Linear Bivariate BMM with calibration of Coleman toy models.

2.5. Linear Bivariate BMM with calibration of Coleman toy models.#

The best way to learn Taweret is to use it. You can run, modify and experiment with this notebook on GitHub Codespaces.

The models can be found in Coleman Thesis : https://go.exlibris.link/3fVZCfhl

This notebook shows how to use the Bayesian model mixing package Taweret for a toy problem.

Author : Dan Liyanage

Date : 11/13/2022

import sys
import os
# You will have to change the following imports depending on where you have 
# the packages installed

# ! pip install Taweret    # if using Colab, uncomment to install

cwd = os.getcwd()

# Get the first part of this path and append to the sys.path
tw_path = cwd.split("Taweret/")[0] + "Taweret"
sys.path.append(tw_path)

# For plotting
import matplotlib.pyplot as plt

! pip install seaborn    # comment if installed
! pip install ptemcee    # comment if installed

import seaborn as sns
sns.set_context('poster')
# To define priors. (uncoment if not using default priors)
# ! pip install bilby     # uncomment this line if bilby is not already installed
# import bilby

# For other operations
import numpy as np

Requirement already satisfied: seaborn in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (0.13.2)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from seaborn) (2.1.1)
Requirement already satisfied: pandas>=1.2 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from seaborn) (2.2.2)
Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from seaborn) (3.9.2)
Requirement already satisfied: contourpy>=1.0.1 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)
Requirement already satisfied: cycler>=0.10 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.1)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)
Requirement already satisfied: packaging>=20.0 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)
Requirement already satisfied: pillow>=8 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)
Requirement already satisfied: python-dateutil>=2.7 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)
Requirement already satisfied: tzdata>=2022.7 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)

Requirement already satisfied: six>=1.5 in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)

Requirement already satisfied: ptemcee in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (1.0.0)
Requirement already satisfied: numpy in /home/runner/work/Taweret/Taweret/.tox/book/lib/python3.12/site-packages (from ptemcee) (2.1.1)

# Import models with a predict method
from Taweret.models import coleman_models as toy_models

m1 = toy_models.coleman_model_1()
m2 = toy_models.coleman_model_2()
truth = toy_models.coleman_truth()

g = np.linspace(-1,9,10)
plot_g = np.linspace(-1,9,100)
true_output = truth.evaluate(plot_g)
exp_data = truth.evaluate(g)

2.5.1. 1. The models and the experimental data.#

Truth

\(f(x) = 2-0.1(x-4)^2\), where \(x \in [-1, 9]\)

Model 1

\(f_1(x,\theta)= 0.5(x+\theta)-2\) , where \(\theta \in [1, 6]\)

Model 2

\(f_2(x,\theta)= -0.5(x-\theta) + 3.7\) , where \(\theta \in [-2, 3]\)

Experimental data

sampled from the Truth with a fixed standard deviation of 0.3

sns.set_context('notebook')
fig, axs = plt.subplots(1,2,figsize=(20,5))
prior_ranges = [(1,6), (-2,3)]
for i in range(0,2):
    ax = axs.flatten()[i]
    ax.plot(plot_g, true_output[0], label='truth', color='black')
    ax.errorbar(g,exp_data[0],exp_data[1], fmt='o', label='experimental data', color='r')
    ax.legend()
    ax.set_ylim(-2,4)
    for value in np.linspace(*prior_ranges[i],10):
        if i==0:
            predict_1 = m1.evaluate(plot_g, value)
            ax.plot(plot_g, predict_1[0])
            ax.set_ylabel(r'$f_1(x)$')
        if i==1:
            predict_2 = m2.evaluate(plot_g, value)
            ax.plot(plot_g, predict_2[0])      
            ax.set_ylabel(r'$f_2(x)$')    
    ax.set_xlabel('x')
../../_images/242b0f4db4ce6b9cadced45ce45aa2513136377b0610cc974a3f50e49479019b.png

