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Oliver Bock authored* Includes a couple of dedicated patches * Uses BOINC's MinGW specific build script (doesn't require configure)
Oliver Bock authored* Includes a couple of dedicated patches * Uses BOINC's MinGW specific build script (doesn't require configure)
mcmc_based_searches.py 97.05 KiB
""" Searches using MCMC-based methods """
from __future__ import division, absolute_import, print_function
import sys
import os
import copy
import logging
from collections import OrderedDict
import subprocess
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from ptemcee import Sampler as PTSampler
import corner
import dill as pickle
import pyfstat.core as core
from pyfstat.core import tqdm, args, read_par
import pyfstat.optimal_setup_functions as optimal_setup_functions
import pyfstat.helper_functions as helper_functions
class MCMCSearch(core.BaseSearchClass):
"""MCMC search using ComputeFstat
Parameters
----------
theta_prior: dict
Dictionary of priors and fixed values for the search parameters.
For each parameters (key of the dict), if it is to be held fixed
the value should be the constant float, if it is be searched, the
value should be a dictionary of the prior.
tref, minStartTime, maxStartTime: int
GPS seconds of the reference time, start time and end time. While tref
is requirede, minStartTime and maxStartTime default to None in which
case all available data is used.
label, outdir: str
A label and output directory (optional, defaults is `'data'`) to
name files
sftfilepattern: str, optional
Pattern to match SFTs using wildcards (*?) and ranges [0-9];
mutiple patterns can be given separated by colons.
detectors: str, optional
Two character reference to the detectors to use, specify None for no
contraint and comma separate for multiple references.
nsteps: list (2,), optional
Number of burn-in and production steps to take, [nburn, nprod]. See
`pyfstat.MCMCSearch.setup_initialisation()` for details on adding
initialisation steps.
nwalkers, ntemps: int, optional
The number of walkers and temperates to use in the parallel
tempered PTSampler.
log10beta_min float < 0, optional
The log_10(beta) value, if given the set of betas passed to PTSampler
are generated from `np.logspace(0, log10beta_min, ntemps)` (given
in descending order to ptemcee).
theta_initial: dict, array, optional
A dictionary of distribution about which to distribute the
initial walkers about
rhohatmax: float, optional
Upper bound for the SNR scale parameter (required to normalise the
Bayes factor) - this needs to be carefully set when using the
evidence.
binary: bool, optional
If true, search over binary parameters
BSGL: bool, optional
If true, use the BSGL statistic
SSBPrec: int, optional
SSBPrec (SSB precision) to use when calling ComputeFstat
minCoverFreq, maxCoverFreq: float, optional
Minimum and maximum instantaneous frequency which will be covered
over the SFT time span as passed to CreateFstatInput
injectSources: dict, optional
If given, inject these properties into the SFT files before running
the search
assumeSqrtSX: float, optional
Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
transientWindowType: str
If 'rect' or 'exp',
compute atoms so that a transient (t0,tau) map can later be computed.
('none' instead of None explicitly calls the transient-window function,
but with the full range, for debugging)
Currently only supported for nsegs=1.
tCWFstatMapVersion: str
Choose between standard 'lal' implementation,
'pycuda' for gpu, and some others for devel/debug.
Attributes
----------
symbol_dictionary: dict
Key, val pairs of the parameters (i.e. `F0`, `F1`), to Latex math
symbols for plots
unit_dictionary: dict
Key, val pairs of the parameters (i.e. `F0`, `F1`), and the
units (i.e. `Hz`)
transform_dictionary: dict
Key, val pairs of the parameters (i.e. `F0`, `F1`), where the key is
itself a dictionary which can item `multiplier`, `subtractor`, or
`unit` by which to transform by and update the units.
"""
symbol_dictionary = dict(
F0='$f$', F1='$\dot{f}$', F2='$\ddot{f}$', Alpha=r'$\alpha$',
Delta='$\delta$', asini='asini', period='P', ecc='ecc', tp='tp',
argp='argp')
unit_dictionary = dict(
F0='Hz', F1='Hz/s', F2='Hz/s$^2$', Alpha=r'rad', Delta='rad',
asini='', period='s', ecc='', tp='', argp='')
transform_dictionary = {}
@helper_functions.initializer
def __init__(self, theta_prior, tref, label, outdir='data',
minStartTime=None, maxStartTime=None, sftfilepattern=None,
detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
log10beta_min=-5, theta_initial=None,
rhohatmax=1000, binary=False, BSGL=False,
SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
injectSources=None, assumeSqrtSX=None,
transientWindowType=None, tCWFstatMapVersion='lal'):
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self._add_log_file()
logging.info('Set-up MCMC search for model {}'.format(self.label))
if sftfilepattern:
logging.info('Using data {}'.format(self.sftfilepattern))
else:
logging.info('No sftfilepattern given')
if injectSources:
logging.info('Inject sources: {}'.format(injectSources))
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self._unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10beta_min:
self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
else:
self.betas = None
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self._set_likelihoodcoef()
self._log_input()
def _set_likelihoodcoef(self):
self.likelihoodcoef = np.log(70./self.rhohatmax**4)
def _log_input(self):
logging.info('theta_prior = {}'.format(self.theta_prior))
logging.info('nwalkers={}'.format(self.nwalkers))
logging.info('nsteps = {}'.format(self.nsteps))
logging.info('ntemps = {}'.format(self.ntemps))
logging.info('log10beta_min = {}'.format(
self.log10beta_min))
def _initiate_search_object(self):
logging.info('Setting up search object')
self.search = core.ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
detectors=self.detectors, BSGL=self.BSGL,
transientWindowType=self.transientWindowType,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
binary=self.binary, injectSources=self.injectSources,
assumeSqrtSX=self.assumeSqrtSX, SSBprec=self.SSBprec,
tCWFstatMapVersion=self.tCWFstatMapVersion)
if self.minStartTime is None:
self.minStartTime = self.search.minStartTime
if self.maxStartTime is None:
self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
twoF = search.get_fullycoherent_twoF(
self.minStartTime, self.maxStartTime, *self.fixed_theta)
return twoF/2.0 + self.likelihoodcoef
def _unpack_input_theta(self):
full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
if self.binary:
full_theta_keys += [
'asini', 'period', 'ecc', 'tp', 'argp']
full_theta_keys_copy = copy.copy(full_theta_keys)
full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
r'$\delta$']
if self.binary:
full_theta_symbols += [
'asini', 'period', 'ecc', 'tp', 'argp']
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
def _evaluate_logpost(self, p0vec):
init_logp = np.array([
self.logp(p, self.theta_prior, self.theta_keys, self.search)
for p in p0vec])
init_logl = np.array([
self.logl(p, self.search)
for p in p0vec])
return init_logl + init_logp
def _check_initial_points(self, p0):
for nt in range(self.ntemps):
logging.info('Checking temperature {} chains'.format(nt))
num = sum(self._evaluate_logpost(p0[nt]) == -np.inf)
if num > 0:
logging.warning(
'Of {} initial values, {} are -np.inf due to the prior'
.format(len(p0[0]), num))
p0 = self._generate_new_p0_to_fix_initial_points(
p0, nt)
def _generate_new_p0_to_fix_initial_points(self, p0, nt):
logging.info('Attempting to correct intial values')
init_logpost = self._evaluate_logpost(p0[nt])
idxs = np.arange(self.nwalkers)[init_logpost == -np.inf]
count = 0
while sum(init_logpost == -np.inf) > 0 and count < 100:
for j in idxs:
p0[nt][j] = (p0[nt][np.random.randint(0, self.nwalkers)]*(
1+np.random.normal(0, 1e-10, self.ndim)))
init_logpost = self._evaluate_logpost(p0[nt])
count += 1
if sum(init_logpost == -np.inf) > 0:
logging.info('Failed to fix initial priors')
else:
logging.info('Suceeded to fix initial priors')
return p0
def setup_initialisation(self, nburn0, scatter_val=1e-10):
""" Add an initialisation step to the MCMC run
If called prior to `run()`, adds an intial step in which the MCMC
simulation is run for `nburn0` steps. After this, the MCMC simulation
continues in the usual manner (i.e. for nburn and nprod steps), but the
walkers are reset scattered around the maximum likelihood position
of the initialisation step.
Parameters
----------
nburn0: int
Number of initialisation steps to take
scatter_val: float
Relative number to scatter walkers around the maximum likelihood
position after the initialisation step
"""
logging.info('Setting up initialisation with nburn0={}, scatter_val={}'
.format(nburn0, scatter_val))
