diff --git a/examples/MCMC_examples/fully_coherent_search_using_MCMC.py b/examples/MCMC_examples/fully_coherent_search_using_MCMC.py
index 72874c74e5b0fdc9270607f7a1a8d1b28f004aed..22a725539afa60e5a59015dfefc0f02b090887f6 100644
--- a/examples/MCMC_examples/fully_coherent_search_using_MCMC.py
+++ b/examples/MCMC_examples/fully_coherent_search_using_MCMC.py
@@ -28,13 +28,13 @@ data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
-print 'Predicted twoF value: {}\n'.format(twoF)
+print('Predicted twoF value: {}\n'.format(twoF))
DeltaF0 = 1e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
+print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
diff --git a/examples/MCMC_examples/semi_coherent_search_using_MCMC.py b/examples/MCMC_examples/semi_coherent_search_using_MCMC.py
index 1422fd344ac0191291832f2cd225d6e6eb06eb84..556e3b14b660a2722c61baa848174d821f0a5dd5 100644
--- a/examples/MCMC_examples/semi_coherent_search_using_MCMC.py
+++ b/examples/MCMC_examples/semi_coherent_search_using_MCMC.py
@@ -28,13 +28,13 @@ data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
-print 'Predicted twoF value: {}\n'.format(twoF)
+print('Predicted twoF value: {}\n'.format(twoF))
DeltaF0 = 1e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
+print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
diff --git a/examples/followup_examples/semi_coherent_directed_follow_up.py b/examples/followup_examples/semi_coherent_directed_follow_up.py
index e84976e34b6e28e68e9dcfe04d8067735843ed6e..e1db02603ffb398fb5eb0a51df91d2e8be1d41a9 100644
--- a/examples/followup_examples/semi_coherent_directed_follow_up.py
+++ b/examples/followup_examples/semi_coherent_directed_follow_up.py
@@ -28,7 +28,7 @@ data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
-print 'Predicted twoF value: {}\n'.format(twoF)
+print('Predicted twoF value: {}\n'.format(twoF))
# Search
VF0 = VF1 = 1e5
diff --git a/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py b/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
index 5d4c0deb7c659e1a469f104d7e81e80b4c0dedf1..e0613d6db641f2854a307ba911f196fb9d29da87 100644
--- a/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
+++ b/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
@@ -66,5 +66,5 @@ mcmc.plot_corner(label_offset=0.25, truths=[0, 0, 0, 0],
mcmc.print_summary()
-print('Prior widths =', F0_width, F1_width)
-print("Actual run time = {}".format(dT))
+print(('Prior widths =', F0_width, F1_width))
+print(("Actual run time = {}".format(dT)))
diff --git a/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py b/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
index 429f64c7bfb6cae9cce78094d01d3590b55a2334..f51b73f2dcc267ac8a21aa70b95c187d8d4c6e8a 100644
--- a/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
+++ b/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
@@ -62,6 +62,6 @@ fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label),
bbox_inches='tight')
-print('Prior widths =', F0_width, F1_width)
-print("Actual run time = {}".format(dT))
-print("Actual number of grid points = {}".format(search.data.shape[0]))
+print(('Prior widths =', F0_width, F1_width))
+print(("Actual run time = {}".format(dT)))
+print(("Actual number of grid points = {}".format(search.data.shape[0])))
diff --git a/examples/other_examples/twoF_cumulative.py b/examples/other_examples/twoF_cumulative.py
index 760fd23e90068ad589918e6a5694e568b0a31cdf..3b14e49b5f3993bbb0b17a26f78206c0206eae7d 100644
--- a/examples/other_examples/twoF_cumulative.py
+++ b/examples/other_examples/twoF_cumulative.py
@@ -27,13 +27,13 @@ data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
-print 'Predicted twoF value: {}\n'.format(twoF)
+print('Predicted twoF value: {}\n'.format(twoF))
DeltaF0 = 1e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
+print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
diff --git a/examples/other_examples/using_initialisation.py b/examples/other_examples/using_initialisation.py
index efe43364f0ea6ada232d6fbb972b39ad9550aef3..c8d8d61398ab4fea08b668315422647e061679e5 100644
--- a/examples/other_examples/using_initialisation.py
+++ b/examples/other_examples/using_initialisation.py
