def main():
"""
Multi-Core Markov-chain Monte Carlo (MC3) wrapper for the command line
Notes
-----
To display the full list of arguments, run from the prompt:
mc3 -h
Command-line arguments overwrite the config-file arguments if the
argument is defined twice.
"""
parser = parse()
# Parse command-line args (right now, just interested in the config file):
args, unknown = parser.parse_known_args()
# Parse configuration file to a dictionary:
if args.cfile is not None and not os.path.isfile(args.cfile):
print("Configuration file: '{:s}' not found.".format(args.cfile))
sys.exit(0)
if args.cfile:
config = configparser.ConfigParser()
config.read([args.cfile])
defaults = dict(config.items("MCMC"))
else:
defaults = {}
# Set defaults from the configuration-file values:
parser.set_defaults(**defaults)
# Overwrite defaults with the command-line arguments:
args, unknown = parser.parse_known_args()
delattr(args, 'cfile')
# Call MCMC driver:
mc3.sample(**vars(args))
def test_mcmc_sampler_error(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
indparams=[x], pstep=pstep,
nsamples=1e4, burnin=100)
captured = capsys.readouterr()
assert output is None
assert "'sampler' is a required argument." in captured.out
def test_mcmc_nsamples_error(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, burnin=100)
captured = capsys.readouterr()
assert output is None
assert "'nsamples' is a required argument for MCMC runs." in captured.out
def test_mcmc_samples_error(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x], pstep=pstep,
nsamples=1e4, burnin=2000)
captured = capsys.readouterr()
assert output is None
assert "The number of burned-in samples (2000) is greater" in captured.out
def test_mcmc_deprecation_full_output(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x], stepsize=pstep,
nsamples=1e3, burnin=2, ncpu=7, full_output=True)
captured = capsys.readouterr()
assert output is not None
assert "full_output argument is deprecated." in captured.out
def test_mcmc_deprecation_walk(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
walk=sampler, indparams=[x], stepsize=pstep,
nsamples=1e3, burnin=2, ncpu=7)
captured = capsys.readouterr()
assert output is not None
assert "walk argument is deprecated. Use sampler instead." in captured.out
def test_mcmc_shared():
output = mc3.sample(data1, uncert1, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=[0.03, -1, 0.05],
nsamples=1e4, burnin=100)
assert output is not None
assert output['bestp'][1] == output['bestp'][0]
def test_mcmc_func_error(capsys):
output = mc3.sample(data=data, uncert=uncert, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100)
captured = capsys.readouterr()
assert output is None
assert "'func' must be either a callable or an iterable" in captured.out
def test_mcmc_gr(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
grtest=True)
captured = capsys.readouterr()
assert output is not None
assert "Gelman-Rubin statistics for free parameters:" in captured.out
def test_mcmc_leastsq_error(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x], pstep=pstep,
leastsq='invalid', nsamples=1e4, burnin=100)
captured = capsys.readouterr()
assert output is None
assert "Invalid 'leastsq' input (invalid). Must select from " \
"['lm', 'trf']." in captured.out
def test_mcmc_deprecation_parname(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x], pstep=pstep,
nsamples=1e3, burnin=2, parname=pnames)
captured = capsys.readouterr()
assert output is not None
assert "parname argument is deprecated. Use pnames instead." \
in captured.out
def test_mcmc_gr_break_iterations(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
grtest=True, grbreak=1.1, grnmin=5000.0)
captured = capsys.readouterr()
assert output is not None
assert "All parameters satisfy the GR convergence threshold of 1.1" \
in captured.out
def test_mcmc_func_as_strings(tmp_path):
p = tmp_path / "quadratic.py"
CONTENT = u'def quad(p, x):\n y = p[0] + p[1]*x + p[2]*x**2.0\n return y'
p.write_text(CONTENT)
