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builders/data_training_builders.py

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+import pandas as pd
+import os
+import math
+import numpy as np
+
+'''
+Checks if the directory exists, if not, creates the directory
+'''
+def create_directory_if_does_not_exist(directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+'''
+Organizes the list of dataset subdirectories 
+in ascending order (Ex: ID1,ID2,ID3 ... ID15).
+'''
+def sort_by_number(id):
+    return int(id[2:])
+
+'''
+Gets the path to access the sampling, acceleration
+ and angular acceleration files of the dataset
+ '''
+def get_file_path(main_directory, subdirectory, position,preprocessing = None):
+    subdirectory_path = os.path.join(main_directory, subdirectory)
+    subdirectory_path_of_subdirectory = os.path.join(subdirectory_path, position)
+
+    file_name = f'{subdirectory}_{position}_acceleration.csv'
+    file_name_2 = f'{subdirectory}_{position}_sampling.csv'
+    file_name_3 = f'{subdirectory}_{position}_angular_speed.csv'
+
+    sampling_file = os.path.join(subdirectory_path_of_subdirectory, file_name_2)
+    acc_file = os.path.join(subdirectory_path_of_subdirectory, file_name)
+    gyr_file = os.path.join(subdirectory_path_of_subdirectory, file_name_3)
+
+    if preprocessing == "sim":
+        return acc_file,gyr_file,sampling_file,file_name,file_name_2,file_name_3
+    return acc_file, gyr_file, sampling_file
+
+
+'''
+Adds the magnitude column {force resulting 
+from the (a,w)x, (a,w)y and (a,w)z axes} in the dataframe.
+'''
+def add_magnitude_column(dataframe, sensor = None):
+    initial_letter = None
+
+    if sensor == "acc":
+        initial_letter = "a"
+    else:
+        initial_letter = "w"
+
+    resultant_force = []
+    i = 0
+
+    while i < len(dataframe[f'{initial_letter}x']):
+        resultant = math.sqrt((dataframe[f'{initial_letter}x'][i]) ** 2 + (dataframe[f'{initial_letter}y'][i]) ** 2 + (dataframe[f'{initial_letter}z'][i]) ** 2)
+        resultant_force.append(resultant)
+        i += 1
+    dataframe.insert(5, "Magnitude", resultant_force, True)
+
+'''
+Creates a data frame from each data file in the dataset.
+'''
+def create_dataframe(acc_file, gyr_file, sampling_file):
+
+    acc_dataframe = pd.DataFrame(pd.read_csv(acc_file))
+    gyr_dataframe= pd.DataFrame(pd.read_csv(gyr_file))
+    sampling_dataframe = pd.DataFrame(pd.read_csv(sampling_file))
+
+    return acc_dataframe,gyr_dataframe,sampling_dataframe
+
+'''
+Apply the fourier transform to data arrays
+'''
+def fourier_transform(time_series):
+    altered_time_series = []
+    mean_time_series = np.mean(time_series)
+
+    for i in time_series:
+        #Subtraction from the average to Remove the DC Component (Zero Frequency Component).
+        data = i - mean_time_series
+        altered_time_series.append(data)
+    altered_time_series = np.array(altered_time_series)
+
+    return np.abs(np.fft.fft(altered_time_series))
+
+'''
+selects the data columns (a,w)x, (a,w)y, (a,w)z and mag
+(gyr,acc) of the dataframe according to the index I of the
+for loop that represents the access key for the file for 
+each volunteer (ID1, ID2, etc.) and according to the activity
+(ADL_1, ADL_2, etc.). Removes the value referring to the 
+first observation of each activity, aiming to minimize 
+any error in data collection.
