def main():
sysrng = random.SystemRandom()
params = load_json(os.path.join(SCRIPT_DIR, 'transient.json'))
params['random_seed'] = sysrng.randint(1, 2**31 - 1)
S_init = [sysrng.random(), sysrng.random()]
params['S_init'] = S_init
I_init = [sysrng.uniform(0.0, S_init[0]), sysrng.uniform(0.0, S_init[1])]
params['I_init'] = I_init
ts_filename = 'transient.npy'
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
sir_out = models.run_via_pypy('multistrain_sde', params)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
years = 100
simulation_samples_per_year = 36
ccm_samples_per_year = 12
add_samples = True
log_transform = False
first_difference = False
standardize = False
# Connect to output database
if os.path.exists('results.sqlite'):
sys.stderr.write('Output database present. Aborting.\n')
sys.exit(1)
db = sqlite3.connect('results.sqlite')
# Set up RNG
rng = numpy.random.RandomState(sysrng.randint(1, 2**31 - 1))
X = subsample_simulation(C, years, simulation_samples_per_year,
ccm_samples_per_year, add_samples, log_transform,
first_difference, standardize)
if numpy.any(numpy.logical_or(numpy.isnan(X), numpy.isinf(X))):
sys.stderr.write('nans or infs in data; skipping all analyses.\n')
sys.exit(0)
x0 = X[:, 0]
x1 = X[:, 1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
plot_timeseries([x0, x1], [x0name, x1name], 'time', 'value',
'timeseries.png')
run_analysis(x0name, x0, x1name, x1, db, rng)
run_analysis(x1name, x1, x0name, x0, db, rng)
db.commit()
db.close()
plot_data_by_lag()
def run_simulation():
try:
db.execute('CREATE TABLE sir_params (params TEXT)')
db.execute('INSERT INTO sir_params VALUES (?)', (params.dump_to_string(),))
return models.run_via_pypy('multistrain_sde', params)
except models.ExecutionException as e:
sys.stderr.write('An exception occurred trying to run simulation...\n')
sys.stderr.write('{0}\n'.format(e.cause))
sys.stderr.write(e.stderr_data)
sys.exit(1)
def run_simulation():
try:
db.execute('CREATE TABLE sir_params (params TEXT)')
db.execute('INSERT INTO sir_params VALUES (?)',
(params.dump_to_string(), ))
return models.run_via_pypy('multistrain_sde', params)
except models.ExecutionException as e:
sys.stderr.write('An exception occurred trying to run simulation...\n')
sys.stderr.write('{0}\n'.format(e.cause))
sys.stderr.write(e.stderr_data)
sys.exit(1)
def main():
ccm_params = load_json(os.path.join('ccm_params.json'))
params = load_json(os.path.join('params.json'))
ts_filename = os.path.join('transient.npy')
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
try:
sir_out = models.run_via_pypy('multistrain_sde', params)
except models.ExecutionException as e:
print e.cause
print e.stdout_data
print e.stderr_data
sys.exit(1)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
years = int(params['t_end'] / 360)
simulation_samples_per_year = 36
ccm_samples_per_year = ccm_params['samples_per_year']
add_samples = True
log_transform = False
first_difference = False
standardize = False
# t = numpy.arange(years * 12) / 12.0
# print t.shape
print C.shape
X = subsample_simulation(C, years, simulation_samples_per_year,
ccm_samples_per_year, add_samples, log_transform,
first_difference, standardize)
if numpy.any(numpy.logical_or(numpy.isnan(X), numpy.isinf(X))):
sys.stderr.write('nans or infs in data; skipping all analyses.\n')
sys.exit(0)
x0 = X[:, 0]
x1 = X[:, 1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
# Set up RNG
rng = numpy.random.RandomState(random.SystemRandom().randint(1, 2**31 - 1))
theiler_window = ccm_params['samples_per_year'] * 2
# RUN ON START
start_range = ccm_params['start_range']
x0_start = x0[start_range[0] * ccm_samples_per_year:start_range[1] *
ccm_samples_per_year]
x1_start = x1[start_range[0] * ccm_samples_per_year:start_range[1] *
ccm_samples_per_year]
plot_timeseries([x0_start, x1_start], [x0name, x1name], 'time', 'value',
'ts_start.png')
mid_range = ccm_params['mid_range']
x0_mid = x0[mid_range[0] * ccm_samples_per_year:mid_range[1] *
ccm_samples_per_year]
