Modifying the population using simple policies¶

This notebook shows how simple policies can be applied to people, households, and entire populations.

In [1]:

from copy import deepcopy
from pprint import pprint

from pam.activity import Activity, Leg
from pam.core import Household, Person, Population
from pam.policy import (
    ActivityPolicy,
    ActivityProbability,
    HouseholdPolicy,
    HouseholdQuarantined,
    MoveActivityTourToHomeLocation,
    PersonAttributeFilter,
    PersonPolicy,
    PersonProbability,
    ReduceSharedActivity,
    RemoveActivity,
    apply_policies,
)
from pam.utils import minutes_to_datetime as mtdt
from pam.variables import END_OF_DAY

from copy import deepcopy from pprint import pprint from pam.activity import Activity, Leg from pam.core import Household, Person, Population from pam.policy import ( ActivityPolicy, ActivityProbability, HouseholdPolicy, HouseholdQuarantined, MoveActivityTourToHomeLocation, PersonAttributeFilter, PersonPolicy, PersonProbability, ReduceSharedActivity, RemoveActivity, apply_policies, ) from pam.utils import minutes_to_datetime as mtdt from pam.variables import END_OF_DAY

In [2]:

def hhld_apply(household, policy):
    new_hhld = deepcopy(household)
    policy.apply_to(new_hhld)
    return new_hhld


def print_attributes(hhld, attribs=None):
    if attribs is not None:
        print(f"Household: {hhld}")
        for pid, person in hhld.people.items():
            print(f"Person: {pid}")
            print("Attributes:")
            for attrib in attribs:
                print(f"\t{attrib}: {person.attributes[attrib]}")
    else:
        print(f"Household: {hhld}")
        for pid, person in hhld.people.items():
            print(f"Person: {pid}")
            print("Attributes:")
            pprint(person.attributes)


def instantiate_household_with(persons: list):
    household = Household(1)
    for person in persons:
        household.add(person)
    return household


def Steve():
    Steve = Person("Steve", attributes={"age": 50, "job": "work", "gender": "male"})
    Steve.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(5 * 60)))
    Steve.add(Leg(1, "car", "a", "b", start_time=mtdt(5 * 60), end_time=mtdt(6 * 60)))
    Steve.add(Activity(2, "work", "b", start_time=mtdt(6 * 60), end_time=mtdt(12 * 60)))
    Steve.add(Leg(2, "walk", "b", "c", start_time=mtdt(12 * 60), end_time=mtdt(12 * 60 + 10)))
    Steve.add(
        Activity(3, "leisure", "c", start_time=mtdt(12 * 60 + 10), end_time=mtdt(13 * 60 - 10))
    )
    Steve.add(Leg(3, "walk", "c", "b", start_time=mtdt(13 * 60 - 10), end_time=mtdt(13 * 60)))
    Steve.add(Activity(4, "work", "b", start_time=mtdt(13 * 60), end_time=mtdt(18 * 60)))
    Steve.add(Leg(4, "car", "b", "a", start_time=mtdt(18 * 60), end_time=mtdt(19 * 60)))
    Steve.add(Activity(5, "home", "a", start_time=mtdt(19 * 60), end_time=mtdt(20 * 60)))
    Steve.add(Leg(5, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20)))
    Steve.add(
        Activity(6, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50))
    )
    Steve.add(Leg(6, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60)))
    Steve.add(Activity(7, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50)))
    Steve.add(Leg(7, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10)))
    Steve.add(Activity(8, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY))
    return Steve


def Hilda():
    Hilda = Person("Hilda", attributes={"age": 45, "job": "influencer", "gender": "female"})
    Hilda.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60)))
    Hilda.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 5)))
    Hilda.add(Activity(2, "escort", "b", start_time=mtdt(8 * 60 + 5), end_time=mtdt(8 * 60 + 30)))
    Hilda.add(Leg(1, "pt", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 30)))
    Hilda.add(Activity(2, "shop", "b", start_time=mtdt(8 * 60 + 30), end_time=mtdt(14 * 60)))
    Hilda.add(Leg(2, "pt", "b", "c", start_time=mtdt(14 * 60), end_time=mtdt(14 * 60 + 20)))
    Hilda.add(
        Activity(3, "leisure", "c", start_time=mtdt(14 * 60 + 20), end_time=mtdt(16 * 60 - 20))
    )
    Hilda.add(Leg(3, "pt", "c", "b", start_time=mtdt(16 * 60 - 20), end_time=mtdt(16 * 60)))
    Hilda.add(Activity(4, "escort", "b", start_time=mtdt(16 * 60), end_time=mtdt(16 * 60 + 30)))
    Hilda.add(Leg(4, "walk", "a", "b", start_time=mtdt(16 * 60 + 30), end_time=mtdt(17 * 60)))
    Hilda.add(Activity(5, "home", "a", start_time=mtdt(17 * 60), end_time=mtdt(20 * 60)))
    Hilda.add(Leg(5, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20)))
    Hilda.add(
        Activity(6, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50))
    )
    Hilda.add(Leg(6, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60)))
    Hilda.add(Activity(7, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50)))
    Hilda.add(Leg(7, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10)))
    Hilda.add(Activity(8, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY))
    return Hilda


def Timmy():
    Timmy = Person("Timmy", attributes={"age": 18, "job": "education", "gender": "male"})
    Timmy.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60)))
    Timmy.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 5)))
    Timmy.add(Activity(2, "sport", "b", start_time=mtdt(8 * 60 + 5), end_time=mtdt(8 * 60 + 55)))
    Timmy.add(Leg(2, "walk", "b", "a", start_time=mtdt(8 * 60 + 55), end_time=mtdt(9 * 60)))
    Timmy.add(Activity(3, "home", "a", start_time=mtdt(9 * 60), end_time=mtdt(10 * 60)))
    Timmy.add(Leg(3, "bike", "a", "b", start_time=mtdt(10 * 60), end_time=mtdt(11 * 60)))
    Timmy.add(Activity(4, "education", "b", start_time=mtdt(11 * 60), end_time=mtdt(13 * 60)))
    Timmy.add(Leg(4, "bike", "b", "c", start_time=mtdt(13 * 60), end_time=mtdt(13 * 60 + 5)))
    Timmy.add(Activity(5, "shop", "c", start_time=mtdt(13 * 60 + 5), end_time=mtdt(13 * 60 + 30)))
    Timmy.add(Leg(5, "bike", "c", "b", start_time=mtdt(13 * 60 + 30), end_time=mtdt(13 * 60 + 35)))
    Timmy.add(Activity(6, "education", "b", start_time=mtdt(13 * 60 + 35), end_time=mtdt(15 * 60)))
    Timmy.add(Leg(6, "bike", "b", "d", start_time=mtdt(15 * 60), end_time=mtdt(15 * 60 + 10)))
    Timmy.add(Activity(7, "leisure", "d", start_time=mtdt(15 * 60 + 10), end_time=mtdt(18 * 60)))
    Timmy.add(Leg(7, "bike", "d", "a", start_time=mtdt(18 * 60), end_time=mtdt(18 * 60 + 20)))
    Timmy.add(Activity(8, "home", "a", start_time=mtdt(18 * 60 + 20), end_time=mtdt(20 * 60)))
    Timmy.add(Leg(8, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20)))
    Timmy.add(
        Activity(9, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50))
    )
    Timmy.add(Leg(9, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60)))
    Timmy.add(Activity(10, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50)))
    Timmy.add(Leg(10, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10)))
    Timmy.add(Activity(11, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY))
    return Timmy


