allesfitter is a convenient wrapper around the packages ellc (light curve and RV models), dynesty (static and dynamic nested sampling) emcee (Markov Chain Monte Carlo sampling) and celerite (Gaussian Process models).
License: MIT License
Python 3.8.5
Tutorial 02_transits - simulate_data.py line 61
See attached screen print
below
Hi,
In the documentation in params file Baselines (red noise) per instrument in point six, you say Matern32 instead of SHO.
Cheers!
I'm finding that "Tranist Nr." instead of "Transit Nr." appears as X label in the ttv results report. Please correct this whenever you find it suitable.
ttv_results_SOI_1.pdf
Regards and thanks again.
Hi, the generation of the ns_output crashes when creating an image. Hence, no single_transits_plot, no corner_plot, neither ns_table or ns_derived_table files are generated. It happened after fitting many TESS sectors together (thus many transits). This is the stack:
Image size of 600x129200 pixels is too large. It must be less than 2^16 in each direction.
Traceback (most recent call last):
File "/home/mdevora/.local/lib/python3.8/site-packages/sherlockpipe/fit.py", line 94, in fit
allesfitter.ns_output(allesfit_dir)
File "/usr/local/lib/python3.8/site-packages/allesfitter/nested_sampling_output.py", line 133, in ns_output
fig.savefig( os.path.join(config.BASEMENT.outdir,'ns_fit_per_transit_'+inst+'_'+companion+'.pdf'), bbox_inches='tight' )
File "/usr/local/lib64/python3.8/site-packages/matplotlib/figure.py", line 2311, in savefig
self.canvas.print_figure(fname, **kwargs)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/backend_bases.py", line 2193, in print_figure
self.figure.draw(renderer)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/artist.py", line 41, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/figure.py", line 1863, in draw
mimage._draw_list_compositing_images(
File "/usr/local/lib64/python3.8/site-packages/matplotlib/image.py", line 131, in _draw_list_compositing_images
a.draw(renderer)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/artist.py", line 41, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/cbook/deprecation.py", line 411, in wrapper
return func(*inner_args, **inner_kwargs)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/axes/_base.py", line 2747, in draw
mimage._draw_list_compositing_images(renderer, self, artists)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/image.py", line 131, in _draw_list_compositing_images
a.draw(renderer)
File "/usr/local/lib64/python3.8/site-packages/matplotlib/artist.py", line 37, in draw_wrapper
renderer.start_rasterizing()
File "/usr/local/lib64/python3.8/site-packages/matplotlib/backends/backend_mixed.py", line 88, in start_rasterizing
self._raster_renderer = self._raster_renderer_class(
File "/usr/local/lib64/python3.8/site-packages/matplotlib/backends/backend_agg.py", line 96, in __init__
self._renderer = _RendererAgg(int(width), int(height), dpi)
ValueError: Image size of 600x129200 pixels is too large. It must be less than 2^16 in each direction.
Generating custom plots
Regards.
Hello.
I just started to use allesfitter.
I runs jupyter-notebook in our server and open the notebook in my laptop's browser.
When I run the GUI.ipynb in server and open it in my laptop's browser, I couldn't select directory because tk() needs browser in the server.
Although I installed a browser in server, the directory-GUI was sent via X-window system. This is very slow.
Do you have any ideas to solve this?
Was trying to set RV offsets for an instrument and kept getting a key error, the key being created was using 'flux' instead of 'rv' in the string. Here's the error message:
Did some poking around, looks like in lines 284, 288, and 289 of the 3rd section of Parameters, the string being created was defaulting to using 'flux' instead of looking at the key ('flux' or 'rv'). Here's what they looked like originally:
It was an easy fix, I switched it to match the formatting of the above parameters and it works fine for me now. Here's what it looked like when I got it working:
Not sure if this is just something I've experienced or if this is a wider issue, just letting you know!
Hi,
I'm trying to model an RV system and it's not working, and I noticed that when I do alles = allesfitter.allesclass('./') and alles.data, the rv_err appears as nans, but my rv file is OK... Do you have any idea of why this happens??
Thanks
Thank you for this amazing package. I ran allesitter to do transit fitting in the past and it worked fine. Now I ran with phase curve fitting using the most updated version in github but I encountered a bug i.e. "missing key ln_yerr_median" in part 6.6 "tighter priors on errors and baseline" in GUI that does not allow me proceed to the last part. Do you need more details to debug this problem?
Hi,
when running the allesfitter ttvs module for curves with long gaps (like TESS ones for very separated sectors) the plots of the transits within the curve are very complicated to be assessed: less x-axis values and huge long horizontal monolithic plot. I don't know what can of improvements you could do (maybe splitting the document with n plots -one per dataset without gaps-), but just noticed that it is complicated to be read. I attach an example.
