Eureka
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Published
20 hours ago •
kevin218
kevin218
[Bug]: Plotting problem in S5 when using GPs
FAQ check
- [X] Yes, I checked the FAQ and my question has not been addressed.
Instrument
Light curve fitting (Stages 4-6)
What happened?
When using GPs (celerite, Matern32 kernel) on a S5 fit, there is a problem with the plotting that impedes the function s5_meta = eureka.S5_lightcurve_fitting.s5_fit.fitlc(eventlabel) to finish, thus not enabling S6 to run properly. The actual fit works and finishes (I've tried lsq and dynesty, both works), the problem seems to be with plotting when isplots_S5 >= 3.
Error traceback output
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[17], line 1
----> 1 s5_meta = eureka.S5_lightcurve_fitting.s5_fit.fitlc(eventlabel)
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/eureka/S5_lightcurve_fitting/s5_fit.py:480, in fitlc(eventlabel, ecf_path, s4_meta, input_meta)
475 # Normalize flux and uncertainties to avoid large
476 # flux values
477 flux, flux_err = util.normalize_spectrum(meta, flux,
478 flux_err)
--> 480 meta, params = fit_channel(meta, time_temp, flux, channel,
481 flux_err, eventlabel, params,
482 log, longparamlist, time_units,
483 paramtitles, chanrng, ld_coeffs,
484 xpos, ypos, xwidth, ywidth)
486 # Save results
487 log.writelog('Saving results', mute=(not meta.verbose))
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/eureka/S5_lightcurve_fitting/s5_fit.py:986, in fit_channel(meta, time, flux, chan, flux_err, eventlabel, params, log, longparamlist, time_units, paramtitles, chanrng, ldcoeffs, xpos, ypos, xwidth, ywidth, white)
984 log.writelog("Starting dynesty fit.")
985 model.fitter = 'dynesty'
--> 986 lc_model.fit(model, meta, log, fitter='dynesty')
987 log.writelog("Completed dynesty fit.")
988 log.writelog("-------------------------")
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/eureka/S5_lightcurve_fitting/lightcurve.py:173, in LightCurve.fit(self, model, meta, log, fitter, **kwargs)
170 raise ValueError("{} is not a valid fitter.".format(fitter))
172 # Run the fit
--> 173 fit_model = self.fitter_func(self, model, meta, log, **kwargs)
175 # Store it
176 if fit_model is not None:
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/eureka/S5_lightcurve_fitting/fitters.py:918, in dynestyfitter(lc, model, meta, log, **kwargs)
916 # Plot residuals distribution
917 if meta.isplots_S5 >= 3:
--> 918 plots.plot_res_distr(lc, model, meta, fitter='dynesty')
920 # plot using corner.py
921 if meta.isplots_S5 >= 5:
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/eureka/S5_lightcurve_fitting/plots_s5.py:683, in plot_res_distr(lc, model, meta, fitter)
680 # Mask out any infinities or nans
681 hist_vals = np.ma.masked_invalid(hist_vals)
--> 683 n, bins, patches = plt.hist(hist_vals, alpha=0.5, color='b',
684 edgecolor='b', lw=1)
685 x = np.linspace(-4., 4., 200)
686 px = stats.norm.pdf(x, loc=0, scale=1)
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/matplotlib/pyplot.py:3236, in hist(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)
3211 @_copy_docstring_and_deprecators(Axes.hist)
3212 def hist(
3213 x: ArrayLike | Sequence[ArrayLike],
(...)
