diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index a19412a78c0974cbf6e095ef4935b39e0e74d3a2..add1efb9d012d318329d90d13c5b1fd39e92e006 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -33,6 +33,7 @@ stages:
- python -c "import bilby.gw.sampler"
- python -c "import bilby.hyper"
- python -c "import cli_bilby"
+ - python test/import_test.py
- for script in $(pip show -f bilby | grep "bin\/" | xargs -I {} basename {}); do
${script} --help;
done
diff --git a/bilby/__init__.py b/bilby/__init__.py
index 30e2531b5a51b892836461372a54cbf8c8907f58..04a29e1bc27008ffed916a8f8f171bc6a2315c7c 100644
--- a/bilby/__init__.py
+++ b/bilby/__init__.py
@@ -29,7 +29,7 @@ __version__ = utils.get_version_information()
if sys.version_info < (3,):
raise ImportError(
-"""You are running bilby 0.6.4 on Python 2
+"""You are running bilby >= 0.6.4 on Python 2
Bilby 0.6.4 and above are no longer compatible with Python 2, and you still
ended up with this version installed. That's unfortunate; sorry about that.
diff --git a/bilby/core/grid.py b/bilby/core/grid.py
index ccc1e9d7dc24acace50f8b994973f760857a173e..8562e6d67b3afbaa863e85fe8125e62248a7bc41 100644
--- a/bilby/core/grid.py
+++ b/bilby/core/grid.py
@@ -1,12 +1,14 @@
-import numpy as np
-import os
import json
+import os
from collections import OrderedDict
+import numpy as np
+
from .prior import Prior, PriorDict
-from .utils import (logtrapzexp, check_directory_exists_and_if_not_mkdir,
- logger)
-from .utils import BilbyJsonEncoder, load_json, move_old_file
+from .utils import (
+ logtrapzexp, check_directory_exists_and_if_not_mkdir, logger,
+ BilbyJsonEncoder, load_json, move_old_file
+)
from .result import FileMovedError
diff --git a/bilby/core/prior/analytical.py b/bilby/core/prior/analytical.py
index 08e56a98d35d7a465e7f7c676c615904bffff19a..63ce9683bfb6a2d0ba80f87173e3c36146dda878 100644
--- a/bilby/core/prior/analytical.py
+++ b/bilby/core/prior/analytical.py
@@ -4,7 +4,7 @@ from scipy.special._ufuncs import xlogy, erf, log1p, stdtrit, gammaln, stdtr, \
btdtri, betaln, btdtr, gammaincinv, gammainc
from .base import Prior
-from bilby.core.utils import logger
+from ..utils import logger
class DeltaFunction(Prior):
diff --git a/bilby/core/prior/base.py b/bilby/core/prior/base.py
index 05df00a7bad5a46021f6597617d833c6ee525816..ab3f2e128408d4dd4b4722ea3c15160e317a3d25 100644
--- a/bilby/core/prior/base.py
+++ b/bilby/core/prior/base.py
@@ -5,7 +5,6 @@ import re
import numpy as np
import scipy.stats
-from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d
from bilby.core.utils import infer_args_from_method, BilbyJsonEncoder, decode_bilby_json, logger, \
@@ -162,6 +161,7 @@ class Prior(object):
def cdf(self, val):
""" Generic method to calculate CDF, can be overwritten in subclass """
+ from scipy.integrate import cumtrapz
if np.any(np.isinf([self.minimum, self.maximum])):
raise ValueError(
"Unable to use the generic CDF calculation for priors with"
diff --git a/bilby/core/prior/conditional.py b/bilby/core/prior/conditional.py
index fbe48469dfdc71b5a38bfa5d50fa242f3478867e..107822828c52ed32111a9462c1d8f4325b143719 100644
--- a/bilby/core/prior/conditional.py
+++ b/bilby/core/prior/conditional.py
@@ -1,11 +1,11 @@
import numpy as np
from .base import Prior, PriorException
-from bilby.core.prior.interpolated import Interped
-from bilby.core.prior.analytical import DeltaFunction, PowerLaw, Uniform, LogUniform, \
+from .interpolated import Interped
+from .analytical import DeltaFunction, PowerLaw, Uniform, LogUniform, \
SymmetricLogUniform, Cosine, Sine, Gaussian, TruncatedGaussian, HalfGaussian, \
LogNormal, Exponential, StudentT, Beta, Logistic, Cauchy, Gamma, ChiSquared, FermiDirac
-from bilby.core.utils import infer_args_from_method, infer_parameters_from_function
+from ..utils import infer_args_from_method, infer_parameters_from_function
def conditional_prior_factory(prior_class):
diff --git a/bilby/core/prior/dict.py b/bilby/core/prior/dict.py
index fc9918b04e37054a309b47390c588128c55648c8..7c82b4f4f2386462ba345bcb97db6d3b52064ccc 100644
--- a/bilby/core/prior/dict.py
+++ b/bilby/core/prior/dict.py
@@ -1,15 +1,14 @@
-from importlib import import_module
-from io import open as ioopen
import json
import os
+from importlib import import_module
+from io import open as ioopen
-from matplotlib.cbook import flatten
import numpy as np
-from bilby.core.prior.analytical import DeltaFunction
-from bilby.core.prior.base import Prior, Constraint
-from bilby.core.prior.joint import JointPrior
-from bilby.core.utils import logger, check_directory_exists_and_if_not_mkdir, BilbyJsonEncoder, decode_bilby_json
+from .analytical import DeltaFunction
+from .base import Prior, Constraint
+from .joint import JointPrior
+from ..utils import logger, check_directory_exists_and_if_not_mkdir, BilbyJsonEncoder, decode_bilby_json
class PriorDict(dict):
@@ -496,6 +495,7 @@ class PriorDict(dict):
=======
list: List of floats containing the rescaled sample
"""
+ from matplotlib.cbook import flatten
return list(flatten([self[key].rescale(sample) for key, sample in zip(keys, theta)]))
def test_redundancy(self, key, disable_logging=False):
diff --git a/bilby/core/prior/interpolated.py b/bilby/core/prior/interpolated.py
index 992a75b50ab8c2a0647f9b42c896786ed4aec08c..ece311a5a5bb475cc543b29ffd37b949592d802f 100644
--- a/bilby/core/prior/interpolated.py
+++ b/bilby/core/prior/interpolated.py
@@ -1,9 +1,8 @@
import numpy as np
-from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d
from .base import Prior
-from bilby.core.utils import logger
+from ..utils import logger
class Interped(Prior):
@@ -164,6 +163,7 @@ class Interped(Prior):
self._initialize_attributes()
def _initialize_attributes(self):
+ from scipy.integrate import cumtrapz
if np.trapz(self._yy, self.xx) != 1:
logger.debug('Supplied PDF for {} is not normalised, normalising.'.format(self.name))
self._yy /= np.trapz(self._yy, self.xx)
diff --git a/bilby/core/prior/joint.py b/bilby/core/prior/joint.py
index b3ee2f0b88a38d1da374f87173a68ad402ce9c82..a10a0add2038e120acd82017a20656e440edd636 100644
--- a/bilby/core/prior/joint.py
+++ b/bilby/core/prior/joint.py
@@ -3,7 +3,7 @@ import scipy.stats
from scipy.special import erfinv
from .base import Prior, PriorException
-from bilby.core.utils import logger, infer_args_from_method, get_dict_with_properties
+from ..utils import logger, infer_args_from_method, get_dict_with_properties
class BaseJointPriorDist(object):
diff --git a/bilby/core/prior/slabspike.py b/bilby/core/prior/slabspike.py
index 54872b8b1c1bcdea91ad567bb85a707207c36b52..8482b81460c799210e923166fd11a14fc53ea520 100644
--- a/bilby/core/prior/slabspike.py
+++ b/bilby/core/prior/slabspike.py
@@ -1,7 +1,7 @@
import numpy as np
-from bilby.core.prior.base import Prior
-from bilby.core.utils import logger
+from .base import Prior
+from ..utils import logger
class SlabSpikePrior(Prior):
diff --git a/bilby/core/result.py b/bilby/core/result.py
index 789fa5d9e9d386063b1be6d3e224dcad07dd04ea..072d1e52ddbc807d62f91aa70893595d4583dd1c 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -1,22 +1,14 @@
import inspect
+import json
import os
from collections import OrderedDict, namedtuple
from copy import copy
-from distutils.version import LooseVersion
from importlib import import_module
from itertools import product
-from tqdm import tqdm
-import corner
-import h5py
-import json
-import matplotlib
-import matplotlib.pyplot as plt
-from matplotlib import lines as mpllines
import numpy as np
import pandas as pd
import scipy.stats
-from scipy.special import logsumexp
