bilby issueshttps://git.ligo.org/lscsoft/bilby/-/issues2019-02-08T02:43:58Zhttps://git.ligo.org/lscsoft/bilby/-/issues/282Save emcee chain during the run2019-02-08T02:43:58ZColm Talbotcolm.talbot@ligo.orgSave emcee chain during the runCurrently, if an emcee job crashes or is killed the chain is lost. There's a suggested method for writing each iteration to disk as it's generated [here](http://dfm.io/emcee/current/user/advanced/#incrementally-saving-progress). We shoul...Currently, if an emcee job crashes or is killed the chain is lost. There's a suggested method for writing each iteration to disk as it's generated [here](http://dfm.io/emcee/current/user/advanced/#incrementally-saving-progress). We should definitely do something like this. Preferably with the ability to resume from an old chain.https://git.ligo.org/lscsoft/bilby/-/issues/316emcee pre-release version does not run2019-02-21T03:45:41ZRhys Greenemcee pre-release version does not runUsing the emcee pre-release version
and running
```
emcee_result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='emcee' )
```
raised the error:
```python
home/c1572221/src/bilby/bilby/core/sampler/emcee.pyc ...Using the emcee pre-release version
and running
```
emcee_result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='emcee' )
```
raised the error:
```python
home/c1572221/src/bilby/bilby/core/sampler/emcee.pyc in run_sampler(self)
194 for sample in tqdm(sampler.sample(**self.sampler_function_kwargs),
195 total=self.nsteps):
--> 196 points = np.hstack([sample[0], np.array(sample[3])])
197 # import IPython; IPython.embed()
198 with open(out_file, "a") as ff:
TypeError: 'State' object does not support indexing
```0.4.1https://git.ligo.org/lscsoft/bilby/-/issues/315Fake sampler for likelihood consistency check2019-03-21T10:24:55ZCarl-Johan HasterFake sampler for likelihood consistency checkImplement a "fake sampler" which reads in a set of parameters from a file, and then computes the likelihood for those paramters.
This would be a really useful consistency check between the ROQ and nonROQ likelihood functions, as well as ...Implement a "fake sampler" which reads in a set of parameters from a file, and then computes the likelihood for those paramters.
This would be a really useful consistency check between the ROQ and nonROQ likelihood functions, as well as an useful check for when the likelihood function is updated/appended (as, in order to accept a merge request to the likelihood function it needs to sufficiently reproduce the likelihood values from a fiducial set of parameter samples)FutureMichael PuerrerMichael Puerrerhttps://git.ligo.org/lscsoft/bilby/-/issues/243improving CPNest integration2019-04-23T06:24:09ZVivien Raymondimproving CPNest integrationAdd cpnest https://github.com/johnveitch/cpnest to bilby. Maybe using something similar to https://git.ligo.org/john-veitch/lalsuite/blob/liwrapper/lalinference/python/lalinference_cpnest.py ...Add cpnest https://github.com/johnveitch/cpnest to bilby. Maybe using something similar to https://git.ligo.org/john-veitch/lalsuite/blob/liwrapper/lalinference/python/lalinference_cpnest.py ...FutureJohn Douglas Veitchjohn.veitch@ligo.orgJohn Douglas Veitchjohn.veitch@ligo.orghttps://git.ligo.org/lscsoft/bilby/-/issues/275Add the "dynamic" nested sampler from `dynesty`.2019-04-23T06:28:37ZAtul DivakarlaAdd the "dynamic" nested sampler from `dynesty`.Is it possible to include 'dynamic' input in the dynesty sampler? It helps to get better posterior distributions, but currently if I write it turned on as:
result = bilby.run_sampler(
likelihood=likelihood, priors=prior, sampler='dy...Is it possible to include 'dynamic' input in the dynesty sampler? It helps to get better posterior distributions, but currently if I write it turned on as:
result = bilby.run_sampler(
likelihood=likelihood, priors=prior, sampler='dynesty', nlive=500,
injection_parameters=injection_param, outdir=outdir, label=label,
bound='multi', sample='rwalk', walks=25, verbose=True, **dynamic=True**)
I get the error:
09:47 **bilby WARNING : Supplied argument 'dynamic' not an argument of 'Dynesty', removing.**
09:47 bilby INFO : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'check_point_delta_t': 600, 'nlive': 500, 'first_update': None, 'npdim': None, 'rstate': None, 'queue_size': None, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': None, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 2.0, 'facc': 0.5, 'slices': 5, 'walks': 25, 'update_interval': 300, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x1a3a11acf8>>, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': True}Futurehttps://git.ligo.org/lscsoft/bilby/-/issues/325Add support for ROQ rescaling2019-05-08T02:35:14ZMichael PuerrerAdd support for ROQ rescalingRescaling of bases as in table I of https://arxiv.org/pdf/1604.08253.pdf.Rescaling of bases as in table I of https://arxiv.org/pdf/1604.08253.pdf.Carl-Johan HasterCarl-Johan Hasterhttps://git.ligo.org/lscsoft/bilby/-/issues/343Combine multiple runs2019-05-23T07:55:34ZColm Talbotcolm.talbot@ligo.orgCombine multiple runsWe need a method for combining the output of multiple runs, this will need to be treated a little differently for different samplers.
Hopefully, outputs from nested samplers can all be combined in this way, if we wish. Similar for MCMC....We need a method for combining the output of multiple runs, this will need to be treated a little differently for different samplers.
Hopefully, outputs from nested samplers can all be combined in this way, if we wish. Similar for MCMC. I'm not sure how best, if at all to combine MCMC runs with nest runs.
Related is thinning for auto-correlation length for MCMC samplers, which may or may not be done currently.
Ideally, this will be possible from the results files.https://git.ligo.org/lscsoft/bilby/-/issues/367Generate initial live points in dynesty2019-05-27T04:09:58ZColm Talbotcolm.talbot@ligo.orgGenerate initial live points in dynestyDynesty provides the option to provide the initial live points to the sampler.
For constrained priors doing this will increase the initial efficiency of the run as you won't place any samples out of the allowed range.
Note that the ini...Dynesty provides the option to provide the initial live points to the sampler.
For constrained priors doing this will increase the initial efficiency of the run as you won't place any samples out of the allowed range.
Note that the initial samples **must be drawn from the prior**.0.5.1https://git.ligo.org/lscsoft/bilby/-/issues/245Proposal library2019-06-19T14:41:01ZVivien RaymondProposal libraryin the `gw` package, we want to have a set of GW-specific jump proposals which can then be used by the samplers able to. The first step of this work is to define a structure and API of such a proposal library.in the `gw` package, we want to have a set of GW-specific jump proposals which can then be used by the samplers able to. The first step of this work is to define a structure and API of such a proposal library.FutureChristopher BerryMatthew CarneyChristopher Berryhttps://git.ligo.org/lscsoft/bilby/-/issues/349CPNest failed to checkpoint in HTCondor2019-06-24T10:55:02ZTsun-Ho PangCPNest failed to checkpoint in HTCondorI have been running a parameter estimation run with CPNest as the sampler on the cluster (CIT) as a condor job. After the job got idle and rerun, all the progress are lost. And there are no .pkl output at the outdir/cpnest/.I have been running a parameter estimation run with CPNest as the sampler on the cluster (CIT) as a condor job. After the job got idle and rerun, all the progress are lost. And there are no .pkl output at the outdir/cpnest/.https://git.ligo.org/lscsoft/bilby/-/issues/303Add detecor frame sky-parametrization2019-06-25T03:51:09ZCarl-Johan HasterAdd detecor frame sky-parametrizationIt's convenient to sample in this parametrization of the sky-coordinates
This is implemented in `LALInference` in https://git.ligo.org/lscsoft/lalsuite/blob/master/lalinference/src/DetectorFixedSkyCoords.cIt's convenient to sample in this parametrization of the sky-coordinates
This is implemented in `LALInference` in https://git.ligo.org/lscsoft/lalsuite/blob/master/lalinference/src/DetectorFixedSkyCoords.cFutureJohn Douglas Veitchjohn.veitch@ligo.orgJohn Douglas Veitchjohn.veitch@ligo.orghttps://git.ligo.org/lscsoft/bilby/-/issues/294Stopping conditions to produce N independent samples2019-06-25T04:04:59ZChristopher BerryStopping conditions to produce N independent samplesInvestigate how different samplers produce independent samples, so we can aim to produce a desired number of samples per runInvestigate how different samplers produce independent samples, so we can aim to produce a desired number of samples per runFutureChristopher BerryChristopher Berryhttps://git.ligo.org/lscsoft/bilby/-/issues/311Determine default for sampler, sampler settings, jump proposals2019-06-25T04:09:18ZEric ThraneDetermine default for sampler, sampler settings, jump proposalsThis work is already underway, but I'm creating a git issue to link to the review readiness [wiki](https://git.ligo.org/lscsoft/bilby/wikis/review_script).
