for i in range(999):
d.set_realisation(i)
if 4 in d.fit_poles:
fitter.add_model_and_dataset(model4,
d,
name=d.name + " " + str(i),
realisation=i)
else:
fitter.add_model_and_dataset(model,
d,
name=d.name + " " + str(i),
realisation=i)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.set_num_concurrent(100)
fitter.fit(file)
# Everything below is nasty plotting code ###########################################################
if fitter.should_plot():
from os import path
import matplotlib.pyplot as plt
import logging
logging.info("Creating plots")
plt.rc("text", usetex=True)
plt.rc("font", family="serif")
res = []
if path.exists(pfn + "_alphameans.pkl"):
logging.info("Found alphameans.pkl, reading from existing file")
# If run on NERSC, this will actually submit 128/num_per_task jobs, and each job will
# do num_per_task.
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
data = PowerSpectrum_SDSS_DR12(isotropic=True, recon="iso")
model = PowerBeutler2017(isotropic=data.isotropic, recon=data.recon, marg="full")
sampler = EnsembleSampler(num_walkers=16, num_steps=5000, num_burn=300, temp_dir=dir_name)
fitter = Fitter(dir_name)
for i in range(128):
data.set_realisation(i)
fitter.add_model_and_dataset(model, data, realisation=i)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.set_num_concurrent(128)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
posterior, weight, chain, evidence, model, data, extra = fitter.load()[0]
model_results, summary = get_model_comparison_dataframe(fitter)
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=model.get_labels())
c.plotter.plot(filename=pfn + "_contour.pdf")