@@ -28,13 +28,13 @@ data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
-print 'Predicted twoF value: {}\n'.format(twoF)
+print('Predicted twoF value: {}\n'.format(twoF))
DeltaF0 = 1e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
-print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
+print('\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1))
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
diff --git a/pyfstat/__init__.py b/pyfstat/__init__.py
index 3516c4a8fdffbedb1697a40dbed4ba2f37dd835c..9c589d6c2a1c75bdb106f69fc5b069593455fb10 100644
--- a/pyfstat/__init__.py
+++ b/pyfstat/__init__.py
@@ -1,4 +1,4 @@
-from __future__ import division as _division
+
from .core import BaseSearchClass, ComputeFstat, SemiCoherentSearch, SemiCoherentGlitchSearch
from .make_sfts import Writer, GlitchWriter, FrequencyModulatedArtifactWriter, FrequencyAmplitudeModulatedArtifactWriter
diff --git a/pyfstat/core.py b/pyfstat/core.py
index a3b6b310495703b4e29fc8d46d6e4ba6c9b07dc2..1e1385f20fc3164d02ec1e7c99375130df9e59d9 100755
--- a/pyfstat/core.py
+++ b/pyfstat/core.py
@@ -1,5 +1,5 @@
""" The core tools used in pyfstat """
-from __future__ import division, absolute_import, print_function
+
import os
import logging
diff --git a/pyfstat/grid_based_searches.py b/pyfstat/grid_based_searches.py
index df4c9d5b5ebb93acd1f2d6251bef644f7d167fca..c4513ac16a2d14b19bad9f046fb4961199ad53c2 100644
--- a/pyfstat/grid_based_searches.py
+++ b/pyfstat/grid_based_searches.py
@@ -1,5 +1,5 @@
""" Searches using grid-based methods """
-from __future__ import division, absolute_import, print_function
+
import os
import logging
@@ -342,7 +342,7 @@ class GridSearch(BaseSearchClass):
def print_max_twoF(self):
d = self.get_max_twoF()
print('Max twoF values for {}:'.format(self.label))
- for k, v in d.iteritems():
+ for k, v in d.items():
print(' {}={}'.format(k, v))
def set_out_file(self, extra_label=None):
@@ -1006,7 +1006,7 @@ class EarthTest(GridSearch):
vals = [self.minStartTime, self.maxStartTime, self.F0, self.F1,
self.F2, self.Alpha, self.Delta]
self.special_data = {'zero': [0, 0, 0]}
- for key, (dR, dphi, dP) in self.special_data.iteritems():
+ for key, (dR, dphi, dP) in self.special_data.items():
rescaleRadius = (1 + dR / lal.REARTH_SI)
rescalePeriod = (1 + dP / lal.DAYSID_SI)
lalpulsar.BarycenterModifyEarthRotation(
diff --git a/pyfstat/helper_functions.py b/pyfstat/helper_functions.py
index 9d22982ef676edceb0e7254ef0e2a2d131cb836b..9bbc2bfb58f20727d2b2895fc6baf03639168bf5 100644
--- a/pyfstat/helper_functions.py
+++ b/pyfstat/helper_functions.py
@@ -110,7 +110,7 @@ def get_ephemeris_files():
logging.warning('No [earth/sun]_ephem found in '+config_file+'. '+please)
earth_ephem = None
sun_ephem = None
- elif env_var in os.environ.keys():
+ elif env_var in list(os.environ.keys()):
earth_ephem = os.path.join(os.environ[env_var],'earth00-40-DE421.dat.gz')
sun_ephem = os.path.join(os.environ[env_var],'sun00-40-DE421.dat.gz')
if not ( os.path.isfile(earth_ephem) and os.path.isfile(sun_ephem) ):
diff --git a/pyfstat/make_sfts.py b/pyfstat/make_sfts.py
index c3a181221095668e9b5e426d5064bb65ab0c11d1..fda0bbe732ac284724fd2ebf4e8a69d79d1bbdc2 100644
--- a/pyfstat/make_sfts.py
+++ b/pyfstat/make_sfts.py
@@ -1,5 +1,5 @@
""" pyfstat tools to generate sfts """
-from __future__ import division, absolute_import, print_function
+
import numpy as np
import logging
@@ -477,7 +477,7 @@ class FrequencyModulatedArtifactWriter(Writer):
linePhi = 0
lineFreq_old = 0
- for i in tqdm(range(self.nsfts)):
+ for i in tqdm(list(range(self.nsfts))):
mid_time = self.tstart + (i+.5)*self.Tsft
lineFreq = self.get_frequency(mid_time)
@@ -517,7 +517,7 @@ class FrequencyModulatedArtifactWriter(Writer):
logging.info('Using {} threads'.format(args.N))
try:
with pathos.pools.ProcessPool(args.N) as p:
- list(tqdm(p.imap(self.make_ith_sft, range(self.nsfts)),
+ list(tqdm(p.imap(self.make_ith_sft, list(range(self.nsfts))),
total=self.nsfts))
except KeyboardInterrupt:
p.terminate()
@@ -525,7 +525,7 @@ class FrequencyModulatedArtifactWriter(Writer):
logging.info(
"No multiprocessing requested or `pathos` not install, cont."