from . import utils
from .utils import (
@@ -126,6 +118,7 @@ def get_weights_for_reweighting(
n_checkpoint: int
Number of samples to reweight before writing a resume file
"""
+ from tqdm.auto import tqdm
nposterior = len(result.posterior)
@@ -257,6 +250,7 @@ def reweight(result, label=None, new_likelihood=None, new_prior=None,
An array of the natural-log priors from the old likelihood
"""
+ from scipy.special import logsumexp
result = copy(result)
@@ -511,6 +505,7 @@ class Result(object):
@classmethod
@docstring(_load_doctstring.format(format="hdf5"))
def from_hdf5(cls, filename=None, outdir=None, label=None):
+ import h5py
filename = _determine_file_name(filename, outdir, label, 'hdf5', False)
with h5py.File(filename, "r") as ff:
data = recursively_load_dict_contents_from_group(ff, '/')
@@ -770,6 +765,7 @@ class Result(object):
with open(filename, 'w') as file:
json.dump(dictionary, file, indent=2, cls=BilbyJsonEncoder)
elif extension == 'hdf5':
+ import h5py
dictionary["__module__"] = self.__module__
dictionary["__name__"] = self.__class__.__name__
with h5py.File(filename, 'w') as h5file:
@@ -984,6 +980,7 @@ class Result(object):
figure: matplotlib.pyplot.figure
A matplotlib figure object
"""
+ import matplotlib.pyplot as plt
logger.info('Plotting {} marginal distribution'.format(key))
label = self.get_latex_labels_from_parameter_keys([key])[0]
fig, ax = plt.subplots()
@@ -1153,6 +1150,8 @@ class Result(object):
A matplotlib figure instance
"""
+ import corner
+ import matplotlib.pyplot as plt
# If in testing mode, not corner plots are generated
if utils.command_line_args.bilby_test_mode:
@@ -1165,12 +1164,7 @@ class Result(object):
truth_color='tab:orange', quantiles=[0.16, 0.84],
levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),
plot_density=False, plot_datapoints=True, fill_contours=True,
- max_n_ticks=3)
-
- if LooseVersion(matplotlib.__version__) < "2.1":
- defaults_kwargs['hist_kwargs'] = dict(normed=True)
- else:
- defaults_kwargs['hist_kwargs'] = dict(density=True)
+ max_n_ticks=3, hist_kwargs=dict(density=True))
if 'lionize' in kwargs and kwargs['lionize'] is True:
defaults_kwargs['truth_color'] = 'tab:blue'
@@ -1282,6 +1276,7 @@ class Result(object):
@latex_plot_format
def plot_walkers(self, **kwargs):
""" Method to plot the trace of the walkers in an ensemble MCMC plot """
+ import matplotlib.pyplot as plt
if hasattr(self, 'walkers') is False:
logger.warning("Cannot plot_walkers as no walkers are saved")
return
@@ -1345,6 +1340,7 @@ class Result(object):
Path to the outdir. Default is the one store in the result object.
"""
+ import matplotlib.pyplot as plt
# Determine model_posterior, the subset of the full posterior which
# should be passed into the model
@@ -1794,6 +1790,7 @@ class ResultList(list):
result: bilby.core.result.Result
The result object with the combined evidences.
"""
+ from scipy.special import logsumexp
self.check_nested_samples()
# Combine evidences
@@ -1886,6 +1883,8 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
A matplotlib figure instance
"""
+ import matplotlib.pyplot as plt
+ import matplotlib.lines as mpllines
kwargs['show_titles'] = False
kwargs['truths'] = None
@@ -1977,6 +1976,7 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=[0.68, 0
matplotlib figure and a NamedTuple with attributes `combined_pvalue`,
`pvalues`, and `names`.
"""
+ import matplotlib.pyplot as plt
if keys is None:
keys = results[0].search_parameter_keys
diff --git a/bilby/core/sampler/__init__.py b/bilby/core/sampler/__init__.py
index 48858d6d4bebeb09726dc95ee95ca65424542e9b..21184ba6054ecd0e0106db9b1c69e40edf5e6393 100644
--- a/bilby/core/sampler/__init__.py
+++ b/bilby/core/sampler/__init__.py
@@ -6,7 +6,6 @@ from collections import OrderedDict
import bilby
from ..utils import command_line_args, logger, loaded_modules_dict
from ..prior import PriorDict, DeltaFunction
-
from .base_sampler import Sampler, SamplingMarginalisedParameterError
from .cpnest import Cpnest
from .dynamic_dynesty import DynamicDynesty
diff --git a/bilby/core/sampler/dynamic_dynesty.py b/bilby/core/sampler/dynamic_dynesty.py
index 88b16763b77cc3d5a6c81494980f2d059bda3554..8bb6d647aad013c5be957cfde6311f10f9feda82 100644
--- a/bilby/core/sampler/dynamic_dynesty.py
+++ b/bilby/core/sampler/dynamic_dynesty.py
@@ -1,6 +1,5 @@
import os
-import dill as pickle
import signal
import numpy as np
@@ -168,18 +167,20 @@ class DynamicDynesty(Dynesty):
def write_current_state(self):
"""
"""
+ import dill
check_directory_exists_and_if_not_mkdir(self.outdir)
with open(self.resume_file, 'wb') as file:
- pickle.dump(self, file)
+ dill.dump(self, file)
def read_saved_state(self, continuing=False):
"""
"""
+ import dill
logger.debug("Reading resume file {}".format(self.resume_file))
if os.path.isfile(self.resume_file):
with open(self.resume_file, 'rb') as file:
- self = pickle.load(file)
+ self = dill.load(file)
else:
logger.debug(
"Failed to read resume file {}".format(self.resume_file))
diff --git a/bilby/core/sampler/dynesty.py b/bilby/core/sampler/dynesty.py
index d453ffd7d9e1c233cc7625dbe75026552c45f1a8..7248ff8012525eb7347e609afda2e46a3dcc22a3 100644
--- a/bilby/core/sampler/dynesty.py
+++ b/bilby/core/sampler/dynesty.py
@@ -1,13 +1,10 @@
import datetime
-import dill
import os
import sys
-import pickle
import signal
import time
+import warnings
-from tqdm.auto import tqdm
-import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame
@@ -20,11 +17,6 @@ from ..utils import (
from .base_sampler import Sampler, NestedSampler
from ..result import rejection_sample
-from numpy import linalg
-from dynesty.utils import unitcheck
-import warnings
-
-
_likelihood = None
_priors = None
_search_parameter_keys = None
@@ -218,6 +210,7 @@ class Dynesty(NestedSampler):
kwargs['queue_size'] = kwargs.pop(equiv)
def _verify_kwargs_against_default_kwargs(self):
+ from tqdm.auto import tqdm
if not self.kwargs['walks']:
self.kwargs['walks'] = self.ndim * 10
if not self.kwargs['update_interval']:
@@ -323,6 +316,7 @@ class Dynesty(NestedSampler):
def run_sampler(self):
import dynesty
+ import dill
logger.info("Using dynesty version {}".format(dynesty.__version__))
if self.kwargs.get("sample", "rwalk") == "rwalk":
@@ -376,7 +370,7 @@ class Dynesty(NestedSampler):
check_directory_exists_and_if_not_mkdir(self.outdir)
dynesty_result = "{}/{}_dynesty.pickle".format(self.outdir, self.label)
with open(dynesty_result, 'wb') as file:
- pickle.dump(out, file)
+ dill.dump(out, file)
self._generate_result(out)
self.calc_likelihood_count()
@@ -482,6 +476,7 @@ class Dynesty(NestedSampler):
"""
from ... import __version__ as bilby_version
from dynesty import __version__ as dynesty_version
+ import dill
versions = dict(bilby=bilby_version, dynesty=dynesty_version)
if os.path.isfile(self.resume_file):
logger.info("Reading resume file {}".format(self.resume_file))
@@ -560,6 +555,7 @@ class Dynesty(NestedSampler):
from ... import __version__ as bilby_version
from dynesty import __version__ as dynesty_version
+ import dill
check_directory_exists_and_if_not_mkdir(self.outdir)
end_time = datetime.datetime.now()
if hasattr(self, 'start_time'):
@@ -605,6 +601,7 @@ class Dynesty(NestedSampler):
df.to_csv(filename, index=False, header=True, sep=' ')
def plot_current_state(self):
+ import matplotlib.pyplot as plt
if self.check_point_plot:
import dynesty.plotting as dyplot
labels = [label.replace('_', ' ') for label in self.search_parameter_keys]
@@ -704,6 +701,7 @@ class Dynesty(NestedSampler):
def sample_rwalk_bilby(args):
""" Modified bilby-implemented version of dynesty.sampling.sample_rwalk """