Regards.
I re-ran my old params+settings file (without any changes) using the latest allesfitter version but this time, the NS sampling does not converge at all.
The initial plots were produced look fine but I notice the logz value is always inf which is probably the reason why the sampling takes too long even if I use ns_tol=1 or so:
1249it [03:18, 3.17s/it, batch: 0 | bound: 1 | nc: 1355 | ncall: 125404 | eff(%): 0.992 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 1.000]
Can you prescribe a way to avoid inf evidence?
I am wondering if it is possible to implement in allesfitter a parameterization where Rp/Rs is bandpass-dependent.
I would like to know whether we can select the x_lims of the phase and T - T0 plots of the final ns_fit pdf. For instance, we run a fit where we have selected a 'fast_fit_width=0.15'. The plots are however plotting the entire phase and -4 to 4 hours in the T-T0 plot. Image provided as an example:
We would like to stretch both plots to the size of our data somehow. Is this possible through the usage of some config params? For instance, given that we can use the fast_fit_width I'd expect some way to focus on the proper range in both plots.
Kind regards and thank you in advance.
Bug in the GUI when using the sample_GP_Matern32 as the baseline: when I'm entering my baseline guess in the Advanced params table and then confirming by creating the params.csv file, the baseline gp Matern32 Insigma and the baseline gp Mater32 Inrho are not writing automatically in the params.csv file.
In the derived_params.csv table, the M_earth and M_jup values are printed incorrectly.
(All other values, especially the fitted ones, are correct.)
Hi,
I observed that the final uncertainties provided by Allesfitter are the values from the median of the normal distributions. Is this the standard approach for all the bayesian models? I was observing the typical corner plots and the allesfitter.ns_output function and thought that we are leaving half the probabilities out of the uncertainty by using the median.
I don't want to ask for a change in allesfitter to use another different percentile, but would it be dumb to ask for a parameter in allesfitter.ns_output where we could select the percentile to be used for the errors? I ran out into a case where a ground observation campaign was completely missing the target real ephemerides because the propagated error was unproperly estimated based on the allesfitter results uncertainties and thought that it might be something to be improved. Of course I could just use two or three times the allesfitter uncertainties values, but I'd like better to stick to some statistical threshold given by a higher percentile than the median.
Again, thank you very much for your help. Hope everything goes fine in the ESA : P
Kind regards.
Hi,
I was trying to run allesfitter without wotan installed and it wouldn't run as it wouldn't import flatten in time_series and thus fail in lightcurves when trying to import flatten.
Cheers
Hi,
I'm running allesfitter on a set of rvs and photometry, the rvs have a lot of noise from stellar activity which cannot be modeled with a single GP, so I'm trying my luck fitting 3 planets, thing is eventually, with both nested sampling and MCMC there comes a point where allesfitter says function eanom failed to converge 6.2506241095242920 0.99989602064794081 1.7918474379209218E+069 8.6196351880484955E+068 9.2988391911607227E+068 and then apparently stops. Have you encountered this before? Is it actually crashing or should I be more patient?
Thank you!
I installed allesfitter in a new python 3.9 environment and got the traceback below when I tried to import it. Installing in a python 3.8 environment instead fixed the issue.
In [1]: import allesfitter
Traceback (most recent call last):
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-006995d2ab9b>", line 1, in <module>
import allesfitter
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/__init__.py", line 45, in <module>
from .mcmc import mcmc_fit
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/mcmc.py", line 37, in <module>
from .computer import update_params, calculate_lnlike_total
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/computer.py", line 47, in <module>
from .lightcurves import translate_limb_darkening_from_q_to_u as q_to_u
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/lightcurves.py", line 39, in <module>
from .plotting import tessplot
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/plotting.py", line 36, in <module>
from allesfitter.io import read_csv
File "/Users/mattcraig/miniconda3/envs/alles/lib/python3.9/site-packages/allesfitter/io.py", line 62
X = np.column_stack((*arrays))
^
SyntaxError: can't use starred expression hereI searched but couldn't find in examples and documentation a params.csv file that uses hybrid_poly_2 (and higher order ones) baseline function. I tried to generate the params.csv file using the GUI but when, for example, hybrid_poly_2 is selected, no relevant parameter appears in the generated params.csv. Actually, why no parameter is specified in params.csv when using any hybrid_* baseline functions?
Anyway, I proceeded to run allesfitter.show_initial_guess but it appears from the resulting plots that the baseline function is constant despite changing the order of hybrid_poly_* baseline function in the settings.csv file. hybrid_spline however works fine.