self.nsteps = [nburn0] + self.nsteps
self.scatter_val = scatter_val
# def setup_burnin_convergence_testing(
# self, n=10, test_type='autocorr', windowed=False, **kwargs):
# """ Set up convergence testing during the MCMC simulation
#
# Parameters
# ----------
# n: int
# Number of steps after which to test convergence
# test_type: str ['autocorr', 'GR']
# If 'autocorr' use the exponential autocorrelation time (kwargs
# passed to `get_autocorr_convergence`). If 'GR' use the Gelman-Rubin
# statistic (kwargs passed to `get_GR_convergence`)
# windowed: bool
# If True, only calculate the convergence test in a window of length
# `n`
# **kwargs:
# Passed to either `_test_autocorr_convergence()` or
# `_test_GR_convergence()` depending on `test_type`.
#
# """
# logging.info('Setting up convergence testing')
# self.convergence_n = n
# self.convergence_windowed = windowed
# self.convergence_test_type = test_type
# self.convergence_kwargs = kwargs
# self.convergence_diagnostic = []
# self.convergence_diagnosticx = []
# if test_type in ['autocorr']:
# self._get_convergence_test = self._test_autocorr_convergence
# elif test_type in ['GR']:
# self._get_convergence_test = self._test_GR_convergence
# else:
# raise ValueError('test_type {} not understood'.format(test_type))
#
#
# def _test_autocorr_convergence(self, i, sampler, test=True, n_cut=5):
# try:
# acors = np.zeros((self.ntemps, self.ndim))
# for temp in range(self.ntemps):
# if self.convergence_windowed:
# j = i-self.convergence_n
# else:
# j = 0
# x = np.mean(sampler.chain[temp, :, j:i, :], axis=0)
# acors[temp, :] = emcee.autocorr.exponential_time(x)
# c = np.max(acors, axis=0)
# except emcee.autocorr.AutocorrError:
# logging.info('Failed to calculate exponential autocorrelation')
# c = np.zeros(self.ndim) + np.nan
# except AttributeError:
# logging.info('Unable to calculate exponential autocorrelation')
# c = np.zeros(self.ndim) + np.nan
#
# self.convergence_diagnosticx.append(i - self.convergence_n/2.)
# self.convergence_diagnostic.append(list(c))
#
# if test:
# return i > n_cut * np.max(c)
#
# def _test_GR_convergence(self, i, sampler, test=True, R=1.1):
# if self.convergence_windowed:
# s = sampler.chain[0, :, i-self.convergence_n+1:i+1, :]
# else:
# s = sampler.chain[0, :, :i+1, :]
# N = float(self.convergence_n)
# M = float(self.nwalkers)
# W = np.mean(np.var(s, axis=1), axis=0)
# per_walker_mean = np.mean(s, axis=1)
# mean = np.mean(per_walker_mean, axis=0)
# B = N / (M-1.) * np.sum((per_walker_mean-mean)**2, axis=0)
# Vhat = (N-1)/N * W + (M+1)/(M*N) * B
# c = np.sqrt(Vhat/W)
# self.convergence_diagnostic.append(c)
# self.convergence_diagnosticx.append(i - self.convergence_n/2.)
#
# if test and np.max(c) < R:
# return True
# else:
# return False
#
# def _test_convergence(self, i, sampler, **kwargs):
# if np.mod(i+1, self.convergence_n) == 0:
# return self._get_convergence_test(i, sampler, **kwargs)
# else:
# return False
#
# def _run_sampler_with_conv_test(self, sampler, p0, nprod=0, nburn=0):
# logging.info('Running {} burn-in steps with convergence testing'
# .format(nburn))
# iterator = tqdm(sampler.sample(p0, iterations=nburn), total=nburn)
# for i, output in enumerate(iterator):
# if self._test_convergence(i, sampler, test=True,
# **self.convergence_kwargs):
# logging.info(
# 'Converged at {} before max number {} of steps reached'
# .format(i, nburn))
# self.convergence_idx = i
# break
# iterator.close()
# logging.info('Running {} production steps'.format(nprod))
# j = nburn
# iterator = tqdm(sampler.sample(output[0], iterations=nprod),
# total=nprod)
# for result in iterator:
# self._test_convergence(j, sampler, test=False,
# **self.convergence_kwargs)
# j += 1
# return sampler
def _run_sampler(self, sampler, p0, nprod=0, nburn=0, window=50):
for result in tqdm(sampler.sample(p0, iterations=nburn+nprod),
total=nburn+nprod):
pass
self.mean_acceptance_fraction = np.mean(
sampler.acceptance_fraction, axis=1)
logging.info("Mean acceptance fraction: {}"
.format(self.mean_acceptance_fraction))
if self.ntemps > 1:
self.tswap_acceptance_fraction = sampler.tswap_acceptance_fraction
logging.info("Tswap acceptance fraction: {}"
.format(sampler.tswap_acceptance_fraction))
self.autocorr_time = sampler.get_autocorr_time(window=window)
logging.info("Autocorrelation length: {}".format(
self.autocorr_time))
return sampler
def _estimate_run_time(self):
""" Print the estimated run time
Uses timing coefficients based on a Lenovo T460p Intel(R)
Core(TM) i5-6300HQ CPU @ 2.30GHz.
"""
# Todo: add option to time on a machine, and move coefficients to
# ~/.pyfstat.conf
if (type(self.theta_prior['Alpha']) == dict or
type(self.theta_prior['Delta']) == dict):
tau0LD = 5.2e-7
tau0T = 1.5e-8
tau0S = 1.2e-4
tau0C = 5.8e-6
else:
tau0LD = 1.3e-7
tau0T = 1.5e-8
tau0S = 9.1e-5
tau0C = 5.5e-6
Nsfts = (self.maxStartTime - self.minStartTime) / 1800.
if hasattr(self, 'run_setup'):
ts = []
for row in self.run_setup:
nsteps = row[0]
nsegs = row[1]
numb_evals = np.sum(nsteps)*self.nwalkers*self.ntemps
t = (tau0S + tau0LD*Nsfts) * numb_evals
if nsegs > 1:
t += (tau0C + tau0T*Nsfts)*nsegs*numb_evals
ts.append(t)
time = np.sum(ts)
else:
numb_evals = np.sum(self.nsteps)*self.nwalkers*self.ntemps
time = (tau0S + tau0LD*Nsfts) * numb_evals
if getattr(self, 'nsegs', 1) > 1:
time += (tau0C + tau0T*Nsfts)*self.nsegs*numb_evals
logging.info('Estimated run-time = {} s = {:1.0f}:{:1.0f} m'.format(
time, *divmod(time, 60)))
def run(self, proposal_scale_factor=2, create_plots=True, window=50,
**kwargs):
""" Run the MCMC simulatation
Parameters
----------
proposal_scale_factor: float
The proposal scale factor used by the sampler, see Goodman & Weare
(2010). If the acceptance fraction is too low, you can raise it by
decreasing the a parameter; and if it is too high, you can reduce
it by increasing the a parameter [Foreman-Mackay (2013)].
create_plots: bool
If true, save trace plots of the walkers
window: int
The minimum number of autocorrelation times needed to trust the
result when estimating the autocorrelation time (see
ptemcee.Sampler.get_autocorr_time for further details.
**kwargs:
Passed to _plot_walkers to control the figures
Returns
-------
sampler: ptemcee.Sampler
The ptemcee ptsampler object
"""
self.old_data_is_okay_to_use = self._check_old_data_is_okay_to_use()
if self.old_data_is_okay_to_use is True:
logging.warning('Using saved data from {}'.format(
self.pickle_path))
d = self.get_saved_data_dictionary()
self.samples = d['samples']
self.lnprobs = d['lnprobs']
self.lnlikes = d['lnlikes']
self.all_lnlikelihood = d['all_lnlikelihood']
self.chain = d['chain']
return
self._initiate_search_object()
self._estimate_run_time()
sampler = PTSampler(
ntemps=self.ntemps, nwalkers=self.nwalkers, dim=self.ndim,
logl=self.logl, logp=self.logp,
logpargs=(self.theta_prior, self.theta_keys, self.search),
loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
p0 = self._generate_initial_p0()
p0 = self._apply_corrections_to_p0(p0)
self._check_initial_points(p0)
# Run initialisation steps if required
ninit_steps = len(self.nsteps) - 2
for j, n in enumerate(self.nsteps[:-2]):
logging.info('Running {}/{} initialisation with {} steps'.format(
j, ninit_steps, n))
sampler = self._run_sampler(sampler, p0, nburn=n, window=window)
if create_plots:
fig, axes = self._plot_walkers(sampler,
**kwargs)
fig.tight_layout()
fig.savefig('{}/{}_init_{}_walkers.png'.format(
self.outdir, self.label, j))
p0 = self._get_new_p0(sampler)
p0 = self._apply_corrections_to_p0(p0)
self._check_initial_points(p0)
sampler.reset()
if len(self.nsteps) > 1:
nburn = self.nsteps[-2]
else:
nburn = 0
nprod = self.nsteps[-1]
logging.info('Running final burn and prod with {} steps'.format(
nburn+nprod))
sampler = self._run_sampler(sampler, p0, nburn=nburn, nprod=nprod)
if create_plots:
try:
fig, axes = self._plot_walkers(sampler, nprod=nprod, **kwargs)
fig.tight_layout()
fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label))
except RuntimeError as e:
logging.warning("Failed to save walker plots due to Erro {}"
.format(e))
samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
lnprobs = sampler.logprobability[0, :, nburn:].reshape((-1))
lnlikes = sampler.loglikelihood[0, :, nburn:].reshape((-1))
all_lnlikelihood = sampler.loglikelihood[:, :, nburn:]
self.samples = samples
self.chain = sampler.chain
self.lnprobs = lnprobs
self.lnlikes = lnlikes
self.all_lnlikelihood = all_lnlikelihood
self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood,
sampler.chain)
return sampler
def _get_rescale_multiplier_for_key(self, key):
""" Get the rescale multiplier from the transform_dictionary
Can either be a float, a string (in which case it is interpretted as
a attribute of the MCMCSearch class, e.g. minStartTime, or non-existent
in which case 0 is returned
"""
if key not in self.transform_dictionary:
return 1
if 'multiplier' in self.transform_dictionary[key]:
val = self.transform_dictionary[key]['multiplier']
if type(val) == str:
if hasattr(self, val):
multiplier = getattr(
self, self.transform_dictionary[key]['multiplier'])
else:
raise ValueError(
"multiplier {} not a class attribute".format(val))
else:
multiplier = val
else:
multiplier = 1
return multiplier
def _get_rescale_subtractor_for_key(self, key):
""" Get the rescale subtractor from the transform_dictionary
Can either be a float, a string (in which case it is interpretted as
a attribute of the MCMCSearch class, e.g. minStartTime, or non-existent
in which case 0 is returned
"""
if key not in self.transform_dictionary:
return 0
if 'subtractor' in self.transform_dictionary[key]:
val = self.transform_dictionary[key]['subtractor']
if type(val) == str:
if hasattr(self, val):
subtractor = getattr(
self, self.transform_dictionary[key]['subtractor'])
else:
raise ValueError(
"subtractor {} not a class attribute".format(val))
else:
subtractor = val
else:
subtractor = 0
return subtractor
def _scale_samples(self, samples, theta_keys):
""" Scale the samples using the transform_dictionary """
for key in theta_keys:
if key in self.transform_dictionary:
idx = theta_keys.index(key)
s = samples[:, idx]
subtractor = self._get_rescale_subtractor_for_key(key)
s = s - subtractor
multiplier = self._get_rescale_multiplier_for_key(key)
s *= multiplier
samples[:, idx] = s
return samples
def _get_labels(self, newline_units=False):
""" Combine the units, symbols and rescaling to give labels """
labels = []
for key in self.theta_keys:
label = None
s = self.symbol_dictionary[key]
s.replace('_{glitch}', r'_\textrm{glitch}')
u = self.unit_dictionary[key]
if key in self.transform_dictionary:
if 'symbol' in self.transform_dictionary[key]:
s = self.transform_dictionary[key]['symbol']
if 'label' in self.transform_dictionary[key]:
label = self.transform_dictionary[key]['label']
if 'unit' in self.transform_dictionary[key]:
u = self.transform_dictionary[key]['unit']
if label is None:
if newline_units:
label = '{} \n [{}]'.format(s, u)
else:
label = '{} [{}]'.format(s, u)
labels.append(label)
return labels
def plot_corner(self, figsize=(7, 7), add_prior=False, nstds=None,
label_offset=0.4, dpi=300, rc_context={},
tglitch_ratio=False, fig_and_axes=None, save_fig=True,
**kwargs):
""" Generate a corner plot of the posterior
Using the `corner` package (https://pypi.python.org/pypi/corner/),
generate estimates of the posterior from the production samples.