+ from dynesty.utils import unitcheck
# Unzipping.
(u, loglstar, axes, scale,
@@ -737,7 +735,7 @@ def sample_rwalk_bilby(args):
# Propose a direction on the unit n-sphere.
drhat = rstate.randn(n)
- drhat /= linalg.norm(drhat)
+ drhat /= np.linalg.norm(drhat)
# Scale based on dimensionality.
dr = drhat * rstate.rand() ** (1.0 / n)
diff --git a/bilby/core/sampler/emcee.py b/bilby/core/sampler/emcee.py
index 81e4eedd0ac072bdc2fccb4e7ad5359727ebd9f2..4718519841143a3d004eaf5be76ccb9c9652d456 100644
--- a/bilby/core/sampler/emcee.py
+++ b/bilby/core/sampler/emcee.py
@@ -1,14 +1,13 @@
-from collections import namedtuple
import os
import signal
import shutil
-from shutil import copyfile
import sys
+from collections import namedtuple
+from distutils.version import LooseVersion
+from shutil import copyfile
import numpy as np
from pandas import DataFrame
-from distutils.version import LooseVersion
-import dill as pickle
from ..utils import logger, check_directory_exists_and_if_not_mkdir
from .base_sampler import MCMCSampler, SamplerError
@@ -254,13 +253,14 @@ class Emcee(MCMCSampler):
def checkpoint(self):
""" Writes a pickle file of the sampler to disk using dill """
+ import dill
logger.info("Checkpointing sampler to file {}"
.format(self.checkpoint_info.sampler_file))
with open(self.checkpoint_info.sampler_file, 'wb') as f:
# Overwrites the stored sampler chain with one that is truncated
# to only the completed steps
self.sampler._chain = self.sampler_chain
- pickle.dump(self._sampler, f)
+ dill.dump(self._sampler, f)
def checkpoint_and_exit(self, signum, frame):
logger.info("Recieved signal {}".format(signum))
@@ -283,10 +283,11 @@ class Emcee(MCMCSampler):
if hasattr(self, '_sampler'):
pass
elif self.resume and os.path.isfile(self.checkpoint_info.sampler_file):
+ import dill
logger.info("Resuming run from checkpoint file {}"
.format(self.checkpoint_info.sampler_file))
with open(self.checkpoint_info.sampler_file, 'rb') as f:
- self._sampler = pickle.load(f)
+ self._sampler = dill.load(f)
self._set_pos0_for_resume()
else:
self._initialise_sampler()
diff --git a/bilby/core/sampler/fake_sampler.py b/bilby/core/sampler/fake_sampler.py
index 1992d74b1628f1d937100bd1645d1d0105fb128e..8d218472d13f7769c4b88cdd0155cb8f7c04dd0d 100644
--- a/bilby/core/sampler/fake_sampler.py
+++ b/bilby/core/sampler/fake_sampler.py
@@ -1,5 +1,6 @@
import numpy as np
+
from .base_sampler import Sampler
from ..result import read_in_result
diff --git a/bilby/core/sampler/kombine.py b/bilby/core/sampler/kombine.py
index d077499cdb27a75b8dbbd1545ef5b54327be2b6d..83947fc88378c5401508eac458192141cd9f221e 100644
--- a/bilby/core/sampler/kombine.py
+++ b/bilby/core/sampler/kombine.py
@@ -1,7 +1,9 @@
-from ..utils import logger
-import numpy as np
import os
+
+import numpy as np
+
from .emcee import Emcee
+from ..utils import logger
class Kombine(Emcee):
diff --git a/bilby/core/sampler/ptemcee.py b/bilby/core/sampler/ptemcee.py
index 2be681f37664ba0e52d4b60a5182658cc9b3ca2e..471439a1dd7a38400e05c3557b334cdba0f29da2 100644
--- a/bilby/core/sampler/ptemcee.py
+++ b/bilby/core/sampler/ptemcee.py
@@ -1,18 +1,14 @@
-
-import os
-import datetime
import copy
+import datetime
+import logging
+import os
import signal
import sys
import time
-import dill
from collections import namedtuple
-import logging
import numpy as np
import pandas as pd
-import matplotlib.pyplot as plt
-import scipy.signal
from ..utils import logger, check_directory_exists_and_if_not_mkdir
from .base_sampler import SamplerError, MCMCSampler
@@ -372,6 +368,7 @@ class Ptemcee(MCMCSampler):
import ptemcee
if os.path.isfile(self.resume_file) and self.resume is True:
+ import dill
logger.info("Resume data {} found".format(self.resume_file))
with open(self.resume_file, "rb") as file:
data = dill.load(file)
@@ -833,10 +830,12 @@ def check_iteration(
def get_max_gradient(x, axis=0, window_length=11, polyorder=2, smooth=False):
+ from scipy.signal import savgol_filter
if smooth:
- x = scipy.signal.savgol_filter(
- x, axis=axis, window_length=window_length, polyorder=3)
- return np.max(scipy.signal.savgol_filter(
+ x = savgol_filter(
+ x, axis=axis, window_length=window_length, polyorder=3
+ )
+ return np.max(savgol_filter(
x, axis=axis, window_length=window_length, polyorder=polyorder,
deriv=1))
@@ -963,6 +962,7 @@ def checkpoint(
Q_list,
time_per_check,
):
+ import dill
logger.info("Writing checkpoint and diagnostics")
ndim = sampler.dim
@@ -1002,6 +1002,7 @@ def checkpoint(
def plot_walkers(walkers, nburn, thin, parameter_labels, outdir, label,
discard=0):
""" Method to plot the trace of the walkers in an ensemble MCMC plot """
+ import matplotlib.pyplot as plt
nwalkers, nsteps, ndim = walkers.shape
if np.isnan(nburn):
nburn = nsteps
@@ -1053,6 +1054,7 @@ def plot_walkers(walkers, nburn, thin, parameter_labels, outdir, label,
def plot_tau(
tau_list_n, tau_list, search_parameter_keys, outdir, label, tau, autocorr_tau,
):
+ import matplotlib.pyplot as plt
fig, ax = plt.subplots()
for i, key in enumerate(search_parameter_keys):
ax.plot(tau_list_n, np.array(tau_list)[:, i], label=key)
@@ -1065,6 +1067,7 @@ def plot_tau(
def plot_mean_log_posterior(mean_log_posterior, outdir, label):
+ import matplotlib.pyplot as plt
ntemps, nsteps = mean_log_posterior.shape
ymax = np.max(mean_log_posterior)
@@ -1085,6 +1088,7 @@ def plot_mean_log_posterior(mean_log_posterior, outdir, label):
def compute_evidence(sampler, log_likelihood_array, outdir, label, discard, nburn, thin,
iteration, make_plots=True):
""" Computes the evidence using thermodynamic integration """
+ import matplotlib.pyplot as plt
betas = sampler.betas
# We compute the evidence without the burnin samples, but we do not thin
lnlike = log_likelihood_array[:, :, discard + nburn : iteration]
diff --git a/bilby/core/utils.py b/bilby/core/utils.py
index 7bfb1faa98f6112e02498eb17ed3c0e7eacd843b..ccb7fc11ebfacb63abbef8706e597ad3b8b9b72b 100644
--- a/bilby/core/utils.py
+++ b/bilby/core/utils.py
@@ -1,26 +1,24 @@
-
-from distutils.spawn import find_executable
-import logging
-import os
-import shutil
-import sys
-from math import fmod
import argparse
-import inspect
import functools
+import inspect
+import json
+import logging
+import multiprocessing
+import os
import types
+import shutil
import subprocess
-import multiprocessing
+import sys
+import warnings
+from distutils.spawn import find_executable
from importlib import import_module
+from math import fmod
from numbers import Number
-import json
-import warnings
import numpy as np
+import pandas as pd
from scipy.interpolate import interp2d
from scipy.special import logsumexp
-import pandas as pd
-import matplotlib.pyplot as plt
logger = logging.getLogger('bilby')
@@ -1208,6 +1206,7 @@ def latex_plot_format(func):
"""
@functools.wraps(func)
def wrapper_decorator(*args, **kwargs):
+ import matplotlib.pyplot as plt
from matplotlib import rcParams
if "BILBY_STYLE" in kwargs:
diff --git a/bilby/gw/conversion.py b/bilby/gw/conversion.py
index f034dfe8458178c91c8b340a8f5ab424ff243cf2..1ded6605e54b2d2ec76dd0517c45cde4ded6dc86 100644
--- a/bilby/gw/conversion.py
+++ b/bilby/gw/conversion.py
@@ -1,7 +1,6 @@
import sys
import multiprocessing
-from tqdm.auto import tqdm
import numpy as np
from pandas import DataFrame
@@ -12,13 +11,6 @@ from .utils import lalsim_SimInspiralTransformPrecessingNewInitialConditions
from .eos.eos import SpectralDecompositionEOS, EOSFamily, IntegrateTOV
from .cosmology import get_cosmology
-try:
- from astropy import units
- from astropy.cosmology import z_at_value
-except ImportError:
- logger.debug("You do not have astropy installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
def redshift_to_luminosity_distance(redshift, cosmology=None):
cosmology = get_cosmology(cosmology)
@@ -32,12 +24,16 @@ def redshift_to_comoving_distance(redshift, cosmology=None):
@np.vectorize
def luminosity_distance_to_redshift(distance, cosmology=None):
+ from astropy import units
+ from astropy.cosmology import z_at_value
cosmology = get_cosmology(cosmology)
return z_at_value(cosmology.luminosity_distance, distance * units.Mpc)
@np.vectorize
def comoving_distance_to_redshift(distance, cosmology=None):
+ from astropy import units
+ from astropy.cosmology import z_at_value
cosmology = get_cosmology(cosmology)
return z_at_value(cosmology.comoving_distance, distance * units.Mpc)
@@ -1148,6 +1144,7 @@ def compute_snrs(sample, likelihood):
sample['{}_optimal_snr'.format(ifo.name)] = \
per_detector_snr.optimal_snr_squared.real ** 0.5
else:
+ from tqdm.auto import tqdm
logger.info(
'Computing SNRs for every sample.')