def Bobby():
    Bobby = Person("Bobby", attributes={"age": 6, "job": "education", "gender": "female"})
    Bobby.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60)))
    Bobby.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 30)))
    Bobby.add(Activity(2, "education", "b", start_time=mtdt(8 * 60 + 30), end_time=mtdt(16 * 60)))
    Bobby.add(Leg(2, "walk", "b", "c", start_time=mtdt(16 * 60), end_time=mtdt(16 * 60 + 30)))
    Bobby.add(Activity(3, "home", "a", start_time=mtdt(16 * 60 + 30), end_time=mtdt(20 * 60)))
    Bobby.add(Leg(3, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20)))
    Bobby.add(
        Activity(4, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50))
    )
    Bobby.add(Leg(4, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60)))
    Bobby.add(Activity(5, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50)))
    Bobby.add(Leg(5, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10)))
    Bobby.add(Activity(6, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY))
    return Bobby

def hhld_apply(household, policy): new_hhld = deepcopy(household) policy.apply_to(new_hhld) return new_hhld def print_attributes(hhld, attribs=None): if attribs is not None: print(f"Household: {hhld}") for pid, person in hhld.people.items(): print(f"Person: {pid}") print("Attributes:") for attrib in attribs: print(f"\t{attrib}: {person.attributes[attrib]}") else: print(f"Household: {hhld}") for pid, person in hhld.people.items(): print(f"Person: {pid}") print("Attributes:") pprint(person.attributes) def instantiate_household_with(persons: list): household = Household(1) for person in persons: household.add(person) return household def Steve(): Steve = Person("Steve", attributes={"age": 50, "job": "work", "gender": "male"}) Steve.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(5 * 60))) Steve.add(Leg(1, "car", "a", "b", start_time=mtdt(5 * 60), end_time=mtdt(6 * 60))) Steve.add(Activity(2, "work", "b", start_time=mtdt(6 * 60), end_time=mtdt(12 * 60))) Steve.add(Leg(2, "walk", "b", "c", start_time=mtdt(12 * 60), end_time=mtdt(12 * 60 + 10))) Steve.add( Activity(3, "leisure", "c", start_time=mtdt(12 * 60 + 10), end_time=mtdt(13 * 60 - 10)) ) Steve.add(Leg(3, "walk", "c", "b", start_time=mtdt(13 * 60 - 10), end_time=mtdt(13 * 60))) Steve.add(Activity(4, "work", "b", start_time=mtdt(13 * 60), end_time=mtdt(18 * 60))) Steve.add(Leg(4, "car", "b", "a", start_time=mtdt(18 * 60), end_time=mtdt(19 * 60))) Steve.add(Activity(5, "home", "a", start_time=mtdt(19 * 60), end_time=mtdt(20 * 60))) Steve.add(Leg(5, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20))) Steve.add( Activity(6, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50)) ) Steve.add(Leg(6, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60))) Steve.add(Activity(7, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50))) Steve.add(Leg(7, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10))) Steve.add(Activity(8, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY)) return Steve def Hilda(): Hilda = Person("Hilda", attributes={"age": 45, "job": "influencer", "gender": "female"}) Hilda.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60))) Hilda.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 5))) Hilda.add(Activity(2, "escort", "b", start_time=mtdt(8 * 60 + 5), end_time=mtdt(8 * 60 + 30))) Hilda.add(Leg(1, "pt", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 30))) Hilda.add(Activity(2, "shop", "b", start_time=mtdt(8 * 60 + 30), end_time=mtdt(14 * 60))) Hilda.add(Leg(2, "pt", "b", "c", start_time=mtdt(14 * 60), end_time=mtdt(14 * 60 + 20))) Hilda.add( Activity(3, "leisure", "c", start_time=mtdt(14 * 60 + 20), end_time=mtdt(16 * 60 - 20)) ) Hilda.add(Leg(3, "pt", "c", "b", start_time=mtdt(16 * 60 - 20), end_time=mtdt(16 * 60))) Hilda.add(Activity(4, "escort", "b", start_time=mtdt(16 * 60), end_time=mtdt(16 * 60 + 30))) Hilda.add(Leg(4, "walk", "a", "b", start_time=mtdt(16 * 60 + 30), end_time=mtdt(17 * 60))) Hilda.add(Activity(5, "home", "a", start_time=mtdt(17 * 60), end_time=mtdt(20 * 60))) Hilda.add(Leg(5, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20))) Hilda.add( Activity(6, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50)) ) Hilda.add(Leg(6, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60))) Hilda.add(Activity(7, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50))) Hilda.add(Leg(7, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10))) Hilda.add(Activity(8, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY)) return Hilda def Timmy(): Timmy = Person("Timmy", attributes={"age": 18, "job": "education", "gender": "male"}) Timmy.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60))) Timmy.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 5))) Timmy.add(Activity(2, "sport", "b", start_time=mtdt(8 * 60 + 5), end_time=mtdt(8 * 60 + 55))) Timmy.add(Leg(2, "walk", "b", "a", start_time=mtdt(8 * 60 + 55), end_time=mtdt(9 * 60))) Timmy.add(Activity(3, "home", "a", start_time=mtdt(9 * 60), end_time=mtdt(10 * 60))) Timmy.add(Leg(3, "bike", "a", "b", start_time=mtdt(10 * 60), end_time=mtdt(11 * 60))) Timmy.add(Activity(4, "education", "b", start_time=mtdt(11 * 60), end_time=mtdt(13 * 60))) Timmy.add(Leg(4, "bike", "b", "c", start_time=mtdt(13 * 60), end_time=mtdt(13 * 60 + 5))) Timmy.add(Activity(5, "shop", "c", start_time=mtdt(13 * 60 + 5), end_time=mtdt(13 * 60 + 30))) Timmy.add(Leg(5, "bike", "c", "b", start_time=mtdt(13 * 60 + 30), end_time=mtdt(13 * 60 + 35))) Timmy.add(Activity(6, "education", "b", start_time=mtdt(13 * 60 + 35), end_time=mtdt(15 * 60))) Timmy.add(Leg(6, "bike", "b", "d", start_time=mtdt(15 * 60), end_time=mtdt(15 * 60 + 10))) Timmy.add(Activity(7, "leisure", "d", start_time=mtdt(15 * 60 + 10), end_time=mtdt(18 * 60))) Timmy.add(Leg(7, "bike", "d", "a", start_time=mtdt(18 * 60), end_time=mtdt(18 * 60 + 20))) Timmy.add(Activity(8, "home", "a", start_time=mtdt(18 * 60 + 20), end_time=mtdt(20 * 60))) Timmy.add(Leg(8, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20))) Timmy.add( Activity(9, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50)) ) Timmy.add(Leg(9, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60))) Timmy.add(Activity(10, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50))) Timmy.add(Leg(10, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10))) Timmy.add(Activity(11, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY)) return Timmy def Bobby(): Bobby = Person("Bobby", attributes={"age": 6, "job": "education", "gender": "female"}) Bobby.add(Activity(1, "home", "a", start_time=mtdt(0), end_time=mtdt(8 * 60))) Bobby.add(Leg(1, "walk", "a", "b", start_time=mtdt(8 * 60), end_time=mtdt(8 * 60 + 30))) Bobby.add(Activity(2, "education", "b", start_time=mtdt(8 * 60 + 30), end_time=mtdt(16 * 60))) Bobby.add(Leg(2, "walk", "b", "c", start_time=mtdt(16 * 60), end_time=mtdt(16 * 60 + 30))) Bobby.add(Activity(3, "home", "a", start_time=mtdt(16 * 60 + 30), end_time=mtdt(20 * 60))) Bobby.add(Leg(3, "car", "a", "b", start_time=mtdt(20 * 60), end_time=mtdt(20 * 60 + 20))) Bobby.add( Activity(4, "shop_1", "b", start_time=mtdt(20 * 60 + 20), end_time=mtdt(20 * 60 + 50)) ) Bobby.add(Leg(4, "walk", "b", "b", start_time=mtdt(20 * 60 + 50), end_time=mtdt(21 * 60))) Bobby.add(Activity(5, "shop_2", "b", start_time=mtdt(21 * 60), end_time=mtdt(21 * 60 + 50))) Bobby.add(Leg(5, "car", "b", "a", start_time=mtdt(21 * 60 + 50), end_time=mtdt(22 * 60 + 10))) Bobby.add(Activity(6, "home", "a", start_time=mtdt(22 * 60 + 10), end_time=END_OF_DAY)) return Bobby

