doc: make sufficient comments to all funcs
in Handle_emg_data.py
This commit is contained in:
Skudalen
parent
588fa7d282
commit
81d4a53335
@ -19,6 +19,9 @@ NR_MEL_BINS = 40 # Number of mel-filter-bins
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class Data_container:
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# Initiates personal data container for each subject. Dict for each session with keys 'left' and 'right',
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# and values equal to lists of EMG data indexed 0-7
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# NB! More sessions has to be added here in the future
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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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@ -32,20 +35,26 @@ class Data_container:
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self.data_dict_round4
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]
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class CSV_handler:
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# Initiates object to store all datapoints in the experiment
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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 = None
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self.data_container_dict = {} # Dict with keys equal subject numbers and values equal to its respective datacontainer
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self.data_type = None # String describing which type of data is stored in the object
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# Makes dataframe from the csv files in the working directory
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# Input: filename of a csv-file
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# Output: DataFrame
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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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# Extracts out the timestamp and the selected emg signal into a new dataframe
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# Input: filename of a csv-file, EMG nr
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# Output: DataFrame(timestamp/EMG)
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def get_time_emg_table(self, filename:str, emg_nr:int):
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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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@ -53,29 +62,31 @@ class CSV_handler:
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return filtered_df
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# Takes in a df and stores the information in a Data_container object
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def store_df_in_container(self, filename:str, emg_nr:int, which_arm:str, data_container:Data_container, round:int):
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# Input: filename of a csv-file, EMG nr, left/right arm, subject's data_container, session nr
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# Output: None -> stores EMG data in data container
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def store_df_in_container(self, filename:str, emg_nr:int, which_arm:str, data_container:Data_container, session:int):
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df = self.get_time_emg_table(filename, emg_nr+1)
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if df.isnull().values.any():
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print('NaN in: subject', data_container.subject_nr, 'arm:', which_arm, 'session:', round, 'emg nr:', emg_nr)
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print('NaN in: subject', data_container.subject_nr, 'arm:', which_arm, 'session:', session, 'emg nr:', emg_nr)
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# Places the data correctly:
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if round == 1:
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if session == 1:
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if which_arm == 'left':
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data_container.data_dict_round1['left'][emg_nr] = df # Zero indexed emg_nr in the dict
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else:
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data_container.data_dict_round1['right'][emg_nr] = df
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elif round == 2:
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elif session == 2:
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if which_arm == 'left':
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data_container.data_dict_round2['left'][emg_nr] = df
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else:
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data_container.data_dict_round2['right'][emg_nr] = df
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elif round == 3:
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elif session == 3:
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if which_arm == 'left':
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data_container.data_dict_round3['left'][emg_nr] = df
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else:
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data_container.data_dict_round3['right'][emg_nr] = df
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elif round == 4:
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elif session == 4:
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if which_arm == 'left':
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data_container.data_dict_round4['left'][emg_nr] = df
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else:
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@ -83,14 +94,25 @@ class CSV_handler:
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else:
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raise IndexError('Not a valid index')
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# Links the data container for a subject to the handler object
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# Links the data container for a subject to the csv_handler object
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# Input: the subject's data_container
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# Output: None -> places the data container correctly in the CSV_handler data_container_dict
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def link_container_to_handler(self, data_container:Data_container):
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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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# Loads the data from the csv files into a storing system in an CSV_handler object
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# (hard, hardPP, soft and softPP)
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# Retrieves df via the data_dict in the CSV_handler object
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# Input: Experiment detailes
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# Output: DataFrame
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def get_df_from_data_dict(self, subject_nr, which_arm, session, emg_nr):
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container:Data_container = self.data_container_dict.get(subject_nr)
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df = container.dict_list[session - 1].get(which_arm)[emg_nr - 1]
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return df
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# Loads the data from the csv files into the storing system of the CSV_handler object
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# Input: None(CSV_handler)
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# Output: None -> load and stores data
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def load_hard_PP_emg_data(self):
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# CSV data from subject 1
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@ -182,7 +204,6 @@ class CSV_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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# CSV data from subject 1
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@ -274,7 +295,6 @@ class CSV_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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# CSV data from subject 1
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@ -366,7 +386,6 @@ class CSV_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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# CSV data from subject 1
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@ -459,14 +478,9 @@ class CSV_handler:
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self.data_type = 'soft'
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return self.data_container_dict
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# Retrieves df via the data_dict in the handler object
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def get_df_from_data_dict(self, subject_nr, which_arm, session, emg_nr):
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container:Data_container = self.data_container_dict.get(subject_nr)
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df = container.dict_list[session - 1].get(which_arm)[emg_nr - 1]
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return df
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# Loads in data to a CSV_handler. Choose data_type: hard, hardPP, soft og softPP as str.
