Automated_classification_of_stroke_deficit/scripts/analyze_GW_2D.py

import pandas as pd
import matplotlib.pyplot as plt
from scipy import signal
import numpy as np
import math
import easygui
from master_funcs import *
import dill
import dataframe_image as dfi

patients=['PB_T2_3_1', 'PB_T2_3_2', 'PB_T2_3_3', 'PB_T2_4_2', 'PB_T2_5_1', 'PB_T2_6_1', 'PB_T2_6_2', 'PB_T3_23_1', 'PB_T3_23_2', 'PB_T3_23_3', 'PB_T3_24_1', 'PB_T3_24_2', 'PB_T3_24_3']

for z in patients:
    print(z)
    pathway='/home/user/owncloud/3D_videos/GW_2D/'+z+'_P3_GW_camera-1DLC_resnet50_GridWalk_camera-1Jun15shuffle1_250000.csv'
    data=prep_dlc(pathway, 0.9, 1080)
    names=[i[0] for i in data.columns[::3]]
    for j in names:
        data[j, 'x']=data[j, 'x'].interpolate(method='polynomial', order=1)
        data[j, 'y']=data[j, 'y'].interpolate(method='polynomial', order=1)
    dill.load_session('/home/user/Documents/Master/GW_2D_'+z+'_data_save.pkl')
    ff=[math.nan]
    length=[len(data), len(df_vel_acc1)]
    for i in range(0, np.min(length)):
            if (df_vel_acc1['angle_acceleration_shoulder_right_forepaw_right_shoulder_left'][i]>30) and (df_vel_acc1['angle_shoulder_right_forepaw_right_shoulder_left'][i]>140):
                ff.append(i)
                print('footfault at ', i)
    if z=='PB_T2_3_1':
        footfault_2d=pd.DataFrame([ff], index=[z])
    else:
        footfault_2d_temp=pd.DataFrame([ff], index=[z])
        footfault_2d=footfault_2d.append(footfault_2d_temp)
    pred_rfc_footfault_2d = best_model.predict(X_test)
    det_footfault_2d=np.where(pred_rfc_footfault_2d)
    if z=='PB_T2_3_1':
        footfault_FI_2d=pd.DataFrame(det_footfault_2d, index=[z])
    else:
        footfault_FI_temp_2d=pd.DataFrame(det_footfault_2d, index=[z])
        footfault_FI_2d=footfault_FI_2d.append(footfault_FI_temp_2d)

footfault_2d.columns=['Non Footfault']+['Footfault']*(footfault_2d.shape[1]-1)
dfi.export(footfault_2d, '/home/user/owncloud/thesis_figures/DLC_GW_2d.png')