def shared_network():
from metawards import Network, Parameters
params = Parameters()
params.set_input_files("2011Data")
params.set_disease("ncov")
network = Network.build(params, profiler=None)
return network
def test_go_partial(prompt=None, nthreads=1):
import random
seed = random.randint(100000, 1000000)
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# the size of the starting population
population = Population(initial=57104043)
nsteps = 20
demographics = Demographics.load(redblue_json)
print("Building the network...")
network = Network.build(params=params)
network = network.specialise(demographics, nthreads=nthreads)
outdir = os.path.join(script_dir, "test_go_to")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population, seed=seed,
output_dir=output_dir,
nsteps=nsteps,
mixer=mix_evenly,
mover=move_partial,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
# 70% of the population (roughly) should be in "blue"
pop = trajectory[-1]
print(pop)
frac0 = pop.subpops[0].population / pop.population
frac1 = pop.subpops[1].population / pop.population
print(frac0, frac1)
# pytest.approx error is percent, e.g. 0.05 is within 5%
assert pytest.approx(frac0, 0.1) == 0.3
assert pytest.approx(frac1, 0.05) == 0.7
assert pytest.approx(frac0+frac1, 0.01) == 1.0
def test_move_isolate(prompt=None, nthreads=1):
# user input parameters
import random
seed = random.randint(100000, 1000000)
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# the size of the starting population
population = Population(initial=57104043)
nsteps = 20
demographics = Demographics.load(isolate_json)
print("Building the network...")
network = Network.build(params=params)
network = network.specialise(demographics, nthreads=nthreads)
outdir = os.path.join(script_dir, "test_zero_demographic_output")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
profiler=None,
mixer=mix_isolate,
mover=move_isolate,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
# there should be no latents in the 'isolate' demographic
assert trajectory[-1].subpops[1].latent == 0
print("End of the run")
def test_go_stage(prompt=None, nthreads=1):
seed = 797747
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# the size of the starting population
population = Population(initial=57104043)
nsteps = 20
demographics = Demographics.load(redblue_json)
print("Building the network...")
network = Network.build(params=params)
network = network.specialise(demographics, nthreads=nthreads)
outdir = os.path.join(script_dir, "test_go_to")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
mixer=mix_evenly,
mover=move_stage,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
# red demographic was seeded, but all moved to blue, so should have
# no outbreak
pop = trajectory[-1]
print(pop)
def _get_network(nthreads):
global _network
if _network is not None:
return _network.copy()
inputfile = ncovparams_csv
line_num = 0
UV = 0.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# start from the parameters in the specified line number of the
# provided input file
variables = params.read_variables(inputfile, line_num)
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
demographics = Demographics.load(redgreenblue_json)
print("Building the network...")
network = Network.build(params=params)
network = network.specialise(demographics, nthreads=nthreads)
_network = network
return network
def test_output():
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease("ncov")
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# the size of the starting population
population = Population(initial=57104043)
print("Building the network...")
network = Network.build(params=params)
from metawards.iterators import build_custom_iterator
iterator = build_custom_iterator(iterate_debug, __name__)
print("Run the model...")
outdir = os.path.join(script_dir, "test_output")
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
with Console.redirect_output(outdir, auto_bzip=True):
trajectory = network.run(population=population,
output_dir=output_dir,
iterator=iterator,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print(trajectory[-1])
print("End of the run")
def test_variable_pathway():
demographics_json = os.path.join(script_dir, "data", "red_one_blue.json")
variables_csv = os.path.join(script_dir, "data", "demographic_scan.csv")
demographics = Demographics.load(demographics_json)
variables = VariableSets.read(variables_csv)
print(variables[0].fingerprint(include_index=True))
params = Parameters.load()
params.set_disease("lurgy")
params.set_input_files("single")
network = Network.build(params)
network = network.specialise(demographics)
params = network.params.set_variables(variables[0])
assert params.disease_params.beta == [0.0, 0.0, 0.1, 0.2, 0.0]
assert params["overall"].disease_params.beta == \
[0.0, 0.0, 0.1, 0.2, 0.0]
assert params["red one"].disease_params.beta == \
[0.0, 0.0, 0.1, 0.5, 0.27]
print(params["blue"].disease_params.beta)
assert params["blue"].disease_params.beta == \
[0.0, 0.0, 0.1, 0.2, 0.25, 0.0]
network.update(params)
d = network.params.disease_params
print(d.beta)
assert d.beta == [0.0, 0.0, 0.1, 0.2, 0.0]
d = network.subnets[0].params.disease_params
print(d.beta)
assert d.beta == [0.0, 0.0, 0.1, 0.5, 0.27]
d = network.subnets[1].params.disease_params
print(d.beta)
assert d.beta == [0.0, 0.0, 0.1, 0.2, 0.25, 0.0]
def test_iterator():
"""This test repeats main_RepeatsNcov.c and validates that the
various stages report the same results as the original C code
for ncov, when using a custom integrator that just calls
iterate_weekday
"""
prompt = None
# user input parameters
seed = 15324
inputfile = ncovparams_csv
line_num = 0
UV = 1.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease("ncov")
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# start from the parameters in the specified line number of the
# provided input file
variables = params.read_variables(inputfile, line_num)
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
profiler = Profiler()
print("Building the network...")