Parameters
----------
figsize: tuple (7, 7)
Figure size in inches (passed to plt.subplots)
add_prior: bool, str
If true, plot the prior as a red line. If 'full' then for uniform
priors plot the full extent of the prior.
nstds: float
The number of standard deviations to plot centered on the mean
label_offset: float
Offset the labels from the plot: useful to precent overlapping the
tick labels with the axis labels
dpi: int
Passed to plt.savefig
rc_context: dict
Dictionary of rc values to set while generating the figure (see
matplotlib rc for more details)
tglitch_ratio: bool
If true, and tglitch is a parameter, plot posteriors as the
fractional time at which the glitch occurs instead of the actual
time
fig_and_axes: tuple
fig and axes to plot on, the axes must be of the right shape,
namely (ndim, ndim)
save_fig: bool
If true, save the figure, else return the fig, axes
**kwargs:
Passed to corner.corner
Returns
-------
fig, axes:
The matplotlib figure and axes, only returned if save_fig = False
"""
if 'truths' in kwargs and len(kwargs['truths']) != self.ndim:
logging.warning('len(Truths) != ndim, Truths will be ignored')
kwargs['truths'] = None
if self.ndim < 2:
with plt.rc_context(rc_context):
if fig_and_axes is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig, ax = fig_and_axes
ax.hist(self.samples, bins=50, histtype='stepfilled')
ax.set_xlabel(self.theta_symbols[0])
fig.savefig('{}/{}_corner.png'.format(
self.outdir, self.label), dpi=dpi)
return
with plt.rc_context(rc_context):
if fig_and_axes is None:
fig, axes = plt.subplots(self.ndim, self.ndim,
figsize=figsize)
else:
fig, axes = fig_and_axes
samples_plt = copy.copy(self.samples)
labels = self._get_labels(newline_units=True)
samples_plt = self._scale_samples(samples_plt, self.theta_keys)
if tglitch_ratio:
for j, k in enumerate(self.theta_keys):
if k == 'tglitch':
s = samples_plt[:, j]
samples_plt[:, j] = (
s - self.minStartTime)/(
self.maxStartTime - self.minStartTime)
labels[j] = r'$R_{\textrm{glitch}}$'
if type(nstds) is int and 'range' not in kwargs:
_range = []
for j, s in enumerate(samples_plt.T):
median = np.median(s)
std = np.std(s)
_range.append((median - nstds*std, median + nstds*std))
elif 'range' in kwargs:
_range = kwargs.pop('range')
else:
_range = None
hist_kwargs = kwargs.pop('hist_kwargs', dict())
if 'normed' not in hist_kwargs:
hist_kwargs['normed'] = True
fig_triangle = corner.corner(samples_plt,
labels=labels,
fig=fig,
bins=50,
max_n_ticks=4,
plot_contours=True,
plot_datapoints=True,
#label_kwargs={'fontsize': 12},
data_kwargs={'alpha': 0.1,
'ms': 0.5},
range=_range,
hist_kwargs=hist_kwargs,
**kwargs)
axes_list = fig_triangle.get_axes()
axes = np.array(axes_list).reshape(self.ndim, self.ndim)
plt.draw()
for ax in axes[:, 0]:
ax.yaxis.set_label_coords(-label_offset, 0.5)
for ax in axes[-1, :]:
ax.xaxis.set_label_coords(0.5, -label_offset)
for ax in axes_list:
ax.set_rasterized(True)
ax.set_rasterization_zorder(-10)
for tick in ax.xaxis.get_major_ticks():
#tick.label.set_fontsize(8)
tick.label.set_rotation('horizontal')
for tick in ax.yaxis.get_major_ticks():
#tick.label.set_fontsize(8)
tick.label.set_rotation('vertical')
plt.tight_layout(h_pad=0.0, w_pad=0.0)
fig.subplots_adjust(hspace=0.05, wspace=0.05)
if add_prior:
self._add_prior_to_corner(axes, self.samples, add_prior)
if save_fig:
fig_triangle.savefig('{}/{}_corner.png'.format(
self.outdir, self.label), dpi=dpi)
else:
return fig, axes
def plot_chainconsumer(
self, save_fig=True, label_offset=0.25, dpi=300, **kwargs):
""" Generate a corner plot of the posterior using chainconsumer
Parameters
----------
dpi: int
Passed to plt.savefig
**kwargs:
Passed to chainconsumer.plotter.plot
"""
if 'truths' in kwargs and len(kwargs['truths']) != self.ndim:
logging.warning('len(Truths) != ndim, Truths will be ignored')
kwargs['truths'] = None
samples_plt = copy.copy(self.samples)
labels = self._get_labels(newline_units=True)
samples_plt = self._scale_samples(samples_plt, self.theta_keys)
import chainconsumer
c = chainconsumer.ChainConsumer()
c.add_chain(samples_plt, parameters=labels)
c.configure(smooth=0, summary=False, sigma2d=True)
fig = c.plotter.plot(**kwargs)
axes_list = fig.get_axes()
axes = np.array(axes_list).reshape(self.ndim, self.ndim)
plt.draw()
for ax in axes[:, 0]:
ax.yaxis.set_label_coords(-label_offset, 0.5)
for ax in axes[-1, :]:
ax.xaxis.set_label_coords(0.5, -label_offset)
for ax in axes_list:
ax.set_rasterized(True)
ax.set_rasterization_zorder(-10)
#for tick in ax.xaxis.get_major_ticks():
# #tick.label.set_fontsize(8)
# tick.label.set_rotation('horizontal')
#for tick in ax.yaxis.get_major_ticks():
# #tick.label.set_fontsize(8)
# tick.label.set_rotation('vertical')
plt.tight_layout(h_pad=0.0, w_pad=0.0)
fig.subplots_adjust(hspace=0.05, wspace=0.05)
if save_fig:
fig.savefig('{}/{}_corner.png'.format(
self.outdir, self.label), dpi=dpi)
else:
return fig
def _add_prior_to_corner(self, axes, samples, add_prior):
for i, key in enumerate(self.theta_keys):
ax = axes[i][i]
s = samples[:, i]
lnprior = self._generic_lnprior(**self.theta_prior[key])
if add_prior == 'full' and self.theta_prior[key]['type'] == 'unif':
lower = self.theta_prior[key]['lower']
upper = self.theta_prior[key]['upper']
r = upper-lower
xlim = [lower-0.05*r, upper+0.05*r]
x = np.linspace(xlim[0], xlim[1], 1000)
else:
xlim = ax.get_xlim()
x = np.linspace(s.min(), s.max(), 1000)
multiplier = self._get_rescale_multiplier_for_key(key)
subtractor = self._get_rescale_subtractor_for_key(key)
ax.plot((x-subtractor)*multiplier,
[np.exp(lnprior(xi)) for xi in x], '-C3',
label='prior')
for j in range(i, self.ndim):
axes[j][i].set_xlim(xlim[0], xlim[1])
for k in range(0, i):
axes[i][k].set_ylim(xlim[0], xlim[1])
def plot_prior_posterior(self, normal_stds=2):
""" Plot the posterior in the context of the prior """
fig, axes = plt.subplots(nrows=self.ndim, figsize=(8, 4*self.ndim))
N = 1000
from scipy.stats import gaussian_kde
for i, (ax, key) in enumerate(zip(axes, self.theta_keys)):
prior_dict = self.theta_prior[key]
prior_func = self._generic_lnprior(**prior_dict)
if prior_dict['type'] == 'unif':
x = np.linspace(prior_dict['lower'], prior_dict['upper'], N)
prior = prior_func(x)
prior[0] = 0
prior[-1] = 0
elif prior_dict['type'] == 'log10unif':
upper = prior_dict['log10upper']
lower = prior_dict['log10lower']
x = np.linspace(lower, upper, N)
prior = [prior_func(xi) for xi in x]
elif prior_dict['type'] == 'norm':
lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = prior_func(x)
elif prior_dict['type'] == 'halfnorm':
lower = prior_dict['loc']
upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = [prior_func(xi) for xi in x]
elif prior_dict['type'] == 'neghalfnorm':
upper = prior_dict['loc']
lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = [prior_func(xi) for xi in x]
else:
raise ValueError('Not implemented for prior type {}'.format(
prior_dict['type']))
priorln = ax.plot(x, prior, 'C3', label='prior')
ax.set_xlabel(self.theta_symbols[i])
s = self.samples[:, i]
while len(s) > 10**4:
# random downsample to avoid slow calculation of kde
s = np.random.choice(s, size=int(len(s)/2.))
