86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
from matplotlib import lines
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from numpy import load
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from Handle_emg_data import *
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import matplotlib.pyplot as plt
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from Signal_prep import *
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def test_df_extraction(emg_nr):
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handler = CSV_handler()
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file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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subject1_left_emg1 = handler.get_time_emg_table(file, emg_nr)
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print(subject1_left_emg1.head)
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return subject1_left_emg1, emg_nr
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def test_hardPP_load_func():
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handler = CSV_handler()
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test_dict = handler.load_hard_PP_emg_data()
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subject2_container = test_dict.get(2)
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print(subject2_container)
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print(subject2_container.subject_name)
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print(subject2_container.data_dict_round1.get('left')[1])
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def test_min_max_func():
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handler = CSV_handler()
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file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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df = handler.get_time_emg_table(file, 1)
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min, max = get_min_max_timestamp(df)
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print(min)
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print(max)
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def test_fft_prep():
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handler = CSV_handler()
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file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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df = handler.get_time_emg_table(file, 1)
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def test_plot_wavelet_both_ways():
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handler = CSV_handler()
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file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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df = handler.get_time_emg_table(file, 1)
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N = get_xory_from_df('x', df)
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plot_df(df)
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#print(len(N))
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#print(len(get_xory_from_df('y', df)))
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x, cA, cD = wavelet_db4_denoising(df)
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plot_arrays(x, cA)
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#print(len(cA))
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cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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plot_arrays(x, cA_filt)
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#print(len(cA_filt))
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y_new_values = inverse_wavelet(df, cA_filt, cD_filt)
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#print(len(y_new_values))
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plot_arrays(N, y_new_values)
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def test_soft_load_func():
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handler = CSV_handler()
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test_dict = handler.load_soft_original_emg_data()
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subject2_container = test_dict.get(4) # Subject 4
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print(subject2_container)
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print(subject2_container.subject_name)
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print(subject2_container.data_dict_round2.get('right')[3]) # Round2, right, emg_4
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def test_total_denoising():
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handler = Handler.CSV_handler('hard')
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handler.load_hard_original_emg_data()
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# Original df:
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df = handler.get_df_from_data_dict(3, 'left', 3, 3)
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print(df.head)
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# Denoised df:
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df_denoised = denoice_dataset(handler, 3, 'left', 3, 3, 0.2)
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print(df_denoised.head)
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x = get_xory_from_df('x', df)
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y1 = get_xory_from_df('y', df)
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y2 = get_xory_from_df('y', df_denoised)
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figure, axis = plt.subplots(1, 2)
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axis[0].plot(x, y1)
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axis[1].plot(x, y2)
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plt.show()
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test_total_denoising()
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