2.5.2. 2. Choose a Mixing method#

# Mixing method
from Taweret.mix.bivariate_linear import BivariateLinear as BL

models= {'model1':m1,'model2':m2}
mix_model = BL(models_dic=models, method='cdf', nargs_model_dic={'model1':1, 'model2':1})

cdf mixing function has 2                   free parameter(s)
Warning : Default prior is set to {'cdf_0': Uniform(minimum=0, maximum=1, name='cdf_0', latex_label='cdf_0', unit=None, boundary=None), 'cdf_1': Uniform(minimum=0, maximum=1, name='cdf_1', latex_label='cdf_1', unit=None, boundary=None)}
To change the prior use `set_prior` method
Using default priors for model 1
{'model1_0': Uniform(minimum=1, maximum=6, name='model1_0', latex_label='model1_0', unit=None, boundary=None)}
Using default priors for model 2
{'model2_0': Uniform(minimum=-2, maximum=3, name='model2_0', latex_label='model2_0', unit=None, boundary=None)}

## uncoment to change the prior from the default
import bilby 

priors = bilby.core.prior.PriorDict()
priors['cdf_0'] = bilby.core.prior.Normal(0, 1, name="cdf_0")
priors['cdf_1'] = bilby.core.prior.Normal(0, 1, name="cdf_1")
mix_model.set_prior(priors)

{'cdf_0': Normal(mu=0, sigma=1, name='cdf_0', latex_label='cdf_0', unit=None, boundary=None),
 'cdf_1': Normal(mu=0, sigma=1, name='cdf_1', latex_label='cdf_1', unit=None, boundary=None),
 'model1_0': Uniform(minimum=1, maximum=6, name='model1_0', latex_label='model1_0', unit=None, boundary=None),
 'model2_0': Uniform(minimum=-2, maximum=3, name='model2_0', latex_label='model2_0', unit=None, boundary=None)}

mix_model.prior

{'cdf_0': Normal(mu=0, sigma=1, name='cdf_0', latex_label='cdf_0', unit=None, boundary=None),
 'cdf_1': Normal(mu=0, sigma=1, name='cdf_1', latex_label='cdf_1', unit=None, boundary=None),
 'model1_0': Uniform(minimum=1, maximum=6, name='model1_0', latex_label='model1_0', unit=None, boundary=None),
 'model2_0': Uniform(minimum=-2, maximum=3, name='model2_0', latex_label='model2_0', unit=None, boundary=None)}

2.5.3. 3. Train to find posterior#

kwargs_for_sampler = {'sampler': 'ptemcee',
                    'ntemps': 5,
                    'nwalkers': 50,
                    'Tmax': 100,
                    'burn_in_fixed_discard': 50,
                    'nsamples': 2000,
                    'threads': 6}

result = mix_model.train(x_exp=g, y_exp=exp_data[0], y_err=exp_data[1],
                        label='cdf_mix', 
                        outdir='outdir/coleman', 
                        kwargs_for_sampler=kwargs_for_sampler,
                        load_previous=True)

14:44 bilby INFO    : Running for label 'cdf_mix', output will be saved to 'outdir/coleman'

Saved results for cdf_mix do not exist in : outdir/coleman
The following settings were                     provided for sampler 
{'sampler': 'ptemcee', 'ntemps': 5, 'nwalkers': 50, 'Tmax': 100, 'burn_in_fixed_discard': 50, 'nsamples': 2000, 'threads': 6}

14:44 bilby INFO    : Analysis priors:

14:44 bilby INFO    : cdf_0=Normal(mu=0, sigma=1, name='cdf_0', latex_label='cdf_0', unit=None, boundary=None)

14:44 bilby INFO    : cdf_1=Normal(mu=0, sigma=1, name='cdf_1', latex_label='cdf_1', unit=None, boundary=None)

14:44 bilby INFO    : model1_0=Uniform(minimum=1, maximum=6, name='model1_0', latex_label='model1_0', unit=None, boundary=None)

14:44 bilby INFO    : model2_0=Uniform(minimum=-2, maximum=3, name='model2_0', latex_label='model2_0', unit=None, boundary=None)

14:44 bilby INFO    : Analysis likelihood class: <class 'Taweret.sampler.likelihood_wrappers.likelihood_wrapper_for_bilby'>

14:44 bilby INFO    : Analysis likelihood noise evidence: nan

14:44 bilby INFO    : Single likelihood evaluation took nan s

14:44 bilby INFO    : Using sampler Ptemcee with kwargs {'ntemps': 5, 'nwalkers': 50, 'Tmax': 100, 'betas': None, 'a': 2.0, 'adaptation_lag': 10000, 'adaptation_time': 100, 'random': None, 'adapt': False, 'swap_ratios': False}

