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-rw-r--r--golemflavor/fr.py18
-rw-r--r--golemflavor/gf.py14
-rw-r--r--golemflavor/llh.py2
-rw-r--r--golemflavor/mcmc.py20
-rw-r--r--golemflavor/misc.py2
-rw-r--r--golemflavor/mn.py2
-rw-r--r--golemflavor/param.py2
-rw-r--r--golemflavor/plot.py26
-rw-r--r--scripts/contour.py8
-rw-r--r--scripts/fr.py8
-rw-r--r--scripts/mc_texture.py8
-rw-r--r--scripts/mc_unitary.py8
-rw-r--r--scripts/mc_x.py8
-rw-r--r--scripts/plot_sens.py24
-rw-r--r--scripts/sens.py22
15 files changed, 86 insertions, 86 deletions
diff --git a/golemflavor/fr.py b/golemflavor/fr.py
index ba9a8e9..6692609 100644
--- a/golemflavor/fr.py
+++ b/golemflavor/fr.py
@@ -66,11 +66,11 @@ def determinant(x):
Examples
----------
- >>> print determinant(
+ >>> print(determinant(
>>> [[-1.65238188-0.59549718j, 0.27486548-0.18437467j, -1.35524534-0.38542072j],
>>> [-1.07480906+0.29630449j, -0.47808456-0.80316821j, -0.88609356-1.50737308j],
>>> [-0.14924144-0.99230446j, 0.49504234+0.63639805j, 2.29258915-0.36537507j]]
- >>> )
+ >>> ))
(2.7797571563274688+3.0841795325804848j)
"""
@@ -94,7 +94,7 @@ def angles_to_fr(src_angles):
Examples
----------
- >>> print angles_to_fr((0.3, 0.4))
+ >>> print(angles_to_fr((0.3, 0.4)))
(0.38340579025361626, 0.16431676725154978, 0.45227744249483393)
"""
@@ -129,7 +129,7 @@ def angles_to_u(bsm_angles):
Examples
----------
>>> from fr import angles_to_u
- >>> print angles_to_u((0.2, 0.3, 0.5, 1.5))
+ >>> print(angles_to_u((0.2, 0.3, 0.5, 1.5)))
array([[ 0.66195018+0.j , 0.33097509+0.j , 0.04757188-0.6708311j ],
[-0.34631487-0.42427084j, 0.61741198-0.21213542j, 0.52331757+0.j ],
[ 0.28614067-0.42427084j, -0.64749908-0.21213542j, 0.52331757+0.j ]])
@@ -184,7 +184,7 @@ def cardano_eqn(ham):
>>> [-0.34631487-0.42427084j, 0.61741198-0.21213542j, 0.52331757+0.j ],
>>> [ 0.28614067-0.42427084j, -0.64749908-0.21213542j, 0.52331757+0.j ]]
>>> )
- >>> print cardano_eqn(ham)
+ >>> print(cardano_eqn(ham))
array([[-0.11143379-0.58863683j, -0.09067747-0.48219068j, 0.34276625-0.08686465j],
[ 0.14835519+0.47511473j, -0.18299305+0.40777481j, 0.31906300+0.82514223j],
[-0.62298966+0.07231745j, -0.61407815-0.42709603j, 0.03660313+0.30160428j]])
@@ -247,7 +247,7 @@ def normalise_fr(fr):
Examples
----------
>>> from fr import normalise_fr
- >>> print normalise_fr((1, 2, 3))
+ >>> print(normalise_fr((1, 2, 3)))
array([ 0.16666667, 0.33333333, 0.5 ])
"""
@@ -347,7 +347,7 @@ def params_to_BSMu(bsm_angles, dim, energy, mass_eigenvalues=MASS_EIGENVALUES,
Examples
----------
>>> from fr import params_to_BSMu
- >>> print params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), dim=3, energy=1000)
+ >>> print(params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), dim=3, energy=1000))
array([[ 0.18658169 -6.34190523e-01j, -0.26460391 +2.01884200e-01j, 0.67247096 -9.86808417e-07j],
[-0.50419832 +2.14420570e-01j, -0.36013768 +5.44254868e-01j, 0.03700961 +5.22039894e-01j],
[-0.32561308 -3.95946524e-01j, 0.64294909 -2.23453580e-01j, 0.03700830 +5.22032403e-01j]])
@@ -475,7 +475,7 @@ def test_unitarity(x, prnt=False, rse=False, epsilon=None):
----------
>>> from fr import test_unitarity
>>> x = np.identity(3)
- >>> print test_unitarity(x)
+ >>> print(test_unitarity(x))
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
@@ -512,7 +512,7 @@ def u_to_fr(source_fr, matrix):
Examples
----------
>>> from fr import params_to_BSMu, u_to_fr