kde = gaussian_kde(s)
ax2 = ax.twinx()
postln = ax2.plot(x, kde.pdf(x), 'k', label='posterior')
ax2.set_yticklabels([])
ax.set_yticklabels([])
lns = priorln + postln
labs = [l.get_label() for l in lns]
axes[0].legend(lns, labs, loc=1, framealpha=0.8)
fig.savefig('{}/{}_prior_posterior.png'.format(
self.outdir, self.label))
def plot_cumulative_max(self, **kwargs):
""" Plot the cumulative twoF for the maximum posterior estimate
See the pyfstat.core.plot_twoF_cumulative function for further details
"""
d, maxtwoF = self.get_max_twoF()
for key, val in self.theta_prior.iteritems():
if key not in d:
d[key] = val
if 'add_pfs' in kwargs:
self.generate_loudest()
if hasattr(self, 'search') is False:
self._initiate_search_object()
if self.binary is False:
self.search.plot_twoF_cumulative(
self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
Alpha=d['Alpha'], Delta=d['Delta'],
tstart=self.minStartTime, tend=self.maxStartTime,
**kwargs)
else:
self.search.plot_twoF_cumulative(
self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
Alpha=d['Alpha'], Delta=d['Delta'], asini=d['asini'],
period=d['period'], ecc=d['ecc'], argp=d['argp'], tp=d['argp'],
tstart=self.minStartTime, tend=self.maxStartTime, **kwargs)
def _generic_lnprior(self, **kwargs):
""" Return a lambda function of the pdf
Parameters
----------
**kwargs:
A dictionary containing 'type' of pdf and shape parameters
"""
def log_of_unif(x, a, b):
above = x < b
below = x > a
if type(above) is not np.ndarray:
if above and below:
return -np.log(b-a)
else:
return -np.inf
else:
idxs = np.array([all(tup) for tup in zip(above, below)])
p = np.zeros(len(x)) - np.inf
p[idxs] = -np.log(b-a)
return p
def log_of_log10unif(x, log10lower, log10upper):
log10x = np.log10(x)
above = log10x < log10upper
below = log10x > log10lower
if type(above) is not np.ndarray:
if above and below:
return -np.log(x*np.log(10)*(log10upper-log10lower))
else:
return -np.inf
else:
idxs = np.array([all(tup) for tup in zip(above, below)])
p = np.zeros(len(x)) - np.inf
p[idxs] = -np.log(x*np.log(10)*(log10upper-log10lower))
return p
def log_of_halfnorm(x, loc, scale):
if x < loc:
return -np.inf
else:
return -0.5*((x-loc)**2/scale**2+np.log(0.5*np.pi*scale**2))
def cauchy(x, x0, gamma):
return 1.0/(np.pi*gamma*(1+((x-x0)/gamma)**2))
def exp(x, x0, gamma):
if x > x0:
return np.log(gamma) - gamma*(x - x0)
else:
return -np.inf
if kwargs['type'] == 'unif':
return lambda x: log_of_unif(x, kwargs['lower'], kwargs['upper'])
if kwargs['type'] == 'log10unif':
return lambda x: log_of_log10unif(
x, kwargs['log10lower'], kwargs['log10upper'])
elif kwargs['type'] == 'halfnorm':
return lambda x: log_of_halfnorm(x, kwargs['loc'], kwargs['scale'])
elif kwargs['type'] == 'neghalfnorm':
return lambda x: log_of_halfnorm(
-x, kwargs['loc'], kwargs['scale'])
elif kwargs['type'] == 'norm':
return lambda x: -0.5*((x - kwargs['loc'])**2/kwargs['scale']**2
+ np.log(2*np.pi*kwargs['scale']**2))
else:
logging.info("kwargs:", kwargs)
raise ValueError("Print unrecognise distribution")
def _generate_rv(self, **kwargs):
dist_type = kwargs.pop('type')
if dist_type == "unif":
return np.random.uniform(low=kwargs['lower'], high=kwargs['upper'])
if dist_type == "log10unif":
return 10**(np.random.uniform(low=kwargs['log10lower'],
high=kwargs['log10upper']))
if dist_type == "norm":
return np.random.normal(loc=kwargs['loc'], scale=kwargs['scale'])
if dist_type == "halfnorm":
return np.abs(np.random.normal(loc=kwargs['loc'],
scale=kwargs['scale']))
if dist_type == "neghalfnorm":
return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
scale=kwargs['scale']))
if dist_type == "lognorm":
return np.random.lognormal(
mean=kwargs['loc'], sigma=kwargs['scale'])
else:
raise ValueError("dist_type {} unknown".format(dist_type))
def _plot_walkers(self, sampler, symbols=None, alpha=0.8, color="k",
temp=0, lw=0.1, nprod=0, add_det_stat_burnin=False,
fig=None, axes=None, xoffset=0, plot_det_stat=False,
context='ggplot', labelpad=5):
""" Plot all the chains from a sampler """
if symbols is None:
symbols = self._get_labels()
if context not in plt.style.available:
raise ValueError((
'The requested context {} is not available; please select a'
' context from `plt.style.available`').format(context))
if np.ndim(axes) > 1:
axes = axes.flatten()
shape = sampler.chain.shape
if len(shape) == 3:
nwalkers, nsteps, ndim = shape
chain = sampler.chain[:, :, :].copy()
if len(shape) == 4:
ntemps, nwalkers, nsteps, ndim = shape
if temp < ntemps:
logging.info("Plotting temperature {} chains".format(temp))
else:
raise ValueError(("Requested temperature {} outside of"
"available range").format(temp))
chain = sampler.chain[temp, :, :, :].copy()
samples = chain.reshape((nwalkers*nsteps, ndim))
samples = self._scale_samples(samples, self.theta_keys)
chain = chain.reshape((nwalkers, nsteps, ndim))
if plot_det_stat:
extra_subplots = 1
else:
extra_subplots = 0
with plt.style.context((context)):
plt.rcParams['text.usetex'] = True
if fig is None and axes is None:
fig = plt.figure(figsize=(4, 3.0*ndim))
ax = fig.add_subplot(ndim+extra_subplots, 1, 1)
axes = [ax] + [fig.add_subplot(ndim+extra_subplots, 1, i)
for i in range(2, ndim+1)]
idxs = np.arange(chain.shape[1])
burnin_idx = chain.shape[1] - nprod
#if hasattr(self, 'convergence_idx'):
# last_idx = self.convergence_idx
#else:
last_idx = burnin_idx
if ndim > 1:
for i in range(ndim):
axes[i].ticklabel_format(useOffset=False, axis='y')
cs = chain[:, :, i].T
if burnin_idx > 0:
axes[i].plot(xoffset+idxs[:last_idx+1],
cs[:last_idx+1],
color="C3", alpha=alpha,
lw=lw)
axes[i].axvline(xoffset+last_idx,
color='k', ls='--', lw=0.5)
axes[i].plot(xoffset+idxs[burnin_idx:],
cs[burnin_idx:],
color="k", alpha=alpha, lw=lw)
axes[i].set_xlim(0, xoffset+idxs[-1])
if symbols:
axes[i].set_ylabel(symbols[i], labelpad=labelpad)
#if subtractions[i] == 0:
# axes[i].set_ylabel(symbols[i], labelpad=labelpad)
#else:
# axes[i].set_ylabel(
# symbols[i]+'$-$'+symbols[i]+'$^\mathrm{s}$',
# labelpad=labelpad)
# if hasattr(self, 'convergence_diagnostic'):
# ax = axes[i].twinx()
# axes[i].set_zorder(ax.get_zorder()+1)
# axes[i].patch.set_visible(False)
# c_x = np.array(self.convergence_diagnosticx)
# c_y = np.array(self.convergence_diagnostic)
# break_idx = np.argmin(np.abs(c_x - burnin_idx))
# ax.plot(c_x[:break_idx], c_y[:break_idx, i], '-C0',
# zorder=-10)
# ax.plot(c_x[break_idx:], c_y[break_idx:, i], '-C0',
# zorder=-10)
# if self.convergence_test_type == 'autocorr':
# ax.set_ylabel(r'$\tau_\mathrm{exp}$')
# elif self.convergence_test_type == 'GR':
# ax.set_ylabel('PSRF')
# ax.ticklabel_format(useOffset=False)
else:
axes[0].ticklabel_format(useOffset=False, axis='y')
cs = chain[:, :, temp].T
if burnin_idx:
axes[0].plot(idxs[:burnin_idx], cs[:burnin_idx],
color="C3", alpha=alpha, lw=lw)
axes[0].plot(idxs[burnin_idx:], cs[burnin_idx:], color="k",
alpha=alpha, lw=lw)
if symbols:
axes[0].set_ylabel(symbols[0], labelpad=labelpad)
axes[-1].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.2)
if plot_det_stat:
if len(axes) == ndim:
axes.append(fig.add_subplot(ndim+1, 1, ndim+1))
lnl = sampler.loglikelihood[temp, :, :]
if burnin_idx and add_det_stat_burnin:
burn_in_vals = lnl[:, :burnin_idx].flatten()
try:
twoF_burnin = (burn_in_vals[~np.isnan(burn_in_vals)]
- self.likelihoodcoef)
axes[-1].hist(twoF_burnin, bins=50, histtype='step',
color='C3')
except ValueError:
logging.info('Det. Stat. hist failed, most likely all '
'values where the same')
pass
else:
twoF_burnin = []
prod_vals = lnl[:, burnin_idx:].flatten()
try:
twoF = prod_vals[~np.isnan(prod_vals)]-self.likelihoodcoef
axes[-1].hist(twoF, bins=50, histtype='step', color='k')
except ValueError:
logging.info('Det. Stat. hist failed, most likely all '
'values where the same')
pass
if self.BSGL:
axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
else:
axes[-1].set_xlabel(r'$\widetilde{2\mathcal{F}}$')
axes[-1].set_ylabel(r'$\textrm{Counts}$')
combined_vals = np.append(twoF_burnin, twoF)
if len(combined_vals) > 0:
minv = np.min(combined_vals)
maxv = np.max(combined_vals)
Range = abs(maxv-minv)
axes[-1].set_xlim(minv-0.1*Range, maxv+0.1*Range)
xfmt = matplotlib.ticker.ScalarFormatter()
xfmt.set_powerlimits((-4, 4))
axes[-1].xaxis.set_major_formatter(xfmt)
return fig, axes
def _apply_corrections_to_p0(self, p0):
""" Apply any correction to the initial p0 values """
return p0
def _generate_scattered_p0(self, p):
""" Generate a set of p0s scattered about p """
p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
for i in xrange(self.nwalkers)]
for j in xrange(self.ntemps)]
return p0
def _generate_initial_p0(self):
""" Generate a set of init vals for the walkers """
if type(self.theta_initial) == dict:
logging.info('Generate initial values from initial dictionary')
if hasattr(self, 'nglitch') and self.nglitch > 1:
raise ValueError('Initial dict not implemented for nglitch>1')
p0 = [[[self._generate_rv(**self.theta_initial[key])
for key in self.theta_keys]
for i in range(self.nwalkers)]
for j in range(self.ntemps)]
elif self.theta_initial is None:
logging.info('Generate initial values from prior dictionary')
p0 = [[[self._generate_rv(**self.theta_prior[key])
for key in self.theta_keys]
for i in range(self.nwalkers)]
for j in range(self.ntemps)]
else:
raise ValueError('theta_initial not understood')
return p0
def _get_new_p0(self, sampler):
""" Returns new initial positions for walkers are burn0 stage
This returns new positions for all walkers by scattering points about
the maximum posterior with scale `scatter_val`.