@gregory.ashton, @christopher\-berry, @paul\-lasky, @vivienThis work is already underway, but I'm creating a git issue to link to the review readiness [wiki](https://git.ligo.org/lscsoft/bilby/wikis/review_script).
@gregory.ashton, @christopher\-berry, @paul\-lasky, @vivien1.0.0Christopher BerryChristopher Berryhttps://git.ligo.org/lscsoft/bilby/-/issues/310Nestle steps argument doesn't work2019-07-26T05:56:21ZRhys GreenNestle steps argument doesn't workIf using the method = 'classic' argument, there should be an option to set the number of mcmc steps between samples, this is currently not accepted by bilbyIf using the method = 'classic' argument, there should be an option to set the number of mcmc steps between samples, this is currently not accepted by bilbyFuturehttps://git.ligo.org/lscsoft/bilby/-/issues/425Recent release of emcee (3.0.0) does not work within BILBY2019-11-07T21:01:34ZEthan PayneRecent release of emcee (3.0.0) does not work within BILBYThe recent update to emcee (to version 3.0.0) has broken its implementation within bilby. The traceback that appears when running a standard `gaussian_example.py` with emcee is:
```
Traceback (most recent call last):
File "gaussian_ex...The recent update to emcee (to version 3.0.0) has broken its implementation within bilby. The traceback that appears when running a standard `gaussian_example.py` with emcee is:
```
Traceback (most recent call last):
File "gaussian_example.py", line 53, in <module>
nwalkers=10, outdir=outdir, label=label, store=True, thin=100, live_dangerously=True)
File "/home/ethan/Research/code_libraries/bilby/bilby/core/sampler/__init__.py", line 175, in run_sampler
result = sampler.run_sampler()
File "/home/ethan/Research/code_libraries/bilby/bilby/core/sampler/emcee.py", line 345, in run_sampler
iterations -= self._previous_iterations
File "/home/ethan/Research/code_libraries/bilby/bilby/core/sampler/emcee.py", line 310, in _previous_iterations
return len(self.sampler.blobs)
File "/home/ethan/Research/dev3/lib/python3.6/site-packages/emcee/utils.py", line 26, in f
return func(*args, **kwargs)
File "/home/ethan/Research/dev3/lib/python3.6/site-packages/emcee/ensemble.py", line 491, in blobs
return self.get_blobs()
File "/home/ethan/Research/dev3/lib/python3.6/site-packages/emcee/ensemble.py", line 509, in get_blobs
return self.get_value("blobs", **kwargs)
File "/home/ethan/Research/dev3/lib/python3.6/site-packages/emcee/ensemble.py", line 524, in get_value
return self.backend.get_value(name, **kwargs)
File "/home/ethan/Research/dev3/lib/python3.6/site-packages/emcee/backends/backend.py", line 43, in get_value
raise AttributeError("you must run the sampler with "
AttributeError: you must run the sampler with 'store == True' before accessing the results
```
@moritz.huebner @colm.talbot
Cheers Gavin Wallace for spotting this issue :smile: https://git.ligo.org/lscsoft/bilby/-/issues/447Dynesty sampling breaks if a checkpoint is being written before all prior sam...2020-01-16T05:55:37ZMoritz HuebnerDynesty sampling breaks if a checkpoint is being written before all prior samples are drawnNoticed because `bilby_pipe` jobs were getting put on hold. If drawing from the prior is not finished by the time the `bilby_pipe` job reaches its `periodic_restart_time` and tries to checkpoint, the below error occurs because the sample...Noticed because `bilby_pipe` jobs were getting put on hold. If drawing from the prior is not finished by the time the `bilby_pipe` job reaches its `periodic_restart_time` and tries to checkpoint, the below error occurs because the sampler is not yet instantiated. This puts `bilby_pipe` jobs on hold.
Drawing from the prior can take a long time if the priors are non-sensical, e.g., if both `prior.minimum = 0` and `prior.maximum = 0`. Therefore, a good solution to this issue would be for `bilby` to flag if priors are weird.
```
Writing checkpoint file bilby_pipe_test/result/S190425_initial_analysis_public_data_bilby_pipe_test_data0_1240215503-0171_analysis_L1V1_dynesty_resume.pickle
Traceback (most recent call last):
File "/local/condor/execute/dir_126817/condor_exec.exe", line 11, in <module>
load_entry_point('bilby-pipe==0.3.8', 'console_scripts', 'bilby_pipe_analysis')()
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby_pipe-0.3.8-py3.6.egg/bilby_pipe/data_analysis.py", line 322, in main
analysis.run_sampler()
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby_pipe-0.3.8-py3.6.egg/bilby_pipe/data_analysis.py", line 299, in run_sampler
**self.sampler_kwargs,
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/__init__.py", line 176, in run_sampler
result = sampler.run_sampler()
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/dynesty.py", line 234, in run_sampler
self.kwargs['nlive']))
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/base_sampler.py", line 449, in get_initial_points_from_prior
if self.check_draw(theta, warning=False):
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/base_sampler.py", line 470, in check_draw
if np.isinf(self.log_prior(theta)):
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/base_sampler.py", line 378, in log_prior
return self.priors.ln_prob(params)
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/prior.py", line 418, in ln_prob
for key in sample], axis=axis)
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/prior.py", line 418, in <listcomp>
for key in sample], axis=axis)
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/prior.py", line 1663, in ln_prob
return -0.5 * ((self.mu - val) ** 2 / self.sigma ** 2 + np.log(2 * np.pi * self.sigma ** 2))
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/dynesty.py", line 415, in write_current_state_and_exit
self.write_current_state(plot=False)
File "/home/isobel.romero-shaw/anaconda3/lib/python3.6/site-packages/bilby-0.6.3-py3.6.egg/bilby/core/sampler/dynesty.py", line 444, in write_current_state
unit_cube_samples=self.sampler.saved_u,
AttributeError: 'Dynesty' object has no attribute 'sampler'
```0.6.4https://git.ligo.org/lscsoft/bilby/-/issues/454Dynesty restarting is inefficient.2020-02-04T21:45:41ZColm Talbotcolm.talbot@ligo.orgDynesty restarting is inefficient.I noticed that after !707 a job restarting after a checkpoint became hugely inefficient.
I think the issue is because we don't store the `dynesty` `bound` object in the pickle file. I'm going to see if adding that back can fix it.
<det...I noticed that after !707 a job restarting after a checkpoint became hugely inefficient.
I think the issue is because we don't store the `dynesty` `bound` object in the pickle file. I'm going to see if adding that back can fix it.