The GUI should not show any planet parameter fields, nor print any keywords like "phase_curve" etc. into the settings file. Also make sure that "do_not_phase_fold" is printed and set to True. Ideally, merge this issue with the major rehaul of all other fitting- and plotting-related keywords.
I got this warning.
What should I do?
Estimating errors and baselines... this will take a couple of minutes. Please be patient, you will get notified once everything is completed.
allesfitter version
--------------------------
v1.1.1
Shifting epochs into the data center:
--------------------------
Companion b
input epoch: 2458931.25
###############################################################################
M1 flux
###############################################################################
Starting...
You are using outdated keywords. Automatically renaming log_err_flux_M1 ---> ln_err_flux_M1. Please fix this before the Duolingo owl comes to get you.
elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
0%| | 3/1000 [00:00<00:42, 23.51it/s]
Is it possible to incorporate additional input covariates e.g. centroid offset, airmass, etc for joint systematics+transit modeling? I have ground-based light curves that have strong in-transit trend that can be explained by telescope pointing offset. Although using e.g. Matern 32 baseline model will fit the trend, it seems reasonable to use physically-motivated covariates to properly take into account the systematics. Is this added complexity necessary at all?
Is there a way to fit double lined binaries within allesfitter, and if so what extra parameters/settings are needed to do this - apologies if this is in the documentation, but I couldn't find anything.
Hey Max! I hope you are well. I'm checking out allesfitter (very nice btw) and I get an error when running the first tutorial. Have you seen this before? Let m know if you need more info. Below is a dump of the output and traceback.
I simply ran the run.py file using the files from the git repo. Cheers, James
allesfitter version
---------------------
v1.2.8
Shifting epochs into the data center
------------------------------------
Companion b
input epoch: 1.09
shifted epoch: 4.5
shifted by 1 periods
Nested Sampling output files already exists in allesfit/results
---------------------------------------------------------------
What do you want to do?
1 : overwrite the output files
2 : abort
1
Plotting collage for companion b...
100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 50.29it/s]
Plotting individual transits for companion b and instrument Leonardo...
100%|█████████████████████████████████████████████| 3/3 [00:00<00:00, 14.83it/s]
Results:
----------
log(Z) = 450.0570070110617 +- 0.13039549733445518
Nr. of posterior samples: 20532
Traceback (most recent call last):
File "run.py", line 23, in <module>
allesfitter.ns_output('allesfit')
File "/Users/jmcc/.pyenv/versions/miniconda3-latest/lib/python3.7/site-packages/allesfitter/nested_sampling_output.py", line 158, in ns_output
results2 = results.copy()
AttributeError: 'Results' object has no attribute 'copy'
>>> results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'results' is not defined
Hi,
I'm noticing that in the ttvs preparation stage the plots are overlapping the time values when they are long sometimes. See example in picture.
Also, when we get the initial guess for the ttvs run, its plots are also overlapping:
And finally for the final ns_fit plots, the same happening:
I'd be great if the size of the font of the x-axis grid values was reduced so they don't overlap.
Kind regards.
Throw an error if luser gives a prior like "normal a b c d" instead of "normal c d"; or a param called "b_perod" instead of "b_period"; or a setting called "shuft_epoch" instead of "shift_epoch"...
I have used the program to fit the EBs, but there were always something wrong on the traceback for Spots_number(I have put the information in the attachment.) Thank you very much.
I tried running the tutorial 1b with the run.py below:
import allesfitter
allesfitter.show_initial_guess('.')
allesfitter.mcmc_fit('.')
allesfitter.mcmc_output('.')When I do that I rapidly get an error raised by emcee:
TypeError Traceback (most recent call last)
~/Dropbox/MSUM/Research/allesfitter/tutorials/01B/mcmc_version/run_mc.py in <module>
22
23 allesfitter.show_initial_guess('.')
---> 24 allesfitter.mcmc_fit('.')
25 allesfitter.mcmc_output('.')
