From e65aa1cd1141e42948c7e38ee7dc60102c7055c8 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Wed, 13 Nov 2024 15:19:06 -0800 Subject: [PATCH 01/27] additional metrics --- .../metrics/trial_metrics.py | 25 ++++++++++++++++++- .../plot/plot_session_scroller.py | 5 ++++ 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index a19eb3d..9299fa0 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -54,8 +54,31 @@ def compute_all_trial_metrics(nwb): df["WENT_RIGHT"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) + # Rolling reward probability for best option + df["IDEAL_OBSERVER_REWARD_PROB"] = df[["reward_probabilityR", "reward_probabilityL"]].max( + axis=1 + ) + df["ideal_observer_reward_rate"] = ( + df["IDEAL_OBSERVER_REWARD_PROB"] + .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) + .mean() + ) + + + # Rolling reward probability for best option with baiting + if 'bait_left' in df: + df["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] = [ + 1 if (x[0] or x[1]) else x[2] + for x in zip(df["bait_left"], df["bait_right"], df["IDEAL_OBSERVER_REWARD_PROB"]) + ] + df["ideal_observer_reward_rate_with_baiting"] = ( + df["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] + .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) + .mean() + ) + # Clean up temp columns - drop_cols = ["RESPONDED", "RESPONSE_REWARD", "WENT_RIGHT"] + drop_cols = ["RESPONDED", "RESPONSE_REWARD", "WENT_RIGHT", "IDEAL_OBSERVER_REWARD_PROB"] df = df.drop(columns=drop_cols) return df diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py index 7c4b0ec..aa573d4 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py @@ -38,6 +38,11 @@ def plot_session_scroller( # noqa: C901 pragma: no cover plot_bouts (bool), if True, plot licks colored by segmented lick bouts + processing (str) processing method for FIP data to plot + + metrics (list of strings), list of metrics to plot. Must be either 'pR','pL' or + columns in nwb.df_trials + EXAMPLES: plot_foraging_session.plot_session_scroller(nwb) plot_foraging_session.plot_session_scroller(nwb, plot_bouts=True) From 5908c4871629cf179a5faa226bb753afaeceb19e Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Thu, 14 Nov 2024 12:32:30 -0800 Subject: [PATCH 02/27] planning --- .../metrics/trial_metrics.py | 23 ++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 9299fa0..44d976c 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -64,9 +64,8 @@ def compute_all_trial_metrics(nwb): .mean() ) - # Rolling reward probability for best option with baiting - if 'bait_left' in df: + if "bait_left" in df: df["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] = [ 1 if (x[0] or x[1]) else x[2] for x in zip(df["bait_left"], df["bait_right"], df["IDEAL_OBSERVER_REWARD_PROB"]) @@ -77,8 +76,26 @@ def compute_all_trial_metrics(nwb): .mean() ) + # TODO, add from process_nwb + # trial duration (stop-time - start-time) (start/stop time, or gocue to gocue?) + # n_licks_left (# of left licks in response window) + # n_licks_left_total (# of left licks from goCue to next go cue) + # Same for Right, same for all + # intertrial choices (boolean) + # number of intertrial choices + # number of intertrial switches + # response switch or repeat + + + # Clean up temp columns - drop_cols = ["RESPONDED", "RESPONSE_REWARD", "WENT_RIGHT", "IDEAL_OBSERVER_REWARD_PROB"] + drop_cols = [ + "RESPONDED", + "RESPONSE_REWARD", + "WENT_RIGHT", + "IDEAL_OBSERVER_REWARD_PROB", + "IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING", + ] df = df.drop(columns=drop_cols) return df From 8858d1b62ffe7efbfb83ee82f933c6aed61f8973 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Fri, 15 Nov 2024 20:54:20 -0800 Subject: [PATCH 03/27] dev --- .../metrics/trial_metrics.py | 84 +++++++++++++++++++ 1 file changed, 84 insertions(+) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 44d976c..1656186 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -4,7 +4,11 @@ """ +import pandas as pd import numpy as np +import matplotlib.pyplot as plt + +import aind_dynamic_foraging_models.logistic_regression.model as model # TODO, we might want to make these parameters metric specific WIN_DUR = 15 @@ -99,3 +103,83 @@ def compute_all_trial_metrics(nwb): df = df.drop(columns=drop_cols) return df + + +def compute_bias(nwb): + n_trials_back = 5 + max_window = 200 + cv = 1 + compute_every = 10 + BIAS_LIMIT = 10 + + if not hasattr(nwb, "df_trials"): + print("You need to compute df_trials: nwb_utils.create_trials_df(nwb)") + return + + df = nwb.df_trials.copy() + df['choice'] = [np.nan if x ==2 else x for x in df['animal_response']] + df['reward'] = [any(x) for x in zip(df['earned_reward'],df['extra_reward'])] + compute_on = np.arange(compute_every, len(df), compute_every) + bias = [] + ci_lower = [] + ci_upper = [] + C = [] + for i in compute_on: + print(i) + start = np.max([0,i-max_window]) + end = i + choice = df.loc[start:end]['choice'].values + reward = df.loc[start:end]['reward'].values + unique = np.unique(choice[~np.isnan(choice)]) + if len(unique) == 0: + bias.append(np.nan) + ci_lower.append(-BIAS_LIMIT) + ci_upper.append(BIAS_LIMIT) + C.append(np.nan) + elif len(unique) == 2: + try: + out = model.fit_logistic_regression(choice, reward, n_trial_back = n_trials_back, cv = cv) + bias.append(out['df_beta'].loc['bias']['bootstrap_mean'].values[0]) + ci_lower.append(out['df_beta'].loc['bias']['bootstrap_CI_lower'].values[0]) + ci_upper.append(out['df_beta'].loc['bias']['bootstrap_CI_upper'].values[0]) + C.append(out['C']) + except Exception as e: + bias.append(np.nan) + ci_lower.append(-BIAS_LIMIT) + ci_upper.append(BIAS_LIMIT) + C.append(np.nan) + elif unique[0] == 0: + bias.append(-1) + ci_lower.append(-BIAS_LIMIT) + ci_upper.append(0) + C.append(np.nan) + elif unique[0] == 1: + bias.append(+1) + ci_lower.append(0) + ci_upper.append(BIAS_LIMIT) + C.append(np.nan) + df = pd.DataFrame() + df['trial'] = compute_on + df['bias'] = bias + df['ci_lower'] = ci_lower + df['ci_upper'] = ci_upper + df['C'] = C + return df + +def plot_bias(df): + plt.figure() + plt.plot(df['trial'], df['bias'], 'b-', linewidth=2) + plt.fill_between(df['trial'], df['ci_lower'],df['ci_upper'],color='b',alpha=.25) + plt.axhline(0, color='k', alpha=.5) + plt.ylim(-1,1) + plt.ylabel('bias') + plt.xlabel('trial #') + ax = plt.gca() + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + +def add_bias(nwb): + df = compute_bias(nwb) + df = pd.merge(nwb.df_trials, df[['trial','bias']], how='left',on=['trial']) + df['bias'] = df['bias'].bfill().ffill() + return df From 8b79f5d79bb1725244e62db10c4c70b9efd305f1 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Fri, 15 Nov 2024 21:08:59 -0800 Subject: [PATCH 04/27] dev --- .../metrics/trial_metrics.py | 103 +++++++++++------- 1 file changed, 63 insertions(+), 40 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 1656186..ed5e2f5 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -87,10 +87,8 @@ def compute_all_trial_metrics(nwb): # Same for Right, same for all # intertrial choices (boolean) # number of intertrial choices - # number of intertrial switches + # number of intertrial switches # response switch or repeat - - # Clean up temp columns drop_cols = [ @@ -106,80 +104,105 @@ def compute_all_trial_metrics(nwb): def compute_bias(nwb): + + # Parameters for computing bias n_trials_back = 5 max_window = 200 cv = 1 compute_every = 10 - BIAS_LIMIT = 10 + BIAS_LIMIT = 10 + # Make sure trials table has been computed if not hasattr(nwb, "df_trials"): print("You need to compute df_trials: nwb_utils.create_trials_df(nwb)") return + # extract choice and reward df = nwb.df_trials.copy() - df['choice'] = [np.nan if x ==2 else x for x in df['animal_response']] - df['reward'] = [any(x) for x in zip(df['earned_reward'],df['extra_reward'])] - compute_on = np.arange(compute_every, len(df), compute_every) + df["choice"] = [np.nan if x == 2 else x for x in df["animal_response"]] + df["reward"] = [any(x) for x in zip(df["earned_reward"], df["extra_reward"])] + + # Set up lists to store results bias = [] ci_lower = [] ci_upper = [] C = [] + + # Iterate over trials and compute + compute_on = np.arange(compute_every, len(df), compute_every) for i in compute_on: - print(i) - start = np.max([0,i-max_window]) + # Determine interval to compute on + start = np.max([0, i - max_window]) end = i - choice = df.loc[start:end]['choice'].values - reward = df.loc[start:end]['reward'].values + + # extract choice and reward + choice = df.loc[start:end]["choice"].values + reward = df.loc[start:end]["reward"].values + + # Determine if we have valid data to fit model unique = np.unique(choice[~np.isnan(choice)]) if len(unique) == 0: + # no choices, report bias confidence as (-inf, +inf) bias.append(np.nan) ci_lower.append(-BIAS_LIMIT) ci_upper.append(BIAS_LIMIT) C.append(np.nan) elif len(unique) == 2: - try: - out = model.fit_logistic_regression(choice, reward, n_trial_back = n_trials_back, cv = cv) - bias.append(out['df_beta'].loc['bias']['bootstrap_mean'].values[0]) - ci_lower.append(out['df_beta'].loc['bias']['bootstrap_CI_lower'].values[0]) - ci_upper.append(out['df_beta'].loc['bias']['bootstrap_CI_upper'].values[0]) - C.append(out['C']) - except Exception as e: - bias.append(np.nan) - ci_lower.append(-BIAS_LIMIT) - ci_upper.append(BIAS_LIMIT) - C.append(np.nan) + # Fit model + out = model.fit_logistic_regression(choice, reward, n_trial_back=n_trials_back, cv=cv) + bias.append(out["df_beta"].loc["bias"]["bootstrap_mean"].values[0]) + ci_lower.append(out["df_beta"].loc["bias"]["bootstrap_CI_lower"].values[0]) + ci_upper.append(out["df_beta"].loc["bias"]["bootstrap_CI_upper"].values[0]) + C.append(out["C"]) elif unique[0] == 0: + # only left choices, report bias confidence as (-inf, 0) bias.append(-1) ci_lower.append(-BIAS_LIMIT) - ci_upper.append(0) - C.append(np.nan) + ci_upper.append(0) + C.append(np.nan) elif unique[0] == 1: + # only right choices, report bias confidence as (0, +inf) bias.append(+1) ci_lower.append(0) - ci_upper.append(BIAS_LIMIT) + ci_upper.append(BIAS_LIMIT) C.append(np.nan) + + # Pack results into a dataframe df = pd.DataFrame() - df['trial'] = compute_on - df['bias'] = bias - df['ci_lower'] = ci_lower - df['ci_upper'] = ci_upper - df['C'] = C + df["trial"] = compute_on + df["bias"] = bias + df["ci_lower"] = ci_lower + df["ci_upper"] = ci_upper + df["C"] = C + return df + def plot_bias(df): plt.figure() - plt.plot(df['trial'], df['bias'], 'b-', linewidth=2) - plt.fill_between(df['trial'], df['ci_lower'],df['ci_upper'],color='b',alpha=.25) - plt.axhline(0, color='k', alpha=.5) - plt.ylim(-1,1) - plt.ylabel('bias') - plt.xlabel('trial #') + + # Plot bias and confidence interval + plt.plot(df["trial"], df["bias"], "b-", linewidth=2) + plt.fill_between(df["trial"], df["ci_lower"], df["ci_upper"], color="b", alpha=0.25) + + # Clean up plot + plt.axhline(0, color="k", alpha=0.5) + plt.ylim(-1, 1) + plt.ylabel("bias") + plt.xlabel("trial #") ax = plt.gca() - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + def add_bias(nwb): + # Compute bias df = compute_bias(nwb) - df = pd.merge(nwb.df_trials, df[['trial','bias']], how='left',on=['trial']) - df['bias'] = df['bias'].bfill().ffill() + + # merge onto trials dataframe + df = pd.merge(nwb.df_trials, df[["trial", "bias"]], how="left", on=["trial"]) + + # fill in bias on non-computed trials + df["bias"] = df["bias"].bfill().ffill() + return df From dd2df839ce196c5f77a08d66bcc1df8498ea4d72 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Sun, 17 Nov 2024 12:06:35 -0800 Subject: [PATCH 05/27] adding wrapper function and bias plot --- .../plot/plot_foraging_session.py | 28 ++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 35860ca..1ebe6a5 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -22,6 +22,18 @@ def moving_average(a, n=3): return ret[(n - 1) :] / n # noqa: E203 +def plot_foraging_session_nwb(nwb): + ''' + Wrapper function that extracts fields + ''' + plot_foraging_session( + [np.nan if x ==2 else x for x in nwb.df_trials['animal_response'].values], + nwb.df_trials['earned_reward'].values, + [nwb.df_trials['reward_probabilityR'], nwb.df_trials['reward_probabilityL']], + bias = nwb.df_trials['bias'].values + ) + + def plot_foraging_session( # noqa: C901 choice_history: Union[List, np.ndarray], reward_history: Union[List, np.ndarray], @@ -34,6 +46,7 @@ def plot_foraging_session( # noqa: C901 base_color: str = "y", ax: plt.Axes = None, vertical: bool = False, + bias: Union[List, np.ndarray] = None, ) -> Tuple[plt.Figure, List[plt.Axes]]: """Plot dynamic foraging session. @@ -183,6 +196,7 @@ def plot_foraging_session( # noqa: C901 label="Autowater ignored", ) + # Base