Skip to content

Commit

Permalink
Oak hoursplit (#156)
Browse files Browse the repository at this point in the history 
modify frequency argument to use the Frequency constant class and handle more summary statistic time window options
  • Loading branch information
@GeorgeEfstathiadis
GeorgeEfstathiadis authored Jan 10, 2023
1 parent 54afb64 commit 5cb9408
Showing 1 changed file with 53 additions and 24 deletions.
77 changes: 53 additions & 24 deletions forest/oak/base.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

from datetime import datetime, timedelta
import logging
import math
import os

from dateutil import tz
Expand All @@ -22,6 +23,7 @@
from scipy.signal.windows import tukey
from ssqueezepy import ssq_cwt

from forest.constants import Frequency
from forest.utils import get_ids

logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -414,8 +416,8 @@ def find_continuous_dominant_peaks(valid_peaks: np.ndarray, min_t: int,


def run(study_folder: str, output_folder: str, tz_str: str = None,
option: str = None, time_start: str = None, time_end: str = None,
users: list = None) -> None:
frequency: Frequency = Frequency.DAILY, time_start: str = None,
time_end: str = None, users: list = None) -> None:
"""Runs walking recognition and step counting algorithm over dataset.
Determine paths to input and output folders, set analysis time frames,
Expand All @@ -428,8 +430,8 @@ def run(study_folder: str, output_folder: str, tz_str: str = None,
local repository to store results
tz_str: string
local time zone, e.g., "America/New_York"
option: string
summary statistics format (accepts 'both', 'hourly', 'daily')
frequency: Frequency
summary statistics format, Frequency class at constants.py
time_start: string
initial date of study in format: 'YYYY-mm-dd HH_MM_SS'
time_end: string
Expand All @@ -446,10 +448,13 @@ def run(study_folder: str, output_folder: str, tz_str: str = None,
to_zone = tz.gettz(tz_str)

# create folders to store results
if option is None or option == 'both' or option == 'daily':
if frequency == Frequency.HOURLY_AND_DAILY:
os.makedirs(os.path.join(output_folder, "daily"), exist_ok=True)
if option is None or option == 'both' or option == 'hourly':
os.makedirs(os.path.join(output_folder, "hourly"), exist_ok=True)
else:
os.makedirs(
os.path.join(output_folder, frequency.name.lower()), exist_ok=True
)

if users is None:
users = get_ids(study_folder)
Expand Down Expand Up @@ -500,8 +505,15 @@ def run(study_folder: str, output_folder: str, tz_str: str = None,
date_end = date_end - timedelta(hours=date_end.hour)

days = pd.date_range(date_start, date_end, freq='D')
if (
frequency == Frequency.HOURLY_AND_DAILY
or frequency == Frequency.HOURLY
):
freq = 'H'
else:
freq = str(frequency.value) + 'H'
days_hourly = pd.date_range(date_start, date_end+timedelta(days=1),
freq='H')[:-1]
freq=freq)[:-1]

# allocate memory
steps_daily = np.full((len(days), 1), np.nan)
Expand Down Expand Up @@ -560,23 +572,36 @@ def run(study_folder: str, output_folder: str, tz_str: str = None,
cadence_bout = find_walking(vm_bout)

# distribute metrics across hours
if option is None or option == 'both' or option == 'hourly':
if frequency != Frequency.DAILY:
for t_unique in np.unique(np.array(t_hours)):
ind_to_store = [t_ind.to_pydatetime() for t_ind in
days_hourly].index(t_unique)
t_ind_pydate = [t_ind.to_pydatetime() for t_ind in
days_hourly]
# get indexes of ranges of dates that contain t_unique
ind_to_store = -1
for ind_to_store, t_ind in enumerate(t_ind_pydate):
if (
t_ind <= t_unique
< t_ind + timedelta(hours=frequency.value)
):
break
cadence_ind = np.array([time == t_unique for time in
t_hours])
cadence_temp = cadence_bout[cadence_ind]
cadence_temp = cadence_temp[np.where(cadence_temp > 0)]

# store hourly metrics
steps_hourly[ind_to_store] = int(np.sum(cadence_temp))
if len(cadence_temp) > 0: # control for empty slices
cadence_hourly[ind_to_store] = np.mean(
cadence_temp)
if math.isnan(steps_hourly[ind_to_store]):
steps_hourly[ind_to_store] = int(np.sum(cadence_temp))
walkingtime_hourly[ind_to_store] = len(cadence_temp)
else:
cadence_hourly[ind_to_store] = np.nan
walkingtime_hourly[ind_to_store] = len(cadence_temp)
steps_hourly[ind_to_store] += int(np.sum(cadence_temp))
walkingtime_hourly[ind_to_store] += len(cadence_temp)

for idx in range(len(cadence_hourly)):
if walkingtime_hourly[idx] > 0:
cadence_hourly[idx] = (
steps_hourly[idx] / walkingtime_hourly[idx]
)

cadence_bout = cadence_bout[np.where(cadence_bout > 0)]

Expand All @@ -588,26 +613,30 @@ def run(study_folder: str, output_folder: str, tz_str: str = None,
cadence_daily[d_ind] = np.nan
walkingtime_daily[d_ind] = len(cadence_bout)

# save results depending on "option"
if option is None or option == 'both' or option == 'daily':

# save results depending on "frequency"
if (
frequency == Frequency.DAILY
or frequency == Frequency.HOURLY_AND_DAILY
):
summary_stats = pd.DataFrame({
'date': days.strftime('%Y-%m-%d'),
'walking_time': walkingtime_daily[:, -1],
'steps': steps_daily[:, -1],
'cadence': cadence_daily[:, -1]})
output_file = user + "_gait_daily.csv"
dest_path = os.path.join(output_folder, "daily",
output_file)
dest_path = os.path.join(output_folder, "daily", output_file)
summary_stats.to_csv(dest_path, index=False)
if option is None or option == 'both' or option == 'hourly':
if frequency != Frequency.DAILY:
summary_stats = pd.DataFrame({
'date': [date.strftime('%Y-%m-%d %H:%M:%S')
for date in days_hourly],
'walking_time': walkingtime_hourly[:, -1],
'steps': steps_hourly[:, -1],
'cadence': cadence_hourly[:, -1]})
output_file = user + "_gait_hourly.csv"
dest_path = os.path.join(output_folder, "hourly",
output_file)
if frequency == Frequency.HOURLY_AND_DAILY:
freq_name = "hourly"
else:
freq_name = frequency.name.lower()
dest_path = os.path.join(output_folder, freq_name, output_file)
summary_stats.to_csv(dest_path, index=False)

0 comments on commit 5cb9408

Please sign in to comment.