" without multiprocessing")
- for i in tqdm(range(self.nsfts)):
+ for i in tqdm(list(range(self.nsfts))):
self.make_ith_sft(i)
self.concatenate_sft_files()
diff --git a/pyfstat/mcmc_based_searches.py b/pyfstat/mcmc_based_searches.py
index 41826f7c0d8f945b6dc8525b85ecfeedbfd7896e..f30c9672e71a58053d3cbe03815f3cdadee4a046 100644
--- a/pyfstat/mcmc_based_searches.py
+++ b/pyfstat/mcmc_based_searches.py
@@ -1,5 +1,5 @@
""" Searches using MCMC-based methods """
-from __future__ import division, absolute_import, print_function
+
import sys
import os
@@ -221,7 +221,7 @@ class MCMCSearch(core.BaseSearchClass):
self.theta_keys = []
fixed_theta_dict = {}
- for key, val in self.theta_prior.iteritems():
+ for key, val in self.theta_prior.items():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
@@ -953,7 +953,7 @@ class MCMCSearch(core.BaseSearchClass):
See the pyfstat.core.plot_twoF_cumulative function for further details
"""
d, maxtwoF = self.get_max_twoF()
- for key, val in self.theta_prior.iteritems():
+ for key, val in self.theta_prior.items():
if key not in d:
d[key] = val
@@ -1223,8 +1223,8 @@ class MCMCSearch(core.BaseSearchClass):
def _generate_scattered_p0(self, p):
""" Generate a set of p0s scattered about p """
p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
- for i in xrange(self.nwalkers)]
- for j in xrange(self.ntemps)]
+ for i in range(self.nwalkers)]
+ for j in range(self.ntemps)]
return p0
def _generate_initial_p0(self):
@@ -1349,7 +1349,7 @@ class MCMCSearch(core.BaseSearchClass):
setattr(self, key, new_d[key])
mod_keys = []
- for key in new_d.keys():
+ for key in list(new_d.keys()):
if key in old_d:
if new_d[key] != old_d[key]:
mod_keys.append((key, old_d[key], new_d[key]))
@@ -1486,10 +1486,10 @@ class MCMCSearch(core.BaseSearchClass):
if hasattr(self, 'theta0_index'):
f.write('theta0_index = {}\n'.format(self.theta0_idx))
if method == 'med':
- for key, val in median_std_d.iteritems():
+ for key, val in median_std_d.items():
f.write('{} = {:1.16e}\n'.format(key, val))
if method == 'twoFmax':
- for key, val in max_twoF_d.iteritems():
+ for key, val in max_twoF_d.items():
f.write('{} = {:1.16e}\n'.format(key, val))
def generate_loudest(self):
@@ -1514,7 +1514,7 @@ class MCMCSearch(core.BaseSearchClass):
f.write(r"\begin{tabular}{c l c} \hline" + '\n'
r"Parameter & & & \\ \hhline{====}")
- for key, prior in self.theta_prior.iteritems():
+ for key, prior in self.theta_prior.items():
if type(prior) is dict:
Type = prior['type']
if Type == "unif":
@@ -1546,10 +1546,10 @@ class MCMCSearch(core.BaseSearchClass):
if hasattr(self, 'theta0_idx'):
logging.info('theta0 index: {}'.format(self.theta0_idx))
logging.info('Max twoF: {} with parameters:'.format(max_twoF))
- for k in np.sort(max_twoFd.keys()):
+ for k in np.sort(list(max_twoFd.keys())):
print(' {:10s} = {:1.9e}'.format(k, max_twoFd[k]))
logging.info('Median +/- std for production values')
- for k in np.sort(median_std_d.keys()):
+ for k in np.sort(list(median_std_d.keys())):
if 'std' not in k:
logging.info(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
@@ -1668,7 +1668,7 @@ class MCMCSearch(core.BaseSearchClass):
def write_evidence_file_from_dict(self, EvidenceDict, evidence_file_name):
with open(evidence_file_name, 'w+') as f:
- for key, val in EvidenceDict.iteritems():
+ for key, val in EvidenceDict.items():
f.write('{} {} {}\n'.format(key, val[0], val[1]))