@@ -1212,6 +1209,7 @@ def generate_posterior_samples_from_marginalized_likelihood(
return samples
elif not isinstance(samples, DataFrame):
raise ValueError("Unable to handle input samples of type {}".format(type(samples)))
+ from tqdm.auto import tqdm
logger.info('Reconstructing marginalised parameters.')
@@ -1242,6 +1240,7 @@ def generate_sky_frame_parameters(samples, likelihood):
return
elif not isinstance(samples, DataFrame):
raise ValueError
+ from tqdm.auto import tqdm
logger.info('Generating sky frame parameters.')
new_samples = list()
diff --git a/bilby/gw/cosmology.py b/bilby/gw/cosmology.py
index 861fdf23f99ef2e8a3190ad47ec0eeaddb44f7d4..ded3f5eb6b01c01c1066afdacb0c13f8f20a7a97 100644
--- a/bilby/gw/cosmology.py
+++ b/bilby/gw/cosmology.py
@@ -1,19 +1,20 @@
-from ..core.utils import logger
+DEFAULT_COSMOLOGY = None
+COSMOLOGY = [None, str(None)]
-try:
+
+def _set_default_cosmology():
from astropy import cosmology as cosmo
- DEFAULT_COSMOLOGY = cosmo.Planck15
- COSMOLOGY = [DEFAULT_COSMOLOGY, DEFAULT_COSMOLOGY.name]
-except ImportError:
- logger.debug("You do not have astropy installed currently. You will"
- " not be able to use some of the prebuilt functions.")
- DEFAULT_COSMOLOGY = None
- COSMOLOGY = [None, str(None)]
+ global DEFAULT_COSMOLOGY, COSMOLOGY
+ if DEFAULT_COSMOLOGY is None:
+ DEFAULT_COSMOLOGY = cosmo.Planck15
+ COSMOLOGY = [DEFAULT_COSMOLOGY, DEFAULT_COSMOLOGY.name]
def get_cosmology(cosmology=None):
+ from astropy import cosmology as cosmo
+ _set_default_cosmology()
if cosmology is None:
- cosmology = COSMOLOGY[0]
+ cosmology = DEFAULT_COSMOLOGY
elif isinstance(cosmology, str):
cosmology = cosmo.__dict__[cosmology]
return cosmology
@@ -41,6 +42,8 @@ def set_cosmology(cosmology=None):
cosmo: astropy.cosmology.FLRW
Cosmology instance
"""
+ from astropy import cosmology as cosmo
+ _set_default_cosmology()
if cosmology is None:
cosmology = DEFAULT_COSMOLOGY
elif isinstance(cosmology, cosmo.FLRW):
diff --git a/bilby/gw/detector/__init__.py b/bilby/gw/detector/__init__.py
index 37d6027634365d2fd7f349a694d0836e19434278..b68313c0fb2f84c3c33dac380a0a1fbadb4c53a2 100644
--- a/bilby/gw/detector/__init__.py
+++ b/bilby/gw/detector/__init__.py
@@ -5,13 +5,6 @@ from .networks import *
from .psd import *
from .strain_data import *
-try:
- import lal
- import lalsimulation as lalsim
-except ImportError:
- logger.debug("You do not have lalsuite installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
def get_safe_signal_duration(mass_1, mass_2, a_1, a_2, tilt_1, tilt_2, flow=10):
""" Calculate the safe signal duration, given the parameters
@@ -30,8 +23,10 @@ def get_safe_signal_duration(mass_1, mass_2, a_1, a_2, tilt_1, tilt_2, flow=10):
to the nearest power of 2)
"""
- chirp_time = lalsim.SimInspiralChirpTimeBound(
- flow, mass_1 * lal.MSUN_SI, mass_2 * lal.MSUN_SI,
+ from lal import MSUN_SI
+ from lalsimulation import SimInspiralChirpTimeBound
+ chirp_time = SimInspiralChirpTimeBound(
+ flow, mass_1 * MSUN_SI, mass_2 * MSUN_SI,
a_1 * np.cos(tilt_1), a_2 * np.cos(tilt_2))
return max(2**(int(np.log2(chirp_time)) + 1), 4)
@@ -329,7 +324,7 @@ def load_data_by_channel_name(
ifo = get_empty_interferometer(det)
ifo.set_strain_data_from_channel_name(
- channel = channel_name,
+ channel=channel_name,
sampling_frequency=sampling_frequency,
duration=segment_duration,
start_time=start_time)
diff --git a/bilby/gw/detector/calibration.py b/bilby/gw/detector/calibration.py
index 88a5a664fbc8f148b6628b50532ec651254d77cf..b52eb9284307bb8f435f0418923bf53ee83c5eee 100644
--- a/bilby/gw/detector/calibration.py
+++ b/bilby/gw/detector/calibration.py
@@ -2,7 +2,6 @@
"""
import numpy as np
-import tables
from scipy.interpolate import interp1d
@@ -29,6 +28,7 @@ def read_calibration_file(filename, frequency_array, number_of_response_curves,
Shape is (number_of_response_curves x len(frequency_array))
"""
+ import tables
calibration_file = tables.open_file(filename, 'r')
calibration_amplitude = \
@@ -71,6 +71,7 @@ def write_calibration_file(filename, frequency_array, calibration_draws, calibra
Parameters used to generate the random draws of the calibration response curves
"""
+ import tables
calibration_file = tables.open_file(filename, 'w')
deltaR_group = calibration_file.create_group(calibration_file.root, 'deltaR')
diff --git a/bilby/gw/detector/interferometer.py b/bilby/gw/detector/interferometer.py
index 72a00168e5a4e1e08f3bc055f9c787ed199ea9d8..7155d0f4445f5c11eae8327845a87dcd3e8622d7 100644
--- a/bilby/gw/detector/interferometer.py
+++ b/bilby/gw/detector/interferometer.py
@@ -2,7 +2,6 @@ import os
import sys
import numpy as np
-from matplotlib import pyplot as plt
from ...core import utils
from ...core.utils import docstring, logger
@@ -12,13 +11,6 @@ from .calibration import Recalibrate
from .geometry import InterferometerGeometry
from .strain_data import InterferometerStrainData
-try:
- import gwpy
- import gwpy.signal
-except ImportError:
- logger.debug("You do not have gwpy installed currently. You will "
- " not be able to use some of the prebuilt functions.")
-
class Interferometer(object):
"""Class for the Interferometer """
@@ -599,6 +591,7 @@ class Interferometer(object):
header='f h(f)')
def plot_data(self, signal=None, outdir='.', label=None):
+ import matplotlib.pyplot as plt
if utils.command_line_args.bilby_test_mode:
return
@@ -656,23 +649,26 @@ class Interferometer(object):
plotting.
"""
+ import matplotlib.pyplot as plt
+ from gwpy.timeseries import TimeSeries
+ from gwpy.signal.filter_design import bandpass, concatenate_zpks, notch
# We use the gwpy timeseries to perform bandpass and notching
if notches is None:
notches = list()
- timeseries = gwpy.timeseries.TimeSeries(
+ timeseries = TimeSeries(
data=self.strain_data.time_domain_strain, times=self.strain_data.time_array)
zpks = []
if bandpass_frequencies is not None:
- zpks.append(gwpy.signal.filter_design.bandpass(
+ zpks.append(bandpass(
bandpass_frequencies[0], bandpass_frequencies[1],
self.strain_data.sampling_frequency))
if notches is not None:
for line in notches:
- zpks.append(gwpy.signal.filter_design.notch(
+ zpks.append(notch(
line, self.strain_data.sampling_frequency))
if len(zpks) > 0:
- zpk = gwpy.signal.filter_design.concatenate_zpks(*zpks)
+ zpk = concatenate_zpks(*zpks)
strain = timeseries.filter(zpk, filtfilt=False)
else:
strain = timeseries
diff --git a/bilby/gw/detector/strain_data.py b/bilby/gw/detector/strain_data.py
index 71d8acf6d52ee7f1eae4b6e176607ebd44523e17..b6e2b46c9a5579892d31c16bc43f60bc13e469a0 100644
--- a/bilby/gw/detector/strain_data.py
+++ b/bilby/gw/detector/strain_data.py
@@ -1,5 +1,4 @@
import numpy as np
-from scipy.signal.windows import tukey
from ...core import utils
from ...core.series import CoupledTimeAndFrequencySeries
@@ -7,19 +6,6 @@ from ...core.utils import logger
from .. import utils as gwutils
from ..utils import PropertyAccessor
-try:
- import gwpy
- import gwpy.signal
-except ImportError:
- logger.debug("You do not have gwpy installed currently. You will "
- " not be able to use some of the prebuilt functions.")
