability, | ||
| # Trial counts and rates | ||
| 'total_trials': n_total_trials_non_autowater, | ||
| 'finished_trials': n_finished_trials_non_autowater, | ||
| 'finished_rate': finished_rate, | ||
| 'total_trials_with_autowater': n_total_trials, | ||
| 'finished_trials_with_autowater': n_finished_trials, | ||
| 'finished_rate_with_autowater': n_finished_trials / n_total_trials, | ||
|
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| "total_trials": n_total_trials_non_autowater, | ||
| "finished_trials": n_finished_trials_non_autowater, | ||
| "finished_rate": finished_rate, | ||
| "total_trials_with_autowater": n_total_trials, | ||
| "finished_trials_with_autowater": n_finished_trials, | ||
| "finished_rate_with_autowater": n_finished_trials / n_total_trials, | ||
| # Reward and foraging metrics | ||
| 'reward_trials': n_reward_trials_non_autowater, | ||
| 'reward_rate': reward_rate_non_autowater_finished, | ||
| 'foraging_eff': foraging_eff, | ||
| 'foraging_eff_random_seed': foraging_eff_random_seed, | ||
| 'foraging_performance': foraging_eff * finished_rate, | ||
| 'foraging_performance_random_seed': foraging_eff_random_seed * finished_rate, | ||
|
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| "reward_trials": n_reward_trials_non_autowater, | ||
| "reward_rate": reward_rate_non_autowater_finished, | ||
| "foraging_eff": foraging_eff, | ||
| "foraging_eff_random_seed": foraging_eff_random_seed, | ||
| "foraging_performance": foraging_eff * finished_rate, | ||
| "foraging_performance_random_seed": foraging_eff_random_seed * finished_rate, | ||
| # Timing metrics | ||
| 'reaction_time_median': df_trial.loc[:, 'reaction_time'].median(), | ||
| 'reaction_time_mean': df_trial.loc[:, 'reaction_time'].mean(), | ||
| 'early_lick_rate': (df_trial.loc[:, 'n_lick_all_delay_period'] > 0).sum() / n_total_trials, | ||
|
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| "reaction_time_median": df.loc[:, "reaction_time"].median(), | ||
| "reaction_time_mean": df.loc[:, "reaction_time"].mean(), | ||
| "early_lick_rate": (df.loc[:, "n_lick_all_delay_period"] > 0).sum() / n_total_trials, | ||
| # Double dipping metrics | ||
| 'double_dipping_rate_finished_trials': | ||
| (df_trial.loc[(df_trial.animal_response != IGNORE), 'n_lick_switches_gocue_stop'] > 0).sum() | ||
| / (df_trial.animal_response != IGNORE).sum(), | ||
| 'double_dipping_rate_finished_reward_trials': | ||
| (df_trial.loc[df_trial.reward, 'n_lick_switches_gocue_stop'] > 0).sum() | ||
| / df_trial.reward.sum(), | ||
| 'double_dipping_rate_finished_noreward_trials': | ||
| (df_trial.loc[(df_trial.animal_response != IGNORE) & (~df_trial.reward), 'n_lick_switches_gocue_stop'] > 0).sum() | ||
| / ((df_trial.animal_response != IGNORE) & (~df_trial.reward)).sum(), | ||
|
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||
| "double_dipping_rate_finished_trials": ( | ||
| df.loc[(df.animal_response != IGNORE), "n_lick_switches_gocue_stop"] > 0 | ||
| ).sum() | ||
| / (df.animal_response != IGNORE).sum(), | ||
| "double_dipping_rate_finished_reward_trials": ( | ||
| df.loc[df.reward, "n_lick_switches_gocue_stop"] > 0 | ||
| ).sum() | ||
| / df.reward.sum(), | ||
| "double_dipping_rate_finished_noreward_trials": ( | ||
| df.loc[ | ||
| (df.animal_response != IGNORE) & (~df.reward), | ||
| "n_lick_switches_gocue_stop", | ||
| ] | ||
| > 0 | ||
| ).sum() | ||
| / ((df.animal_response != IGNORE) & (~df.reward)).sum(), | ||
| # Lick consistency metrics | ||
| 'lick_consistency_mean_finished_trials': | ||
| df_trial.loc[(df_trial.animal_response != IGNORE), 'n_lick_consistency_gocue_stop'].mean(), | ||
| 'lick_consistency_mean_finished_reward_trials': | ||
| df_trial.loc[df_trial.reward, 'n_lick_consistency_gocue_stop'].mean(), | ||
| 'lick_consistency_mean_finished_noreward_trials': | ||
| df_trial.loc[(df_trial.animal_response != IGNORE) & (~df_trial.reward), 'n_lick_consistency_gocue_stop'].mean(), | ||
| "lick_consistency_mean_finished_trials": df.loc[ | ||
| (df.animal_response != IGNORE), "n_lick_consistency_gocue_stop" | ||
| ].mean(), | ||
| "lick_consistency_mean_finished_reward_trials": df.loc[ | ||
| df.reward, "n_lick_consistency_gocue_stop" | ||
| ].mean(), | ||
| "lick_consistency_mean_finished_noreward_trials": df.loc[ | ||
| (df.animal_response != IGNORE) & (~df.reward), | ||
| "n_lick_consistency_gocue_stop", | ||
| ].mean(), | ||
| } | ||
|
|
||
| # Create DataFrame with hierarchical columns | ||
| df_meta = pd.DataFrame(dict_meta, | ||
| index=session_index) | ||
| df_meta.columns = pd.MultiIndex.from_product([['metadata'], dict_meta.keys()], | ||
| names=['type', 'variable']) | ||
| return df_meta | ||
| df_meta = pd.DataFrame(dict_meta, index=session_index) | ||
| df_meta.columns = pd.MultiIndex.from_product( | ||
| [["metadata"], dict_meta.keys()], names=["type", "variable"] | ||
| ) | ||
|
|
||
| return df_meta |