probability xx = np.arange(0, n_trials) + 1 yy = p_reward_fraction @@ -193,6 +207,7 @@ def plot_foraging_session( # noqa: C901 lw=1.5, ) + # Smoothed choice history y = moving_average(choice_history, smooth_factor) / ( moving_average(~np.isnan(choice_history), smooth_factor) + 1e-6 @@ -218,6 +233,16 @@ def plot_foraging_session( # noqa: C901 label="Finished (smooth = %g)" % smooth_factor, ) + # Bias + if bias is not None: + bias = (np.array(bias)+1)/(2) + ax_choice_reward.plot( + xx, + bias, + color='g', + lw=1.5 + ) + # add valid ranage if valid_range is not None: add_range = ax_choice_reward.axhline if vertical else ax_choice_reward.axvline @@ -305,5 +330,6 @@ def plot_foraging_session( # noqa: C901 ax_reward_schedule.set(ylabel="Trial number") ax.remove() - + plt.tight_layout() + return ax_choice_reward.get_figure(), [ax_choice_reward, ax_reward_schedule] From b3e992039577a422730cc8d7c4c3a1753b213c6c Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 08:55:37 -0800 Subject: [PATCH 06/27] cleaning up --- .../metrics/trial_metrics.py | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index ed5e2f5..0d07354 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -1,6 +1,7 @@ """ Tools for computing trial by trial metrics - df_trials = compute_all_trial_metrics(nwb) + df_trials = compute_trial_metrics(nwb) + df_trials = compute_bias(nwb) """ @@ -15,9 +16,9 @@ MIN_EVENTS = 2 -def compute_all_trial_metrics(nwb): +def compute_trial_metrics(nwb): """ - Computes all trial by trial metrics + Computes trial by trial metrics response_rate, fraction of trials with a response gocue_reward_rate, fraction of trials with a reward @@ -175,9 +176,21 @@ def compute_bias(nwb): df["ci_upper"] = ci_upper df["C"] = C - return df + # merge onto trials dataframe + df_trials = pd.merge( + nwb.df_trials.drop(columns=["bias"], errors="ignore"), + df[["trial", "bias"]], + how="left", + on=["trial"], + ) + # fill in bias on non-computed trials + df_trials["bias"] = df_trials["bias"].bfill().ffill() + return df_trials + + +# TODO, remove this. Just for debugging def plot_bias(df): plt.figure() @@ -193,16 +206,3 @@ def plot_bias(df): ax = plt.gca() ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) - - -def add_bias(nwb): - # Compute bias - df = compute_bias(nwb) - - # merge onto trials dataframe - df = pd.merge(nwb.df_trials, df[["trial", "bias"]], how="left", on=["trial"]) - - # fill in bias on non-computed trials - df["bias"] = df["bias"].bfill().ffill() - - return df From 7a3a28f5ac3cf6c4daaf84b88d3c212987aca73e Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 08:58:54 -0800 Subject: [PATCH 07/27] disabling exponential fit --- .../metrics/trial_metrics.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 0d07354..5843f0f 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -150,7 +150,9 @@ def compute_bias(nwb): C.append(np.nan) elif len(unique) == 2: # Fit model - out = model.fit_logistic_regression(choice, reward, n_trial_back=n_trials_back, cv=cv) + out = model.fit_logistic_regression( + choice, reward, n_trial_back=n_trials_back, cv=cv, fit_exponential=False + ) bias.append(out["df_beta"].loc["bias"]["bootstrap_mean"].values[0]) ci_lower.append(out["df_beta"].loc["bias"]["bootstrap_CI_lower"].values[0]) ci_upper.append(out["df_beta"].loc["bias"]["bootstrap_CI_upper"].values[0]) From 847a6d8bc93890b14c56c8627085f1a9f1527366 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 09:20:45 -0800 Subject: [PATCH 08/27] adding confidence intervals to df_trials --- .../metrics/trial_metrics.py | 30 +++++-------------- 1 file changed, 7 insertions(+), 23 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 5843f0f..f247fde 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -174,37 +174,21 @@ def compute_bias(nwb): df = pd.DataFrame() df["trial"] = compute_on df["bias"] = bias - df["ci_lower"] = ci_lower - df["ci_upper"] = ci_upper - df["C"] = C + df["bias_ci_lower"] = ci_lower + df["bias_ci_upper"] = ci_upper + df["bias_C"] = C # merge onto trials dataframe df_trials = pd.merge( - nwb.df_trials.drop(columns=["bias"], errors="ignore"), - df[["trial", "bias"]], + nwb.df_trials.drop(columns=["bias", "bias_ci_lower", "bias_ci_upper"], errors="ignore"), + df[["trial", "bias", "bias_ci_lower", "bias_ci_upper"]], how="left", on=["trial"], ) # fill in bias on non-computed trials df_trials["bias"] = df_trials["bias"].bfill().ffill() + df_trials["bias_ci_lower"] = df_trials["bias_ci_lower"].bfill().ffill() + df_trials["bias_ci_upper"] = df_trials["bias_ci_upper"].bfill().ffill() return df_trials - - -# TODO, remove this. Just for debugging -def plot_bias(df): - plt.figure() - - # Plot bias and confidence interval - plt.plot(df["trial"], df["bias"], "b-", linewidth=2) - plt.fill_between(df["trial"], df["ci_lower"], df["ci_upper"], color="b", alpha=0.25) - - # Clean up plot - plt.axhline(0, color="k", alpha=0.5) - plt.ylim(-1, 1) - plt.ylabel("bias") - plt.xlabel("trial #") - ax = plt.gca() - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) From 8a92407daceeeacadcb440494f2fe1ac5b313054 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 09:43:11 -0800 Subject: [PATCH 09/27] updating plotting --- .../plot/plot_foraging_session.py | 87 ++++++++++--------- 1 file changed, 47 insertions(+), 40 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 1ebe6a5..0139398 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -22,17 +22,20 @@ def moving_average(a, n=3): return ret[(n - 1) :] / n # noqa: E203 -def plot_foraging_session_nwb(nwb): - ''' - Wrapper function that extracts fields - ''' +def plot_foraging_session_nwb(nwb, **kwargs): + """ + Wrapper function that extracts fields + """ plot_foraging_session( - [np.nan if x ==2 else x for x in nwb.df_trials['animal_response'].values], - nwb.df_trials['earned_reward'].values, - [nwb.df_trials['reward_probabilityR'], nwb.df_trials['reward_probabilityL']], - bias = nwb.df_trials['bias'].values + [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], + nwb.df_trials["earned_reward"].values, + [nwb.df_trials["reward_probabilityR"], nwb.df_trials["reward_probabilityL"]], + bias=nwb.df_trials["bias"].values, + bias_lower=nwb.df_trials["bias_ci_lower"].values, + bias_upper=nwb.df_trials["bias_ci_upper"].values, + **kwargs, ) - + def plot_foraging_session( # noqa: C901 choice_history: Union[List, np.ndarray], @@ -47,6 +50,9 @@ def plot_foraging_session( # noqa: C901 ax: plt.Axes = None, vertical: bool = False, bias: Union[List, np.ndarray] = None, + bias_lower: Union[List, np.ndarray] = None, + bias_upper: Union[List, np.ndarray] = None, + plot_list: List = ["choice", "finished", "reward_prob"], ) -> Tuple[plt.Figure, List[plt.Axes]]: """Plot dynamic foraging session. @@ -196,17 +202,16 @@ def plot_foraging_session( # noqa: C901 label="Autowater ignored", ) - # Base probability xx = np.arange(0, n_trials) + 1 yy = p_reward_fraction - ax_choice_reward.plot( - *(xx, yy) if not vertical else [*(yy, xx)], - color=base_color, - label="Base rew. prob.", - lw=1.5, - ) - + if "reward_prob" in plot_list: + ax_choice_reward.plot( + *(xx, yy) if not vertical else [*(yy, xx)], + color=base_color, + label="Base rew. prob.", + lw=1.5, + ) # Smoothed choice history y = moving_average(choice_history, smooth_factor) / ( @@ -214,34 +219,36 @@ def plot_foraging_session( # noqa: C901 ) y[y > 100] = np.nan x = np.arange(0, len(y)) + int(smooth_factor / 2) + 1 - ax_choice_reward.plot( - *(x, y) if not vertical else [*(y, x)], - linewidth=1.5, - color="black", - label="Choice (smooth = %g)" % smooth_factor, - ) + if "choice" in plot_list: + ax_choice_reward.plot( + *(x, y) if not vertical else [*(y, x)], + linewidth=1.5, + color="black", + label="Choice (smooth = %g)" % smooth_factor, + ) # finished ratio if np.sum(np.isnan(choice_history)): x = np.arange(0, len(y)) + int(smooth_factor / 2) + 1 y = moving_average(~np.isnan(choice_history), smooth_factor) - ax_choice_reward.plot( - *(x, y) if not vertical else [*(y, x)], - linewidth=0.8, - color="m", - alpha=1, - label="Finished (smooth = %g)" % smooth_factor, - ) + if "finished" in plot_list: + ax_choice_reward.plot( + *(x, y) if not vertical else [*(y, x)], + linewidth=0.8, + color="m", + alpha=1, + label="Finished (smooth = %g)" % smooth_factor, + ) # Bias - if bias is not None: - bias = (np.array(bias)+1)/(2) - ax_choice_reward.plot( - xx, - bias, - color='g', - lw=1.5 - ) + if "bias" in plot_list: + bias = (np.array(bias) + 1) / (2) + bias_lower = (np.array(bias_lower) + 1) / (2) + bias_upper = (np.array(bias_upper) + 1) / (2) + bias_lower[bias_lower < 0] = 0 + bias_upper[bias_upper > 1] = 1 + ax_choice_reward.plot(xx, bias, color="g", lw=1.5, label="bias") + ax_choice_reward.fill_between(xx, bias_lower, bias_upper, color="g", alpha=0.25) # add valid ranage if valid_range is not None: @@ -294,7 +301,7 @@ def plot_foraging_session( # noqa: C901 if not vertical: ax_choice_reward.set_yticks([0, 1]) ax_choice_reward.set_yticklabels(["Left", "Right"]) - ax_choice_reward.legend(fontsize=6, loc="upper left", bbox_to_anchor=(0.6, 1.3), ncol=3) + ax_choice_reward.legend(fontsize=6, loc="upper left", bbox_to_anchor=(0, 1.25), ncol=5) # sns.despine(trim=True, bottom=True, ax=ax_1) ax_choice_reward.spines["top"].set_visible(False) @@ -331,5 +338,5 @@ def plot_foraging_session( # noqa: C901 ax.remove() plt.tight_layout() - + return ax_choice_reward.get_figure(), [ax_choice_reward, ax_reward_schedule] From 08d90853fff5a95239e0639500f272c80a53b2aa Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 09:46:14 -0800 Subject: [PATCH 10/27] linting --- .../metrics/trial_metrics.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index f247fde..80b6293 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -7,7 +7,6 @@ import pandas as pd import numpy as np -import matplotlib.pyplot as plt import aind_dynamic_foraging_models.logistic_regression.model as model From 9ef4d5a4349ae94f52a93f15067f399e47882b44 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 09:50:19 -0800 Subject: [PATCH 11/27] linting --- .../metrics/trial_metrics.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 80b6293..8265118 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -104,6 +104,13 @@ def compute_trial_metrics(nwb): def compute_bias(nwb): + ''' + Computes side bias by fitting a logistic regression model + returns trials table with the following columns: + bias, the side bias + bias_ci_lower, the lower confidence interval on the bias + bias_ci_upper, the uppwer confidence interval on the bias + ''' # Parameters for computing bias n_trials_back = 5 From 46657d369fedf3fb172849dba23c9f4418dbf023 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 09:52:59 -0800 Subject: [PATCH 12/27] linting --- .../metrics/trial_metrics.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 8265118..a3c4e12 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -104,13 +104,13 @@ def compute_trial_metrics(nwb): def compute_bias(nwb): - ''' - Computes side bias by fitting a logistic regression model - returns trials table with the following columns: - bias, the side bias - bias_ci_lower, the lower confidence interval on the bias - bias_ci_upper, the uppwer confidence interval on the bias - ''' + """ + Computes side bias by fitting a logistic regression model + returns trials table with the following columns: + bias, the side bias + bias_ci_lower, the lower confidence interval on the bias + bias_ci_upper, the uppwer confidence interval on the bias + """ # Parameters for computing bias n_trials_back = 5 From c3ff7c4b722d1179466887271fc1e84a01d2685d Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 11:44:45 -0800 Subject: [PATCH 13/27] plotting updates --- .../plot/plot_foraging_session.py | 25 ++++++++++++++++--- .../plot/plot_session_scroller.py | 4 +-- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 0139398..74878ad 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -26,16 +26,34 @@ def plot_foraging_session_nwb(nwb, **kwargs): """ Wrapper function that extracts fields """ - plot_foraging_session( + fig, axes = plot_foraging_session( [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], nwb.df_trials["earned_reward"].values, - [nwb.df_trials["reward_probabilityR"], nwb.df_trials["reward_probabilityL"]], + [nwb.df_trials["reward_probabilityL"], nwb.df_trials["reward_probabilityR"]], bias=nwb.df_trials["bias"].values, bias_lower=nwb.df_trials["bias_ci_lower"].values, bias_upper=nwb.df_trials["bias_ci_upper"].values, **kwargs, ) + # Add some text info + # TODO, waiting for AIND metadata to get integrated before adding this info: + # {df_session.metadata.rig.iloc[0]}, {df_session.metadata.user_name.iloc[0]}\n' + # f'FORAGING