def adjust_commute_from_work(self, data, no_variation=False):
"""
This function adjusts the household parameters to reflect the sampled parameters \
(mean and standard deviation of start time, end time, and \
duration, respectively), from the CHAD data for the commuting from work \
activity.
:param data: relevant parameters for each person in the household for \
commuting from work. The tuple contains the following: mean start time, standard \
deviation of start time, mean end time, standard deviation of end time, mean duration, \
and standard deviation of duration for each person in the household.
:type data: tuple of numpy.ndarray, numpy.ndarray, numpy.ndarray, \
numpy.ndarray, numpy.ndarray, numpy.ndarray
:param bool no_variation: whether (if True) or not (if False) intra-individual \
variation is set to zero among the activities
:return:
"""
# the activity-related parameters (in hours)
start_mean, start_std, end_mean, end_std, dt_mean, dt_std = data
# set standard deviation to zero
if no_variation:
dt_std[:] = 0
# set the commuting from work mean duration (in minutes)
self.params.commute_from_work_dt_mean = mg.hours_to_minutes(dt_mean)
# set the commuting from work standard deviation for duration (in minutes)
self.params.commute_from_work_dt_std = mg.hours_to_minutes(dt_std)
return
def adjust_sleep(self, data, no_variation=False):
"""
This function adjusts the household parameters to reflect the sampled parameters \
(mean and standard deviation of start time, end time, and \
duration, respectively), from the CHAD data for the sleeping \
activity.
:param data: relevant parameters for each person in the household for \
sleeping. The tuple contains the following: mean start time, standard \
deviation of start time, mean end time, standard deviation of end time, mean duration, \
and standard deviation of duration for each person in the household.
:type data: tuple of numpy.ndarray, numpy.ndarray, numpy.ndarray, \
numpy.ndarray, numpy.ndarray, numpy.ndarray
:param bool no_variation: whether (if True) or not (if False) intra-individual \
variation is set to zero among the activities
:return:
"""
# the amount of minutes in 1 hour
HOUR_2_MIN = temporal.HOUR_2_MIN
# the activity-related parameters (in hours)
start_mean, start_std, end_mean, end_std, dt_mean, dt_std = data
# set the standard deviations to zero (time is in hours)
if no_variation:
start_std[:] = 0
end_std[:] = 0
# since the start time may occur over midnight, express time as [-12, 12)
start_mean = mg.from_periodic(start_mean)
# for the demographics with a "work" or "school" occupation
# set the sleep end time to the breakfast start time
if self.demographic in [dmg.ADULT_WORK, dmg.CHILD_SCHOOL]:
# breakfast start time in HOURS [0, 24)
bf_start = np.array([x.t_start
for x in self.params.breakfasts]) / HOUR_2_MIN
# sleep end time is the breakfast start time
# make sure time is in [0, 24) format
end_mean = bf_start
# set the mean start time (in minutes)
self.params.sleep_start_mean = mg.hours_to_minutes(start_mean)
# set the standard deviation of start time (in minutes)
self.params.sleep_start_std = mg.hours_to_minutes(start_std)
# set the mean end time (in minutes)
self.params.sleep_end_mean = mg.hours_to_minutes(end_mean)
# set the standard deviation of end time (in mintues)
self.params.sleep_end_std = mg.hours_to_minutes(end_std)
return
def adjust_work(self, data, no_variation=False):
"""
This function adjusts the household parameters to reflect the sampled parameters \
(mean and standard deviation of start time, end time, and \
duration, respectively), from the CHAD data for the working \
activity.
:param data: relevant parameters for each person in the household for \
working. The tuple contains the following: mean start time, standard \
deviation of start time, mean end time, standard deviation of end time, mean duration, \
and standard deviation of duration for each person in the household.
:type data: tuple of numpy.ndarray, numpy.ndarray, numpy.ndarray, \
numpy.ndarray, numpy.ndarray, numpy.ndarray
:param bool no_variation: whether (if True) or not (if False) intra-individual \
variation is set to zero among the activities
:return:
"""
# the activity-related parameters (in hours)
start_mean, start_std, end_mean, end_std, dt_mean, dt_std = data
# set the standard deviations to zero (time is in hours)
if no_variation:
start_std[:] = 0
end_std[:] = 0
# set the jobs for working adults
if self.demographic == dmg.ADULT_WORK:
job_id = occupation.STANDARD_JOB
# set the jobs for school-age children
elif self.demographic == dmg.CHILD_SCHOOL:
job_id = occupation.STUDENT
# set the job in the ABMHAP parameters
self.params.job_id = (job_id, ) * self.params.num_people
# set the mean start time (in minutes)
self.params.work_start_mean = mg.hours_to_minutes(start_mean)
# set the standard deviation of start time (in minutes)
self.params.work_start_std = mg.hours_to_minutes(start_std)
# set the mean end time (in minutes)
self.params.work_end_mean = mg.hours_to_minutes(end_mean)
# set the standard deviation of end time (in minutes)
self.params.work_end_std = mg.hours_to_minutes(end_std)
return
def save_output(df, fname):
"""
This function saves the output of the simulation.