<details><summary>log output</summary>
```python
2045it [01:11, 12.65it/s, bound:0 nc:27 ncall:11016 eff:18.6% logz-ratio=1853.30+/-0.12 dlogz:1636.812>0.10]
2718it [02:04, 9.83it/s, bound:0 nc:25 ncall:20989 eff:12.9% logz-ratio=2254.53+/-0.12 dlogz:1234.631>0.10]
3099it [02:58, 2.89it/s, bound:1 nc:101 ncall:31101 eff:10.0% logz-ratio=2450.14+/-0.13 dlogz:1038.968>0.10]
3177it [04:05, 1.04it/s, bound:15 nc:155 ncall:41105 eff:7.7% logz-ratio=2491.49+/-0.13 dlogz:997.578>0.10]
3241it [05:16, 1.15s/it, bound:30 nc:172 ncall:51275 eff:6.3% logz-ratio=2528.86+/-0.13 dlogz:966.665>0.10]
3298it [06:29, 1.37s/it, bound:45 nc:162 ncall:61352 eff:5.4% logz-ratio=2560.72+/-0.13 dlogz:934.598>0.10]
3351it [07:42, 1.63s/it, bound:60 nc:281 ncall:71493 eff:4.7% logz-ratio=2577.80+/-0.13 dlogz:917.460>0.10]
3405it [08:55, 1.26s/it, bound:74 nc:181 ncall:81622 eff:4.2% logz-ratio=2601.54+/-0.13 dlogz:893.913>0.10]
3465it [10:06, 1.32s/it, bound:89 nc:192 ncall:91633 eff:3.8% logz-ratio=2624.83+/-0.13 dlogz:870.338>0.10]
3527it [11:20, 1.20s/it, bound:104 nc:155 ncall:101667 eff:3.5% logz-ratio=2649.51+/-0.13 dlogz:857.522>0.10]
3586it [12:33, 1.54s/it, bound:118 nc:271 ncall:111708 eff:3.2% logz-ratio=2674.93+/-0.13 dlogz:832.041>0.10]
3646it [13:45, 1.27s/it, bound:133 nc:165 ncall:121816 eff:3.0% logz-ratio=2697.53+/-0.13 dlogz:827.981>0.10]
3715it [14:59, 1.14s/it, bound:148 nc:196 ncall:131988 eff:2.8% logz-ratio=2731.60+/-0.14 dlogz:794.266>0.10]
3781it [16:12, 1.23s/it, bound:164 nc:236 ncall:142163 eff:2.7% logz-ratio=2758.13+/-0.13 dlogz:766.821>0.10]
3839it [17:25, 1.33s/it, bound:179 nc:171 ncall:152255 eff:2.5% logz-ratio=2779.89+/-0.14 dlogz:745.389>0.10]
3897it [18:37, 1.20s/it, bound:193 nc:168 ncall:162291 eff:2.4% logz-ratio=2802.56+/-0.13 dlogz:722.284>0.10]
3961it [19:46, 1.03s/it, bound:208 nc:184 ncall:172342 eff:2.3% logz-ratio=2822.48+/-0.13 dlogz:702.342>0.10]
4029it [20:57, 1.00s/it, bound:222 nc:138 ncall:182450 eff:2.2% logz-ratio=2844.71+/-0.13 dlogz:679.958>0.10]
4097it [22:08, 1.00it/s, bound:238 nc:118 ncall:192479 eff:2.1% logz-ratio=2868.61+/-0.14 dlogz:656.271>0.10]
4165it [23:19, 1.11s/it, bound:252 nc:184 ncall:202543 eff:2.1% logz-ratio=2891.39+/-0.13 dlogz:633.225>0.10]
4229it [24:28, 1.14s/it, bound:267 nc:156 ncall:212687 eff:2.0% logz-ratio=2911.83+/-0.13 dlogz:612.673>0.10]
4296it [25:41, 1.22it/s, bound:282 nc:113 ncall:222749 eff:1.9% logz-ratio=2933.81+/-0.14 dlogz:590.787>0.10]
4369it [26:47, 1.22it/s, bound:297 nc:115 ncall:232765 eff:1.9% logz-ratio=2954.94+/-0.13 dlogz:569.337>0.10]
4445it [27:55, 1.04s/it, bound:312 nc:157 ncall:242790 eff:1.8% logz-ratio=2979.66+/-0.13 dlogz:544.659>0.10]
4522it [29:06, 1.03it/s, bound:327 nc:144 ncall:252837 eff:1.8% logz-ratio=2996.55+/-0.13 dlogz:527.708>0.10]
4599it [30:18, 1.17s/it, bound:342 nc:182 ncall:263011 eff:1.7% logz-ratio=3022.97+/-0.13 dlogz:501.142>0.10]
4675it [31:31, 1.11it/s, bound:357 nc:148 ncall:273129 eff:1.7% logz-ratio=3043.69+/-0.13 dlogz:480.360>0.10]
4751it [32:46, 1.11it/s, bound:373 nc:105 ncall:283219 eff:1.7% logz-ratio=3061.06+/-0.13 dlogz:462.912>0.10]
4830it [34:01, 1.05s/it, bound:388 nc:135 ncall:293353 eff:1.6% logz-ratio=3081.62+/-0.13 dlogz:442.246>0.10]
4914it [35:20, 1.08it/s, bound:403 nc:101 ncall:303383 eff:1.6% logz-ratio=3099.45+/-0.14 dlogz:424.405>0.10]
4997it [36:36, 1.11it/s, bound:419 nc:131 ncall:313497 eff:1.6% logz-ratio=3118.84+/-0.14 dlogz:404.871>0.10]
5078it [37:53, 1.03it/s, bound:434 nc:101 ncall:323524 eff:1.6% logz-ratio=3133.55+/-0.14 dlogz:390.111>0.10]
5165it [39:12, 1.22it/s, bound:449 nc:119 ncall:333592 eff:1.5% logz-ratio=3150.60+/-0.14 dlogz:372.901>0.10]
5253it [40:33, 1.05it/s, bound:464 nc:101 ncall:343674 eff:1.5% logz-ratio=3166.91+/-0.14 dlogz:356.477>0.10]
5341it [41:54, 1.14it/s, bound:479 nc:125 ncall:353701 eff:1.5% logz-ratio=3179.19+/-0.14 dlogz:344.129>0.10]
5429it [43:18, 1.08it/s, bound:494 nc:101 ncall:363796 eff:1.5% logz-ratio=3193.32+/-0.14 dlogz:329.999>0.10]
5519it [44:43, 1.24it/s, bound:509 nc:108 ncall:373902 eff:1.5% logz-ratio=3207.75+/-0.14 dlogz:315.382>0.10]
5610it [46:09, 1.16it/s, bound:525 nc:101 ncall:384003 eff:1.5% logz-ratio=3221.81+/-0.14 dlogz:301.295>0.10]
5700it [47:36, 1.04it/s, bound:540 nc:101 ncall:394049 eff:1.4% logz-ratio=3234.31+/-0.14 dlogz:288.671>0.10]
5794it [49:07, 1.01s/it, bound:556 nc:119 ncall:404163 eff:1.4% logz-ratio=3247.08+/-0.14 dlogz:275.826>0.10]
5889it [50:33, 1.13it/s, bound:571 nc:101 ncall:414216 eff:1.4% logz-ratio=3259.53+/-0.14 dlogz:263.222>0.10]
5985it [51:58, 1.20it/s, bound:587 nc:101 ncall:424217 eff:1.4% logz-ratio=3269.11+/-0.14 dlogz:253.525>0.10]
6083it [53:27, 1.17it/s, bound:604 nc:101 ncall:434237 eff:1.4% logz-ratio=3280.14+/-0.14 dlogz:242.397>0.10]
6181it [55:00, 1.05it/s, bound:620 nc:101 ncall:444261 eff:1.4% logz-ratio=3290.66+/-0.14 dlogz:231.761>0.10]
6280it [56:36, 1.04s/it, bound:637 nc:134 ncall:454356 eff:1.4% logz-ratio=3301.98+/-0.14 dlogz:220.381>0.10]
6379it [58:03, 1.11it/s, bound:653 nc:101 ncall:464385 eff:1.4% logz-ratio=3310.68+/-0.14 dlogz:211.582>0.10]
6478it [59:32, 1.12it/s, bound:670 nc:101 ncall:474456 eff:1.4% logz-ratio=3319.91+/-0.14 dlogz:202.195>0.10]
6577it [1:00:59, 1.17it/s, bound:686 nc:101 ncall:484484 eff:1.4% logz-ratio=3328.27+/-0.14 dlogz:193.781>0.10]
6676it [1:02:26, 1.20it/s, bound:703 nc:101 ncall:494489 eff:1.4% logz-ratio=3337.37+/-0.14 dlogz:184.549>0.10]
6775it [1:03:55, 1.07it/s, bound:719 nc:101 ncall:504556 eff:1.3% logz-ratio=3344.63+/-0.14 dlogz:177.190>0.10]
6875it [1:05:25, 1.14it/s, bound:736 nc:101 ncall:514656 eff:1.3% logz-ratio=3352.55+/-0.14 dlogz:169.163>0.10]
6974it [1:06:55, 1.14it/s, bound:752 nc:101 ncall:524698 eff:1.3% logz-ratio=3358.52+/-0.14 dlogz:163.103>0.10]
7072it [1:08:24, 1.03it/s, bound:769 nc:101 ncall:534747 eff:1.3% logz-ratio=3364.89+/-0.14 dlogz:156.608>0.10]
7171it [1:09:53, 1.14it/s, bound:785 nc:101 ncall:544750 eff:1.3% logz-ratio=3370.60+/-0.14 dlogz:150.796>0.10]