-
-try:
- import lal
-except ImportError:
- logger.debug("You do not have lalsuite installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
class InterferometerStrainData(object):
""" Strain data for an interferometer """
@@ -198,6 +184,7 @@ class InterferometerStrainData(object):
window: array
Window function over time array
"""
+ from scipy.signal.windows import tukey
if roll_off is not None:
self.roll_off = roll_off
elif alpha is not None:
@@ -253,11 +240,15 @@ class InterferometerStrainData(object):
"""
Output the time series strain data as a :class:`gwpy.timeseries.TimeSeries`.
"""
+ try:
+ from gwpy.timeseries import TimeSeries
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError("Cannot output strain data as gwpy TimeSeries")
- return gwpy.timeseries.TimeSeries(self.time_domain_strain,
- sample_rate=self.sampling_frequency,
- t0=self.start_time,
- channel=self.channel)
+ return TimeSeries(
+ self.time_domain_strain, sample_rate=self.sampling_frequency,
+ t0=self.start_time, channel=self.channel
+ )
def to_pycbc_timeseries(self):
"""
@@ -265,37 +256,48 @@ class InterferometerStrainData(object):
"""
try:
- import pycbc
- except ImportError:
- raise ImportError("Cannot output strain data as PyCBC TimeSeries")
+ from pycbc.types.timeseries import TimeSeries
+ from lal import LIGOTimeGPS
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError("Cannot output strain data as PyCBC TimeSeries")
- return pycbc.types.timeseries.TimeSeries(self.time_domain_strain,
- delta_t=(1. / self.sampling_frequency),
- epoch=lal.LIGOTimeGPS(self.start_time))
+ return TimeSeries(
+ self.time_domain_strain, delta_t=(1. / self.sampling_frequency),
+ epoch=LIGOTimeGPS(self.start_time)
+ )
def to_lal_timeseries(self):
"""
Output the time series strain data as a LAL TimeSeries object.
"""
+ try:
+ from lal import CreateREAL8TimeSeries, LIGOTimeGPS, SecondUnit
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError("Cannot output strain data as PyCBC TimeSeries")
- laldata = lal.CreateREAL8TimeSeries("",
- lal.LIGOTimeGPS(self.start_time),
- 0., (1. / self.sampling_frequency),
- lal.SecondUnit,
- len(self.time_domain_strain))
- laldata.data.data[:] = self.time_domain_strain
+ lal_data = CreateREAL8TimeSeries(
+ "", LIGOTimeGPS(self.start_time), 0, 1 / self.sampling_frequency,
+ SecondUnit, len(self.time_domain_strain)
+ )
+ lal_data.data.data[:] = self.time_domain_strain
- return laldata
+ return lal_data
def to_gwpy_frequencyseries(self):
"""
Output the frequency series strain data as a :class:`gwpy.frequencyseries.FrequencySeries`.
"""
+ try:
+ from gwpy.frequencyseries import FrequencySeries
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError("Cannot output strain data as gwpy FrequencySeries")
- return gwpy.frequencyseries.FrequencySeries(self.frequency_domain_strain,
- frequencies=self.frequency_array,
- epoch=self.start_time,
- channel=self.channel)
+ return FrequencySeries(
+ self.frequency_domain_strain,
+ frequencies=self.frequency_array,
+ epoch=self.start_time,
+ channel=self.channel
+ )
def to_pycbc_frequencyseries(self):
"""
@@ -303,31 +305,42 @@ class InterferometerStrainData(object):
"""
try:
- import pycbc
+ from pycbc.types.frequencyseries import FrequencySeries
+ from lal import LIGOTimeGPS
except ImportError:
raise ImportError("Cannot output strain data as PyCBC FrequencySeries")
- return pycbc.types.frequencyseries.FrequencySeries(self.frequency_domain_strain,
- delta_f=(self.frequency_array[1] - self.frequency_array[0]),
- epoch=lal.LIGOTimeGPS(self.start_time))
+ return FrequencySeries(
+ self.frequency_domain_strain,
+ delta_f=1 / self.duration,
+ epoch=LIGOTimeGPS(self.start_time)
+ )
def to_lal_frequencyseries(self):
"""
Output the frequency series strain data as a LAL FrequencySeries object.
"""
-
- laldata = lal.CreateCOMPLEX16FrequencySeries("",
- lal.LIGOTimeGPS(self.start_time),
- self.frequency_array[0],
- (self.frequency_array[1] - self.frequency_array[0]),
- lal.SecondUnit,
- len(self.frequency_domain_strain))
- laldata.data.data[:] = self.frequency_domain_strain
-
- return laldata
+ try:
+ from lal import CreateCOMPLEX16FrequencySeries, LIGOTimeGPS, SecondUnit
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError("Cannot output strain data as PyCBC TimeSeries")
+
+ lal_data = CreateCOMPLEX16FrequencySeries(
+ "",
+ LIGOTimeGPS(self.start_time),
+ self.frequency_array[0],
+ 1 / self.duration,
+ SecondUnit,
+ len(self.frequency_domain_strain)
+ )
+ lal_data.data.data[:] = self.frequency_domain_strain
+
+ return lal_data
def low_pass_filter(self, filter_freq=None):
""" Low pass filter the data """
+ from gwpy.signal.filter_design import lowpass
+ from gwpy.timeseries import TimeSeries
if filter_freq is None:
logger.debug(
@@ -342,10 +355,8 @@ class InterferometerStrainData(object):
return
logger.debug("Applying low pass filter with filter frequency {}".format(filter_freq))
- bp = gwpy.signal.filter_design.lowpass(
- filter_freq, self.sampling_frequency)
- strain = gwpy.timeseries.TimeSeries(
- self.time_domain_strain, sample_rate=self.sampling_frequency)
+ bp = lowpass(filter_freq, self.sampling_frequency)
+ strain = TimeSeries(self.time_domain_strain, sample_rate=self.sampling_frequency)
strain = strain.filter(bp, filtfilt=True)
self._time_domain_strain = strain.value
@@ -379,6 +390,7 @@ class InterferometerStrainData(object):
The frequencies and power spectral density array
"""
+ from gwpy.timeseries import TimeSeries
data = self.time_domain_strain
@@ -394,7 +406,7 @@ class InterferometerStrainData(object):
data = data[idxs]
# WARNING this line can cause issues if the data is non-contiguous
- strain = gwpy.timeseries.TimeSeries(data=data, sample_rate=self.sampling_frequency)
+ strain = TimeSeries(data=data, sample_rate=self.sampling_frequency)
psd_alpha = 2 * self.roll_off / fft_length
logger.info(
"Tukey window PSD data with alpha={}, roll off={}".format(
@@ -500,8 +512,9 @@ class InterferometerStrainData(object):
The data to use
"""
+ from gwpy.timeseries import TimeSeries
logger.debug('Setting data using provided gwpy TimeSeries object')
- if type(time_series) != gwpy.timeseries.TimeSeries:
+ if not isinstance(time_series, TimeSeries):
raise ValueError("Input time_series is not a gwpy TimeSeries")
self._times_and_frequencies = \
CoupledTimeAndFrequencySeries(duration=time_series.duration.value,
@@ -557,7 +570,8 @@ class InterferometerStrainData(object):
The path to the file to read in
"""
- timeseries = gwpy.timeseries.TimeSeries.read(filename, format='csv')
+ from gwpy.timeseries import TimeSeries
+ timeseries = TimeSeries.read(filename, format='csv')
self.set_from_gwpy_timeseries(timeseries)
def set_from_frequency_domain_strain(
@@ -700,6 +714,7 @@ class InterferometerStrainData(object):
The sampling frequency (in Hz)
"""
+ from gwpy.timeseries import TimeSeries
channel_comp = channel.split(':')
if len(channel_comp) != 2:
raise IndexError('Channel name must have format `IFO:Channel`')
@@ -709,8 +724,7 @@ class InterferometerStrainData(object):
start_time=start_time)
logger.info('Fetching data using channel {}'.format(channel))
- strain = gwpy.timeseries.TimeSeries.get(
- channel, start_time, start_time + duration)
+ strain = TimeSeries.get(channel, start_time, start_time + duration)
strain = strain.resample(sampling_frequency)
self.set_from_gwpy_timeseries(strain)
diff --git a/bilby/gw/eos/eos.py b/bilby/gw/eos/eos.py
index b2a45dc222c08756ec033b96b864f49813e8842e..f63c2b83fd2cb0d8d371addd7dcc472f51efb043 100644
--- a/bilby/gw/eos/eos.py
+++ b/bilby/gw/eos/eos.py
@@ -1,9 +1,6 @@
import os
import numpy as np
-import matplotlib.pyplot as plt
-from scipy.integrate import cumtrapz, quad
from scipy.interpolate import interp1d, CubicSpline
-from scipy.optimize import minimize_scalar
from .tov_solver import IntegrateTOV
from ...core import utils
@@ -58,6 +55,7 @@ class TabularEOS(object):
"""
def __init__(self, eos, sampling_flag=False, warning_flag=False):
+ from scipy.integrate import cumtrapz
self.sampling_flag = sampling_flag
self.warning_flag = warning_flag
@@ -398,6 +396,7 @@ class TabularEOS(object):
fig: matplotlib.figure.Figure
EOS plot.
