pfn, dir_name, file = setup(__file__) c = getCambGenerator() r_s = c.get_data()[0] postprocess = BAOExtractor(r_s) sampler = DynestySampler(temp_dir=dir_name) fitter = Fitter(dir_name) for r in [True, False]: rt = "Recon" if r else "Prerecon" data = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, postprocess=postprocess) n = PowerNoda2019(postprocess=postprocess, recon=r, nonlinear_type="spt") n2 = PowerNoda2019(postprocess=postprocess, recon=r, nonlinear_type="halofit") fitter.add_model_and_dataset(n, data, name=f"N19 {rt} SPT", color="r", shade_alpha=0.7, linestyle="-" if r else "--", zorder=10 if r else 2) fitter.add_model_and_dataset(n2, data, name=f"N19 {rt} Halofit",
fitter = Fitter(dir_name) p = BAOExtractor(r_s) cs = ["#262232", "#116A71", "#48AB75", "#b7c742"] for r in [True]: t = "Recon" if r else "Prerecon" datae = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, min_k=0.03, max_k=0.30, postprocess=p) for ls, n in zip(["-", ":"], ["", " (No Poly)"]): if n: fix = ["om", "f", "a1", "a2", "a3", "a4", "a5"] else: fix = ["om", "f"] fitter.add_model_and_dataset(PowerBeutler2017(postprocess=p, recon=r, fix_params=fix), datae, name=f"Beutler 2017{n}", linestyle=ls, color=cs[0]) fitter.add_model_and_dataset(PowerSeo2016(postprocess=p, recon=r, fix_params=fix), datae, name=f"Seo 2016{n}", linestyle=ls, color=cs[1]) fitter.add_model_and_dataset(PowerDing2018(postprocess=p, recon=r, fix_params=fix), datae, name=f"Ding 2018{n}", linestyle=ls, color=cs[2]) fitter.add_model_and_dataset(PowerNoda2019(postprocess=p, recon=r), datae, name=f"Noda 2019", color=cs[3]) sampler = DynestySampler(temp_dir=dir_name, nlive=300) fitter.set_sampler(sampler) fitter.set_num_walkers(10) fitter.fit(file) if fitter.should_plot(): from chainconsumer import ChainConsumer c = ChainConsumer() for posterior, weight, chain, evidence, model, data, extra in fitter.load(): c.add_chain(chain, weights=weight, parameters=model.get_labels(), **extra) c.configure(shade=True, bins=30, legend_artists=True) c.analysis.get_latex_table(filename=pfn + "_params.txt") c.plotter.plot_summary(filename=pfn + "_summary.png", errorbar=True, truth={"$\\Omega_m$": 0.31, "$\\alpha$": 0.9982})
ls = "-" if r else "--" d = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, realisation=0) de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, postprocess=p, realisation=0) beutler_not_fixed = PowerBeutler2017(recon=r) beutler = PowerBeutler2017(recon=r) sigma_nl = 6.0 if r else 9.3 beutler.set_default("sigma_nl", sigma_nl) beutler.set_fix_params(["om", "sigma_nl"]) seo = PowerSeo2016(recon=r) ding = PowerDing2018(recon=r) noda = PowerNoda2019(recon=r, postprocess=p) for i in range(999): d.set_realisation(i) de.set_realisation(i) fitter.add_model_and_dataset( beutler_not_fixed, d, name=f"Beutler 2017 {t}, mock number {i}", linestyle=ls, color="p", realisation=i) fitter.add_model_and_dataset( beutler, d,
BAOExtractor(r_s, extra_ks=(0.095, 0.15)), BAOExtractor(r_s, extra_ks=(0.095, 0.16)), BAOExtractor(r_s, extra_ks=(0.095, 0.17)), BAOExtractor(r_s, extra_ks=(0.095, 0.18)), BAOExtractor(r_s, extra_ks=(0.095, 0.19)), BAOExtractor(r_s, extra_ks=(0.095, 0.20)), BAOExtractor(r_s, extra_ks=(0.095, 0.21)), BAOExtractor(r_s, extra_ks=(0.095, 0.22)), BAOExtractor(r_s, extra_ks=(0.095, 0.23)), BAOExtractor(r_s, extra_ks=(0.095, 0.24)), ] recon = True for p in ps: n = f"$k = {p.extra_ks[1]:0.2f}\, h / {{\\rm Mpc}}$" model = PowerNoda2019(postprocess=p, recon=recon) data = PowerSpectrum_SDSS_DR12_Z061_NGC(min_k=0.02, max_k=0.30, postprocess=p, recon=recon) fitter.add_model_and_dataset(model, data, name=n) sampler = DynestySampler(temp_dir=dir_name) fitter.set_sampler(sampler) fitter.set_num_walkers(30) fitter.fit(file) if fitter.should_plot(): from chainconsumer import ChainConsumer c = ChainConsumer() for posterior, weight, chain, model, data, extra in fitter.load(): print(extra["name"])