7270it [1:11:20, 1.15it/s, bound:802 nc:101 ncall:554752 eff:1.3% logz-ratio=3376.15+/-0.14 dlogz:145.143>0.10]
7370it [1:12:50, 1.22it/s, bound:818 nc:101 ncall:564852 eff:1.3% logz-ratio=3381.15+/-0.14 dlogz:140.055>0.10]
7469it [1:14:21, 1.09it/s, bound:835 nc:101 ncall:574867 eff:1.3% logz-ratio=3386.62+/-0.14 dlogz:134.476>0.10]
7568it [1:15:52, 1.04it/s, bound:851 nc:101 ncall:584886 eff:1.3% logz-ratio=3390.72+/-0.14 dlogz:141.777>0.10]
7668it [1:17:21, 1.13it/s, bound:868 nc:101 ncall:594986 eff:1.3% logz-ratio=3395.39+/-0.14 dlogz:140.208>0.10]
7767it [1:18:50, 1.09it/s, bound:884 nc:101 ncall:604996 eff:1.3% logz-ratio=3400.42+/-0.15 dlogz:135.088>0.10]
7866it [1:20:19, 1.16it/s, bound:901 nc:101 ncall:615086 eff:1.3% logz-ratio=3405.35+/-0.15 dlogz:130.046>0.10]
7965it [1:21:49, 1.04it/s, bound:917 nc:101 ncall:625098 eff:1.3% logz-ratio=3409.35+/-0.15 dlogz:125.950>0.10]
8064it [1:23:20, 1.16it/s, bound:934 nc:101 ncall:635113 eff:1.3% logz-ratio=3413.18+/-0.15 dlogz:122.008>0.10]
8163it [1:24:50, 1.15it/s, bound:950 nc:101 ncall:645136 eff:1.3% logz-ratio=3416.97+/-0.15 dlogz:126.862>0.10]
8262it [1:26:20, 1.08it/s, bound:967 nc:101 ncall:655153 eff:1.3% logz-ratio=3420.92+/-0.15 dlogz:122.818>0.10]
8361it [1:27:50, 1.24it/s, bound:983 nc:101 ncall:665231 eff:1.3% logz-ratio=3424.07+/-0.15 dlogz:119.563>0.10]
8459it [1:29:11, 1.21it/s, bound:1000 nc:101 ncall:675236 eff:1.3% logz-ratio=3427.09+/-0.15 dlogz:116.437>0.10]
8558it [1:30:32, 1.35it/s, bound:1016 nc:101 ncall:685246 eff:1.2% logz-ratio=3430.42+/-0.15 dlogz:113.011>0.10]
8657it [1:31:59, 1.12it/s, bound:1033 nc:101 ncall:695273 eff:1.2% logz-ratio=3433.30+/-0.15 dlogz:117.069>0.10]
8756it [1:33:26, 1.17it/s, bound:1049 nc:101 ncall:705343 eff:1.2% logz-ratio=3435.77+/-0.15 dlogz:114.491>0.10]
8855it [1:34:50, 1.30it/s, bound:1066 nc:101 ncall:715344 eff:1.2% logz-ratio=3438.21+/-0.15 dlogz:111.963>0.10]
8952it [1:36:10, 1.25it/s, bound:1082 nc:101 ncall:725416 eff:1.2% logz-ratio=3440.78+/-0.15 dlogz:109.289>0.10]
9047it [1:37:35, 1.39it/s, bound:1098 nc:101 ncall:735492 eff:1.2% logz-ratio=3443.44+/-0.15 dlogz:106.547>0.10]
9146it [1:38:55, 1.23it/s, bound:1115 nc:101 ncall:745502 eff:1.2% logz-ratio=3446.25+/-0.15 dlogz:103.631>0.10]
9245it [1:40:17, 1.22it/s, bound:1131 nc:101 ncall:755577 eff:1.2% logz-ratio=3449.04+/-0.15 dlogz:100.741>0.10]
9344it [1:41:41, 1.11it/s, bound:1148 nc:101 ncall:765593 eff:1.2% logz-ratio=3451.43+/-0.15 dlogz:98.248>0.10]
9444it [1:43:10, 1.12it/s, bound:1164 nc:101 ncall:775693 eff:1.2% logz-ratio=3453.59+/-0.15 dlogz:95.983>0.10]
9543it [1:44:37, 1.15it/s, bound:1181 nc:101 ncall:785714 eff:1.2% logz-ratio=3456.00+/-0.15 dlogz:93.483>0.10]
9642it [1:46:04, 1.12it/s, bound:1197 nc:101 ncall:795776 eff:1.2% logz-ratio=3458.27+/-0.15 dlogz:91.112>0.10]
9742it [1:47:30, 1.14it/s, bound:1214 nc:101 ncall:805876 eff:1.2% logz-ratio=3460.43+/-0.15 dlogz:88.845>0.10]
9839it [1:48:57, 1.24it/s, bound:1230 nc:101 ncall:815917 eff:1.2% logz-ratio=3462.71+/-0.15 dlogz:86.470>0.10]
9938it [1:50:23, 1.10it/s, bound:1247 nc:101 ncall:825944 eff:1.2% logz-ratio=3464.67+/-0.15 dlogz:84.407>0.10]
10036it [1:51:48, 1.12it/s, bound:1263 nc:101 ncall:835952 eff:1.2% logz-ratio=3466.78+/-0.16 dlogz:88.744>0.10]
10136it [1:53:13, 1.19it/s, bound:1280 nc:101 ncall:846052 eff:1.2% logz-ratio=3468.85+/-0.16 dlogz:86.573>0.10]
10234it [1:54:40, 1.11it/s, bound:1296 nc:101 ncall:856116 eff:1.2% logz-ratio=3470.93+/-0.16 dlogz:84.394>0.10]
10333it [1:56:10, 1.08it/s, bound:1313 nc:101 ncall:866167 eff:1.2% logz-ratio=3473.08+/-0.16 dlogz:82.141>0.10]
10429it [1:57:42, 1.02it/s, bound:1329 nc:101 ncall:876207 eff:1.2% logz-ratio=3474.91+/-0.16 dlogz:80.216>0.10]
10523it [1:59:20, 1.02s/it, bound:1345 nc:101 ncall:886275 eff:1.2% logz-ratio=3477.07+/-0.16 dlogz:77.964>0.10]
10622it [2:00:53, 1.06it/s, bound:1361 nc:101 ncall:896292 eff:1.2% logz-ratio=3479.12+/-0.16 dlogz:75.822>0.10]
10721it [2:02:23, 1.14it/s, bound:1378 nc:101 ncall:906378 eff:1.2% logz-ratio=3481.56+/-0.16 dlogz:73.284>0.10]
10819it [2:03:53, 1.17it/s, bound:1394 nc:101 ncall:916433 eff:1.2% logz-ratio=3483.75+/-0.16 dlogz:70.990>0.10]
10918it [2:05:21, 1.10it/s, bound:1410 nc:101 ncall:926520 eff:1.2% logz-ratio=3485.81+/-0.16 dlogz:72.060>0.10]
11018it [2:06:51, 1.10it/s, bound:1427 nc:101 ncall:936620 eff:1.2% logz-ratio=3487.91+/-0.16 dlogz:69.867>0.10]
11111it [2:08:20, 1.00it/s, bound:1443 nc:101 ncall:946711 eff:1.2% logz-ratio=3489.75+/-0.16 dlogz:67.935>0.10]
11209it [2:09:50, 1.08it/s, bound:1459 nc:101 ncall:956732 eff:1.2% logz-ratio=3491.72+/-0.16 dlogz:65.862>0.10]
11308it [2:11:19, 1.10it/s, bound:1476 nc:101 ncall:966741 eff:1.2% logz-ratio=3493.55+/-0.16 dlogz:65.003>0.10]
11407it [2:12:48, 1.09it/s, bound:1492 nc:101 ncall:976808 eff:1.2% logz-ratio=3495.22+/-0.16 dlogz:63.226>0.10]
11507it [2:14:20, 1.07it/s, bound:1509 nc:101 ncall:986908 eff:1.2% logz-ratio=3497.10+/-0.16 dlogz:61.249>0.10]
11606it [2:15:52, 1.08it/s, bound:1525 nc:101 ncall:996928 eff:1.2% logz-ratio=3498.97+/-0.16 dlogz:59.283>0.10]
11705it [2:17:20, 1.07it/s, bound:1542 nc:101 ncall:1006965 eff:1.2% logz-ratio=3500.61+/-0.16 dlogz:65.772>0.10]
11804it [2:18:53, 1.15it/s, bound:1558 nc:101 ncall:1017017 eff:1.2% logz-ratio=3502.33+/-0.16 dlogz:63.955>0.10]
11902it [2:20:26, 1.12it/s, bound:1575 nc:101 ncall:1027054 eff:1.2% logz-ratio=3503.88+/-0.16 dlogz:62.300>0.10]
12001it [2:21:59, 1.09it/s, bound:1591 nc:101 ncall:1037078 eff:1.2% logz-ratio=3505.35+/-0.17 dlogz:60.742>0.10]
12100it [2:23:31, 1.10it/s, bound:1608 nc:101 ncall:1047114 eff:1.2% logz-ratio=3506.98+/-0.17 dlogz:59.006>0.10]
12199it [2:25:03, 1.03it/s, bound:1624 nc:101 ncall:1057129 eff:1.2% logz-ratio=3508.50+/-0.17 dlogz:57.391>0.10]
12298it [2:26:37, 1.05it/s, bound:1641 nc:101 ncall:1067218 eff:1.2% logz-ratio=3510.04+/-0.17 dlogz:55.755>0.10]