14:44 bilby INFO    : Using convergence inputs: ConvergenceInputs(autocorr_c=5, autocorr_tol=50, autocorr_tau=1, gradient_tau=0.1, gradient_mean_log_posterior=0.1, Q_tol=1.02, safety=1, burn_in_nact=50, burn_in_fixed_discard=50, mean_logl_frac=0.01, thin_by_nact=0.5, nsamples=2000, ignore_keys_for_tau=None, min_tau=1, niterations_per_check=5)

14:44 bilby INFO    : Generating pos0 samples

14:44 bilby INFO    : Starting to sample

1|0:00:00|nc:1.2e+03|a0:0.00-0.80|swp:0.30-0.64|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.20ms/ev

2|0:00:00|nc:2.5e+03|a0:0.20-0.90|swp:0.21-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.18ms/ev

3|0:00:00|nc:3.8e+03|a0:0.20-0.93|swp:0.19-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.17ms/ev

4|0:00:00|nc:5.0e+03|a0:0.20-0.85|swp:0.18-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

5|0:00:00|nc:6.2e+03|a0:0.24-0.80|swp:0.18-0.64|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

6|0:00:01|nc:7.5e+03|a0:0.27-0.80|swp:0.18-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

7|0:00:01|nc:8.8e+03|a0:0.29-0.80|swp:0.19-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

8|0:00:01|nc:1.0e+04|a0:0.35-0.80|swp:0.20-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

9|0:00:01|nc:1.1e+04|a0:0.40-0.78|swp:0.21-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

10|0:00:01|nc:1.2e+04|a0:0.46-0.76|swp:0.22-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

11|0:00:01|nc:1.4e+04|a0:0.47-0.75|swp:0.22-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

12|0:00:02|nc:1.5e+04|a0:0.47-0.75|swp:0.21-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

13|0:00:02|nc:1.6e+04|a0:0.48-0.72|swp:0.21-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

14|0:00:02|nc:1.8e+04|a0:0.49-0.71|swp:0.21-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

15|0:00:02|nc:1.9e+04|a0:0.48-0.72|swp:0.21-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

16|0:00:02|nc:2.0e+04|a0:0.46-0.70|swp:0.22-0.65|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

17|0:00:02|nc:2.1e+04|a0:0.47-0.68|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

18|0:00:03|nc:2.2e+04|a0:0.49-0.68|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

19|0:00:03|nc:2.4e+04|a0:0.47-0.67|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

20|0:00:03|nc:2.5e+04|a0:0.47-0.66|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

21|0:00:03|nc:2.6e+04|a0:0.48-0.65|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

22|0:00:03|nc:2.8e+04|a0:0.48-0.65|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

23|0:00:03|nc:2.9e+04|a0:0.49-0.65|swp:0.22-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

24|0:00:04|nc:3.0e+04|a0:0.49-0.64|swp:0.23-0.66|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

25|0:00:04|nc:3.1e+04|a0:0.50-0.64|swp:0.23-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

26|0:00:04|nc:3.2e+04|a0:0.50-0.64|swp:0.23-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

27|0:00:04|nc:3.4e+04|a0:0.50-0.64|swp:0.23-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

28|0:00:04|nc:3.5e+04|a0:0.50-0.65|swp:0.24-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

29|0:00:04|nc:3.6e+04|a0:0.50-0.66|swp:0.24-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

30|0:00:05|nc:3.8e+04|a0:0.51-0.65|swp:0.24-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

31|0:00:05|nc:3.9e+04|a0:0.50-0.65|swp:0.24-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

32|0:00:05|nc:4.0e+04|a0:0.50-0.66|swp:0.24-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

33|0:00:05|nc:4.1e+04|a0:0.50-0.66|swp:0.24-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

34|0:00:05|nc:4.2e+04|a0:0.51-0.66|swp:0.24-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

35|0:00:06|nc:4.4e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

36|0:00:06|nc:4.5e+04|a0:0.52-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

37|0:00:06|nc:4.6e+04|a0:0.51-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

38|0:00:06|nc:4.8e+04|a0:0.52-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

39|0:00:06|nc:4.9e+04|a0:0.51-0.66|swp:0.25-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

40|0:00:06|nc:5.0e+04|a0:0.51-0.66|swp:0.24-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

41|0:00:07|nc:5.1e+04|a0:0.50-0.65|swp:0.25-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

42|0:00:07|nc:5.2e+04|a0:0.51-0.65|swp:0.25-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

43|0:00:07|nc:5.4e+04|a0:0.51-0.66|swp:0.25-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

