Merge branch 'remove-future' into 'master'

remove future imports

See merge request lscsoft/bilby!911

bilby/__init__.py

@@ -16,7 +16,6 @@ https://lscsoft.docs.ligo.org/bilby/installation.html.
 """
 
 
-from __future__ import absolute_import
 import sys
 
 from . import core, gw, hyper

bilby/core/__init__.py

-from __future__ import absolute_import
 from . import grid, likelihood, prior, result, sampler, series, utils

bilby/core/grid.py

-from __future__ import division
-
 import numpy as np
 import os
 import json

bilby/core/likelihood.py

-from __future__ import division, print_function
 import copy
 
 import numpy as np

bilby/core/prior/dict.py

@@ -3,7 +3,6 @@ from io import open as ioopen
 import json
 import os
 
-from future.utils import iteritems
 from matplotlib.cbook import flatten
 import numpy as np
 
@@ -185,7 +184,7 @@ class PriorDict(dict):
 
     def from_dictionary(self, dictionary):
         eval_dict = dict(inf=np.inf)
-        for key, val in iteritems(dictionary):
+        for key, val in dictionary.items():
             if isinstance(val, Prior):
                 continue
             elif isinstance(val, (int, float)):

bilby/core/result.py

-from __future__ import division
-
 import inspect
 import os
 from collections import OrderedDict, namedtuple

bilby/core/sampler/base_sampler.py

-from __future__ import absolute_import
 import datetime
 import distutils.dir_util
 import numpy as np

bilby/core/sampler/cpnest.py

-from __future__ import absolute_import
 
 import array
 import copy

bilby/core/sampler/dynamic_dynesty.py

-from __future__ import absolute_import
 
 import os
 import dill as pickle

bilby/core/sampler/fake_sampler.py

-from __future__ import absolute_import
 
 import numpy as np
 from .base_sampler import Sampler

bilby/core/sampler/nestle.py

-from __future__ import absolute_import
 
 import numpy as np
 from pandas import DataFrame

bilby/core/sampler/polychord.py

-from __future__ import absolute_import
 
 import numpy as np
 

bilby/core/sampler/ptemcee.py

-from __future__ import absolute_import, division, print_function
 
 import os
 import datetime

bilby/core/sampler/ptmcmc.py

-from __future__ import absolute_import, print_function
 
 import glob
 import shutil

bilby/core/sampler/pymc3.py

-from __future__ import absolute_import, print_function
 
 from collections import OrderedDict
 from distutils.version import StrictVersion

bilby/core/sampler/ultranest.py

-from __future__ import absolute_import
 
 import datetime
 import distutils.dir_util

bilby/core/utils.py

-from __future__ import division
 
 from distutils.spawn import find_executable
 import logging

bilby/gw/likelihood.py

-from __future__ import division
 
 import gc
 import os

bilby/gw/result.py

-from __future__ import division
 
 import json
 import pickle

bilby/gw/sampler/__init__.py

-from __future__ import absolute_import
 from . import proposal

bilby/gw/utils.py

-from __future__ import division
 import os
 import json
 from math import fmod

bilby/hyper/likelihood.py

-from __future__ import division, print_function
 
 import logging
 

cli_bilby/__init__.py

-from __future__ import absolute_import

examples/core_examples/gaussian_example.py

@@ -3,7 +3,6 @@
 An example of how to use bilby to perform paramater estimation for
 non-gravitational wave data consisting of a Gaussian with a mean and variance
 """
-from __future__ import division
 import bilby
 import numpy as np
 

examples/core_examples/grid_example.py

@@ -5,7 +5,6 @@ non-gravitational wave data. In this case, fitting a linear function to
 data with background Gaussian noise
 
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/hyper_parameter_example.py

@@ -2,7 +2,6 @@
 """
 An example of how to use bilby to perform parameter estimation for hyper params
 """
-from __future__ import division
 import numpy as np
 import matplotlib.pyplot as plt
 from bilby.core.likelihood import GaussianLikelihood

examples/core_examples/linear_regression.py

@@ -5,7 +5,6 @@ non-gravitational wave data. In this case, fitting a linear function to
 data with background Gaussian noise
 
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/linear_regression_grid.py

@@ -5,7 +5,6 @@ fitting a linear function to data with background Gaussian noise.
 This will compare the output of using a stochastic sampling method
 to evaluating the posterior on a grid.
 """
-from __future__ import division
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/core_examples/linear_regression_pymc3.py

@@ -5,7 +5,6 @@ non-gravitational wave data. In this case, fitting a linear function to
 data with background Gaussian noise
 
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/linear_regression_pymc3_custom_likelihood.py

@@ -8,7 +8,6 @@ would give equivalent results as using the pre-defined 'Gaussian Likelihood'
 
 """
 
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/linear_regression_unknown_noise.py

@@ -5,7 +5,6 @@ non-gravitational wave data. In this case, fitting a linear function to
 data with background Gaussian noise with unknown variance.
 