~/miniconda3/envs/alles/lib/python3.8/site-packages/allesfitter/mcmc.py in mcmc_fit(datadir)
205 pool=pool,
206 backend=backend)
--> 207 sampler = run_mcmc(sampler)
208 t1 = timer()
209 timemcmc = (t1-t0)
~/miniconda3/envs/alles/lib/python3.8/site-packages/allesfitter/mcmc.py in run_mcmc(sampler)
183 #::: run the sampler
184 logprint("\nRunning full MCMC")
--> 185 sampler.run_mcmc(p0,
186 (config.BASEMENT.settings['mcmc_total_steps'] - already_completed_steps)/config.BASEMENT.settings['mcmc_thin_by'],
187 thin_by=config.BASEMENT.settings['mcmc_thin_by'],
~/miniconda3/envs/alles/lib/python3.8/site-packages/emcee/ensemble.py in run_mcmc(self, initial_state, nsteps, **kwargs)
441
442 results = None
--> 443 for results in self.sample(initial_state, iterations=nsteps, **kwargs):
444 pass
445
~/miniconda3/envs/alles/lib/python3.8/site-packages/emcee/ensemble.py in sample(self, initial_state, log_prob0, rstate0, blobs0, iterations, tune, skip_initial_state_check, thin_by, thin, store, progress, progress_kwargs)
394 with get_progress_bar(progress, total, **progress_kwargs) as pbar:
395 i = 0
--> 396 for _ in count() if iterations is None else range(iterations):
397 for _ in range(yield_step):
398 # Choose a random moveA full list of the packages in my environment is below:
allesfitter 1.2.4 pypi_0 pypi
anyio 3.3.0 py38h50d1736_0 conda-forge
appnope 0.1.2 py38h50d1736_1 conda-forge
argon2-cffi 20.1.0 py38h96a0964_2 conda-forge
arviz 0.11.2 pyhd8ed1ab_1 conda-forge
astropy 4.3.1 py38hbe852b5_1 conda-forge
async_generator 1.10 py_0 conda-forge
attrs 21.2.0 pyhd8ed1ab_0 conda-forge
babel 2.9.1 pyh44b312d_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
bleach 4.1.0 pyhd8ed1ab_0 conda-forge
brotlipy 0.7.0 py38h96a0964_1001 conda-forge
bzip2 1.0.8 h0d85af4_4 conda-forge
c-ares 1.17.2 h0d85af4_0 conda-forge
ca-certificates 2021.5.30 h033912b_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
celerite 0.4.0 py38he9f00de_1 conda-forge
certifi 2021.5.30 py38h50d1736_0 conda-forge
cffi 1.14.6 py38h1a44b6c_1 conda-forge
cftime 1.5.0 py38h0793694_0 conda-forge
chardet 4.0.0 py38h50d1736_1 conda-forge
charset-normalizer 2.0.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
corner 2.2.1 pyhd8ed1ab_0 conda-forge
cryptography 3.4.7 py38h1fa4640_0 conda-forge
curl 7.79.0 hb861fe1_0 conda-forge
cycler 0.10.0 py_2 conda-forge
debugpy 1.4.1 py38ha048514_0 conda-forge
decorator 5.1.0 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
dynesty 1.1 pyh44b312d_0 conda-forge
ellc 1.8.5 py38hdbaa049_0 conda-forge
emcee 3.1.1 pyh8a188c0_0 conda-forge
entrypoints 0.3 py38h32f6830_1002 conda-forge
freetype 2.10.4 h4cff582_1 conda-forge
h5py 3.4.0 nompi_py38h9f21798_101 conda-forge
hdf4 4.2.15 hefd3b78_3 conda-forge
hdf5 1.12.1 nompi_h2f0ef1a_100 conda-forge
idna 3.2 pyhd3eb1b0_0
importlib-metadata 4.8.1 py38h50d1736_0 conda-forge
importlib_metadata 4.8.1 hd8ed1ab_0 conda-forge
ipykernel 6.4.1 py38h5fd9f69_0 conda-forge
ipython 7.27.0 py38h5fd9f69_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 7.6.5 pyhd8ed1ab_0 conda-forge
jbig 2.1 h0d85af4_2003 conda-forge
jedi 0.18.0 py38h50d1736_2 conda-forge
jinja2 3.0.1 pyhd8ed1ab_0 conda-forge
joblib 1.0.1 pyhd8ed1ab_0 conda-forge
jpeg 9d hbcb3906_0 conda-forge
json5 0.9.6 pyhd3eb1b0_0
jsonschema 3.2.0 py38h32f6830_1 conda-forge
jupyter_client 7.0.3 pyhd8ed1ab_0 conda-forge
jupyter_core 4.8.1 py38h50d1736_0 conda-forge
jupyter_server 1.11.0 pyhd8ed1ab_0 conda-forge
jupyterlab 3.1.12 pyhd8ed1ab_0 conda-forge
jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge
jupyterlab_server 2.8.1 pyhd8ed1ab_0 conda-forge
jupyterlab_widgets 1.0.2 pyhd8ed1ab_0 conda-forge
kiwisolver 1.3.2 py38h12bbefe_0 conda-forge