12397it [2:28:11, 1.06it/s, bound:1657 nc:101 ncall:1077314 eff:1.2% logz-ratio=3511.47+/-0.17 dlogz:54.216>0.10]
12496it [2:29:45, 1.14it/s, bound:1674 nc:101 ncall:1087408 eff:1.1% logz-ratio=3512.82+/-0.17 dlogz:52.766>0.10]
12594it [2:31:17, 1.01it/s, bound:1690 nc:101 ncall:1097409 eff:1.1% logz-ratio=3514.09+/-0.17 dlogz:51.402>0.10]
12693it [2:32:51, 1.05it/s, bound:1707 nc:101 ncall:1107453 eff:1.1% logz-ratio=3515.43+/-0.17 dlogz:49.965>0.10]
12793it [2:34:27, 1.12it/s, bound:1723 nc:101 ncall:1117553 eff:1.1% logz-ratio=3516.62+/-0.17 dlogz:48.670>0.10]
12890it [2:36:03, 1.01it/s, bound:1739 nc:101 ncall:1127649 eff:1.1% logz-ratio=3517.74+/-0.17 dlogz:47.456>0.10]
12989it [2:37:37, 1.10it/s, bound:1756 nc:101 ncall:1137703 eff:1.1% logz-ratio=3518.86+/-0.17 dlogz:46.235>0.10]
13088it [2:39:12, 1.10it/s, bound:1772 nc:101 ncall:1147708 eff:1.1% logz-ratio=3520.15+/-0.17 dlogz:44.850>0.10]
13185it [2:40:48, 1.01it/s, bound:1789 nc:101 ncall:1157727 eff:1.1% logz-ratio=3521.38+/-0.17 dlogz:43.521>0.10]
13285it [2:42:22, 1.07it/s, bound:1805 nc:101 ncall:1167827 eff:1.1% logz-ratio=3522.74+/-0.17 dlogz:42.057>0.10]
13379it [2:43:55, 1.07it/s, bound:1822 nc:101 ncall:1177911 eff:1.1% logz-ratio=3523.86+/-0.17 dlogz:40.848>0.10]
13478it [2:45:27, 1.06it/s, bound:1838 nc:101 ncall:1187916 eff:1.1% logz-ratio=3524.97+/-0.17 dlogz:39.631>0.10]
13573it [2:47:02, 1.14it/s, bound:1854 nc:101 ncall:1198011 eff:1.1% logz-ratio=3525.95+/-0.17 dlogz:38.565>0.10]
13672it [2:48:33, 1.08it/s, bound:1871 nc:101 ncall:1208095 eff:1.1% logz-ratio=3526.91+/-0.17 dlogz:37.499>0.10]
13770it [2:50:05, 1.15s/it, bound:1887 nc:221 ncall:1218268 eff:1.1% logz-ratio=3527.94+/-0.17 dlogz:36.376>0.10]
13868it [2:51:33, 1.12it/s, bound:1904 nc:101 ncall:1228352 eff:1.1% logz-ratio=3528.96+/-0.17 dlogz:35.253>0.10]
13967it [2:53:00, 1.19it/s, bound:1920 nc:101 ncall:1238360 eff:1.1% logz-ratio=3529.84+/-0.17 dlogz:34.273>0.10]
14064it [2:54:26, 1.23s/it, bound:1937 nc:253 ncall:1248591 eff:1.1% logz-ratio=3530.67+/-0.17 dlogz:33.352>0.10]
14163it [2:55:52, 1.03it/s, bound:1953 nc:101 ncall:1258660 eff:1.1% logz-ratio=3531.41+/-0.17 dlogz:32.510>0.10]
14258it [2:57:19, 1.02it/s, bound:1970 nc:101 ncall:1268678 eff:1.1% logz-ratio=3532.09+/-0.17 dlogz:31.733>0.10]
14356it [2:58:49, 1.06it/s, bound:1986 nc:101 ncall:1278714 eff:1.1% logz-ratio=3532.84+/-0.17 dlogz:30.882>0.10]
14453it [3:00:18, 1.04it/s, bound:2002 nc:101 ncall:1288744 eff:1.1% logz-ratio=3533.53+/-0.17 dlogz:30.098>0.10]
14551it [3:01:50, 1.28it/s, bound:2019 nc:101 ncall:1298845 eff:1.1% logz-ratio=3534.20+/-0.18 dlogz:29.326>0.10]
14649it [3:03:26, 1.11it/s, bound:2035 nc:101 ncall:1308875 eff:1.1% logz-ratio=3534.88+/-0.18 dlogz:28.556>0.10]
14743it [3:04:55, 1.15it/s, bound:2051 nc:101 ncall:1318886 eff:1.1% logz-ratio=3535.51+/-0.18 dlogz:27.829>0.10]
14841it [3:06:25, 1.32it/s, bound:2068 nc:101 ncall:1328903 eff:1.1% logz-ratio=3536.21+/-0.18 dlogz:27.026>0.10]
14940it [3:07:54, 1.04it/s, bound:2084 nc:101 ncall:1338990 eff:1.1% logz-ratio=3536.90+/-0.18 dlogz:26.241>0.10]
15039it [3:09:25, 1.10it/s, bound:2101 nc:101 ncall:1349083 eff:1.1% logz-ratio=3537.55+/-0.18 dlogz:25.487>0.10]
15114it [3:10:58, 7.08s/it, bound:2114 nc:2480 ncall:1359836 eff:1.1% logz-ratio=3538.04+/-0.18 dlogz:24.927>0.10]
15167it [3:12:24, 1.70s/it, bound:2128 nc:164 ncall:1369975 eff:1.1% logz-ratio=3538.38+/-0.18 dlogz:24.534>0.10]
15245it [3:13:50, 1.04s/it, bound:2143 nc:110 ncall:1380026 eff:1.1% logz-ratio=3538.85+/-0.18 dlogz:23.986>0.10]
15339it [3:15:20, 1.28it/s, bound:2160 nc:101 ncall:1390057 eff:1.1% logz-ratio=3539.40+/-0.18 dlogz:23.342>0.10]
15438it [3:16:49, 1.17it/s, bound:2176 nc:101 ncall:1400144 eff:1.1% logz-ratio=3539.98+/-0.18 dlogz:22.663>0.10]
15534it [3:18:20, 1.02it/s, bound:2192 nc:101 ncall:1410149 eff:1.1% logz-ratio=3540.50+/-0.18 dlogz:22.048>0.10]
15632it [3:19:48, 1.17it/s, bound:2209 nc:101 ncall:1420171 eff:1.1% logz-ratio=3541.02+/-0.18 dlogz:21.426>0.10]
15731it [3:21:18, 1.14it/s, bound:2225 nc:101 ncall:1430233 eff:1.1% logz-ratio=3541.56+/-0.18 dlogz:20.791>0.10]
15821it [3:22:46, 1.62s/it, bound:2241 nc:104 ncall:1440294 eff:1.1% logz-ratio=3542.06+/-0.18 dlogz:20.198>0.10]
15915it [3:24:19, 1.07it/s, bound:2257 nc:101 ncall:1450387 eff:1.1% logz-ratio=3542.56+/-0.18 dlogz:19.602>0.10]
16008it [3:25:50, 1.04it/s, bound:2273 nc:101 ncall:1460389 eff:1.1% logz-ratio=3543.04+/-0.18 dlogz:19.030>0.10]
16107it [3:27:24, 1.02it/s, bound:2289 nc:101 ncall:1470427 eff:1.1% logz-ratio=3543.54+/-0.18 dlogz:18.436>0.10]
16205it [3:28:55, 1.15it/s, bound:2305 nc:101 ncall:1480443 eff:1.1% logz-ratio=3544.02+/-0.18 dlogz:17.852>0.10]
16283it [3:30:23, 1.13it/s, bound:2319 nc:101 ncall:1490465 eff:1.1% logz-ratio=3544.38+/-0.18 dlogz:17.412>0.10]
16378it [3:31:52, 1.22it/s, bound:2335 nc:101 ncall:1500480 eff:1.1% logz-ratio=3544.81+/-0.18 dlogz:17.417>0.10]
16475it [3:33:23, 1.12s/it, bound:2351 nc:188 ncall:1510534 eff:1.1% logz-ratio=3545.26+/-0.18 dlogz:16.875>0.10]
16572it [3:34:52, 1.11it/s, bound:2368 nc:101 ncall:1520605 eff:1.1% logz-ratio=3545.69+/-0.18 dlogz:16.344>0.10]
16669it [3:36:19, 1.20it/s, bound:2384 nc:101 ncall:1530652 eff:1.1% logz-ratio=3546.14+/-0.18 dlogz:15.802>0.10]
16766it [3:37:48, 1.04it/s, bound:2400 nc:101 ncall:1540665 eff:1.1% logz-ratio=3546.58+/-0.18 dlogz:15.940>0.10]
16860it [3:39:17, 1.00s/it, bound:2416 nc:101 ncall:1550752 eff:1.1% logz-ratio=3546.98+/-0.19 dlogz:15.452>0.10]
16955it [3:40:48, 1.14it/s, bound:2432 nc:101 ncall:1560825 eff:1.1% logz-ratio=3547.37+/-0.19 dlogz:14.959>0.10]
17046it [3:42:19, 1.11it/s, bound:2449 nc:101 ncall:1570898 eff:1.1% logz-ratio=3547.73+/-0.19 dlogz:14.510>0.10]
17127it [3:43:49, 1.12s/it, bound:2463 nc:119 ncall:1580952 eff:1.1% logz-ratio=3548.05+/-0.19 dlogz:14.115>0.10]
17221it [3:45:21, 1.03it/s, bound:2479 nc:101 ncall:1591017 eff:1.1% logz-ratio=3548.39+/-0.19 dlogz:13.677>0.10]