- >>> print u_to_fr((1, 2, 0), params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), 3, 1000))
+ >>> print(u_to_fr((1, 2, 0), params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), 3, 1000)))
array([ 0.33740075, 0.33176584, 0.33083341])
"""
diff --git a/golemflavor/gf.py b/golemflavor/gf.py
index fbabd49..252e706 100644
--- a/golemflavor/gf.py
+++ b/golemflavor/gf.py
@@ -16,7 +16,7 @@ import numpy as np
try:
import GolemFitPy as gf
except:
- print 'Running without GolemFit'
+ print('Running without GolemFit')
pass
from golemflavor.enums import DataType, Likelihood, SteeringCateg
@@ -53,8 +53,8 @@ def fit_flags(llh_paramset):
gf_nuisance = []
for param in llh_paramset:
if param.name in default_flags:
- print 'Setting param {0:<15} to float in the ' \
- 'minimisation'.format(param.name)
+ print('Setting param {0:<15} to float in the ' \
+ 'minimisation'.format(param.name))
flags.__setattr__(param.name, False)
gf_nuisance.append(param)
return flags, ParamSet(gf_nuisance)
@@ -85,7 +85,7 @@ def steering_params(args):
def setup_asimov(params):
- print 'Injecting the model', params
+ print('Injecting the model', params)
asimov_params = gf.FitParameters(gf.sampleTag.MagicTau)
for parm in params:
asimov_params.__setattr__(parm.name, float(parm.value))
@@ -93,7 +93,7 @@ def setup_asimov(params):
def setup_realisation(params, seed):
- print 'Injecting the model', params
+ print('Injecting the model', params)
asimov_params = gf.FitParameters(gf.sampleTag.MagicTau)
for parm in params:
asimov_params.__setattr__(parm.name, float(parm.value))
@@ -112,7 +112,7 @@ def setup_fitter(args, asimov_paramset):
seed = args.seed if args.seed is not None else 1
setup_realisation(FITTER, asimov_paramset, seed)
elif args.data is DataType.REAL:
- print 'Using MagicTau DATA'
+ print('Using MagicTau DATA')
def get_llh(params):
@@ -124,7 +124,7 @@ def get_llh(params):
def get_llh_freq(params):
- print 'setting to {0}'.format(params)
+ print('setting to {0}'.format(params))
fitparams = gf.FitParameters(gf.sampleTag.MagicTau)
for parm in params:
fitparams.__setattr__(parm.name, float(parm.value))
diff --git a/golemflavor/llh.py b/golemflavor/llh.py
index 93ae3bd..aa5d32d 100644
--- a/golemflavor/llh.py
+++ b/golemflavor/llh.py
@@ -88,7 +88,7 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset):
fr = fr_utils.flux_averaged_BSMu(theta, args, spectral_index, llh_paramset)
flavour_angles = fr_utils.fr_to_angles(fr)
- # print 'flavour_angles', map(float, flavour_angles)
+ # print('flavour_angles', map(float, flavour_angles))
for idx, param in enumerate(hypo_paramset.from_tag(ParamTag.BESTFIT)):
param.value = flavour_angles[idx]
diff --git a/golemflavor/mcmc.py b/golemflavor/mcmc.py
index 606da2d..4cad6e7 100644
--- a/golemflavor/mcmc.py
+++ b/golemflavor/mcmc.py
@@ -26,26 +26,26 @@ def mcmc(p0, ln_prob, ndim, nwalkers, burnin, nsteps, args, threads=1):
nwalkers, ndim, ln_prob, threads=threads
)
- print "Running burn-in"
+ print("Running burn-in")
for result in tqdm.tqdm(sampler.sample(p0, iterations=burnin), total=burnin):
pos, prob, state = result
sampler.reset()
- print "Finished burn-in"
+ print("Finished burn-in")
args.burnin = False
- print "Running"
+ print("Running")
for _ in tqdm.tqdm(sampler.sample(pos, iterations=nsteps), total=nsteps):
pass
- print "Finished"
+ print("Finished")
samples = sampler.chain.reshape((-1, ndim))
- print 'acceptance fraction', sampler.acceptance_fraction
