52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
from Handle_emg_data import CSV_handler, get_min_max_timestamp
|
|
import matplotlib.pyplot as plt
|
|
import Signal_prep
|
|
|
|
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_load_func():
|
|
test_dict = Signal_prep.load_user_emg_data()
|
|
subject2_container = test_dict[2]
|
|
print(subject2_container.data_dict['left'][1])
|
|
|
|
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)
|
|
|
|
def test_fft_prep():
|
|
handler = CSV_handler()
|
|
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
|
|
df = handler.get_time_emg_table(file, 1)
|
|
|
|
x, y, d = Signal_prep.prep_df_for_trans(df)
|
|
print(x)
|
|
print(y)
|
|
|
|
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)
|
|
N = handler.get_xory_from_df('x', df)
|
|
#plot_df(df)
|
|
#print(len(N))
|
|
#print(len(get_xory_from_df('y', df)))
|
|
x, cA, cD = handler.wavelet_db4_denoising(df)
|
|
#plot_arrays(x, cA)
|
|
#print(len(cA))
|
|
cA_filt, cD_filt = soft_threshold_filter(cA, cD)
|
|
#plot_arrays(x, cA_filt)
|
|
#print(len(cA_filt))
|
|
y_new_values = handler.inverse_wavelet(df, cA, cD)
|
|
#print(len(y_new_values))
|
|
handler.plot_arrays(N, y_new_values) |