regress_rr.py

import glob
from os import makedirs, mkdir
from os.path import join, exists
import pandas as pd
import numpy as np
import json
import ipdb
import re
import pickle
import sys
import time
from zipfile import ZipFile

import argparse
from datetime import datetime, timedelta, timezone, timedelta
import pytz

import matplotlib.pyplot as plt
from functools import partial
from collections import Counter
from itertools import repeat, chain, combinations
from multiprocessing import Pool, cpu_count
import tensorflow as tf

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.preprocessing import PolynomialFeatures, LabelEncoder
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import accuracy_score

from tsfresh.feature_extraction import extract_features
from tsfresh.feature_extraction import settings as tsfresh_settings
from tsfresh.utilities.string_manipulation import get_config_from_string

from modules.datapipeline import get_file_list, load_and_snip, load_data, \
        load_split_data, load_harness_data
from modules.digitalsignalprocessing import vectorized_slide_win as vsw
from modules.digitalsignalprocessing import imu_signal_processing
from modules.digitalsignalprocessing import bvp_signal_processing
from modules.digitalsignalprocessing import hernandez_sp, reject_artefact
from modules.digitalsignalprocessing import do_pad_fft,\
        pressure_signal_processing, infer_frequency, movingaverage
from modules.utils import *

from modules.evaluations import Evaluation
from modules.datapipeline import get_windowed_data, DataSynchronizer,\
        parallelize_dataframe
from modules.datapipeline import ProjectFileHandler
from models.ardregression import ARDRegressionClass
from models.knn import KNNClass
from models.svm import SVMClass
from models.lda import LDAClass
from models.svr import SVRClass
from models.logisticregression import LogisticRegressionClass
from models.linearregression import LinearRegressionClass
from models.neuralnet import FNN_HyperModel, LSTM_HyperModel, TunerClass,\
        CNN1D_HyperModel
from models.ridgeclass import RidgeClass
from models.resnet import Regressor_RESNET, Classifier_RESNET
from models.xgboostclass import XGBoostClass

from pprint import PrettyPrinter

from sktime.transformations.panel.rocket import (
        MiniRocket,
        MiniRocketMultivariate,
        MiniRocketMultivariateVariable,
)

from config import WINDOW_SIZE, WINDOW_SHIFT, IMU_FS, DATA_DIR, BR_FS\
        , FS_RESAMPLE, PPG_FS

IMU_COLS =  ['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z']

def utc_to_local(utc_dt, tz=None):
    return utc_dt.replace(tzinfo=timezone.utc).astimezone(tz=tz)

def datetime_from_utc_to_local(utc_datetime):
    now_timestamp = time.time()
    offset = datetime.fromtimestamp(now_timestamp) - datetime.utcfromtimestamp(now_timestamp)
    return utc_datetime + offset

# Load data
def load_bioharness_file(f:str, skiprows=0, skipfooter=0, **kwargs):
    df_list = []
    method = partial(pd.read_csv, skipinitialspace=True,
                     skiprows=list(range(1, skiprows+1)),
                     skipfooter=skipfooter,
                     header=0,
                     **kwargs
                    )
    df = method(f)
    if 'Time' not in df.columns.values:
        df['Time'] = pd.to_datetime(
            df.rename(columns={'Date':'Day'})[
                ['Day','Month','Year']]) \
                + pd.to_timedelta(df['ms'], unit='ms')
        if pd.isna(df['Time']).any():
            df['Time'].interpolate(inplace=True)
        df['Time'] = pd.to_datetime(df['Time'], format="%d/%m/%Y %H:%M:%S.%f")
        df['Time'] = df['Time'].dt.strftime("%d/%m/%Y %H:%M:%S.%f")
    return df

def load_bioharness_files(f_list:list, skiprows=0, skipfooter=0, **kwargs):
    df_list = []
    method = partial(pd.read_csv, skipinitialspace=True,
                     skiprows=list(range(1, skiprows+1)),
                     skipfooter=skipfooter,
                     header=0, **kwargs)
    for f in f_list:
        df_list.append(load_bioharness_file(f))

    df = pd.concat(df_list, ignore_index=True)
    return df

def bioharness_datetime_to_seconds(val):
    fmt = "%d/%m/%Y %H:%M:%S.%f" 
    dstr = datetime.strptime(val, fmt)
    seconds = dstr.timestamp()
    return seconds