3234 BarContainer | Polygon | list[BarContainer | Polygon],
3235 ]:
-> 3236 return gca().hist(
3237 x,
3238 bins=bins,
3239 range=range,
3240 density=density,
3241 weights=weights,
3242 cumulative=cumulative,
3243 bottom=bottom,
3244 histtype=histtype,
3245 align=align,
3246 orientation=orientation,
3247 rwidth=rwidth,
3248 log=log,
3249 color=color,
3250 label=label,
3251 stacked=stacked,
3252 **({"data": data} if data is not None else {}),
3253 **kwargs,
3254 )
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/matplotlib/__init__.py:1465, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
1462 @functools.wraps(func)
1463 def inner(ax, *args, data=None, **kwargs):
1464 if data is None:
-> 1465 return func(ax, *map(sanitize_sequence, args), **kwargs)
1467 bound = new_sig.bind(ax, *args, **kwargs)
1468 auto_label = (bound.arguments.get(label_namer)
1469 or bound.kwargs.get(label_namer))
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/matplotlib/axes/_axes.py:6862, in Axes.hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
6858 # Loop through datasets
6859 for i in range(nx):
6860 # this will automatically overwrite bins,
6861 # so that each histogram uses the same bins
-> 6862 m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
6863 tops.append(m)
6864 tops = np.array(tops, float) # causes problems later if it's an int
File <__array_function__ internals>:180, in histogram(*args, **kwargs)
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/numpy/lib/histograms.py:793, in histogram(a, bins, range, normed, weights, density)
681 r"""
682 Compute the histogram of a dataset.
683
(...)
789
790 """
791 a, weights = _ravel_and_check_weights(a, weights)
--> 793 bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
795 # Histogram is an integer or a float array depending on the weights.
796 if weights is None:
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/numpy/lib/histograms.py:426, in _get_bin_edges(a, bins, range, weights)
423 if n_equal_bins < 1:
424 raise ValueError('`bins` must be positive, when an integer')
--> 426 first_edge, last_edge = _get_outer_edges(a, range)
428 elif np.ndim(bins) == 1:
429 bin_edges = np.asarray(bins)
File ~/miniconda3/envs/eureka/lib/python3.9/site-packages/numpy/lib/histograms.py:323, in _get_outer_edges(a, range)
321 first_edge, last_edge = a.min(), a.max()
322 if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
--> 323 raise ValueError(
324 "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
326 # expand empty range to avoid divide by zero
327 if first_edge == last_edge:
ValueError: autodetected range of [nan, nan] is not finite
What operating system are you using?
MacOS Monterey (12.6.5)
What version of Python are you running?
Python 3.9.7
What Python packages do you have installed?
# packages in environment at /Users/rluque/miniconda3/envs/eureka:
#
# Name Version Build Channel
affine 2.4.0 pyhd8ed1ab_0 conda-forge
alabaster 0.7.16 pyhd8ed1ab_0 conda-forge
anyio 4.2.0 pyhd8ed1ab_0 conda-forge
aom 3.8.1 h078ce10_0 conda-forge
appnope 0.1.4 pyhd8ed1ab_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py39h0f82c59_4 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