@@ -1801,7 +1801,7 @@ class MCMCGlitchSearch(MCMCSearch):
r'$\delta$'] + full_glitch_symbols)
self.theta_keys = []
fixed_theta_dict = {}
- for key, val in self.theta_prior.iteritems():
+ for key, val in self.theta_prior.items():
if type(val) is dict:
fixed_theta_dict[key] = 0
if key in glitch_keys:
@@ -1863,7 +1863,7 @@ class MCMCGlitchSearch(MCMCSearch):
fig, ax = plt.subplots()
d, maxtwoF = self.get_max_twoF()
- for key, val in self.theta_prior.iteritems():
+ for key, val in self.theta_prior.items():
if key not in d:
d[key] = val
@@ -2223,7 +2223,7 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
def check_old_run_setup(self, old_setup, **kwargs):
try:
- truths = [val == old_setup[key] for key, val in kwargs.iteritems()]
+ truths = [val == old_setup[key] for key, val in kwargs.items()]
if all(truths):
return True
else:
@@ -2540,7 +2540,7 @@ class MCMCTransientSearch(MCMCSearch):
self.theta_keys = []
fixed_theta_dict = {}
- for key, val in self.theta_prior.iteritems():
+ for key, val in self.theta_prior.items():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
diff --git a/pyfstat/optimal_setup_functions.py b/pyfstat/optimal_setup_functions.py
index 9de3c2b17c95174ccf408f2dfe846c82090bfa07..5481bf6aff2570c5581c14697723822f200efff5 100644
--- a/pyfstat/optimal_setup_functions.py
+++ b/pyfstat/optimal_setup_functions.py
@@ -3,7 +3,7 @@
Provides functions to aid in calculating the optimal setup for zoom follow up
"""
-from __future__ import division, absolute_import, print_function
+
import logging
import numpy as np
diff --git a/pyfstat/tcw_fstat_map_funcs.py b/pyfstat/tcw_fstat_map_funcs.py
index 5a05cba50fb8a4e2b38f0ddd131c90fa5ef65590..2676c1a00e7ff5f038eaf6f4226595fadad85b1b 100644
--- a/pyfstat/tcw_fstat_map_funcs.py
+++ b/pyfstat/tcw_fstat_map_funcs.py
@@ -31,7 +31,7 @@ def _optional_import ( modulename, shorthand=None ):
logging.debug('Successfully imported module %s%s.'
% (modulename, shorthandbit))
success = True
- except ImportError, e:
+ except ImportError as e:
if e.message == 'No module named '+modulename:
logging.debug('No module {:s} found.'.format(modulename))
success = False
@@ -111,7 +111,7 @@ def init_transient_fstat_map_features ( wantCuda=False, cudaDeviceName=None ):
' then checking all available devices...')
try:
context0 = pycuda.tools.make_default_context()
- except pycuda._driver.LogicError, e:
+ except pycuda._driver.LogicError as e:
if e.message == 'cuDeviceGet failed: invalid device ordinal':
devn = int(os.environ['CUDA_DEVICE'])
raise RuntimeError('Requested CUDA device number {} exceeds' \
diff --git a/tests.py b/tests.py
index 7ec645afe77de39f486520b0ab2c9bcf41da1136..284d32545100429da1eec235c0f8a4cf6e0c70c9 100644
--- a/tests.py
+++ b/tests.py
@@ -315,10 +315,10 @@ class SemiCoherentGlitchSearch(Test):
Writer.tend = maxStartTime
FSB = Writer.predict_fstat()
- print FSA, FSB
+ print(FSA, FSB)
predicted_FS = (FSA + FSB)
- print(predicted_FS, FS)
+ print((predicted_FS, FS))
self.assertTrue(np.abs((FS - predicted_FS))/predicted_FS < 0.3)
@@ -359,8 +359,8 @@ class MCMCSearch(Test):
search.run(create_plots=False)
_, FS = search.get_max_twoF()
- print('Predicted twoF is {} while recovered is {}'.format(
- predicted_FS, FS))
+ print(('Predicted twoF is {} while recovered is {}'.format(
+ predicted_FS, FS)))
self.assertTrue(
FS > predicted_FS or np.abs((FS-predicted_FS))/predicted_FS < 0.3)