def load_imu_file(imu_file:str):
    hdr_file = imu_file.replace('imudata.gz', 'recording.g3')

    df = pd.read_json(imu_file, lines=True, compression='gzip')
    hdr = pd.read_json(hdr_file, orient='index')
    hdr = hdr.to_dict().pop(0)

    if df.empty: return df, hdr

    data_df = pd.DataFrame(df['data'].tolist())
    df = pd.concat([df.drop('data', axis=1), data_df], axis=1)

    iso_tz = hdr['created']
    tzinfo = pytz.timezone(hdr['timezone'])
    # adjust for UTC
    start_time = datetime.fromisoformat(iso_tz[:-1])
    start_time = utc_to_local(start_time, tz=tzinfo).astimezone(tzinfo)

    na_inds = df.loc[pd.isna(df['accelerometer']), :].index.values
    df.drop(index=na_inds, inplace=True)

    imu_times = df['timestamp'].values
    df['timestamp_interp'] = imu_times
    df['timestamp_interp'] = df['timestamp_interp'].interpolate()
    imu_times = df['timestamp_interp'].values
    imu_datetimes = [start_time + timedelta(seconds=val) \
                     for val in imu_times]
    imu_s = np.array([time.timestamp() for time in imu_datetimes])
    df['sec'] = imu_s

    time_check_thold = df['sec'].min() + 3*3600
    mask = df['sec'] > time_check_thold
    if np.any(mask):
        df = df[np.logical_not(mask)]

    return df, hdr

def load_imu_files(f_list:list):
    data, hdr = [], []
    tmp = []
    for f in f_list:
        tmp.append(load_imu_file(f))
    for l in tmp:
        data.append(l[0])
        hdr.append(l[1])
    data_df = pd.concat(data, axis=0)
    return data_df, hdr

def load_e4_file(e4_file:str):
    ''' First row is the initial time of the session as unix time.
    Second row is the sample rate in Hz'''
    zip_file = ZipFile(e4_file)
    dfs = {csv_file.filename: pd.read_csv(zip_file.open(csv_file.filename)
                                          ,header=None)
           for csv_file in zip_file.infolist()
           if csv_file.filename.endswith('.csv')}
    bvp = dfs["BVP.csv"]
    t0 = bvp.iloc[0].values[0]
    fs = bvp.iloc[1].values[0]
    nsamples = len(bvp) - 2

    t0_datetime = datetime.utcfromtimestamp(t0)
    t0_local = datetime_from_utc_to_local(t0_datetime)
    time = [t0_local.timestamp() + ind*(1/fs) for ind in
            range(nsamples)]
    tmp = [np.nan, np.nan]
    time = tmp + time
    bvp.rename(columns={0: "bvp"}, inplace=True)
    bvp['sec'] = np.array(time)

    head = bvp.iloc[[0, 1]]
    bvp.drop(inplace=True, index=[0, 1])

    hdr = {'start_time': head.iloc[0,0],
           'fs': head.iloc[0,1]}

    return bvp, hdr

def load_e4_files(f_list:list):
    tmp = []
    data = []
    hdr = []
    for f in f_list:
        tmp.append(load_e4_file(f))
    for d, h in tmp:
        data.append(d)
        hdr.append(h)
    data_df = pd.concat(data, axis=0)
    return data_df, hdr

# Synchronising data
def sync_to_ref(df0, df1):
    dsync0 = DataSynchronizer()
    dsync1 = DataSynchronizer()

    time0 = df0['sec'].values
    time1 = df1['sec'].values

    t0 = max((time0[0], time1[0]))
    t1 = min((time0[-1], time1[-1]))
    dsync0.set_bounds(time0, t0, t1)
    dsync1.set_bounds(time1, t0, t1)

    return dsync0.sync_df(df0), dsync1.sync_df(df1)

def pss_br_calculations(win, pss_df=None, br_df=None):
    n_out = 5
    if win[-1] == 0: return [None]*n_out

    dsync = DataSynchronizer()
    pss_fs = BR_FS
    pss_col = [col for col in pss_df.columns.values if\
               'breathing' in col.lower()][0]
    pss_ms = pss_df['ms'].values
    br_ms  = br_df['ms'].values
    t0, t1 = pss_ms[win][0], pss_ms[win][-1]

    diff = pss_ms[win][1:] - pss_ms[win][:-1]
    mask = np.abs(diff/1e3) > 60
    diff_chk = np.any(mask)
    if diff_chk: return [None]*n_out