arviz 0.17.0 pyhd8ed1ab_0 conda-forge
asciitree 0.3.3 py_2 conda-forge
asdf 3.0.1 pyhd8ed1ab_0 conda-forge
asdf-astropy 0.5.0 pyhd8ed1ab_0 conda-forge
asdf-coordinates-schemas 0.2.0 pyhd8ed1ab_1 conda-forge
asdf-standard 1.0.3 pyhd8ed1ab_0 conda-forge
asdf-transform-schemas 0.4.0 pyhd8ed1ab_0 conda-forge
asdf-unit-schemas 0.1.0 pyhd8ed1ab_0 conda-forge
asdf-wcs-schemas 0.3.0 pyhd8ed1ab_0 conda-forge
asteval 0.9.31 pyhd8ed1ab_0 conda-forge
astraeus 0.3 pypi_0 pypi
astropy 6.0.0 py39h373d45f_0 conda-forge
astropy-healpix 1.0.2 py39h373d45f_1 conda-forge
astropy-iers-data 0.2024.2.12.0.31.0 pyhd8ed1ab_0 conda-forge
astroquery 0.4.6 pypi_0 pypi
astroscrappy 1.1.0 py39h4d8bf0d_2 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
async-lru 2.0.4 pyhd8ed1ab_0 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.7.15 h8117f06_0 conda-forge
aws-c-cal 0.6.9 h4fd42c2_3 conda-forge
aws-c-common 0.9.12 h93a5062_0 conda-forge
aws-c-compression 0.2.17 h4fd42c2_8 conda-forge
aws-c-event-stream 0.4.1 hf6cc7c5_5 conda-forge
aws-c-http 0.8.0 hf1748bb_5 conda-forge
aws-c-io 0.14.3 h8daa835_1 conda-forge
aws-c-mqtt 0.10.1 h7f0f801_3 conda-forge
aws-c-s3 0.5.0 hbb97ff1_2 conda-forge
aws-c-sdkutils 0.1.14 h4fd42c2_0 conda-forge
aws-checksums 0.1.17 h4fd42c2_7 conda-forge
aws-crt-cpp 0.26.1 h9b04f48_9 conda-forge
aws-sdk-cpp 1.11.242 h26e3666_0 conda-forge
azure-core-cpp 1.10.3 he231e37_1 conda-forge
azure-storage-blobs-cpp 12.10.0 h6aa02a4_0 conda-forge
azure-storage-common-cpp 12.5.0 h607ffeb_2 conda-forge
babel 2.14.0 pyhd8ed1ab_0 conda-forge
batman-package 2.4.9 py39h4d8bf0d_1 conda-forge
bayesicfitting 3.2.0 pypi_0 pypi
beautifulsoup4 4.12.3 pyha770c72_0 conda-forge
bleach 6.1.0 pyhd8ed1ab_0 conda-forge
blosc 1.21.5 hc338f07_0 conda-forge
bokeh 2.4.3 pyhd8ed1ab_3 conda-forge
bottleneck 1.3.7 py39hf4a74a7_1 conda-forge
brotli 1.1.0 hb547adb_1 conda-forge
brotli-bin 1.1.0 hb547adb_1 conda-forge
brotli-python 1.1.0 py39hb198ff7_1 conda-forge
brunsli 0.1 h9f76cd9_0 conda-forge
bzip2 1.0.8 h93a5062_5 conda-forge
c-ares 1.26.0 h93a5062_0 conda-forge
c-blosc2 2.13.2 ha57e6be_0 conda-forge
ca-certificates 2024.2.2 hf0a4a13_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cairo 1.18.0 hd1e100b_0 conda-forge
ccdproc 2.4.1 pyhd8ed1ab_0 conda-forge
celerite 0.4.2 py39h425d09f_4 conda-forge
celerite2 0.3.0 pypi_0 pypi
certifi 2024.2.2 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py39he153c15_0 conda-forge
cfitsio 4.3.1 h808cd33_0 conda-forge
cftime 1.6.3 pypi_0 pypi
charls 2.4.2 h13dd4ca_0 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
click 8.1.7 unix_pyh707e725_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.7.2 pyhd8ed1ab_1 conda-forge
cloudpickle 3.0.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
comm 0.2.1 pyhd8ed1ab_0 conda-forge
contourpy 1.2.0 py39he9de807_0 conda-forge
corner 2.2.2 pyhd8ed1ab_0 conda-forge
coverage 7.4.1 py39h17cfd9d_0 conda-forge
crds 11.17.16 pypi_0 pypi
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
cytoolz 0.12.3 py39h17cfd9d_0 conda-forge
dask 2024.2.0 pyhd8ed1ab_0 conda-forge