- print 'sum of acceptance fraction', np.sum(sampler.acceptance_fraction)
- print 'np.unique(samples[:,0]).shape', np.unique(samples[:,0]).shape
+ print('acceptance fraction', sampler.acceptance_fraction)
+ print('sum of acceptance fraction', np.sum(sampler.acceptance_fraction))
+ print('np.unique(samples[:,0]).shape', np.unique(samples[:,0]).shape)
try:
- print 'autocorrelation', sampler.acor
+ print('autocorrelation', sampler.acor)
except:
- print 'WARNING : NEED TO RUN MORE SAMPLES'
+ print('WARNING : NEED TO RUN MORE SAMPLES')
return samples
@@ -115,6 +115,6 @@ def save_chains(chains, outfile):
"""
make_dir(outfile)
- print 'Saving chains to location {0}'.format(outfile+'.npy')
+ print('Saving chains to location {0}'.format(outfile+'.npy'))
np.save(outfile+'.npy', chains)
diff --git a/golemflavor/misc.py b/golemflavor/misc.py
index 4974775..d83bb99 100644
--- a/golemflavor/misc.py
+++ b/golemflavor/misc.py
@@ -77,7 +77,7 @@ def parse_bool(s):
Examples
----------
>>> from misc import parse_bool
- >>> print parse_bool('true')
+ >>> print(parse_bool('true'))
True
"""
diff --git a/golemflavor/mn.py b/golemflavor/mn.py
index e8ff3b4..1e6c9fc 100644
--- a/golemflavor/mn.py
+++ b/golemflavor/mn.py
@@ -85,7 +85,7 @@ def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, prefix='mn'):
if args.run_mn:
make_dir(prefix)
- print 'Running evidence calculation for {0}'.format(prefix)
+ print('Running evidence calculation for {0}'.format(prefix))
run(
LogLikelihood = lnProbEval,
Prior = CubePrior,
diff --git a/golemflavor/param.py b/golemflavor/param.py
index 076386f..941f265 100644
--- a/golemflavor/param.py
+++ b/golemflavor/param.py
@@ -7,7 +7,7 @@
Param class and functions for the BSM flavour ratio analysis
"""
-from __future__ import absolute_import, division, print_function
+from __future__ import absolute_import, division
import sys
diff --git a/golemflavor/plot.py b/golemflavor/plot.py
index 2fc3210..caaed47 100644
--- a/golemflavor/plot.py
+++ b/golemflavor/plot.py
@@ -157,10 +157,10 @@ def get_limit(scales, statistic, args, mask_initial=False, return_interp=False):
min_idx = np.argmin(scales)
null = statistic[min_idx]
# if np.abs(statistic_rm[0] - null) > 0.8:
- # print 'Warning, null incompatible with smallest scanned scale! For ' \
+ # print('Warning, null incompatible with smallest scanned scale! For ' \
# 'DIM {0} [{1}, {2}, {3}]!'.format(
# args.dimension, *args.source_ratio
- # )
+ # ))
# null = statistic_rm[0]
if args.stat_method is StatCateg.BAYESIAN:
reduced_ev = -(statistic_rm - null)
@@ -175,16 +175,16 @@ def get_limit(scales, statistic, args, mask_initial=False, return_interp=False):
return None
re = -(statistic-null)[scales > al[0]]
if np.sum(re < np.log(10**(BAYES_K)) - 0.1) >= 2:
- print 'Warning, peaked contour does not exclude large scales! For ' \
+ print('Warning, peaked contour does not exclude large scales! For ' \
'DIM {0} [{1}, {2}, {3}]!'.format(
args.dimension, *args.source_ratio
- )
+ ))
return None
if np.sum(re >= np.log(10**(BAYES_K)) + 0.0) < 2:
- print 'Warning, only single point above threshold! For ' \
+ print('Warning, only single point above threshold! For ' \
'DIM {0} [{1}, {2}, {3}]!'.format(
args.dimension, *args.source_ratio
- )
+ ))
return None
if return_interp:
@@ -193,7 +193,7 @@ def get_limit(scales, statistic, args, mask_initial=False, return_interp=False):
# Divide by 2 to convert to standard SME coefficient
lim = al[0] - np.log10(2.)