finished {df_session.session_stats.finished_trials.iloc[0]} ' + # f'ignored {df_session.session_stats.ignored_trials.iloc[0]} + ' + # f'AUTOWATER collected {df_session.session_stats.autowater_collected.iloc[0]} ' + # f'ignored {df_session.session_stats.autowater_ignored.iloc[0]}\n' + # f'FORAGING finished rate {df_session.session_stats.finished_rate.iloc[0]:.2%}, ' + axes[0].text( + 0, + 1.05, + f"{nwb.session_id}\n" + f'Total trials {len(nwb.df_trials)}, ignored {np.sum(nwb.df_trials["animal_response"]==2)},' + f' left {np.sum(nwb.df_trials["animal_response"] == 0)}, right {np.sum(nwb.df_trials["animal_response"] == 1)}', + fontsize=8, + transform=axes[0].transAxes, + ) + def plot_foraging_session( # noqa: C901 choice_history: Union[List, np.ndarray], @@ -249,6 +267,7 @@ def plot_foraging_session( # noqa: C901 bias_upper[bias_upper > 1] = 1 ax_choice_reward.plot(xx, bias, color="g", lw=1.5, label="bias") ax_choice_reward.fill_between(xx, bias_lower, bias_upper, color="g", alpha=0.25) + ax_choice_reward.plot(xx, [0.5] * len(xx), color="g", linestyle="--", alpha=0.2, lw=1) # add valid ranage if valid_range is not None: @@ -301,7 +320,7 @@ def plot_foraging_session( # noqa: C901 if not vertical: ax_choice_reward.set_yticks([0, 1]) ax_choice_reward.set_yticklabels(["Left", "Right"]) - ax_choice_reward.legend(fontsize=6, loc="upper left", bbox_to_anchor=(0, 1.25), ncol=5) + ax_choice_reward.legend(fontsize=6, loc="upper left", bbox_to_anchor=(0.4, 1.3), ncol=3) # sns.despine(trim=True, bottom=True, ax=ax_1) ax_choice_reward.spines["top"].set_visible(False) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py index aa573d4..ed64d36 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_session_scroller.py @@ -338,11 +338,11 @@ def plot_session_scroller( # noqa: C901 pragma: no cover if "pR" in metrics: pR = params["metrics_bottom"] + df_trials["reward_probabilityR"] pR = np.repeat(pR, 2)[:-1] - ax.plot(go_cue_times_doubled, pR, color="r", label="pR") + ax.plot(go_cue_times_doubled, pR, color="b", label="pR") if "pL" in metrics: pL = params["metrics_bottom"] + df_trials["reward_probabilityL"] pL = np.repeat(pL, 2)[:-1] - ax.plot(go_cue_times_doubled, pL, color="b", label="pL") + ax.plot(go_cue_times_doubled, pL, color="r", label="pL") # plot metrics if they are available for metric in metrics: From 52c275a1e245f21507fef18125f088a0c91457e2 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 12:13:19 -0800 Subject: [PATCH 14/27] cleaning up --- .../plot/plot_foraging_session.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 74878ad..ede4b83 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -26,6 +26,8 @@ def plot_foraging_session_nwb(nwb, **kwargs): """ Wrapper function that extracts fields """ + # TODO, should check for nwb.df_trials (and return) + # TODO, should check for bias (and not pass it in) fig, axes = plot_foraging_session( [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], nwb.df_trials["earned_reward"].values, From 0b1f00f3636b31be1214c4cac02802cca55bcc2e Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 12:15:15 -0800 Subject: [PATCH 15/27] linting --- .../plot/plot_foraging_session.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index ede4b83..77311e7 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -51,7 +51,8 @@ def plot_foraging_session_nwb(nwb, **kwargs): 1.05, f"{nwb.session_id}\n" f'Total trials {len(nwb.df_trials)}, ignored {np.sum(nwb.df_trials["animal_response"]==2)},' - f' left {np.sum(nwb.df_trials["animal_response"] == 0)}, right {np.sum(nwb.df_trials["animal_response"] == 1)}', + f' left {np.sum(nwb.df_trials["animal_response"] == 0)},' + f' right {np.sum(nwb.df_trials["animal_response"] == 1)}', fontsize=8, transform=axes[0].transAxes, ) From f6cc42397b5c2370f678056dc7d42b967c7a9b1c Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 12:21:46 -0800 Subject: [PATCH 16/27] linting --- .../plot/plot_foraging_session.py | 35 ++++++++++++------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 77311e7..ce0c92d 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -26,17 +26,28 @@ def plot_foraging_session_nwb(nwb, **kwargs): """ Wrapper function that extracts fields """ - # TODO, should check for nwb.df_trials (and return) - # TODO, should check for bias (and not pass it in) - fig, axes = plot_foraging_session( - [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], - nwb.df_trials["earned_reward"].values, - [nwb.df_trials["reward_probabilityL"], nwb.df_trials["reward_probabilityR"]], - bias=nwb.df_trials["bias"].values, - bias_lower=nwb.df_trials["bias_ci_lower"].values, - bias_upper=nwb.df_trials["bias_ci_upper"].values, - **kwargs, - ) + + if not hasattr(nwb, "df_trials"): + print("You need to compute df_trials: nwb_utils.create_trials_df(nwb)") + return + + if "bias" not in nwb.df_trials: + fig, axes = plot_foraging_session( + [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], + nwb.df_trials["earned_reward"].values, + [nwb.df_trials["reward_probabilityL"], nwb.df_trials["reward_probabilityR"]], + **kwargs, + ) + else: + fig, axes = plot_foraging_session( + [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], + nwb.df_trials["earned_reward"].values, + [nwb.df_trials["reward_probabilityL"], nwb.df_trials["reward_probabilityR"]], + bias=nwb.df_trials["bias"].values, + bias_lower=nwb.df_trials["bias_ci_lower"].values, + bias_upper=nwb.df_trials["bias_ci_upper"].values, + **kwargs, + ) # Add some text info # TODO, waiting for AIND metadata to get integrated before adding this info: @@ -262,7 +273,7 @@ def plot_foraging_session( # noqa: C901 ) # Bias - if "bias" in plot_list: + if ("bias" in plot_list) and (bias is not None): bias = (np.array(bias) + 1) / (2) bias_lower = (np.array(bias_lower) + 1) / (2) bias_upper = (np.array(bias_upper) + 1) / (2) From 7305afe6acf08aa76f383d8c62fa1b75826ef1eb Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 13:34:11 -0800 Subject: [PATCH 17/27] unit tests --- tests/data/test_plot_session.png | Bin 196951 -> 210242 bytes tests/data/test_plot_session_vertical.png | Bin 168722 -> 138823 bytes tests/test_plot_foraging_session.py | 33 ++++++++++++++++++++++ 3 files changed, 33 insertions(+) diff --git a/tests/data/test_plot_session.png b/tests/data/test_plot_session.png 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z1K$w6VT~lc3qV|j_eeXAeaUh^>D*^u$n6ff5tSsi?EDRK#|Y{r89z9I-&xuFlmU}D z50Jke2YrcvY2ZsugA@dewk2R_Xv?i$MsTUhr$}O|uEQ*Rc-J5E1vyw{RVi910>q6W zAr5Bh2z6Q0*f<9XQAo-(&q1H%NY5VkacR^ac$b|mT#m4N5Q4{h0&>wv((P~Sk;si? zkzJ;NQsIm6p3_z|SkqOIB@vi69lj%b9yUi^1jo-d2(s9I&(&#`6DJg6<4)?7om|rW z16BN*P-s&|41=wO9~JB^)W9Mm8g@^|ktChi$$&DEZR3Q}MPy`53=tMAE(L@pR5Ac5 zqc*e}L95&i7MG(F=ggu~!0NG-SmOvG(6By(0rhPUB4?