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/multidimensional_gaussian.py

@@ -3,7 +3,6 @@
 Testing the recovery of a multi-dimensional
 Gaussian with zero mean and unit variance
 """
-from __future__ import division
 import bilby
 import numpy as np
 

examples/core_examples/multivariate_gaussian_prior.py

@@ -4,7 +4,6 @@ An example of how to use bilby with a (multi-modal) multivariate
 Gaussian prior distribution.
 """
 
-from __future__ import division
 import bilby
 import numpy as np
 from scipy import linalg, stats

examples/core_examples/occam_factor_example.py

@@ -30,7 +30,6 @@ Note - the code uses a course 100-point estimation for speed, results can be
 improved by increasing this to say 500 or 1000.
 
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/radioactive_decay.py

@@ -4,7 +4,6 @@ An example of how to use bilby to perform paramater estimation for
 non-gravitational wave data. In this case, fitting the half-life and
 initial radionuclide number for Polonium 214.
 """
-from __future__ import division
 import bilby
 import numpy as np
 import matplotlib.pyplot as plt

examples/core_examples/starting_mcmc_chains_near_to_the_peak.py

@@ -4,7 +4,6 @@ An example of using emcee, but starting the walkers off close to the peak (or
 any other arbitrary point). This is based off the
 linear_regression_with_unknown_noise.py example.
 """
-from __future__ import division
 import bilby
 import numpy as np
 import pandas as pd

examples/gw_examples/data_examples/GW150914.py

@@ -9,7 +9,6 @@ the LIGO Data Grid instead.
 
 [1] https://gwpy.github.io/docs/stable/timeseries/remote-access.html
 """
-from __future__ import division, print_function
 import bilby
 from gwpy.timeseries import TimeSeries
 

examples/gw_examples/data_examples/GW150914_advanced.py

@@ -10,7 +10,6 @@ LIST OF AVAILABLE EVENTS:
 
 List of events in BILBY dict: run -> help(bilby.gw.utils.get_event_time(event))
 """
-from __future__ import division, print_function
 import bilby
 from gwpy.timeseries import TimeSeries
 

examples/gw_examples/data_examples/GW170817.py

@@ -4,7 +4,6 @@ This tutorial includes advanced specifications
 for analysing binary neutron star event data.
 Here GW170817 is used as an example.
 """
-from __future__ import division, print_function
 import bilby
 
 outdir = 'outdir'

examples/gw_examples/data_examples/get_LOSC_event_data.py

@@ -7,7 +7,6 @@ $ python get_LOSC_event_data -e GW150914
 
 """
 
-from __future__ import division
 import numpy as np
 import os
 import argparse

examples/gw_examples/injection_examples/australian_detector.py

@@ -5,7 +5,6 @@ Tutorial to demonstrate a new interferometer
 We place a new instrument in Gingin, with an A+ sensitivity in a network of A+
 interferometers at Hanford and Livingston
 """
-from __future__ import division, print_function
 
 import numpy as np
 

examples/gw_examples/injection_examples/binary_neutron_star_example.py

@@ -8,7 +8,6 @@ and also estimates the tidal deformabilities using a uniform prior in both
 tidal deformabilities
 """
 
-from __future__ import division, print_function
 
 import numpy as np
 

examples/gw_examples/injection_examples/bns_eos_example.py

@@ -8,7 +8,6 @@ and also estimates the tidal deformabilities using a uniform prior in both
 tidal deformabilities
 """
 
-from __future__ import division, print_function
 
 import numpy as np
 

examples/gw_examples/injection_examples/calibration_example.py

@@ -3,7 +3,6 @@
 Tutorial to demonstrate running parameter estimation with calibration
 uncertainties included.
 """
-from __future__ import division, print_function
 
 import numpy as np
 import bilby

examples/gw_examples/injection_examples/change_sampled_parameters.py

@@ -7,7 +7,6 @@ This example estimates the masses using a uniform prior in chirp mass,
 mass ratio and redshift.
 The cosmology is according to the Planck 2015 data release.
 """
-from __future__ import division, print_function
 import bilby
 import numpy as np
 

examples/gw_examples/injection_examples/create_your_own_source_model.py

@@ -2,7 +2,6 @@
 """
 A script to demonstrate how to use your own source model
 """
-from __future__ import division, print_function
 import bilby
 import numpy as np
 