Sampling and Policy Levels¶

In [3]:

smiths = instantiate_household_with([Steve(), Hilda(), Timmy(), Bobby()])

smiths = instantiate_household_with([Steve(), Hilda(), Timmy(), Bobby()])

In [4]:

smiths.plot()

smiths.plot()

Household¶

$$P_{household} = 1 - (1-P_{person})^n$$

If you have a probability that drives of any one person living in the household.

The probability of the household being chosen is then $$1 - (1-P)^n$$ where $P$ is the probability any one person being chosen and $n$ is the number of people in the household; $(1-P)^n$ is the probability of no one being picked.

In [5]:

hhld_apply(smiths, HouseholdPolicy(RemoveActivity(["education"]), 1)).plot()

hhld_apply(smiths, HouseholdPolicy(RemoveActivity(["education"]), 1)).plot()

Person¶

If you have a probability that drives of any one activity a person does.

The probability of the person being chosen is then $$1 - (1-P)^n$$ where $P$ is the probability any one (relevant) activity being chosen and $n$ is the number of (relevant) activities in the person's plan; $(1-P)^n$ is the probability of no relevant activities being picked.

In [6]:

hhld_apply(smiths, PersonPolicy(RemoveActivity(["shop_1", "shop_2"]), 0.5)).plot()

hhld_apply(smiths, PersonPolicy(RemoveActivity(["shop_1", "shop_2"]), 0.5)).plot()

Activity¶

If you have a probability that drives of any one activity a person does and you want to affect particular activities with no consequence to the person or household.

Notice that given a probability $P$ for an activity you can affect the whole household. The probability of the household being chosen is then $$1 - (1-P)^n$$ where $P$ is the probability any one (relevant) activity being chosen and $n$ is then the number of all (relevant) activities in all of the persons plans within a household; $(1-P)^n$ is the probability of no relevant activities being picked for the household.

In [7]:

hhld_apply(smiths, ActivityPolicy(RemoveActivity(["shop_1", "shop_2"]), 0.5)).plot()

hhld_apply(smiths, ActivityPolicy(RemoveActivity(["shop_1", "shop_2"]), 0.5)).plot()

The Policies expect certain levels of probability. For example, performing a HouseholdPolicy is easy, you can affect a whole household using a probability at any level, household, person or activity. Performing an ActivityPolicy however means you need information at the level of the activities you want to affect.

	HouseholdProbability	PersonProbability	ActivityProbability
HouseholdPolicy	Yes	Yes	Yes
PersonPolicy	---	Yes	Yes
ActivityPolicy	---	---	Yes

Joint distributions¶

You can give the SamplingProbability classes custom samplers with joint (or not) distributions. That changes the following table

	HouseholdProbability	PersonProbability	ActivityProbability
HouseholdPolicy	$$p$$	$$1-(1-p)^{n_p}$$	$$1-(1-p)^{n_{a,h}}$$
PersonPolicy	$$-$$	$$p$$	$$1-(1-p)^{n_{a,p}}$$
ActivityPolicy	$$-$$	$$-$$	$$p$$

where

• $n_p$ is the number of people in a household
• $n_{a,p}$ is the number of (relevant) activities in a person's plan
• $n_{a,h}$ is the number of (relevant) activities in a household

to

	HouseholdProbability	PersonProbability	ActivityProbability
HouseholdPolicy	$$p$$	$$1-\prod_x (1-p_x)^{n_x}$$	$$1-\prod_{x,a}(1-p_{x,a})^{n_{x,a}}$$
PersonPolicy	$$-$$	$$p_x$$	$$1-\prod_a (1-p_a)^{n_a}$$
ActivityPolicy	$$-$$	$$-$$	$$p_a, p_{x,a}$$

where

• $p_x$ is the probability for person $x$
• $n_x$ is the number of people with probability $p_x$
• $p_a$ is the probability for activity $a$
• $n_a$ is the number of activities $a$ in a person's plan
• $p_{x,a}$ is the probability for activity $a$ for person $x$
• $n_{x,a}$ is the number of activities $a$ in a person $x$ plan

Modifiers¶

Modifier perform the actions of removing or moving activities on the chosen Policy Level and sampled using the chosen Sampling Level.

RemoveActivity¶

Takes a list of activities to be removed.

In [8]:

hhld_apply(smiths, HouseholdPolicy(RemoveActivity(["education"]), 1)).plot()

hhld_apply(smiths, HouseholdPolicy(RemoveActivity(["education"]), 1)).plot()

ReduceSharedActivity¶

This is a HouseholdPolicy level modifier which takes a list of activities, checks the household for those being shared (like going shopping together) and selects a person at random to still perform those activities. Those activities will be deleted for everyone else in the household.