krb5 1.19.2 hcfbf3a7_0 conda-forge
lcms2 2.12 h577c468_0 conda-forge
lerc 2.2.1 h046ec9c_0 conda-forge
libblas 3.9.0 11_osx64_openblas conda-forge
libcblas 3.9.0 11_osx64_openblas conda-forge
libcurl 7.79.0 hf45b732_0 conda-forge
libcxx 12.0.1 habf9029_0 conda-forge
libdeflate 1.7 h35c211d_5 conda-forge
libedit 3.1.20210714 h9ed2024_0
libev 4.33 haf1e3a3_1 conda-forge
libffi 3.4.2 he49afe7_1 conda-forge
libgfortran 5.0.0 9_3_0_h6c81a4c_23 conda-forge
libgfortran5 9.3.0 h6c81a4c_23 conda-forge
liblapack 3.9.0 11_osx64_openblas conda-forge
libnetcdf 4.8.1 nompi_h6609ca0_101 conda-forge
libnghttp2 1.43.0 h07e645a_0 conda-forge
libopenblas 0.3.17 openmp_h3351f45_1 conda-forge
libpng 1.6.37 h7cec526_2 conda-forge
libsodium 1.0.18 hbcb3906_1 conda-forge
libssh2 1.10.0 h52ee1ee_0 conda-forge
libtiff 4.3.0 h1167814_1 conda-forge
libwebp-base 1.2.1 h0d85af4_0 conda-forge
libzip 1.8.0 h8b0c345_0 conda-forge
llvm-openmp 12.0.1 hda6cdc1_1 conda-forge
lz4-c 1.9.3 he49afe7_1 conda-forge
markupsafe 2.0.1 py38h96a0964_0 conda-forge
matplotlib 3.4.3 py38h50d1736_0 conda-forge
matplotlib-base 3.4.3 py38hc7d2367_0 conda-forge
matplotlib-inline 0.1.3 pyhd8ed1ab_0 conda-forge
mistune 0.8.4 py38h96a0964_1004 conda-forge
mpi 1.0 mpich conda-forge
mpi4py 3.1.1 py38hcd6a4d3_0 conda-forge
mpich 3.4.2 hd33e60e_100 conda-forge
nbclassic 0.3.2 pyhd8ed1ab_0 conda-forge
nbclient 0.5.4 pyhd8ed1ab_0 conda-forge
nbconvert 6.1.0 py38h50d1736_1 conda-forge
nbformat 5.1.3 pyhd8ed1ab_0 conda-forge
ncurses 6.2 h2e338ed_4 conda-forge
nest-asyncio 1.5.1 pyhd8ed1ab_0 conda-forge
netcdf4 1.5.7 nompi_py38h465ef27_102 conda-forge
notebook 6.4.4 pyha770c72_0 conda-forge
numpy 1.21.2 py38h49b9922_0 conda-forge
olefile 0.46 pyh9f0ad1d_1 conda-forge
openjpeg 2.4.0 h6e7aa92_1 conda-forge
openssl 1.1.1l h0d85af4_0 conda-forge
packaging 21.0 pyhd8ed1ab_0 conda-forge
pandas 1.3.3 py38ha53d530_0 conda-forge
pandoc 2.14.2 h0d85af4_0 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
parso 0.8.2 pyhd8ed1ab_0 conda-forge
patsy 0.5.1 py_0 conda-forge
pexpect 4.8.0 py38h32f6830_1 conda-forge
pickleshare 0.7.5 py38h32f6830_1002 conda-forge
pillow 8.3.2 py38hee640a0_0 conda-forge
pip 21.2.4 pyhd8ed1ab_0 conda-forge
prometheus_client 0.11.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.20 pyha770c72_0 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pyerfa 2.0.0 py38h0793694_0 conda-forge
pygments 2.10.0 pyhd8ed1ab_0 conda-forge
pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyrsistent 0.17.3 py38h96a0964_2 conda-forge
pysocks 1.7.1 py38h50d1736_3 conda-forge
python 3.8.12 h17280f6_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.8 2_cp38 conda-forge
pytz 2021.1 pyhd8ed1ab_0 conda-forge
pyzmq 22.3.0 py38hd3b92b6_0 conda-forge
readline 8.1 h05e3726_0 conda-forge
rebound 3.17.3 py38h96a0964_0 conda-forge
requests 2.26.0 pyhd8ed1ab_0 conda-forge
requests-unixsocket 0.2.0 py_0 conda-forge
schwimmbad 0.3.2 py38h50d1736_0 conda-forge
scikit-learn 0.24.2 py38h1021539_1 conda-forge
scipy 1.7.1 py38hd329d04_0 conda-forge
seaborn 0.11.2 hd8ed1ab_0 conda-forge
seaborn-base 0.11.2 pyhd8ed1ab_0 conda-forge
send2trash 1.8.0 pyhd8ed1ab_0 conda-forge
setuptools 58.0.4 py38h50d1736_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sniffio 1.2.0 py38h50d1736_1 conda-forge
sqlite 3.36.0 h23a322b_1 conda-forge
statsmodels 0.12.2 py38hbe852b5_0 conda-forge
terminado 0.12.1 py38h50d1736_0 conda-forge
testpath 0.5.0 pyhd8ed1ab_0 conda-forge
threadpoolctl 2.2.0 pyh8a188c0_0 conda-forge
tk 8.6.11 h5dbffcc_1 conda-forge
tornado 6.1 py38h96a0964_1 conda-forge
tqdm 4.62.2 pyhd8ed1ab_0 conda-forge
traitlets 5.1.0 pyhd8ed1ab_0 conda-forge
typing_extensions 3.10.0.2 pyh06a4308_0
urllib3 1.26.6 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