network = Network.build(params=params,
profiler=profiler)
params = params.set_variables(variables[0])
network.update(params, profiler=profiler)
# Here is a custom integrator function that just calls
# iterate_weekday after 'print_hello'
def print_hello(**kwargs):
print(f"Hello")
def my_iterator(**kwargs):
from metawards.iterators import iterate_weekday
return [print_hello] + iterate_weekday(**kwargs)
from metawards.iterators import build_custom_iterator
iterator = build_custom_iterator(my_iterator, __name__)
print("Run the model...")
outdir = os.path.join(script_dir, "test_integrator_output")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.run(population=population, seed=seed,
output_dir=output_dir,
nsteps=29, profiler=profiler,
iterator=iterator,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print("End of the run")
print(f"Model output: {trajectory}")
print(profiler)
# The original C code has this expected population after 47 steps
expected = Population(initial=57104043,
susceptibles=56081710,
latent=145,
total=48,
recovereds=174,
n_inf_wards=39,
day=29)
print(f"Expect output: {expected}")
assert trajectory[-1] == expected
def test_pathway():
demographics = Demographics.load(demographics_json)
assert len(demographics) == 2
disease_home = Disease.load(filename=home_json)
disease_super = Disease.load(filename=super_json)
assert demographics[1].disease is None
assert demographics[0].disease == disease_super
params = Parameters.load()
params.set_disease(disease_home)
params.set_input_files("single")
params.add_seeds("ExtraSeedsOne.dat")
network = Network.build(params)
print(network.params.disease_params)
print(disease_home)
assert network.params.disease_params == disease_home
network = network.specialise(demographics)
print(network.params.disease_params)
print(disease_home)
assert network.params.disease_params == disease_home
print(network.subnets[1].params.disease_params)
print(disease_home)
assert network.subnets[1].params.disease_params == disease_home
print(network.subnets[0].params.disease_params)
print(disease_super)
assert network.subnets[0].params.disease_params == disease_super
infections = network.initialise_infections()
assert infections.N_INF_CLASSES == disease_home.N_INF_CLASSES()
assert \
infections.subinfs[1].N_INF_CLASSES == disease_home.N_INF_CLASSES()
assert \
infections.subinfs[0].N_INF_CLASSES == disease_super.N_INF_CLASSES()
assert disease_super.N_INF_CLASSES() != disease_home.N_INF_CLASSES()
outdir = os.path.join(script_dir, "test_pathway")
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
results = network.copy().run(population=Population(),
output_dir=output_dir,
mixer=mix_evenly,
nthreads=1,
seed=36538943)
# using one thread, but if use 2 then have a system crash after
# any other test that uses the big network. This is because we
# have intialised some global data that assumes a large network,
# which then fails for the small network
OutputFiles.remove(outdir, prompt=None)
print(results[-1])
print(results[-1].initial)
expected = Population(susceptibles=519,
latent=0,
total=0,
recovereds=481,
n_inf_wards=0,
day=90)
print(expected)
assert results[-1].has_equal_SEIR(expected)
assert results[-1].day == expected.day
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
results = network.copy().run(population=Population(),
output_dir=output_dir,
mixer=mix_evenly,
nthreads=1,
seed=36538943)
OutputFiles.remove(outdir, prompt=None)
print(results[-1])
print(results[-1].initial)
print(expected)
assert results[-1].has_equal_SEIR(expected)
assert results[-1].day == expected.day
variables = VariableSet()
print("\nUpdate with null variables")
oldparams = network.params
params = network.params.set_variables(variables)
network.update(params)
assert oldparams == network.params
print(network.params.disease_params)
print(disease_home)
assert network.params.disease_params == disease_home
print(network.subnets[1].params.disease_params)
print(disease_home)
assert network.subnets[1].params.disease_params == disease_home
print(network.subnets[0].params.disease_params)
print(disease_super)
assert network.subnets[0].params.disease_params == disease_super
infections = network.initialise_infections()
assert infections.N_INF_CLASSES == disease_home.N_INF_CLASSES()
assert \
infections.subinfs[1].N_INF_CLASSES == disease_home.N_INF_CLASSES()
assert \
infections.subinfs[0].N_INF_CLASSES == disease_super.N_INF_CLASSES()
assert disease_super.N_INF_CLASSES() != disease_home.N_INF_CLASSES()
outdir = os.path.join(script_dir, "test_pathway")
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
results = network.copy().run(population=Population(),
output_dir=output_dir,
mixer=mix_evenly,
nthreads=1,
seed=36538943)
OutputFiles.remove(outdir, prompt=None)
print(results[-1])
print(expected)
assert results[-1].has_equal_SEIR(expected)
assert results[-1].day == expected.day
def test_demographics_reset(prompt=None, nthreads=1, force_multi=False):
"""This test runs several runs one after another with the expectation
that they should all give the same result. This tests that the
network is being correctly reset after each run. This test
uses a mixer and demographics to show that these can be reset
"""
# user input parameters
import random
seed = random.randint(100000, 1000000)
inputfile = ncovparams_csv
line_num = 0
UV = 0.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# start from the parameters in the specified line number of the
# provided input file
variables = params.read_variables(inputfile, line_num)
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
profiler = Profiler()
nsteps = 20
demographics = Demographics.load(redblue_json)
print("Building the network...")
