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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
8 files changed, 43 insertions, 43 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
generated by cgit v1.2.3 (git 2.25.1) at 2025-12-17 11:24:13 +0000