x1_mid = x1[mid_range[0] * ccm_samples_per_year:mid_range[1] *
ccm_samples_per_year]
plot_timeseries([x0_mid, x1_mid], [x0name, x1name], 'time', 'value',
'ts_mid.png')
end_range = ccm_params['end_range']
x0_end = x0[end_range[0] * ccm_samples_per_year:end_range[1] *
ccm_samples_per_year]
x1_end = x1[end_range[0] * ccm_samples_per_year:end_range[1] *
ccm_samples_per_year]
plot_timeseries([x0_end, x1_end], [x0name, x1name], 'time', 'value',
'ts_end.png')
if os.path.exists('results_start.sqlite'):
sys.stderr.write('Output database present. Skipping CCM run.\n')
else:
db = sqlite3.connect('results_start.sqlite')
run_analysis(x0name, x0_start, x1name, x1_start, db, rng,
theiler_window)
run_analysis(x1name, x1_start, x0name, x0_start, db, rng,
theiler_window)
db.commit()
db.close()
plot_data_by_lag('results_start.sqlite', 'ccm_by_lag_start.png')
# RUN ON END
if os.path.exists('results_end.sqlite'):
sys.stderr.write('Output database present. Skipping CCM run.\n')
else:
db = sqlite3.connect('results_end.sqlite')
run_analysis(x0name, x0_end, x1name, x1_end, db, rng, theiler_window)
run_analysis(x1name, x1_end, x0name, x0_end, db, rng, theiler_window)
db.commit()
db.close()
plot_data_by_lag('results_end.sqlite', 'ccm_by_lag_end.png')
# RUN ON MIDDLE
if os.path.exists('results_mid.sqlite'):
sys.stderr.write('Output database present. Skipping CCM run.\n')
else:
db = sqlite3.connect('results_mid.sqlite')
run_analysis(x0name, x0_mid, x1name, x1_mid, db, rng, theiler_window)
run_analysis(x1name, x1_mid, x0name, x0_mid, db, rng, theiler_window)
db.commit()
db.close()
plot_data_by_lag('results_mid.sqlite', 'ccm_by_lag_mid.png')
def main():
# params = load_json(os.path.join(SCRIPT_DIR, 'transient.json'))
#
ts_filename = os.path.join(SCRIPT_DIR, 'transient.npy')
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
sir_out = models.run_via_pypy('multistrain_sde', params)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
#
years = 100
simulation_samples_per_year = 36
ccm_samples_per_year = 12
add_samples = True
log_transform = False
first_difference = False
standardize = False
#
# t = numpy.arange(100*12) / 12.0
#
# print t.shape
# print C.shape
#
# # Connect to output database
# if os.path.exists('results.sqlite'):
# sys.stderr.write('Output database present. Aborting.\n')
# sys.exit(1)
# db = sqlite3.connect('results.sqlite')
#
# # Set up RNG
# random_seed = 1234
# rng = numpy.random.RandomState(random_seed)
X = subsample_simulation(
C, years, simulation_samples_per_year, ccm_samples_per_year,
add_samples, log_transform, first_difference, standardize
)
#
# if numpy.any(numpy.logical_or(
# numpy.isnan(X),
# numpy.isinf(X)
# )):
# sys.stderr.write('nans or infs in data; skipping all analyses.\n')
# sys.exit(0)
#
x0 = X[:,0]
x1 = X[:,1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
plot_timeseries([x0, x1], [x0name, x1name], 'time', 'value', 'timeseries.png')
#
# run_analysis(x0name, x0, x1name, x1, db, rng)
# run_analysis(x1name, x1, x0name, x0, db, rng)
#
# db.commit()
# db.close()
plot_data_by_lag()
def main():
sysrng = random.SystemRandom()
params = load_json(os.path.join(SCRIPT_DIR, 'transient.json'))
params['random_seed'] = sysrng.randint(1, 2**31 - 1)
S_init = [sysrng.random(), sysrng.random()]
params['S_init'] = S_init
I_init = [sysrng.uniform(0.0, S_init[0]), sysrng.uniform(0.0, S_init[1])]
params['I_init'] = I_init
ts_filename = 'transient.npy'
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
sir_out = models.run_via_pypy('multistrain_sde', params)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
years = 100
simulation_samples_per_year = 36
ccm_samples_per_year = 12
add_samples = True
log_transform = False
first_difference = False
standardize = False
# Connect to output database
if os.path.exists('results.sqlite'):
sys.stderr.write('Output database present. Aborting.\n')
sys.exit(1)
db = sqlite3.connect('results.sqlite')
# Set up RNG
rng = numpy.random.RandomState(sysrng.randint(1, 2**31 - 1))
X = subsample_simulation(