# lim = al[0]
- print 'limit = {0}'.format(lim)
+ print('limit = {0}'.format(lim))
return lim
@@ -323,7 +323,7 @@ def alpha_shape(points, alpha):
area = np.sqrt(s*(s-a)*(s-b)*(s-c))
circum_r = a*b*c/(4.0*area)
# Here's the radius filter.
- #print circum_r
+ #print(circum_r)
if circum_r < 1.0/alpha:
add_edge(edges, edge_points, coords, ia, ib)
add_edge(edges, edge_points, coords, ib, ic)
@@ -562,9 +562,9 @@ def plot_statistic(data, outfile, outformat, args, scale_param, label=None):
fig_text = gen_figtext(args)
if label is not None: fig_text += '\n' + label
- print 'data', data
- print 'data.shape', data.shape
- print 'outfile', outfile
+ print('data', data)
+ print('data.shape', data.shape)
+ print('outfile', outfile)
try:
scales, statistic = ma.compress_rows(data).T
lim = get_limit(deepcopy(scales), deepcopy(statistic), args, mask_initial=True)
@@ -620,7 +620,7 @@ def plot_statistic(data, outfile, outformat, args, scale_param, label=None):
def plot_table_sens(data, outfile, outformat, args, show_lvatmo=True):
- print 'Making TABLE sensitivity plot'
+ print('Making TABLE sensitivity plot')
argsc = deepcopy(args)
dims = args.dimensions
@@ -648,7 +648,7 @@ def plot_table_sens(data, outfile, outformat, args, show_lvatmo=True):
legend_elements = []
for idim, dim in enumerate(dims):
- print '|||| DIM = {0}'.format(dim)
+ print('|||| DIM = {0}'.format(dim))
argsc.dimension = dim
gs0 = gridspec.GridSpecFromSubplotSpec(
len(textures), 1, subplot_spec=gs[idim], hspace=0
diff --git a/scripts/contour.py b/scripts/contour.py
index 6f08617..cbb6c87 100644
--- a/scripts/contour.py
+++ b/scripts/contour.py
@@ -194,10 +194,10 @@ def main():
prefix = ''
outfile = args.datadir + '/contour' + prefix + gen_identifier(args)
- print '== {0:<25} = {1}'.format('outfile', outfile)
+ print('== {0:<25} = {1}'.format('outfile', outfile))
- print 'asimov_paramset', asimov_paramset
- print 'hypo_paramset', hypo_paramset
+ print('asimov_paramset', asimov_paramset)
+ print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
gf_utils.setup_fitter(args, asimov_paramset)
@@ -250,7 +250,7 @@ def main():
labels = labels
)
- print "DONE!"
+ print("DONE!")
main.__doc__ = __doc__
diff --git a/scripts/fr.py b/scripts/fr.py
index e32d7d0..25cc1f8 100644
--- a/scripts/fr.py
+++ b/scripts/fr.py
@@ -171,13 +171,13 @@ def main():
outfile = args.datadir + '/{0}/{1}/chains_'.format(
*map(misc_utils.parse_enum, [args.stat_method, args.data])
) + misc_utils.gen_identifier(args)
- print '== {0:<25} = {1}'.format('outfile', outfile)
+ print('== {0:<25} = {1}'.format('outfile', outfile))
if args.run_mcmc:
gf_utils.setup_fitter(args, asimov_paramset)
- print 'asimov_paramset', asimov_paramset
- print 'llh_paramset', llh_paramset
+ print('asimov_paramset', asimov_paramset)
+ print('llh_paramset', llh_paramset)
ln_prob = partial(
llh_utils.ln_prob,
@@ -240,7 +240,7 @@ def main():
labels = labels,
ranges = ranges
)
- print "DONE!"
