def showForms():
form = ShiftLogForm()
shiftLogDAO = shift_log_DAO()
resident_list = shiftLogDAO.get_incompleted_residents()
form.resident.choices = [(resident_map['resident_id'],
resident_map['name'])
for resident_map in resident_list]
resident_dict = {
resident_map['resident_id']: resident_map['name']
for resident_map in resident_list
}
if request.method == 'POST':
if form.validate_on_submit():
# handle submitted data here
# process form here
submitted_name = form.resident.data
# name_to_show = "testing"
name_to_show = resident_dict[submitted_name]
submitted_date = form.date.data
submitted_time = form.time.data
submitted_falls = form.falls.data
submitted_near_falls = form.near_falls.data
submitted_consumption = form.consumption.data
submitted_toilet_visits = 0
submitted_temperature = form.temperature.data
submitted_sbp = form.sbp.data
submitted_dbp = form.dbp.data
submitted_pulse = form.pulse.data
# shiftLogDAO = shift_log_DAO()
if submitted_time == 1:
day_night = "Day"
else:
day_night = "Night"
shiftLog = Shift_log(submitted_date, submitted_time,
submitted_name, submitted_falls,
submitted_near_falls, submitted_consumption,
submitted_toilet_visits,
submitted_temperature, submitted_sbp,
submitted_dbp, submitted_pulse)
response = 'Shift log for ' + submitted_date.strftime(
'%Y-%m-%d'
) + '(' + day_night + ') for ' + name_to_show + ' has already been recorded. Please enter another date.'
try:
shiftLogDAO.insert_shift_log(shiftLog)
except:
flash(response)
return render_template('eosforms.html', form=form)
response = 'Shift log for ' + name_to_show + ' has been successfully recorded. Click <a href="/admin/shift_log" class="alert-link">here</a> to view/edit responses.'
flash(Markup(response))
return redirect(url_for('showForms'))
else:
return render_template('eosforms.html', form=form)
else:
return render_template('eosforms.html', form=form)
def create_view(self):
form = ShiftLogForm()
shiftLogDAO = shift_log_DAO()
resident_list = shiftLogDAO.get_incompleted_residents()
form.resident.choices = [(resident_map['resident_id'],
resident_map['name'])
for resident_map in resident_list]
return render_template('eosforms.html', form=form)
def showOverviewResidents():
'''
This method prepares all the necessary variables to pass into the html for display
Each resident will have the following:
Name ('name'), List of Toilet Alerts ('toilet_alerts'),
List of Sleep Alerts (WIP), Overall Alert Level ('alert_highest')
NOTE: jinja templates do not allow for import of python modules, so all calculation will be done here
'''
residents_raw = resident_DAO.get_list_of_residents()
residents = []
date_in_use = datetime.datetime.now()
# date_in_use = datetime.datetime(2018, 4, 19, 23, 34, 12) # TODO: change to current system time once live data is available
juvo_date_in_use = datetime.datetime.now(
) # datetime.datetime(2018, 8, 12, 22, 34, 12) # TODO: change to current system time once live data is available
for resident in residents_raw:
r = {}
r['name'] = resident['name']
r['resident_id'] = resident['resident_id']
# settle night toilet usage
r['toilet_alerts'], __ = input_data.input_data.get_nightly_toilet_indicator(
int(resident['resident_id']), date_in_use)
r['toilet_tooltip'] = []
if len(r['toilet_alerts']) == 0:
r['toilet_tooltip'].append(
"Night toilet visit numbers appear normal")
else: # NOTE: right now we just append, but use separate list for tooltips in case of future changes
r['toilet_tooltip'].extend(r['toilet_alerts'])
# settle sleep duration
r['sleep_alerts'], __, __, __, __, __, __ = input_data.input_data.get_nightly_sleep_indicator(
int(resident['resident_id']), date_in_use)
r['sleep_tooltip'] = []
if len(r['sleep_alerts']) == 0:
r['sleep_tooltip'].append(
"Normal level of motion during sleep detected")
else: # NOTE: for future changes
r['sleep_tooltip'].extend(r['sleep_alerts'])
r['vitals_alerts'], __, __, __, __ = input_data.input_data.get_vital_signs_indicator(
resident['resident_id'], juvo_date_in_use)
r['vitals_tooltip'] = []
if len(r['vitals_alerts']) == 0:
r['vitals_tooltip'].append(
"Vital signs from previous week appear to be normal")
else:
r['vitals_tooltip'].extend(r['vitals_alerts'])
# print("DEBUG resident id sleep_alerts", resident['resident_id'], r['sleep_alerts'])
r['alert_highest'] = max(0, len(r['toilet_alerts']),
len(r['sleep_alerts']),
len(r['vitals_alerts']))
residents.append(r)
