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MIT-BIH-ECG
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[4d064f]: / python / aux1 / generate_graphics.py

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import matplotlib.pyplot as plt

import numpy as np

from load_MITBIH import *



# Generate graphics for paper



db_path = '/home/mondejar/dataset/ECG/mitdb/m_learning/scikit/'

winL = 90

winR = 90

do_preprocess = True

maxRR = True



use_RR = False

norm_RR = False



reduced_DS = False

leads_flag = [1, 0]

# Load train data 

compute_morph = {'raw'}



label_name = ['N', 'S', 'V', 'F']

line_styles =  ['-', '--', ':', '-.']

l_width = 2





plt.figure(figsize=(8.27, 11.69))



print("1 Raw")

compute_morph = {'raw'}



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))





ax1 = plt.subplot(321)

ax1.title.set_text('(a)')



raw_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    raw_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(raw_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)





# 1 Average wavelets

print("2 wavelets")

compute_morph = {'wvlt'}



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))





ax2 = plt.subplot(322)

ax2.title.set_text('(b)')



wvlt_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    wvlt_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(wvlt_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)





# 2 Average HOS

compute_morph = {'HOS'}

print("3 HOS")



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))





ax3 = plt.subplot(323)

ax3.title.set_text('(c)')



hos_avg = np.zeros((4, features.shape[1]))



for n in range(0,4):

    hos_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(hos_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)



# 3 Average U-LBP1D



compute_morph = {'u-lbp'}

print("4 U LBP")



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))



ax4 = plt.subplot(324)

ax4.title.set_text('(d)')



lbp_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    lbp_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(lbp_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)





"""

compute_morph = {'hbf'}

print("4 HBF")



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))



ax4 = plt.subplot(324)

ax4.title.set_text('d')



hbf_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    hbf_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(hbf_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)

"""



# 4 Average MyMorph

compute_morph = {'myMorph'}

print("5 myMorph")



[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))





ax5 = plt.subplot(325)

ax5.title.set_text('(e)')



myMorph_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    myMorph_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(myMorph_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)



# 5 Average RR intervals

compute_morph = {''}

print("6 RR")



use_RR = True

norm_RR = True

[features_1, labels_1, patient_num_beats_1] = load_mit_db('DS1', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



[features_2, labels_2, patient_num_beats_2] = load_mit_db('DS2', winL, winR, do_preprocess,

    maxRR, use_RR, norm_RR, compute_morph, db_path, reduced_DS, leads_flag)



features = np.vstack((features_1, features_2))

labels = np.concatenate((labels_1, labels_2))





ax6 = plt.subplot(326)

ax6.title.set_text('(f)')



myMorph_avg = np.zeros((4, features.shape[1]))

for n in range(0,4):

    myMorph_avg[n] = np.average(features[labels == n], axis=0)

    plt.plot(myMorph_avg[n], label=label_name[n], linestyle=line_styles[n], linewidth=l_width)



leg = plt.legend(loc='best', ncol=1, shadow=False, fancybox=True)

leg.get_frame().set_alpha(0.5)







#plt.show()



plt.savefig('/home/mondejar/graphic.pdf', dpi=None, facecolor='w', edgecolor='w',

    orientation='portrait', papertype='a4', format='pdf', transparent=True, bbox_inches=None, 

    pad_inches=0.1, frameon=None)



# A4 figsize=(11.69,8.27)