+ print("DONE!")
main.__doc__ = __doc__
diff --git a/scripts/mc_texture.py b/scripts/mc_texture.py
index 2813dc0..9e3f0eb 100644
--- a/scripts/mc_texture.py
+++ b/scripts/mc_texture.py
@@ -182,10 +182,10 @@ def main():
prefix = ''
outfile = args.datadir + '/mc_texture' + prefix + gen_identifier(args)
- print '== {0:<25} = {1}'.format('outfile', outfile)
+ print('== {0:<25} = {1}'.format('outfile', outfile))
- print 'asimov_paramset', asimov_paramset
- print 'llh_paramset', llh_paramset
+ print('asimov_paramset', asimov_paramset)
+ print('llh_paramset', llh_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
@@ -223,7 +223,7 @@ def main():
frs_scale = np.vstack((frs.T, samples[:-1].T)).T
mcmc_utils.save_chains(frs_scale, outfile)
- print "DONE!"
+ print("DONE!")
main.__doc__ = __doc__
diff --git a/scripts/mc_unitary.py b/scripts/mc_unitary.py
index bbd5a28..0f0e8f4 100644
--- a/scripts/mc_unitary.py
+++ b/scripts/mc_unitary.py
@@ -155,10 +155,10 @@ def main():
prefix = ''
outfile = args.datadir + '/mc_unitary' + prefix + gen_identifier(args)
- print '== {0:<25} = {1}'.format('outfile', outfile)
+ print('== {0:<25} = {1}'.format('outfile', outfile))
- print 'asimov_paramset', asimov_paramset
- print 'hypo_paramset', hypo_paramset
+ print('asimov_paramset', asimov_paramset)
+ print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
@@ -193,7 +193,7 @@ def main():
)
mcmc_utils.save_chains(frs, outfile)
- print "DONE!"
+ print("DONE!")
main.__doc__ = __doc__
diff --git a/scripts/mc_x.py b/scripts/mc_x.py
index 04319c4..875c40b 100644
--- a/scripts/mc_x.py
+++ b/scripts/mc_x.py
@@ -151,10 +151,10 @@ def main():
prefix = ''
outfile = args.datadir + '/mc_x' + prefix
- print '== {0:<25} = {1}'.format('outfile', outfile)
+ print('== {0:<25} = {1}'.format('outfile', outfile))
- print 'asimov_paramset', asimov_paramset
- print 'hypo_paramset', hypo_paramset
+ print('asimov_paramset', asimov_paramset)
+ print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
@@ -192,7 +192,7 @@ def main():
)
mcmc_utils.save_chains(frs, outfile)
- print "DONE!"
+ print("DONE!")
main.__doc__ = __doc__
diff --git a/scripts/plot_sens.py b/scripts/plot_sens.py
index 18e0c03..2c827ca 100644
--- a/scripts/plot_sens.py
+++ b/scripts/plot_sens.py
@@ -82,7 +82,7 @@ def process_args(args):
skip = False
for a in anon:
if (a[1] > x) & (x > a[0]):
- print 'Skipping src', x
+ print('Skipping src', x)
skip = True
break
if skip: continue
@@ -171,7 +171,7 @@ def main():
