def __init__(self, n_doctors=3, n_init_arg=5, experiment_case=1):
self.n_init_arg = n_init_arg
# Create a line chart tracking avg_belief for all the initial arguments
list_var = []
for i in range(self.n_init_arg):
dict = {"Label": ARGUMENT_NAMES[i], "Color": COLORS[i]}
list_var.append(dict)
avg_belief_line_chart = ChartModule(list_var)
# Create a line chart tracking the committee decision progress
list_var = []
for i, disease in enumerate(MedicalModel.LIST_OF_DISEASES.values()):
dict = {"Label": disease, "Color": COLORS[i]}
list_var.append(dict)
disease_line_chart = ChartModule(list_var)
# Create bar chart to display the agents belief arrays
list_var = []
for i in range(self.n_init_arg):
dict = {"Label": ARGUMENT_NAMES[i], "Color": COLORS[i]}
list_var.append(dict)
bar_chart = BarChartModule(list_var, scope="agent")
diagnosis = PrintedDiagnosis()
legend_belief_array = LegendElement(
'<font size="5"><b>1. </b></font> The graph below displays the belief '
'array for each of the doctors (e.g. Doctor 0, Doctor 1..)')
legend_conclusion = LegendElement(
'<font size="5"><b>2. </b></font> The graph below displays the probability '
'of the committee in each conclusion.')
legend_avg_belief = LegendElement(
'The graph below represents the average belief between all doctors '
'for each of the possible arguments')
list_of_visualizations = [
legend_belief_array, bar_chart, legend_conclusion,
disease_line_chart, diagnosis, legend_avg_belief,
avg_belief_line_chart
]
model_legend = """<h1>Default Case Scenario.</h1><br><h3>The initial set of arguments is the
following:</h3><br>
"""
for arg_name, arg_idx in MedicalModel.LIST_OF_ARGUMENTS.items():
model_legend += "<b>" + arg_name + "</b>" + ": " + arg_idx + "<br>"
model_params = {
"Legend": UserSettableParameter('static_text', value=model_legend),
"N": n_doctors,
"n_init_arg": n_init_arg,
"experiment_case": experiment_case
}
# Create server
self.server = ModularServer(MedicalModel, list_of_visualizations,
"ABM medical diagnosis", model_params)
self.server.port = 8521
"Label": "Poor",
"Color": POOR_COLOR
},
{
"Label": "Middle Class",
"Color": MID_COLOR
},
])
model_bar = BarChartModule([
{
"Label": "Rich",
"Color": RICH_COLOR
},
{
"Label": "Poor",
"Color": POOR_COLOR
},
{
"Label": "Middle Class",
"Color": MID_COLOR
},
])
agent_bar = BarChartModule(
[{
"Label": "Wealth",
"Color": MID_COLOR
}],
scope="agent",
sorting="ascending",
sort_by="Wealth",
}],
data_collector_name='datacollector')
chartConsumidores = ChartModule([{
"Label": "Gini",
"Color": "#18496D"
}, {
"Label": "S80/S20",
"Color": "#A43DC6"
}, {
"Label": "Consumidores",
"Color": "#993599"
}],
data_collector_name='datacollector')
agent_bar = BarChartModule(
fields=[{
"Label": "Ingreso total",
"Color": "#4CCE59"
}],
scope="agent",
sorting="ascending",
sort_by="ingreso_total",
)
server = ModularServer(
EconomiaSocialista,
[chartGini, chartGiniS80S20, chartConsumidores, agent_bar],
"Economia socialista", model_params)
server.port = 8521 # The default
server.launch()
xCollectors = list()
yCollectors = list()
# Loop to create bars for bar graphs
coord = 0
for x in range(masterHeight):
xCollectors.append({"Label": ("x: " + str(coord)), "Color": "#85c6e7"})
coord += 1
coord = 0
for y in range(masterWidth):
yCollectors.append({"Label": ("y: " + str(coord)), "Color": "#85c6e7"})
coord += 1
# Create a bar charts to represent column and row amounts of relative to grid
bar_chart_element_col = BarChartModule(xCollectors, canvas_width=550)
bar_chart_element_row = BarChartModule(yCollectors, canvas_width=550)
# Create a chart to represent total amount of cAMP on the grid
chart_element = ChartModule([{
"Label": "Total Amount of cAMP",
"Color": "#85c6e7"
}])
# Setting size of model
model_params = {
"height":
200,
"width":
220,
"numAgents":
import math
from mesa.visualization.ModularVisualization import ModularServer
from mesa.visualization.UserParam import UserSettableParameter
from mesa.visualization.modules import BarChartModule, NetworkModule
from .model import WasteNet
from .enums import WasteNetMode
from .visualization import network_portrayal, level_fields, ResultsElement
network = NetworkModule(network_portrayal, 400, 500, library="d3")
barchart = BarChartModule(level_fields, canvas_width=500, scope="agent")
text = ResultsElement()
model_params = {
"mode": UserSettableParameter(
"choice",
"Agent Mode",
value=WasteNetMode.COMPLETE.name,
choices=WasteNetMode.names(),
),
"nb_episodes": UserSettableParameter(
"slider",
"Number of Episodes",
value=1,
min_value=1,
max_value=2000,
step=1,
),
}
portrayal["r"] = 0.5
portrayal["text"] = str(agent.unique_id)
portrayal["text_color"] = "black"
if (agent.status != Status.Comeback and agent.status != Status.Loading and agent.status != Status.Free):
portrayal["Color"] = "blue"
elif (agent.status == Status.Loading):
portrayal["Color"] = "orange"
else:
portrayal["Color"] = "yellow"
return portrayal
grid = CanvasGrid(agent_portrayal, 10, 10, 800, 800)
total_moving_step_graph = BarChartModule(
[{"Label": "Total_Moving_Seconds", "Color": "Red"}],
scope="agent",
data_collector_name='data_collector'
)
total_working_step_graph = BarChartModule(
[{"Label": "Total_Working_Seconds", "Color": "Red"}],
scope="agent",
data_collector_name='data_collector'
)
total_waiting_step_graph = BarChartModule(
[{"Label": "Total_Waiting_Seconds", "Color": "Red"}],
scope="agent",
data_collector_name='data_collector'
)
"Color": "orange"
}, {
"Label": "Strategies Min Score",
"Color": "blue"
}],
data_collector_name='datacollector')
fluctuation_chart = ChartModule([{
"Label": "Fluctuation",
"Color": "purple"
}],
data_collector_name='datacollector')
agent_bar = BarChartModule([{
"Label": "Wealth",
"Color": "blue"
}],
scope="agent")
producer_pie_chart = PieChartModule([{
"Label": "Good Producer",
"Color": "red"
}, {
"Label": "Bad Producer",
"Color": "green"
}])
speculator_pie_chart = PieChartModule([{
"Label": "Good Speculator",
"Color": "red"
}, {
"Label": "min_elo",
"Color": "Black"
}],
data_collector_name='datacollector')
# Bar Charts
rank_pop_chart = BarChartModule([{
"Label": "rank_zero_pop",
"Color": _COLORS[0]
}, {
"Label": "rank_one_pop",
"Color": _COLORS[1]
}, {
"Label": "rank_two_pop",
"Color": _COLORS[2]
}, {
"Label": "rank_three_pop",
"Color": _COLORS[3]
}, {
"Label": "rank_four_pop",
"Color": _COLORS[4]
}],
canvas_height=250,
canvas_width=500,
scope='model')
# Sliders
init_elo = UserSettableParameter('slider', 'Initial ELO Rating', 1500, 10,
3000, 10)
n_agents = UserSettableParameter('slider', 'Number of Agents', 500, 2, 2000, 5)
overcrowding_threshold = 60
# tamanho do grid definido em função do número de pessoas
grid_size = round(2 * round(math.sqrt(number_persons)))
crowded_element = CrowdedElement()
reward_element = RewardElement()
# 500 é o tamanho do grid em pixels
grid = CanvasGrid(agent_portrayal, grid_size, grid_size, 500, 500)
chart = ChartModule([{"Label": "Attendance", "Color": "Black"}])
# esse histograma mostra o valor das recompensas de cada agente
# a extensão três do NetLogo, diferentemente, mostra quantos agentes têm
# determinado valro de recompensas, do mínimo ao máximo
hist_agents_reward = BarChartModule([{
"Label": "Reward",
"Color": "Black"
}],
scope="agent",
sorting="ascending",
sort_by="unique_id")
# TERCEIRA EXTENSÃO
# tentativa de criar um histograma como o do NetLogo, mas aparentemente
# não é trivial; os valores a exibir não podem ser listas ou arrays;
# assim, optei por fixar em 5 o número de segmentos do histograma, mas isso
# aparentemente não é parametrizável e, portanto, não é uma boa solução
hist_reward_agents = BarChartModule([{
"Label": "Bin0",
"Color": "Blue"
}, {
"Label": "Bin1",
"Color": "Blue"