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add ability to cache detector response to marginalize time model and us… #4806

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Jul 22, 2024
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add ability to cache detector response to marginalize time model and us… #4806

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add ability to cache detect response to marginalize time model and us…
ahnitz Jun 28, 2024
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ahnitz Jul 2, 2024
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ahnitz Jul 22, 2024
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ahnitz Jul 22, 2024
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ahnitz Jul 22, 2024
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5 changes: 4 additions & 1 deletion examples/inference/margtime/margtime.ini
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,11 @@
name = marginalized_time
low-frequency-cutoff = 30.0

# This is the sample rate used for the model and marginalization
sample_rate = 4096

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I updated the example to make explicit use of this feature.

marginalize_vector_params = tc, ra, dec, polarization
marginalize_vector_samples = 500
marginalize_vector_samples = 2000

; You shouldn't use phase marginalization if the approximant has
; higher-order modes
Expand Down
3 changes: 2 additions & 1 deletion examples/inference/margtime/run.sh
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
OMP_NUM_THREADS=1 pycbc_inference \
--config-file `dirname "$0"`/margtime.ini \
--nprocesses 2 \
--nprocesses 1 \
--processing-scheme mkl \
--output-file marg_150914.hdf \
--seed 0 \
Expand All @@ -23,4 +23,5 @@ pycbc_inference_plot_posterior \
"primary_mass(mass1, mass2) / (1 + redshift(distance)):srcmass1" \
"secondary_mass(mass1, mass2) / (1 + redshift(distance)):srcmass2" \
ra dec tc inclination coa_phase polarization distance \
--vmin 23.2 \
--z-arg snr
43 changes: 33 additions & 10 deletions pycbc/inference/models/marginalized_gaussian_noise.py
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Expand Up @@ -19,6 +19,7 @@
"""

import itertools
import logging
import numpy
from scipy import special

Expand Down Expand Up @@ -207,8 +208,10 @@ class MarginalizedTime(DistMarg, BaseGaussianNoise):
def __init__(self, variable_params,
data, low_frequency_cutoff, psds=None,
high_frequency_cutoff=None, normalize=False,
sample_rate=None,
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What's the advantage of doing this instead of just increasing the sample rate of the data? Also, this might need some documentation, or maybe change the name of the argument to make it clearer what it is. People are going to think this is the data sample rate otherwise.

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Increasing the sample rate of the data means that you need to do data conditioning and parts of the generation at a higher sample rate. That's only useful if you actually care about the frequency band at higher frequencies. If you just need better timing resolution that would be extremely slow and memory intensive.

I think the separation is clear as the data sample rate is in the data section. This is only about the model itself. BTW, we already have this distinction for the heterodyne likelihoods when doing sky marginalization. This was just advancing the same interface / feature to the more regular model

**kwargs):

self.sample_rate = float(sample_rate)
self.kwargs = kwargs
variable_params, kwargs = self.setup_marginalization(
variable_params,
Expand Down Expand Up @@ -241,6 +244,14 @@ def __init__(self, variable_params,

self.dets = {}

if sample_rate is not None:
for ifo in self.data:
if self.sample_rate < self.data[ifo].sample_rate:
raise ValueError("Model sample rate was set less than the"
" data. ")
logging.info("Using %s sample rate for marginalization",
sample_rate)

def _nowaveform_loglr(self):
"""Convenience function to set loglr values if no waveform generated.
"""
Expand Down Expand Up @@ -296,8 +307,15 @@ def _loglr(self):
hp[self._kmin[det]:kmax] *= self._weight[det][slc]
hc[self._kmin[det]:kmax] *= self._weight[det][slc]

hp.resize(len(self._whitened_data[det]))
hc.resize(len(self._whitened_data[det]))
# Use a higher sample rate if requested
if self.sample_rate is not None:
tlen = int(round(self.sample_rate *
self.whitened_data[det].duration))
flen = tlen // 2 + 1
hp.resize(flen)
hc.resize(flen)
self._whitened_data[det].resize(flen)

cplx_hpd[det], _, _ = matched_filter_core(
hp,
self._whitened_data[det],
Expand Down Expand Up @@ -325,15 +343,20 @@ def _loglr(self):
for det in wfs:
if det not in self.dets:
self.dets[det] = Detector(det)
fp, fc = self.dets[det].antenna_pattern(
params['ra'],
params['dec'],
params['polarization'],
params['tc'])
dt = self.dets[det].time_delay_from_earth_center(params['ra'],
params['dec'],
params['tc'])

if self.precalc_antenna_factors:
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How is precalc_antenna_factors set to True in a run? Is this an argument for the model? I couldn't find where it would be set.

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It's already handled by the marginalization parent class. The features were implemented first for other cases, and this just extends the use to the marginalized time model.

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Ok, but I was asking how you set that in the config file and how that's passed to the model initialization. I couldn't figure it out from looking at the code, although I didn't dig too deep.

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Ah, it's automatically populated if you do sky marginalization so it's the same set of settings really. These factors are calculated in any case when the sky grid is set up.

fp, fc, dt = self.get_precalc_antenna_factors(det)
else:
fp, fc = self.dets[det].antenna_pattern(
params['ra'],
params['dec'],
params['polarization'],
params['tc'])
dt = self.dets[det].time_delay_from_earth_center(params['ra'],
params['dec'],
params['tc'])
dtc = params['tc'] + dt

cplx_hd = fp * cplx_hpd[det].at_time(dtc,
interpolate='quadratic')
cplx_hd += fc * cplx_hcd[det].at_time(dtc,
Expand Down
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