feat: complete a denoise func for part of the dataset
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9634a63d31
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@ -13,12 +13,18 @@ class Data_container:
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self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8}
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self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8}
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self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8}
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self.dict_list = [self.data_dict_round1,
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self.data_dict_round2,
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self.data_dict_round3,
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self.data_dict_round4
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]
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class CSV_handler:
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def __init__(self):
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self.working_dir = str(Path.cwd())
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self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer
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self.data_type = ''
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# Makes dataframe from the csv files in the working directory
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def make_df(self, filename):
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@ -158,6 +164,7 @@ class CSV_handler:
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self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
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# Links the stored data in the data_container to the Handler
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self.link_container_to_handler(data_container)
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self.data_type = 'hardPP'
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return self.data_container_dict
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def load_soft_PP_emg_data(self):
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@ -249,6 +256,7 @@ class CSV_handler:
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self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
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# Links the stored data in the data_container to the Handler
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self.link_container_to_handler(data_container)
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self.data_type = 'softPP'
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return self.data_container_dict
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def load_hard_original_emg_data(self):
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@ -340,7 +348,7 @@ class CSV_handler:
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self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
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# Links the stored data in the data_container to the Handler
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self.link_container_to_handler(data_container)
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self.data_type = 'hard'
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return self.data_container_dict
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def load_soft_original_emg_data(self):
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@ -432,6 +440,7 @@ class CSV_handler:
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self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
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# Links the stored data in the data_container to the Handler
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self.link_container_to_handler(data_container)
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self.data_type = 'soft'
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return self.data_container_dict
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# Help: gets the str from emg nr
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@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas
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from pandas.core.frame import DataFrame
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from scipy.fft import fft, fftfreq
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import pywt
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@ -69,6 +70,23 @@ def inverse_wavelet(df, cA_filt, cD_filt):
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old_len = len(get_xory_from_df('y', df))
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return y_new_values
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def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr, round):
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data_type = handler.data_type
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container = handler.data_container_dict.get(subject_nr)
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df = container.dict_list[round - 1].get(which_arm)[emg_nr]
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print(df.head)
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N = get_xory_from_df('x', df)
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N_trans, cA, cD = wavelet_db4_denoising(df)
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cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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y_values = inverse_wavelet(df, cA_filt, cD_filt)
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return pandas.DataFrame([N_trans, y_values])
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# MOVE TO Present_data.py
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# Plots DataFrame objects
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def plot_df(df:DataFrame):
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lines = df.plot.line(x='timestamp')
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