44 lines
1.0 KiB
Python
44 lines
1.0 KiB
Python
import pandas as pd
|
|
from pathlib import Path
|
|
|
|
class Data_container:
|
|
|
|
def __init__(self):
|
|
self.subject_nr
|
|
self.subject_name
|
|
self.data_dict
|
|
|
|
|
|
|
|
class CSV_handler:
|
|
|
|
def __init__(self):
|
|
self.working_dir = str(Path.cwd())
|
|
self.data_container_dict = {i: []}
|
|
|
|
# Makes dataframe from the csv files in the working directory
|
|
def make_df(self, filename):
|
|
filepath = self.working_dir + str(filename)
|
|
df = pd.read_csv(filepath)
|
|
return df
|
|
|
|
# Extracts out the timestamp and the selected emg signal into a new dataframe
|
|
def get_time_emg_table(self, filename: str, subject_nr: int, which_arm: str, emg_nr: int):
|
|
tot_data_frame = self.make_df(filename)
|
|
emg_str = 'emg' + str(emg_nr)
|
|
filtered_df = tot_data_frame[["timestamp", emg_str]]
|
|
|
|
#self.data_dict[subject_nr] = [which_arm, emg1]
|
|
|
|
return filtered_df
|
|
|
|
def get_emg_str(emg_nr):
|
|
return 'emg' + str(emg_nr)
|
|
|
|
def get_min_max_timestamp():
|
|
|
|
|
|
|
|
|
|
|