from numpy import load from Handle_emg_data import * import matplotlib.pyplot as plt 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]) 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) 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 = get_xory_from_df('x', df) plot_df(df) #print(len(N)) #print(len(get_xory_from_df('y', df))) x, cA, cD = 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 = inverse_wavelet(df, cA_filt, cD_filt) #print(len(y_new_values)) 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 def test_total_denoising(): handler = Handler.CSV_handler('hard') handler.load_hard_original_emg_data() # Original df: df = handler.get_df_from_data_dict(3, 'left', 3, 3) print(df.head) plot_df(df) # Denoised df: N_trans, y_values, df_denoised = denoice_dataset(handler, 3, 'left', 3, 3) print(df_denoised.head) plot_df(df_denoised) test_total_denoising()