EMG_Biometrics_2021/Present_data.py

from logging import error

from matplotlib.cbook import get_sample_data
from Handle_emg_data import *
from Signal_prep import *
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.ticker as ticker


SAMPLE_RATE = 200
mfcc_stepsize = 0.5
mfcc_windowsize = 2

# PLOT FUNCTIONS:

# Plots DataFrame objects
def plot_df(df:DataFrame):
    lines = df.plot.line(x='timestamp')
    plt.show()

# Plots ndarrays after transformations 
def plot_array(N, y):
    plt.plot(N, np.abs(y))
    plt.show()

def plot_compare_two_df(df_old, old_name, df_new, new_name):
    x = get_xory_from_df('x', df_old)
    y1 = get_xory_from_df('y', df_old)
    y2 = get_xory_from_df('y', df_new)

    figure, axis = plt.subplots(1, 2)
    axis[0].plot(x, y1)
    axis[0].set_title(old_name)
    axis[1].plot(x, y2)
    axis[1].set_title(new_name)
    plt.show()

def plot_mfcc(mfcc_data):
    #plt.rcParams["figure.figsize"] = [7.50, 15]
    #plt.rcParams["figure.autolayout"] = True

    fig, ax = plt.subplots()
    mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
    #ax.axis([0, mfcc_stepsize * len(mfcc_data[0]), 0, len(mfcc_data[:,0])])
    #ax.set_xticks(range(int(len(mfcc_data) * 1 / mfcc_stepsize)))
    ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
    ax.set_title('MFCC')
    plt.show() 

# DATA FUNCTIONS:

# The CSV_handler takes in data_type, but only for visuals. 
# E.g. handler = CSV_handler('soft')

# Loads in data. Choose data_type: hard, hardPP, soft og softPP as str. Returns None
def load_data(csv_handler:CSV_handler, data_type):
    if data_type == 'hard': 
        csv_handler.load_hard_original_emg_data()
    elif data_type == 'hardPP':
        csv_handler.load_hard_PP_emg_data()
    elif data_type == 'soft':
        csv_handler.load_soft_original_emg_data()
    elif data_type == 'softPP':
        csv_handler.load_soft_PP_emg_data()
    else:
        raise Exception('Wrong input')

# Retrieved data. Send in loaded csv_handler and data detailes you want. Returns DataFrame
def get_data(csv_handler:CSV_handler, subject_nr, which_arm, session, emg_nr):
    data_frame = csv_handler.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
    return data_frame
    
#Takes in handler and detailes to denoise. Returns arrays and df
def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, emg_nr):
    df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)

    N = get_xory_from_df('x', df)
    N_trans, cA, cD = wavelet_db4(df)
    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
    y_values = inverse_wavelet(df, cA_filt, cD_filt)

    df_new = Handler.make_df_from_xandy(N, y_values, emg_nr)
    return df_new

def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
    N = get_xory_from_df('x', df)
    y = get_xory_from_df('y', df)
    return N, base.mfcc(y, samplesize, windowsize, stepsize)

# CASE FUNTIONS 

def compare_with_wavelet(data_frame):
    N_trans, cA, cD = wavelet_db4(data_frame)
    data_frame_freq = make_df_from_xandy(N_trans, cA, 1)

    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
    data_frame_freq_filt = make_df_from_xandy(N_trans, cD_filt, 1)

    plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')


# MAIN:

def main():

    csv_handler = CSV_handler()
    load_data(csv_handler, 'hard')
    data_frame = get_data(csv_handler, 2, 'left', 1, 1)

    print(get_samplerate(data_frame))

    N, mfcc_feat = mfcc_custom(data_frame[:5000], SAMPLE_RATE, mfcc_windowsize, mfcc_stepsize)
    plot_mfcc(mfcc_feat)
    print(get_sample_data)
    
    return None

main()