ms762wu/EMG_Biometrics_2021
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feat: complete a denoise func for part of the dataset

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Skudalen 2021-06-25 13:41:57 +02:00
parent 9634a63d31
commit 8f34ff6abf
2 changed files with 29 additions and 2 deletions
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11
Handle_emg_data.py
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@ -13,12 +13,18 @@ class Data_container:
self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8}
self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8}
self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8}
self.dict_list = [self.data_dict_round1,
self.data_dict_round2,
self.data_dict_round3,
self.data_dict_round4
]
class CSV_handler: class CSV_handler:
def __init__(self): def __init__(self):
self.working_dir = str(Path.cwd()) self.working_dir = str(Path.cwd())
self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer
self.data_type = ''
# Makes dataframe from the csv files in the working directory # Makes dataframe from the csv files in the working directory
def make_df(self, filename): def make_df(self, filename):
@ -158,6 +164,7 @@ class CSV_handler:
self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
# Links the stored data in the data_container to the Handler # Links the stored data in the data_container to the Handler
self.link_container_to_handler(data_container) self.link_container_to_handler(data_container)
self.data_type = 'hardPP'
return self.data_container_dict return self.data_container_dict
def load_soft_PP_emg_data(self): def load_soft_PP_emg_data(self):
@ -249,6 +256,7 @@ class CSV_handler:
self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
# Links the stored data in the data_container to the Handler # Links the stored data in the data_container to the Handler
self.link_container_to_handler(data_container) self.link_container_to_handler(data_container)
self.data_type = 'softPP'
return self.data_container_dict return self.data_container_dict
def load_hard_original_emg_data(self): def load_hard_original_emg_data(self):
@ -340,7 +348,7 @@ class CSV_handler:
self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
# Links the stored data in the data_container to the Handler # Links the stored data in the data_container to the Handler
self.link_container_to_handler(data_container) self.link_container_to_handler(data_container)
self.data_type = 'hard'
return self.data_container_dict return self.data_container_dict
def load_soft_original_emg_data(self): def load_soft_original_emg_data(self):
@ -432,6 +440,7 @@ class CSV_handler:
self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
# Links the stored data in the data_container to the Handler # Links the stored data in the data_container to the Handler
self.link_container_to_handler(data_container) self.link_container_to_handler(data_container)
self.data_type = 'soft'
return self.data_container_dict return self.data_container_dict
# Help: gets the str from emg nr # Help: gets the str from emg nr

18
Signal_prep.py
View File

@ -1,5 +1,6 @@
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import pandas
from pandas.core.frame import DataFrame from pandas.core.frame import DataFrame
from scipy.fft import fft, fftfreq from scipy.fft import fft, fftfreq
import pywt import pywt
@ -69,6 +70,23 @@ def inverse_wavelet(df, cA_filt, cD_filt):
old_len = len(get_xory_from_df('y', df)) old_len = len(get_xory_from_df('y', df))
return y_new_values return y_new_values
def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr, round):
data_type = handler.data_type
container = handler.data_container_dict.get(subject_nr)
df = container.dict_list[round - 1].get(which_arm)[emg_nr]
print(df.head)
N = get_xory_from_df('x', df)
N_trans, cA, cD = wavelet_db4_denoising(df)
cA_filt, cD_filt = soft_threshold_filter(cA, cD)
y_values = inverse_wavelet(df, cA_filt, cD_filt)
return pandas.DataFrame([N_trans, y_values])
# MOVE TO Present_data.py
# Plots DataFrame objects # Plots DataFrame objects
def plot_df(df:DataFrame): def plot_df(df:DataFrame):
lines = df.plot.line(x='timestamp') lines = df.plot.line(x='timestamp')