output = mc3.sample(func=('quad', 'quadratic', str(tmp_path)),
params=np.copy(params),
data=data, uncert=uncert, indparams=[x], pstep=pstep,
sampler=sampler, nsamples=1e4, burnin=100)
assert output is not None
def test_mcmc_optimize(capsys, leastsq):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
leastsq=leastsq)
captured = capsys.readouterr()
assert output is not None
assert "Least-squares best-fitting parameters:" in captured.out
np.testing.assert_allclose(output['bestp'],
np.array([4.28263253, -2.40781859, 0.49534411]), rtol=1e-7)
def test_mcmc_pnames_texnames(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
pnames=pnames, texnames=texnames)
captured = capsys.readouterr()
assert output is not None
assert "constant" in captured.out
assert "linear" in captured.out
assert "quadratic" in captured.out
def test_mcmc_texnames(capsys):
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
texnames=texnames)
captured = capsys.readouterr()
assert output is not None
assert "$\\alpha$" in captured.out
assert "$\\log(\\beta" in captured.out
assert "quadratic" in captured.out
def test_mcmc_savefile(capsys, tmp_path):
os.chdir(str(tmp_path))
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
savefile='MCMC.npz')
captured = capsys.readouterr()
assert output is not None
assert "'MCMC.npz'" in captured.out
assert "MCMC.npz" in os.listdir(".")
def test_mcmc_priors_gauss():
prior = np.array([ 4.5, 0.0, 0.0])
priorlow = np.array([ 0.1, 0.0, 0.0])
priorup = np.array([ 0.1, 0.0, 0.0])
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
prior=prior, priorlow=priorlow, priorup=priorup)
assert output is not None
assert -2*output['best_log_post'] > output['best_chisq']
assert np.all(-2*output['log_post'] > output['chisq'])
def test_mcmc_fixed():
pars = np.copy(params)
pars[0] = p0[0]
output = mc3.sample(data, uncert, func=quad, params=np.copy(pars),
sampler=sampler, indparams=[x],
pstep=[0, 0.03, 0.05], nsamples=1e4, burnin=100)
assert output is not None
assert len(output['bestp']) == len(params)
assert output['bestp'][0] == pars[0]
assert output['CRlo'][0] == 0
assert output['CRhi'][0] == 0
assert output['stdp'][0] == 0
def test_dynesty_pmax_error(capsys):
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(params),
sampler=sampler,
indparams=[x],
pstep=pstep,
pmin=pmin)
captured = capsys.readouterr()
assert output is None
assert "Parameter space must be constrained by pmin and pmax." \
in captured.out
def test_dynesty_minimal():
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(params),
indparams=[x],
pmin=pmin,
pmax=pmax,
pstep=pstep,
sampler=sampler,
maxiter=5000)
# No error? that's a pass.
assert output is not None
def test_dynesty_ncpu():
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(params),
indparams=[x],
pmin=pmin,
pmax=pmax,
ncpu=8,
pstep=pstep,
sampler=sampler,
maxiter=5000)
assert output is not None
def test_dynesty_shared():
output = mc3.sample(data1,
uncert1,
func=quad,
params=np.copy(params),
sampler=sampler,
indparams=[x],
pmin=pmin,
pmax=pmax,
pstep=[0.03, -1, 0.05],
maxiter=5000)
assert output is not None
assert output['bestp'][1] == output['bestp'][0]
def test_mcmc_optimize_chisqscale(capsys):
unc = np.copy(uncert)
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
leastsq='lm', chisqscale=True)
captured = capsys.readouterr()
assert output is not None
assert "Least-squares best-fitting parameters (rescaled chisq):" \
in captured.out
assert "Reduced chi-squared: 1.0000" in captured.out
# Assert that uncert has not mutated:
np.testing.assert_equal(uncert, unc)
def test_mcmc_plots(capsys, tmp_path):
os.chdir(str(tmp_path))
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x],
pstep=pstep, nsamples=1e4, burnin=100,
plots=True)
captured = capsys.readouterr()
assert output is not None
assert "snooker_trace.png" in captured.out
assert "snooker_pairwise.png" in captured.out
assert "snooker_posterior.png" in captured.out
assert "snooker_model.png" in captured.out
assert "snooker_trace.png" in os.listdir(".")