websocket-client 0.58.0 py38hecd8cb5_4
wheel 0.37.0 pyhd8ed1ab_1 conda-forge
widgetsnbextension 3.5.1 py38h50d1736_4 conda-forge
xarray 0.19.0 pyhd8ed1ab_1 conda-forge
xz 5.2.5 haf1e3a3_1 conda-forge
zeromq 4.3.4 he49afe7_1 conda-forge
zipp 3.5.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h7795811_1010 conda-forge
zstd 1.5.0 h582d3a0_0 conda-forge
(Edited)
Exactly the title. I'd suggest you add wotan in the required packages, otherwise, you can't import allesfitter
Hi,
Is it possible to fit both an offset and a GP to the RVs?
Cheers,
Jose V.
Just like we can use "draw_mcmc_posterior_samples_at_maximum_likelihood()" for MCMC, we also want to be able to do this for Nested Sampling.
If there are any rows that are not recognized (e.g., because of spelling mistakes, missing the _[inst] decoration, or having superfluous _[inst] decorations) in params.csv and settings.csv, the user should be alerted.
Instead of fast_fit=True/False and keys like secondary_eclipse and phase_curve, combine all options into one.
Deprecate the old keys, and implement the new key
fit_type
with options
transit: fit only the transit and mask out the rest (currently done by fast_fit=True)primary_eclipse: same as transittransit_and_occultation: fit the transit and occultation and mask out the rest (currently done by combining fast_fit=True and secondary_eclipse=True)primary_and_secondary_eclipse: same as transit_and_occultationfull_lightcurve: fit the full lightcurve (currently done by fast_fit=False)phase_curve: like full_lightcurve but with the extra phase curve plots and calculations (could be redundant; could instead simply check if the user uses phase_curve related parameters)Add infos for RV errors
"err_rv_inst"
Hi,
I was able to generate the results directory for a 5-candidates system. I found out a very weird system, which might probably point to False candidates. I wanted to do some checks in the derived data when I realised that the last companion was missing the Total and Full Transit Duration from the ns_derived_table files (latex tables and csv file). I'm attaching the entire allesfitter working directory for you to be able to debug the case.
The link to the allesfitter dir (the file is too large to be attached here): https://drive.google.com/file/d/1AQO_MTCutPOQjlfOHgqfFMYrdeElv0O0/view?usp=sharing
I'm running allesfitter 1.2.2.
Regards.
allesclass: Improve the API documentation & shift code into separate file
use method from Florian Lienhard instead of checking for matching periods
from allesfitter import allesclass
alles_old = allesclass(datadir_old)
alles_new = allesclass(datadir_new)
plt.hist(alles_old.posterior_params[‘b_rr’])
plt.hist(alles_new.posterior_params[‘b_rr’])
etc.
automatically for all possible fitted parameters.
I know that it is fine to ignore these warnings but wanted to make you aware of them. At the step of generating output warnings like this are raised (allesfitter version 1.2.4, matplotlib version 3.4.3):
! WARNING:
color is redundantly defined by the 'color' keyword argument and the fmt string "k-" (-> color='k'). The keyword argument will take precedence.
type: <class 'UserWarning'>, file: /Users/mattcraig/miniconda3/envs/alles/lib/python3.8/site-packages/allesfitter/general_output.py, line: 811
TLDR: If you use Python 3.8, update all your packages and use allesfitter version > 1.2.
There have been some issues reported with the "experimental" Python 3.8 releases in the past, but now that the "stable" Python 3.8 seems to be officially supported by Anaconda, we can revisit it.
Until then, if you encounter any issues with Python 3.8, please set up a fresh Python 3.7 environment for allesfitter.