network = Network.build(params=params, profiler=profiler)
network = network.specialise(demographics, nthreads=2, profiler=profiler)
outdir = os.path.join(script_dir, "test_integration_output")
if can_run_multiprocessing(force_multi):
print("Running parallel...")
variable = variables[0]
variables = VariableSets()
variables.append(variable)
variables = variables.repeat(3)
params = params.set_variables(variables[0])
network.update(params, profiler=profiler)
with OutputFiles(outdir, force_empty=True,
prompt=prompt) as output_dir:
results = run_models(network=network,
mixer=mix_shield,
output_dir=output_dir,
variables=variables,
population=population,
nsteps=nsteps,
nthreads=nthreads,
nprocs=2,
seed=seed,
debug_seeds=True)
OutputFiles.remove(outdir, prompt=None)
assert len(results) == 3
print(f"Result 1\n{results[0][1][-1]}")
print(f"Result 2\n{results[1][1][-1]}")
print(f"Result 3\n{results[2][1][-1]}")
assert results[0][1] == results[1][1]
assert results[0][1] == results[2][1]
print("Running model 1...")
network.update(params, profiler=profiler)
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory1 = network.run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
profiler=None,
mixer=mix_shield,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
# this should reset the network
print("Running model 2...")
network.update(params, profiler=profiler)
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory2 = network.run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
profiler=None,
mixer=mix_shield,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
# this should reset the network
print("Running model 3...")
network.update(params, profiler=profiler)
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory3 = network.run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
profiler=None,
mixer=mix_shield,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
print("End of the run")
print(profiler)
print(f"Model 1 output: {trajectory1}")
print(f"Model 2 output: {trajectory2}")
print(f"Model 3 output: {trajectory3}")
assert trajectory1 == trajectory2
assert trajectory1 == trajectory3
if can_run_multiprocessing(force_multi):
# this should also be the same result as the multiprocessing run
assert trajectory1 == results[0][1]
def test_not_infected():
# create a disease where it will finish with everyone in the V state
lurgy = Disease("lurgy")
lurgy.add("E", beta=0.0, progress=1.0)
lurgy.add("I1", beta=0.4, progress=0.2)
lurgy.add("I2", beta=0.5, progress=0.5, too_ill_to_move=0.5)
lurgy.add("I3", beta=0.5, progress=0.8, too_ill_to_move=0.8)
lurgy.add("V", beta=0.0, progress=0.0, is_infected=False)
lurgy.add("R")
params = Parameters()
params.set_input_files("single")
params.add_seeds("5")
params.set_disease(lurgy)
network = Network.build(params=params, population=Population(1000))
outdir = os.path.join(script_dir, "test_not_infected")
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
trajectory = network.copy().run(population=Population(),
output_dir=output_dir,
nsteps=1000,
nthreads=1)
# should finish at about 60 days and not run to 1000
p = trajectory[-1]
assert p.day < 900
# there should be no recovereds, as all who were ill went to 'others'
assert p.recovereds == 0
assert p.others == p.population - p.susceptibles
# Now repeat with more threads
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
trajectory = network.copy().run(population=Population(),
output_dir=output_dir,
nsteps=1000,
nthreads=8)
# should finish at about 60 days and not run to 1000
p = trajectory[-1]
assert p.day < 900
# there should be no recovereds, as all who were ill went to 'others'
assert p.recovereds == 0
assert p.others == p.population - p.susceptibles
# now repeat with demographics
demographics = Demographics.load(redblue_json)
network.params.add_seeds("demographic,number\\n1,10")
network = network.specialise(demographics)
with OutputFiles(outdir, force_empty=True, prompt=None) as output_dir:
trajectory = network.copy().run(population=Population(),
output_dir=output_dir,
nsteps=1000)
# should finish at about 60 days and not run to 1000
p = trajectory[-1]
assert p.day < 900
assert p.recovereds == 0
assert p.others > 0
assert p.others == p.population - p.susceptibles
p_red = p.subpops[0]
p_blue = p.subpops[1]
assert p_red.day < 900
assert p_red.recovereds == 0
assert p_red.others > 0
assert p_red.others == p_red.population - p_red.susceptibles
assert p_blue.day < 900
assert p_blue.recovereds == 0
assert p_blue.others > 0
assert p_blue.others == p_blue.population - p_blue.susceptibles
OutputFiles.remove(outdir, prompt=None)
def test_integration():
"""This test repeats main_RepeatsNcov.c and validates that the
various stages report the same results as the original C code
"""
# user input parameters
seed = 15324
inputfile = ncovparams_csv
line_num = 0
UV = 1.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease("ncov")
params.set_input_files("2011Data")
# start from the parameters in the specified line number of the
# provided input file
params.read_file(inputfile, line_num)
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
print("Building the network...")