:param pandas.core.frame.DataFrame df: the activity-diary output of the simulation
:param str fname: the file name of the saved file. It must end with ".csv"
"""
# the conversion of 1 hour into minutes
HOUR_2_MIN = temporal.HOUR_2_MIN
# copy the data frame to avoid changing the original data in df
data = df.copy()
# convert the end time in minutes.
# Add the + 1 minute to the end time so that 16:00 - 16:59 becomes 16:00 - 17:00
end = mg.hours_to_minutes(data.end) + 1
# convert the end time into hours
data.end = end / HOUR_2_MIN
# create the directory for the save file if it does not exist
os.makedirs(os.path.dirname(fname), exist_ok=True)
# save the data
data.to_csv(fname, index=False)
return
def adjust_params(self, start_mean, start_std, end_mean, end_std):
"""
This function adjusts the values for the mean and standard deviation of both work \
duration and work start time in the key-word arguments based on the CHAD data \
that was sampled. These new values will be used in the runs.
:param numpy.ndarray dt_mean: the work duration mean [minutes] for each person
:param numpy.ndarray dt_std: the work duration standard deviation [minutes] for each person
:param numpy.ndarray start_mean: the mean work start time [minutes] for each person
:param numpy.ndarray start_std: the standard deviation of start time [minutes] for each person
:return:
"""
# the amount of minutes in 1 hour
HOUR_2_MIN = temporal.HOUR_2_MIN
# the amount of minutes in 1 day
DAY_2_MIN = temporal.DAY_2_MIN
# set the start time to be Day 1, Monday, at 14:00
t_monday = temporal.MONDAY * DAY_2_MIN
self.params.t_start = t_monday + 4 * HOUR_2_MIN
# set the standard deviations to zero (time is in hours)
start_std[:] = 0
end_std[:] = 0
# set the mean start time for work (in minutes)
self.params.work_start_mean = mg.hours_to_minutes(start_mean)
# set the standard deviation for start time for work (in minutes)
self.params.work_start_std = mg.hours_to_minutes(start_std)
# set the mean end time for work (in minutes)
self.params.work_end_mean = mg.hours_to_minutes(end_mean)
# set the standard deviation for end time for work (in minutes)
self.params.work_end_std = mg.hours_to_minutes(end_std)
return
def adjust_params(self, commute_dt_mean, commute_dt_std, work_start_mean, work_start_std, \
work_end_mean, work_end_std):
"""
This function adjusts the values for the mean and standard deviation of both commute to work \
duration and start time in the key-word arguments based on the CHAD data \
that was sampled. These new values will be used in the runs.
:param numpy.ndarray commute_dt_mean: the commute duration mean [hours] for each person
:param numpy.ndarray commute_dt_std: the commute duration standard deviation [hours] for each person
:param numpy.ndarray work_start_mean: the mean work start time [hours] for each person
:param numpy.ndarray work_start_std: the standard deviation of work start time [hours] for each person
:param numpy.ndarray work_end_mean: the mean work end time [hours] for each person
:param numpy.ndarray work_end_std: the standard deviation of work end time [hours] for each person
:return:
"""
# the number of minutes in 1 hour and 1 day, respectively
HOUR_2_MIN, DAY_2_MIN = temporal.HOUR_2_MIN, temporal.DAY_2_MIN
# set the initial time
t_monday = temporal.MONDAY * DAY_2_MIN
self.params.t_start = t_monday + 4 * HOUR_2_MIN
# set the work parameters for start time (mean start time, standard deviation of start time)
self.params.work_start_mean = mg.hours_to_minutes(work_start_mean)
self.params.work_start_std = mg.hours_to_minutes(work_start_std)
# set the work parameters for end time (mean start time, standard deviation of end time)
self.params.work_end_mean = mg.hours_to_minutes(work_end_mean)
self.params.work_end_std = mg.hours_to_minutes(work_end_std)
# set the commute to work information (mean duration and standard deviation of duration)
self.params.commute_to_work_dt_mean = mg.hours_to_minutes(
commute_dt_mean)
self.params.commute_to_work_dt_std = mg.hours_to_minutes(
commute_dt_std)
return