    # Get pressure estimate for window
    pss_win = pss_df.iloc[win]
    pss_data = pss_win[pss_col]
    pss_filt = pressure_signal_processing(pss_data, fs=pss_fs)
    xf, yf = do_pad_fft(pss_filt, fs=pss_fs)
    pss_est = xf[yf.argmax()]*60

    # Sync and get summary br output
    dsync.set_bounds(br_ms, t0, t1)
    br_win = dsync.sync_df(br_df)

    br_out = np.median(br_win['BR'].values)

    # Get subject and condition
    sbj_out = pss_win['subject'].values[0]
    time_out = np.median(pss_win['sec'].values)
    return time_out, pss_est, br_out, sbj_out, cond_out

def get_pss_br_estimates(pss_df, br_df, window_size=12, window_shift=1):
    pss_fs = BR_FS
    # pss_col = [col for col in pss_df.columns.values if\
    #            'breathing' in col.lower()][0]
    pss_ms = pss_df['sec'].values
    br_ms  = br_df['sec'].values

    inds = np.arange(0, len(pss_ms))
    vsw_out = vsw(inds, len(inds), sub_window_size=int(window_size*pss_fs),
                  stride_size=int(window_shift*pss_fs))

    # dsync = DataSynchronizer()
    pss_est, br_out = [], []
    cond_out, sbj_out = [], []
    func = partial(pss_br_calculations, pss_df=pss_df, br_df=br_df)
    # for i, win in enumerate(vsw_out):
    #     tmp = func(win)

    with Pool(cpu_count()) as p:
        tmp = p.map(func, vsw_out)

    time_out, pss_est, br_out, sbj_out, cond_out = zip(*tmp)

    time_array = np.array(time_out)
    pss_est_array = np.array(pss_est)
    br_out_array = np.array(br_out)
    sbj_out_array = np.array(sbj_out)
    cond_out_array = np.array(cond_out)

    df = pd.DataFrame(
        np.array(
            [time_array, sbj_out_array, cond_out_array,
             pss_est_array, br_out_array]
        ).T,
        columns=['ms', 'subject', 'condition', 'pss', 'br'])
    df.dropna(inplace=True)

    return df

# Multiprocessing task for windowing dataframe
def imu_df_win_task(w_inds, df, i, cols):
    time = df['sec'].values
    if w_inds[-1] == 0: return
    w_df = df.iloc[w_inds]
    t0, t1 = time[w_inds][0], time[w_inds][-1]
    diff = time[w_inds[1:]] - time[w_inds[0:-1]]
    mask = np.abs(diff)>20
    diff_chk = np.any(mask)
    if diff_chk:
        return

    # sbj = w_df['subject'].values.astype(int)
    # sbj_mask = np.any((sbj[1:] - sbj[:-1])>0)
    # if sbj_mask:
    #     return

    if cols is None:
        cols = IMU_COLS

    data = w_df[cols].values

    # DSP
    sd_data = (data - np.mean(data, axis=0))/np.std(data, axis=0)
    # ys = cubic_interp(sd_data, BR_FS, FS_RESAMPLE)
    filt_data = imu_signal_processing(sd_data, IMU_FS)
    x_out = pd.DataFrame(filt_data,
                         columns=IMU_COLS)

    sm_out = w_df['BR'].values
    ps_out = w_df['PSS'].values

    x_vec_time = np.median(time[w_inds])

    fs = 1/np.mean(diff)
    ps_freq = int(get_max_frequency(ps_out, fs=fs))

    y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])

    x_out['sec'] = x_vec_time
    x_out['id'] = i
    y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])

    return x_out, y_out

def bvp_df_win_task(w_inds, df, i, cols):
    time = df['sec'].values

    fs = PPG_FS

    if w_inds[-1] == 0: return
    w_df = df.iloc[w_inds]
    t0, t1 = time[w_inds][0], time[w_inds][-1]
    diff = time[w_inds[1:]] - time[w_inds[0:-1]]
    mask = np.abs(diff)>20
    diff_chk = np.any(mask)
    if diff_chk:
        return