17315it [3:46:52, 1.07it/s, bound:2495 nc:101 ncall:1601055 eff:1.1% logz-ratio=3548.73+/-0.19 dlogz:14.878>0.10]
17410it [3:48:24, 1.08it/s, bound:2511 nc:101 ncall:1611082 eff:1.1% logz-ratio=3549.06+/-0.19 dlogz:14.456>0.10]
17508it [3:49:57, 1.07it/s, bound:2528 nc:101 ncall:1621141 eff:1.1% logz-ratio=3549.38+/-0.19 dlogz:15.007>0.10]
17561it [3:51:28, 7.49s/it, bound:2537 nc:269 ncall:1631220 eff:1.1% logz-ratio=3549.54+/-0.19 dlogz:14.791>0.10]
17603it [3:52:57, 1.81s/it, bound:2551 nc:204 ncall:1641246 eff:1.1% logz-ratio=3549.67+/-0.19 dlogz:14.622>0.10]
17665it [3:54:28, 1.01s/it, bound:2566 nc:111 ncall:1651275 eff:1.1% logz-ratio=3549.86+/-0.19 dlogz:14.367>0.10]
17758it [3:55:59, 1.02it/s, bound:2581 nc:101 ncall:1661364 eff:1.1% logz-ratio=3550.14+/-0.19 dlogz:13.994>0.10]
17856it [3:57:30, 1.23it/s, bound:2598 nc:101 ncall:1671391 eff:1.1% logz-ratio=3550.43+/-0.19 dlogz:13.607>0.10]
17952it [3:59:03, 1.09s/it, bound:2614 nc:101 ncall:1681406 eff:1.1% logz-ratio=3550.69+/-0.19 dlogz:13.252>0.10]
18044it [4:00:30, 1.21it/s, bound:2630 nc:101 ncall:1691412 eff:1.1% logz-ratio=3550.93+/-0.19 dlogz:12.915>0.10]
18143it [4:02:00, 1.24it/s, bound:2646 nc:101 ncall:1701501 eff:1.1% logz-ratio=3551.19+/-0.19 dlogz:12.558>0.10]
18230it [4:03:28, 1.14it/s, bound:2662 nc:101 ncall:1711516 eff:1.1% logz-ratio=3551.41+/-0.19 dlogz:12.255>0.10]
18321it [4:04:56, 1.10it/s, bound:2677 nc:101 ncall:1721555 eff:1.1% logz-ratio=3551.62+/-0.19 dlogz:11.950>0.10]
18416it [4:06:34, 1.34s/it, bound:2693 nc:186 ncall:1731625 eff:1.1% logz-ratio=3551.83+/-0.19 dlogz:11.641>0.10]
18513it [4:08:09, 1.05it/s, bound:2710 nc:105 ncall:1741632 eff:1.1% logz-ratio=3552.05+/-0.19 dlogz:11.331>0.10]
18611it [4:09:40, 1.05it/s, bound:2726 nc:101 ncall:1751679 eff:1.1% logz-ratio=3552.27+/-0.19 dlogz:11.015>0.10]
18708it [4:11:12, 1.05it/s, bound:2742 nc:101 ncall:1761706 eff:1.1% logz-ratio=3552.48+/-0.19 dlogz:10.702>0.10]
18806it [4:12:43, 1.10it/s, bound:2758 nc:101 ncall:1771720 eff:1.1% logz-ratio=3552.69+/-0.19 dlogz:10.395>0.10]
18904it [4:14:13, 1.14it/s, bound:2775 nc:101 ncall:1781736 eff:1.1% logz-ratio=3552.89+/-0.19 dlogz:10.095>0.10]
19000it [4:15:45, 1.16s/it, bound:2791 nc:178 ncall:1791902 eff:1.1% logz-ratio=3553.09+/-0.19 dlogz:9.805>0.10]
19096it [4:17:17, 1.04it/s, bound:2807 nc:101 ncall:1801929 eff:1.1% logz-ratio=3553.28+/-0.19 dlogz:9.648>0.10]
19187it [4:18:46, 1.13it/s, bound:2823 nc:101 ncall:1811982 eff:1.1% logz-ratio=3553.45+/-0.19 dlogz:9.386>0.10]
19282it [4:20:15, 1.97s/it, bound:2839 nc:517 ncall:1821993 eff:1.1% logz-ratio=3553.62+/-0.19 dlogz:9.117>0.10]
19380it [4:21:46, 1.09it/s, bound:2856 nc:101 ncall:1832054 eff:1.1% logz-ratio=3553.80+/-0.19 dlogz:8.841>0.10]
19478it [4:23:17, 1.07it/s, bound:2872 nc:101 ncall:1842138 eff:1.1% logz-ratio=3553.97+/-0.19 dlogz:8.572>0.10]
19573it [4:24:46, 1.08it/s, bound:2888 nc:101 ncall:1852148 eff:1.1% logz-ratio=3554.13+/-0.19 dlogz:8.320>0.10]
19672it [4:26:16, 1.11it/s, bound:2905 nc:101 ncall:1862226 eff:1.1% logz-ratio=3554.28+/-0.19 dlogz:8.063>0.10]
19770it [4:27:49, 1.03it/s, bound:2921 nc:101 ncall:1872285 eff:1.1% logz-ratio=3554.43+/-0.19 dlogz:7.885>0.10]
19857it [4:29:25, 1.04s/it, bound:2937 nc:101 ncall:1882321 eff:1.1% logz-ratio=3554.55+/-0.19 dlogz:7.676>0.10]
19955it [4:30:57, 1.01s/it, bound:2953 nc:101 ncall:1892323 eff:1.1% logz-ratio=3554.68+/-0.19 dlogz:7.446>0.10]
20053it [4:32:31, 1.08it/s, bound:2969 nc:101 ncall:1902325 eff:1.1% logz-ratio=3554.81+/-0.19 dlogz:7.220>0.10]
20152it [4:34:05, 1.04s/it, bound:2986 nc:101 ncall:1912370 eff:1.1% logz-ratio=3554.93+/-0.19 dlogz:6.999>0.10]
20251it [4:35:39, 1.05it/s, bound:3002 nc:101 ncall:1922469 eff:1.1% logz-ratio=3555.05+/-0.19 dlogz:6.782>0.10]
20348it [4:37:12, 1.24it/s, bound:3019 nc:101 ncall:1932497 eff:1.1% logz-ratio=3555.16+/-0.20 dlogz:6.574>0.10]
20447it [4:38:50, 1.04s/it, bound:3035 nc:101 ncall:1942573 eff:1.1% logz-ratio=3555.27+/-0.20 dlogz:6.367>0.10]
20546it [4:40:28, 1.00it/s, bound:3052 nc:101 ncall:1952667 eff:1.1% logz-ratio=3555.38+/-0.20 dlogz:6.164>0.10]
20645it [4:42:00, 1.04it/s, bound:3068 nc:101 ncall:1962713 eff:1.1% logz-ratio=3555.48+/-0.20 dlogz:5.966>0.10]
20727it [4:43:35, 1.04it/s, bound:3083 nc:101 ncall:1972714 eff:1.1% logz-ratio=3555.56+/-0.20 dlogz:5.805>0.10]
20821it [4:45:12, 1.04it/s, bound:3098 nc:101 ncall:1982744 eff:1.1% logz-ratio=3555.65+/-0.20 dlogz:5.622>0.10]
20919it [4:46:46, 1.01s/it, bound:3115 nc:112 ncall:1992802 eff:1.0% logz-ratio=3555.73+/-0.20 dlogz:5.440>0.10]
21015it [4:48:18, 1.04it/s, bound:3131 nc:101 ncall:2002900 eff:1.0% logz-ratio=3555.82+/-0.20 dlogz:5.304>0.10]
21114it [4:49:48, 1.28it/s, bound:3148 nc:101 ncall:2013000 eff:1.0% logz-ratio=3555.90+/-0.20 dlogz:5.169>0.10]
21213it [4:51:22, 1.03it/s, bound:3164 nc:101 ncall:2023055 eff:1.0% logz-ratio=3555.98+/-0.20 dlogz:4.991>0.10]
21312it [4:52:57, 1.05it/s, bound:3181 nc:101 ncall:2033061 eff:1.0% logz-ratio=3556.06+/-0.20 dlogz:4.816>0.10]
21412it [4:54:31, 1.03it/s, bound:3197 nc:101 ncall:2043161 eff:1.0% logz-ratio=3556.13+/-0.20 dlogz:4.641>0.10]
21510it [4:56:07, 1.00it/s, bound:3214 nc:101 ncall:2053262 eff:1.0% logz-ratio=3556.21+/-0.20 dlogz:4.472>0.10]
21608it [4:57:41, 1.03it/s, bound:3230 nc:101 ncall:2063349 eff:1.0% logz-ratio=3556.28+/-0.20 dlogz:4.307>0.10]
21706it [4:59:19, 1.02it/s, bound:3247 nc:101 ncall:2073435 eff:1.0% logz-ratio=3556.34+/-0.20 dlogz:4.144>0.10]
21802it [5:00:55, 1.09it/s, bound:3263 nc:101 ncall:2083439 eff:1.0% logz-ratio=3556.41+/-0.20 dlogz:3.987>0.10]
21899it [5:02:31, 1.12it/s, bound:3279 nc:101 ncall:2093453 eff:1.0% logz-ratio=3556.47+/-0.20 dlogz:3.830>0.10]
21998it [5:04:05, 1.03it/s, bound:3296 nc:101 ncall:2103493 eff:1.0% logz-ratio=3556.53+/-0.20 dlogz:3.674>0.10]
22097it [5:05:37, 1.08it/s, bound:3312 nc:101 ncall:2113509 eff:1.0% logz-ratio=3556.59+/-0.20 dlogz:3.584>0.10]
22197it [5:07:12, 1.07it/s, bound:3329 nc:101 ncall:2123609 eff:1.0% logz-ratio=3556.65+/-0.20 dlogz:3.504>0.10]
