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NZ simulation params #232

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3 changes: 3 additions & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -169,6 +169,9 @@ python_examples/intervention_example/intervention_outputs/*.csv
# PyEpiabm travelling output files
python_examples/travel_example/travelling_outputs/*.csv

# PyEpiabm New Zealand input file
python_examples/NZ_example/NZ_inputs/*.csv

# Profiling script
kernprof.py
*.prof
Expand Down
118 changes: 118 additions & 0 deletions python_examples/NZ_example/NZ_flow.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,118 @@
#
# Example simulation script running with New Zealand parameters
# Incorporates both age and spatial stratification.
#

import os
import sys
import logging
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import pyEpiabm as pe

# Add plotting functions to path
sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir,
"./age_stratified_example"))
from age_stratified_plot import Plotter # noqa

# Setup output for logging file
logging.basicConfig(filename='sim.log', filemode='w+', level=logging.DEBUG,
format=('%(asctime)s - %(name)s'
+ '- %(levelname)s - %(message)s'))

# Set config file for Parameters
pe.Parameters.set_file(os.path.join(os.path.dirname(__file__),
"NewZealand_parameters.json"))

# Generate population from input file
# (Input converted from CovidSim with `microcell_conversion.py`)
file_loc = os.path.join(os.path.dirname(__file__),
"NZ_inputs", "NZ_input.csv")
population = pe.routine.FilePopulationFactory.make_pop(file_loc,
random_seed=42)


# sim_ and file_params give details for the running of the simulations and
# where output should be written to.
sim_params = {"simulation_start_time": 0, "simulation_end_time": 90,
"initial_infected_number": 0, "initial_infect_cell": True,
"simulation_seed": 42}

file_params = {"output_file": "output_NZ.csv",
"output_dir": os.path.join(os.path.dirname(__file__),
"simulation_outputs"),
"spatial_output": True,
"age_stratified": True}

# Create a simulation object, configure it with the parameters given, then
# run the simulation.
sim = pe.routine.Simulation()
sim.configure(
population,
[pe.sweep.InitialHouseholdSweep(),
pe.sweep.InitialInfectedSweep(),
pe.sweep.InitialisePlaceSweep()],
[
pe.sweep.TravelSweep(),
pe.sweep.InterventionSweep(),
pe.sweep.UpdatePlaceSweep(),
pe.sweep.HouseholdSweep(),
pe.sweep.PlaceSweep(),
pe.sweep.SpatialSweep(),
pe.sweep.QueueSweep(),
pe.sweep.HostProgressionSweep(),
],
sim_params,
file_params,
)
sim.run_sweeps()

# Need to close the writer object at the end of each simulation.
del (sim.writer)
del (sim)

# Creation of a plot of results (plotter from spatial_simulation_flow)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
filename = os.path.join(os.path.dirname(__file__), "simulation_outputs",
"output_NZ.csv")
SIRdf = pd.read_csv(filename)
total = SIRdf[list(SIRdf.filter(regex='InfectionStatus.Infect'))]
SIRdf["Infected"] = total.sum(axis=1)
SIRdf = SIRdf.groupby(["time"]).agg(
{"InfectionStatus.Susceptible": 'sum',
"Infected": 'sum',
"InfectionStatus.Recovered": 'sum',
"InfectionStatus.Dead": 'sum'})
SIRdf.rename(columns={"InfectionStatus.Susceptible": "Susceptible",
"InfectionStatus.Recovered": "Recovered",
"InfectionStatus.Dead": "Dead"},
inplace=True)
# Create plot to show SIR curves against time
SIRdf.plot(y=["Infected", "Recovered"])
plt.savefig(os.path.join(os.path.dirname(__file__),
"simulation_outputs/SIR.png"))
plt.close()

# Create plot to show new cases and new deaths against time
newdf = pd.DataFrame()
newdf['New_cases'] = np.insert(-np.diff(SIRdf['Susceptible']),
0, SIRdf['Infected'][0])
newdf['New_deaths'] = np.insert(np.diff(SIRdf['Dead']), 0, SIRdf['Dead'][0])
newdf.plot(y=["New_cases", "New_deaths"])
plt.savefig(os.path.join(os.path.dirname(__file__),
"simulation_outputs/SIR_new.png"))
plt.close()

# Creation of a plot of results with age stratification
# if file_params["age_stratified"]:
p = Plotter(os.path.join(os.path.dirname(__file__),
"simulation_outputs/output_NZ.csv"),
start_date='03-01-2020', sum_weekly=True)
p.barchart(os.path.join(os.path.dirname(__file__),
"simulation_outputs/age_stratify.png"),
write_Df_toFile=os.path.join(os.path.dirname(__file__),
"simulation_outputs/NZ_weeky_cases.csv"),
param_file=os.path.join(os.path.dirname(__file__),
"NewZealand_parameters.json"))
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