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# Returns None.
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# Loads data the to the CSV_handler(general load func). Choose data_type: hard, hardPP, soft og softPP as str.
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# Input: String(datatype you want)
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# Output: None -> load and stores data
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def load_data(self, data_type):
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if data_type == 'hard':
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self.load_hard_original_emg_data()
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@ -480,28 +494,31 @@ class CSV_handler:
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raise Exception('Wrong input')
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# Retrieved data. Send in loaded csv_handler and data detailes you want.
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# Returns DataFrame and samplerate
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# Input: Experiment detailes
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# Output: DataFrame, samplerate:int
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def get_data(self, subject_nr, which_arm, session, emg_nr):
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data_frame = self.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
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samplerate = get_samplerate(data_frame)
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return data_frame, samplerate
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# NOT IMPLEMENTED
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'''
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def get_keyboard_data(self, filename:str, pres_or_release:str='pressed'):
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filepath = self.working_dir + str(filename)
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df = pd.read_csv(filepath)
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if pres_or_release == 'pressed':
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df = df[(df['event'] == 'KeyPressed') and (df['event'] == 'KeyPressed')]
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else
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'''
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else: pass
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pass
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class NN_handler:
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# Paths for data storage in json to later use in Neural_Network_Analysis.py
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JSON_PATH_REG = "reg_data.json"
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JSON_PATH_MFCC = "mfcc_data.json"
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# Class to manipulate data from the CSV_handler and store it for further analysis
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# NB! More subject needs to be added manually
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def __init__(self, csv_handler:CSV_handler) -> None:
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self.csv_handler = csv_handler
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# Should med 4 sessions * split nr of samples per person. Each sample is structured like this: [sample_df, samplerate]
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@ -519,13 +536,17 @@ class NN_handler:
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5: None
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}
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def get_reg_samples_dict(self):
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# GET method for reg_samples_dict
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def get_reg_samples_dict(self) -> dict:
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return self.reg_samples_per_subject
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def get_mfcc_samples_dict(self):
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# GET method for mfcc_samples_dict
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def get_mfcc_samples_dict(self) -> dict:
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return self.mfcc_samples_per_subject
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# Retrieves all EMG data from one subject and one session, and makes a list of the DataFrames
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# Input: Subject nr, Session nr (norm, not 0-indexed)
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# Output: List(df_1, ..., df_16)
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def get_emg_list(self, subject_nr, session_nr) -> list:
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list_of_emgs = []
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df, _ = self.csv_handler.get_data(subject_nr, 'left', session_nr, 1)
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@ -537,10 +558,13 @@ class NN_handler:
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df, _ = self.csv_handler.get_data(subject_nr, 'right', session_nr, emg_nr+1)
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list_of_emgs.append(DataFrame(df[get_emg_str(emg_nr+1)]))
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return list_of_emgs # list of emg data where first element also has timestamp column
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return list_of_emgs # list of emg data
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# Creates one Dataframe of all EMG data(one session, one subject). One column for each EMG array
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# Input: List(emg1, ..., emg16)
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# Output: DataFrame(shape[1]=16)
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def make_subj_sample(self, list_of_emgs_):
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# Test and fix if the emgs have different size
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# Test and fix if the left/right EMGs have different size
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list_of_emgs = []
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length_left_emgs = int(len(list_of_emgs_[0].index))
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length_right_emgs = int(len(list_of_emgs_[-1].index))
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@ -568,6 +592,9 @@ class NN_handler:
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return tot_session_df
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# Takes in all EMG session Dataframe and merges the EMG data into one column, creating one signal
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# Input: DataFrame(shape[1]=16, EMG data)
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# Output: DataFrame(signal), samplerate of it
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def reshape_session_df_to_signal(self, df:DataFrame):
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main_df = df[['timestamp', 1]].rename(columns={1: 'emg'})
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for i in range(2, 17):
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@ -576,6 +603,9 @@ class NN_handler:
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samplerate = get_samplerate(main_df)
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return main_df, samplerate
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# Stores split, merged signals in the NN-handler's reg_samples_per_subject