network = Network.build(params=params, calculate_distances=True)
print("Run the model...")
population = network.run(population=population, seed=seed, s=-1, nsteps=20)
print("End of the run")
print(f"Model output: {population}")
# The original C code has this expected population after 20 steps
expected = Population(initial=57104043,
susceptibles=56081923,
latent=61,
total=17,
recovereds=76,
n_inf_wards=24)
print(f"Expect output: {expected}")
assert population == expected
def test_network_copy(prompt=None, nthreads=1):
# user input parameters
import random
seed = random.randint(100000, 1000000)
UV = 0.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(os.path.join(script_dir, "data", "ncov.json"))
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
nsteps = 20
print("Building the network...")
network = Network.build(params=params)
outdir = os.path.join(script_dir, "test_network_copy")
print("Run 1")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
t1 = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
extractor=extract_none,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
print("Run 2")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
t2 = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
extractor=extract_none,
nthreads=nthreads)
print("Run 3")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
t3 = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=nsteps,
extractor=extract_none,
nthreads=nthreads)
OutputFiles.remove(outdir, prompt=None)
print(t1)
print(t2)
print(t3)
assert t1 == t2
assert t1 == t3
def cli():
import sys
import argparse
from metawards import Parameters, Network, Population
parser = argparse.ArgumentParser(
description="MetaWards epidemic modelling - see "
"https://github.com/chryswoods/metawards "
"for more information",
prog="metawards")
parser.add_argument('-i',
'--input',
type=str,
help="Input file for the simulation")
parser.add_argument('-l',
'--line',
type=int,
default=0,
help="Line number from the inputfile to run "
"(default 0 as 0-indexed line number)")
parser.add_argument('-s',
'--seed',
type=int,
default=None,
help="Random number seed for this run "
"(default is to use a random seed)")
parser.add_argument('-u',
'--UV',
type=float,
default=1.0,
help="Value for the UV parameter for the model "
"(default is 1.0)")
parser.add_argument('-d',
'--disease',
type=str,
default="ncov",
help="Name of the disease to model "
"(default is 'ncov')")
parser.add_argument('-I',
'--input-data',
type=str,
default="2011Data",
help="Name of the input data set for the network "
"(default is '2011Data')")
parser.add_argument('-p',
'--parameters',
type=str,
default="march29",
help="Name of the input parameter set used to "
"control the simulation (default 'march29')")
parser.add_argument('-r',
'--repository',
type=str,
default=None,
help="Path to the MetaWardsData repository. If "
"unspecified this defaults to the value "
"in the environment variable METAWARDSDATA "
"or, if that isn't specified, to "
"$HOME/GitHub/MetaWardsData")
parser.add_argument('-P',
'--population',
type=int,
default=57104043,
help="Initial population (default 57104043)")
parser.add_argument('-n',
'--nsteps',
type=int,
default=None,
help="Maximum number of steps to run for the "
"simulation (default is to run until the "
"epidemic has finished)")
parser.add_argument('-o',
'--output',
type=str,
default="output",
help="Path to the directory in which to place all "
"output files (default 'output')")
args = parser.parse_args()
print(args)
if args.input is None:
parser.print_help(sys.stdout)
sys.exit(0)
# load all of the parameters
try:
params = Parameters.load(parameters=args.parameters)
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease(args.disease)
params.set_input_files(args.input_data)
# start from the parameters in the specified line number of the
# provided input file
params.read_file(args.input, args.line)
# extra parameters that are set
params.UV = args.UV
# set these extra parameters to 0
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=args.population)
print("Building the network...")
network = Network.build(params=params, calculate_distances=True)
print("Run the model...")
population = network.run(population=population,
seed=args.seed,
s=-1,
nsteps=args.nsteps,
output_dir=args.output)
print("End of the run")
print(f"Model output: {population}")
def cli():
"""Main function for the command line interface. This does one of three
things:
1. If this is the main process, then it parses the arguments and
runs and manages the jobs
2. If this is a worker process, then it starts up and waits for work
3. If this is a supervisor process, then it query the job scheduling
system for information about the compute nodes to use, and will then
set up and run a manager (main) process that will use those
nodes to run the jobs
"""
from metawards.utils import Console
# get the parallel scheme now before we import any other modules
# so that it is clear if mpi4py or scoop (or another parallel module)
# has been imported via the required "-m module" syntax
parallel_scheme = get_parallel_scheme()
if parallel_scheme == "mpi4py":
from mpi4py import MPI
comm = MPI.COMM_WORLD
nprocs = comm.Get_size()
rank = comm.Get_rank()
if rank != 0:
# this is a worker process, so should not do anything
# more until it is given work in the pool
Console.print(f"Starting worker process {rank+1} of {nprocs-1}...")
