Segmentation fault during run_pycurv.py
#27
Comments
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I've also experienced the same problem recently. No advice yet, I haven't figured out what is different this time compared to the last time I tried to use this (quite some time ago) |
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This issue #24 from two weeks ago has the suggestion to try:
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Thanks Genevieve! I had missed that issue and will try the two suggestions and report back. |
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Okay, so even with a lot of available memory (128GB) and The segmentation faults appear just after the curvature warnings: I've experienced segmentation faults before when using |
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So it looks like the input surfaces I was making are malformed vtk files some of the time. Something is wrong with this new meshing protocol - I am working on it. In the mean time, try turning "ultrafine" off in the config. I'll hopefully have a fix in the next couple days - we are now reproducibly getting this issue in-house which of course makes it much easier for me to fix! |
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Thanks a lot for looking into this @bbarad! Happy to help with any testing from 15/7 on. |
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Thanks for working on a fix! I am also very happy to help test things. |
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I've narrowed things down to a new issue with either pymeshlab's ply writer, vtk's ply reader, or vtk's vtp writer (seems least likely!). The VTP file that gets fed into pycurv etc is malformed - paraview can't load it either. Would either of you mind sharing a pip freeze of your environment that is causing segfaults? |
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OK - I've narrowed it down to just VTK operations, as the ply file that is generated seems to work perfectly with other analysis. |
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Disregard that - any VTK that I make from one of these ply files won't work. Seems to be coming from the pymeshlab side... |
pip freeze (click to expand)conda list (click to expand) |
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Sadly, setting "ultrafine: false" did not help. |
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Yeah, I can't get anything from meshlab or pymeshlab through with any
settings on the workstation. Works fine on my laptop of course... Just a
matter of identifying the difference now!
…On Mon, Jul 8, 2024 at 6:03 PM Genevieve Buckley ***@***.***> wrote:
Sadly, setting "ultrafine: false" did not help.
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You said you have a reproducible example now, would it help to share that so we can both be looking at the same thing? |
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Although I not running on a HPC. Some of the tomograms succesfully run the run_pycurv.py step and some don't. So I will also share my pip freeze and conda list I am using the surface_morphometrics-1.0beta1 version pip freeze``` absl-py==2.0.0 alabaster==0.7.16 annotated-types==0.7.0 app-model==0.2.7 appdirs==1.4.4 asteval==0.9.32 asttokens==2.4.1 astunparse==1.6.3 attrs==23.2.0 Babel==2.15.0 build==1.2.1 cachetools==5.3.2 cachey==0.2.1 certifi @ file:///croot/certifi_1717618050233/work/certifi cffi @ file:///croot/cffi_1714483155441/work click==8.1.7 cloudpickle==3.0.0 comm==0.2.2 contourpy==1.2.0 cryoCAT==0.2.0 cycler==0.12.1 dask==2024.5.1 debugpy==1.8.1 decorator==5.1.1 dill==0.3.8 distlib==0.3.8 docstring_parser==0.16 