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# Input: Split_nr:int(how many times to split this merged signal)
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# Output: None -> stores in NN_handler
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def store_samples(self, split_nr) -> None:
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for subject_nr in range(5):
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subj_samples = []
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@ -595,11 +625,12 @@ class NN_handler:
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self.reg_samples_per_subject[subject_nr+1] = subj_samples
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# Takes in all EMG session Dataframe and creates DataFrame of MFCC samples
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# Input: DataFrame(shape[1]=16, EMG data)
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# Output: DataFrame(merged MFCC data, shape: (n, 13*16))
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def make_mfcc_df_from_session_df(self, session_df) -> DataFrame:
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session_df.rename(columns={0:'timestamp'}, inplace=True)
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samplerate = get_samplerate(session_df)
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#attach_func = lambda list_1, list_2: list_1.tolist().extend(list_2.tolist())
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attach_func = lambda list_1, list_2: list_1.extend(list_2)
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signal = session_df[1]
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@ -607,7 +638,6 @@ class NN_handler:
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df = DataFrame(mfcc_0).dropna()
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df['combined'] = df.values.tolist()
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result_df = df['combined']
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#print(result_df)
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for i in range(2, 17):
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signal_i = session_df[i]
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@ -618,6 +648,10 @@ class NN_handler:
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return result_df
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# Merges MFCC data from all sessions and stores the sample data in
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# the NN_handler's mfcc_samples_per_subject dict
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# Input: None(NN_handler)
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# Output: None -> stores in NN_handler
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def store_mfcc_samples(self) -> None:
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for subject_nr in range(5):
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subj_samples = []
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@ -636,9 +670,12 @@ class NN_handler:
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self.mfcc_samples_per_subject[subject_nr+1] = result_df
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# Makes MFCC data from reg_samples_per_subject and stores it in a json file
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# Input: Path to the json file
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# Output: None -> stores in json
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def save_json_reg(self, json_path=JSON_PATH_REG):
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# dictionary to store mapping, labels, and MFCCs
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# Dictionary to store mapping, labels, and MFCCs
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data = {
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"mapping": [],
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"labels": [],
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@ -647,7 +684,7 @@ class NN_handler:
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raw_data_dict = self.get_reg_samples_dict()
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# loop through all subjects to get samples
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# Loop through all subjects to get samples
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mfcc_list = []
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mfcc_frame_list = []
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@ -689,6 +726,9 @@ class NN_handler:
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with open(json_path, "w") as fp:
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json.dump(data, fp, indent=4)
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# Stores MFCC data from mfcc_samples_per_subject in a json file
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# Input: Path to the json file
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# Output: None -> stores in json
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def save_json_mfcc(self, json_path=JSON_PATH_MFCC):
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# dictionary to store mapping, labels, and MFCCs
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@ -752,7 +792,7 @@ def get_samplerate(df:DataFrame):
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samplerate = samples / seconds
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return int(samplerate)
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# Takes in a df and outputs np arrays for x and y values
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# Help: takes in a df and outputs np arrays for x and y values
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def get_xory_from_df(x_or_y, df:DataFrame):
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swither = {
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'x': df.iloc[:,0].to_numpy(),
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@ -760,15 +800,15 @@ def get_xory_from_df(x_or_y, df:DataFrame):
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}
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return swither.get(x_or_y, 0)
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# Slightly modified mfcc with inputs like below.
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# Returns N (x_values from original df) and mfcc_y_values
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def mfcc_custom(signal, samplesize, windowsize=MFCC_WINDOWSIZE,
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# Help: slightly modified mfcc with inputs like below. Returns N (x_values from original df) and mfcc_y_values
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def mfcc_custom(signal, samplerate, windowsize=MFCC_WINDOWSIZE,
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stepsize=MFCC_STEPSIZE,
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nr_coefficients=NR_COEFFICIENTS,
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nr_mel_filters=NR_MEL_BINS):
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return mfcc(signal, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters)
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return mfcc(signal, samplerate, windowsize, stepsize, nr_coefficients, nr_mel_filters)
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# Help: test for unregularities in DataFrame obj
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def test_df_for_bugs(signal, key, placement_index):
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df = DataFrame(signal)
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if df.isnull().values.any():
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