assert "snooker_pairwise.png" in os.listdir(".")
assert "snooker_posterior.png" in os.listdir(".")
assert "snooker_model.png" in os.listdir(".")
def test_dynesty_log(capsys, tmp_path):
os.chdir(str(tmp_path))
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(params),
sampler=sampler,
indparams=[x],
pmin=pmin,
pmax=pmax,
pstep=pstep,
maxiter=5000,
log='NS.log')
captured = capsys.readouterr()
assert output is not None
assert "NS.log" in captured.out
assert "NS.log" in os.listdir(".")
def test_mcmc_deprecation_leastsq(capsys, lm, leastsq):
if leastsq is True and lm is False:
ls = 'trf'
elif leastsq in [True, 'lm']:
ls = 'lm'
else:
ls = None
output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
sampler=sampler, indparams=[x], stepsize=pstep,
nsamples=1e3, burnin=2, ncpu=7, leastsq=leastsq, lm=lm)
captured = capsys.readouterr()
assert output is not None
if isinstance(leastsq, bool):
assert "leastsq as boolean is deprecated. See docs for new usage. " \
"Set\nleastsq={}".format(repr(ls)) in captured.out
if isinstance(lm, bool):
assert "lm argument is deprecated. See new usage of leastsq. " \
"Set\nleastsq={}".format(repr(ls)) in captured.out
def test_dynesty_fixed():
pars = np.copy(params)
pars[0] = p0[0]
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(pars),
sampler=sampler,
indparams=[x],
pmin=pmin,
pmax=pmax,
pstep=[0, 0.03, 0.05],
maxiter=5000)
assert output is not None
assert len(output['bestp']) == len(params)
assert output['bestp'][0] == pars[0]
assert output['CRlo'][0] == 0
assert output['CRhi'][0] == 0
assert output['stdp'][0] == 0
def MCMC(dfs_dict):
# dfoutput = pd.DataFrame(columns = ['peak_L1635','width_L1635','L1635_amplitude','peak_G1515',
# 'std_G1515','G1515_amplitude','peak_G1430', 'std_G1430', 'std_G1430'])
# Run the MCMC:
for files, data in dfs_dict.items():
spec = data['Absorbance'].to_numpy()
uncert = np.ones_like(spec)*0.01
mc3_output = mc3.sample(data=spec, uncert=uncert, func=func, params=params,
indparams=indparams, pstep=pstep, pmin=pmin, pmax=pmax,
priorlow=priorlow, priorup=priorup, pnames=pnames, texnames=texnames,
sampler=sampler, nsamples=nsamples, nchains=nchains,
ncpu=ncpu, burnin=burnin, thinning=thinning,
leastsq=leastsq, chisqscale=chisqscale,
grtest=grtest, grbreak=grbreak, grnmin=grnmin,
hsize=hsize, kickoff=kickoff, wlike=wlike, log=log,
plots=plots, savefile=savefile, rms=rms)
def test_dynesty_priors_gauss():
prior = np.array([4.5, 0.0, 0.0])
priorlow = np.array([0.1, 0.0, 0.0])
priorup = np.array([0.1, 0.0, 0.0])
output = mc3.sample(data,
uncert,
func=quad,
params=np.copy(params),
sampler=sampler,
indparams=[x],
pmin=pmin,
pmax=pmax,
pstep=pstep,
maxiter=5000,
prior=prior,
priorlow=priorlow,
priorup=priorup)
assert output is not None
assert -2 * output['best_log_post'] > output['best_chisq']
assert np.all(-2 * output['log_post'] > output['chisq'])
