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pbilby_gen [2022/08/19 14:16]
theoastro 		pbilby_gen [2022/08/30 14:32] (current)
theoastro
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 ===The Basic Idea=== ===The Basic Idea===
-To run efficiently, parallel bilby requires some preparatory work, just as efficient construction work requires some preparatory logistical planning. Fortunately, you do not need to do this on your own. Instead, you can outsource this task to a ''parallel_bilby_generation'' command, which will meet your initial demands if they are well specified in a configuration file. You will find this mostly referred to as ''config.ini''. It should be used to specify non-[[https://lscsoft.docs.ligo.org/parallel_bilby/data_generation.html|default arguments]] that are taken by ''parallel_bilby_generation''. Several default values are taken from [[https://lscsoft.docs.ligo.org/bilby_pipe/master/user-interface.html#Positional%20Arguments|bilby_pipe]], too. Find below some recommended configurations that should comprise your ''.ini'', but see the ''parallel_bilby''-[https://lscsoft.docs.ligo.org/parallel_bilby/examples.html|docs]], too.+To run efficiently, parallel bilby requires some preparatory work, just as efficient construction work requires some preparatory logistical planning. Fortunately, you do not need to do this on your own. Instead, you can outsource this task to a ''parallel_bilby_generation'' command, which will meet your initial demands if they are well specified in a configuration file. You will find this mostly referred to as ''config.ini''. It should be used to specify non-[[https://lscsoft.docs.ligo.org/parallel_bilby/data_generation.html|default arguments]] that are taken by ''parallel_bilby_generation''. Several default values are taken from [[https://lscsoft.docs.ligo.org/bilby_pipe/master/user-interface.html#Positional%20Arguments|bilby_pipe]], too. Find below some recommended configurations that should comprise your ''.ini'', but see the ''parallel_bilby''-[[https://lscsoft.docs.ligo.org/parallel_bilby/examples.html|docs]], too.
  
 ===Data Generation arguments=== ===Data Generation arguments===
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 You likely have reasons to ascribe a higher prior belief to some of the EoSs. This can be expressed by using sampling weights. They need again to be stored in a single txt-file ''[YOUR_EOS_WEIGHTS].dat'' where the $i$-th line (only) contains the weight of the $i$-th EoS.  Because ''bilby'' does not know how to handle truly discrete quantities, we have to find a workaround, using an interpolation. In order to minimise the effects of interpolation-increased/reduced weights, you need to reorder the the EoS as well by increasing/decreasing weights and provide the argument. You likely have reasons to ascribe a higher prior belief to some of the EoSs. This can be expressed by using sampling weights. They need again to be stored in a single txt-file ''[YOUR_EOS_WEIGHTS].dat'' where the $i$-th line (only) contains the weight of the $i$-th EoS.  Because ''bilby'' does not know how to handle truly discrete quantities, we have to find a workaround, using an interpolation. In order to minimise the effects of interpolation-increased/reduced weights, you need to reorder the the EoS as well by increasing/decreasing weights and provide the argument.
   path-to-eos-weight =[YOUR_ORDERED_EOS_WEIGHTS.dat/txt]   path-to-eos-weight =[YOUR_ORDERED_EOS_WEIGHTS.dat/txt]
-Find some routines to help you with this task as well as example EoS- and weight-files in this ''zip''-directory.+Find some routines to help you with this task as well as example EoS- and weight-files in this ''zip''-{{:eos_routines.zip|directory}}.
  
-################################################################################ +=== Injection arguments=== 
-## Injection arguments +These are required if you do not provide data to be analysed but rather wish that ''bilby'' generates it for you, based upon some parameters you specify. 
-################################################################################+  injection = True  
 +  gaussian-noise = True 
 +  injection-numbers=[0] 
 +  n-simulation = 1 
 +  injection-file =[YOUR_INJECTION].json 
 +The injected parameters in ''injection-file'' are used to create a waveform model that is superseeded by noise. 
 +If you are interested in optimal conditions, you might also want to use ''zero-noise'' instead of ''gaussian-noise''. 
 +The ''injection-numbers'' specify the rows to use in your ''injection-file'', making use of the ordinary python slicing-syntax. ''n-simulation'' must match the total number of injections , that is to say ''n-simulation= len(injection-numbers)''.  
  
-injection = True 
-gaussian-noise = True 
-n-simulation = 1 
-injection-file =aux_large_injection.json 
  
 === Waveform arguments=== === Waveform arguments===
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   catch-waveform-errors=True   catch-waveform-errors=True
  
-################################################################################ +===sampler arguments===
-## sampler arguments +
-################################################################################ +
 nact = 30 nact = 30
 nlive = 2048 nlive = 2048
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 #generation-seed = DUMMY #generation-seed = DUMMY
  
-################################################################################ 
-## slurm arguments 
-################################################################################ 
- 
-nodes = 24 
-ntasks-per-node = 32 
-time = 48:00:00 
 ===Job Submission arguments=== ===Job Submission arguments===
   label = inj   label = inj
Last modified: le 2022/08/19 14:16

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