# Initialise data structure.
statistic_arr = np.full((dims, srcs, texs, args.segments, 2), np.nan)
- print 'Loading data'
+ print('Loading data')
argsc = deepcopy(args)
for idim, dim in enumerate(args.dimensions):
argsc.dimension = dim
@@ -194,7 +194,7 @@ def main():
*map(parse_enum, [args.stat_method, args.data])
) + r'{0}/fr_stat'.format(prefix) + gen_identifier(argsc)
- print '== {0:<25} = {1}'.format('base_infile', base_infile)
+ print('== {0:<25} = {1}'.format('base_infile', base_infile))
if args.split_jobs:
for idx_sc, scale in enumerate(eval_scales):
@@ -202,31 +202,31 @@ def main():
np.power(10, scale)
)
try:
- print 'Loading from {0}'.format(infile+'.npy')
+ print('Loading from {0}'.format(infile+'.npy'))
statistic_arr[idim][isrc][itex][idx_sc] = \
np.load(infile+'.npy')[0]
except:
- print 'Unable to load file {0}'.format(
+ print('Unable to load file {0}'.format(
infile+'.npy'
- )
+ ))
# raise
continue
else:
- print 'Loading from {0}'.format(base_infile+'.npy')
+ print('Loading from {0}'.format(base_infile+'.npy'))
try:
statistic_arr[idim][isrc][itex] = \
np.load(base_infile+'.npy')
except:
- print 'Unable to load file {0}'.format(
+ print('Unable to load file {0}'.format(
base_infile+'.npy'
- )
+ ))
continue
data = ma.masked_invalid(statistic_arr)
- print 'data', data
+ print('data', data)
if args.plot_statistic:
- print 'Plotting statistic'
+ print('Plotting statistic')
for idim, dim in enumerate(args.dimensions):
argsc.dimension = dim
@@ -268,7 +268,7 @@ def main():
argsc = deepcopy(args)
if args.plot_x:
for idim, dim in enumerate(args.dimensions):
- print '|||| DIM = {0}'.format(dim)
+ print('|||| DIM = {0}'.format(dim))
argsc.dimension = dim
plot_utils.plot_x(
data = data[idim],
diff --git a/scripts/sens.py b/scripts/sens.py
index 6368159..5198016 100644
--- a/scripts/sens.py
+++ b/scripts/sens.py
@@ -209,8 +209,8 @@ def main():
) + misc_utils.gen_identifier(args)
if not args.overwrite and os.path.isfile(outfile+'.npy'):
- print 'FILE EXISTS {0}'.format(outfile+'.npy')
- print 'Exiting...'
+ print('FILE EXISTS {0}'.format(outfile+'.npy'))
+ print('Exiting...')
return
# Setup Golemfit.
@@ -225,16 +225,16 @@ def main():
if idx_sc == args.eval_segment:
outfile += '_scale_{0:.0E}'.format(np.power(10, scale))
else: continue
- print '|||| SCALE = {0:.0E}'.format(np.power(10, scale))
+ print('|||| SCALE = {0:.0E}'.format(np.power(10, scale)))
if not args.overwrite and os.path.isfile(outfile+'.npy'):
- print 'FILE EXISTS {0}'.format(outfile+'.npy')
+ print('FILE EXISTS {0}'.format(outfile+'.npy'))
t = np.load(outfile+'.npy')
if np.any(~np.isfinite(t)):
- print 'nan found, rerunning...'
+ print('nan found, rerunning...')
pass
else:
- print 'Exiting...'
+ print('Exiting...')
return
# Lower scale boundary for first (NULL) point and set the scale param.
@@ -262,9 +262,9 @@ def main():
prefix = prefix
)
except:
- print 'Failed run'
+ print('Failed run')
raise
- print '## Evidence = {0}'.format(stat)
+ print('## Evidence = {0}'.format(stat))
if args.eval_segment is not None:
stat_arr[0] = np.array([scale, stat])
@@ -278,14 +278,14 @@ def main():
if args.run_mn and not args.debug:
try:
for f in glob.glob(prefix + '*'):
- print 'cleaning file {0}'.format(f)
+ print('cleaning file {0}'.format(f))
os.remove(f)
except:
- print 'got error trying to cleanup, continuing'
+ print('got error trying to cleanup, continuing')
pass
misc_utils.make_dir(outfile)
- print 'Saving to {0}'.format(outfile+'.npy')
+ print('Saving to {0}'.format(outfile+'.npy'))
np.save(outfile+'.npy', stat_arr)
generated by cgit v1.2.3 (git 2.25.1) at 2025-12-17 06:16:11 +0000