Hi. I'm beginning to use allesfitter for transit fitting. I was wondering why there is no input parameter for the transit duration. Would you please help me with this? I'm using the light curve where I previously found a possible transit candidate with X duration, but a nested sampling run is returning very big uncertainties and the transit duration expands from X to 2*X (the light curve shows a little bit of variability before the transit). I would like to know how could I tell allesfitter that I know an estimation of the duration so the model can adjust better to it.
I'm sorry if I'm misunderstanding something but I'm very new with transit modeling.
Kind regards.
Hello,
I launched a fit for 3 planets. It seemed to run fine, even though it took 14 hours : ) The problem appeared when calling allesfitter.ns_output. This is the stack I found:
Filling the Basement
allesfitter version
---------------------
v1.2.2
Shifting epochs into the data center
------------------------------------
Companion SOI_1
input epoch: 1691.0105488696813
shifted epoch: 1810.854818157887
shifted by 11 periods
Companion SOI_2
input epoch: 1689.975239903956
shifted epoch: 1802.7797961014235
shifted by 6 periods
Companion SOI_3
input epoch: 1693.4444089962353
shifted epoch: 1814.7627571365817
shifted by 9 periods
100%|██████████| 235/235 [11:53<00:00, 3.04s/it]
100%|██████████| 235/235 [11:51<00:00, 3.03s/it]
100%|██████████| 235/235 [12:04<00:00, 3.08s/it]
Traceback (most recent call last):
File "/home/martin/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/plugins/python/helpers/pydev/pydevd.py", line 1438, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/martin/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/home/martin/git_repositories/sherlockpipe/test.py", line 12, in <module>
allesfitter.ns_output("/mnt/0242522242521AAD/dev/workspaces/git_repositories/sherlockpipe/run_tests/analysis/dietrich/TIC467179528_all/fit_0")
File "/home/martin/.local/lib/python3.8/site-packages/allesfitter/nested_sampling_output.py", line 132, in ns_output
fig, axes = afplot_per_transit(posterior_samples_for_plot, inst, companion)
File "/home/martin/.local/lib/python3.8/site-packages/allesfitter/general_output.py", line 794, in afplot_per_transit
xx = np.arange(x[ind][0], x[ind][-1]+dt, dt)
IndexError: index 0 is out of bounds for axis 0 with size 0
Process finished with exit code 1
Running allesfitter 1.2.2 version with Python 3.8.
Results: Only the PDFs appear in the results folder together to the log files, which are not showing the exception. I'm attaching my working allesfitter folder so you can try debugging this.
Expected: I'm missing the rest of files with the fit data, like the ns_table and ns_derived_table CSVs.
Can you tell whether it is a coding bug or I did something wrong with the settings or params files?
Kind regards,
Martín.
I'm fitting an RV data set with only one variable, K. In mcmc_output.py lines 203-205, the code breaks with error:
AttributeError: 'numpy.ndarray' object has no attribute 'set_title'
To fix it, I changed lines 203-205 to:
caxes[i,i].set_title(ctitle)
caxes[i,i].xaxis.set_label_coords(0.5, -0.5)
caxes[i,i].yaxis.set_label_coords(-0.5, 0.5)
This now works and I just wanted to let you know about it.
Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ?
The interface should be very similar to dynesty.
UltraNest is a very reliable tuning-parameter-free algorithm. I have published some examples where the UltraNest algorithm is unbiased while multinest's algorithm (implemented in pymultinest,nestle,dynesty) gives a different answer. UltraNest is a pure-python package and very easy to install with pip or conda.
UltraNest also supports resuming from disk and MPI parallelisation, if that is useful to you. It can be faster than dynesty, especially if you take advantage of vectorization.