docutils==0.21.2 doit==0.36.0 emfile==0.3.0 exceptiongroup==1.2.1 executing==2.0.1 filelock==3.13.1 fire==0.5.0 flatbuffers==23.5.26 fonttools==4.45.1 freetype-py==2.4.0 fsspec==2024.5.0 future==1.0.0 gast==0.5.4 google-auth==2.23.4 google-auth-oauthlib==1.0.0 google-pasta==0.2.0 grpcio==1.59.3 h5py==3.10.0 HeapDict==1.0.1 hsluv==5.0.4 imageio==2.33.0 imagesize==1.4.1 importlib-metadata==6.8.0 importlib-resources==6.1.1 in-n-out==0.2.1 iniconfig==2.0.0 ipykernel==6.29.4 ipython==8.18.1 jedi==0.19.1 Jinja2==3.1.4 joblib==1.3.2 jsonschema==4.22.0 jsonschema-specifications==2023.12.1 jupyter_client==8.6.2 jupyter_core==5.7.2 keras==2.14.0 kiwisolver==1.4.5 lazy_loader==0.3 libclang==16.0.6 lmfit==1.2.2 locket==1.0.0 magicgui==0.8.2 Markdown==3.5.1 markdown-it-py==3.0.0 MarkupSafe==2.1.3 matplotlib==3.8.2 matplotlib-inline==0.1.7 mdurl==0.1.2 ml-dtypes==0.2.0 mpi4py_mpich==3.1.5 mrcfile==1.4.3 multiprocess==0.70.16 napari==0.4.19.post1 napari-console==0.0.9 -e git+ssh://git@gitlab.mpcdf.mpg.de/mpibr/scic/napari-particle-extraction.git@a70184f1ec570e1801a4e3d49894648736e0c604#egg=napari_particle_extraction napari-plugin-engine==0.2.0 napari-svg==0.1.10 nest-asyncio==1.6.0 networkx==3.2.1 nibabel==5.2.1 npe2==0.7.5 numpy==1.23.5 numpydoc==1.7.0 nvidia-cublas-cu11==11.11.3.6 nvidia-cublas-cu12==12.5.2.13 nvidia-cuda-cupti-cu11==11.8.87 nvidia-cuda-nvcc-cu11==11.8.89 nvidia-cuda-runtime-cu11==11.8.89 nvidia-cudnn-cu11==8.7.0.84 nvidia-cudnn-cu12==9.1.1.17 nvidia-cufft-cu11==10.9.0.58 nvidia-curand-cu11==10.3.0.86 nvidia-cusolver-cu11==11.4.1.48 nvidia-cusparse-cu11==11.7.5.86 nvidia-nccl-cu11==2.16.5 oauthlib==3.2.2 opt-einsum==3.3.0 packaging==24.0 pandas @ file:///home/conda/feedstock_root/build_artifacts/pandas_1715897627815/work parso==0.8.4 partd==1.4.2 pathlib==1.0.1 pathos==0.3.2 patsy==0.5.6 pexpect==4.9.0 Pillow==10.1.0 Pint==0.23 platformdirs==4.1.0 pluggy==1.5.0 ply==3.11 pooch==1.8.1 pox==0.3.4 ppft==1.7.6.8 prompt_toolkit==3.0.45 protobuf==4.25.1 psutil==5.9.8 psygnal==0.11.1 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==15.0.2 pyasn1==0.5.1 pyasn1-modules==0.3.0 pycairo==1.23.0 pyconify==0.1.6 pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work pycurv @ git+https://github.com/kalemaria/pycurv.git@6dd72f64455b3c8abfa0a152ab54894a27043080 pydantic-compat==0.1.2 pydantic_core==2.18.2 Pygments==2.18.0 PyGObject==3.48.2 pymeshlab==2022.2.post3 PyOpenGL==3.1.7 pyparsing==3.1.1 pyproject_hooks==1.1.0 PyQt5==5.15.10 PyQt5-Qt5==5.15.2 PyQt5-sip==12.13.0 pytest==8.2.1 python-dateutil==2.8.2 pyto @ git+https://github.com/vladanl/Pyto.git@5cd9c556fee69a270dae5257c7dc78288c934a2c pytz==2024.1 PyWavelets==1.5.0 PyYAML==6.0.1 pyzmq==26.0.3 qtconsole==5.5.2 QtPy==2.4.1 referencing==0.35.1 requests-oauthlib==1.3.1 rich==13.7.1 rpds-py==0.18.1 rsa==4.9 scikit-image==0.22.0 scikit-learn==1.4.1.post1 scipy==1.11.4 seaborn==0.13.2 shellingham==1.5.4 sip @ file:///croot/sip_1698675935381/work six==1.16.0 snowballstemmer==2.2.0 Sphinx==7.3.7 sphinxcontrib-applehelp==1.0.8 sphinxcontrib-devhelp==1.0.6 sphinxcontrib-htmlhelp==2.0.5 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.7 sphinxcontrib-serializinghtml==1.1.10 stack-data==0.6.3 starfile==0.5.6 statsmodels==0.14.2 superqt==0.6.6 tabulate==0.9.0 tensorboard==2.14.1 tensorboard-data-server==0.7.2 tensorflow==2.14.1 tensorflow-estimator==2.14.0 tensorflow-io-gcs-filesystem==0.34.0 tensorrt==8.5.3.1 termcolor==2.3.0 threadpoolctl==3.4.0 tifffile==2023.9.26 toml @ file:///tmp/build/80754af9/toml_1616166611790/work tomli==2.0.1 tomli_w==1.0.0 tornado==6.4 traitlets==5.14.3 typer==0.12.3 typing_extensions==4.8.0 tzdata==2024.1 uncertainties==3.1.7 virtualenv==20.25.0 vispy==0.14.2 vtk==9.3.0 wcwidth==0.2.13 Werkzeug==3.0.1 wrapt==1.14.1 zipp==3.17.0 zstandard @ file:///croot/zstandard_1714677652653/work ```conda listpackages in environment at /opt/local/software/miniconda/envs/morphometrics:Name Version Build Channel_libgcc_mutex 0.1 conda_forge conda-forge |