"""
+ import matplotlib.pyplot as plt
# Set data based on specified representation
varnames = rep.split('-')
@@ -538,7 +537,7 @@ class SpectralDecompositionEOS(TabularEOS):
return 1. / spectral_adiabatic_index(self.gammas, x)
def mu(self, x):
-
+ from scipy.integrate import quad
return np.exp(-quad(self.__mu_integrand, 0, x)[0])
def __eps_integrand(self, x):
@@ -546,7 +545,7 @@ class SpectralDecompositionEOS(TabularEOS):
return np.exp(x) * self.mu(x) / spectral_adiabatic_index(self.gammas, x)
def energy_density(self, x, eps0):
-
+ from scipy.integrate import quad
quad_result, quad_err = quad(self.__eps_integrand, 0, x)
eps_of_x = (eps0 * C_CGS ** 2.) / self.mu(x) + self.p0 / self.mu(x) * quad_result
return eps_of_x
@@ -638,6 +637,7 @@ class EOSFamily(object):
populated here via the TOV solver upon object construction.
"""
def __init__(self, eos, npts=500):
+ from scipy.optimize import minimize_scalar
self.eos = eos
# FIXME: starting_energy_density is set somewhat arbitrarily
@@ -781,6 +781,7 @@ class EOSFamily(object):
fig: matplotlib.figure.Figure
EOS Family plot.
"""
+ import matplotlib.pyplot as plt
# Set data based on specified representation
varnames = rep.split('-')
diff --git a/bilby/gw/eos/tov_solver.py b/bilby/gw/eos/tov_solver.py
index 5b5d7e13260ebff83318ab1478299db1a43f3602..0135086efaaf3bc9ac7741b03be8c4367e44a926 100644
--- a/bilby/gw/eos/tov_solver.py
+++ b/bilby/gw/eos/tov_solver.py
@@ -1,7 +1,6 @@
# Monica Rizzo, 2019
import numpy as np
-from scipy.integrate import solve_ivp
class IntegrateTOV:
@@ -111,6 +110,7 @@ class IntegrateTOV:
"""
Evolves TOV+k2 equations and returns final quantities
"""
+ from scipy.integrate import solve_ivp
# integration settings the same as in lalsimulation
rel_err = 1e-4
diff --git a/bilby/gw/likelihood.py b/bilby/gw/likelihood.py
index 0f113683972874cf4d9267b6e07682205c6bd53e..b6fdfea7354099a9247c21d51af46518714fcc63 100644
--- a/bilby/gw/likelihood.py
+++ b/bilby/gw/likelihood.py
@@ -5,16 +5,9 @@ import json
import copy
import numpy as np
-import scipy.integrate as integrate
-from scipy.interpolate import interp1d
import pandas as pd
-from tqdm import tqdm
-
-try:
- from scipy.special import logsumexp
-except ImportError:
- from scipy.misc import logsumexp
-from scipy.special import i0e
+from scipy.interpolate import interp1d
+from scipy.special import logsumexp, i0e
from ..core.likelihood import Likelihood
from ..core.utils import BilbyJsonEncoder, decode_bilby_json
@@ -939,10 +932,11 @@ class GravitationalWaveTransient(Likelihood):
self.cache_lookup_table()
def _setup_phase_marginalization(self, min_bound=-5, max_bound=10):
+ x_values = np.logspace(min_bound, max_bound, int(1e6))
self._bessel_function_interped = interp1d(
- np.logspace(-5, max_bound, int(1e6)), np.logspace(-5, max_bound, int(1e6)) +
- np.log([i0e(snr) for snr in np.logspace(-5, max_bound, int(1e6))]),
- bounds_error=False, fill_value=(0, np.nan))
+ x_values, x_values + np.log([i0e(snr) for snr in x_values]),
+ bounds_error=False, fill_value=(0, np.nan)
+ )
def _setup_time_marginalization(self):
self._delta_tc = 2 / self.waveform_generator.sampling_frequency
@@ -980,6 +974,7 @@ class GravitationalWaveTransient(Likelihood):
self.number_of_response_curves, self.starting_index)
else: # generate the fake curves
+ from tqdm.auto import tqdm
self.calibration_parameter_draws[interferometer.name] =\
pd.DataFrame(calibration_priors.sample(self.number_of_response_curves))
@@ -1609,10 +1604,11 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
f_high: float
The maximum frequency which must be considered
"""
+ from scipy.integrate import simps
integrand1 = np.power(freq, -7. / 3) / psd
- integral1 = integrate.simps(integrand1, freq)
+ integral1 = simps(integrand1, freq)
integrand3 = np.power(freq, 2. / 3.) / (psd * integral1)
- f_3_bar = integrate.simps(integrand3, freq)
+ f_3_bar = simps(integrand3, freq)
f_high = scaling * f_3_bar**(1 / 3)
diff --git a/bilby/gw/prior.py b/bilby/gw/prior.py
index 899e98060cf7023eb616ec4eeda6ee14089c6e30..8c21948a767ae06faaea64dce0226981dfde81b9 100644
--- a/bilby/gw/prior.py
+++ b/bilby/gw/prior.py
@@ -3,7 +3,6 @@ import copy
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline, interp1d
-from scipy.integrate import cumtrapz
from scipy.special import hyp2f1
from scipy.stats import norm
@@ -22,12 +21,6 @@ from .conversion import (
total_mass_and_mass_ratio_to_component_masses)
from .cosmology import get_cosmology
-try:
- from astropy import cosmology as cosmo, units
-except ImportError:
- logger.debug("You do not have astropy installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
DEFAULT_PRIOR_DIR = os.path.join(os.path.dirname(__file__), 'prior_files')
@@ -99,6 +92,7 @@ class Cosmological(Interped):
@property
def _default_args_dict(self):
+ from astropy import units
return dict(
redshift=dict(name='redshift', latex_label='$z$', unit=None),
luminosity_distance=dict(
@@ -108,6 +102,7 @@ class Cosmological(Interped):
def __init__(self, minimum, maximum, cosmology=None, name=None,
latex_label=None, unit=None, boundary=None):
+ from astropy import units
self.cosmology = get_cosmology(cosmology)
if name not in self._default_args_dict:
raise ValueError(
@@ -172,6 +167,7 @@ class Cosmological(Interped):
recalculate_array: boolean
Determines if the distance arrays are recalculated
"""
+ from astropy.cosmology import z_at_value
cosmology = get_cosmology(self.cosmology)
limit_dict[self.name] = value
if self.name == 'redshift':
@@ -183,8 +179,9 @@ class Cosmological(Interped):
if value == 0:
limit_dict['redshift'] = 0
else:
- limit_dict['redshift'] = cosmo.z_at_value(
- cosmology.luminosity_distance, value * self.unit)
+ limit_dict['redshift'] = z_at_value(
+ cosmology.luminosity_distance, value * self.unit
+ )
limit_dict['comoving_distance'] = (
cosmology.comoving_distance(limit_dict['redshift']).value
)
@@ -192,8 +189,9 @@ class Cosmological(Interped):
if value == 0:
limit_dict['redshift'] = 0
else:
- limit_dict['redshift'] = cosmo.z_at_value(
- cosmology.comoving_distance, value * self.unit)
+ limit_dict['redshift'] = z_at_value(
+ cosmology.comoving_distance, value * self.unit
+ )
limit_dict['luminosity_distance'] = (
cosmology.luminosity_distance(limit_dict['redshift']).value
)
@@ -255,8 +253,10 @@ class Cosmological(Interped):
"""
Get a dictionary containing the arguments needed to reproduce this object.