C, years, simulation_samples_per_year, ccm_samples_per_year,
add_samples, log_transform, first_difference, standardize
)
if numpy.any(numpy.logical_or(
numpy.isnan(X),
numpy.isinf(X)
)):
sys.stderr.write('nans or infs in data; skipping all analyses.\n')
sys.exit(0)
x0 = X[:,0]
x1 = X[:,1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
plot_timeseries([x0, x1], [x0name, x1name], 'time', 'value', 'timeseries.png')
run_analysis(x0name, x0, x1name, x1, db, rng)
run_analysis(x1name, x1, x0name, x0, db, rng)
db.commit()
db.close()
plot_data_by_lag()
def main():
# params = load_json(os.path.join(SCRIPT_DIR, 'transient.json'))
#
ts_filename = os.path.join(SCRIPT_DIR, 'transient.npy')
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
sir_out = models.run_via_pypy('multistrain_sde', params)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
#
years = 100
simulation_samples_per_year = 36
ccm_samples_per_year = 12
add_samples = True
log_transform = False
first_difference = False
standardize = False
#
# t = numpy.arange(100*12) / 12.0
#
# print t.shape
# print C.shape
#
# # Connect to output database
# if os.path.exists('results.sqlite'):
# sys.stderr.write('Output database present. Aborting.\n')
# sys.exit(1)
# db = sqlite3.connect('results.sqlite')
#
# # Set up RNG
# random_seed = 1234
# rng = numpy.random.RandomState(random_seed)
X = subsample_simulation(C, years, simulation_samples_per_year,
ccm_samples_per_year, add_samples, log_transform,
first_difference, standardize)
#
# if numpy.any(numpy.logical_or(
# numpy.isnan(X),
# numpy.isinf(X)
# )):
# sys.stderr.write('nans or infs in data; skipping all analyses.\n')
# sys.exit(0)
#
x0 = X[:, 0]
x1 = X[:, 1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
plot_timeseries([x0, x1], [x0name, x1name], 'time', 'value',
'timeseries.png')
#
# run_analysis(x0name, x0, x1name, x1, db, rng)
# run_analysis(x1name, x1, x0name, x0, db, rng)
#
# db.commit()
# db.close()
plot_data_by_lag()
def main():
ccm_params = load_json(os.path.join('ccm_params.json'))
params = load_json(os.path.join('params.json'))
ts_filename = os.path.join('transient.npy')
if os.path.exists(ts_filename):
C = numpy.load(ts_filename)
else:
try:
sir_out = models.run_via_pypy('multistrain_sde', params)
except models.ExecutionException as e:
print e.cause
print e.stdout_data
print e.stderr_data
sys.exit(1)
C = numpy.array(sir_out['C'])
numpy.save(ts_filename, C)
years = int(params['t_end'] / 360)
simulation_samples_per_year = 36
ccm_samples_per_year = ccm_params['samples_per_year']
add_samples = True
log_transform = False
first_difference = False
standardize = False
# t = numpy.arange(years * 12) / 12.0
# print t.shape
print C.shape
X = subsample_simulation(
C, years, simulation_samples_per_year, ccm_samples_per_year,
add_samples, log_transform, first_difference, standardize
)
if numpy.any(numpy.logical_or(
numpy.isnan(X),
numpy.isinf(X)
)):
sys.stderr.write('nans or infs in data; skipping all analyses.\n')
sys.exit(0)
x0 = X[:,0]
x1 = X[:,1]
variable_name = 'C'
x0name = variable_name + '0'
x1name = variable_name + '1'
# Set up RNG
rng = numpy.random.RandomState(random.SystemRandom().randint(1, 2**31 - 1))
theiler_window = ccm_params['samples_per_year'] * 2
# RUN ON START
start_range = ccm_params['start_range']
x0_start = x0[start_range[0] * ccm_samples_per_year:start_range[1] * ccm_samples_per_year]
x1_start = x1[start_range[0] * ccm_samples_per_year:start_range[1] * ccm_samples_per_year]
save_timeseries(x0_start, x1_start, 'ts_start.json')
mid_range = ccm_params['mid_range']
x0_mid = x0[mid_range[0] * ccm_samples_per_year:mid_range[1] * ccm_samples_per_year]
x1_mid = x1[mid_range[0] * ccm_samples_per_year:mid_range[1] * ccm_samples_per_year]
save_timeseries(x0_mid, x1_mid, 'ts_mid.json')
end_range = ccm_params['end_range']
x0_end = x0[end_range[0] * ccm_samples_per_year:end_range[1] * ccm_samples_per_year]
x1_end = x1[end_range[0] * ccm_samples_per_year:end_range[1] * ccm_samples_per_year]
save_timeseries(x0_end, x1_end, 'ts_end.json')
