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lscsoft
bilby
Commits
286011fd
Commit
286011fd
authored
May 17, 2018
by
Colm Talbot
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bbh parameter filling uses likelihood rather than wg and ifos
parent
6e7a6c13
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1
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1 changed file
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38 additions
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32 deletions
+38
-32
tupak/conversion.py
tupak/conversion.py
+38
-32
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tupak/conversion.py
View file @
286011fd
...
...
@@ -98,7 +98,7 @@ def convert_to_lal_binary_black_hole_parameters(parameters, search_keys, remove=
return
ignored_keys
def
generate_all_bbh_parameters
(
sample
,
waveform_generator
=
None
,
interferometers
=
None
,
priors
=
None
):
def
generate_all_bbh_parameters
(
sample
,
likelihood
=
None
,
priors
=
None
):
"""
From either a single sample or a set of samples fill in all missing BBH parameters, in place.
...
...
@@ -106,24 +106,21 @@ def generate_all_bbh_parameters(sample, waveform_generator=None, interferometers
----------
sample: dict or pandas.DataFrame
Samples to fill in with extra parameters, this may be either an injection or posterior samples.
waveform_generator: tupak.waveform_generator.WaveformGenerator, optional
If the waveform generator and interferometers are provided, the SNRs will be recorded.
interferometers: list, optional
List of tupak.detector.Interferometer objects.
If the waveform generator and interferometers are provided, the SNRs will be recorded.
likelihood: tupak.likelihood.Likelihood
Likelihood used for sampling, used for waveform and likelihood.interferometers.
priors: dict, optional
Dictionary of prior objects, used to fill in non-sampled parameters.
"""
if
waveform_generator
is
not
None
:
sample
[
'reference_frequency'
]
=
waveform_generator
.
parameters
[
'reference_frequency'
]
sample
[
'waveform_approximant'
]
=
waveform_generator
.
parameters
[
'waveform_approximant'
]
if
likelihood
is
not
None
:
sample
[
'reference_frequency'
]
=
likelihood
.
waveform_generator
.
parameters
[
'reference_frequency'
]
sample
[
'waveform_approximant'
]
=
likelihood
.
waveform_generator
.
parameters
[
'waveform_approximant'
]
fill_from_fixed_priors
(
sample
,
priors
)
convert_to_lal_binary_black_hole_parameters
(
sample
,
[
key
for
key
in
sample
.
keys
()],
remove
=
False
)
generate_non_standard_parameters
(
sample
)
generate_component_spins
(
sample
)
compute_snrs
(
sample
,
waveform_generator
,
interferometers
)
compute_snrs
(
sample
,
likelihood
)
def
fill_from_fixed_priors
(
sample
,
priors
):
...
...
@@ -194,39 +191,48 @@ def generate_component_spins(sample):
logging
.
warning
(
"Component spin extraction failed."
)
def
compute_snrs
(
sample
,
waveform_generator
,
interferometers
):
def
compute_snrs
(
sample
,
likelihood
):
"""Compute the optimal and matched filter snrs of all posterior samples."""
temp_sample
=
sample
.
copy
()
if
waveform_generator
is
not
None
and
interferometers
is
not
None
:
temp_sample
=
sample
if
likelihood
is
not
None
:
if
isinstance
(
temp_sample
,
dict
):
for
key
in
waveform_generator
.
parameters
.
keys
():
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
]
signal_polarizations
=
waveform_generator
.
frequency_domain_strain
()
for
interferometer
in
interferometers
:
signal
=
interferometer
.
get_detector_response
(
signal_polarizations
,
waveform_generator
.
parameters
)
for
key
in
likelihood
.
waveform_generator
.
parameters
.
keys
():
likelihood
.