    # sbj = w_df['subject'].values.astype(int)
    # sbj_mask = np.any((sbj[1:] - sbj[:-1])>0)
    # if sbj_mask:
    #     return

    if cols is None:
        cols = ['bvp']

    data = w_df[cols].values

    # DSP
    sd_data = (data - np.mean(data, axis=0))/np.std(data, axis=0)
    filt_data = bvp_signal_processing(sd_data.copy(), fs)
    x_out = pd.DataFrame(filt_data,
                         columns=cols)

    sm_out = w_df['BR'].values
    ps_out = w_df['PSS'].values

    x_vec_time = np.median(time[w_inds])

    ps_freq = int(get_max_frequency(ps_out, fs=fs))

    y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])

    x_out['sec'] = x_vec_time
    x_out['id'] = i
    y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])

    return x_out, y_out

def df_win_task(w_inds, df, i, cols):
    time = df['sec'].values
    if w_inds[-1] == 0: return
    w_df = df.iloc[w_inds]
    t0, t1 = time[w_inds][0], time[w_inds][-1]
    diff = time[w_inds[1:]] - time[w_inds[0:-1]]

    fs_est = 1/np.mean(diff)
    if fs_est > 70 and 'acc_x' in cols: fs = IMU_FS
    elif fs_est < 70 and 'bvp' in cols: fs = PPG_FS
    mask = np.abs(diff)>20
    diff_chk = np.any(mask)
    if diff_chk:
        return

    filt_out = []
    for col in cols:
        data = w_df[col].values
        # DSP
        sd_data = (data - np.mean(data, axis=0))/np.std(data, axis=0)
        # ys = cubic_interp(sd_data, BR_FS, FS_RESAMPLE)
        if col != 'bvp':
            filt_out.append(imu_signal_processing(sd_data, fs))
        else:
            bvp_filt = bvp_signal_processing(sd_data, fs)
            filt_out.append(bvp_filt)
    
    x_out = pd.DataFrame(np.array(filt_out).T, columns=cols)

    sm_out = w_df['BR'].values
    ps_out = w_df['PSS'].values

    x_vec_time = np.median(time[w_inds])

    fs = 1/np.mean(diff)
    ps_freq = int(get_max_frequency(ps_out, fs=fs))
    y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])

    x_out['sec'] = x_vec_time
    x_out['id'] = i
    y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])

    if 'cpm' in w_df.columns.tolist():
        cpm_out = int(np.median(w_df['cpm'].values))
        y_out['cpm'] = cpm_out
    if 'bvp' in cols:
        xf, yf = do_pad_fft(bvp_filt, fs=fs)
        bv_freq = int(xf[yf.argmax()]*60)
        y_out['bvp_est'] = bv_freq

    return x_out, y_out

def get_max_frequency(data, fs=IMU_FS):
    data = pressure_signal_processing(data, fs=fs)

    xf, yf = do_pad_fft(data, fs=fs)
    max_freq = xf[yf.argmax()]*60
    return max_freq

def convert_to_float(df):
    cols = df.columns.values
    if 'sec' in cols:
        df['sec'] = df['sec'].astype(float)
    if 'pss' in cols:
        df['pss'] = df['pss'].astype(float)
    if 'br' in cols:
        df['br'] = df['br'].astype(float)
    if 'subject' in cols:
        df['subject'] = df['subject'].astype(float)

def load_and_sync_xsens(subject, sens_list:list=['imu', 'bvp']):
    assert 'imu' in sens_list or 'bvp' in sens_list, \
            f"{sens_list} is not supported, must contain"\
            "'imu', 'bvp' or 'imu, bvp'"

    pss_df, br_df, imu_df, bvp_df = None, None, None, None
    acc_data, gyr_data, bvp_data = None, None, None
    # load imu
    if 'imu' in sens_list:
        imu_list = get_file_list('imudata.gz', sbj=subject)
        imu_df_all, imu_hdr_df_all = load_imu_files(imu_list)

    # load bioharness
    pss_list = get_file_list('*Breathing.csv', sbj=subject)
    if len(pss_list) == 0:
        pss_list = get_file_list('BR*.csv', sbj=subject)

    br_list = get_file_list('*Summary*.csv', sbj=subject)