"""
+ from astropy.cosmology.core import Cosmology
+ from astropy import units
dict_with_properties = super(Cosmological, self)._repr_dict
- if isinstance(dict_with_properties['cosmology'], cosmo.core.Cosmology):
+ if isinstance(dict_with_properties['cosmology'], Cosmology):
if dict_with_properties['cosmology'].name is not None:
dict_with_properties['cosmology'] = dict_with_properties['cosmology'].name
if isinstance(dict_with_properties['unit'], units.Unit):
@@ -1113,6 +1113,7 @@ class HealPixMapPriorDist(BaseJointPriorDist):
"""
Method that builds the inverse cdf of the P(pixel) distribution for rescaling
"""
+ from scipy.integrate import cumtrapz
yy = self._all_interped(self.pix_xx)
yy /= np.trapz(yy, self.pix_xx)
YY = cumtrapz(yy, self.pix_xx, initial=0)
diff --git a/bilby/gw/result.py b/bilby/gw/result.py
index 7116a5bf82bdb5723f9eba80fcdbf0186d34a599..fc599232b3a8cf71633825a4c941b61347b12ba3 100644
--- a/bilby/gw/result.py
+++ b/bilby/gw/result.py
@@ -1,10 +1,7 @@
-
import json
-import pickle
import os
+import pickle
-import matplotlib.pyplot as plt
-from matplotlib import rcParams
import numpy as np
from ..core.result import Result as CoreResult
@@ -149,6 +146,7 @@ class CompactBinaryCoalescenceResult(CoreResult):
format: str
Format to save the plot, default=png, options are png/pdf
"""
+ import matplotlib.pyplot as plt
if format not in ["png", "pdf"]:
raise ValueError("Format should be one of png or pdf")
@@ -394,6 +392,8 @@ class CompactBinaryCoalescenceResult(CoreResult):
)
)
else:
+ import matplotlib.pyplot as plt
+ from matplotlib import rcParams
old_font_size = rcParams["font.size"]
rcParams["font.size"] = 20
fig, axs = plt.subplots(
@@ -734,6 +734,8 @@ class CompactBinaryCoalescenceResult(CoreResult):
If true, load the cached pickle file (default name), or the
pickle-file give as a path.
"""
+ import matplotlib.pyplot as plt
+ from matplotlib import rcParams
try:
from astropy.time import Time
diff --git a/bilby/gw/source.py b/bilby/gw/source.py
index 96e61afc7c06cab2c57d925d9cda0e2ca20ab08f..b874b8a03abe711531d4160c214a8919e13c345f 100644
--- a/bilby/gw/source.py
+++ b/bilby/gw/source.py
@@ -10,13 +10,6 @@ from .utils import (lalsim_GetApproximantFromString,
lalsim_SimInspiralWaveformParamsInsertTidalLambda2,
lalsim_SimInspiralChooseFDWaveformSequence)
-try:
- import lal
- import lalsimulation as lalsim
-except ImportError:
- logger.debug("You do not have lalsuite installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
def lal_binary_black_hole(
frequency_array, mass_1, mass_2, luminosity_distance, a_1, tilt_1,
@@ -293,6 +286,9 @@ def _base_lal_cbc_fd_waveform(
=======
dict: A dictionary with the plus and cross polarisation strain modes
"""
+ import lal
+ import lalsimulation as lalsim
+
waveform_approximant = waveform_kwargs['waveform_approximant']
reference_frequency = waveform_kwargs['reference_frequency']
minimum_frequency = waveform_kwargs['minimum_frequency']
@@ -509,6 +505,7 @@ def _base_roq_waveform(
Dict containing plus and cross modes evaluated at the linear and
quadratic frequency nodes.
"""
+ from lal import CreateDict
frequency_nodes_linear = waveform_arguments['frequency_nodes_linear']
frequency_nodes_quadratic = waveform_arguments['frequency_nodes_quadratic']
reference_frequency = waveform_arguments['reference_frequency']
@@ -519,7 +516,7 @@ def _base_roq_waveform(
mass_1 = mass_1 * utils.solar_mass
mass_2 = mass_2 * utils.solar_mass
- waveform_dictionary = lal.CreateDict()
+ waveform_dictionary = CreateDict()
lalsim_SimInspiralWaveformParamsInsertTidalLambda1(
waveform_dictionary, lambda_1)
lalsim_SimInspiralWaveformParamsInsertTidalLambda2(
diff --git a/bilby/gw/utils.py b/bilby/gw/utils.py
index cf7c0f3e65d8cc0edcc584d6caa7830b96dff45a..fa81766bf1694ace94e4e655b2b761fbd3b6306a 100644
--- a/bilby/gw/utils.py
+++ b/bilby/gw/utils.py
@@ -1,29 +1,15 @@
-import os
import json
+import os
from math import fmod
import numpy as np
from scipy.interpolate import interp1d
-import matplotlib.pyplot as plt
from ..core.utils import (ra_dec_to_theta_phi,
speed_of_light, logger, run_commandline,
check_directory_exists_and_if_not_mkdir,
SamplesSummary, theta_phi_to_ra_dec)
-try:
- from gwpy.timeseries import TimeSeries
-except ImportError:
- logger.debug("You do not have gwpy installed currently. You will "
- " not be able to use some of the prebuilt functions.")
-
-try:
- import lal
- import lalsimulation as lalsim
-except ImportError:
- logger.debug("You do not have lalsuite installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
def asd_from_freq_series(freq_data, df):
"""
@@ -88,7 +74,8 @@ def time_delay_geocentric(detector1, detector2, ra, dec, time):
float: Time delay between the two detectors in the geocentric frame
"""
- gmst = fmod(lal.GreenwichMeanSiderealTime(time), 2 * np.pi)
+ from lal import GreenwichMeanSiderealTime
+ gmst = fmod(GreenwichMeanSiderealTime(time), 2 * np.pi)
theta, phi = ra_dec_to_theta_phi(ra, dec, gmst)
omega = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)])
delta_d = detector2 - detector1
@@ -122,7 +109,8 @@ def get_polarization_tensor(ra, dec, time, psi, mode):
array_like: A 3x3 representation of the polarization_tensor for the specified mode.
"""
- gmst = fmod(lal.GreenwichMeanSiderealTime(time), 2 * np.pi)
+ from lal import GreenwichMeanSiderealTime
+ gmst = fmod(GreenwichMeanSiderealTime(time), 2 * np.pi)
theta, phi = ra_dec_to_theta_phi(ra, dec, gmst)
u = np.array([np.cos(phi) * np.cos(theta), np.cos(theta) * np.sin(phi), -np.sin(theta)])
v = np.array([-np.sin(phi), np.cos(phi), 0])
@@ -390,8 +378,9 @@ def zenith_azimuth_to_ra_dec(zenith, azimuth, geocent_time, ifos):
ra, dec: float
The zenith and azimuthal angles in the sky frame.
"""
+ from lal import GreenwichMeanSiderealTime
theta, phi = zenith_azimuth_to_theta_phi(zenith, azimuth, ifos)
- gmst = lal.GreenwichMeanSiderealTime(geocent_time)
+ gmst = GreenwichMeanSiderealTime(geocent_time)
ra, dec = theta_phi_to_ra_dec(theta, phi, gmst)
ra = ra % (2 * np.pi)
return ra, dec
@@ -478,6 +467,7 @@ def get_open_strain_data(
fails, this function retruns `None`.