~L%PyVZN795+poKA2JB~e= z?kR|Zcgyiwx55H!LIfK#VFkBp)`57)Q@y&)kyqgm9~{)NO*%2M)x*4H2toX2K2lY# zflV4G@}0aJ*O06W;IrhN>@J&-K430p$DhF|i9=Elisxb(2ArQ7Ce5B6M8%6D90=UZ ie7qX|zb;*j(08U0E}nUq5w?naUj Date: Mon, 18 Nov 2024 13:35:50 -0800 Subject: [PATCH 18/27] linting --- tests/test_plot_foraging_session.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/tests/test_plot_foraging_session.py b/tests/test_plot_foraging_session.py index 1880b11..266c6a2 100644 --- a/tests/test_plot_foraging_session.py +++ b/tests/test_plot_foraging_session.py @@ -14,6 +14,7 @@ import aind_dynamic_foraging_basic_analysis.plot.plot_foraging_session as pfs from tests.nwb_io import get_history_from_nwb + class EmptyNWB: """ Just an empty class for saving attributes to @@ -21,6 +22,7 @@ class EmptyNWB: pass + class TestPlotSession(unittest.TestCase): """Test plot session""" @@ -43,23 +45,23 @@ def test_nwb_wrapper(self): pfs.plot_foraging_session_nwb(nwb) # Test without bias column - choices = np.array([0,0,1,1,2,2]) + choices = np.array([0, 0, 1, 1, 2, 2]) rewards = np.array([True, False, True, False, False, False]) - pL = [0.1]*6 - pR = [0.8]*6 + pL = [0.1] * 6 + pR = [0.8] * 6 df = pd.DataFrame() - df['animal_response'] = choices - df['earned_reward'] = rewards - df['reward_probabilityL'] = pL - df['reward_probabilityR'] = pR + df["animal_response"] = choices + df["earned_reward"] = rewards + df["reward_probabilityL"] = pL + df["reward_probabilityR"] = pR nwb.df_trials = df - nwb.session_id ='test' + nwb.session_id = "test" pfs.plot_foraging_session_nwb(nwb) - + # Test with bias column - nwb.df_trials['bias'] = np.array([0,0,.1,.1,.05,.05]) - nwb.df_trials['bias_ci_lower'] = np.array([0]*6) - nwb.df_trials['bias_ci_upper'] = np.array([.2]*6) + nwb.df_trials["bias"] = np.array([0, 0, 0.1, 0.1, 0.05, 0.05]) + nwb.df_trials["bias_ci_lower"] = np.array([0] * 6) + nwb.df_trials["bias_ci_upper"] = np.array([0.2] * 6) pfs.plot_foraging_session_nwb(nwb) def test_plot_session(self): From 0bd20847ab225e24ae34c07c50aee17c6904540d Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 13:38:10 -0800 Subject: [PATCH 19/27] linting --- tests/test_plot_foraging_session.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/test_plot_foraging_session.py b/tests/test_plot_foraging_session.py index 266c6a2..3d163e8 100644 --- a/tests/test_plot_foraging_session.py +++ b/tests/test_plot_foraging_session.py @@ -40,6 +40,9 @@ def setUpClass(cls): ) = get_history_from_nwb(nwb_file) def test_nwb_wrapper(self): + """ + Test wrapper function that plots foraging session from nwb file + """ # Test we have df_trials nwb = EmptyNWB() pfs.plot_foraging_session_nwb(nwb) From 4ed2b5c98184553074c1a1ad7ea1cee5693b983e Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 13:41:09 -0800 Subject: [PATCH 20/27] linting --- .../plot/plot_foraging_session.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index ce0c92d..386d22b 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -46,6 +46,7 @@ def plot_foraging_session_nwb(nwb, **kwargs): bias=nwb.df_trials["bias"].values, bias_lower=nwb.df_trials["bias_ci_lower"].values, bias_upper=nwb.df_trials["bias_ci_upper"].values, + plot_list=["bias"], **kwargs, ) From 5a2d204d3354c2859c559c72ca28911a8159d1a7 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 13:56:48 -0800 Subject: [PATCH 21/27] linting --- .../plot/plot_foraging_session.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 386d22b..1ea7951 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -39,6 +39,8 @@ def plot_foraging_session_nwb(nwb, **kwargs): **kwargs, ) else: + if "plot_list" not in kwargs: + kwargs["plot_list"] = ["choice", "finished", "reward_prob", "bias"] fig, axes = plot_foraging_session( [np.nan if x == 2 else x for x in nwb.df_trials["animal_response"].values], nwb.df_trials["earned_reward"].values, @@ -46,7 +48,6 @@ def plot_foraging_session_nwb(nwb, **kwargs): bias=nwb.df_trials["bias"].values, bias_lower=nwb.df_trials["bias_ci_lower"].values, bias_upper=nwb.df_trials["bias_ci_upper"].values, - plot_list=["bias"], **kwargs, ) From 612f380c7d1baa6e54e5955cfe7d2d8121473318 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 14:45:30 -0800 Subject: [PATCH 22/27] linting, clarifying df vs df_trials --- .../metrics/trial_metrics.py | 68 ++++++++++--------- 1 file changed, 37 insertions(+), 31 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index a3c4e12..7d25e6b 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -31,51 +31,55 @@ def compute_trial_metrics(nwb): print("You need to compute df_trials: nwb_utils.create_trials_df(nwb)") return - df = nwb.df_trials.copy() + df_trials = nwb.df_trials.copy() - df["RESPONDED"] = [x in [0, 1] for x in df["animal_response"].values] + df_trials["RESPONDED"] = [x in [0, 1] for x in df_trials["animal_response"].values] # Rolling fraction of goCues with a response - df["response_rate"] = ( - df["RESPONDED"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() + df_trials["response_rate"] = ( + df_trials["RESPONDED"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) # Rolling fraction of goCues with a response - df["gocue_reward_rate"] = ( - df["earned_reward"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() + df_trials["gocue_reward_rate"] = ( + df_trials["earned_reward"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) # Rolling fraction of responses with a response - df["RESPONSE_REWARD"] = [ - x[0] if x[1] else np.nan for x in zip(df["earned_reward"], df["RESPONDED"]) + df_trials["RESPONSE_REWARD"] = [ + x[0] if x[1] else np.nan for x in zip(df_trials["earned_reward"], df_trials["RESPONDED"]) ] - df["response_reward_rate"] = ( - df["RESPONSE_REWARD"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() + df_trials["response_reward_rate"] = ( + df_trials["RESPONSE_REWARD"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) # Rolling fraction of choosing right - df["WENT_RIGHT"] = [x if x in [0, 1] else np.nan for x in df["animal_response"]] - df["choose_right_rate"] = ( - df["WENT_RIGHT"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() + df_trials["WENT_RIGHT"] = [x if x in [0, 1] else np.nan for x in df_trials["animal_response"]] + df_trials["choose_right_rate"] = ( + df_trials["WENT_RIGHT"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) # Rolling reward probability for best option - df["IDEAL_OBSERVER_REWARD_PROB"] = df[["reward_probabilityR", "reward_probabilityL"]].max( - axis=1 - ) - df["ideal_observer_reward_rate"] = ( - df["IDEAL_OBSERVER_REWARD_PROB"] + df_trials["IDEAL_OBSERVER_REWARD_PROB"] = df_trials[ + ["reward_probabilityR", "reward_probabilityL"] + ].max(axis=1) + df_trials["ideal_observer_reward_rate"] = ( + df_trials["IDEAL_OBSERVER_REWARD_PROB"] .