feat: implement func that gets df from
data_dict in handler
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8f34ff6abf
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@ -21,10 +21,10 @@ class Data_container:
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class CSV_handler:
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class CSV_handler:
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def __init__(self):
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def __init__(self, data_type):
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self.working_dir = str(Path.cwd())
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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_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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self.data_type = data_type
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# Makes dataframe from the csv files in the working directory
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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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def make_df(self, filename):
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@ -443,6 +443,13 @@ class CSV_handler:
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self.data_type = 'soft'
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self.data_type = 'soft'
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return self.data_container_dict
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return self.data_container_dict
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def get_df_from_data_dict(self, subject_nr, which_arm, round, emg_nr):
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data_type = self.data_type
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container = self.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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return df
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# Help: gets the str from emg nr
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# Help: gets the str from emg nr
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def get_emg_str(emg_nr):
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def get_emg_str(emg_nr):
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return 'emg' + str(emg_nr)
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return 'emg' + str(emg_nr)
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@ -70,11 +70,9 @@ 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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old_len = len(get_xory_from_df('y', df))
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return y_new_values
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return y_new_values
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# Takes in handler and detailes to denoise. Returns arrays and df
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def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr, round):
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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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print(df.head)
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N = get_xory_from_df('x', df)
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N = get_xory_from_df('x', df)
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@ -82,7 +80,8 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr,
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cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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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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y_values = inverse_wavelet(df, cA_filt, cD_filt)
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return pandas.DataFrame([N_trans, y_values])
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df_new = pandas.DataFrame([N_trans, y_values])
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return N, y_values, df_new
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