analyse_file NO PROCESS.py

# Math time
import numpy as np
from scipy import signal as sp
import matplotlib.pyplot as plt
import csv

def get_distance(first, second):
    first_distance = []
    second_distance = []

    first_index_list = []
    second_index_list = []

    for i in range(len(first)):
        first_index_list.append(i)
        first_index_list.append(i)
    
    for i in range(len(second)):
        second_index_list.append(i)
        second_index_list.append(i)

    if len(first) == len(second):
        second_index_list = second_index_list[:-1]
        first_index_list = first_index_list[1:]
    else:
        first_index_list = first_index_list[1:-1]

    for i in first_index_list:
        first_distance.append(first[i])
    for i in second_index_list:
        second_distance.append(second[i])
    
    first_distance = np.array(first_distance)
    second_distance = np.array(second_distance)

    distance = first_distance + second_distance

    return distance[1:][::2], distance[0:][::2]

def get_time(a):
    time_mm = []
    for i in range(1, len(a)):
        time_mm.append(round((a[i] - a[i - 1])/30*1000))
    return time_mm

def get_time_difference(first, second):
    first_times = []
    second_times = []

    first_index_list = []
    second_index_list = []

    for i in range(len(first)):
        first_index_list.append(i)
        first_index_list.append(i)
    
    for i in range(len(second)):
        second_index_list.append(i)
        second_index_list.append(i)

    if len(first) == len(second):
        second_index_list = second_index_list[:-1]
        first_index_list = first_index_list[1:]
    else:
        first_index_list = first_index_list[1:-1]

    for i in first_index_list:
        first_times.append(first[i])
    for i in second_index_list:
        second_times.append(second[i])
    
    first_times = np.array(first_times)
    second_times = np.array(second_times)

    time_differences = []
    for i in range(len(second_times)):
        if i % 2 == 0:
            time_differences.append(int(second_times[i]) - int(first_times[i]))
        else:
            time_differences.append(int(first_times[i]) - int(second_times[i]))

    time_differences = np.array(time_differences) / 30 * 1000

    return time_differences[1:][::2], time_differences[0:][::2]

def get_distance_difference(first, second):
    first_times = []
    second_times = []

    first_index_list = []
    second_index_list = []

    for i in range(len(first)):
        first_index_list.append(i)
        first_index_list.append(i)
    
    for i in range(len(second)):
        second_index_list.append(i)
        second_index_list.append(i)

    if len(first) == len(second):
        second_index_list = second_index_list[:-1]
        first_index_list = first_index_list[1:]
    else:
        first_index_list = first_index_list[1:-1]

    for i in first_index_list:
        first_times.append(first[i])
    for i in second_index_list:
        second_times.append(second[i])
    
    first_times = np.array(first_times)
    second_times = np.array(second_times)

    distance_differences = []
    for i in range(len(second_times)):
        if i % 2 == 0:
            distance_differences.append(int(second_times[i]) - int(first_times[i]))
        else:
            distance_differences.append(int(first_times[i]) - int(second_times[i]))

    distance_differences = np.array(distance_differences)

    return distance_differences[1:][::2], distance_differences[0:][::2]

def limp_calc(right, left):
    right_avg = np.average(right)
    left_avg = np.average(left)

    return right_avg - left_avg

def get_margin_for_valley(peaks, min_values):
    # Will get x values of where the peaks are at the same hight
    idx = np.argwhere(np.diff(np.sign(peaks[0] - peaks[1]))).flatten()
    
    # take average of min_values distance when the peaks overlap
    average_distance = np.median(np.hstack([min_values[0][idx], min_values[1][idx]]))
    return average_distance

def main(min_values, peak, f_name, display_visual=True):
    min_values = np.array(min_values)
    peak = np.array(peak)
    
    min_values_invert = min_values * -1

    margin = get_margin_for_valley(peak, min_values_invert)
    valley_right_x, _ = sp.find_peaks(min_values_invert[0], height=margin, width=10)
    valley_left_x, _ = sp.find_peaks(min_values_invert[1], height=margin, width=10)

    valley_right_y = min_values[0][valley_right_x]
    valley_left_y = min_values[1][valley_left_x]

    # if np.min(valley_left_x) < np.min(valley_right_x): # select the foot that has first IC
    #     # If this is the left foot
    #     left_stride_lengths, right_stride_lengths = get_distance(min_values[0][valley_left_x] - valley_left_y, min_values[1][valley_right_x] - valley_right_y)
    #     left_step_to_time, right_step_to_time = get_time_difference(valley_left_x, valley_right_x)
    #     left_step_to_lengths, right_step_to_lengths = get_distance_difference(valley_left_y, valley_right_y)
    # else:
    #     # If this is the right foot
    #     right_stride_lengths, left_stride_lengths = get_distance(min_values[1][valley_right_x] - valley_right_y, min_values[0][valley_left_x] - valley_left_y)
    #     right_step_to_time, left_step_to_time = get_time_difference(valley_right_x, valley_left_x)
    #     right_step_to_lengths, left_step_to_lengths = get_distance_difference(valley_right_y, valley_left_y)
    