from barry.fitter import Fitter if __name__ == "__main__": pfn, dir_name, file = setup(__file__) sampler = DynestySampler(temp_dir=dir_name, nlive=1000) fitter = Fitter(dir_name) cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"] d = PowerSpectrum_DESIMockChallenge_Handshake(min_k=0.005, max_k=0.3, isotropic=False, realisation="data", fit_poles=[0, 2]) fitter.add_model_and_dataset(PowerBeutler2017(isotropic=False), d, name=f"Beutler 2017 Prerecon", color=cs[0]) fitter.add_model_and_dataset(PowerSeo2016(isotropic=False), d, name=f"Seo 2016 Prerecon", color=cs[1]) fitter.add_model_and_dataset(PowerDing2018(isotropic=False), d, name=f"Ding 2018 Prerecon", color=cs[2]) fitter.add_model_and_dataset(PowerNoda2019(isotropic=False), d, name=f"Noda 2019 Prerecon", color=cs[3]) fitter.set_sampler(sampler) fitter.set_num_walkers(10) fitter.fit(file) if fitter.should_plot(): import logging logging.info("Creating plots") res = fitter.load() from chainconsumer import ChainConsumer import copy c = ChainConsumer()
if __name__ == "__main__": logging.basicConfig( level=logging.DEBUG, format="[%(levelname)7s |%(funcName)20s] %(message)s") logging.getLogger("matplotlib").setLevel(logging.ERROR) c = getCambGenerator() r_s = c.get_data()["r_s"] postprocess = BAOExtractor(r_s, mink=0.15) for recon in [True, False]: model1 = PowerNoda2019(recon=recon, name=f"Noda2019, recon={recon}", postprocess=postprocess, fix_params=["om", "f", "gamma"]) dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=recon, postprocess=postprocess, min_k=0.03, max_k=0.15) data1 = dataset1.get_data() print(list(data1[0].keys())) print(data1[0]["ks_output"]) exit() # First comparison - the actual recon data model1.set_data(data1) p, minv = model1.optimize() print(recon) print(p) print(minv)
model, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}", linestyle=ls, color=cs[0]) fitter.add_model_and_dataset(PowerSeo2016(recon=r), d, name=f"Seo 2016 {t}", linestyle=ls, color=cs[1]) fitter.add_model_and_dataset(PowerDing2018(recon=r), d, name=f"Ding 2018 {t}", linestyle=ls, color=cs[2]) fitter.add_model_and_dataset(PowerNoda2019(recon=r, postprocess=p), de, name=f"Noda 2019 {t}", linestyle=ls, color=cs[3]) fitter.set_sampler(sampler) fitter.set_num_walkers(10) fitter.fit(file) if fitter.should_plot(): import logging logging.info("Creating plots") from chainconsumer import ChainConsumer
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC from barry.postprocessing import BAOExtractor, PureBAOExtractor from barry.cosmology.camb_generator import getCambGenerator from barry.samplers import DynestySampler from barry.fitter import Fitter import numpy as np if __name__ == "__main__": pfn, dir_name, file = setup(__file__) c = getCambGenerator() r_s = c.get_data()["r_s"] postprocess = BAOExtractor(r_s) r = True model = PowerNoda2019(postprocess=postprocess, recon=r, name="") mink = 0.03 maxk = 0.30 datas = [ PowerSpectrum_SDSS_DR12_Z061_NGC(name="Mock covariance", recon=r, min_k=mink, max_k=maxk, postprocess=postprocess), PowerSpectrum_SDSS_DR12_Z061_NGC(name="Nishimichi, full", recon=r, min_k=mink, max_k=maxk, postprocess=postprocess), PowerSpectrum_SDSS_DR12_Z061_NGC(name="Nishimichi, diag", recon=r,
from barry.postprocessing import BAOExtractor if __name__ == "__main__": logging.basicConfig( level=logging.DEBUG, format="[%(levelname)7s |%(funcName)20s] %(message)s") logging.getLogger("matplotlib").setLevel(logging.ERROR) recon = True c = getCambGenerator() r_s = c.get_data()[0] postprocess = BAOExtractor(r_s) model1 = PowerNoda2019(recon=recon, name=f"Noda2019, recon={recon}", postprocess=postprocess) from barry.datasets.mock_power import PowerSpectrum_SDSS_DR12_Z061_NGC from barry.datasets.dummy import DummyPowerSpectrum_SDSS_DR12_Z061_NGC dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=recon, postprocess=postprocess, min_k=0.03, max_k=0.25) dataset2 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC( name="Dummy data, real window fn", dummy_window=False, postprocess=postprocess) data1 = dataset1.get_data() data2 = dataset2.get_data()
pfn, dir_name, file = setup(__file__) c = getCambGenerator() r_s = c.get_data()["r_s"] postprocess = BAOExtractor(r_s) sampler = DynestySampler(temp_dir=dir_name) fitter = Fitter(dir_name) for r in [True, False]: rt = "Recon" if r else "Prerecon" data = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, postprocess=postprocess) n = PowerNoda2019(postprocess=postprocess, recon=r, fix_params=["om", "f", "gamma", "b"]) n.param_dict["b"].default = 1.992 if r else 1.996 fitter.add_model_and_dataset( n, data, name=f"N19 {rt} fixed $f$, $\\gamma_{{rec}}$, $b$", linestyle="-" if r else "--", color="o", shade_alpha=0.7, zorder=10) fitter.add_model_and_dataset( PowerNoda2019(postprocess=postprocess, recon=r, fix_params=["om", "f", "gamma"]), data,