+'''
+def section_data_array(acc_dataframe, gyr_dataframe, i,use_in_media_generator = None):
+    magacc = acc_dataframe.loc[acc_dataframe["sampling"] == i, "Magnitude"]
+    magacc = magacc.reset_index(drop=True)
+
+    xacc = acc_dataframe.loc[acc_dataframe["sampling"] == i, "ax"]
+    xacc = xacc.reset_index(drop=True)
+
+    yacc = acc_dataframe.loc[acc_dataframe["sampling"] == i, "ay"]
+    yacc = yacc.reset_index(drop=True)
+
+    zacc = acc_dataframe.loc[acc_dataframe["sampling"] == i, "az"]
+    zacc = zacc.reset_index(drop=True)
+
+    maggyr = gyr_dataframe.loc[gyr_dataframe["sampling"] == i, "Magnitude"]
+    maggyr = maggyr.reset_index(drop=True)
+
+    xgyr = gyr_dataframe.loc[gyr_dataframe["sampling"] == i, "wx"]
+    xgyr = xgyr.reset_index(drop=True)
+
+    ygyr = gyr_dataframe.loc[gyr_dataframe["sampling"] == i, "wy"]
+    ygyr = ygyr.reset_index(drop=True)
+
+    zgyr = gyr_dataframe.loc[gyr_dataframe["sampling"] == i, "wz"]
+    zgyr = zgyr.reset_index(drop=True)
+
+    timestamp_acc = acc_dataframe.loc[acc_dataframe["sampling"] == i, "timestamp"]
+    timestamp_acc = timestamp_acc.reset_index(drop=True)
+    timestamp_acc = timestamp_acc.drop(0)
+    timestamp_acc = timestamp_acc.reset_index(drop=True)
+
+    timestamp_gyr = gyr_dataframe.loc[gyr_dataframe["sampling"] == i, "timestamp"]
+    timestamp_gyr = timestamp_gyr.reset_index(drop=True)
+    timestamp_gyr = timestamp_gyr.drop(0)
+    timestamp_gyr = timestamp_gyr.reset_index(drop=True)
+
+    if use_in_media_generator == "yes":
+        return timestamp_acc, timestamp_gyr, magacc, xacc, yacc, zacc, maggyr, xgyr, ygyr, zgyr
+    return magacc, xacc, yacc, zacc, maggyr, xgyr, ygyr, zgyr
+'''
+Adds data arrays formatted in the size of observations equivalent to five seconds 
+(480 observations for the right and left wrist and 1050 observations for the chest) 
+in the time domain and frequency domain lists.
+'''
+def add_data_arrays_to_time_and_frequency_data_lists(initial_index,final_index,array_size,data_array,data_array_list,fourier_transformed_data_array_list):
+    data_array = data_array[initial_index:final_index]
+    numpy_data_array = np.array(data_array)
+    numpy_data_array = np.expand_dims(numpy_data_array, axis=1)
+    data_array_list.append(numpy_data_array)
+
+    transformed_data_array = fourier_transform(data_array)
+    transformed_data_array = transformed_data_array[:int(array_size / 2)]
+    numpy_transformed_data_array = np.array(transformed_data_array)
+    numpy_transformed_data_array = np.expand_dims(numpy_transformed_data_array, axis=1)
+    fourier_transformed_data_array_list.append(numpy_transformed_data_array)
+
+
+'''
+Returns the activity label for the four approaches.
+'''
+def create_labels(activity):
+
+    #Multiple_classes_labels_1 represents the approach where each activity, whether performed with or without a rifle,
+    # receives a distinct label. With the exception of activities FALL_5 and FALL_6, which receive a single label and
+    # are considered only as lateral falls regardless of the fall position. The same applies to FALL_5_with_rifle and FALL_6_with_rifle.
+    multiple_classes_labels_1 = {"ADL_1": 0, "ADL_2": 1, "ADL_3": 2, "ADL_4": 3, "ADL_5": 4, "ADL_6": 5, "ADL_7": 6, "ADL_8": 7,
+                               "ADL_11": 8, "ADL_12": 9, "ADL_13": 10, "ADL_14": 11, "ADL_15": 12, "OM_1": 13, "OM_2": 14,
+                               "OM_3": 15, "OM_4": 16, "OM_5": 17, "OM_6": 18, "OM_7": 19, "OM_8": 20, "OM_9": 21,
+                               "FALL_1": 22, "FALL_2": 23, "FALL_3": 24, "FALL_5": 25, "FALL_6": 25,"FALL_1_with_rifle": 26,
+                               "FALL_3_with_rifle": 27, "FALL_5_with_rifle": 28, "FALL_6_with_rifle": 28,"ADL_1_with_rifle": 29,
+                               "ADL_4_with_rifle": 30, "ADL_5_with_rifle": 31, "ADL_6_with_rifle": 32,"ADL_11_with_rifle": 33,
+                               "ADL_12_with_rifle": 34, "ADL_13_with_rifle": 35, "ADL_14_with_rifle": 36}
+
+
+    #Multiple_classes_labels_2 represents the approach in which each activity performed, regardless of whether a rifle
+    # was used or not, receives a label according to the activity. For example, activity ADL_1 receives the label 0 and
+    # encompasses standing activities recorded with and without the use of a rifle. The exception is activities FALL_5
+    # and FALL_6, which receive a single label and are considered only as a lateral fall, regardless of the fall position.