44|0:00:07|nc:5.5e+04|a0:0.51-0.66|swp:0.25-0.69|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

45|0:00:07|nc:5.6e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

46|0:00:07|nc:5.8e+04|a0:0.51-0.67|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

47|0:00:08|nc:5.9e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

48|0:00:08|nc:6.0e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

49|0:00:08|nc:6.1e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

50|0:00:08|nc:6.2e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

51|0:00:08|nc:6.4e+04|a0:0.51-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

52|0:00:08|nc:6.5e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

53|0:00:09|nc:6.6e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

54|0:00:09|nc:6.8e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

55|0:00:09|nc:6.9e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

56|0:00:09|nc:7.0e+04|a0:0.50-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

57|0:00:09|nc:7.1e+04|a0:0.49-0.66|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

58|0:00:09|nc:7.2e+04|a0:0.50-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

59|0:00:10|nc:7.4e+04|a0:0.50-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

60|0:00:10|nc:7.5e+04|a0:0.50-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

61|0:00:10|nc:7.6e+04|a0:0.50-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

62|0:00:10|nc:7.8e+04|a0:0.50-0.65|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

63|0:00:10|nc:7.9e+04|a0:0.50-0.64|swp:0.25-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

64|0:00:10|nc:8.0e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

65|0:00:11|nc:8.1e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

66|0:00:11|nc:8.2e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

67|0:00:11|nc:8.4e+04|a0:0.51-0.66|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

68|0:00:11|nc:8.5e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

69|0:00:11|nc:8.6e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

70|0:00:11|nc:8.8e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

71|0:00:12|nc:8.9e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

72|0:00:12|nc:9.0e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

73|0:00:12|nc:9.1e+04|a0:0.52-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

74|0:00:12|nc:9.2e+04|a0:0.52-0.66|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

75|0:00:12|nc:9.4e+04|a0:0.52-0.66|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

76|0:00:12|nc:9.5e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

77|0:00:13|nc:9.6e+04|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

78|0:00:13|nc:9.8e+04|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

79|0:00:13|nc:9.9e+04|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

80|0:00:13|nc:1.0e+05|a0:0.52-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

81|0:00:13|nc:1.0e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

82|0:00:14|nc:1.0e+05|a0:0.52-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

83|0:00:14|nc:1.0e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

84|0:00:14|nc:1.0e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

85|0:00:14|nc:1.1e+05|a0:0.52-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

86|0:00:14|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

87|0:00:14|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

88|0:00:15|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

89|0:00:15|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

90|0:00:15|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

91|0:00:15|nc:1.1e+05|a0:0.51-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

92|0:00:15|nc:1.2e+05|a0:0.51-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

93|0:00:15|nc:1.2e+05|a0:0.52-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

94|0:00:16|nc:1.2e+05|a0:0.52-0.65|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

95|0:00:16|nc:1.2e+05|a0:0.53-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

96|0:00:16|nc:1.2e+05|a0:0.53-0.65|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

97|0:00:16|nc:1.2e+05|a0:0.53-0.64|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

98|0:00:16|nc:1.2e+05|a0:0.53-0.64|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

99|0:00:16|nc:1.2e+05|a0:0.53-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

100|0:00:17|nc:1.2e+05|a0:0.53-0.64|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

101|0:00:17|nc:1.3e+05|a0:0.53-0.63|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

102|0:00:17|nc:1.3e+05|a0:0.54-0.63|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

103|0:00:17|nc:1.3e+05|a0:0.54-0.63|swp:0.27-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

104|0:00:17|nc:1.3e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

105|0:00:17|nc:1.3e+05|a0:0.54-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

106|0:00:18|nc:1.3e+05|a0:0.54-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

107|0:00:18|nc:1.3e+05|a0:0.54-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

108|0:00:18|nc:1.4e+05|a0:0.54-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

109|0:00:18|nc:1.4e+05|a0:0.54-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

110|0:00:18|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

111|0:00:18|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

112|0:00:19|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

113|0:00:19|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

114|0:00:19|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

115|0:00:19|nc:1.4e+05|a0:0.54-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

116|0:00:19|nc:1.4e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

117|0:00:19|nc:1.5e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

118|0:00:20|nc:1.5e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

119|0:00:20|nc:1.5e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

120|0:00:20|nc:1.5e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

121|0:00:20|nc:1.5e+05|a0:0.55-0.64|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

