GeneralProcessing.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import joblib
from utils import normalize_MPU9250_data, split_df, get_intervals_from_moments, EventIntervals
from GeneralAnalyser import GeneralAnalyser, plot_measurements

plt.interactive(True)
pd.options.display.max_columns = 15
pic_prefix = 'pic/'

sessions_dict = joblib.load('data/sessions_dict')
# sessions_dict = joblib.load('data/data_dict')
gamedata_dict = joblib.load('data/gamedata_dict_old')

# gamedata_dict.update(gamedata_dict_update)


sensors_columns_dict = {
    'hrm': ['hrm'],
    'datalog': ['resistance', 'muscle_activity'],
    # 'datalog': ['muscle_activity'],
    'envibox': ['co2', 'temperature', 'humidity'],
    'eyetracker': ['gaze_x', 'gaze_y'],
    'mxy': ['mouse_dx', 'mouse_dy'],
    'schairlog': ['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z'],
}

total_len = sum([len(value) for value in sensors_columns_dict.values()])



sensors_list = list(sensors_columns_dict.keys())
sensors_columns_list = []

for sensor in sensors_columns_dict:
    for column in sensors_columns_dict[sensor]:
        sensors_columns_list.append([sensor, column])


for session_id, session_data_dict in sessions_dict.items():
    df_dict = {}

    if not set(sensors_list).issubset(set(session_data_dict.keys())):
        print("not set(sensors_list).issubset(set(session_data_dict.keys()))")
        ### If not all the sensors provided
        continue  # TODO: THIS IS DANGEROUS AND SHOULD BE UNCOMMENTED BACK
        # pass

    if session_id not in gamedata_dict:
        continue
    
    print("Processing!")
    moments_kills = gamedata_dict[session_id]['times_kills']
    moments_death = gamedata_dict[session_id]['times_is_killed']
    duration = 1

    # intervals_shootout = gamedata_dict[session_id]['shootout_times_start_end']
    intervals_kills = get_intervals_from_moments(moments_kills, interval_start=-duration, interval_end=duration)
    intervals_death = get_intervals_from_moments(moments_death, interval_start=-duration, interval_end=duration)

    # event_intervals_shootout = EventIntervals(intervals_list=intervals_shootout, label='shootouts', color='blue')
    event_intervals_kills = EventIntervals(intervals_list=intervals_kills, label='kills', color='limegreen')
    event_intervals_death = EventIntervals(intervals_list=intervals_death, label='deaths', color='red')

    # events_intervals_list = [event_intervals_shootout, event_intervals_kills, event_intervals_death]
    events_intervals_list = [event_intervals_kills, event_intervals_death]

    for sensor_name in sensors_columns_dict:
        df = session_data_dict[sensor_name].copy()

        # if sensor_name == 'schairlog':
        #     chair_features = get_chair_features(df, session_id)  # TMP
        #     chair_features_list.append(chair_features)

        # ss = StandardScaler()
        # # df.values = ss.fit_transform(df.values)
        # df.loc[:, sensors_columns_dict[sensor_name]] = ss.fit_transform(df.loc[:, sensors_columns_dict[sensor_name]])

        # ###  WARNING: it is CUSTOM PART
        # if sensor_name == 'schairlog':
        #     chair_analyser = GeneralAnalyser(
        #         df,
        #         pic_prefix=pic_prefix,
        #         sensor_name='Chair',  # Manual assignment
        #         session_id=session_id,
        #         events_intervals_list=events_intervals_list,
        #         interval=interval,
        #         reaction_multiplier=reaction_multiplier,
        #     )
        #     # chair_analyser.get_floating_features()  # Need to be refactored
        #     chair_analyser._append_floating_features(interval=interval)
        #
        #     for column in sensors_columns_dict[sensor_name]:
        #         analyser_column_pairs_list.append([chair_analyser, column])


    ### VISUALIZATION
    analyser_column_pairs_list = []

    for sensor_name in sensors_columns_dict:
        df = session_data_dict[sensor_name]
        analyser = GeneralAnalyser(df, pic_prefix=pic_prefix, sensor_name=sensor_name, session_id=session_id)
        for column in sensors_columns_dict[sensor_name]:
            analyser_column_pairs_list.append([analyser, column])

    plot_measurements(
        analyser_column_pairs_list=analyser_column_pairs_list,
        pic_prefix=pic_prefix,
        session_id=session_id,
        event_intervals_list=events_intervals_list,
        n_rows=4,  # TODO: automatically adjust number of rows and cols
        n_cols=4,
        figsize=(21, 8),
        plot_suptitle=False,
        alpha=0.8,
        alpha_background=0.5,
    )
    # general_analyser.plot_measurements_timeline(column_name=sensor_name, intervals_dicts_list=intervals_dicts_list, alpha=0.9)