# return render_template('overview_residents_amanda.html', residents=residents)
information = {}
information['num_residents'] = len(
resident_DAO.get_list_of_residents(location_filter='STB'))
num_good_health = 0
for r_dict in residents:
if r_dict['alert_highest'] == 0:
num_good_health += 1
information['health_percentage'] = num_good_health / information[
'num_residents'] * 100 # in percentage
sldao = shift_log_DAO.shift_log_DAO()
information['num_shift_forms'] = sldao.get_today_logs()
sensor_statuses = sensor_mgmt.Sensor_mgmt.get_all_sensor_status_v2(
retBatteryLevel=True)
# print(sensor_statuses)
sensor_down_count = 0
for _sensor in sensor_statuses:
if _sensor[1][0] > 0:
sensor_down_count += 1
information['sensors_down'] = sensor_down_count
return render_template('overview_residents.html',
residents=residents,
information=information)
class input_shiftlogs(object):
# input_raw_data = pd.DataFrame()
sldao = shift_log_DAO()
input_raw_data = sldao.get_all_logs()
input_raw_data['datetime'] = pd.to_datetime(input_raw_data['datetime'],
format='%Y-%m-%dT%H:%M:%S')
input_raw_data['food_consumption'] = pd.to_numeric(
input_raw_data['food_consumption'])
input_raw_data['pulse_pressure'] = input_raw_data[
'systolic_bp'] - input_raw_data['diastolic_bp']
input_raw_max_date = input_raw_data['datetime'].max()
input_raw_min_date = input_raw_data['datetime'].min()
# print(input_raw_data)
daytime_start = datetime.time(7, 30)
daytime_end = datetime.time(19, 30)
# changeable parameters NOTE: should be changeable
para_temperature_max = 37.6
para_temperature_min = 35.5
para_temperature_sd = 0.66
para_pulse_pressure_max = 50
@staticmethod
def update_shiftlogs_data():
input_shiftlogs.input_raw_data = input_shiftlogs.sldao.get_all_logs()
input_shiftlogs.input_raw_data['datetime'] = pd.to_datetime(
input_shiftlogs.input_raw_data['datetime'],
format='%Y-%m-%dT%H:%M:%S')
input_shiftlogs.input_raw_data['food_consumption'] = pd.to_numeric(
input_shiftlogs.input_raw_data['food_consumption'])
input_shiftlogs.input_raw_data[
'pulse_pressure'] = input_shiftlogs.input_raw_data[
'systolic_bp'] - input_shiftlogs.input_raw_data['diastolic_bp']
input_shiftlogs.input_raw_max_date = input_shiftlogs.input_raw_data[
'datetime'].max()
input_shiftlogs.input_raw_min_date = input_shiftlogs.input_raw_data[
'datetime'].min()
@staticmethod
def date_only(original_date):
return original_date.replace(hour=0, minute=0, second=0, microsecond=0)
@staticmethod
def get_logs_filter_options():
'''Returns labels and values in an array of tuples'''
return [('No. of Falls', 'num_falls'),
('No. of Near Falls', 'num_near_falls'),
('Food Consumption', 'food_consumption'),
('Temperature', 'temperature'),
('Systolic//Diastolic Bp', 'sys_dia'),
('Pulse Pressure', 'pulse_pressure'),
('Pulse Rate', 'pulse_rate')]
@staticmethod
def get_relevant_data(start_date, end_date, patient_id):
'''
Retrieve sensor data based on location, start and end dates, and the device
grouped=True to get grouped data for toilet visits
'''
# TODO: this part probably is the best to convert to retrieve from DB
relevant_data = input_shiftlogs.input_raw_data.loc[
(input_shiftlogs.input_raw_data['patient_id'] == patient_id)
& (input_shiftlogs.input_raw_data['datetime'] < end_date)
& (input_shiftlogs.input_raw_data['datetime'] > start_date), [
'patient_id', 'datetime', 'num_falls', 'num_near_falls',
'food_consumption', 'temperature', 'systolic_bp',
'diastolic_bp', 'pulse_pressure', 'pulse_rate'
]]
return relevant_data
@staticmethod
def get_logs_by_date(start_date=input_raw_min_date,
end_date=input_raw_max_date,
patient_id=1,
time_period=None):
"""
Function returns dates and aggregated number of times the sensor was activated
To get day only, use time_period='Day' and to get night_only use time_period='Night'
"""
# NOTE: last day of the returned output is not accurate if offset is used because the next day's data is needed to get the current night's data
current_data = input_shiftlogs.get_relevant_data(
start_date, end_date, patient_id)
# print(current_data)
# print(current_data)
if time_period == 'Day':
current_data = current_data.loc[(current_data['datetime'].dt.time
>= input_shiftlogs.daytime_start)
& (current_data['datetime'].dt.time
< input_shiftlogs.daytime_end)]
elif time_period == 'Night':
current_data = current_data.loc[
(current_data['datetime'].dt.time < input_shiftlogs.