"""
temp_idx = 0
pF = sampler.chain[temp_idx, :, :, :]
lnl = sampler.loglikelihood[temp_idx, :, :]
lnp = sampler.logprobability[temp_idx, :, :]
# General warnings about the state of lnp
if np.any(np.isnan(lnp)):
logging.warning(
"Of {} lnprobs {} are nan".format(
np.shape(lnp), np.sum(np.isnan(lnp))))
if np.any(np.isposinf(lnp)):
logging.warning(
"Of {} lnprobs {} are +np.inf".format(
np.shape(lnp), np.sum(np.isposinf(lnp))))
if np.any(np.isneginf(lnp)):
logging.warning(
"Of {} lnprobs {} are -np.inf".format(
np.shape(lnp), np.sum(np.isneginf(lnp))))
lnp_finite = copy.copy(lnp)
lnp_finite[np.isinf(lnp)] = np.nan
idx = np.unravel_index(np.nanargmax(lnp_finite), lnp_finite.shape)
p = pF[idx]
p0 = self._generate_scattered_p0(p)
self.search.BSGL = False
twoF = self.logl(p, self.search)
self.search.BSGL = self.BSGL
logging.info(('Gen. new p0 from pos {} which had det. stat.={:2.1f},'
' twoF={:2.1f} and lnp={:2.1f}')
.format(idx[1], lnl[idx], twoF, lnp_finite[idx]))
return p0
def _get_data_dictionary_to_save(self):
d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior,
log10beta_min=self.log10beta_min,
BSGL=self.BSGL, minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime)
return d
def _save_data(self, sampler, samples, lnprobs, lnlikes, all_lnlikelihood,
chain):
d = self._get_data_dictionary_to_save()
d['samples'] = samples
d['lnprobs'] = lnprobs
d['lnlikes'] = lnlikes
d['chain'] = chain
d['all_lnlikelihood'] = all_lnlikelihood
if os.path.isfile(self.pickle_path):
logging.info('Saving backup of {} as {}.old'.format(
self.pickle_path, self.pickle_path))
os.rename(self.pickle_path, self.pickle_path+".old")
with open(self.pickle_path, "wb") as File:
pickle.dump(d, File)
def get_saved_data_dictionary(self):
""" Returns dictionary of the data saved in the pickle """
with open(self.pickle_path, "r") as File:
d = pickle.load(File)
return d
def _check_old_data_is_okay_to_use(self):
if os.path.isfile(self.pickle_path) is False:
logging.info('No pickled data found')
return False
if self.sftfilepattern is not None:
oldest_sft = min([os.path.getmtime(f) for f in
self._get_list_of_matching_sfts()])
if os.path.getmtime(self.pickle_path) < oldest_sft:
logging.info('Pickled data outdates sft files')
return False
old_d = self.get_saved_data_dictionary().copy()
new_d = self._get_data_dictionary_to_save().copy()
old_d.pop('samples')
old_d.pop('lnprobs')
old_d.pop('lnlikes')
old_d.pop('all_lnlikelihood')
old_d.pop('chain')
for key in 'minStartTime', 'maxStartTime':
if new_d[key] is None:
new_d[key] = old_d[key]
setattr(self, key, new_d[key])
mod_keys = []
for key in new_d.keys():
if key in old_d:
if new_d[key] != old_d[key]:
mod_keys.append((key, old_d[key], new_d[key]))
else:
raise ValueError('Keys {} not in old dictionary'.format(key))
if len(mod_keys) == 0:
return True
else:
logging.warning("Saved data differs from requested")
logging.info("Differences found in following keys:")
for key in mod_keys:
if len(key) == 3:
if np.isscalar(key[1]) or key[0] == 'nsteps':
logging.info(" {} : {} -> {}".format(*key))
else:
logging.info(" " + key[0])
else:
logging.info(key)
return False
def get_max_twoF(self, threshold=0.05):
""" Returns the max likelihood sample and the corresponding 2F value
Note: the sample is returned as a dictionary along with an estimate of
the standard deviation calculated from the std of all samples with a
twoF within `threshold` (relative) to the max twoF
"""
if any(np.isposinf(self.lnlikes)):
logging.info('lnlike values contain positive infinite values')
if any(np.isneginf(self.lnlikes)):
logging.info('lnlike values contain negative infinite values')
if any(np.isnan(self.lnlikes)):
logging.info('lnlike values contain nan')
idxs = np.isfinite(self.lnlikes)
jmax = np.nanargmax(self.lnlikes[idxs])
maxlogl = self.lnlikes[jmax]
d = OrderedDict()
if self.BSGL:
if hasattr(self, 'search') is False:
self._initiate_search_object()
p = self.samples[jmax]
self.search.BSGL = False
maxtwoF = self.logl(p, self.search)
self.search.BSGL = self.BSGL
else:
maxtwoF = (maxlogl - self.likelihoodcoef)*2
repeats = []
for i, k in enumerate(self.theta_keys):
if k in d and k not in repeats:
d[k+'_0'] = d[k] # relabel the old key
d.pop(k)
repeats.append(k)
if k in repeats:
k = k + '_0'
count = 1
while k in d:
k = k.replace('_{}'.format(count-1), '_{}'.format(count))
count += 1
d[k] = self.samples[jmax][i]
return d, maxtwoF
def get_median_stds(self):
""" Returns a dict of the median and std of all production samples """
d = OrderedDict()
repeats = []
for s, k in zip(self.samples.T, self.theta_keys):
if k in d and k not in repeats:
d[k+'_0'] = d[k] # relabel the old key
d[k+'_0_std'] = d[k+'_std']
d.pop(k)
d.pop(k+'_std')
repeats.append(k)
if k in repeats:
k = k + '_0'
count = 1
while k in d:
k = k.replace('_{}'.format(count-1), '_{}'.format(count))
count += 1
d[k] = np.median(s)
d[k+'_std'] = np.std(s)
return d
def check_if_samples_are_railing(self, threshold=0.01):
""" Returns a boolean estimate of if the samples are railing
Parameters
----------
threshold: float [0, 1]
Fraction of the uniform prior to test (at upper and lower bound)
Returns
-------
return_flag: bool
IF true, the samples are railing
"""
return_flag = False
for s, k in zip(self.samples.T, self.theta_keys):
prior = self.theta_prior[k]
if prior['type'] == 'unif':
prior_range = prior['upper'] - prior['lower']
edges = []
fracs = []
for l in ['lower', 'upper']:
bools = np.abs(s - prior[l])/prior_range < threshold
if np.any(bools):
edges.append(l)
fracs.append(str(100*float(np.sum(bools))/len(bools)))
if len(edges) > 0:
logging.warning(
'{}% of the {} posterior is railing on the {} edges'
.format('% & '.join(fracs), k, ' & '.join(edges)))
return_flag = True
return return_flag
def write_par(self, method='med'):
""" Writes a .par of the best-fit params with an estimated std """
logging.info('Writing {}/{}.par using the {} method'.format(
self.outdir, self.label, method))
median_std_d = self.get_median_stds()
max_twoF_d, max_twoF = self.get_max_twoF()
logging.info('Writing par file with max twoF = {}'.format(max_twoF))
filename = '{}/{}.par'.format(self.outdir, self.label)
with open(filename, 'w+') as f:
f.write('MaxtwoF = {}\n'.format(max_twoF))
f.write('tref = {}\n'.format(self.tref))
if hasattr(self, 'theta0_index'):
f.write('theta0_index = {}\n'.format(self.theta0_idx))
if method == 'med':
for key, val in median_std_d.iteritems():
f.write('{} = {:1.16e}\n'.format(key, val))