122|0:00:20|nc:1.5e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

123|0:00:20|nc:1.5e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

124|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

125|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

126|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

127|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

128|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

129|0:00:21|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.68|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

130|0:00:22|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

131|0:00:22|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

132|0:00:22|nc:1.6e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

133|0:00:22|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

134|0:00:22|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

135|0:00:22|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

136|0:00:23|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

137|0:00:23|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

138|0:00:23|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

139|0:00:23|nc:1.7e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

140|0:00:23|nc:1.8e+05|a0:0.55-0.63|swp:0.27-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

141|0:00:24|nc:1.8e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.14ms/ev

142|0:00:24|nc:1.8e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

143|0:00:24|nc:1.8e+05|a0:0.55-0.63|swp:0.26-0.67|n:nan<2000|t!nan(+nan,+nan)|q:inf|0.13ms/ev

144|0:00:24|nc:1.8e+05|a0:0.55-0.62|swp:0.26-0.67|n:-2450<2000|t!2(+nan,+0.50)|q:1.01|0.14ms/ev

145|0:00:24|nc:1.8e+05|a0:0.55-0.62|swp:0.26-0.67|n:-2400<2000|t!2(+nan,+0.14)|q:1.01|0.14ms/ev

146|0:00:24|nc:1.8e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2350<2000|t!2(+nan,+0.22)|q:1.01|0.15ms/ev

147|0:00:25|nc:1.8e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2300<2000|t!2(+nan,+0.16)|q:1.01|0.15ms/ev

148|0:00:25|nc:1.8e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2250<2000|t!2(+nan,+0.40)|q:1.01|0.15ms/ev

149|0:00:25|nc:1.9e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2200<2000|t!2(+nan,+0.40)|q:1.01|0.15ms/ev

150|0:00:25|nc:1.9e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2150<2000|t!2(+nan,+0.48)|q:1.01|0.15ms/ev

151|0:00:25|nc:1.9e+05|a0:0.55-0.62|swp:0.27-0.67|n:-2100<2000|t!2(+nan,+0.33)|q:1.01|0.15ms/ev

152|0:00:26|nc:1.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:-2050<2000|t!2(+nan,+0.35)|q:1.01|0.15ms/ev

153|0:00:26|nc:1.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:-2000<2000|t!2(+0.03,+0.15)|q:1.01|0.15ms/ev

154|0:00:26|nc:1.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1950<2000|t!2(+0.05,+0.33)|q:1.01|0.15ms/ev

155|0:00:26|nc:1.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1900<2000|t!2(+0.06,+0.26)|q:1.01|0.15ms/ev

156|0:00:26|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1850<2000|t!2(+0.05,+0.29)|q:1.01|0.15ms/ev

157|0:00:26|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1800<2000|t!2(+0.05,+0.48)|q:1.01|0.15ms/ev

158|0:00:27|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1750<2000|t!2(+0.05,+0.39)|q:1.01|0.15ms/ev

159|0:00:27|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1700<2000|t!2(+0.03,+0.36)|q:1.01|0.15ms/ev

160|0:00:27|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1650<2000|t!2(+0.03,+0.16)|q:1.01|0.15ms/ev

161|0:00:27|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1600<2000|t!2(+0.05,+0.09)|q:1.01|0.15ms/ev

162|0:00:27|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1550<2000|t!2(+0.05,+0.35)|q:1.01|0.15ms/ev

163|0:00:28|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1500<2000|t!2(+0.05,+0.21)|q:1.01|0.15ms/ev

164|0:00:28|nc:2.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1450<2000|t!2(+0.04,+0.30)|q:1.01|0.15ms/ev

165|0:00:28|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1400<2000|t!2(+0.04,+0.21)|q:1.01|0.15ms/ev

166|0:00:28|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1350<2000|t!2(+0.05,+0.36)|q:1.01|0.15ms/ev

167|0:00:28|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1300<2000|t!2(+0.04,+0.39)|q:1.01|0.15ms/ev

168|0:00:29|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1250<2000|t!2(+0.04,+0.36)|q:1.01|0.15ms/ev

169|0:00:29|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1200<2000|t!2(+0.06,+0.26)|q:1.01|0.15ms/ev

170|0:00:29|nc:2.1e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1150<2000|t!2(+0.05,+0.25)|q:1.01|0.15ms/ev

171|0:00:29|nc:2.1e+05|a0:0.55-0.62|swp:0.27-0.67|n:-1100<2000|t!2(+0.02,+0.32)|q:1.01|0.15ms/ev

172|0:00:29|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1050<2000|t!2(+0.02,+0.24)|q:1.01|0.15ms/ev

173|0:00:29|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-1000<2000|t!2(+0.04,+0.27)|q:1.01|0.15ms/ev

174|0:00:30|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-950<2000|t!2(+0.04,+0.33)|q:1.01|0.15ms/ev

175|0:00:30|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-900<2000|t!2(+0.02,+0.21)|q:1.01|0.15ms/ev

176|0:00:30|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-850<2000|t!2(+0.03,+0.30)|q:1.01|0.15ms/ev

177|0:00:30|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-800<2000|t!2(+0.03,+0.10)|q:1.01|0.15ms/ev

178|0:00:30|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-750<2000|t!2(+0.03,+0.32)|q:1.00|0.15ms/ev

179|0:00:31|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-700<2000|t!2(+0.02,+0.53)|q:1.00|0.15ms/ev

180|0:00:31|nc:2.2e+05|a0:0.54-0.62|swp:0.27-0.67|n:-650<2000|t!2(+0.02,+0.24)|q:1.01|0.15ms/ev

181|0:00:31|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-600<2000|t!2(+0.02,+0.64)|q:1.01|0.15ms/ev

182|0:00:31|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-550<2000|t!2(+0.02,+0.31)|q:1.01|0.15ms/ev

183|0:00:31|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-500<2000|t!2(+0.02,+0.17)|q:1.01|0.15ms/ev

184|0:00:32|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-450<2000|t!2(+0.03,+0.25)|q:1.01|0.15ms/ev

185|0:00:32|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-400<2000|t!2(+0.03,+0.15)|q:1.00|0.15ms/ev

186|0:00:32|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-350<2000|t!2(+0.02,+0.36)|q:1.00|0.15ms/ev

187|0:00:32|nc:2.3e+05|a0:0.54-0.62|swp:0.27-0.67|n:-300<2000|t!2(+0.02,+0.36)|q:1.00|0.15ms/ev

188|0:00:32|nc:2.4e+05|a0:0.54-0.62|swp:0.27-0.67|n:-250<2000|t!2(+0.02,+0.70)|q:1.00|0.15ms/ev

189|0:00:32|nc:2.4e+05|a0:0.54-0.62|swp:0.27-0.67|n:-200<2000|t!2(+0.02,+0.34)|q:1.00|0.15ms/ev

190|0:00:33|nc:2.4e+05|a0:0.54-0.62|swp:0.27-0.67|n:-150<2000|t!2(+0.02,+0.26)|q:1.00|0.15ms/ev

191|0:00:33|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:-100<2000|t!2(+0.03,+0.27)|q:1.00|0.15ms/ev

192|0:00:33|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:-50<2000|t!2(+0.03,+0.22)|q:1.00|0.15ms/ev

193|0:00:33|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:0<2000|t!2(+0.03,+0.18)|q:1.00|0.15ms/ev

194|0:00:33|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:50<2000|t!2(+0.02,+0.20)|q:1.00|0.15ms/ev

195|0:00:34|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:100<2000|t!2(+0.04,+0.31)|q:1.00|0.15ms/ev

196|0:00:34|nc:2.4e+05|a0:0.53-0.62|swp:0.27-0.67|n:150<2000|t!2(+0.02,+0.53)|q:1.00|0.15ms/ev

197|0:00:34|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:200<2000|t!2(+0.02,+0.27)|q:1.00|0.15ms/ev

198|0:00:34|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:250<2000|t!2(+0.02,+0.19)|q:1.00|0.15ms/ev

199|0:00:34|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:300<2000|t!2(+0.06,+0.32)|q:1.00|0.15ms/ev

200|0:00:35|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:350<2000|t!2(+0.05,+0.23)|q:1.00|0.15ms/ev

201|0:00:35|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:400<2000|t!2(+0.04,+0.28)|q:1.00|0.15ms/ev

202|0:00:35|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:450<2000|t!2(+0.04,+0.40)|q:1.00|0.15ms/ev

203|0:00:35|nc:2.5e+05|a0:0.53-0.62|swp:0.27-0.67|n:500<2000|t!2(+0.04,+0.25)|q:1.00|0.15ms/ev