daytime_start)
|
(current_data['datetime'].dt.time > input_shiftlogs.daytime_end
)]
# group by date only
current_data['date_only'] = current_data['datetime'].apply(
input_shiftlogs.date_only)
result_data = current_data.groupby(
['date_only'],
as_index=False)['num_falls', 'num_near_falls', 'food_consumption',
'temperature', 'systolic_bp', 'diastolic_bp',
'pulse_pressure', 'pulse_rate'].mean()
# add 0 for days with no data
result_data.set_index('date_only', inplace=True)
if isinstance(start_date, str):
start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')
if isinstance(end_date, str):
end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')
all_days_range = pd.date_range(start_date.date(),
end_date.date() +
datetime.timedelta(days=-1),
freq='D')
try:
result_data = result_data.loc[all_days_range]
except KeyError as e:
erroroutput = pd.DataFrame()
erroroutput['num_falls'] = []
erroroutput['num_near_falls'] = []
erroroutput['food_consumption'] = []
erroroutput['temperature'] = []
erroroutput['systolic_bp'] = []
erroroutput['diastolic_bp'] = []
erroroutput['pulse_pressure'] = []
erroroutput['pulse_rate'] = []
erroroutput['date_only'] = []
result_data.fillna(0, inplace=True)
# undo set index
result_data.reset_index(inplace=True)
result_data.rename(columns={'index': 'date_only'}, inplace=True)
# print("result data from get_num_visits_by_date\n", result_data)
return result_data
@staticmethod
def get_residents_options():
return input_shiftlogs.input_raw_data['patient_id'].unique().tolist()
@staticmethod
def get_shiftlog_indicators(patient_id, current_sys_time=None):
ret_alerts = []
if not current_sys_time:
current_sys_time = datetime.datetime.now()
current_sys_date = current_sys_time.date()
three_weeks_ago = current_sys_date + datetime.timedelta(days=-21)
one_week_ago = current_sys_date + datetime.timedelta(days=-7)
four_weeks_ago = current_sys_date + datetime.timedelta(days=-28)
# get data first
current_data = input_shiftlogs.get_relevant_data(
four_weeks_ago, current_sys_date, patient_id)
# print(current_data)
# patient_id,datetime,num_falls,num_near_falls,food_consumption,temperature,systolic_bp,diastolic_bp,pulse_pressure,pulse_rate
# compare averages
three_week_data = current_data.loc[(current_data['datetime'] <
one_week_ago)]
# print(three_week_data)
past_week_data = current_data.loc[
current_data['datetime'] > one_week_ago]
# print(past_week_data)
# check averages then check for significant out-of-range numbers
# check temperatures
temperature_sd = three_week_data['temperature'].std(
) # NOTE: maybe can change to some other stdevs
three_week_average_temp = three_week_data['temperature'].mean()
past_week_average_temp = past_week_data['temperature'].mean()
if (past_week_average_temp - input_shiftlogs.para_temperature_sd *
temperature_sd) > three_week_average_temp:
ret_alerts.append(
"Significant increase in temperature in the past week")
elif (past_week_average_temp + input_shiftlogs.para_temperature_sd *
temperature_sd) < three_week_average_temp:
ret_alerts.append(
"Significant decrease in temperature in the past week")
if any((past_week_data['temperature'] <
input_shiftlogs.para_temperature_min)
| (past_week_data['temperature'] >
input_shiftlogs.para_temperature_max)):
ret_alerts.append("Abnormal temperatures detected in past week")
return ret_alerts
import pandas as pd
import datetime
# pandas settings
pd.options.mode.chained_assignment = None # default='warn
pd.set_option('display.expand_frame_repr', False)
if __name__ == '__main__': # we want to import from same directory if using this
# module as-is (for debugging mainly, or for loading data in the future)
sys.path.append(".")
from DAOs.shift_log_DAO import shift_log_DAO
else: # if called from index.py
from DAOs.shift_log_DAO import shift_log_DAO
# input_raw_data = pd.DataFrame()
sldao = shift_log_DAO()
input_raw_data = sldao.get_all_logs()
input_raw_data['datetime'] = pd.to_datetime(input_raw_data['datetime'],
format='%Y-%m-%dT%H:%M:%S')
input_raw_data['food_consumption'] = pd.to_numeric(
input_raw_data['food_consumption'])
input_raw_data['pulse_pressure'] = input_raw_data[
'systolic_bp'] - input_raw_data['diastolic_bp']
input_raw_max_date = input_raw_data['datetime'].max()
input_raw_min_date = input_raw_data['datetime'].min()
# print(input_raw_data)
daytime_start = datetime.time(7, 30)
daytime_end = datetime.time(19, 30)
def date_only(original_date):