return
else:
Console.print("Starting main process...")
elif parallel_scheme == "scoop":
Console.print("STARTING SCOOP PROCESS")
import sys
args, parser = parse_args()
if not args.already_supervised:
hostfile = get_hostfile(args)
if hostfile:
# The user has asked to run a parallel job - this means that this
# process is the parallel supervisor
if args.mpi:
mpi_supervisor(hostfile, args)
return
elif args.scoop:
scoop_supervisor(hostfile, args)
return
# neither is preferred - if scoop is installed then use that
try:
import scoop # noqa - disable unused warning
have_scoop = True
except Exception:
have_scoop = False
if have_scoop:
scoop_supervisor(hostfile, args)
return
# do we have MPI?
try:
import mpi4py # noqa - disable unused warning
have_mpi4py = True
except Exception:
have_mpi4py = False
if have_mpi4py:
mpi_supervisor(hostfile, args)
return
# we don't have any other option, just keep going and
# use multiprocessing - in this case we don't need a
# supervisor and this is the main process
# This is now the code for the main process
# WE NEED ONE OF these listed options;
should_run = False
for arg in [
args.input, args.repeats, args.disease, args.additional,
args.model, args.iterator, args.extractor, args.demographics,
args.mixer, args.mover
]:
if arg is not None:
should_run = True
break
if not should_run:
parser.print_help(sys.stdout)
sys.exit(0)
if args.repeats is None:
args.repeats = [1]
# import the parameters here to speed up the display of help
from metawards import Parameters, Network, Population, print_version_string
# print the version information first, so that there is enough
# information to enable someone to reproduce this run
print_version_string()
Console.rule("Initialise")
if args.input:
# get the line numbers of the input file to read
if args.line is None or len(args.line) == 0:
linenums = None
Console.print(f"* Using parameters from all lines of {args.input}",
markdown=True)
else:
from metawards.utils import string_to_ints
linenums = string_to_ints(args.line)
if len(linenums) == 0:
Console.error(f"You cannot read no lines from {args.input}?")
sys.exit(-1)
elif len(linenums) == 1:
Console.print(
f"* Using parameters from line {linenums[0]} of "
f"{args.input}",
markdown=True)
else:
Console.print(
f"* Using parameters from lines {linenums} of "
f"{args.input}",
markdown=True)
from metawards import VariableSets, VariableSet
variables = VariableSets.read(filename=args.input,
line_numbers=linenums)
else:
from metawards import VariableSets, VariableSet
# create a VariableSets with one null VariableSet
variables = VariableSets()
variables.append(VariableSet())
nrepeats = args.repeats
if nrepeats is None or len(nrepeats) < 1:
nrepeats = [1]
if len(nrepeats) > 1 and len(variables) != len(nrepeats):
Console.error(f"The number of repeats {len(nrepeats)} must equal the "
f"number of adjustable variable lines {len(variables)}")
raise ValueError("Disagreement in the number of repeats and "
"adjustable variables")
# ensure that all repeats are >= 0
nrepeats = [0 if int(x) < 0 else int(x) for x in nrepeats]
if sum(nrepeats) == 0:
Console.error(f"The number of the number of repeats is 0. Are you "
f"sure that you don't want to run anything?")
raise ValueError("Cannot run nothing")
if len(nrepeats) == 1 and nrepeats[0] == 1:
Console.print("* Performing a single run of each set of parameters",
markdown=True)
elif len(nrepeats) == 1:
Console.print(
f"* Performing {nrepeats[0]} runs of each set of parameters",
markdown=True)
else:
Console.print(
f"* Performing {nrepeats} runs applied to the parameters",
markdown=True)
variables = variables.repeat(nrepeats)
# working out the number of processes and threads...
from metawards.utils import guess_num_threads_and_procs
(nthreads,
nprocs) = guess_num_threads_and_procs(njobs=len(variables),
nthreads=args.nthreads,
nprocs=args.nprocs,
parallel_scheme=parallel_scheme)
Console.print(
f"\n* Number of threads to use for each model run is {nthreads}",
markdown=True)
if nprocs > 1:
Console.print(
f"* Number of processes used to parallelise model "
f"runs is {nprocs}",
markdown=True)
Console.print(
f"* Parallelisation will be achieved using {parallel_scheme}",
markdown=True)
# sort out the random number seed
seed = args.seed
if seed is None:
import random
seed = random.randint(10000, 99999999)
if seed == 0:
# this is a special mode that a developer can use to force
# all jobs to use the same random number seed (15324) that
# is used for comparing outputs. This should NEVER be used
# for production code
Console.warning("Using special mode to fix all random number"
"seeds to 15324. DO NOT USE IN PRODUCTION!!!")
else:
Console.print(f"* Using random number seed {seed}", markdown=True)
# get the starting day and date
start_day = args.start_day
if start_day < 0:
raise ValueError(f"You cannot use a start day {start_day} that is "
f"less than zero!")