In [9]:

hhld_apply(smiths, HouseholdPolicy(ReduceSharedActivity(["shop_1"]), 1)).plot()

hhld_apply(smiths, HouseholdPolicy(ReduceSharedActivity(["shop_1"]), 1)).plot()

MoveActivityTourToHomeLocation¶

Takes a list of activities and moves those to the home location if they form a tour. A tour is a chain of activities sandwiched by two home activities (or boundary of persons plan e.g. if person's activities are: 'work', 'home', 'shop', 'work', 'home' (in that order), their tours are: ['work'] and ['shop', work']).

In [10]:

hhld_apply(smiths, HouseholdPolicy(MoveActivityTourToHomeLocation(["shop_1", "shop_2"]), 1)).plot()

hhld_apply(smiths, HouseholdPolicy(MoveActivityTourToHomeLocation(["shop_1", "shop_2"]), 1)).plot()

The result of this modifier do not show up on the plot. Below are the details of Hilda's original plan.

In [11]:

smiths["Hilda"].print()

smiths["Hilda"].print()

Person: Hilda
{'age': 45, 'job': 'influencer', 'gender': 'female'}
0:	Activity(act:home, location:a, time:00:00:00 --> 08:00:00, duration:8:00:00)
1:	Leg(mode:walk, area:a --> b, time:08:00:00 --> 08:05:00, duration:0:05:00)
2:	Activity(act:escort, location:b, time:08:05:00 --> 08:30:00, duration:0:25:00)
3:	Leg(mode:pt, area:a --> b, time:08:00:00 --> 08:30:00, duration:0:30:00)
4:	Activity(act:shop, location:b, time:08:30:00 --> 14:00:00, duration:5:30:00)
5:	Leg(mode:pt, area:b --> c, time:14:00:00 --> 14:20:00, duration:0:20:00)
6:	Activity(act:leisure, location:c, time:14:20:00 --> 15:40:00, duration:1:20:00)
7:	Leg(mode:pt, area:c --> b, time:15:40:00 --> 16:00:00, duration:0:20:00)
8:	Activity(act:escort, location:b, time:16:00:00 --> 16:30:00, duration:0:30:00)
9:	Leg(mode:walk, area:a --> b, time:16:30:00 --> 17:00:00, duration:0:30:00)
10:	Activity(act:home, location:a, time:17:00:00 --> 20:00:00, duration:3:00:00)
11:	Leg(mode:car, area:a --> b, time:20:00:00 --> 20:20:00, duration:0:20:00)
12:	Activity(act:shop_1, location:b, time:20:20:00 --> 20:50:00, duration:0:30:00)
13:	Leg(mode:walk, area:b --> b, time:20:50:00 --> 21:00:00, duration:0:10:00)
14:	Activity(act:shop_2, location:b, time:21:00:00 --> 21:50:00, duration:0:50:00)
15:	Leg(mode:car, area:b --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
16:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

Here are details of Hilda's plan post application of.MoveActivityTourToHomeLocation to 'shop' activities. Compare the location of Activities 6 and 7 with the ones above. At the moment only the Leg's destination and origin get updated, the mode choice and duration remains unchanged.

In [12]:

hhld_apply(smiths, HouseholdPolicy(MoveActivityTourToHomeLocation(["shop_1", "shop_2"]), 1))[
    "Hilda"
].print()

hhld_apply(smiths, HouseholdPolicy(MoveActivityTourToHomeLocation(["shop_1", "shop_2"]), 1))[ "Hilda" ].print()

Person: Hilda
{'age': 45, 'job': 'influencer', 'gender': 'female'}
0:	Activity(act:home, location:a, time:00:00:00 --> 08:00:00, duration:8:00:00)
1:	Leg(mode:walk, area:a --> b, time:08:00:00 --> 08:05:00, duration:0:05:00)
2:	Activity(act:escort, location:b, time:08:05:00 --> 08:30:00, duration:0:25:00)
3:	Leg(mode:pt, area:a --> b, time:08:00:00 --> 08:30:00, duration:0:30:00)
4:	Activity(act:shop, location:b, time:08:30:00 --> 14:00:00, duration:5:30:00)
5:	Leg(mode:pt, area:b --> c, time:14:00:00 --> 14:20:00, duration:0:20:00)
6:	Activity(act:leisure, location:c, time:14:20:00 --> 15:40:00, duration:1:20:00)
7:	Leg(mode:pt, area:c --> b, time:15:40:00 --> 16:00:00, duration:0:20:00)
8:	Activity(act:escort, location:b, time:16:00:00 --> 16:30:00, duration:0:30:00)
9:	Leg(mode:walk, area:a --> b, time:16:30:00 --> 17:00:00, duration:0:30:00)
10:	Activity(act:home, location:a, time:17:00:00 --> 20:00:00, duration:3:00:00)
11:	Leg(mode:walk, area:a --> a, time:20:00:00 --> 20:20:00, duration:0:20:00)
12:	Activity(act:shop_1, location:a, time:20:20:00 --> 20:50:00, duration:0:30:00)
13:	Leg(mode:walk, area:a --> a, time:20:50:00 --> 21:00:00, duration:0:10:00)
14:	Activity(act:shop_2, location:a, time:21:00:00 --> 21:50:00, duration:0:50:00)
15:	Leg(mode:walk, area:a --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
16:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

Attribute Filters¶

PersonAttributeFilter is a conveniece class to apply policies to selected people in the population which satisfy given attributes.

In [13]:

print_attributes(smiths, ["gender", "age", "job"])

print_attributes(smiths, ["gender", "age", "job"])

Household: Household: 1
Person: Steve
Attributes:
	gender: male
	age: 50
	job: work
Person: Hilda
Attributes:
	gender: female
	age: 45
	job: influencer
Person: Timmy
Attributes:
	gender: male
	age: 18
	job: education
Person: Bobby
Attributes:
	gender: female
	age: 6
	job: education

In [14]:

def is_male(attribute_value):
    return attribute_value == "male"


condition = {"gender": is_male}

hhld_apply(
    smiths, PersonPolicy(RemoveActivity(["shop_1", "shop_2"]), 1, PersonAttributeFilter(condition))
).plot()

def is_male(attribute_value): return attribute_value == "male" condition = {"gender": is_male} hhld_apply( smiths, PersonPolicy(RemoveActivity(["shop_1", "shop_2"]), 1, PersonAttributeFilter(condition)) ).plot()

Policies¶

Person-based Household Quarantine¶

In [15]:

smiths.plot()

smiths.plot()

In [16]:

policy_household_quarantine_per_person = HouseholdQuarantined(PersonProbability(0.01))

policy_household_quarantine_per_person = HouseholdQuarantined(PersonProbability(0.01))

In [17]:

hhld_apply(smiths, policy_household_quarantine_per_person).plot()

hhld_apply(smiths, policy_household_quarantine_per_person).plot()

In [18]:

hhld_apply(smiths, HouseholdQuarantined(1)).plot()

hhld_apply(smiths, HouseholdQuarantined(1)).plot()

Remove Higher Education¶

Remove all education activity for persons over age of 17

In [19]:

print_attributes(smiths, ["age", "job"])

print_attributes(smiths, ["age", "job"])

