50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import pandas as pd
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from pathlib import Path
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from pandas.core.frame import DataFrame
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class Data_container:
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def __init__(self, subject_nr:int, subject_name:str):
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self.subject_nr = subject_nr
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self.subject_name = subject_name
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self.data_dict = {'left': [None]*8, 'right': [None]*8}
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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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# Makes dataframe from the csv files in the working directory
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def make_df(self, filename):
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filepath = self.working_dir + str(filename)
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df = pd.read_csv(filepath)
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return df
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# Extracts out the timestamp and the selected emg signal into a new dataframe and stores the data on the subject
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def get_time_emg_table(self, filename:str, emg_nr:int, which_arm:str, data_container:Data_container):
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tot_data_frame = self.make_df(filename)
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emg_str = 'emg' + str(emg_nr)
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filtered_df = tot_data_frame[["timestamp", emg_str]]
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# Links the retrieved data with the subjects data_container
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subject_nr = data_container.subject_nr
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self.data_container_dict[subject_nr] = data_container
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# Places the data correctly:
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if which_arm is 'left':
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data_container.data_dict['left'][emg_nr+1] = filtered_df
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return filtered_df
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def get_emg_str(emg_nr):
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return 'emg' + str(emg_nr)
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def get_min_max_timestamp(df:DataFrame):
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min = df['timestamp'].argmin
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max = df['timestamp'].argmax
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return min, max
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