import csv

# mask_included = eye0_df['pupil_timestamp'].between(trial_start, trial_end)
# trial_data_eye0 = eye0_df.loc[mask_included]


all_labels = annotations_pd_frame['label']
label_names = list(all_labels)
# all_trials = len(all_labels)


# creates new column and fills with None
eye0_high_conf_df["trial"] = None
eye1_high_conf_df["trial"] = None
# all_trial_labels = None


all_indices = annotations_pd_frame['index']
# index_names = list(all_indices)

groups = annotations_pd_frame.groupby("label").timestamp.agg(["min", "max"])


for label in label_names:
    
#     trial_label = annotations_pd_frame['label'].loc[annotations_pd_frame['index'] == index]
#     trial_data = annotations_pd_frame[annotations_pd_frame.label == str(trial_label)]

    trial_label = label
    trial_start_timestamp = groups['min'].loc[trial_label]
    trial_end_timestamp = groups['max'].loc[trial_label]
    trial_boolean_mask_eye0 = eye0_high_conf_df['pupil_timestamp'].between(trial_start_timestamp,trial_end_timestamp)
    trial_boolean_mask_eye1 = eye1_high_conf_df['pupil_timestamp'].between(trial_start_timestamp,trial_end_timestamp)
    eye0_high_conf_df["trial"].loc[trial_boolean_mask_eye0] = trial_label
    eye1_high_conf_df["trial"].loc[trial_boolean_mask_eye1] = trial_label
    mask_trial_not_available_eye0 = eye0_high_conf_df["trial"].isna()
    mask_trial_not_available_eye1 = eye1_high_conf_df["trial"].isna()
    mask_trial_available_eye0 = ~mask_trial_not_available_eye0
    mask_trial_available_eye1 = ~mask_trial_not_available_eye1
    pupil_data_in_trial_eye0 = eye0_high_conf_df.loc[mask_trial_available_eye0]
    pupil_data_in_trial_eye1 = eye1_high_conf_df.loc[mask_trial_available_eye1]
#     pupil_clean = detector_3d_data[detector_3d_data.trial == trial_label]
#     trial_data_eye0 = pupil_data_in_trial[pupil_data_in_trial.eye_id == 0]
#     trial_data_eye1 = pupil_data_in_trial[pupil_data_in_trial.eye_id == 1]

#     data_sequence_eye0 = pupil_data_in_trial_eye0[pupil_data_in_trial_eye0.trial == label]
#     data_sequence_eye1 = pupil_data_in_trial_eye1[pupil_data_in_trial_eye1.trial == label]

    
label_names = list(dict.fromkeys(label_names))
for label in list(label_names):
    plt.figure(figsize=(16, 5))
    plt.plot(pupil_data_in_trial_eye0['pupil_timestamp'], pupil_data_in_trial_eye0['diameter_3d'])
    plt.plot(pupil_data_in_trial_eye1['pupil_timestamp'], pupil_data_in_trial_eye1['diameter_3d'])
    plt.legend(['eye0', 'eye1'])
    plt.xlabel('Timestamps [s]')
    plt.ylabel('Diameter [mm]')
    plt.title('Pupil Diameter in ' + str(label) + ' sequences' )