Household: Household: 1
Person: Steve
Attributes:
	age: 50
	job: work
Person: Hilda
Attributes:
	age: 45
	job: influencer
Person: Timmy
Attributes:
	age: 18
	job: education
Person: Bobby
Attributes:
	age: 6
	job: education

In [20]:

smiths.plot()

smiths.plot()

In [21]:

def over_17(attribute_value):
    return attribute_value > 17


policy_remove_higher_education = PersonPolicy(
    RemoveActivity(["education"]),
    PersonProbability(1),
    PersonAttributeFilter({"age": over_17}, how="all"),
)

def over_17(attribute_value): return attribute_value > 17 policy_remove_higher_education = PersonPolicy( RemoveActivity(["education"]), PersonProbability(1), PersonAttributeFilter({"age": over_17}, how="all"), )

In [22]:

hhld_apply(smiths, policy_remove_higher_education).plot()

hhld_apply(smiths, policy_remove_higher_education).plot()

Remove Education¶

Probabilistically remove education activities from a person, and escort from people who share the household. Contraint based on the house not being care constrainted and people being able to work from home.

Example of non care constrained household¶

Let's make the Smiths household a non care constrained household with at least one adult is able to work from home and the household is not care constrained.

In [23]:

smiths["Steve"].attributes["wfh"] = True
smiths["Steve"].attributes["care_constrained"] = False
smiths["Hilda"].attributes["wfh"] = True
smiths["Hilda"].attributes["care_constrained"] = False
smiths["Timmy"].attributes["wfh"] = False
smiths["Timmy"].attributes["care_constrained"] = False
smiths["Bobby"].attributes["wfh"] = False
smiths["Bobby"].attributes["care_constrained"] = False

smiths["Steve"].attributes["wfh"] = True smiths["Steve"].attributes["care_constrained"] = False smiths["Hilda"].attributes["wfh"] = True smiths["Hilda"].attributes["care_constrained"] = False smiths["Timmy"].attributes["wfh"] = False smiths["Timmy"].attributes["care_constrained"] = False smiths["Bobby"].attributes["wfh"] = False smiths["Bobby"].attributes["care_constrained"] = False

In [24]:

print_attributes(smiths, ["wfh", "care_constrained"])

print_attributes(smiths, ["wfh", "care_constrained"])

Household: Household: 1
Person: Steve
Attributes:
	wfh: True
	care_constrained: False
Person: Hilda
Attributes:
	wfh: True
	care_constrained: False
Person: Timmy
Attributes:
	wfh: False
	care_constrained: False
Person: Bobby
Attributes:
	wfh: False
	care_constrained: False

In [25]:

def attribute_False(attribute_value):
    return not bool(attribute_value)


def attribute_True(attribute_value):
    return bool(attribute_value)


conditions = {"care_constrained": attribute_False, "wfh": attribute_True}

# notice that the probability of the household (`HouseholdPolicy`) being chosen is driven
# by the presence of education activities (`ActivityProbability(['education'], 0.95)`)
# in the end the policy removes both education and escort (`RemoveActivity(['education', 'escort'])`)
# but the presence of escort doesn't affect the likelihood
policy_remove_any_education = HouseholdPolicy(
    RemoveActivity(["education", "escort"]),
    ActivityProbability(["education"], 0.95),
    PersonAttributeFilter(conditions),
)

def attribute_False(attribute_value): return not bool(attribute_value) def attribute_True(attribute_value): return bool(attribute_value) conditions = {"care_constrained": attribute_False, "wfh": attribute_True} # notice that the probability of the household (`HouseholdPolicy`) being chosen is driven # by the presence of education activities (`ActivityProbability(['education'], 0.95)`) # in the end the policy removes both education and escort (`RemoveActivity(['education', 'escort'])`) # but the presence of escort doesn't affect the likelihood policy_remove_any_education = HouseholdPolicy( RemoveActivity(["education", "escort"]), ActivityProbability(["education"], 0.95), PersonAttributeFilter(conditions), )

Non care constrained household is affected¶

In [26]:

hhld_apply(smiths, policy_remove_any_education).plot()

hhld_apply(smiths, policy_remove_any_education).plot()

Example of care constrained household¶

In [27]:

smiths["Steve"].attributes["wfh"] = False
smiths["Steve"].attributes["care_constrained"] = True
smiths["Hilda"].attributes["wfh"] = False
smiths["Hilda"].attributes["care_constrained"] = True
smiths["Timmy"].attributes["wfh"] = False
smiths["Timmy"].attributes["care_constrained"] = False
smiths["Bobby"].attributes["wfh"] = False
smiths["Bobby"].attributes["care_constrained"] = False

print_attributes(smiths, ["wfh", "care_constrained"])

smiths["Steve"].attributes["wfh"] = False smiths["Steve"].attributes["care_constrained"] = True smiths["Hilda"].attributes["wfh"] = False smiths["Hilda"].attributes["care_constrained"] = True smiths["Timmy"].attributes["wfh"] = False smiths["Timmy"].attributes["care_constrained"] = False smiths["Bobby"].attributes["wfh"] = False smiths["Bobby"].attributes["care_constrained"] = False print_attributes(smiths, ["wfh", "care_constrained"])

Household: Household: 1
Person: Steve
Attributes:
	wfh: False
	care_constrained: True
Person: Hilda
Attributes:
	wfh: False
	care_constrained: True
Person: Timmy
Attributes:
	wfh: False
	care_constrained: False
Person: Bobby
Attributes:
	wfh: False
	care_constrained: False

Care constrained household is not affected¶

In [28]:

hhld_apply(smiths, policy_remove_any_education).plot()

hhld_apply(smiths, policy_remove_any_education).plot()

Remove Leisure Activities¶

Remove all leisure activities

In [29]:

policy_remove_leisure = PersonPolicy(RemoveActivity(["leisure"]), PersonProbability(1))

policy_remove_leisure = PersonPolicy(RemoveActivity(["leisure"]), PersonProbability(1))

In [30]:

hhld_apply(smiths, policy_remove_leisure).plot()

hhld_apply(smiths, policy_remove_leisure).plot()

Move Sport Activities¶

Probabilistically move Sport activities to home location

In [31]:

smiths["Timmy"].print()

smiths["Timmy"].print()

