de,
name=f"D18 + Extractor",
linestyle=ls,
color="p",
realisation=i)
fitter.add_model_and_dataset(beutler_extracted,
de,
name=f"B17 + Extractor",
linestyle=ls,
color="p",
realisation=i)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.set_num_concurrent(700)
if not fitter.should_plot():
fitter.fit(file)
if fitter.should_plot():
import matplotlib.pyplot as plt
import logging
logging.info("Creating plots")
model_results, summary = get_model_comparison_dataframe(fitter)
# Define colour scheme and plt defaults
c4 = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
plt.rc("text", usetex=True)
plt.rc("font", family="serif")
PowerBeutler2017(recon=r, smooth_type="eh1998", name="EH1998")
]
data = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean",
recon=r,
min_k=0.02,
max_k=0.30)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(models[0], data, name="Hinton2017")
fitter.add_model_and_dataset(models[1], data, name="EH1998")
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
pks = {}
for posterior, weight, chain, model, data, extra in fitter.load():
c.add_chain(chain,
weights=weight,
parameters=model.get_labels(),
**extra)
# params = dict([(p.name, v) for p, v in zip(model.get_active_params(), chain[posterior.argmax(), :])])
# params["om"] = 0.3121
# model.set_data(datas[0].get_data())
# key = f"{model.name}, alpha={params['alpha']:0.4f}"
# pks[key] = model.get_model(datas[0].get_data(), params)
from barry.fitter import Fitter
from barry.models.test import TestModel
from barry.datasets.test import TestDataset
from barry.samplers import MetropolisHastings
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
model = TestModel()
data = TestDataset()
sampler = MetropolisHastings(num_steps=1000,
num_burn=300,
temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, data)
fitter.set_num_walkers(2)
fitter.set_sampler(sampler)
fitter.fit(file)
if fitter.should_plot(): # As I'm not sure if the cluster has matplotlib
from chainconsumer import ChainConsumer
res, = fitter.load()
posterior, weight, chain, model, data, extra = res
labels = model.get_labels()
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=labels)
c.plotter.plot(filename=pfn + "_contour.png")
