Merge branch '479-double-check-theta_jn-and-iota-consistency-in-the-docs' into 'master'

Resolve "Double check `theta_jn` and `iota` consistency in the docs" Closes #479 See merge request !777

Merge branch '479-double-check-theta_jn-and-iota-consistency-in-the-docs' into 'master'
65ba5713 · Colm Talbot · dded5f6c · 7f17df4c · 65ba5713
Commit 65ba5713 authored 4 years ago by Colm Talbot
--- a/examples/tutorials/compare_samplers.ipynb
+++ b/examples/tutorials/compare_samplers.ipynb
@@ -40,7 +40,7 @@
    "phi_12=0,                            # azimuthal angle between primary and secondary spin (radians)\n",
    "phi_jl=0,                            # azimuthal angle between total angular momentum and orbital angular momentum (radians)\n",
    "luminosity_distance=100.,            # luminosity distance to source (Mpc)\n",
-    "theta_jn=0.4,                        # inclination angle between line of sight and orbital angular momentum (radians)\n",
+    "theta_jn=0.4,                        # angle between the total angular momentum (both spin and orbital) and the line of sight\n",
    "phase=1.3,                           # phase (radians)\n",
    "waveform_approximant='IMRPhenomPv2', # waveform approximant name\n",
    "reference_frequency=50.,             # gravitational waveform reference frequency (Hz)\n",
@@ -215,7 +215,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.6"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id: tags:

 # Compare samplers

 In this notebook, we'll compare the different samplers implemented in `bilby`. As of this version, we don't compare the outputs, only how to run them and the timings for their default setup.

 ## Setup

 %% Cell type:code id: tags:

 ``` python
 import numpy as np
 import pylab as plt

 %load_ext autoreload
 %autoreload 2

 import bilby

 bilby.utils.setup_logger()

 time_duration = 1.         # set the signal duration (seconds)
 sampling_frequency = 4096. # set the data sampling frequency (Hz)

 injection_parameters = dict(
 mass_1=36.,                          # detector frame (redshifted) primary mass (solar masses)
 mass_2=29.,                          # detector frame (redshifted) secondary mass (solar masses)
 a_1=0,                               # primary dimensionless spin magnitude
 a_2=0,                               # secondary dimensionless spin magnitude
 tilt_1=0,                            # polar angle between primary spin and the orbital angular momentum (radians)
 tilt_2=0,                            # polar angle between secondary spin and the orbital angular momentum
 phi_12=0,                            # azimuthal angle between primary and secondary spin (radians)
 phi_jl=0,                            # azimuthal angle between total angular momentum and orbital angular momentum (radians)
 luminosity_distance=100.,            # luminosity distance to source (Mpc)
-theta_jn=0.4,                        # inclination angle between line of sight and orbital angular momentum (radians)
+theta_jn=0.4,                        # angle between the total angular momentum (both spin and orbital) and the line of sight
 phase=1.3,                           # phase (radians)
 waveform_approximant='IMRPhenomPv2', # waveform approximant name
 reference_frequency=50.,             # gravitational waveform reference frequency (Hz)
 ra=1.375,                            # source right ascension (radians)
 dec=-1.2108,                         # source declination (radians)
 geocent_time=1126259642.413,         # reference time at geocentre (time of coalescence or peak amplitude) (GPS seconds)
 psi=2.659                            # gravitational wave polarisation angle
 )


 # initialise the waveform generator
 waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
    sampling_frequency=sampling_frequency,
    duration=time_duration,
    frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
    parameters=injection_parameters)

 # generate a frequency-domain waveform
 hf_signal = waveform_generator.frequency_domain_strain()

 # initialise a single interferometer representing LIGO Hanford
 H1 = bilby.gw.detector.get_empty_interferometer('H1')
 # set the strain data at the interferometer
 H1.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency, duration=time_duration)
 # inject the gravitational wave signal into the interferometer model
 H1.inject_signal(injection_polarizations=hf_signal, parameters=injection_parameters)

 IFOs = [H1]

 # compute the likelihood on each of the signal parameters
 likelihood = bilby.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
 ```

 %% Cell type:markdown id: tags:

 ## Prior

 For this test, we will simply search of the sky position, setting the other parameters to their simulated values.

 %% Cell type:code id: tags:

 ``` python
 # set the priors on each of the injection parameters to be a delta function at their given value
 priors = bilby.gw.prior.BBHPriorDict()
 for key in injection_parameters.keys():
    priors[key] = injection_parameters[key]

 # now reset the priors on the sky position co-ordinates in order to conduct a sky position search
 priors['ra'] = bilby.prior.Uniform(0, 2*np.pi, 'ra')
 priors['dec'] = bilby.prior.Cosine(name='dec', minimum=-np.pi/2, maximum=np.pi/2)
 ```

 %% Cell type:markdown id: tags:

 ## PyMultinest

 %% Cell type:code id: tags:

 ``` python
 %%time
 result = bilby.core.sampler.run_sampler(
    likelihood, priors=priors, sampler='pymultinest', label='pymultinest',
    npoints=200, verbose=False, resume=False)
 fig = result.plot_corner(save=False)
 # show the corner plot
 plt.show()
 print(result)
 ```

 %% Cell type:markdown id: tags:

 # dynesty

 %% Cell type:code id: tags:

 ``` python
 %%time
 result = bilby.core.sampler.run_sampler(
    likelihood, priors=priors, sampler='dynesty', label='dynesty',
    bound='multi', sample='rwalk', npoints=200, walks=1, verbose=False,
    update_interval=100)
 fig = result.plot_corner(save=False)
 # show the corner plot
 plt.show()
 print(result)
 ```

 %% Cell type:markdown id: tags:

 # Dynamic Nested Sampling (Dynesty)

 See [the dynesty docs](http://dynesty.readthedocs.io/en/latest/dynamic.html#). Essentially, this methods improves the posterior estimation over that of standard nested sampling.

 %% Cell type:code id: tags:

 ``` python
 %%time
 result = bilby.core.sampler.run_sampler(
    likelihood, priors=priors, sampler='dynesty', label='dynesty_dynamic',
    bound='multi', nlive=250, sample='unif', verbose=True,
    update_interval=100, dynamic=True)
 fig = result.plot_corner(save=False)
 # show the corner plot
 plt.show()
 print(result)
 ```

 %% Cell type:markdown id: tags:

 # ptemcee

 %% Cell type:code id: tags:

 ``` python
 %%time
 result = bilby.core.sampler.run_sampler(
    likelihood, priors=priors, sampler='ptemcee', label='ptemcee',
    nwalkers=100, nsteps=200, nburn=100, ntemps=2,
    tqdm='tqdm_notebook')
 fig = result.plot_corner(save=False)
 # show the corner plot
 plt.show()
 print(result)
 ```