Person: Timmy
{'age': 18, 'job': 'education', 'gender': 'male', 'wfh': False, 'care_constrained': False}
0:	Activity(act:home, location:a, time:00:00:00 --> 08:00:00, duration:8:00:00)
1:	Leg(mode:walk, area:a --> b, time:08:00:00 --> 08:05:00, duration:0:05:00)
2:	Activity(act:sport, location:b, time:08:05:00 --> 08:55:00, duration:0:50:00)
3:	Leg(mode:walk, area:b --> a, time:08:55:00 --> 09:00:00, duration:0:05:00)
4:	Activity(act:home, location:a, time:09:00:00 --> 10:00:00, duration:1:00:00)
5:	Leg(mode:bike, area:a --> b, time:10:00:00 --> 11:00:00, duration:1:00:00)
6:	Activity(act:education, location:b, time:11:00:00 --> 13:00:00, duration:2:00:00)
7:	Leg(mode:bike, area:b --> c, time:13:00:00 --> 13:05:00, duration:0:05:00)
8:	Activity(act:shop, location:c, time:13:05:00 --> 13:30:00, duration:0:25:00)
9:	Leg(mode:bike, area:c --> b, time:13:30:00 --> 13:35:00, duration:0:05:00)
10:	Activity(act:education, location:b, time:13:35:00 --> 15:00:00, duration:1:25:00)
11:	Leg(mode:bike, area:b --> d, time:15:00:00 --> 15:10:00, duration:0:10:00)
12:	Activity(act:leisure, location:d, time:15:10:00 --> 18:00:00, duration:2:50:00)
13:	Leg(mode:bike, area:d --> a, time:18:00:00 --> 18:20:00, duration:0:20:00)
14:	Activity(act:home, location:a, time:18:20:00 --> 20:00:00, duration:1:40:00)
15:	Leg(mode:car, area:a --> b, time:20:00:00 --> 20:20:00, duration:0:20:00)
16:	Activity(act:shop_1, location:b, time:20:20:00 --> 20:50:00, duration:0:30:00)
17:	Leg(mode:walk, area:b --> b, time:20:50:00 --> 21:00:00, duration:0:10:00)
18:	Activity(act:shop_2, location:b, time:21:00:00 --> 21:50:00, duration:0:50:00)
19:	Leg(mode:car, area:b --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
20:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

In [32]:

policy_move_sport = PersonPolicy(MoveActivityTourToHomeLocation(["sport"]), PersonProbability(1))

policy_move_sport = PersonPolicy(MoveActivityTourToHomeLocation(["sport"]), PersonProbability(1))

In [33]:

hhld_apply(smiths, policy_move_sport)["Timmy"].print()

hhld_apply(smiths, policy_move_sport)["Timmy"].print()

Person: Timmy
{'age': 18, 'job': 'education', 'gender': 'male', 'wfh': False, 'care_constrained': False}
0:	Activity(act:home, location:a, time:00:00:00 --> 08:00:00, duration:8:00:00)
1:	Leg(mode:walk, area:a --> a, time:08:00:00 --> 08:05:00, duration:0:05:00)
2:	Activity(act:sport, location:a, time:08:05:00 --> 08:55:00, duration:0:50:00)
3:	Leg(mode:walk, area:a --> a, time:08:55:00 --> 09:00:00, duration:0:05:00)
4:	Activity(act:home, location:a, time:09:00:00 --> 10:00:00, duration:1:00:00)
5:	Leg(mode:bike, area:a --> b, time:10:00:00 --> 11:00:00, duration:1:00:00)
6:	Activity(act:education, location:b, time:11:00:00 --> 13:00:00, duration:2:00:00)
7:	Leg(mode:bike, area:b --> c, time:13:00:00 --> 13:05:00, duration:0:05:00)
8:	Activity(act:shop, location:c, time:13:05:00 --> 13:30:00, duration:0:25:00)
9:	Leg(mode:bike, area:c --> b, time:13:30:00 --> 13:35:00, duration:0:05:00)
10:	Activity(act:education, location:b, time:13:35:00 --> 15:00:00, duration:1:25:00)
11:	Leg(mode:bike, area:b --> d, time:15:00:00 --> 15:10:00, duration:0:10:00)
12:	Activity(act:leisure, location:d, time:15:10:00 --> 18:00:00, duration:2:50:00)
13:	Leg(mode:bike, area:d --> a, time:18:00:00 --> 18:20:00, duration:0:20:00)
14:	Activity(act:home, location:a, time:18:20:00 --> 20:00:00, duration:1:40:00)
15:	Leg(mode:car, area:a --> b, time:20:00:00 --> 20:20:00, duration:0:20:00)
16:	Activity(act:shop_1, location:b, time:20:20:00 --> 20:50:00, duration:0:30:00)
17:	Leg(mode:walk, area:b --> b, time:20:50:00 --> 21:00:00, duration:0:10:00)
18:	Activity(act:shop_2, location:b, time:21:00:00 --> 21:50:00, duration:0:50:00)
19:	Leg(mode:car, area:b --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
20:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

Remove Health Activities¶

Probabilistically remove individual health activities from a person. Make vulnerable people more likely to have their health activities removed.

In [34]:

def discrete_joint_distribution_sampler(person, mapping, distribution):
    p = distribution
    for key in mapping:
        value = person.attributes.get(key)
        if value is None:
            msg = f"Cannot find mapping: {key} in sampling features: {person.attributes}"
            raise KeyError(msg)
        p = p.get(value)
        if p is None:
            msg = f"Cannot find feature for {key}: {value} in distribution: {p}"
            raise KeyError(msg)
    return p

def discrete_joint_distribution_sampler(person, mapping, distribution): p = distribution for key in mapping: value = person.attributes.get(key) if value is None: msg = f"Cannot find mapping: {key} in sampling features: {person.attributes}" raise KeyError(msg) p = p.get(value) if p is None: msg = f"Cannot find feature for {key}: {value} in distribution: {p}" raise KeyError(msg) return p

In [35]:

vulnerable_mapping = ["age", "gender"]
vulnerable_distribution = dict(
    zip(
        list(range(101)),
        [{"male": i / 100, "female": i / 100, "other": i / 100} for i in range(101)],
    )
)

vulnerable_mapping = ["age", "gender"] vulnerable_distribution = dict( zip( list(range(101)), [{"male": i / 100, "female": i / 100, "other": i / 100} for i in range(101)], ) )

In [36]:

dict(list(vulnerable_distribution.items())[0:15])

dict(list(vulnerable_distribution.items())[0:15])

Out[36]:

{0: {'male': 0.0, 'female': 0.0, 'other': 0.0},
 1: {'male': 0.01, 'female': 0.01, 'other': 0.01},
 2: {'male': 0.02, 'female': 0.02, 'other': 0.02},
 3: {'male': 0.03, 'female': 0.03, 'other': 0.03},
 4: {'male': 0.04, 'female': 0.04, 'other': 0.04},
 5: {'male': 0.05, 'female': 0.05, 'other': 0.05},
 6: {'male': 0.06, 'female': 0.06, 'other': 0.06},
 7: {'male': 0.07, 'female': 0.07, 'other': 0.07},
 8: {'male': 0.08, 'female': 0.08, 'other': 0.08},
 9: {'male': 0.09, 'female': 0.09, 'other': 0.09},
 10: {'male': 0.1, 'female': 0.1, 'other': 0.1},
 11: {'male': 0.11, 'female': 0.11, 'other': 0.11},
 12: {'male': 0.12, 'female': 0.12, 'other': 0.12},
 13: {'male': 0.13, 'female': 0.13, 'other': 0.13},
 14: {'male': 0.14, 'female': 0.14, 'other': 0.14}}

In [37]:

policy_remove_health = PersonPolicy(
    RemoveActivity(["medical"]),
    [
        PersonProbability(
            discrete_joint_distribution_sampler,
            {"mapping": vulnerable_mapping, "distribution": vulnerable_distribution},
        ),
        ActivityProbability(["medical"], 0.5),
    ],
)

policy_remove_health = PersonPolicy( RemoveActivity(["medical"]), [ PersonProbability( discrete_joint_distribution_sampler, {"mapping": vulnerable_mapping, "distribution": vulnerable_distribution}, ), ActivityProbability(["medical"], 0.5), ], )

In [38]:

print_attributes(smiths, ["age", "gender"])

print_attributes(smiths, ["age", "gender"])

Household: Household: 1
Person: Steve
Attributes:
	age: 50
	gender: male
Person: Hilda
Attributes:
	age: 45
	gender: female
Person: Timmy
Attributes:
	age: 18
	gender: male
Person: Bobby
Attributes:
	age: 6
	gender: female

In [39]:

smiths.plot()

smiths.plot()

In [40]:

hhld_apply(smiths, policy_remove_health).plot()

hhld_apply(smiths, policy_remove_health).plot()

Unemployment/Furlough¶

Probabilistically remove all work activities from a person

In [41]:

policy_unemployment_and_furlough = PersonPolicy(RemoveActivity(["work"]), PersonProbability(0.1))

policy_unemployment_and_furlough = PersonPolicy(RemoveActivity(["work"]), PersonProbability(0.1))

In [42]:

hhld_apply(smiths, PersonPolicy(RemoveActivity(["work"]), 1)).plot()

hhld_apply(smiths, PersonPolicy(RemoveActivity(["work"]), 1)).plot()

Work from Home¶

Probabilistically remove all work activities from a person

In [43]:

smiths["Steve"].attributes["wfh"] = True
smiths["Steve"].attributes["key_worker"] = False
smiths["Hilda"].attributes["wfh"] = False
smiths["Hilda"].attributes["key_worker"] = False
smiths["Timmy"].attributes["wfh"] = False
smiths["Timmy"].attributes["key_worker"] = False
smiths["Bobby"].attributes["wfh"] = False
smiths["Bobby"].attributes["key_worker"] = False

print_attributes(smiths, ["wfh", "key_worker"])

smiths["Steve"].attributes["wfh"] = True smiths["Steve"].attributes["key_worker"] = False smiths["Hilda"].attributes["wfh"] = False smiths["Hilda"].attributes["key_worker"] = False smiths["Timmy"].attributes["wfh"] = False smiths["Timmy"].attributes["key_worker"] = False smiths["Bobby"].attributes["wfh"] = False smiths["Bobby"].attributes["key_worker"] = False print_attributes(smiths, ["wfh", "key_worker"])

Household: Household: 1
Person: Steve
Attributes:
	wfh: True
	key_worker: False
Person: Hilda
Attributes:
	wfh: False
	key_worker: False
Person: Timmy
Attributes:
	wfh: False
	key_worker: False
Person: Bobby
Attributes:
	wfh: False
	key_worker: False

In [44]:

conditions = {"key_worker": attribute_False, "wfh": attribute_True}

conditions = {"key_worker": attribute_False, "wfh": attribute_True}

In [45]:

policy_work_from_home = PersonPolicy(
    RemoveActivity(["work"]), PersonProbability(0.5), PersonAttributeFilter(conditions)
)

policy_work_from_home = PersonPolicy( RemoveActivity(["work"]), PersonProbability(0.5), PersonAttributeFilter(conditions) )

In [46]:

hhld_apply(
    smiths, PersonPolicy(RemoveActivity(["work"]), 1, PersonAttributeFilter(conditions))
).plot()

hhld_apply( smiths, PersonPolicy(RemoveActivity(["work"]), 1, PersonAttributeFilter(conditions)) ).plot()

In [47]:

smiths["Steve"].attributes["wfh"] = False
smiths["Steve"].attributes["key_worker"] = True
smiths["Hilda"].attributes["wfh"] = False
smiths["Hilda"].attributes["key_worker"] = False
smiths["Timmy"].attributes["wfh"] = False
smiths["Timmy"].attributes["key_worker"] = False
smiths["Bobby"].attributes["wfh"] = False
smiths["Bobby"].attributes["key_worker"] = False

print_attributes(smiths, ["wfh", "key_worker"])

smiths["Steve"].attributes["wfh"] = False smiths["Steve"].attributes["key_worker"] = True smiths["Hilda"].attributes["wfh"] = False smiths["Hilda"].attributes["key_worker"] = False smiths["Timmy"].attributes["wfh"] = False smiths["Timmy"].attributes["key_worker"] = False smiths["Bobby"].attributes["wfh"] = False smiths["Bobby"].attributes["key_worker"] = False print_attributes(smiths, ["wfh", "key_worker"])

Household: Household: 1
Person: Steve
Attributes:
	wfh: False
	key_worker: True
Person: Hilda
Attributes:
	wfh: False
	key_worker: False
Person: Timmy
Attributes:
	wfh: False
	key_worker: False
Person: Bobby
Attributes:
	wfh: False
	key_worker: False

In [48]:

hhld_apply(
    smiths, PersonPolicy(RemoveActivity(["work"]), 1, PersonAttributeFilter(conditions))
).plot()

hhld_apply( smiths, PersonPolicy(RemoveActivity(["work"]), 1, PersonAttributeFilter(conditions)) ).plot()

Reduced Work Activity¶

Probabilistically remove individual work activities from a person

In [49]:

policy_reduced_work_activity = ActivityPolicy(
    RemoveActivity(["work"]), ActivityProbability(["work"], 0.2)
)

policy_reduced_work_activity = ActivityPolicy( RemoveActivity(["work"]), ActivityProbability(["work"], 0.2) )

In [50]:

hhld_apply(smiths, policy_reduced_work_activity).plot()

hhld_apply(smiths, policy_reduced_work_activity).plot()

Shopping¶

Reduce Shared Shopping Activities¶

In [51]:

policy_reduce_shopping_activities = HouseholdPolicy(
    ReduceSharedActivity(["shop", "shop_1", "shop_2"]),
    ActivityProbability(["shop", "shop_1", "shop_2"], 1),
)

policy_reduce_shopping_activities = HouseholdPolicy( ReduceSharedActivity(["shop", "shop_1", "shop_2"]), ActivityProbability(["shop", "shop_1", "shop_2"], 1), )

In [52]:

smiths_shop = hhld_apply(smiths, policy_reduce_shopping_activities)

smiths_shop = hhld_apply(smiths, policy_reduce_shopping_activities)

In [53]:

smiths_shop.plot()

smiths_shop.plot()

In [54]:

person_with_shopping = [
    p for p in smiths_shop.people.values() if "shop_1" in [act.act for act in p.activities]
][0].pid

person_with_shopping = [ p for p in smiths_shop.people.values() if "shop_1" in [act.act for act in p.activities] ][0].pid

In [55]:

smiths_shop.people[person_with_shopping].print()

smiths_shop.people[person_with_shopping].print()