+    multiple_classes_labels_2 = {"ADL_1": 0, "ADL_2": 1, "ADL_3": 2, "ADL_4": 3, "ADL_5": 4, "ADL_6": 5, "ADL_7": 6,
+                               "ADL_8": 7,"ADL_11": 8, "ADL_12": 9, "ADL_13": 10, "ADL_14": 11, "ADL_15": 12, "OM_1": 13,
+                               "OM_2": 14,"OM_3": 15, "OM_4": 16, "OM_5": 17, "OM_6": 18, "OM_7": 19, "OM_8": 20, "OM_9": 21,
+                               "FALL_1": 22, "FALL_2": 23, "FALL_3": 24, "FALL_5": 25, "FALL_6": 25}
+
+    # binary_classes_labels_1 is the approach where all activities of daily living and military operations receive
+    # label and falls receive label 0. However, activities 0M6 to OM8 relating to the transition to the prone
+    # firing position are considered as falling activities.
+    binary_classes_labels_1 = {"ADL_1": 1, "ADL_2": 1, "ADL_3": 1, "ADL_4": 1, "ADL_5": 1, "ADL_6": 1, "ADL_7": 1, "ADL_8": 1,
+                       "ADL_11": 1, "ADL_12": 1, "ADL_13": 1, "ADL_14": 1, "ADL_15": 1, "OM_1": 1,
+                       "OM_2": 1, "OM_3": 1, "OM_4": 1, "OM_5": 1, "OM_6": 0, "OM_7": 0, "OM_8": 0, "OM_9": 1,
+                       "FALL_1": 0, "FALL_2": 0, "FALL_3": 0, "FALL_5": 0, "FALL_6": 0}
+
+    #binary_classes_labels_2 is the approach where all activities of daily living and military operations receive
+    # label 1 and falls receive label 0. However, activities 0M6 to OM8 relating to the transition to the prone
+    # shooting position are not considered as fall activities and receive label 1.
+    binary_classes_labels_2 = {"ADL_1": 1, "ADL_2": 1, "ADL_3": 1, "ADL_4": 1, "ADL_5": 1, "ADL_6": 1, "ADL_7": 1, "ADL_8": 1,
+                       "ADL_11": 1, "ADL_12": 1, "ADL_13": 1, "ADL_14": 1, "ADL_15": 1, "OM_1": 1,
+                       "OM_2": 1, "OM_3": 1, "OM_4": 1, "OM_5": 1, "OM_6": 1, "OM_7": 1, "OM_8": 1, "OM_9": 1,
+                       "FALL_1": 0, "FALL_2": 0, "FALL_3": 0, "FALL_5": 0, "FALL_6": 0}
+
+    activity_without_rifle = activity.split("_with_rifle")[0]
+
+    multiple_class_label_1 = multiple_classes_labels_1.get(activity)
+    multiple_class_label_2 = multiple_classes_labels_2.get(activity_without_rifle)
+    binary_class_label_1 = binary_classes_labels_1.get(activity_without_rifle)
+    binary_class_label_2 = binary_classes_labels_2.get(activity_without_rifle)
+
+    return multiple_class_label_1,multiple_class_label_2,binary_class_label_1,binary_class_label_2
+
+'''
+Add the labels to the accelerometer and gyroscope label lists.
+'''
+def add_labels(multiple_class_label_1, multiple_class_label_2, binary_class_label_1, binary_class_label_2, labels_list):
+
+    labels_list[0].append(multiple_class_label_1)
+    labels_list[1].append(multiple_class_label_2)
+    labels_list[2].append(binary_class_label_1)
+    labels_list[3].append(binary_class_label_2)
+