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
]
signal_polarizations
=
likelihood
.
waveform_generator
.
frequency_domain_strain
()
for
interferometer
in
likelihood
.
interferometers
:
signal
=
interferometer
.
get_detector_response
(
signal_polarizations
,
likelihood
.
waveform_generator
.
parameters
)
sample
[
'{}_matched_filter_snr'
.
format
(
interferometer
.
name
)]
=
\
tupak
.
utils
.
matched_filter_snr_squared
(
signal
,
interferometer
,
waveform_generator
.
time_duration
)
**
0.5
likelihood
.
waveform_generator
.
time_duration
)
**
0.5
sample
[
'{}_optimal_snr'
.
format
(
interferometer
.
name
)]
=
tupak
.
utils
.
optimal_snr_squared
(
signal
,
interferometer
,
waveform_generator
.
time_duration
)
**
0.5
signal
,
interferometer
,
likelihood
.
waveform_generator
.
time_duration
)
**
0.5
else
:
logging
.
info
(
'Computing SNRs for every sample, this may take some time.'
)
matched_filter_snrs
=
{
interferometer
.
name
:
[]
for
interferometer
in
interferometers
}
optimal_snrs
=
{
interferometer
.
name
:
[]
for
interferometer
in
interferometers
}
all_interferometers
=
likelihood
.
interferometers
matched_filter_snrs
=
{
interferometer
.
name
:
[]
for
interferometer
in
all_interferometers
}
optimal_snrs
=
{
interferometer
.
name
:
[]
for
interferometer
in
all_interferometers
}
likelihoods
=
{
interferometer
.
name
:
[]
for
interferometer
in
all_interferometers
}
for
ii
in
range
(
len
(
temp_sample
)):
for
key
in
set
(
temp_sample
.
keys
()).
intersection
(
waveform_generator
.
parameters
.
keys
()):
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
][
ii
]
for
key
in
waveform_generator
.
search_parameter_keys
:
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
][
ii
]
signal_polarizations
=
waveform_generator
.
frequency_domain_strain
()
for
interferometer
in
interferometers
:
signal
=
interferometer
.
get_detector_response
(
signal_polarizations
,
waveform_generator
.
parameters
)
for
key
in
set
(
temp_sample
.
keys
()).
intersection
(
likelihood
.
waveform_generator
.
parameters
.
keys
()):
likelihood
.
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
][
ii
]
for
key
in
likelihood
.
waveform_generator
.
sampling_parameter_keys
:
likelihood
.
waveform_generator
.
parameters
[
key
]
=
temp_sample
[
key
][
ii
]
signal_polarizations
=
likelihood
.
waveform_generator
.
frequency_domain_strain
()
for
interferometer
in
all_interferometers
:
signal
=
interferometer
.
get_detector_response
(
signal_polarizations
,
likelihood
.
waveform_generator
.
parameters
)
matched_filter_snrs
[
interferometer
.
name
].
append
(
tupak
.
utils
.
matched_filter_snr_squared
(
signal
,
interferometer
,
waveform_generator
.
time_duration
)
**
0.5
)
signal
,
interferometer
,
likelihood
.
waveform_generator
.
time_duration
)
**
0.5
)
optimal_snrs
[
interferometer
.
name
].
append
(
tupak
.
utils
.
optimal_snr_squared
(
signal
,
interferometer
,
waveform_generator
.
time_duration
)
**
0.5
)
for
interferometer
in
interferometers
:
signal
,
interferometer
,
likelihood
.
waveform_generator
.
time_duration
)
**
0.5
)
for
interferometer
in
likelihood
.
interferometers
:
sample
[
'{}_matched_filter_snr'
.
format
(
interferometer
.
name
)]
=
matched_filter_snrs
[
interferometer
.
name
]
sample
[
'{}_optimal_snr'
.
format
(
interferometer
.
name
)]
=
optimal_snrs
[
interferometer
.
name
]
likelihood
.
interferometers
=
all_interferometers
print
([
interferometer
.
name
for
interferometer
in
likelihood
.
interferometers
])
else
:
logging
.
info
(
'Not computing SNRs.'
)
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