if method == 'twoFmax':
for key, val in max_twoF_d.iteritems():
f.write('{} = {:1.16e}\n'.format(key, val))
def generate_loudest(self):
""" Use lalapps_ComputeFstatistic_v2 to produce a .loudest file """
self.write_par()
params = read_par(label=self.label, outdir=self.outdir)
for key in ['Alpha', 'Delta', 'F0', 'F1']:
if key not in params:
params[key] = self.theta_prior[key]
cmd = ('lalapps_ComputeFstatistic_v2 -a {} -d {} -f {} -s {} -D "{}"'
' --refTime={} --outputLoudest="{}/{}.loudest" '
'--minStartTime={} --maxStartTime={}').format(
params['Alpha'], params['Delta'], params['F0'],
params['F1'], self.sftfilepattern, params['tref'],
self.outdir, self.label, self.minStartTime,
self.maxStartTime)
subprocess.call([cmd], shell=True)
def write_prior_table(self):
""" Generate a .tex file of the prior """
with open('{}/{}_prior.tex'.format(self.outdir, self.label), 'w') as f:
f.write(r"\begin{tabular}{c l c} \hline" + '\n'
r"Parameter & & & \\ \hhline{====}")
for key, prior in self.theta_prior.iteritems():
if type(prior) is dict:
Type = prior['type']
if Type == "unif":
a = prior['lower']
b = prior['upper']
line = r"{} & $\mathrm{{Unif}}$({}, {}) & {}\\"
elif Type == "norm":
a = prior['loc']
b = prior['scale']
line = r"{} & $\mathcal{{N}}$({}, {}) & {}\\"
elif Type == "halfnorm":
a = prior['loc']
b = prior['scale']
line = r"{} & $|\mathcal{{N}}$({}, {})| & {}\\"
u = self.unit_dictionary[key]
s = self.symbol_dictionary[key]
f.write("\n")
a = helper_functions.texify_float(a)
b = helper_functions.texify_float(b)
f.write(" " + line.format(s, a, b, u) + r" \\")
f.write("\n\end{tabular}\n")
def print_summary(self):
""" Prints a summary of the max twoF found to the terminal """
max_twoFd, max_twoF = self.get_max_twoF()
median_std_d = self.get_median_stds()
logging.info('Summary:')
if hasattr(self, 'theta0_idx'):
logging.info('theta0 index: {}'.format(self.theta0_idx))
logging.info('Max twoF: {} with parameters:'.format(max_twoF))
for k in np.sort(max_twoFd.keys()):
print(' {:10s} = {:1.9e}'.format(k, max_twoFd[k]))
logging.info('Median +/- std for production values')
for k in np.sort(median_std_d.keys()):
if 'std' not in k:
logging.info(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
logging.info('\n')
def _CF_twoFmax(self, theta, twoFmax, ntrials):
Fmax = twoFmax/2.0
return (np.exp(1j*theta*twoFmax)*ntrials/2.0
* Fmax*np.exp(-Fmax)*(1-(1+Fmax)*np.exp(-Fmax))**(ntrials-1))
def _pdf_twoFhat(self, twoFhat, nglitch, ntrials, twoFmax=100, dtwoF=0.1):
if np.ndim(ntrials) == 0:
ntrials = np.zeros(nglitch+1) + ntrials
twoFmax_int = np.arange(0, twoFmax, dtwoF)
theta_int = np.arange(-1/dtwoF, 1./dtwoF, 1./twoFmax)
CF_twoFmax_theta = np.array(
[[np.trapz(self._CF_twoFmax(t, twoFmax_int, ntrial), twoFmax_int)
for t in theta_int]
for ntrial in ntrials])
CF_twoFhat_theta = np.prod(CF_twoFmax_theta, axis=0)
pdf = (1/(2*np.pi)) * np.array(
[np.trapz(np.exp(-1j*theta_int*twoFhat_val)
* CF_twoFhat_theta, theta_int) for twoFhat_val in twoFhat])
return pdf.real
def _p_val_twoFhat(self, twoFhat, ntrials, twoFhatmax=500, Npoints=1000):
""" Caluculate the p-value for the given twoFhat in Gaussian noise
Parameters
----------
twoFhat: float
The observed twoFhat value
ntrials: int, array of len Nglitch+1
The number of trials for each glitch+1
"""
twoFhats = np.linspace(twoFhat, twoFhatmax, Npoints)
pdf = self._pdf_twoFhat(twoFhats, self.nglitch, ntrials)
return np.trapz(pdf, twoFhats)
def get_p_value(self, delta_F0, time_trials=0):
""" Get's the p-value for the maximum twoFhat value """
d, max_twoF = self.get_max_twoF()
if self.nglitch == 1:
tglitches = [d['tglitch']]
else:
tglitches = [d['tglitch_{}'.format(i)]
for i in range(self.nglitch)]
tboundaries = [self.minStartTime] + tglitches + [self.maxStartTime]
deltaTs = np.diff(tboundaries)
ntrials = [time_trials + delta_F0 * dT for dT in deltaTs]
p_val = self._p_val_twoFhat(max_twoF, ntrials)
print('p-value = {}'.format(p_val))
return p_val
def compute_evidence(self, make_plots=False, write_to_file=None):
""" Computes the evidence/marginal likelihood for the model """
betas = self.betas
mean_lnlikes = np.mean(np.mean(self.all_lnlikelihood, axis=1), axis=1)
mean_lnlikes = mean_lnlikes[::-1]
betas = betas[::-1]
if any(np.isinf(mean_lnlikes)):
print("WARNING mean_lnlikes contains inf: recalculating without"
" the {} infs".format(len(betas[np.isinf(mean_lnlikes)])))
idxs = np.isinf(mean_lnlikes)
mean_lnlikes = mean_lnlikes[~idxs]
betas = betas[~idxs]
log10evidence = np.trapz(mean_lnlikes, betas)/np.log(10)
z1 = np.trapz(mean_lnlikes, betas)
z2 = np.trapz(mean_lnlikes[::-1][::2][::-1],
betas[::-1][::2][::-1])
log10evidence_err = np.abs(z1 - z2) / np.log(10)
logging.info("log10 evidence for {} = {} +/- {}".format(
self.label, log10evidence, log10evidence_err))
if write_to_file:
EvidenceDict = self.read_evidence_file_to_dict(write_to_file)
EvidenceDict[self.label] = [log10evidence, log10evidence_err]
self.write_evidence_file_from_dict(EvidenceDict, write_to_file)
if make_plots:
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 8))
ax1.semilogx(betas, mean_lnlikes, "-o")
ax1.set_xlabel(r"$\beta$")
ax1.set_ylabel(r"$\langle \log(\mathcal{L}) \rangle$")
min_betas = []
evidence = []
for i in range(int(len(betas)/2.)):
min_betas.append(betas[i])
lnZ = np.trapz(mean_lnlikes[i:], betas[i:])
evidence.append(lnZ/np.log(10))
ax2.semilogx(min_betas, evidence, "-o")
ax2.set_ylabel(r"$\int_{\beta_{\textrm{Min}}}^{\beta=1}" +
r"\langle \log(\mathcal{L})\rangle d\beta$",
size=16)
ax2.set_xlabel(r"$\beta_{\textrm{min}}$")
plt.tight_layout()
fig.savefig("{}/{}_beta_lnl.png".format(self.outdir, self.label))
return log10evidence, log10evidence_err
@staticmethod
def read_evidence_file_to_dict(evidence_file_name='Evidences.txt'):
EvidenceDict = OrderedDict()
if os.path.isfile(evidence_file_name):
with open(evidence_file_name, 'r') as f:
for line in f:
key, log10evidence, log10evidence_err = line.split(' ')
EvidenceDict[key] = [
float(log10evidence), float(log10evidence_err)]
return EvidenceDict
def write_evidence_file_from_dict(self, EvidenceDict, evidence_file_name):
with open(evidence_file_name, 'w+') as f:
for key, val in EvidenceDict.iteritems():
f.write('{} {} {}\n'.format(key, val[0], val[1]))
class MCMCGlitchSearch(MCMCSearch):
"""MCMC search using the SemiCoherentGlitchSearch
See parent MCMCSearch for a list of all additional parameters, here we list
only the additional init parameters of this class.
