diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index 83e1556f4c8b0315d83a4459139040c99516ef03..b1c78f9910dd412d6636b1e117ebb8de04a9b6f5 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -9,15 +9,21 @@ from __future__ import division, print_function
import tupak
import numpy as np
-
+# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
time_duration = 4.
sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'basic_tutorial'
tupak.utils.setup_logger(outdir=outdir, label=label)
-np.random.seed(170809)
+# Set up a random seed for result reproducibility. This is optional!
+np.random.seed(170801)
+# We are going to inject a binary black hole waveform. We first establish a dictionary of parameters that
+# includes all of the different waveform parameters, including masses of the two black holes (mass_1, mass_2),
+# spins of both black holes (a, tilt, phi), etc.
injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
@@ -29,24 +35,33 @@ waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=ti
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
-# Set up interferometers.
+# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
+# and Virgo (V1)). These default to their design sensitivity
IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
-# Set up prior
+# Set up prior, which is a dictionary
priors = dict()
-# These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'iota', 'ra', 'dec', 'geocent_time']:
+# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
+# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
+# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
+# not sample any parameter that has a delta-function prior.
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
+
+# The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
+# that will be included in the sampler. If we do nothing, then the default priors get used.
priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
-# Initialise Likelihood
+# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
-# Run sampler
+# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# make some plots of the outputs
result.plot_corner()
result.plot_walks()
result.plot_distributions()
diff --git a/tupak/result.py b/tupak/result.py
index d4d56431c4453fc4fc62c6abff345c35883cbe1d..1c803bfbc4253553eeb7fbed8485dad3ac0bde43 100644
--- a/tupak/result.py
+++ b/tupak/result.py
@@ -18,13 +18,26 @@ def result_file_name(outdir, label):
return '{}/{}_result.h5'.format(outdir, label)
-def read_in_result(outdir, label):
- """ Read in a saved .h5 data file """
- filename = result_file_name(outdir, label)
+def read_in_result(outdir=None, label=None, filename=None):
+ """ Read in a saved .h5 data file
+
+ Parameters
+ ----------
+ outdir, label: str
+ If given, use the default naming convention for saved results file
+ filename: str
+ If given, try to load from this filename
+
+ Returns:
+ result: tupak.result.Result instance
+
+ """
+ if filename is None:
+ filename = result_file_name(outdir, label)
if os.path.isfile(filename):
return Result(deepdish.io.load(filename))
else:
- return None
+ raise ValueError("No information given to load file")
class Result(dict):
diff --git a/tupak/sampler.py b/tupak/sampler.py
index 2817de857008c62fc6e1b9b53c7d03ba2d0e0282..1cb7a4e9ccf7b4329bfbff19643219fe4adaeea9 100644
--- a/tupak/sampler.py
+++ b/tupak/sampler.py
@@ -205,7 +205,12 @@ class Sampler(object):
logging.debug("Command line argument clean given, forcing rerun")
self.cached_result = None
return
- self.cached_result = read_in_result(self.outdir, self.label)
+
+ try:
+ self.cached_result = read_in_result(self.outdir, self.label)
+ except ValueError:
+ self.cached_result = None
+
if utils.command_line_args.use_cached:
logging.debug("Command line argument cached given, no cache check performed")
return