start_date = None
if args.start_date:
try:
from dateparser import parse
start_date = parse(args.start_date).date()
except Exception:
pass
if start_date is None:
from datetime import date
try:
start_date = date.fromisoformat(args.start_date)
except Exception as e:
raise ValueError(f"Cannot interpret a valid date from "
f"'{args.start_date}'. Error is "
f"{e.__class__} {e}")
if start_date is None:
from datetime import date
start_date = date.today()
Console.print(f"* Day zero is {start_date.strftime('%A %B %d %Y')}",
markdown=True)
if start_day != 0:
from datetime import timedelta
start_day_date = start_date + timedelta(days=start_day)
Console.print(f"Starting on day {start_day}, which is "
f"{start_day_date.strftime('%A %B %d %Y')}")
else:
start_day_date = start_date
# now find the MetaWardsData repository as this will be needed
# for the repeat command line too
(repository,
repository_version) = Parameters.get_repository(args.repository)
Console.print(f"* Using MetaWardsData at {repository}", markdown=True)
if repository_version["is_dirty"]:
Console.warning("This repository is dirty, meaning that the data"
"has not been committed to git. This may make "
"this calculation very difficult to reproduce")
# now work out the minimum command line needed to repeat this job
args.seed = seed
args.nprocs = nprocs
args.nthreads = nthreads
args.start_date = start_date.isoformat()
args.repository = repository
# also print the source of all inputs
import configargparse
Console.rule("Souce of inputs")
p = configargparse.get_argument_parser("main")
Console.print(p.format_values())
# print out the command used to repeat this job
repeat_cmd = "metawards"
for key, value in vars(args).items():
if value is not None:
k = key.replace("_", "-")
if isinstance(value, bool):
if value:
repeat_cmd += f" --{k}"
elif isinstance(value, list):
repeat_cmd += f" --{k}"
for val in value:
v = str(val)
if " " in v:
repeat_cmd += f" '{v}''"
else:
repeat_cmd += f" {v}"
else:
v = str(value)
if " " in v:
repeat_cmd += f" --{k} '{v}''"
else:
repeat_cmd += f" --{k} {v}"
Console.rule("Repeating this run")
Console.print("To repeat this job use the command;")
Console.command(repeat_cmd)
Console.print("Or alternatively use the config.yaml file that will be "
"written to the output directory and use the command;")
Console.command("metawards -c config.yaml")
# load all of the parameters
try:
params = Parameters.load(parameters=args.parameters)
except Exception as e:
Console.warning(
f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# should we profile the code? (default no as it prints a lot)
profiler = None
if args.no_profile:
profiler = None
elif args.profile:
from metawards.utils import Profiler
profiler = Profiler()
# load the disease and starting-point input files
Console.rule("Disease")
if args.disease:
params.set_disease(args.disease)
else:
params.set_disease("ncov")
Console.rule("Model data")
if args.model:
params.set_input_files(args.model)
else:
params.set_input_files("2011Data")
# load the user-defined custom parameters
Console.rule("Custom parameters and seeds")
if args.user_variables:
custom = VariableSet.read(args.user_variables)
Console.print(f"Adjusting variables to {custom}")
custom.adjust(params)
else:
Console.print("Not adjusting any parameters...")
# read the additional seeds
if args.additional is None or len(args.additional) == 0:
Console.print("Not using any additional seeds...")
else:
for additional in args.additional:
Console.print(f"Loading additional seeds from {additional}")
params.add_seeds(additional)
# what to do with the 0 state?
stage_0 = "R"
if args.disable_star:
Console.print("Disabling the * state. Stage 0 is the one and "
"only E state.")
stage_0 = "disable"
elif args.star_is_E:
Console.print("Setting the * state as an additional E state.")
stage_0 = "E"
else:
Console.print("Setting the * state as an additional R state.")