dask-core 2024.2.0 pyhd8ed1ab_0 conda-forge
dav1d 1.2.1 hb547adb_0 conda-forge
debugpy 1.8.1 py39hf3050f2_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
distributed 2024.2.0 pyhd8ed1ab_0 conda-forge
docutils 0.20.1 py39h2804cbe_3 conda-forge
drizzle 1.15.0 pypi_0 pypi
dynesty 2.1.2 pyhd8ed1ab_0 conda-forge
emcee 3.1.4 pyhd8ed1ab_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
eureka 0.11.dev236+g413299ee pypi_0 pypi
exceptiongroup 1.2.0 pyhd8ed1ab_2 conda-forge
executing 2.0.1 pyhd8ed1ab_0 conda-forge
exotic-ld 3.0.0 pypi_0 pypi
expat 2.5.0 hb7217d7_1 conda-forge
fasteners 0.17.3 pyhd8ed1ab_0 conda-forge
filelock 3.13.1 pypi_0 pypi
flake8 7.0.0 pyhd8ed1ab_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 h77eed37_1 conda-forge
fontconfig 2.14.2 h82840c6_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.49.0 py39h17cfd9d_0 conda-forge
fqdn 1.5.1 pyhd8ed1ab_0 conda-forge
freetype 2.12.1 hadb7bae_2 conda-forge
freexl 2.0.0 hfbad9fb_0 conda-forge
fsspec 2024.2.0 pyhca7485f_0 conda-forge
future 0.18.3 pyhd8ed1ab_0 conda-forge
george 0.4.0 py39hf8cecc8_6 conda-forge
geos 3.12.1 h965bd2d_0 conda-forge
geotiff 1.7.1 h7bcba05_15 conda-forge
gettext 0.21.1 h0186832_0 conda-forge
gflags 2.2.2 hc88da5d_1004 conda-forge
giflib 5.2.1 h1a8c8d9_3 conda-forge
glog 0.6.0 h6da1cb0_0 conda-forge
gwcs 0.20.0 pyhd8ed1ab_0 conda-forge
h11 0.14.0 pyhd8ed1ab_0 conda-forge
h2 4.1.0 pyhd8ed1ab_0 conda-forge
h5netcdf 1.3.0 pyhd8ed1ab_0 conda-forge
h5py 3.10.0 nompi_py39hd62a535_101 conda-forge
hdf4 4.2.15 h2ee6834_7 conda-forge
hdf5 1.14.3 nompi_h5bb55e9_100 conda-forge
hpack 4.0.0 pyh9f0ad1d_0 conda-forge
html5lib 1.1 pypi_0 pypi
httpcore 1.0.3 pyhd8ed1ab_0 conda-forge
httpx 0.26.0 pyhd8ed1ab_0 conda-forge
hyperframe 6.0.1 pyhd8ed1ab_0 conda-forge
icu 73.2 hc8870d7_0 conda-forge
idna 3.6 pyhd8ed1ab_0 conda-forge
image-registration 0.2.8.dev12+gf95d959 pypi_0 pypi
imagecodecs 2024.1.1 py39h60fbabe_0 conda-forge
imageio 2.34.0 pyh4b66e23_0 conda-forge
imagesize 1.4.1 pyhd8ed1ab_0 conda-forge
importlib-metadata 7.0.1 pyha770c72_0 conda-forge
importlib-resources 6.1.1 pyhd8ed1ab_0 conda-forge
importlib_metadata 7.0.1 hd8ed1ab_0 conda-forge
importlib_resources 6.1.1 pyhd8ed1ab_0 conda-forge
iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge
ipykernel 6.29.2 pyh3cd1d5f_0 conda-forge
ipython 8.18.1 pyh707e725_3 conda-forge
ipywidgets 8.1.2 pyhd8ed1ab_0 conda-forge
isoduration 20.11.0 pyhd8ed1ab_0 conda-forge
jaraco-classes 3.3.1 pypi_0 pypi
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
jinja2 3.1.3 pyhd8ed1ab_0 conda-forge
jmespath 1.0.1 pyhd8ed1ab_0 conda-forge
joblib 1.3.2 pyhd8ed1ab_0 conda-forge
json-c 0.17 h40ed0f5_0 conda-forge
json5 0.9.14 pyhd8ed1ab_0 conda-forge
jsonpointer 2.4 py39h2804cbe_3 conda-forge
jsonschema 4.21.1 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.12.1 pyhd8ed1ab_0 conda-forge
jsonschema-with-format-nongpl 4.21.1 pyhd8ed1ab_0 conda-forge
jupyter 1.0.0 pyhd8ed1ab_10 conda-forge
jupyter-lsp 2.2.2 pyhd8ed1ab_0 conda-forge
jupyter_client 8.6.0 pyhd8ed1ab_0 conda-forge
jupyter_console 6.6.3 pyhd8ed1ab_0 conda-forge