22297it [5:08:45, 1.07it/s, bound:3345 nc:101 ncall:2133709 eff:1.0% logz-ratio=3556.70+/-0.20 dlogz:3.355>0.10]
22397it [5:10:27, 1.06it/s, bound:3362 nc:101 ncall:2143809 eff:1.0% logz-ratio=3556.75+/-0.20 dlogz:3.513>0.10]
22495it [5:12:00, 1.34it/s, bound:3379 nc:101 ncall:2153821 eff:1.0% logz-ratio=3556.80+/-0.20 dlogz:3.370>0.10]
22594it [5:13:29, 1.14it/s, bound:3395 nc:101 ncall:2163869 eff:1.0% logz-ratio=3556.85+/-0.20 dlogz:3.227>0.10]
22691it [5:15:00, 1.12it/s, bound:3411 nc:101 ncall:2173896 eff:1.0% logz-ratio=3556.90+/-0.20 dlogz:3.089>0.10]
22791it [5:16:31, 1.07it/s, bound:3428 nc:110 ncall:2184005 eff:1.0% logz-ratio=3556.95+/-0.20 dlogz:2.949>0.10]
22891it [5:18:06, 1.09it/s, bound:3445 nc:101 ncall:2194105 eff:1.0% logz-ratio=3556.99+/-0.20 dlogz:2.812>0.10]
22989it [5:19:32, 1.13it/s, bound:3461 nc:101 ncall:2204163 eff:1.0% logz-ratio=3557.04+/-0.20 dlogz:2.680>0.10]
23088it [5:21:04, 1.07it/s, bound:3478 nc:101 ncall:2214227 eff:1.0% logz-ratio=3557.08+/-0.20 dlogz:2.549>0.10]
23187it [5:22:39, 1.08it/s, bound:3494 nc:101 ncall:2224280 eff:1.0% logz-ratio=3557.12+/-0.20 dlogz:3.110>0.10]
23287it [5:24:09, 1.11it/s, bound:3511 nc:101 ncall:2234380 eff:1.0% logz-ratio=3557.16+/-0.20 dlogz:2.978>0.10]
23387it [5:25:41, 1.12it/s, bound:3527 nc:101 ncall:2244480 eff:1.0% logz-ratio=3557.20+/-0.20 dlogz:2.848>0.10]
23486it [5:27:09, 1.14it/s, bound:3544 nc:101 ncall:2254512 eff:1.0% logz-ratio=3557.23+/-0.20 dlogz:2.721>0.10]
23586it [5:28:44, 1.02it/s, bound:3561 nc:101 ncall:2264612 eff:1.0% logz-ratio=3557.27+/-0.20 dlogz:2.595>0.10]
23685it [5:30:17, 1.13it/s, bound:3577 nc:101 ncall:2274673 eff:1.0% logz-ratio=3557.30+/-0.20 dlogz:2.473>0.10]
23784it [5:31:52, 1.04it/s, bound:3594 nc:101 ncall:2284721 eff:1.0% logz-ratio=3557.33+/-0.20 dlogz:2.354>0.10]
23883it [5:33:26, 1.11it/s, bound:3610 nc:101 ncall:2294735 eff:1.0% logz-ratio=3557.36+/-0.20 dlogz:2.237>0.10]
23983it [5:34:59, 1.02it/s, bound:3627 nc:101 ncall:2304835 eff:1.0% logz-ratio=3557.39+/-0.20 dlogz:2.123>0.10]
24081it [5:36:33, 1.04it/s, bound:3643 nc:101 ncall:2314928 eff:1.0% logz-ratio=3557.42+/-0.20 dlogz:2.013>0.10]
24178it [5:38:01, 1.09it/s, bound:3659 nc:101 ncall:2324966 eff:1.0% logz-ratio=3557.45+/-0.20 dlogz:1.908>0.10]
24277it [5:39:34, 1.10it/s, bound:3676 nc:101 ncall:2335011 eff:1.0% logz-ratio=3557.47+/-0.20 dlogz:1.803>0.10]
24375it [5:41:04, 1.15it/s, bound:3692 nc:101 ncall:2345091 eff:1.0% logz-ratio=3557.49+/-0.20 dlogz:1.703>0.10]
24475it [5:42:38, 1.12it/s, bound:3709 nc:101 ncall:2355191 eff:1.0% logz-ratio=3557.52+/-0.20 dlogz:1.603>0.10]
24574it [5:44:13, 1.50s/it, bound:3726 nc:328 ncall:2365453 eff:1.0% logz-ratio=3557.54+/-0.20 dlogz:1.507>0.10]
24671it [5:45:48, 1.01it/s, bound:3742 nc:101 ncall:2375494 eff:1.0% logz-ratio=3557.56+/-0.20 dlogz:1.417>0.10]
24770it [5:47:23, 1.03it/s, bound:3759 nc:101 ncall:2385515 eff:1.0% logz-ratio=3557.58+/-0.20 dlogz:1.327>0.10]
24867it [5:48:57, 1.08it/s, bound:3775 nc:101 ncall:2395523 eff:1.0% logz-ratio=3557.60+/-0.20 dlogz:1.483>0.10]
24963it [5:50:31, 1.02it/s, bound:3791 nc:101 ncall:2405539 eff:1.0% logz-ratio=3557.62+/-0.20 dlogz:1.421>0.10]
25062it [5:52:06, 1.11it/s, bound:3808 nc:101 ncall:2415575 eff:1.0% logz-ratio=3557.64+/-0.20 dlogz:1.333>0.10]
25160it [5:53:38, 1.12it/s, bound:3824 nc:101 ncall:2425654 eff:1.0% logz-ratio=3557.66+/-0.20 dlogz:1.249>0.10]
25221it [5:55:15, 1.75s/it, bound:3837 nc:356 ncall:2435969 eff:1.0% logz-ratio=3557.67+/-0.20 dlogz:1.199>0.10]
25311it [5:56:52, 1.20s/it, bound:3853 nc:209 ncall:2446152 eff:1.0% logz-ratio=3557.68+/-0.20 dlogz:1.127>0.10]
25401it [5:58:21, 1.07it/s, bound:3869 nc:101 ncall:2456160 eff:1.0% logz-ratio=3557.69+/-0.20 dlogz:1.058>0.10]
25487it [5:59:52, 1.08it/s, bound:3884 nc:101 ncall:2466169 eff:1.0% logz-ratio=3557.71+/-0.20 dlogz:0.995>0.10]
25490it [5:59:56, 1.07s/it, bound:3884 nc:101 ncall:2466472 eff:1.0% logz-ratio=3557.71+/-0.20 dlogz:0.992>0.10]
Exception while calling loglikelihood function:
params: [ 1.38841380e+02 6.59811639e-01 9.23852574e-01 4.04885104e-01
1.63076244e+00 1.86805552e+00 1.52219177e+00 3.52768238e+00
1.25288603e+00 6.35165638e-01 1.04867019e+00 2.65968080e+00
1.99279577e+00 -8.57930140e-06]
args: []
kwargs: {}
exception:
25491it [36:32, 11.63it/s, bound:1 nc:258874 ncall:2725346 eff:0.9% logz-ratio=3557.71+/-0.20 dlogz:0.992>0.10]
25492it [44:10, 137.46s/it, bound:2 nc:54520 ncall:2779866 eff:0.9% logz-ratio=3557.71+/-0.20 dlogz:0.991>0.10]
25493it [1:08:12, 528.76s/it, bound:3 nc:179179 ncall:2959045 eff:0.9% logz-ratio=3557.71+/-0.20 dlogz:0.990>0.10]
25494it [1:18:36, 557.37s/it, bound:4 nc:89886 ncall:3048931 eff:0.8% logz-ratio=3557.71+/-0.20 dlogz:0.990>0.10]
```
</details>https://git.ligo.org/lscsoft/bilby/-/issues/321Evidence calculation for all!2020-02-06T11:50:33ZCarl-Johan HasterEvidence calculation for all!Make sure to check which samplers can produce evidences (and Bayes Factors), and how good they are at computing them.Make sure to check which samplers can produce evidences (and Bayes Factors), and how good they are at computing them.https://git.ligo.org/lscsoft/bilby/-/issues/456Add implementation of the UltraNest sampler2020-05-02T05:14:44ZMatthew PitkinAdd implementation of the UltraNest samplerThere has been a [suggestion](https://github.com/mattpitkin/theonlinemcmc/issues/27) to implement the [UltraNest](https://johannesbuchner.github.io/UltraNest/) sampler in bilby. This looks like it should be relatively simple as it has a ...There has been a [suggestion](https://github.com/mattpitkin/theonlinemcmc/issues/27) to implement the [UltraNest](https://johannesbuchner.github.io/UltraNest/) sampler in bilby. This looks like it should be relatively simple as it has a similar interface to Nestle.
