add brackets to all print statements

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