    # load e4 wristband
    if 'bvp' in sens_list:
        e4_list = get_file_list('*.zip', sbj=subject)
        bvp_df_all, bvp_hdr = load_e4_files(e4_list)
        bvp_fs = bvp_hdr[0]['fs']

    xsens_list = []
    # skip the first and last x minute(s)
    minutes_to_skip = .5
    br_skiprows = br_skipfooter = int(minutes_to_skip*60)
    pss_skiprows = pss_skipfooter = int(minutes_to_skip*60*BR_FS)

    # load each bioharness file and sync the imu to it
    for pss_file, br_file in zip(pss_list, br_list):
        xsens_data = {}

        pss_df = load_bioharness_file(pss_file, skiprows=pss_skiprows,
                                      skipfooter=pss_skipfooter,
                                      engine='python')
        pss_time = pss_df['Time'].map(bioharness_datetime_to_seconds).values\
                .reshape(-1, 1)
        pss_df['sec'] = pss_time

        br_df = load_bioharness_file(br_file, skiprows=br_skiprows,
                                     skipfooter=br_skipfooter,
                                     engine='python')
        br_time = br_df['Time'].map(bioharness_datetime_to_seconds).values\
                .reshape(-1, 1)
        br_df['sec'] = br_time

        # sync
        if 'imu' in sens_list and 'bvp' in sens_list:
            br_df, imu_df = sync_to_ref(br_df, imu_df_all.copy())
            pss_df, _ = sync_to_ref(pss_df, imu_df_all.copy())
            bvp_df, _ = sync_to_ref(bvp_df_all.copy(), pss_df.copy())
        elif 'imu' in sens_list and not 'bvp' in sens_list:
            br_df, imu_df = sync_to_ref(br_df, imu_df_all.copy())
            pss_df, _ = sync_to_ref(pss_df, imu_df_all.copy())
        elif not 'imu' in sens_list and 'bvp' in sens_list:
            br_df, bvp_df = sync_to_ref(br_df, bvp_df_all.copy())
            pss_df, _ = sync_to_ref(pss_df, bvp_df_all.copy())

        # extract relevant data
        if 'imu' in sens_list:
            axes = ['x', 'y', 'z']
            acc_data = np.stack(imu_df['accelerometer'].values)
            gyr_data = np.stack(imu_df['gyroscope'].values)
            x_time = imu_df['sec'].values.reshape(-1, 1)

        if 'bvp' in sens_list and 'imu' in sens_list:
            bvp_data = bvp_df['bvp'].values
            bvp_data = np.interp(x_time, bvp_df['sec'].values, bvp_data)\
                    .reshape(-1, 1)
        elif 'bvp' in sens_list and not 'imu' in sens_list:
            bvp_data = bvp_df['bvp'].values
            x_time = bvp_df['sec'].values

        xsens_data['sec'] = x_time.flatten()

        br_col = [col for col in pss_df.columns.values if\
                  'breathing' in col.lower()][0]
        pss_data = pss_df[br_col].values
        pss_data = np.interp(x_time, pss_df['sec'].values, pss_data)\
                .reshape(-1, 1)
        xsens_data['PSS'] = pss_data.flatten()

        br_lbl = [col for col in br_df.columns.values if\
                  'br' in col.lower()][0]
        br_data = br_df['BR'].values
        br_data = np.interp(x_time, br_df['sec'].values, br_data)\
                .reshape(-1, 1)
        xsens_data['BR'] = br_data.flatten()

        if 'imu' in sens_list:
            for i, axis in enumerate(axes):
                xsens_data['acc_'+axis] = acc_data.T[i].flatten()
                xsens_data['gyro_'+axis] = gyr_data.T[i].flatten()

        if 'bvp' in sens_list:
            xsens_data['bvp'] = bvp_data.flatten()

        xsens_df_tmp = pd.DataFrame(xsens_data)

        xsens_list.append(xsens_df_tmp)

    if len(xsens_list) > 1:
        xsens_df = pd.concat(xsens_list, axis=0, ignore_index=True)
        xsens_df.reset_index(drop=True, inplace=True)
    else:
        xsens_df = xsens_list[0]

    return xsens_df

def load_tsfresh(xsens_df, project_dir,
                 sens_list:list=['imu', 'bvp'],
                 window_size=12, window_shift=0.2, fs=IMU_FS,
                 overwrite=False, data_cols=None):
    """
    Loads the tsfresh pickle file, or generates if it does not exist for the
    given configuration