Parameters
----------
nglitch: int
The number of glitches to allow
dtglitchmin: int
The minimum duration (in seconds) of a segment between two glitches
or a glitch and the start/end of the data
theta0_idx, int
Index (zero-based) of which segment the theta refers to - useful
if providing a tight prior on theta to allow the signal to jump
too theta (and not just from)
"""
symbol_dictionary = dict(
F0='$f$', F1='$\dot{f}$', F2='$\ddot{f}$', Alpha=r'$\alpha$',
Delta='$\delta$', delta_F0='$\delta f$',
delta_F1='$\delta \dot{f}$', tglitch='$t_\mathrm{glitch}$')
unit_dictionary = dict(
F0='Hz', F1='Hz/s', F2='Hz/s$^2$', Alpha=r'rad', Delta='rad',
delta_F0='Hz', delta_F1='Hz/s', tglitch='s')
transform_dictionary = dict(
tglitch={
'multiplier': 1/86400.,
'subtractor': 'minStartTime',
'unit': 'day',
'label': '$t^{g}_0$ \n [d]'}
)
@helper_functions.initializer
def __init__(self, theta_prior, tref, label, outdir='data',
minStartTime=None, maxStartTime=None, sftfilepattern=None,
detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
log10beta_min=-5, theta_initial=None,
rhohatmax=1000, binary=False, BSGL=False,
SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
injectSources=None, assumeSqrtSX=None,
dtglitchmin=1*86400, theta0_idx=0, nglitch=1):
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self._add_log_file()
logging.info(('Set-up MCMC glitch search with {} glitches for model {}'
' on data {}').format(self.nglitch, self.label,
self.sftfilepattern))
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self._unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10beta_min:
self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
else:
self.betas = None
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self.old_data_is_okay_to_use = self._check_old_data_is_okay_to_use()
self._log_input()
self._set_likelihoodcoef()
def _set_likelihoodcoef(self):
self.likelihoodcoef = (self.nglitch+1)*np.log(70./self.rhohatmax**4)
def _initiate_search_object(self):
logging.info('Setting up search object')
self.search = core.SemiCoherentGlitchSearch(
label=self.label, outdir=self.outdir,
sftfilepattern=self.sftfilepattern, tref=self.tref,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
detectors=self.detectors, BSGL=self.BSGL, nglitch=self.nglitch,
theta0_idx=self.theta0_idx, injectSources=self.injectSources)
if self.minStartTime is None:
self.minStartTime = self.search.minStartTime
if self.maxStartTime is None:
self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
if self.nglitch > 1:
ts = ([self.minStartTime] + list(theta_vals[-self.nglitch:])
+ [self.maxStartTime])
if np.array_equal(ts, np.sort(ts)) is False:
return -np.inf
if any(np.diff(ts) < self.dtglitchmin):
return -np.inf
H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
if self.nglitch > 1:
ts = ([self.minStartTime] + list(theta[-self.nglitch:])
+ [self.maxStartTime])
if np.array_equal(ts, np.sort(ts)) is False:
return -np.inf
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
twoF = search.get_semicoherent_nglitch_twoF(*self.fixed_theta)
return twoF/2.0 + self.likelihoodcoef
def _unpack_input_theta(self):
glitch_keys = ['delta_F0', 'delta_F1', 'tglitch']
full_glitch_keys = list(np.array(
[[gk]*self.nglitch for gk in glitch_keys]).flatten())
if 'tglitch_0' in self.theta_prior:
full_glitch_keys[-self.nglitch:] = [
'tglitch_{}'.format(i) for i in range(self.nglitch)]
full_glitch_keys[-2*self.nglitch:-1*self.nglitch] = [
'delta_F1_{}'.format(i) for i in range(self.nglitch)]
full_glitch_keys[-4*self.nglitch:-2*self.nglitch] = [
'delta_F0_{}'.format(i) for i in range(self.nglitch)]
full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']+full_glitch_keys
full_theta_keys_copy = copy.copy(full_theta_keys)
glitch_symbols = ['$\delta f$', '$\delta \dot{f}$', r'$t_{glitch}$']
full_glitch_symbols = list(np.array(
[[gs]*self.nglitch for gs in glitch_symbols]).flatten())
full_theta_symbols = (['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
r'$\delta$'] + full_glitch_symbols)
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
if key in glitch_keys:
for i in range(self.nglitch):
self.theta_keys.append(key)
else:
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
if key in glitch_keys:
for i in range(self.nglitch):
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
else:
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
# Correct for number of glitches in the idxs
self.theta_idxs = np.array(self.theta_idxs)
while np.sum(self.theta_idxs[:-1] == self.theta_idxs[1:]) > 0:
for i, idx in enumerate(self.theta_idxs):
if idx in self.theta_idxs[:i]:
self.theta_idxs[i] += 1
def _get_data_dictionary_to_save(self):
d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior,
log10beta_min=self.log10beta_min,
theta0_idx=self.theta0_idx, BSGL=self.BSGL,
minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime)
return d
def _apply_corrections_to_p0(self, p0):
p0 = np.array(p0)
if self.nglitch > 1:
p0[:, :, -self.nglitch:] = np.sort(p0[:, :, -self.nglitch:],
axis=2)
return p0
def plot_cumulative_max(self):
fig, ax = plt.subplots()
d, maxtwoF = self.get_max_twoF()
for key, val in self.theta_prior.iteritems():
if key not in d:
d[key] = val
if self.nglitch > 1:
delta_F0s = [d['delta_F0_{}'.format(i)] for i in
range(self.nglitch)]
delta_F0s.insert(self.theta0_idx, 0)
delta_F0s = np.array(delta_F0s)
delta_F0s[:self.theta0_idx] *= -1
tglitches = [d['tglitch_{}'.format(i)] for i in
range(self.nglitch)]
elif self.nglitch == 1:
delta_F0s = [d['delta_F0']]
delta_F0s.insert(self.theta0_idx, 0)
delta_F0s = np.array(delta_F0s)
delta_F0s[:self.theta0_idx] *= -1
tglitches = [d['tglitch']]
tboundaries = [self.minStartTime] + tglitches + [self.maxStartTime]
for j in range(self.nglitch+1):
ts = tboundaries[j]
te = tboundaries[j+1]
if (te - ts)/86400 < 5:
logging.info('Period too short to perform cumulative search')
continue
if j < self.theta0_idx:
summed_deltaF0 = np.sum(delta_F0s[j:self.theta0_idx])
F0_j = d['F0'] - summed_deltaF0
taus, twoFs = self.search.calculate_twoF_cumulative(
F0_j, F1=d['F1'], F2=d['F2'], Alpha=d['Alpha'],
Delta=d['Delta'], tstart=ts, tend=te)
elif j >= self.theta0_idx:
summed_deltaF0 = np.sum(delta_F0s[self.theta0_idx:j+1])
F0_j = d['F0'] + summed_deltaF0
taus, twoFs = self.search.calculate_twoF_cumulative(
F0_j, F1=d['F1'], F2=d['F2'], Alpha=d['Alpha'],
Delta=d['Delta'], tstart=ts, tend=te)
ax.plot(ts+taus, twoFs)
ax.set_xlabel('GPS time')
fig.savefig('{}/{}_twoFcumulative.png'.format(self.outdir, self.label))
class MCMCSemiCoherentSearch(MCMCSearch):
""" MCMC search for a signal using the semi-coherent ComputeFstat
See parent MCMCSearch for a list of all additional parameters, here we list
only the additional init parameters of this class.
Parameters
----------
nsegs: int
The number of segments
"""
@helper_functions.initializer
def __init__(self, theta_prior, tref, label, outdir='data',
minStartTime=None, maxStartTime=None, sftfilepattern=None,
detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
log10beta_min=-5, theta_initial=None,
rhohatmax=1000, binary=False, BSGL=False,
SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
injectSources=None, assumeSqrtSX=None,
nsegs=None):
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self._add_log_file()
logging.info(('Set-up MCMC semi-coherent search for model {} on data'
'{}').format(
self.label, self.sftfilepattern))
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self._unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10beta_min:
self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
else:
self.betas = None
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self._log_input()
if self.nsegs:
self._set_likelihoodcoef()
else:
logging.info('Value `nsegs` not yet provided')
def _set_likelihoodcoef(self):
self.likelihoodcoef = self.nsegs * np.log(70./self.rhohatmax**4)
def _get_data_dictionary_to_save(self):
d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior,
log10beta_min=self.log10beta_min,
BSGL=self.BSGL, nsegs=self.nsegs,
minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime)
return d
def _initiate_search_object(self):
logging.info('Setting up search object')
self.search = core.SemiCoherentSearch(
label=self.label, outdir=self.outdir, tref=self.tref,
nsegs=self.nsegs, sftfilepattern=self.sftfilepattern,
binary=self.binary, BSGL=self.BSGL, minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime, minCoverFreq=self.minCoverFreq,
maxCoverFreq=self.maxCoverFreq, detectors=self.detectors,
injectSources=self.injectSources, assumeSqrtSX=self.assumeSqrtSX)
if self.minStartTime is None:
self.minStartTime = self.search.minStartTime
if self.maxStartTime is None:
self.maxStartTime = self.search.maxStartTime
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
twoF = search.get_semicoherent_twoF(
*self.fixed_theta)
return twoF/2.0 + self.likelihoodcoef
class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
""" A follow up procudure increasing the coherence time in a zoom
See parent MCMCSemiCoherentSearch for a list of all additional parameters
"""
def _get_data_dictionary_to_save(self):
d = dict(nwalkers=self.nwalkers, ntemps=self.ntemps,
theta_keys=self.theta_keys, theta_prior=self.theta_prior,
log10beta_min=self.log10beta_min,
BSGL=self.BSGL, minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime, run_setup=self.run_setup)
return d
def update_search_object(self):
logging.info('Update search object')
self.search.init_computefstatistic_single_point()
def get_width_from_prior(self, prior, key):
if prior[key]['type'] == 'unif':
return prior[key]['upper'] - prior[key]['lower']
def get_mid_from_prior(self, prior, key):
if prior[key]['type'] == 'unif':
return .5*(prior[key]['upper'] + prior[key]['lower'])
def read_setup_input_file(self, run_setup_input_file):
with open(run_setup_input_file, 'r+') as f:
d = pickle.load(f)
return d
def write_setup_input_file(self, run_setup_input_file, NstarMax, Nsegs0,