"""
+ from gwpy.timeseries import TimeSeries
filename = '{}/{}_{}_{}.txt'.format(outdir, name, start_time, end_time)
if buffer_time < 0:
@@ -529,6 +519,7 @@ def read_frame_file(file_name, start_time, end_time, channel=None, buffer_time=0
strain: gwpy.timeseries.TimeSeries
"""
+ from gwpy.timeseries import TimeSeries
loaded = False
strain = None
@@ -793,17 +784,19 @@ def convert_args_list_to_float(*args_list):
def lalsim_SimInspiralTransformPrecessingNewInitialConditions(
theta_jn, phi_jl, tilt_1, tilt_2, phi_12, a_1, a_2, mass_1, mass_2,
reference_frequency, phase):
+ from lalsimulation import SimInspiralTransformPrecessingNewInitialConditions
args_list = convert_args_list_to_float(
theta_jn, phi_jl, tilt_1, tilt_2, phi_12, a_1, a_2, mass_1, mass_2,
reference_frequency, phase)
- return lalsim.SimInspiralTransformPrecessingNewInitialConditions(*args_list)
+ return SimInspiralTransformPrecessingNewInitialConditions(*args_list)
def lalsim_GetApproximantFromString(waveform_approximant):
+ from lalsimulation import GetApproximantFromString
if isinstance(waveform_approximant, str):
- return lalsim.GetApproximantFromString(waveform_approximant)
+ return GetApproximantFromString(waveform_approximant)
else:
raise ValueError("waveform_approximant must be of type str")
@@ -840,6 +833,7 @@ def lalsim_SimInspiralFD(
waveform_dictionary: None, lal.Dict
approximant: int, str
"""
+ from lalsimulation import SimInspiralFD
args = convert_args_list_to_float(
mass_1, mass_2, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z,
@@ -849,7 +843,7 @@ def lalsim_SimInspiralFD(
approximant = _get_lalsim_approximant(approximant)
- return lalsim.SimInspiralFD(*args, waveform_dictionary, approximant)
+ return SimInspiralFD(*args, waveform_dictionary, approximant)
def lalsim_SimInspiralChooseFDWaveform(
@@ -884,6 +878,7 @@ def lalsim_SimInspiralChooseFDWaveform(
waveform_dictionary: None, lal.Dict
approximant: int, str
"""
+ from lalsimulation import SimInspiralChooseFDWaveform
args = convert_args_list_to_float(
mass_1, mass_2, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z,
@@ -893,7 +888,7 @@ def lalsim_SimInspiralChooseFDWaveform(
approximant = _get_lalsim_approximant(approximant)
- return lalsim.SimInspiralChooseFDWaveform(*args, waveform_dictionary, approximant)
+ return SimInspiralChooseFDWaveform(*args, waveform_dictionary, approximant)
def _get_lalsim_approximant(approximant):
@@ -931,6 +926,8 @@ def lalsim_SimInspiralChooseFDWaveformSequence(
approximant: int, str
frequency_array: np.ndarray, lal.REAL8Vector
"""
+ from lal import REAL8Vector, CreateREAL8Vector
+ from lalsimulation import SimInspiralChooseFDWaveformSequence
[mass_1, mass_2, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z,
luminosity_distance, iota, phase, reference_frequency] = convert_args_list_to_float(
@@ -944,12 +941,12 @@ def lalsim_SimInspiralChooseFDWaveformSequence(
else:
raise ValueError("approximant not an int")
- if not isinstance(frequency_array, lal.REAL8Vector):
+ if not isinstance(frequency_array, REAL8Vector):
old_frequency_array = frequency_array.copy()
- frequency_array = lal.CreateREAL8Vector(len(old_frequency_array))
+ frequency_array = CreateREAL8Vector(len(old_frequency_array))
frequency_array.data = old_frequency_array
- return lalsim.SimInspiralChooseFDWaveformSequence(
+ return SimInspiralChooseFDWaveformSequence(
phase, mass_1, mass_2, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y,
spin_2z, reference_frequency, luminosity_distance, iota,
waveform_dictionary, approximant, frequency_array)
@@ -957,23 +954,25 @@ def lalsim_SimInspiralChooseFDWaveformSequence(
def lalsim_SimInspiralWaveformParamsInsertTidalLambda1(
waveform_dictionary, lambda_1):
+ from lalsimulation import SimInspiralWaveformParamsInsertTidalLambda1
try:
lambda_1 = float(lambda_1)
except ValueError:
raise ValueError("Unable to convert lambda_1 to float")
- return lalsim.SimInspiralWaveformParamsInsertTidalLambda1(
+ return SimInspiralWaveformParamsInsertTidalLambda1(
waveform_dictionary, lambda_1)
def lalsim_SimInspiralWaveformParamsInsertTidalLambda2(
waveform_dictionary, lambda_2):
+ from lalsimulation import SimInspiralWaveformParamsInsertTidalLambda2
try:
lambda_2 = float(lambda_2)
except ValueError:
raise ValueError("Unable to convert lambda_2 to float")
- return lalsim.SimInspiralWaveformParamsInsertTidalLambda2(
+ return SimInspiralWaveformParamsInsertTidalLambda2(
waveform_dictionary, lambda_2)
@@ -1020,6 +1019,7 @@ def plot_spline_pos(log_freqs, samples, nfreqs=100, level=0.9, color='k', label=
Function to transform the spline into plotted values.
"""
+ import matplotlib.pyplot as plt
freq_points = np.exp(log_freqs)
freqs = np.logspace(min(log_freqs), max(log_freqs), nfreqs, base=np.exp(1))
diff --git a/requirements.txt b/requirements.txt
index 05d86e2f5459f636abf631452a48535d12d4393f..4c80f78a58f55ca3ba6b373ff37f694a72525eab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ dynesty
emcee
corner
numpy<1.20
-matplotlib>=2.0
+matplotlib>=2.1
scipy>=0.16
pandas
mock
diff --git a/sampler_requirements.txt b/sampler_requirements.txt
index 2e7155585bedfbc9630e2a2406dc3c171d95f7b0..69a03701aa337928a0112046a74f6a44677487fe 100644
--- a/sampler_requirements.txt
+++ b/sampler_requirements.txt
@@ -3,8 +3,7 @@ dynesty
emcee
nestle
ptemcee
-pymc3==3.6; python_version <= '2.7'
-pymc3>=3.6; python_version > '3.4'
+pymc3>=3.6
pymultinest
kombine
ultranest>=3.0.0
diff --git a/setup.cfg b/setup.cfg
index a9ae5b2d2c05c11e57bbd05cbc5b1b3ea3eb7a8a..8dd2e017db2db04a5d92044bc4fc5ed0040bd572 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -5,6 +5,7 @@ ignore = E129 W503 W504 W605 E203 E402
[tool:pytest]
addopts =
+ --ignore test/import_test.py
--ignore test/integration/other_test.py
--ignore test/integration/example_test.py
--ignore test/integration/sample_from_the_prior_test.py
diff --git a/test/gw/cosmology_test.py b/test/gw/cosmology_test.py
index 158375d5a5dcb172c03b4c9267c8a792b4d5f509..0864c4cc03afd995f22cc1190978e012baebac3a 100644
--- a/test/gw/cosmology_test.py
+++ b/test/gw/cosmology_test.py
@@ -46,7 +46,7 @@ class TestGetCosmology(unittest.TestCase):
self.assertEqual(cosmology.get_cosmology("WMAP9").name, "WMAP9")
def test_getting_cosmology_with_default(self):
- self.assertEqual(cosmology.get_cosmology(), cosmology.COSMOLOGY[0])
+ self.assertEqual(cosmology.get_cosmology().name, "Planck15")
if __name__ == "__main__":
diff --git a/test/gw/utils_test.py b/test/gw/utils_test.py
index fee929faaae9572b6d351f610b5085a84bd13a54..a3a7582f1f2b94c796156fb9cc031c37a3bb5aee 100644
--- a/test/gw/utils_test.py
+++ b/test/gw/utils_test.py
@@ -3,9 +3,10 @@ import os
from shutil import rmtree
import numpy as np
-import gwpy
import lal
import lalsimulation as lalsim
+from gwpy.timeseries import TimeSeries
+from gwpy.detector import Channel
from scipy.stats import ks_2samp
import bilby
@@ -125,8 +126,8 @@ class TestGWUtils(unittest.TestCase):
N = 100
times = np.linspace(start_time, end_time, N)
data = np.random.normal(0, 1, N)
- ts = gwpy.timeseries.TimeSeries(data=data, times=times, t0=0)
- ts.channel = gwpy.detector.Channel(channel)
+ ts = TimeSeries(data=data, times=times, t0=0)
+ ts.channel = Channel(channel)
ts.name = channel
filename = os.path.join(self.outdir, "test.gwf")
ts.write(filename, format="gwf")
@@ -158,7 +159,7 @@ class TestGWUtils(unittest.TestCase):
)
self.assertTrue(np.all(strain.value == data[:-1]))
- ts = gwpy.timeseries.TimeSeries(data=data, times=times, t0=0)
+ ts = TimeSeries(data=data, times=times, t0=0)
ts.name = "NOT-A-KNOWN-CHANNEL"
ts.write(filename, format="gwf")
strain = gwutils.read_frame_file(filename, start_time=None, end_time=None)
diff --git a/test/import_test.py b/test/import_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..29a4c602d094e1ed04ea0f086db7048c7bf4536e
--- /dev/null
+++ b/test/import_test.py
@@ -0,0 +1,22 @@
+import sys
+
+import bilby # noqa
+
+unique_packages = set(sys.modules)
+
+unwanted = {
+ "lal", "lalsimulation", "matplotlib",
+ "h5py", "dill", "tqdm", "tables", "deepdish", "corner",
+}
+
+for filename in ["sampler_requirements.txt", "optional_requirements.txt"]:
+ with open(filename, "r") as ff:
+ packages = ff.readlines()
+ for package in packages:
+ package = package.split(">")[0].split("<")[0].split("=")[0].strip()
+ unwanted.add(package)
+
+if not unique_packages.isdisjoint(unwanted):
+ raise ImportError(
+ f"{' '.join(unique_packages.intersection(unwanted))} imported with Bilby"
+ )