jupyter_core 5.7.1 py39h2804cbe_0 conda-forge
jupyter_events 0.9.0 pyhd8ed1ab_0 conda-forge
jupyter_server 2.12.5 pyhd8ed1ab_0 conda-forge
jupyter_server_terminals 0.5.2 pyhd8ed1ab_0 conda-forge
jupyterlab 4.1.1 pyhd8ed1ab_0 conda-forge
jupyterlab_pygments 0.3.0 pyhd8ed1ab_1 conda-forge
jupyterlab_server 2.25.3 pyhd8ed1ab_0 conda-forge
jupyterlab_widgets 3.0.10 pyhd8ed1ab_0 conda-forge
jwst 1.12.2 pypi_0 pypi
jxrlib 1.1 h93a5062_3 conda-forge
kealib 1.5.3 h210d843_0 conda-forge
keyring 24.3.0 pypi_0 pypi
kiwisolver 1.4.5 py39hbd775c9_1 conda-forge
krb5 1.21.2 h92f50d5_0 conda-forge
lazy_loader 0.3 pyhd8ed1ab_0 conda-forge
lcms2 2.16 ha0e7c42_0 conda-forge
lerc 4.0.0 h9a09cb3_0 conda-forge
libabseil 20230802.1 cxx17_h13dd4ca_0 conda-forge
libaec 1.1.2 h13dd4ca_1 conda-forge
libarchive 3.7.2 hcacb583_1 conda-forge
libarrow 15.0.0 h906e67b_2_cpu conda-forge
libarrow-acero 15.0.0 h13dd4ca_2_cpu conda-forge
libarrow-dataset 15.0.0 h13dd4ca_2_cpu conda-forge
libarrow-flight 15.0.0 h7c660a6_2_cpu conda-forge
libarrow-flight-sql 15.0.0 h6dff610_2_cpu conda-forge
libarrow-gandiva 15.0.0 hf757142_2_cpu conda-forge
libarrow-substrait 15.0.0 h3c8a37a_2_cpu conda-forge
libavif16 1.0.4 h76840f6_0 conda-forge
libblas 3.9.0 21_osxarm64_openblas conda-forge
libboost-headers 1.84.0 hce30654_1 conda-forge
libbrotlicommon 1.1.0 hb547adb_1 conda-forge
libbrotlidec 1.1.0 hb547adb_1 conda-forge
libbrotlienc 1.1.0 hb547adb_1 conda-forge
libcblas 3.9.0 21_osxarm64_openblas conda-forge
libcrc32c 1.1.2 hbdafb3b_0 conda-forge
libcurl 8.5.0 h2d989ff_0 conda-forge
libcxx 16.0.6 h4653b0c_0 conda-forge
libdeflate 1.19 hb547adb_0 conda-forge
libedit 3.1.20191231 hc8eb9b7_2 conda-forge
libev 4.33 h93a5062_2 conda-forge
libevent 2.1.12 h2757513_1 conda-forge
libexpat 2.5.0 hb7217d7_1 conda-forge
libffi 3.4.2 h3422bc3_5 conda-forge
libgdal 3.8.3 ha86f356_3 conda-forge
libgfortran 5.0.0 13_2_0_hd922786_3 conda-forge
libgfortran5 13.2.0 hf226fd6_3 conda-forge
libglib 2.78.3 hb438215_0 conda-forge
libgoogle-cloud 2.12.0 h49bbb43_5 conda-forge
libgrpc 1.60.1 hfc68871_0 conda-forge
libiconv 1.17 h0d3ecfb_2 conda-forge
libjpeg-turbo 3.0.0 hb547adb_1 conda-forge
libkml 1.3.0 h1eb4d9f_1018 conda-forge
liblapack 3.9.0 21_osxarm64_openblas conda-forge
libllvm15 15.0.7 h2621b3d_4 conda-forge
libnetcdf 4.9.2 nompi_h291a7c2_113 conda-forge
libnghttp2 1.58.0 ha4dd798_1 conda-forge
libopenblas 0.3.26 openmp_h6c19121_0 conda-forge
libparquet 15.0.0 hf6ce1d5_2_cpu conda-forge
libpng 1.6.42 h091b4b1_0 conda-forge
libpq 16.2 h0f8b458_0 conda-forge
libprotobuf 4.25.1 h810fc01_1 conda-forge
libre2-11 2023.06.02 h1753957_0 conda-forge
librttopo 1.1.0 hc8f776e_15 conda-forge
libsodium 1.0.18 h27ca646_1 conda-forge
libspatialite 5.1.0 h69abc6b_4 conda-forge
libsqlite 3.45.1 h091b4b1_0 conda-forge
libssh2 1.11.0 h7a5bd25_0 conda-forge
libthrift 0.19.0 h026a170_1 conda-forge
libtiff 4.6.0 ha8a6c65_2 conda-forge
libutf8proc 2.8.0 h1a8c8d9_0 conda-forge
libwebp-base 1.3.2 hb547adb_0 conda-forge
libxcb 1.15 hf346824_0 conda-forge
libxml2 2.12.5 h0d0cfa8_0 conda-forge
libzip 1.10.1 ha0bc3c6_3 conda-forge
libzlib 1.2.13 h53f4e23_5 conda-forge