Check that RV instrument errors are plotted correctly
Hi. First of all I don't want to state this issue as a bug before gathering info from you. Let me expose what I'm trying:
I'm running allesfitter with:
allesfitter.ns_fit(allesfit_dir) allesfitter.ns_output(allesfit_dir)
I'm providing settings.csv, params.csv with this content:
#companion b astrophysical params,,,,,
SOI_5_rr,0.026,1,trunc_normal 0 1 0.026 0.05,$R_b / R_\star$,
SOI_5_rsuma,0.11011430055938672,1,trunc_normal 0 1 0.11011430055938672 0.1,$(R_\star + R_b) / a_b$,
SOI_5_cosi,0.0,1,uniform 0.0 0.06,$\cos{i_b}$,
SOI_5_epoch,1362.8847530987089,1,trunc_normal -1000000000000.0 1000000000000.0 1362.8847530987089 0.05,$T_{0;b}$,$\mathrm{BJD}$
SOI_5_period,13.189658223908912,1,trunc_normal -1000000000000.0 1000000000000.0 13.189658223908912 0.05,$P_b$,$\mathrm{d}$
SOI_5_f_c,0.0,0,trunc_normal -1 1 0.0 0.0,$\sqrt{e_b} \cos{\omega_b}$,
SOI_5_f_s,0.0,0,trunc_normal -1 1 0.0 0.0,$\sqrt{e_b} \sin{\omega_b}$,
#dilution per instrument,,,,,
dil_lc,0.0,0,trunc_normal 0 1 0.0 0.0,$D_\mathrm{0; lc}$,
#limb darkening coefficients per instrument,,,,,
host_ldc_q1_lc,0.1998,0,uniform 0.0 1.0,$q_{1; \mathrm{lc}}$,
host_ldc_q2_lc,0.5387,0,uniform 0.0 1.0,$q_{2; \mathrm{lc}}$,
#surface brightness per instrument and companion,,,,,
SOI_5_sbratio_lc,0.0,0,trunc_normal 0 1 0.0 0.0,$J_{b; \mathrm{lc}}$,
#albedo per instrument and companion,,,,,
host_geom_albedo_lc,0.0,0,trunc_normal 0 1 0.0 0.0,$A_{\mathrm{geom}; host; \mathrm{lc}}$,
SOI_5_geom_albedo_lc,0.0,0,trunc_normal 0 1 0.0 0.0,$A_{\mathrm{geom}; b; \mathrm{lc}}$,
#gravity darkening per instrument and companion,,,,,
host_gdc_lc,0.0,0,trunc_normal 0 1 0.0 0.0,$Grav. dark._{b; \mathrm{lc}}$,
#spots per instrument and companion,,,,,
#errors per instrument,
ln_err_flux_lc,-7.0,1,uniform -15.0 0.0,$\log{\sigma_\mathrm{lc}}$,$\log{ \mathrm{rel. flux.} }$
#baseline per instrument,
baseline_gp_matern32_lnsigma_flux_lc,0.0,1,uniform -15.0 15.0,$\mathrm{gp: \ln{\sigma} (lc)}$,
baseline_gp_matern32_lnrho_flux_lc,0.0,1,uniform -15.0 15.0,$\mathrm{gp: \ln{\rho} (lc)}$,
As you can see, I'm providing a period of 13.19d and a rr of 0.026.
and params_star.csv with content:
165.691476612015,-16.4061904736575,1.133,0.0645,0.0645,0.992,0.082,0.082,5540.0,220,220,0.1998,0.5387
As you can see, I'm providing a R_star of 1.133 with little uncertainty.
Given these priors, I obtain the next posteriors:
Companion radius SOI_5; $R_\mathrm{SOI_5}$
($\mathrm{R_{\oplus}}$),12.997667627520098,3.6315454554099897,4.360371356068896,derived
SOI_5_rr,0.10548749875981736,0.02878261480797195,0.033790848181602606,$R_b / R_\star$,
Transit depth (dil.) SOI_5; $\delta_\mathrm{tr; dil; SOI_5; lc}$ (ppt),0.0012701552787879322,0.0001110527739457412,0.00011386618070774457,derived
Which seems to be unlikely with the star params I provided because:
With the obtained depth of 1 ppt (0.00127) the R_b/R_star value would be aproximately 0.04 (given sqrt(depth) = R_b/R_star). Am I missing anything? I know I'm not restricting the uncertainties from params.csv but I'd expect that the star parameters that I'm providing as input would act as a hard limit.
Kind regards and again, thank you in advance for your attention.
Hi,
while trying out the TTVs module I realized about something. When I open the ttpvs-per-transit plot of my second candidate, the transit numbering differs to the TTVs initial guess transit numbering. See attached image:
You can see that transit 5 data is skipped in the initial_guess plot (right) and then its transit 5 matches the ttv_preparation transit 6. I guess this is somehow related to the transit lacking some data, but the point is that the numbering should be kept. I don't know whether this is also related to the transit fit missing the model at all (I only see the baseline).
In addition, this also would be affecting the final TTVs plot. See next attached image:
You can see that the transit 6 ttv from the final fit doesn't match the 6th point in the graph, but the 7th. I'm guessing that the transit numbering is matching the ttvs_preparation stage one instead of the transit numbering from the fit. Hence the mess. If this was the case, I don't know where the transit 5 data was obtained either.
Sorry if it sounds chaotic, but these are only some previous findings I've been getting. For clarity purposes, I'm also attaching my entire allesfitter working dir: https://drive.google.com/file/d/18t--3SnycEIjBxgYkNKr5S8uYiGu1uc9/view?usp=sharing
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.