nsegs_vals, Nstar_vals, theta_prior):
d = dict(NstarMax=NstarMax, Nsegs0=Nsegs0, nsegs_vals=nsegs_vals,
theta_prior=theta_prior, Nstar_vals=Nstar_vals)
with open(run_setup_input_file, 'w+') as f:
pickle.dump(d, f)
def check_old_run_setup(self, old_setup, **kwargs):
try:
truths = [val == old_setup[key] for key, val in kwargs.iteritems()]
if all(truths):
return True
else:
logging.info(
"Old setup doesn't match one of NstarMax, Nsegs0 or prior")
except KeyError as e:
logging.info(
'Error found when comparing with old setup: {}'.format(e))
return False
def init_run_setup(self, run_setup=None, NstarMax=1000, Nsegs0=None,
log_table=True, gen_tex_table=True):
if run_setup is None and Nsegs0 is None:
raise ValueError(
'You must either specify the run_setup, or Nsegs0 and NStarMax'
' from which the optimal run_setup can be estimated')
if run_setup is None:
logging.info('No run_setup provided')
run_setup_input_file = '{}/{}_run_setup.p'.format(
self.outdir, self.label)
if os.path.isfile(run_setup_input_file):
logging.info('Checking old setup input file {}'.format(
run_setup_input_file))
old_setup = self.read_setup_input_file(run_setup_input_file)
if self.check_old_run_setup(old_setup, NstarMax=NstarMax,
Nsegs0=Nsegs0,
theta_prior=self.theta_prior):
logging.info(
'Using old setup with NstarMax={}, Nsegs0={}'.format(
NstarMax, Nsegs0))
nsegs_vals = old_setup['nsegs_vals']
Nstar_vals = old_setup['Nstar_vals']
generate_setup = False
else:
generate_setup = True
else:
generate_setup = True
if generate_setup:
nsegs_vals, Nstar_vals = (
optimal_setup_functions.get_optimal_setup(
NstarMax, Nsegs0, self.tref, self.minStartTime,
self.maxStartTime, self.theta_prior,
self.search.detector_names))
self.write_setup_input_file(run_setup_input_file, NstarMax,
Nsegs0, nsegs_vals, Nstar_vals,
self.theta_prior)
run_setup = [((self.nsteps[0], 0), nsegs, False)
for nsegs in nsegs_vals[:-1]]
run_setup.append(
((self.nsteps[0], self.nsteps[1]), nsegs_vals[-1], False))
else:
logging.info('Calculating the number of templates for this setup')
Nstar_vals = []
for i, rs in enumerate(run_setup):
rs = list(rs)
if len(rs) == 2:
rs.append(False)
if np.shape(rs[0]) == ():
rs[0] = (rs[0], 0)
run_setup[i] = rs
if args.no_template_counting:
Nstar_vals.append([1, 1, 1])
else:
Nstar = optimal_setup_functions.get_Nstar_estimate(
rs[1], self.tref, self.minStartTime, self.maxStartTime,
self.theta_prior, self.search.detector_names)
Nstar_vals.append(Nstar)
if log_table:
logging.info('Using run-setup as follows:')
logging.info(
'Stage | nburn | nprod | nsegs | Tcoh d | resetp0 | Nstar')
for i, rs in enumerate(run_setup):
Tcoh = (self.maxStartTime - self.minStartTime) / rs[1] / 86400
if Nstar_vals[i] is None:
vtext = 'N/A'
else:
vtext = '{:0.3e}'.format(int(Nstar_vals[i]))
logging.info('{} | {} | {} | {} | {} | {} | {}'.format(
str(i).ljust(5), str(rs[0][0]).ljust(5),
str(rs[0][1]).ljust(5), str(rs[1]).ljust(5),
'{:6.1f}'.format(Tcoh), str(rs[2]).ljust(7),
vtext))
if gen_tex_table:
filename = '{}/{}_run_setup.tex'.format(self.outdir, self.label)
with open(filename, 'w+') as f:
f.write(r'\begin{tabular}{c|ccc}' + '\n')
f.write(r'Stage & $N_\mathrm{seg}$ &'
r'$T_\mathrm{coh}^{\rm days}$ &'
r'$\mathcal{N}^*(\Nseg^{(\ell)}, \Delta\mathbf{\lambda}^{(0)})$ \\ \hline'
'\n')
for i, rs in enumerate(run_setup):
Tcoh = float(
self.maxStartTime - self.minStartTime)/rs[1]/86400
line = r'{} & {} & {} & {} \\' + '\n'
if Nstar_vals[i] is None:
Nstar = 'N/A'
else:
Nstar = Nstar_vals[i]
line = line.format(i, rs[1], '{:1.1f}'.format(Tcoh),
helper_functions.texify_float(Nstar))
f.write(line)
f.write(r'\end{tabular}' + '\n')
if args.setup_only:
logging.info("Exit as requested by setup_only flag")
sys.exit()
else:
return run_setup
def run(self, run_setup=None, proposal_scale_factor=2, NstarMax=10,
Nsegs0=None, create_plots=True, log_table=True, gen_tex_table=True,
fig=None, axes=None, return_fig=False, window=50, **kwargs):
""" Run the follow-up with the given run_setup
Parameters
----------
run_setup: list of tuples, optional
proposal_scale_factor: float
The proposal scale factor used by the sampler, see Goodman & Weare
(2010). If the acceptance fraction is too low, you can raise it by
decreasing the a parameter; and if it is too high, you can reduce
it by increasing the a parameter [Foreman-Mackay (2013)].
create_plots: bool
If true, save trace plots of the walkers
window: int
The minimum number of autocorrelation times needed to trust the
result when estimating the autocorrelation time (see
ptemcee.Sampler.get_autocorr_time for further details.
**kwargs:
Passed to _plot_walkers to control the figures
"""
self.nsegs = 1
self._set_likelihoodcoef()
self._initiate_search_object()
run_setup = self.init_run_setup(
run_setup, NstarMax=NstarMax, Nsegs0=Nsegs0, log_table=log_table,
gen_tex_table=gen_tex_table)
self.run_setup = run_setup
self._estimate_run_time()
self.old_data_is_okay_to_use = self._check_old_data_is_okay_to_use()
if self.old_data_is_okay_to_use is True:
logging.warning('Using saved data from {}'.format(
self.pickle_path))
d = self.get_saved_data_dictionary()
self.samples = d['samples']
self.lnprobs = d['lnprobs']
self.lnlikes = d['lnlikes']
self.all_lnlikelihood = d['all_lnlikelihood']
self.chain = d['chain']
self.nsegs = run_setup[-1][1]
return
nsteps_total = 0
for j, ((nburn, nprod), nseg, reset_p0) in enumerate(run_setup):
if j == 0:
p0 = self._generate_initial_p0()
p0 = self._apply_corrections_to_p0(p0)
elif reset_p0:
p0 = self._get_new_p0(sampler)
p0 = self._apply_corrections_to_p0(p0)
# self._check_initial_points(p0)
else:
p0 = sampler.chain[:, :, -1, :]
self.nsegs = nseg
self._set_likelihoodcoef()
self.search.nsegs = nseg
self.update_search_object()
self.search.init_semicoherent_parameters()
sampler = PTSampler(
ntemps=self.ntemps, nwalkers=self.nwalkers, dim=self.ndim,
logl=self.logl, logp=self.logp,
logpargs=(self.theta_prior, self.theta_keys, self.search),
loglargs=(self.search,), betas=self.betas,
a=proposal_scale_factor)
Tcoh = (self.maxStartTime-self.minStartTime)/nseg/86400.
logging.info(('Running {}/{} with {} steps and {} nsegs '
'(Tcoh={:1.2f} days)').format(
j+1, len(run_setup), (nburn, nprod), nseg, Tcoh))
sampler = self._run_sampler(sampler, p0, nburn=nburn, nprod=nprod,
window=window)
logging.info('Max detection statistic of run was {}'.format(
np.max(sampler.loglikelihood)))
if create_plots:
fig, axes = self._plot_walkers(
sampler, fig=fig, axes=axes,
nprod=nprod, xoffset=nsteps_total, **kwargs)
for ax in axes[:self.ndim]:
ax.axvline(nsteps_total, color='k', ls='--', lw=0.25)
nsteps_total += nburn+nprod
if create_plots:
nstep_list = np.array(
[el[0][0] for el in run_setup] + [run_setup[-1][0][1]])
mids = np.cumsum(nstep_list) - nstep_list/2
mid_labels = ['{:1.0f}'.format(i) for i in np.arange(0, len(mids)-1)]
mid_labels += ['Production']
for ax in axes[:self.ndim]:
axy = ax.twiny()
axy.tick_params(pad=-10, direction='in', axis='x', which='major')
axy.minorticks_off()
axy.set_xlim(ax.get_xlim())
axy.set_xticks(mids)
axy.set_xticklabels(mid_labels)
samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
lnprobs = sampler.logprobability[0, :, nburn:].reshape((-1))
lnlikes = sampler.loglikelihood[0, :, nburn:].reshape((-1))
all_lnlikelihood = sampler.loglikelihood
self.samples = samples
self.lnprobs = lnprobs
self.lnlikes = lnlikes
self.all_lnlikelihood = all_lnlikelihood
self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood,
sampler.chain)
if create_plots:
try:
fig.tight_layout()
except (ValueError, RuntimeError) as e:
logging.warning('Tight layout encountered {}'.format(e))
if return_fig:
return fig, axes
else:
fig.savefig('{}/{}_walkers.png'.format(
self.outdir, self.label))
class MCMCTransientSearch(MCMCSearch):
""" MCMC search for a transient signal using ComputeFstat
See parent MCMCSearch for a list of all additional parameters, here we list
only the additional init parameters of this class.
"""
symbol_dictionary = dict(
F0='$f$', F1='$\dot{f}$', F2='$\ddot{f}$',
Alpha=r'$\alpha$', Delta='$\delta$',
transient_tstart='$t_\mathrm{start}$', transient_duration='$\Delta T$')
unit_dictionary = dict(
F0='Hz', F1='Hz/s', F2='Hz/s$^2$', Alpha=r'rad', Delta='rad',
transient_tstart='s', transient_duration='s')
transform_dictionary = dict(
transient_duration={'multiplier': 1/86400.,
'unit': 'day',
'symbol': 'Transient duration'},
transient_tstart={
'multiplier': 1/86400.,
'subtractor': 'minStartTime',
'unit': 'day',
'label': 'Transient start-time \n days after minStartTime'}
)
def _initiate_search_object(self):
logging.info('Setting up search object')
if not self.transientWindowType:
self.transientWindowType = 'rect'
self.search = core.ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
detectors=self.detectors,
transientWindowType=self.transientWindowType,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, binary=self.binary,
injectSources=self.injectSources,
tCWFstatMapVersion=self.tCWFstatMapVersion)
if self.minStartTime is None:
self.minStartTime = self.search.minStartTime
if self.maxStartTime is None:
self.maxStartTime = self.search.maxStartTime
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
in_theta = copy.copy(self.fixed_theta)
in_theta[1] = in_theta[0] + in_theta[1]
if in_theta[1] > self.maxStartTime:
return -np.inf
twoF = search.get_fullycoherent_twoF(*in_theta)
return twoF/2.0 + self.likelihoodcoef
def _unpack_input_theta(self):
full_theta_keys = ['transient_tstart',
'transient_duration', 'F0', 'F1', 'F2', 'Alpha',
'Delta']
if self.binary:
full_theta_keys += [
'asini', 'period', 'ecc', 'tp', 'argp']
full_theta_keys_copy = copy.copy(full_theta_keys)
full_theta_symbols = [r'$t_{\rm start}$', r'$\Delta T$',
'$f$', '$\dot{f}$', '$\ddot{f}$',
r'$\alpha$', r'$\delta$']
if self.binary:
full_theta_symbols += [
'asini', 'period', 'period', 'ecc', 'tp', 'argp']
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]