diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index b88a77cdbcf2dfc1106d3725cd8638c6aebb4d00..6621a33c0ae2bb32f36c0c115ce23ff20bfa6040 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -21,11 +21,13 @@ exitcode-jessie:
- pip install -r requirements.txt
- pip install coverage
- pip install coverage-badge
+ - python setup.py install
- coverage erase
- coverage run --include=tupak/* -a test/prior_tests.py
- coverage run --include=tupak/* -a test/tests.py
- coverage run --include=tupak/* -a test/waveform_generator_tests.py
- coverage run --include=tupak/* -a test/noise_realisation_tests.py
+ - coverage run --include=tupak/* -a test/example_tests.py
- coverage html
- coverage-badge -o coverage_badge.svg -f
artifacts:
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index 4563e767c718f92252ecb439316f6fc9616cf28e..eb95e7f379cfd73e30b872a274e3ea4b18ed2f2f 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -64,7 +64,7 @@ prior['phase'] = tupak.prior.Uniform(-np.pi/2, np.pi/2, r'$\phi$')
# define likelihood
-likelihood = tupak.likelihood.Likelihood(IFOs, waveform)
+likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform)
# launch sampler
result = tupak.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
diff --git a/examples/other_examples/alternative_likelihoods.py b/examples/other_examples/alternative_likelihoods.py
index 329f9f339053ad67bc598a8cb83be0e169ab6f2a..0bc5227759fef817ae62c4fbb584f84590713eb2 100644
--- a/examples/other_examples/alternative_likelihoods.py
+++ b/examples/other_examples/alternative_likelihoods.py
@@ -20,7 +20,7 @@ outdir = 'outdir'
# use of the `tupak` waveform_generator to make the signal (more on this later)
# But, one could make this work without the waveform generator.
-class GaussianLikelihood():
+class GaussianLikelihood(tupak.likelihood.Likelihood):
def __init__(self, x, y, waveform_generator):
self.x = x
self.y = y
diff --git a/requirements.txt b/requirements.txt
index 636cb022bbb446aa2d3eb86133f7b5fdc72d9fb9..f71732b9408956a5cac6709a0079630222d20fde 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ future
dynesty
corner
numpy
-matplotlib>=1.4
+matplotlib>=2.0
scipy
gwpy
pandas
diff --git a/test/example_tests.py b/test/example_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab7a738cf11ce2f6530daa7711b445efdf9a9a61
--- /dev/null
+++ b/test/example_tests.py
@@ -0,0 +1,47 @@
+import unittest
+import os
+import shutil
+import logging
+import subprocess
+
+
+class Test(unittest.TestCase):
+ outdir = 'outdir'
+ dir_path = os.path.dirname(os.path.realpath(__file__))
+ dir_path = os.path.abspath(os.path.join(dir_path, os.path.pardir))
+
+ @classmethod
+ def setUpClass(self):
+ if os.path.isdir(self.outdir):
+ try:
+ shutil.rmtree(self.outdir)
+ except OSError:
+ logging.warning(
+ "{} not removed prior to tests".format(self.outdir))
+
+ @classmethod
+ def tearDownClass(self):
+ if os.path.isdir(self.outdir):
+ try:
+ shutil.rmtree(self.outdir)
+ except OSError:
+ logging.warning(
+ "{} not removed prior to tests".format(self.outdir))
+
+ def test_examples(self):
+ """ Loop over examples to check they run """
+ examples = ['examples/injection_examples/basic_tutorial.py',
+ 'examples/injection_examples/change_sampled_parameters.py',
+ 'examples/injection_examples/marginalized_likelihood.py',
+ 'examples/injection_examples/create_your_own_time_domain_source_model.py',
+ 'examples/other_examples/alternative_likelihoods.py',
+ ]
+ for filename in examples:
+ print("Testing {}".format(filename))
+ subprocess.check_call(['python', filename, '--test'])
+
+
+if __name__ == '__main__':
+ unittest.main()
+
+
diff --git a/tupak/result.py b/tupak/result.py
index 3ef71a34cebf5d37ba05652b026c0922aa3726e7..07023b03cace71d61d721e7fffcd2817e547c1b0 100644
--- a/tupak/result.py
+++ b/tupak/result.py
@@ -3,14 +3,14 @@ import os
import numpy as np
import deepdish
import pandas as pd
-import tupak
try:
from chainconsumer import ChainConsumer
except ImportError:
def ChainConsumer():
- raise ImportError(
- "You do not have the optional module chainconsumer installed")
+ logging.warning(
+ "You do not have the optional module chainconsumer installed"
+ " unable to generate a corner plot")
def result_file_name(outdir, label):
@@ -142,10 +142,11 @@ class Result(dict):
if label is None:
self.parameter_labels[i] = 'Unknown'