Person: Steve
{'age': 50, 'job': 'work', 'gender': 'male', 'wfh': False, 'care_constrained': True, 'key_worker': True}
0:	Activity(act:home, location:a, time:00:00:00 --> 05:00:00, duration:5:00:00)
1:	Leg(mode:car, area:a --> b, time:05:00:00 --> 06:00:00, duration:1:00:00)
2:	Activity(act:work, location:b, time:06:00:00 --> 12:00:00, duration:6:00:00)
3:	Leg(mode:walk, area:b --> c, time:12:00:00 --> 12:10:00, duration:0:10:00)
4:	Activity(act:leisure, location:c, time:12:10:00 --> 12:50:00, duration:0:40:00)
5:	Leg(mode:walk, area:c --> b, time:12:50:00 --> 13:00:00, duration:0:10:00)
6:	Activity(act:work, location:b, time:13:00:00 --> 18:00:00, duration:5:00:00)
7:	Leg(mode:car, area:b --> a, time:18:00:00 --> 19:00:00, duration:1:00:00)
8:	Activity(act:home, location:a, time:19:00:00 --> 20:00:00, duration:1:00:00)
9:	Leg(mode:car, area:a --> b, time:20:00:00 --> 20:20:00, duration:0:20:00)
10:	Activity(act:shop_1, location:b, time:20:20:00 --> 20:50:00, duration:0:30:00)
11:	Leg(mode:walk, area:b --> b, time:20:50:00 --> 21:00:00, duration:0:10:00)
12:	Activity(act:shop_2, location:b, time:21:00:00 --> 21:50:00, duration:0:50:00)
13:	Leg(mode:car, area:b --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
14:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

Move Shopping Activities closer to home¶

In [56]:

policy_move_shopping = PersonPolicy(
    MoveActivityTourToHomeLocation(["shop_1", "shop", "shop_2"]),
    ActivityProbability(["shop_1", "shop", "shop_2"], 1.0),
)

policy_move_shopping = PersonPolicy( MoveActivityTourToHomeLocation(["shop_1", "shop", "shop_2"]), ActivityProbability(["shop_1", "shop", "shop_2"], 1.0), )

In [57]:

smiths_shop = hhld_apply(smiths_shop, policy_move_shopping)

smiths_shop = hhld_apply(smiths_shop, policy_move_shopping)

In [58]:

smiths_shop.plot()

smiths_shop.plot()

In [59]:

smiths.people[person_with_shopping].print()

smiths.people[person_with_shopping].print()

Person: Steve
{'age': 50, 'job': 'work', 'gender': 'male', 'wfh': False, 'care_constrained': True, 'key_worker': True}
0:	Activity(act:home, location:a, time:00:00:00 --> 05:00:00, duration:5:00:00)
1:	Leg(mode:car, area:a --> b, time:05:00:00 --> 06:00:00, duration:1:00:00)
2:	Activity(act:work, location:b, time:06:00:00 --> 12:00:00, duration:6:00:00)
3:	Leg(mode:walk, area:b --> c, time:12:00:00 --> 12:10:00, duration:0:10:00)
4:	Activity(act:leisure, location:c, time:12:10:00 --> 12:50:00, duration:0:40:00)
5:	Leg(mode:walk, area:c --> b, time:12:50:00 --> 13:00:00, duration:0:10:00)
6:	Activity(act:work, location:b, time:13:00:00 --> 18:00:00, duration:5:00:00)
7:	Leg(mode:car, area:b --> a, time:18:00:00 --> 19:00:00, duration:1:00:00)
8:	Activity(act:home, location:a, time:19:00:00 --> 20:00:00, duration:1:00:00)
9:	Leg(mode:car, area:a --> b, time:20:00:00 --> 20:20:00, duration:0:20:00)
10:	Activity(act:shop_1, location:b, time:20:20:00 --> 20:50:00, duration:0:30:00)
11:	Leg(mode:walk, area:b --> b, time:20:50:00 --> 21:00:00, duration:0:10:00)
12:	Activity(act:shop_2, location:b, time:21:00:00 --> 21:50:00, duration:0:50:00)
13:	Leg(mode:car, area:b --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
14:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

In [60]:

smiths_shop.people[person_with_shopping].print()

smiths_shop.people[person_with_shopping].print()

Person: Steve
{'age': 50, 'job': 'work', 'gender': 'male', 'wfh': False, 'care_constrained': True, 'key_worker': True}
0:	Activity(act:home, location:a, time:00:00:00 --> 05:00:00, duration:5:00:00)
1:	Leg(mode:car, area:a --> b, time:05:00:00 --> 06:00:00, duration:1:00:00)
2:	Activity(act:work, location:b, time:06:00:00 --> 12:00:00, duration:6:00:00)
3:	Leg(mode:walk, area:b --> c, time:12:00:00 --> 12:10:00, duration:0:10:00)
4:	Activity(act:leisure, location:c, time:12:10:00 --> 12:50:00, duration:0:40:00)
5:	Leg(mode:walk, area:c --> b, time:12:50:00 --> 13:00:00, duration:0:10:00)
6:	Activity(act:work, location:b, time:13:00:00 --> 18:00:00, duration:5:00:00)
7:	Leg(mode:car, area:b --> a, time:18:00:00 --> 19:00:00, duration:1:00:00)
8:	Activity(act:home, location:a, time:19:00:00 --> 20:00:00, duration:1:00:00)
9:	Leg(mode:walk, area:a --> a, time:20:00:00 --> 20:20:00, duration:0:20:00)
10:	Activity(act:shop_1, location:a, time:20:20:00 --> 20:50:00, duration:0:30:00)
11:	Leg(mode:walk, area:a --> a, time:20:50:00 --> 21:00:00, duration:0:10:00)
12:	Activity(act:shop_2, location:a, time:21:00:00 --> 21:50:00, duration:0:50:00)
13:	Leg(mode:walk, area:a --> a, time:21:50:00 --> 22:10:00, duration:0:20:00)
14:	Activity(act:home, location:a, time:22:10:00 --> 00:00:00, duration:1:50:00)

All together now!¶

In [61]:

population = Population(1)
population.add(smiths)

all_together_pop = apply_policies(
    population,
    [
        policy_household_quarantine_per_person,
        policy_remove_higher_education,
        policy_remove_any_education,
        policy_remove_leisure,
        policy_move_sport,
        policy_remove_health,
        policy_unemployment_and_furlough,
        policy_work_from_home,
        policy_reduced_work_activity,
        policy_reduce_shopping_activities,
        policy_move_shopping,
    ],
)

population = Population(1) population.add(smiths) all_together_pop = apply_policies( population, [ policy_household_quarantine_per_person, policy_remove_higher_education, policy_remove_any_education, policy_remove_leisure, policy_move_sport, policy_remove_health, policy_unemployment_and_furlough, policy_work_from_home, policy_reduced_work_activity, policy_reduce_shopping_activities, policy_move_shopping, ], )

In [ ]: