EMG_Biometrics_2021/Test_functions.py

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from Handle_emg_data import *
import matplotlib.pyplot as plt
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from Signal_prep import *
def test_df_extraction(emg_nr):
handler = CSV_handler()
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
subject1_left_emg1 = handler.get_time_emg_table(file, emg_nr)
print(subject1_left_emg1.head)
return subject1_left_emg1, emg_nr
def test_hardPP_load_func():
handler = CSV_handler()
test_dict = handler.load_hard_PP_emg_data()
subject2_container = test_dict.get(2)
print(subject2_container)
print(subject2_container.subject_name)
print(subject2_container.data_dict_round1.get('left')[1])
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def test_min_max_func():
handler = CSV_handler()
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
df = handler.get_time_emg_table(file, 1)
min, max = get_min_max_timestamp(df)
print(min)
print(max)
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def test_fft_prep():
handler = CSV_handler()
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
df = handler.get_time_emg_table(file, 1)
def test_plot_wavelet_both_ways():
handler = CSV_handler()
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
df = handler.get_time_emg_table(file, 1)
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N = get_xory_from_df('x', df)
plot_df(df)
#print(len(N))
#print(len(get_xory_from_df('y', df)))
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x, cA, cD = wavelet_db4_denoising(df)
plot_arrays(x, cA)
#print(len(cA))
cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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plot_arrays(x, cA_filt)
#print(len(cA_filt))
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y_new_values = inverse_wavelet(df, cA_filt, cD_filt)
#print(len(y_new_values))
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plot_arrays(N, y_new_values)
def test_soft_load_func():
handler = CSV_handler()
test_dict = handler.load_soft_original_emg_data()
subject2_container = test_dict.get(4) # Subject 4
print(subject2_container)
print(subject2_container.subject_name)
print(subject2_container.data_dict_round2.get('right')[3]) # Round2, right, emg_4
test_soft_load_func()