Maintenance will be performed on git.ligo.org, containers.ligo.org, and docs.ligo.org on Tuesday 1 April 2025 starting at approximately 9am PDT. It is expected to take around 30 minutes and there will be several periods of downtime throughout the maintenance. Please address any comments, concerns, or questions to the helpdesk.
## Here we create arbitrary jump proposals. This will highlight the necessary features of a jump proposal in ptmcmc. That is it takes the current position, x, then outputs a new position , q, and the jump probability i.e. p(x -> q). These will then be passed to the standard metropolis hastings condition.
## The two proposals below are probably not very good ones, ideally we would use proposals based upon our kmowledge of the problem/parameter space. In general for these proposals lqxy will certainly not be 0
%% Cell type:code id: tags:
``` python
classUniformJump(object):
def__init__(self,pmin,pmax):
"""Draw random parameters from pmin, pmax"""
self.pmin=pmin
self.pmax=pmax
defunjump(self,x,it,beta):
"""
Function prototype must read in parameter vector x,
sampler iteration number it, and inverse temperature beta
"""
# log of forward-backward jump probability
lqxy=0
# uniformly drawm parameters
q=np.random.uniform(self.pmin,self.pmax,len(x))
returnq,lqxy
```
%% Cell type:code id: tags:
``` python
classNormJump(object):
def__init__(self,step_size):
"""Draw random parameters from pmin, pmax"""
self.step_size=step_size
defnormjump(self,x,it,beta):
"""
Function prototype must read in parameter vector x,
sampler iteration number it, and inverse temperature beta
"""
# log of forward-backward jump probability. this is only zero for simple examples.
### Below we create a dictionary containing our jump proposals and the relative weight of that proposal in the proposal cycle, these are then passed to bilby.run_sampler under the keyword argument custom_proposals =
%% Cell type:code id: tags:
``` python
normjump=NormJump(1)
normweight=5
ujump=UniformJump(20,40)
uweight=1
custom={'uniform':[ujump.unjump,uweight],
'normal':[normjump.normjump,normweight]}
```
%% Cell type:code id: tags:
``` python
# Initialise the likelihood by passing in the interferometer data (ifos) and
### PTMCMC produces the acceptance rate for each of the proposals (including the ones built in). This is taken as an average at a specified checkpoint. This is one (acceptnace rate is certainly not the only/even the best metric here. Think exploration v exploitation problem ) indicators of whether our jump proposal is a good one
%% Cell type:code id: tags:
``` python
sampler_meta=result.meta_data['sampler_meta']
jumps=sampler_meta['proposals']
```
%% Cell type:code id: tags:
``` python
plt.figure()
plt.xlabel('epoch')
plt.ylabel('acceptance rate')
fori,proposalinenumerate(jumps):
plt.plot(jumps[proposal],label=proposal)
plt.legend(loc='best',frameon=True)
```
%% Output
<matplotlib.legend.Legend at 0x7f2ed97a0f50>
%% Cell type:markdown id: tags:
## We can generate the 1d chains for each of the parameters too and the likelihood of those points on the chain
"reference_frequency=50., # gravitational waveform reference frequency (Hz)\n",
...
...
%% 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
importnumpyasnp
importpylabasplt
%load_extautoreload
%autoreload2
importbilby
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.,# source frame (non-redshifted) primary mass (solar masses)
mass_2=29.,# source frame (non-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)
iota=0.4,# inclination angle between line of sight and orbital angular momentum (radians)
theta_jn=0.4,# inclination angle between line of sight and orbital angular momentum (radians)
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)
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.
