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plot.py
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plot.py
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"""Master script to plot machine learning results in order to
evaluate TES calibration. Command line arguments may be
used to specify target, learning rate, batch size, and model.
Options are also availible for plotting over part of the dataset.
Authors: Miles Cochran-Branson and Quentin Buat
Date: Summer 2022
"""
import subprocess
from genericpath import exists
import os
from taunet.database import PATH, DATASET, testing_data
from taunet.fields import FEATURES, TRUTH_FIELDS, OTHER_TES, FEATURES_NEW
if __name__ == '__main__':
from taunet.parser import plot_parser
args = plot_parser.parse_args()
if args.newTarget:
FEATURES = FEATURES_NEW
target_normalize_var = 'TauJetsAuxDyn.ptTauEnergyScale'
else:
target_normalize_var = 'TauJetsAuxDyn.ptCombined'
n_files = args.nfiles
path = args.path # path to where training is stored
# make plot folder for plots if it doesn't already exist
if not os.path.exists(os.path.join(path, 'plots')):
cmd = 'mkdir -p {}'.format(os.path.join(path, 'plots'))
subprocess.run(cmd, shell=True)
# if not using cache load these packages
if not args.use_cache:
import tensorflow as tf
import tensorflow_probability as tfp
from taunet.utils import tf_mdn_loss
if path != '' and not args.use_cache:
regressor = tf.keras.models.load_model(os.path.join(path, args.model),
custom_objects={'MixtureNormal': tfp.layers.MixtureNormal, 'tf_mdn_loss': tf_mdn_loss})
elif not args.use_cache:
regressor = tf.keras.models.load_model(os.path.join('cache', args.model),
custom_objects={'MixtureNormal': tfp.layers.MixtureNormal, 'tf_mdn_loss': tf_mdn_loss})
else:
regressor = ''
if args.get_above_below:
d, d_above, d_below = testing_data(PATH, DATASET, FEATURES, TRUTH_FIELDS + OTHER_TES, regressor, getAboveBelow=True,
nfiles=n_files, optional_path=path, select_1p=args.oneProng, select_3p=args.threeProngs, no_normalize=args.no_normalize,
no_norm_target=args.no_norm_target, debug=args.debug, noCombined=args.newTarget)
elif args.get_GMM_components:
d, d_GMM_output = testing_data(PATH, DATASET, FEATURES, TRUTH_FIELDS + OTHER_TES, regressor, getGMMcomponents=True,
nfiles=n_files, optional_path=path, select_1p=args.oneProng, select_3p=args.threeProngs, no_normalize=args.no_normalize,
no_norm_target=args.no_norm_target, debug=args.debug, noCombined=args.newTarget)
else:
d = testing_data(PATH, DATASET, FEATURES, TRUTH_FIELDS + OTHER_TES, regressor,
nfiles=n_files, optional_path=path, select_1p=args.oneProng, select_3p=args.threeProngs, no_normalize=args.no_normalize,
no_norm_target=args.no_norm_target, debug=args.debug, noCombined=args.newTarget)
from taunet.plotting import nn_history
nn_history(os.path.join(path, 'history.p'), path)
from taunet.plotting import pt_lineshape
pt_lineshape(d, path, target_normalize_var=target_normalize_var, nbins=100)
from taunet.plotting import response_lineshape
response_lineshape(d, path)
response_lineshape(d, path, plotSaveName='plots/tes_response_lineshape_zoomedin.pdf',
Range=(0.9, 1.1), scale='linear', lineat1=True, target_normalize_var=target_normalize_var)
from taunet.plotting import target_lineshape
target_lineshape(d, plotSaveLoc=path, target_normalize_var=target_normalize_var)
target_lineshape(d, bins=100, range=(0.5, 1.5), basename='tes_target_lineshape_zoomedin',
logy=False, plotSaveLoc=path, target_normalize_var=target_normalize_var)
from taunet.plotting import response_and_resol_vs_var
response_and_resol_vs_var(d, path, target_normalize_var=target_normalize_var)
response_and_resol_vs_var(d[d['TauJetsAuxDyn.truthPtVisDressed']/1000 > 10], path,
xvar='eta', target_normalize_var=target_normalize_var, nbins=35)
response_and_resol_vs_var(d[d['TauJetsAuxDyn.truthPtVisDressed']/1000 > 10], path,
xvar='mu', target_normalize_var=target_normalize_var, nbins=35)
if args.get_above_below:
from taunet.plotting import response_lineshape_above_below, response_above_below
response_above_below(d, d_above, d_below, path, 'all')
response_lineshape_above_below(d, d_above, path, 'plots/response_lineshape_above.pdf', txt='$|\\frac{\\sigma}{\\mu}| > 1$')
response_lineshape_above_below(d, d_below, path, 'plots/response_lineshape_below.pdf', txt='$|\\frac{\\sigma}{\\mu}| < 1$')
if args.get_GMM_components:
from taunet.plotting import visualize_GMM_vars
truth_pt = d['TauJetsAuxDyn.truthPtVisDressed']
for i in range(int(len(d_GMM_output[0])/3)):
visualize_GMM_vars(truth_pt/1000, d_GMM_output['mu{}'.format(i+1)],
path, ytitle='$\\mu_{}$'.format(i+1), plotSaveName='plots/mu{}.pdf'.format(i+1))
visualize_GMM_vars(truth_pt/1000, d_GMM_output['sig{}'.format(i+1)],
path, ytitle='$\\sigma_{}$'.format(i+1), plotSaveName='plots/sig{}.pdf'.format(i+1))
visualize_GMM_vars(truth_pt/1000, d_GMM_output['pi{}'.format(i+1)],
path, ytitle='$\\pi_{}$'.format(i+1), plotSaveName='plots/pi{}.pdf'.format(i+1))
if len(d_GMM_output[0]) > 3:
visualize_GMM_vars(truth_pt/1000, abs(d_GMM_output['mu1']-d_GMM_output['mu2']), path,
ytitle='$|\\mu_1 - \\mu_2|$', plotSaveName='plots/mu1_minus_mu2_vs_pt.pdf')
visualize_GMM_vars(truth_pt/1000, abs(d_GMM_output['sig1']-d_GMM_output['sig2']), path,
ytitle='$|\\sigma_1 - \\sigma_2|$', plotSaveName='plots/sig1_minus_sig2_vs_pt.pdf')
if args.copy_to_cernbox:
from taunet.utils import copy_plots_to_cernbox
if path != '':
copy_plots_to_cernbox(location=path)
else:
copy_plots_to_cernbox()