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gstlal-inspiral
0.4.2
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gstlal-inspiral
0.4.2
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Public Member Functions | |
| def | __init__ |
| def | __iadd__ |
| def | get_snr_joint_pdf |
| def | coinc_params |
| def | lnP_signal |
| def | add_background_prior |
| def | add_instrument_combination_counts |
| def | add_foreground_snrchi_prior |
| def | populate_prob_of_instruments_given_signal |
| def | pdf_from_rates_instruments |
| def | pdf_from_rates_snrchi2 |
| def | from_xml |
| def | to_xml |
| def | count_above_threshold |
| def | count_above_threshold |
| def | Pinstrument_noise |
| def | Pinstrument_signal |
| def | random_params |
| def | random_sim_params |
| def | joint_pdf_of_snrs |
Static Public Member Functions | |
| def | quantize_horizon_distances |
Public Attributes | |
| horizon_history | |
| pdf_from_rates_func | |
| def far.ThincaCoincParamsDistributions.count_above_threshold | ( | self | ) |
| def far.ThincaCoincParamsDistributions.joint_pdf_of_snrs | ( | cls, | |
| instruments, | |||
| inst_horiz_mapping, | |||
n_samples = 80000, |
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bins = rate.ATanLogarithmicBins(3.6, 120., |
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progressbar = None |
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| ) |
Return a BinnedArray representing the joint probability density of measuring a set of SNRs from a network of instruments. The inst_horiz_mapping is a dictionary mapping instrument name (e.g., "H1") to horizon distance (arbitrary units). n_samples is the number of lines over which to calculate the density in the SNR space. The axes of the PDF correspond to the instruments in alphabetical order.
| def far.ThincaCoincParamsDistributions.random_params | ( | self, | |
| instruments | |||
| ) |
Generator that yields an endless sequence of randomly
generated parameter dictionaries for the given instruments.
NOTE: the parameters will be within the domain of the
repsective binnings but are not drawn from the PDF stored
in those binnings --- this is not an MCMC style sampler.
The return value is a tuple, the first element of which is
the random parameter dictionary and the second is the
natural logarithm (up to an arbitrary constant) of the PDF
from which the parameters have been drawn evaluated at the
co-ordinates in the parameter dictionary.
Example:
>>> x = iter(ThincaCoincParamsDistributions().random_params(("H1", "L1", "V1")))
>>> x.next()
See also:
random_sim_params()
The sequence is suitable for input to the
pylal.snglcoinc.LnLikelihoodRatio.samples() log likelihood
ratio generator.
| def far.ThincaCoincParamsDistributions.random_sim_params | ( | self, | |
| sim, | |||
horizon_distance = None, |
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snr_min = None, |
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snr_efficiency = 1.0 |
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| ) |
Generator that yields an endless sequence of randomly generated parameter dictionaries drawn from the distribution of parameters expected for the given injection, which is an instance of a SimInspiral table row object (see glue.ligolw.lsctables.SimInspiral for more information). The return value is a tuple, the first element of which is the random parameter dictionary and the second is 0. See also: random_params() The sequence is suitable for input to the pylal.snglcoinc.LnLikelihoodRatio.samples() log likelihood ratio generator. Bugs: The second element in each tuple in the sequence is merely a placeholder, not the natural logarithm of the PDF from which the sample has been drawn, as in the case of random_params(). Therefore, when used in combination with pylal.snglcoinc.LnLikelihoodRatio.samples(), the two probability densities computed and returned by that generator along with each log likelihood ratio value will simply be the probability densities of the signal and noise populations at that point in parameter space. They cannot be used to form an importance weighted sampler of the log likeklihood ratios.
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static |
1.8.1.2