from barry.fitter import Fitter from barry.models.test import TestModel from barry.datasets.test import TestDataset from barry.samplers import DynestySampler if __name__ == "__main__": import sys sys.path.append("..") pfn, dir_name, file = setup(__file__) model = TestModel() data = TestDataset() sampler = DynestySampler(temp_dir=dir_name, max_iter=None) fitter = Fitter(dir_name) fitter.add_model_and_dataset(model, data) fitter.set_sampler(sampler) fitter.set_num_walkers(1) fitter.fit(file) if fitter.should_plot(): res, = fitter.load() posterior, weight, chain, evidence, model, data, extra = res import matplotlib.pyplot as plt fig, ax = plt.subplots(nrows=2)
from barry.config import setup from barry.models import PowerBeutler2017 from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge_Post from barry.fitter import Fitter import numpy as np import pandas as pd from barry.models.model import Correction from barry.utils import weighted_avg_and_cov, break_vector_and_get_blocks import matplotlib as plt from matplotlib import cm if __name__ == "__main__": pfn, dir_name, file = setup(__file__) fitter = Fitter(dir_name, remove_output=True) sampler = DynestySampler(temp_dir=dir_name, nlive=500) names = [ "PostRecon Yuyu NonFix ", "PostRecon Yuyu NonFix ", ] cmap = plt.cm.get_cmap("viridis") smoothtypes = [1, 2, 3, 4] # [5, 10, 15, 20] Mpc/h kmaxs = [0.15, 0.20, 0.25, 0.30] allnames = [] counter = 0 fit_poles = [0, 2, 4] n_poly = 3 for i, recon in enumerate(["iso"]):
from barry.samplers import DynestySampler from barry.fitter import Fitter if __name__ == "__main__": pfn, dir_name, file = setup(__file__) r = True models = [ PowerBeutler2017(recon=r, smooth_type="hinton2017", name="Hinton2017"), 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, evidence, model, data, extra in fitter.load( ):