204|0:00:35|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:550<2000|t!2(+0.03,+0.17)|q:1.00|0.15ms/ev

205|0:00:35|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:600<2000|t!2(+0.03,+0.13)|q:1.00|0.15ms/ev

206|0:00:36|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:650<2000|t!2(+0.03,+0.28)|q:1.00|0.15ms/ev

207|0:00:36|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:700<2000|t!2(+0.05,+0.34)|q:1.00|0.15ms/ev

208|0:00:36|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:750<2000|t!2(+0.03,+0.33)|q:1.00|0.15ms/ev

209|0:00:36|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:800<2000|t!2(+0.05,+0.43)|q:1.00|0.15ms/ev

210|0:00:36|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:850<2000|t!2(+0.04,+0.35)|q:1.00|0.15ms/ev

211|0:00:37|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:900<2000|t!2(+0.02,+0.31)|q:1.00|0.15ms/ev

212|0:00:37|nc:2.6e+05|a0:0.53-0.62|swp:0.27-0.67|n:950<2000|t!2(+0.02,+0.36)|q:1.00|0.15ms/ev

213|0:00:37|nc:2.7e+05|a0:0.54-0.62|swp:0.27-0.67|n:1000<2000|t!2(+0.02,+0.42)|q:1.00|0.15ms/ev

214|0:00:37|nc:2.7e+05|a0:0.53-0.62|swp:0.27-0.67|n:1050<2000|t!2(+0.03,+0.15)|q:1.00|0.15ms/ev

215|0:00:37|nc:2.7e+05|a0:0.54-0.62|swp:0.27-0.67|n:1100<2000|t!2(+0.01,+0.25)|q:1.00|0.15ms/ev

216|0:00:38|nc:2.7e+05|a0:0.54-0.62|swp:0.27-0.67|n:1150<2000|t!2(+0.02,+0.38)|q:1.00|0.15ms/ev

217|0:00:38|nc:2.7e+05|a0:0.54-0.62|swp:0.27-0.67|n:1200<2000|t!2(+0.02,+0.22)|q:1.00|0.15ms/ev

218|0:00:38|nc:2.7e+05|a0:0.53-0.62|swp:0.27-0.67|n:1250<2000|t!2(+0.02,+0.45)|q:1.00|0.15ms/ev

219|0:00:38|nc:2.7e+05|a0:0.54-0.62|swp:0.27-0.67|n:1300<2000|t!2(+0.02,+0.20)|q:1.00|0.15ms/ev

220|0:00:38|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1350<2000|t!2(+0.03,+0.45)|q:1.00|0.15ms/ev

221|0:00:38|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1400<2000|t!2(+0.03,+0.40)|q:1.00|0.15ms/ev

222|0:00:39|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1450<2000|t!2(+0.02,+0.37)|q:1.00|0.15ms/ev

223|0:00:39|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1500<2000|t!2(+0.02,+0.35)|q:1.00|0.15ms/ev

224|0:00:39|nc:2.8e+05|a0:0.53-0.62|swp:0.27-0.67|n:1550<2000|t!2(+0.01,+0.40)|q:1.00|0.15ms/ev

225|0:00:39|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1600<2000|t!2(+0.01,+0.11)|q:1.00|0.15ms/ev

226|0:00:39|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1650<2000|t!2(+0.02,+0.43)|q:1.00|0.15ms/ev

227|0:00:40|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1700<2000|t!2(+0.02,+0.34)|q:1.00|0.15ms/ev

228|0:00:40|nc:2.8e+05|a0:0.54-0.62|swp:0.27-0.67|n:1750<2000|t!2(+0.04,+0.26)|q:1.00|0.15ms/ev

229|0:00:40|nc:2.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:1800<2000|t!2(+0.03,+0.52)|q:1.00|0.15ms/ev

230|0:00:40|nc:2.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:1850<2000|t!2(+0.02,+0.29)|q:1.00|0.15ms/ev

231|0:00:40|nc:2.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:1900<2000|t!2(+0.02,+0.19)|q:1.00|0.15ms/ev

232|0:00:41|nc:2.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:1950<2000|t!2(+0.02,+0.14)|q:1.00|0.15ms/ev

233|0:00:41|nc:2.9e+05|a0:0.53-0.62|swp:0.27-0.67|n:2000<2000|t!2(+0.03,+0.19)|q:1.00|0.15ms/ev