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) .mean() ) # Rolling reward probability for best option with baiting - if "bait_left" in df: - df["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] = [ + if "bait_left" in df_trials: + df_trials["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] = [ 1 if (x[0] or x[1]) else x[2] - for x in zip(df["bait_left"], df["bait_right"], df["IDEAL_OBSERVER_REWARD_PROB"]) + for x in zip( + df_trials["bait_left"], + df_trials["bait_right"], + df_trials["IDEAL_OBSERVER_REWARD_PROB"], + ) ] - df["ideal_observer_reward_rate_with_baiting"] = ( - df["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] + df_trials["ideal_observer_reward_rate_with_baiting"] = ( + df_trials["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) .mean() ) @@ -98,9 +102,9 @@ def compute_trial_metrics(nwb): "IDEAL_OBSERVER_REWARD_PROB", "IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING", ] - df = df.drop(columns=drop_cols) + df_trials = df_trials.drop(columns=drop_cols) - return df + return df_trials def compute_bias(nwb): @@ -125,9 +129,11 @@ def compute_bias(nwb): return # extract choice and reward - df = nwb.df_trials.copy() - df["choice"] = [np.nan if x == 2 else x for x in df["animal_response"]] - df["reward"] = [any(x) for x in zip(df["earned_reward"], df["extra_reward"])] + df_trials = nwb.df_trials.copy() + df_trials["choice"] = [np.nan if x == 2 else x for x in df_trials["animal_response"]] + df_trials["reward"] = [ + any(x) for x in zip(df_trials["earned_reward"], df_trials["extra_reward"]) + ] # Set up lists to store results bias = [] @@ -136,15 +142,15 @@ def compute_bias(nwb): C = [] # Iterate over trials and compute - compute_on = np.arange(compute_every, len(df), compute_every) + compute_on = np.arange(compute_every, len(df_trials), compute_every) for i in compute_on: # Determine interval to compute on start = np.max([0, i - max_window]) end = i # extract choice and reward - choice = df.loc[start:end]["choice"].values - reward = df.loc[start:end]["reward"].values + choice = df_trials.loc[start:end]["choice"].values + reward = df_trials.loc[start:end]["reward"].values # Determine if we have valid data to fit model unique = np.unique(choice[~np.isnan(choice)]) From be1653c5b3ad82a6e45bf19cc25bf64da14461b3 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 15:27:15 -0800 Subject: [PATCH 23/27] plot formatting --- .../plot/plot_foraging_session.py | 90 ++++++++++++++----- 1 file changed, 69 insertions(+), 21 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 1ea7951..0d21b67 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -177,30 +177,79 @@ def plot_foraging_session( # noqa: C901 # Rewarded trials (real foraging, autowater excluded) xx = np.nonzero(rewarded_excluding_autowater)[0] + 1 yy = 0.5 + (choice_history[rewarded_excluding_autowater] - 0.5) * 1.4 - ax_choice_reward.plot( - *(xx, yy) if not vertical else [*(yy, xx)], - "|" if not vertical else "_", - color="black", - markersize=10, - markeredgewidth=2, - label="Rewarded choices", - ) + yy_temp = choice_history[rewarded_excluding_autowater] + yy_right = yy_temp[yy_temp > 0.5] + 0.05 + xx_right = xx[yy_temp > 0.5] + yy_left = yy_temp[yy_temp < 0.5] - 0.05 + xx_left = xx[yy_temp < 0.5] + if not vertical: + ax_choice_reward.vlines( + xx_right, + yy_right, + yy_right + 0.1, + alpha=1, + linewidth=1, + color="black", + label="Rewarded choices", + ) + ax_choice_reward.vlines( + xx_left, + yy_left - 0.1, + yy_left, + alpha=1, + linewidth=1, + color="black", + label="Rewarded choices", + ) + else: + ax_choice_reward.plot( + *(xx, yy) if not vertical else [*(yy, xx)], + "|" if not vertical else "_", + color="black", + markersize=10, + markeredgewidth=2, + label="Rewarded choices", + ) # Unrewarded trials (real foraging; not ignored or autowater trials) xx = np.nonzero(unrewarded_trials)[0] + 1 yy = 0.5 + (choice_history[unrewarded_trials] - 0.5) * 1.4 - ax_choice_reward.plot( - *(xx, yy) if not vertical else [*(yy, xx)], - "|" if not vertical else "_", - color="gray", - markersize=6, - markeredgewidth=1, - label="Unrewarded choices", - ) + yy_temp = choice_history[unrewarded_trials] + yy_right = yy_temp[yy_temp > 0.5] + xx_right = xx[yy_temp > 0.5] + yy_left = yy_temp[yy_temp < 0.5] + xx_left = xx[yy_temp < 0.5] + if not vertical: + ax_choice_reward.vlines( + xx_right, + yy_right + 0.05, + yy_right + 0.1, + alpha=1, + linewidth=1, + color="gray", + label="Unrewarded choices", + ) + ax_choice_reward.vlines( + xx_left, + yy_left - 0.1, + yy_left - 0.05, + alpha=1, + linewidth=1, + color="gray", + ) + else: + ax_choice_reward.plot( + *(xx, yy) if not vertical else [*(yy, xx)], + "|" if not vertical else "_", + color="gray", + markersize=6, + markeredgewidth=1, + label="Unrewarded choices", + ) # Ignored trials xx = np.nonzero(ignored & ~autowater_ignored)[0] + 1 - yy = [1.1] * sum(ignored & ~autowater_ignored) + yy = [1.2] * sum(ignored & ~autowater_ignored) ax_choice_reward.plot( *(xx, yy) if not vertical else [*(yy, xx)], "x", @@ -334,18 +383,17 @@ def plot_foraging_session( # noqa: C901 ax_reward_schedule.legend(fontsize=5, ncol=1, loc="upper left", bbox_to_anchor=(0, 1)) if not vertical: - ax_choice_reward.set_yticks([0, 1]) - ax_choice_reward.set_yticklabels(["Left", "Right"]) + ax_choice_reward.set_yticks([0, 1, 1.2]) + ax_choice_reward.set_yticklabels(["Left", "Right", "Ignored"]) ax_choice_reward.legend(fontsize=6, loc="upper left", bbox_to_anchor=(0.4, 