I'll try and take this on when I have some time.Matthew PitkinMatthew Pitkinhttps://git.ligo.org/lscsoft/bilby/-/issues/485MultiNest MPI not working2020-05-09T02:59:29ZColm Talbotcolm.talbot@ligo.orgMultiNest MPI not workingIt looks like the recent changes to the `pymultinest` implementation have broken the MPI compatibility. I think it's because of the use of the temporary directory.
I think there are two ways around this:
- use a deterministic temporary ...It looks like the recent changes to the `pymultinest` implementation have broken the MPI compatibility. I think it's because of the use of the temporary directory.
I think there are two ways around this:
- use a deterministic temporary directory name
- allow users to turn off the temporary directory usage.
Thoughts? @gregory.ashton
```python
*****************************************************
MultiNest v3.10
Copyright Farhan Feroz & Mike Hobson
Release Jul 2015
no. of live points = 100
dimensionality = 9
*****************************************************
Starting MultiNest
Traceback (most recent call last):
File "fast_tutorial.py", line 106, in <module>
injection_parameters=injection_parameters, outdir=outdir, label=label)
File "/Users/colm/modules/bilby/bilby/core/sampler/__init__.py", line 178, in run_sampler
result = sampler.run_sampler()
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 186, in run_sampler
self.temporary_outputfiles_basename = temporary_outputfiles_basename
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 147, in temporary_outputfiles_basename
self.outputfiles_basename, self.temporary_outputfiles_basename
File "/opt/anaconda3/envs/dev/lib/python3.7/shutil.py", line 368, in copytree
raise Error(errors)
shutil.Error: [('outdir/pm_fast_tutorial/post_equal_weights.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/post_equal_weights.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/post_equal_weights.dat'"), ('outdir/pm_fast_tutorial/resume.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/resume.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/resume.dat'"), ('outdir/pm_fast_tutorial/live.points', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/live.points', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/live.points'"), ('outdir/pm_fast_tutorial/ev.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/ev.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/ev.dat'"), ('outdir/pm_fast_tutorial/summary.txt', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/summary.txt', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/summary.txt'"), ('outdir/pm_fast_tutorial/stats.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/stats.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/stats.dat'"), ('outdir/pm_fast_tutorial/phys_live.points', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmp_c1pz_a1/phys_live.points', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/phys_live.points'")]
Traceback (most recent call last):
File "fast_tutorial.py", line 106, in <module>
injection_parameters=injection_parameters, outdir=outdir, label=label)
File "/Users/colm/modules/bilby/bilby/core/sampler/__init__.py", line 178, in run_sampler
result = sampler.run_sampler()
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 186, in run_sampler
self.temporary_outputfiles_basename = temporary_outputfiles_basename
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 147, in temporary_outputfiles_basename
self.outputfiles_basename, self.temporary_outputfiles_basename
File "/opt/anaconda3/envs/dev/lib/python3.7/shutil.py", line 368, in copytree
raise Error(errors)
shutil.Error: [('outdir/pm_fast_tutorial/stats.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpof4eyttp/stats.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/stats.dat'"), ('outdir/pm_fast_tutorial/phys_live.points', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpof4eyttp/phys_live.points', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/phys_live.points'"), ('outdir/pm_fast_tutorial/post_separate.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpof4eyttp/post_separate.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/post_separate.dat'"), ('outdir/pm_fast_tutorial', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpof4eyttp/', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial'")]
Traceback (most recent call last):
File "fast_tutorial.py", line 106, in <module>
injection_parameters=injection_parameters, outdir=outdir, label=label)
File "/Users/colm/modules/bilby/bilby/core/sampler/__init__.py", line 178, in run_sampler
result = sampler.run_sampler()
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 186, in run_sampler
self.temporary_outputfiles_basename = temporary_outputfiles_basename
File "/Users/colm/modules/bilby/bilby/core/sampler/pymultinest.py", line 147, in temporary_outputfiles_basename
self.outputfiles_basename, self.temporary_outputfiles_basename
File "/opt/anaconda3/envs/dev/lib/python3.7/shutil.py", line 368, in copytree
raise Error(errors)
shutil.Error: [('outdir/pm_fast_tutorial/ev.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/ev.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/ev.dat'"), ('outdir/pm_fast_tutorial/summary.txt', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/summary.txt', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/summary.txt'"), ('outdir/pm_fast_tutorial/stats.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/stats.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/stats.dat'"), ('outdir/pm_fast_tutorial/phys_live.points', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/phys_live.points', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/phys_live.points'"), ('outdir/pm_fast_tutorial/post_separate.dat', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/post_separate.dat', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial/post_separate.dat'"), ('outdir/pm_fast_tutorial', '/var/folders/ch/sb6js20s48xbkqbd4tn70j9m0000gn/T/tmpdppblma0/', "[Errno 2] No such file or directory: 'outdir/pm_fast_tutorial'")]
```