234|0:00:41|nc:2.9e+05|a0:0.53-0.62|swp:0.27-0.67|n:2050<2000|t!2(+0.02,+0.37)|q:1.00|0.15ms/ev

235|0:00:41|nc:2.9e+05|a0:0.54-0.62|swp:0.27-0.67|n:2100<2000|t!2(+0.02,+0.12)|q:1.00|0.15ms/ev

236|0:00:41|nc:3.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:2150<2000|t!2(+0.04,+0.31)|q:1.00|0.15ms/ev

237|0:00:41|nc:3.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:2200<2000|t!2(+0.03,+0.37)|q:1.00|0.15ms/ev

238|0:00:42|nc:3.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:2250<2000|t!2(+0.01,+0.38)|q:1.00|0.15ms/ev

239|0:00:42|nc:3.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:2300<2000|t!2(+0.01,+0.19)|q:1.00|0.15ms/ev

240|0:00:42|nc:3.0e+05|a0:0.54-0.62|swp:0.27-0.67|n:2350<2000|t=2(+0.03,+0.09)|q:1.00|0.15ms/ev

14:44 bilby INFO    : Finished sampling

14:44 bilby INFO    : Writing checkpoint and diagnostics

14:44 bilby INFO    : Finished writing checkpoint

14:44 bilby INFO    : Sampling time: 0:00:42.520053

14:44 bilby INFO    : Summary of results:
nsamples: 2350
ln_noise_evidence:    nan
ln_evidence: -9.566 +/-  3.012
ln_bayes_factor:    nan +/-  3.012

result.plot_corner()
../../_images/92dc8691916bd7526201dfab0b392c986bfb2993d15e3970eac7958be64d431e.png

2.5.3.1. 4. Predictions#

_,mean_prior,CI_prior, _ = mix_model.prior_predict(plot_g, CI=[5,20,80,95])
_,mean,CI, _ = mix_model.predict(plot_g, CI=[5,20,80,95])

(10000, 4)
using provided samples instead of posterior

per5, per20, per80, per95 = CI
prior5, prior20, prior80, prior95 = CI_prior

mix_model.map

array([ 3.32489141, -0.92966127,  4.66597839,  1.37327561])

# Map value prediction for the step mixing function parameter
model_params = [np.array(mix_model.map[2]), np.array(mix_model.map[3])]
map_prediction = mix_model.evaluate(mix_model.map[0:2], plot_g, model_params=model_params)

_,_,CI_weights,_=mix_model.predict_weights(plot_g, CI=[5,20, 80, 95])
perw_5, perw_20, perw_80, perw_95 = CI_weights

(2350, 100)

%matplotlib inline
fig, ax = plt.subplots()
ax.fill_between(plot_g,perw_5,perw_95,color=sns.color_palette()[4], alpha=0.2, label='90% C.I.')
ax.fill_between(plot_g,perw_20,perw_80, color=sns.color_palette()[4], alpha=0.3, label='60% C.I.')
ax.legend()
#ax.plot(plot_g, true_output[0], label='truth')
#ax.set_ylim(1.2,3.2)

<matplotlib.legend.Legend at 0x7fef71772d20>
../../_images/bb675a26c0b7e47e515c515af4d5c331542263150d3001f0d003b93ca338b1b7.png 
%matplotlib inline
sns.set_context('poster')
fig, ax = plt.subplots(figsize=(10,10))
ax.plot(plot_g, mean.flatten(), label='posterior mean')
ax.fill_between(plot_g,per5.flatten(),per95.flatten(),color=sns.color_palette()[4], alpha=0.2, label='90% C.I.')
ax.fill_between(plot_g,per20.flatten(),per80.flatten(), color=sns.color_palette()[4], alpha=0.3, label='60% C.I.')
ax.fill_between(plot_g,prior20.flatten(),prior80.flatten(),color=sns.color_palette()[2], alpha=0.2, label='60% C.I. Prior')
ax.errorbar(g,exp_data[0],yerr=exp_data[1], marker='x', label='experimental data')
ax.plot(plot_g, mean_prior.flatten(), label='prior mean')
ax.plot(plot_g, map_prediction.flatten(), label='MAP prediction')
ax.set_ybound(-2,4)
ax.legend()

<matplotlib.legend.Legend at 0x7fef7164b740>
../../_images/0689cad8ae924dab12b65d31c37a3087ec6167c1bdc7a40d4209bba8a9ee5d83.png
Contents