libzopfli 1.0.3 h9f76cd9_0 conda-forge
llvm-openmp 17.0.6 hcd81f8e_0 conda-forge
lmfit 1.2.2 pyhd8ed1ab_1 conda-forge
locket 1.0.0 pyhd8ed1ab_0 conda-forge
lz4 4.3.3 py39hf99b9d6_0 conda-forge
lz4-c 1.9.4 hb7217d7_0 conda-forge
lzo 2.10 h642e427_1000 conda-forge
markdown-it-py 3.0.0 pypi_0 pypi
markupsafe 2.1.5 py39h17cfd9d_0 conda-forge
matplotlib 3.8.3 py39hdf13c20_0 conda-forge
matplotlib-base 3.8.3 py39hbab7938_0 conda-forge
matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge
mc3 3.1.3 pypi_0 pypi
mccabe 0.7.0 pyhd8ed1ab_0 conda-forge
mdit-py-plugins 0.4.0 pypi_0 pypi
mdurl 0.1.2 pypi_0 pypi
minizip 4.0.4 hc35e051_0 conda-forge
mistune 3.0.2 pyhd8ed1ab_0 conda-forge
more-itertools 10.2.0 pypi_0 pypi
msgpack-python 1.0.7 py39he9de807_0 conda-forge
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Thanks for bringing this to our attention! Just to confirm, as far as you can tell from what outputs and plots were made, the fit looks like it converged well to a reasonable set of values and it is only this plot that is causing an issue? I think I've likely encountered this before as well - I'll see if I've already patched this in one of my in prep. pull requests.
I'd say so, but it is hard to judge from the plots because the transit model + systematics or the data - systematics do not get drawn in the figures produced when isplots_S5=1
Sorry, I had a paper that needed to be resubmitted rapidly that is now taken care of. I'll try my best to resolve this before the end of the week.
I'd say so, but it is hard to judge from the plots because the transit model + systematics or the data - systematics do not get drawn in the figures produced when isplots_S5=1
Wait, do you mean that there are no model lines drawn on these figures? That's a different issue that what I thought was going on, but I think I know how to take care of that issue too. Can you confirm what reduced chi-squared value and scatter_mult value you're getting (should be very near 1.0 and >1.0, respectively); I ask since sometimes issues happen when scatter_mult goes negative which can happen with broad Normal priors.
But other issues have sometimes arisen for me where masked values don't play nicely with the GP code for reasons I don't fully understand
In Figs510X:
- 5101: top data-only (no model); middle model-only (no data); bottom nothing
- 5102: top data-only (no model); middle nothing; bottom nothing
- 5103: data-only
- 5104: model-only
scatter_mult is 1.4+-0.01, no negative values (I've already avoided that with U-priors) reduced chi-squared ---
So the fit apparently is done correctly, I've tried dynasty emcee and lsq. All break the code when isplots>=3, but when isplots=1 and the fit finishes, then I have those figures lacking the model and/or data
the models are certainly missing the GP term for what I can see in Fig 5104
FYI, I was able to reproduce this bug with one of my own datasets today which is helping me narrow down the issue. Still working on the solution though
@rluquer, I just came up with a quick patch that works for me - you could try checking out the pull request above