c = ChainConsumer()
- c.add_chain(self.samples, parameters=self.parameter_labels,
- name=self.label)
- fig = c.plotter.plot(**kwargs)
- return fig
+ if c:
+ c.add_chain(self.samples, parameters=self.parameter_labels,
+ name=self.label)
+ fig = c.plotter.plot(**kwargs)
+ return fig
def plot_walks(self, save=True, **kwargs):
""" Plot the chain walks using chain-consumer
@@ -165,12 +166,13 @@ class Result(dict):
if save:
kwargs['filename'] = '{}/{}_walks.png'.format(self.outdir, self.label)
logging.info('Saving walker plot to {}'.format(kwargs['filename']))
- if self.injection_parameters is not None:
+ if getattr(self, 'injection_parameters', None) is not None:
kwargs['truth'] = [self.injection_parameters[key] for key in self.search_parameter_keys]
c = ChainConsumer()
- c.add_chain(self.samples, parameters=self.parameter_labels)
- fig = c.plotter.plot_walks(**kwargs)
- return fig
+ if c:
+ c.add_chain(self.samples, parameters=self.parameter_labels)
+ fig = c.plotter.plot_walks(**kwargs)
+ return fig
def plot_distributions(self, save=True, **kwargs):
""" Plot the chain walks using chain-consumer
@@ -190,12 +192,13 @@ class Result(dict):
if save:
kwargs['filename'] = '{}/{}_distributions.png'.format(self.outdir, self.label)
logging.info('Saving distributions plot to {}'.format(kwargs['filename']))
- if self.injection_parameters is not None:
+ if getattr(self, 'injection_parameters', None) is not None:
kwargs['truth'] = [self.injection_parameters[key] for key in self.search_parameter_keys]
c = ChainConsumer()
- c.add_chain(self.samples, parameters=self.parameter_labels)
- fig = c.plotter.plot_distributions(**kwargs)
- return fig
+ if c:
+ c.add_chain(self.samples, parameters=self.parameter_labels)
+ fig = c.plotter.plot_distributions(**kwargs)
+ return fig
def write_prior_to_file(self, outdir):
"""
diff --git a/tupak/sampler.py b/tupak/sampler.py
index c6e9565b9574ec9ff0b86ddfc4b88d241373cbcf..16f7a34fe8d2ca72c66f1c67b238911224e03bd6 100644
--- a/tupak/sampler.py
+++ b/tupak/sampler.py
@@ -190,6 +190,9 @@ class Sampler(object):
def run_sampler(self):
pass
+ def _run_test(self):
+ raise ValueError("Method not yet implemented")
+
def check_cached_result(self):
""" Check if the cached data file exists and can be used """
@@ -263,6 +266,18 @@ class Nestle(Sampler):
self.result.logzerr = out.logzerr
return self.result
+ def _run_test(self):
+ nestle = self.external_sampler
+ self.external_sampler_function = nestle.sample
+ self.external_sampler_function(
+ loglikelihood=self.log_likelihood,
+ prior_transform=self.prior_transform,
+ ndim=self.ndim, maxiter=10, **self.kwargs)
+ self.result.samples = np.random.uniform(0, 1, (100, self.ndim))
+ self.result.logz = np.nan
+ self.result.logzerr = np.nan
+ return self.result
+
class Dynesty(Sampler):
@@ -312,6 +327,22 @@ class Dynesty(Sampler):
self.result.logzerr = out.logzerr[-1]
return self.result
+ def _run_test(self):
+ dynesty = self.external_sampler
+ nested_sampler = dynesty.NestedSampler(
+ loglikelihood=self.log_likelihood,
+ prior_transform=self.prior_transform,
+ ndim=self.ndim, **self.kwargs)
+ nested_sampler.run_nested(
+ dlogz=self.kwargs['dlogz'],
+ print_progress=self.kwargs['verbose'],
+ maxiter=10)
+
+ self.result.samples = np.random.uniform(0, 1, (100, self.ndim))
+ self.result.logz = np.nan
+ self.result.logzerr = np.nan
+ return self.result
+
class Pymultinest(Sampler):
@@ -458,7 +489,11 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
logging.info("Using cached result")
return sampler.cached_result
- result = sampler.run_sampler()
+ if utils.command_line_args.test:
+ result = sampler._run_test()
+ else:
+ result = sampler.run_sampler()
+
result.noise_logz = likelihood.noise_log_likelihood()
if use_ratio:
result.log_bayes_factor = result.logz
diff --git a/tupak/utils.py b/tupak/utils.py
index 3bf1baf0e8cd785c911a0670fade064ab96eb985..5d096ace0321700ff570da440916bad3305324e0 100644
--- a/tupak/utils.py
+++ b/tupak/utils.py
@@ -530,6 +530,9 @@ def set_up_command_line_arguments():
default=['H1', 'L1', 'V1'],
help=("List of detectors to use in open data calls, "
"e.g. -d H1 L1 for H1 and L1"))
+ parser.add_argument("-t", "--test", action="store_true",
+ help=("Used for testing only: don't run full PE, but"
+ " just check nothing breaks"))
args, _ = parser.parse_known_args()
if args.quite: