Introductory Tutorial

Instalation

First of all, you need to install the package using pip.

$ pip install soba

In case of error, this other command should be used, ensuring to have installed python 3 and pip 3.

$ pip3 install soba

Tutorial

The SOBA tool can be provided to be used directly on two scenarios:

1. Generic case with a space defined as a grid of a given square size (by default, half a meter on each side).
2. Simplified case with a room defined by rooms, to perform simulations in simplified buildings that require less consumption of resources and specifications.

An introductory tutorial will be presented for each case, although most parameters are common or similar.

SOBA enables the performance of the simulations in two modes:

1. With visual representation.
2. In batch mode.

In the tutorials, the small modifications required to use each posibility are reflected.

IMPORTANT NOTE: The .py files described in this tutorial are available in the github repository https://github.com/gsi-upm/soba/tree/master/projects/basicExamples

Implementing a sample model with continuous space

Once soba is installed, the implementation can be started. First we define the generic parameters to both types of scenario.

1.- We define the characteristics of the occupants

from collections import OrderedDict
#JSON to store all the informacion.
jsonsOccupants = []

#Number of occupants
N = 12

#Definition of the states
states = OrderedDict([('Leaving','out'), ('Resting', 'sofa'), ('Working in my laboratory', 'wp')])

#Definition of the schedule
schedule = {'t1': "09:00:00", 't2': "13:00:00", 't3': "14:10:00"}

#Possible Variation on the schedule
variation = {'t1': "00:10:00", 't2': "01:20:00", 't3': "00:20:00"}

#Probability of state change associated with the Markovian chain as a function of the temporal period.
markovActivity = {
    '-t1': [[100, 0, 0], [0, 0, 0], [0, 0, 0]],
    't1-t2': [[30, 40, 30], [0, 50, 50], [0, 50, 50]],
    't2-t3': [[0, 0, 0], [50, 50, 0], [0, 0, 0]],
    't3-': [[0, 50, 50], [10, 90, 0], [0, 0, 0]]
}

#Time associated to each state (minutes)
timeActivity = {
    '-t1': [60, 0, 0], 't1-t2': [2, 60, 15], 't2-t3': [60, 10, 15], 't3-': [60, 20, 15]
}

#Store the information
jsonOccupant = {'type': 'example' , 'N': N, 'states': states , 'schedule': schedule, 'variation': variation, 'markovActivity': markovActivity, 'timeActivity': timeActivity}
jsonsOccupants.append(jsonOccupant)

2.- We define the building plan or the distribution of the space.

import soba.visualization.ramen.mapGenerator as ramen

with open('labgsi.blueprint3d') as data_file:
    jsonMap = ramen.returnMap(data_file)

3.- We implement a Model inheriting a base class of SOBA.

from soba.model.model import ContinuousModel
import datetime as dt

class ModelExample(ContinuousModel):

    def __init__(self, width, height, jsonMap, jsonsOccupants, seed = dt.datetime.now()):
        super().__init__(width, height, jsonMap, jsonsOccupants, seed = seed)

    def step(self):
        if self.clock.clock.day > 3:
            self.finishSimulation = True
        super().step()

4.- We call the execution methods.

4.1-With visual representation.

import soba.run

soba.run.run(ModelExample, [], cellW, cellH, jsonMap, jsonsOccupants)

4.1- Bacth mode.

#Fixed parameters during iterations
fixed_params = {"width": cellW, "height": cellH, "jsonMap": jsonMap, "jsonsOccupants": jsonsOccupants}
#Variable parameters to each iteration
variable_params = {"seed": range(10, 500, 10)}

soba.run.run(ModelExample, fixed_params, variable_params)

Implementing a sample model with simplified space

Once soba is installed, the implementation can be started. First we define the generic parameters to both types of scenario.

1.- We define the characteristics of the occupants

from collections import OrderedDict
#JSON to store all the informacion.
jsonsOccupants = []

#Number of occupants
N = 3

#Definition of the states
states = OrderedDict([('out','Pos1'), ('Working in my laboratory', {'Pos2': 1, 'Pos3': 2})])

#Definition of the schedule
schedule = {'t1': "09:00:00", 't2': "13:00:00", 't3': "14:10:00"}

#Possible Variation on the schedule
variation = {'t1': "00:10:00", 't2': "01:20:00", 't3': "00:20:00"}

#Probability of state change associated with the Markovian chain as a function of the temporal period.
markovActivity = {
    '-t1': [[100, 0], [0, 0]],
    't1-t2': [[50, 50], [0, 0]],
    't2-t3': [[0, 0], [50, 0]],
    't3-': [[0, 50], [10, 90]]
}

#Time associated to each state (minutes)
timeActivity = {
    '-t1': [60, 0],
    't1-t2': [2, 60],
    't2-t3': [60, 10],
    't3-': [60, 20]
}

#Store the information
jsonOccupant = {'type': 'example' , 'N': N, 'states': states , 'schedule': schedule, 'variation': variation,
                'markovActivity': markovActivity, 'timeActivity': timeActivity}
jsonsOccupants.append(jsonOccupant)

2.- We define the building plan or the distribution of the space.

jsonMap = {
  'Pos1': {'entrance':'', 'conectedTo': {'U':'Pos2'}, 'measures': {'dx':2, 'dy':2}},
  'Pos2': {'measures': {'dx':3, 'dy':3.5}, 'conectedTo': {'R':'Pos3'}},
  'Pos3': {'measures': {'dx':3, 'dy':3.5}}
}

3.- We implement a Model inheriting a base class of SOBA.

from soba.model.model import ContinuousModel
import datetime as dt

class ModelExample(RoomsModel):

    def __init__(self, width, height, jsonMap, jsonsOccupants, seed = dt.datetime.now()):
        super().__init__(width, height, jsonMap, jsonsOccupants, seed = seed)

    def step(self):
        if self.clock.clock.day > 3:
            self.finishSimulation = True
        super().step()

4.- We call the execution methods. 4.1- With visual representation.

cellW = 4
cellH = 4

soba.run.run(ModelExample, [], cellW, cellH, jsonMap, jsonsOccupants)

4.1- Bacth mode.

#Fixed parameters during iterations
fixed_params = {"width": cellW, "height": cellH, "jsonMap": jsonMap, "jsonsOccupants": jsonsOccupants}
#Variable parameters to each iteration
variable_params = {"seed": range(10, 500, 10)}

soba.run.run(ModelExample, fixed_params, variable_params)

Running the simulation using the terminal

$ python exampleContinuous.py -v

Options:

-v,     Visual option on browser

-b,     Background option

-r,     Ramen option