stage_0 = "R"
params.stage_0 = stage_0
# extra parameters that are set
params.UV = args.UV
# set these extra parameters to 0
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
Console.rule("Parameters")
Console.print(params, markdown=True)
# the size of the starting population
population = Population(initial=args.population,
date=start_day_date,
day=start_day)
Console.rule("Building the network")
network = Network.build(params=params,
population=population,
max_nodes=args.max_nodes,
max_links=args.max_links,
profiler=profiler)
if args.demographics:
from metawards import Demographics
Console.rule("Specialising into demographics")
demographics = Demographics.load(args.demographics)
Console.print(demographics)
network = network.specialise(demographics,
profiler=profiler,
nthreads=nthreads)
Console.rule("Preparing to run")
from metawards import OutputFiles
from metawards.utils import run_models
outdir = args.output
if outdir is None:
outdir = "output"
if args.force_overwrite_output:
prompt = None
else:
from metawards import input
def prompt(x):
return input(x, default="y")
auto_bzip = True
if args.auto_bzip:
auto_bzip = True
elif args.no_auto_bzip:
auto_bzip = False
if args.iterator:
iterator = args.iterator
else:
iterator = None
if args.extractor:
extractor = args.extractor
else:
extractor = None
if args.mixer:
mixer = args.mixer
else:
mixer = None
if args.mover:
mover = args.mover
else:
mover = None
with OutputFiles(outdir,
force_empty=args.force_overwrite_output,
auto_bzip=auto_bzip,
prompt=prompt) as output_dir:
# write the config file for this job to output/config.yaml
Console.rule("Running the model")
CONSOLE = output_dir.open("console.log")
Console.save(CONSOLE)
lines = []
max_keysize = None
for key, value in vars(args).items():
if max_keysize is None:
max_keysize = len(key)
elif len(key) > max_keysize:
max_keysize = len(key)
for key, value in vars(args).items():
if value is not None:
key = key.replace("_", "-")
spaces = " " * (max_keysize - len(key))
if isinstance(value, bool):
if value:
lines.append(f"{key}:{spaces} true")
else:
lines.append(f"{key}:{spaces} false")
elif isinstance(value, list):
s_value = [str(x) for x in value]
lines.append(f"{key}:{spaces} [ {', '.join(s_value)} ]")
else:
lines.append(f"{key}:{spaces} {value}")
CONFIG = output_dir.open("config.yaml", auto_bzip=False)
lines.sort(key=str.swapcase)
CONFIG.write("\n".join(lines))
CONFIG.write("\n")
CONFIG.flush()
CONFIG.close()
lines = None
result = run_models(network=network,
variables=variables,
population=population,
nprocs=nprocs,
nthreads=nthreads,
seed=seed,
nsteps=args.nsteps,
output_dir=output_dir,
iterator=iterator,
extractor=extractor,
mixer=mixer,
mover=mover,
profiler=profiler,
parallel_scheme=parallel_scheme)
if result is None or len(result) == 0:
Console.print("No output - end of run")
return 0
Console.rule("End of the run", style="finish")
Console.save(CONSOLE)
return 0
def test_local():
prompt = None
# user input parameters
seed = 15324
UV = 1.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease("ncov")
params.set_input_files("2011Data")
params.add_seeds("1 5 2124") # seeding 5 into ward 2124
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
print("Building the network...")
network = Network.build(params=params)
outdir = os.path.join(script_dir, "test_local_output")
# First check that setting the local cutoff has the same effect
# as setting the global cutoff
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=50,
iterator=iterate_cutoff,
extractor=extract_cutoff,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
# run setting the global dyn_dist_cutoff to zero
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
net2 = network.copy()
net2.params.dyn_dist_cutoff = 0.0
trajectory2 = net2.run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=50,
extractor=extract_cutoff,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print(f"Model output: {trajectory}")
print(f"Model output: {trajectory2}")
assert trajectory == trajectory2
# now test that setting the scale_uv has the same effect as
# setting the global scale_uv
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=50,
iterator=iterate_scale,
extractor=extract_scale,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
# run setting the global dyn_dist_cutoff to zero
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
net2 = network.copy()
pop2 = deepcopy(population)
pop2.scale_uv = 0.0
trajectory2 = net2.run(population=pop2,
seed=seed,
output_dir=output_dir,
nsteps=50,
extractor=extract_scale,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print(f"Model output: {trajectory}")
print(f"Model output: {trajectory2}")
p = trajectory[-1]
p2 = trajectory2[-1]
# won't be identical as different scale_uv causes different order
# of random numbers - but should still have 0 infections
assert p.has_equal_SEIR(p2)
# now test that setting both to non-zero values has the same effect
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
trajectory = network.copy().run(population=population,
seed=seed,
output_dir=output_dir,
nsteps=50,
iterator=iterate_both,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
# run setting the global dyn_dist_cutoff to zero
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
net2 = network.copy()
net2.params.dyn_dist_cutoff = 42.0
pop2 = deepcopy(population)
pop2.scale_uv = 0.5
trajectory2 = net2.run(population=pop2,
seed=seed,
output_dir=output_dir,
nsteps=50,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print(f"Model output: {trajectory}")
print(f"Model output: {trajectory2}")
p = trajectory[-1]
p2 = trajectory2[-1]
p2.scale_uv = 1
assert p == p2
def test_integration_pox(prompt=None):
"""This test repeats main_RepeatsNcov.c and validates that the
various stages report the same results as the original C code
for the POX
"""
# user input parameters
seed = 15324
inputfile = ncovparams_csv
line_num = 0
UV = 1.0
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
# load the disease and starting-point input files
params.set_disease("pox")
params.set_input_files("2011Data")
params.add_seeds("ExtraSeedsBrighton.dat")
# start from the parameters in the specified line number of the
# provided input file
variables = params.read_variables(inputfile, line_num)
# extra parameters that are set
params.UV = UV
params.static_play_at_home = 0
params.play_to_work = 0
params.work_to_play = 0
params.daily_imports = 0.0
# the size of the starting population
population = Population(initial=57104043)
profiler = Profiler()
print("Building the network...")