    # right_stride_times = get_time(valley_right_x)
    # left_stride_times = get_time(valley_left_x)

    # limp_distance = limp_calc(valley_right_y, valley_left_y)
    # limp_time = limp_calc(right_step_to_time, left_step_to_time)

    # write to file
    f = open(f"graphs/{f_name}.txt", "w")
    f.write(f"##############\n")
    f.write(f"# Raw Values #\n")
    f.write(f"##############\n")
    f.write(f"IC's Right Foot (frame): {valley_right_x}\n")
    f.write(f"IC's Depth Right Foot (mm): {valley_right_y}\n")
    f.write(f"Presw's Depth Left Foot (mm): {min_values[1][valley_right_x]}\n")
    f.write(f"---\n")
    f.write(f"IC's Left Foot (frame): {valley_left_x}\n")
    f.write(f"IC's Depth Left Foot (mm): {valley_left_y}\n")
    f.write(f"Presw's Depth Right Foot (mm): {min_values[0][valley_left_x]}\n\n")
    f.write(f"##############################\n")
    f.write(f"# Stride Distance & Duration #\n")
    f.write(f"##############################\n")
    # f.write(f"Stride distance(s) right foot (mm): {right_stride_lengths}\n")
    # f.write(f"Stride duration(s) right foot (ms): {right_stride_times}\n")
    # f.write(f"Stride step to distance(s) right foot (mm): {right_step_to_lengths}\n")
    # f.write(f"Stride step to duration(s) right foot (ms): {right_step_to_time}\n")
    # f.write(f"---\n")
    # f.write(f"Stride distance(s) left foot (mm): {left_stride_lengths}\n")
    # f.write(f"Stride duration(s) left foot (ms): {left_stride_times}\n")
    # f.write(f"Stride step to distance(s) left foot (mm): {left_step_to_lengths}\n")
    # f.write(f"Stride step to duration(s) left foot (ms): {left_step_to_time}\n\n")
    # f.write(f"###################\n")
    # f.write(f"# Stride Analysis #\n")
    # f.write(f"###################\n")
    # f.write(f"IC difference duration (ms): {limp_time}\n")
    # if abs(limp_time) > 100:
    #     if limp_time > 0:
    #         f.write(f"Potential limp left leg\n")
    #     else:
    #         f.write(f"Potential limp right leg\n")
    # f.write(f"---\n")
    # f.write(f"IC difference distance (mm): {limp_distance}\n")
    # if abs(limp_distance) > 20:
    #     if limp_distance < 0:
    #         f.write(f"Potential limp left leg")
    #     else:
    #         f.write(f"Potential limp right leg")
    f.close()

    # Display Plot
    plt.plot(min_values[0], color="red", label="right foot")
    plt.plot(min_values[1], color="blue", label="left foot")

    plt.scatter(valley_right_x, valley_right_y, color="yellow")
    plt.scatter(valley_right_x, min_values[1][valley_right_x], color="yellow")
    plt.scatter(valley_left_x, valley_left_y, color="yellow")
    plt.scatter(valley_left_x, min_values[0][valley_left_x], color="yellow")

    valleys_x = np.hstack([valley_right_x, valley_left_x])
    valleys_y = np.hstack([valley_right_y, valley_left_y])

    arrlinds = valleys_x.argsort()
    sorted_x = valleys_x[arrlinds]
    sorted_y = valleys_y[arrlinds]

    for i in range(len(sorted_x)):
        plt.annotate(f"IC {i + 1}", xy=(sorted_x[i], sorted_y[i]), xytext=(sorted_x[i] + 3, sorted_y[i] - 25))

    plt.plot([valley_right_x, valley_right_x], [valley_right_y, min_values[1][valley_right_x]], color="yellow")
    plt.plot([valley_left_x, valley_left_x], [valley_left_y, min_values[0][valley_left_x]], color="yellow")

    plt.title("Minimum Depth of Right and Left Foot")
    plt.ylabel('depth')
    plt.xlabel('frames', loc="left")
    plt.legend(bbox_to_anchor=(0.5,-0.1,0.5,0.2),
                mode="expand", borderaxespad=0, ncol=3)
    plt.savefig(f"graphs/{f_name}.png")
    
    if display_visual:
        plt.show()
    plt.close()

def get_csv(f_name):
    min_values = []
    peaks = []
    with open(f"csv/{f_name}_min_values.csv") as csvfile:
        reader = csv.reader(csvfile, delimiter=';', quoting=csv.QUOTE_NONNUMERIC) # change contents to floats
        for row in reader: # each row is a list
            min_values.append(row)
    with open(f"csv/{f_name}_peaks.csv") as csvfile:
        reader = csv.reader(csvfile, delimiter=';', quoting=csv.QUOTE_NONNUMERIC) # change contents to floats
        for row in reader: # each row is a list
            peaks.append(row)
    return min_values, peaks

if __name__ == "__main__":
    f_name = "live"
    min_value, peak = get_csv(f_name)
    main(min_value, peak, f_name)