examples/gw_examples/injection_examples/custom_proposal_example.py

@@ -2,7 +2,6 @@
 """
 Tutorial for running cpnest with custom jump proposals.
 """
-from __future__ import division, print_function
 
 import numpy as np
 import bilby.gw.sampler.proposal

examples/gw_examples/injection_examples/eccentric_inspiral.py

@@ -10,7 +10,6 @@ Lower et al. (2018) -> arXiv:1806.05350.
 For a more comprehensive look at what goes on in each step, refer to the
 "basic_tutorial.py" example.
 """
-from __future__ import division
 
 import numpy as np
 import bilby

examples/gw_examples/injection_examples/fake_sampler_example.py

@@ -2,7 +2,6 @@
 """
 Read ROQ posterior and calculate full likelihood at same parameter space points.
 """
-from __future__ import division, print_function
 
 import numpy as np
 import deepdish as dd

examples/gw_examples/injection_examples/fast_tutorial.py

@@ -7,7 +7,6 @@ This example estimates the masses using a uniform prior in both component masses
 and distance using a uniform in comoving volume prior on luminosity distance
 between luminosity distances of 100Mpc and 5Gpc, the cosmology is Planck15.
 """
-from __future__ import division, print_function
 
 import numpy as np
 import bilby

examples/gw_examples/injection_examples/how_to_specify_the_prior.py

@@ -3,7 +3,6 @@
 Tutorial to demonstrate how to specify the prior distributions used for
 parameter estimation.
 """
-from __future__ import division, print_function
 
 import numpy as np
 import bilby

examples/gw_examples/injection_examples/marginalized_likelihood.py

@@ -6,7 +6,6 @@ estimation on an injected signal using time, phase and distance marginalisation.
 We also demonstrate how the posterior distribution for the marginalised
 parameter can be recovered in post-processing.
 """
-from __future__ import division, print_function
 import bilby
 import numpy as np
 

examples/gw_examples/injection_examples/non_tensor.py

@@ -6,7 +6,6 @@ allowed in general relativity.
 We adapt the sine-Gaussian burst model to include vector polarizations with an
 unknown contribution from the vector modes.
 """
-from __future__ import division, print_function
 import bilby
 import numpy as np
 

examples/gw_examples/injection_examples/plot_skymap.py

@@ -3,7 +3,6 @@
 Example script which produces posterior samples of ra and dec and generates a
 skymap
 """
-from __future__ import division, print_function
 import bilby
 
 duration = 4.

examples/gw_examples/injection_examples/plot_time_domain_data.py

 #!/usr/bin/env python
 """
 """
-from __future__ import division, print_function
 import numpy as np
 import bilby
 

examples/gw_examples/injection_examples/roq_example.py

@@ -8,7 +8,6 @@ This requires files specifying the appropriate basis weights.
 These aren't shipped with Bilby, but are available on LDG clusters and
 from the public repository https://git.ligo.org/lscsoft/ROQ_data.
 """
-from __future__ import division, print_function
 
 import numpy as np
 

examples/gw_examples/injection_examples/sine_gaussian_example.py

@@ -3,7 +3,6 @@
 Tutorial to demonstrate running parameter estimation on a sine gaussian
 injected signal.
 """
-from __future__ import division, print_function
 import bilby
 import numpy as np
 

examples/gw_examples/injection_examples/standard_15d_cbc_tutorial.py

@@ -4,7 +4,6 @@ Tutorial to demonstrate running parameter estimation on a full 15 parameter
 space for an injected cbc signal. This is the standard injection analysis script
 one can modify for the study of injected CBC events.
 """
-from __future__ import division, print_function
 import numpy as np
 import bilby
 

examples/gw_examples/injection_examples/using_gwin.py

@@ -15,7 +15,6 @@ of the model. So, in the following, we only create priors for the parameters
 to be searched over.
 
 """
-from __future__ import division, print_function
 import numpy as np
 import bilby
 

examples/gw_examples/supernova_example/supernova_example.py

@@ -7,7 +7,6 @@ supernova waveforms. The first few PCs are then linearly combined with a scale
 factor. (See https://arxiv.org/pdf/1202.3256.pdf)
 
 """
-from __future__ import division, print_function
 import numpy as np
 import bilby
 

requirements.txt

-future
 dynesty
 emcee
 corner

setup.py

@@ -87,7 +87,6 @@ setup(name='bilby',
                     'bilby': [version_file]},
       python_requires='>=3.5',
       install_requires=[
-          'future',
           'dynesty>=1.0.0',
           'emcee',
           'corner',

test/core/prior/prior_test.py

-from __future__ import absolute_import, division
 import bilby
 import unittest
 import numpy as np