    Arguments
    ---------
    xsens_df : pandas.DataFrame
        synchronised and frequency matched DataFrame with all data and labels
    
    Returns
    -------
    pd.DataFrame
    """

    raise NotImplementedError("To be implemented")

    assert data_cols is not None, "invalid selection for data columns"
    pkl_file = join(project_dir, 'tsfresh.pkl')
    if exists(pkl_file) and not overwrite:
        return pd.read_pickle(pkl_file)

    x_df, y_df = get_df_windows(xsens_df,
                                df_win_task,
                                window_size=window_size,
                                window_shift=window_shift,
                                fs=fs,
                                cols=data_cols,
                               )
    x_features_df = extract_features(
        x_df, column_sort='sec',
        column_id='id',
        # default_fc_parameters=tsfresh_settings.MinimalFCParameters(),
    )
    x_features_df.fillna(0, inplace=True)

    cols = x_features_df.columns.values

    df_out = pd.concat([y_df, x_features_df], axis=1)
    df_out.to_pickle(pkl_file)
    return df_out

def get_activity_log(subject):
    """
    Loads and retrieves the sit and stand file

    Arguments
    ---------
    subject: str
        subject to retrieve (i.e. 'Pilot02', 'S02')
    
    Returns
    -------
    pd.DataFrame
    """
    activity_list = get_file_list('activity*.csv', sbj=subject)
    activity_dfs = [pd.read_csv(f) for f in activity_list]
    return pd.concat(activity_dfs, axis=0)

def get_respiration_log(subject):
    """
    Loads and retrieves the respiration calibration events, timestamps,
    inhale/exhale

    Arguments
    ---------
    subject: str
        subject to retrieve (i.e. 'Pilot02', 'S02')
    
    Returns
    -------
    pd.DataFrame
    """

    log_list = get_file_list('*.json', sbj=subject)
    log_dfs = [pd.read_json(f) for f in log_list]
    return pd.concat(log_dfs, axis=0)

def get_cal_data(event_df, xsens_df):
    """
    Loads and retrieves the respiration calibration data

    Arguments
    ---------
    event_df : pandas.DataFrame
        timestamp, inhalation, exhalation, and event data from calibration
        process
    xsens_df : pandas.DataFrame
        synchronised and frequency matched DataFrame with all data and labels
    
    Returns
    -------
    pd.DataFrame
    """

    fmt ="%Y-%m-%d %H.%M.%S" 
    cal_list = []
    cpms = []
    start_sec = 0
    stop_sec = 0
    for index, row in event_df.iterrows():
        event = row['eventTag']
        timestamp = row['timestamp']
        inhalePeriod = row['inhalePeriod']
        exhalePeriod = row['exhalePeriod']

        cpm = np.round( 60/(inhalePeriod + exhalePeriod) )

        sec = timestamp.to_pydatetime().timestamp()

        if event == 'Start':
            start_sec = sec
            continue
        elif event == 'Stop':
            stop_sec = sec

            dsync = DataSynchronizer()
            dsync.set_bounds(xsens_df['sec'].values, start_sec, stop_sec)

            sync_df = dsync.sync_df(xsens_df.copy())
            cal_data = {'cpm': cpm, 'data': sync_df}
            cal_list.append(cal_data)

            assert np.round(sync_df.sec.iloc[0])==np.round(start_sec), \
            "error with start sync"
            assert np.round(sync_df.sec.iloc[-1])==np.round(stop_sec), \
            "error with stop sync"

    return pd.DataFrame(cal_list)

def get_test_data(cal_df, activity_df, xsens_df, test_standing):
    """
    Loads and retrieves the activity timestamps from sitting and standing
    events

    Arguments
    ---------
    cal_df : pandas.DataFrame
        synchronised and frequency matched respiration calibration data
    activity_df : pandas.DataFrame
        timestamps of activity events
    xsens_df : pandas.DataFrame
        synchronised and frequency matched DataFrame with all data and labels
    test_standing : bool
        list of column str
    