network = Network.build(params=params,
profiler=profiler)
params = params.set_variables(variables[0])
network.update(params, profiler=profiler)
outdir = os.path.join(script_dir, "test_integration_output")
with OutputFiles(outdir, force_empty=True, prompt=prompt) as output_dir:
print("Run the model...")
trajectory = network.run(population=population, seed=seed,
output_dir=output_dir,
nsteps=31, profiler=profiler,
nthreads=1)
OutputFiles.remove(outdir, prompt=None)
print("End of the run")
Console.print_profiler(profiler)
Console.rule("Model output")
Console.print_population(trajectory[-1])
# The original C code has this expected population after 47 steps
expected = Population(initial=57104043,
susceptibles=56080780,
latent=374,
total=370,
recovereds=553,
n_inf_wards=289,
day=31)
Console.rule("Expected output")
Console.print_population(expected)
assert trajectory[-1] == expected
def test_ward_conversion():
# load all of the parameters
try:
params = Parameters.load(parameters="march29")
except Exception as e:
print(f"Unable to load parameter files. Make sure that you have "
f"cloned the MetaWardsData repository and have set the "
f"environment variable METAWARDSDATA to point to the "
f"local directory containing the repository, e.g. the "
f"default is $HOME/GitHub/MetaWardsData")
raise e
params.set_input_files("2011Data")
print("Building the network...")
network = Network.build(params=params)
profiler = Profiler()
profiler = profiler.start("to_json")
wards = network.to_wards(profiler=profiler)
print(f"{wards.num_workers()} / {wards.num_players()}")
_assert_equal(wards.num_workers(), network.work_population)
_assert_equal(wards.num_players(), network.play_population)
print(f"{wards.num_work_links()} / {wards.num_play_links()}")
_assert_equal(wards.num_work_links(), network.nlinks)
_assert_equal(wards.num_play_links(), network.nplay)
print("Converting to data...")
data = wards.to_data(profiler=profiler)
print("Converting to json...")
profiler = profiler.start("Convert to JSON")
s = json.dumps(data)
profiler = profiler.stop()
profiler = profiler.stop() # end to_json
print(f"Done - {len(s)/(1024*1024.0)} MB : {s[0:1024]}...")
print(f"Converting from json...")
profiler = profiler.start("Convert from JSON")
profiler = profiler.start("from_json")
data = json.loads(s)
profiler = profiler.stop()
wards2 = Wards.from_data(data, profiler=profiler)
assert wards2 == wards
network2 = Network.from_wards(wards2, profiler=profiler)
profiler = profiler.stop()
Console.print(profiler)
Console.print("Validating equality - may take some time...")
_assert_equal(network2.nnodes, network.nnodes)
_assert_equal(network2.nlinks, network.nlinks)
_assert_equal(network2.nplay, network.nplay)
if network.info is None:
assert network2.info is None
_assert_equal(len(network.info), len(network2.info))
Console.print(f"{len(network.info)}, {network.nnodes}")
with Console.progress() as progress:
task1 = progress.add_task("Validating info", total=len(network.info))
task2 = progress.add_task("Validating nodes", total=network.nnodes)
task3 = progress.add_task("Validating work", total=network.nlinks)
task4 = progress.add_task("Validating play", total=network.nplay)
for i in range(0, len(network.info)):
assert network.info[i] == network2.info[i]
progress.update(task1, advance=1)
progress.update(task1, completed=len(network.info), force_update=True)
for i in range(1, network.nnodes + 1):
_assert_equal(network.nodes.label[i], network2.nodes.label[i])
_assert_equal(network.nodes.begin_to[i],
network2.nodes.begin_to[i])
_assert_equal(network.nodes.end_to[i], network2.nodes.end_to[i])
_assert_equal(network.nodes.self_w[i], network2.nodes.self_w[i])
_assert_equal(network.nodes.begin_p[i], network2.nodes.begin_p[i])
_assert_equal(network.nodes.end_p[i], network2.nodes.end_p[i])
_assert_equal(network.nodes.self_p[i], network2.nodes.self_p[i])
_assert_equal(network.nodes.x[i], network2.nodes.x[i])
_assert_equal(network.nodes.y[i], network2.nodes.y[i])
progress.update(task2, advance=1)
progress.update(task2, completed=network.nnodes, force_update=True)
for i in range(1, network.nlinks + 1):
_assert_equal(network.links.ifrom[i], network2.links.ifrom[i])
_assert_equal(network.links.ito[i], network2.links.ito[i])
_assert_equal(network.links.weight[i], network2.links.weight[i])
_assert_equal(network.links.suscept[i], network2.links.suscept[i])
progress.update(task3, advance=1)
progress.update(task3, completed=network.nlinks, force_update=True)
for i in range(1, network.nplay + 1):
_assert_equal(network.play.ifrom[i], network2.play.ifrom[i])
_assert_equal(network.play.ito[i], network2.play.ito[i])
_assert_equal(network.play.weight[i], network2.play.weight[i])
_assert_equal(network.play.suscept[i], network2.play.suscept[i])
progress.update(task4, advance=1)
progress.update(task4, completed=network.nplay, force_update=True)