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(
):