1.3), ncol=3) - # sns.despine(trim=True, bottom=True, ax=ax_1) ax_choice_reward.spines["top"].set_visible(False) ax_choice_reward.spines["right"].set_visible(False) ax_choice_reward.spines["bottom"].set_visible(False) ax_choice_reward.tick_params(labelbottom=False) ax_choice_reward.xaxis.set_ticks_position("none") + ax_choice_reward.set_ylim([-0.15, 1.25]) - # sns.despine(trim=True, ax=ax_2) ax_reward_schedule.set_ylim([0, 1]) ax_reward_schedule.spines["top"].set_visible(False) ax_reward_schedule.spines["right"].set_visible(False) From a9617d07dfb71f991118914da041e2140584ba26 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 15:39:24 -0800 Subject: [PATCH 24/27] dealing with autowater --- .../plot/plot_foraging_session.py | 46 +++++++++++++++---- 1 file changed, 36 insertions(+), 10 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py index 0d21b67..8e0bde3 100644 --- a/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py +++ b/src/aind_dynamic_foraging_basic_analysis/plot/plot_foraging_session.py @@ -48,6 +48,7 @@ def plot_foraging_session_nwb(nwb, **kwargs): bias=nwb.df_trials["bias"].values, bias_lower=nwb.df_trials["bias_ci_lower"].values, bias_upper=nwb.df_trials["bias_ci_upper"].values, + autowater_offered=nwb.df_trials[["auto_waterL", "auto_waterR"]].any(axis=1), **kwargs, ) @@ -199,7 +200,6 @@ def plot_foraging_session( # noqa: C901 alpha=1, linewidth=1, color="black", - label="Rewarded choices", ) else: ax_choice_reward.plot( @@ -264,18 +264,44 @@ def plot_foraging_session( # noqa: C901 # Autowater offered and collected xx = np.nonzero(autowater_collected)[0] + 1 yy = 0.5 + (choice_history[autowater_collected] - 0.5) * 1.4 - ax_choice_reward.plot( - *(xx, yy) if not vertical else [*(yy, xx)], - "|" if not vertical else "_", - color="royalblue", - markersize=10, - markeredgewidth=2, - label="Autowater collected", - ) + + yy_temp = choice_history[autowater_collected] + yy_right = yy_temp[yy_temp > 0.5] + 0.05 + xx_right = xx[yy_temp > 0.5] + yy_left = yy_temp[yy_temp < 0.5] - 0.05 + xx_left = xx[yy_temp < 0.5] + + if not vertical: + ax_choice_reward.vlines( + xx_right, + yy_right, + yy_right + 0.1, + alpha=1, + linewidth=1, + color="royalblue", + label="Autowater collected", + ) + ax_choice_reward.vlines( + xx_left, + yy_left - 0.1, + yy_left, + alpha=1, + linewidth=1, + color="royalblue", + ) + else: + ax_choice_reward.plot( + *(xx, yy) if not vertical else [*(yy, xx)], + "|" if not vertical else "_", + color="royalblue", + markersize=10, + markeredgewidth=2, + label="Autowater collected", + ) # Also highlight the autowater offered but still ignored xx = np.nonzero(autowater_ignored)[0] + 1 - yy = [1.1] * sum(autowater_ignored) + yy = [1.2] * sum(autowater_ignored) ax_choice_reward.plot( *(xx, yy) if not vertical else [*(yy, xx)], "x", From fe428b2974862488c46d9b478d3d20606e1a6d01 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 15:42:24 -0800 Subject: [PATCH 25/27] linting and tests --- tests/test_plot_foraging_session.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test_plot_foraging_session.py b/tests/test_plot_foraging_session.py index 3d163e8..563d6e0 100644 --- a/tests/test_plot_foraging_session.py +++ b/tests/test_plot_foraging_session.py @@ -57,6 +57,8 @@ def test_nwb_wrapper(self): df["earned_reward"] = rewards df["reward_probabilityL"] = pL df["reward_probabilityR"] = pR + df["auto_waterL"] = [0] * 6 + df["auto_waterR"] = [0] * 6 nwb.df_trials = df nwb.session_id = "test" pfs.plot_foraging_session_nwb(nwb) From 44eefc5099309207ffaa4c9efac126a211fc490b Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Mon, 18 Nov 2024 15:48:56 -0800 Subject: [PATCH 26/27] test coverage --- tests/test_plot_foraging_session.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/tests/test_plot_foraging_session.py b/tests/test_plot_foraging_session.py index 563d6e0..cf5f134 100644 --- a/tests/test_plot_foraging_session.py +++ b/tests/test_plot_foraging_session.py @@ -141,6 +141,20 @@ def test_plot_session_vertical(self): bbox_inches="tight", ) + fig, _ = plot_foraging_session( + choice_history=self.choice_history, + reward_history=self.reward_history, + p_reward=self.p_reward, + autowater_offered=np.array([0] * len(self.choice_history)), + fitted_data=None, + photostim=None, # trial, power, s_type + valid_range=None, + smooth_factor=5, + base_color="y", + ax=None, + vertical=True, + ) + def test_plot_session_wrong_format(self): """Some wrong input format""" with self.assertRaises(ValueError): From dab633aeee64d7d615072627bef216fded64fe43 Mon Sep 17 00:00:00 2001 From: Alex Piet Date: Thu, 21 Nov 2024 11:15:28 -0800 Subject: [PATCH 27/27] removing ideal observer metrics --- .../metrics/trial_metrics.py | 28 ------------------- 1 file changed, 28 deletions(-) diff --git a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py index 7d25e6b..7439e82 100644 --- a/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py +++ b/src/aind_dynamic_foraging_basic_analysis/metrics/trial_metrics.py @@ -58,32 +58,6 @@ def compute_trial_metrics(nwb): df_trials["WENT_RIGHT"].rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True).mean() ) - # Rolling reward probability for best option - df_trials["IDEAL_OBSERVER_REWARD_PROB"] = df_trials[ - ["reward_probabilityR", "reward_probabilityL"] - ].max(axis=1) - df_trials["ideal_observer_reward_rate"] = ( - df_trials["IDEAL_OBSERVER_REWARD_PROB"] - .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) - .mean() - ) - - # Rolling reward probability for best option with baiting - if "bait_left" in df_trials: - df_trials["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] = [ - 1 if (x[0] or x[1]) else x[2] - for x in zip( - df_trials["bait_left"], - df_trials["bait_right"], - df_trials["IDEAL_OBSERVER_REWARD_PROB"], - ) - ] - df_trials["ideal_observer_reward_rate_with_baiting"] = ( - df_trials["IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING"] - .rolling(WIN_DUR, min_periods=MIN_EVENTS, center=True) - .mean() - ) - # TODO, add from process_nwb # trial duration (stop-time - start-time) (start/stop time, or gocue to gocue?) # n_licks_left (# of left licks in response window) @@ -99,8 +73,6 @@ def compute_trial_metrics(nwb): "RESPONDED", "RESPONSE_REWARD", "WENT_RIGHT", - "IDEAL_OBSERVER_REWARD_PROB", - "IDEAL_OBSERVER_REWARD_PROB_WITH_BAITING", ] df_trials = df_trials.drop(columns=drop_cols)