    Returns
    -------
    pd.DataFrame
    """

    fmt = "%d/%m/%Y %H:%M:%S"
    start_time = cal_df.iloc[-1]['data'].sec.values[-1]
    data_df = xsens_df[xsens_df.sec > start_time]
    activity_start = 0
    activity_end = 0

    activity_list = []

    for index, row in activity_df.iterrows():
        sec = datetime.strptime(row['Timestamps'], fmt).timestamp()
        if not test_standing and row['Activity'] == 'standing':
            continue
        if row['Event'] == 'start':
            activity_start = sec
        elif row['Event'] == 'end':
            activity_stop = sec

            dsync = DataSynchronizer()
            dsync.set_bounds(data_df['sec'].values, activity_start,
                             activity_stop)

            sync_df = dsync.sync_df(data_df.copy())
            activity_data = {'activity': row['Activity'], 'data': sync_df}
            activity_list.append(activity_data)

    return pd.DataFrame(activity_list)

def dsp_win_func(w_inds, df, i, cols):
    """
    Runs artefact rejection, PCA, and Hernandez DSP for a window of data

    Arguments
    ---------
    w_inds : numpy.ndarray
        set of indexes for a given window
    df : pandas.DataFrame
        synchronised and frequency matched DataFrame with all data and labels
    i : int
        window index
    cols : list
        list of column str
    
    Returns
    -------
    y_hat : pandas.DataFrame
        estimated respiration rate from Hernandez method
    y_out : pandas.DataFrame
        max PSS frequency and median breathing rate from bioharness summary 
        file
    """
    time = df['sec'].values
    if w_inds[-1] == 0: return
    w_df = df.iloc[w_inds]
    t0, t1 = time[w_inds][0], time[w_inds][-1]
    diff = time[w_inds[1:]] - time[w_inds[0:-1]]
    mask = np.abs(diff)>20
    diff_chk = np.any(mask)
    if diff_chk:
        return

    data = w_df[cols].values

    if reject_artefact((data-np.mean(data,axis=0))/np.std(data,axis=0)):
        return

    # DSP
    pca = PCA(n_components=1, random_state=3)

    # do hernandez sp on datacols for df
    filt = hernandez_sp(data=data, fs=IMU_FS)[1]

    # pca
    pca_out = pca.fit_transform(filt)

    std = StandardScaler().fit_transform(pca_out)

    pred = get_max_frequency(std, fs=FS_RESAMPLE)

    # get pss / br estimates
    # x_time median, pss max_freq, br median
    sm_out = w_df['BR'].values
    ps_out = w_df['PSS'].values

    x_vec_time = np.median(time[w_inds])

    fs = 1/np.mean(diff)
    ps_freq = int(get_max_frequency(ps_out, fs=IMU_FS))

    y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])

    y_hat = pd.DataFrame([ {'sec': x_vec_time, 'pred': pred} ])
    y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])

    return y_hat, y_out

# save evaluation metrics in single file that handles the models for the
# subject and config
class EvalHandler():
    """
    Handles the evaluation metric for each subject and configuration.
    ...

    Attributes
    ----------
    y_true : numpy.ndarray
        a numpy array of the respiration rate ground truth values from the
        bioharness
    y_pred : numpy.ndarray
        a numpy array of the predicted respiration rate
    subject : str
        the subject in format Pilot01, S01 etc.
    pfh : ProjectFileHandler
        custom class detailing the directories, metafile, and configurations
    mdl_str : str
        a string to inform what model was used
    overwrite : bool
        overwrites the evaluations (default False)

    Methods
    -------
    load_eval_history()
        loads the evaluation file
    save_eval_history()
        saves the evaluation file
    update_eval_history()
        updates the evaluation file using the new entry if there is no matching
        model or configuration for the given subject
    """
    def __init__(self, y_true, y_pred, subject, pfh, mdl_str, overwrite=False):
        self.subject = subject
        self.config = pfh.config
        self.parent_directory = join(DATA_DIR, 'subject_specific')
        self.fset_id = pfh.fset_id
        self.mdl_str = mdl_str
        self.overwrite = overwrite

        self.evals = Evaluation(y_true, y_pred)

        entry = {'subject': self.subject,
                 'config_id': self.fset_id,
                 'mdl_str': self.mdl_str,
                }
        self.entry = {**entry, **self.config, **self.evals.get_evals()}

        self.eval_history_file = join(self.parent_directory,
                                      'eval_history.csv')
        self.eval_hist = self.load_eval_history()

    def load_eval_history(self):
        if not exists(self.eval_history_file):
            return None
        else:
            return pd.read_csv(self.eval_history_file)

    def update_eval_history(self):
        eval_hist = self.eval_hist
        if eval_hist is None:
            eval_hist = pd.DataFrame([self.entry])
        else:
            index_list = eval_hist[
                (eval_hist['subject'] == self.entry['subject']) &\
                (eval_hist['config_id'] == self.entry['config_id']) &\
                (eval_hist['mdl_str'] == self.entry['mdl_str']) &\
                (eval_hist['cpm'] == self.entry['cpm']) &\
                (eval_hist['sens_list'] == self.entry['sens_list'])\
            ].index.tolist()
            if len(index_list) == 0:
                print("adding new entry")
                eval_hist = eval_hist._append(self.entry, ignore_index=True)
            elif index_list is not None and self.overwrite:
                eval_hist.loc[index_list[0]] = self.entry
        self.eval_hist = eval_hist

    def save_eval_history(self):
        self.eval_hist.to_csv(self.eval_history_file, index=False)

def imu_rr_dsp(subject,
               window_size=12,
               window_shift=0.2,
               lbl_str='pss',
               overwrite=False,
               train_len:int=3,
               test_standing=False,
              ):
    # TODO: 
        # implement evaluation saving
    """Loads, preprocesses, and performs Hernandez digital signal processing
    pipeline on the selected subject. Uses the specified parameters. Runs on
    both accelerometer and gyroscope.

    Attributes
    ----------
    subject: str
        specify the subject code (i.e. 'Pilot02', 'S02')
    window_size : float
        a numpy array of the respiration rate ground truth values from the
        bioharness
    window_shift : float
        a portion of the window size between 0 and 1
    mdl_str : str
        a string to infoa portion of the window size between 0 and 1rm what model was used
    overwrite : bool
        overwrites the evaluations, models, and graphs (default False)
    test_standing : bool
        boolean to use standing data

    Returns
    -------
    None
    """
    cal_str = 'cpm'
    fs = IMU_FS
    tmp = []
    imu_cols = ['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z']
    parent_directory_string = "imu_rr_dsp"

    do_minirocket = False
    use_tsfresh   = False
    overwrite_tsfresh = True
    train_size = int(train_len)

    config = {'window_size'   : window_size,
              'window_shift'  : window_shift,
              'lbl_str'       : lbl_str,
              'train_len'     : train_len,
              'test_standing' : test_standing,
              'sens_list'     : 'imu',
             }

    pfh = ProjectFileHandler(config)
    pfh.set_home_directory(join(DATA_DIR, 'subject_specific', subject))
    pfh.set_parent_directory(parent_directory_string)
    id_check = pfh.get_id_from_config()
    if id_check is None:
        pfh.set_project_directory()
        pfh.save_metafile()
    else:
        pfh.set_id(int(id_check))
        pfh.set_project_directory()
        print('Using pre-set data id: ', pfh.fset_id)
    project_dir = pfh.project_directory

    xsens_df = load_and_sync_xsens(subject, sens_list=['imu'])
    activity_df = get_activity_log(subject)
    event_df = get_respiration_log(subject)

    cal_df = get_cal_data(event_df, xsens_df)

    # include standing or not
    test_df_tmp = get_test_data(cal_df, activity_df, xsens_df, test_standing)
    test_df = pd.concat([df for df in test_df_tmp['data']], axis=0)
    x_test_df, y_test_df = get_df_windows(test_df, 
                                          imu_df_win_task,
                                          window_size=window_size,
                                          window_shift=window_shift,
                                          fs=fs,
                                         )

    acc_dsp_df, acc_y_dsp_df =  get_df_windows(test_df, dsp_win_func,
                                               window_size=window_size, 
                                               window_shift=window_shift,
                                               fs=fs,
                                               cols=['acc_x', 'acc_y', 'acc_z'])
    gyr_dsp_df, gyr_y_dsp_df =  get_df_windows(test_df, dsp_win_func,
                                               window_size=window_size, 
                                               window_shift=window_shift,
                                               fs=fs,
                                               cols=['gyr_x', 'gyr_y', 'gyr_z'])

    acc_evals = Evaluation(acc_y_dsp_df[lbl_str], acc_dsp_df['pred'])
    gyr_evals = Evaluation(gyr_y_dsp_df[lbl_str], gyr_dsp_df['pred'])
    print("acc evals: \n", acc_evals.get_evals())
    print("gyr evals: \n", gyr_evals.get_evals())
    plt.subplot(211)