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I am actually running into my problems with the example data, which shows its really generic. Its happening with or without the new ultrafine sampling, so I don't believe thats the issue. I've attached the ply and surface.vtp files. What's odd is that both of these seem to work fine on my m1 mac but not on my ubuntu workstations... |
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The issue always occurs to me when I am running it on the Inner mitochondria membrane and always works with the OMM without any issues! But wow, were you able to find a workaround or a solution that could work? |
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Hi, I installed the morphometrics package a week ago and I've also been steadily getting the Segmentation fault (core dumped) error when executing run_pycurv.py script. I'm working on a Ubuntu 22.04 server that I ssh to, as it has an up-to-date GNU C Library and 64 cores. I've been running the run_pycurv analysis on a segmented volume with four labels (all output .ply and .vtk files look fine to me, I've played around with different parameters). The script normally runs for multiple hours per label before I get the Segmentation fault (core dumped) error. Looking at the log files, I don't see a clear pattern, i.e. a failure during a particular step of the analysis. Sometimes it happens after the estimation principle curvatures and directions has started, sometimes it's after this step has been completed and I already see the "Extracting curvatures for all surfaces" message. The analysis somehow ran through for 1 label on my segmentation, which should be very similar in terms of curvature and thicknesses compared to the others, so I can't really identify a clear pattern. I don't know if this information is of any help but I created a new conda environment with Python 3.9 and installed Pyto and PyCurv. All imports were successful but when I tried running the If it's of any use, my environment information: pip freezeaiohttp==3.8.6 aiosignal==1.3.1 alabaster==0.7.13 alphashape==1.3.1 anyio==4.4.0 app-model==0.2.3 appdirs==1.4.4 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asteval==0.9.31 asttokens==2.4.1 async-lru==2.0.4 async-timeout==4.0.3 attrs==23.1.0 Babel==2.14.0 beautifulsoup4==4.12.3 bleach==6.1.0 build==1.0.3 cachey==0.2.1 caerus==0.1.9 certifi @ file:///croot/certifi_1720453481653/work/certifi cffi @ file:///croot/cffi_1714483155441/work charset-normalizer==3.3.2 click==8.1.7 click-log==0.4.0 cloudpickle==3.0.0 colorama==0.4.6 comm==0.2.0 contourpy==1.2.0 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work dask==2023.12.1 debugpy==1.8.0 decorator==5.1.1 defusedxml==0.7.1 dill==0.3.8 docstring-parser==0.15 docutils==0.17.1 doit==0.36.0 emfile==0.3.0 exceptiongroup==1.2.0 executing==2.0.1 fastjsonschema==2.20.0 filelock==3.13.1 findpeaks==2.6.1 fqdn==1.5.1 freetype-py==2.4.0 frozenlist==1.4.0 fsspec==2023.10.0 future==0.18.3 h11==0.14.0 HeapDict==1.0.1 hsluv==5.0.4 httpcore==1.0.5 httpx==0.27.2 idna==3.8 imageio==2.32.0 imagesize==1.4.1 importlib-metadata==7.0.0 importlib-resources==6.1.1 in-n-out==0.1.9 iniconfig==2.0.0 ipykernel==6.27.1 ipython==8.18.1 ipywidgets==8.1.5 isoduration==20.11.0 jedi==0.19.1 Jinja2==3.1.2 joblib==1.4.2 json5==0.9.25 jsonpointer==3.0.0 jsonschema==4.20.0 jsonschema-specifications==2023.11.2 jupyter==1.0.0 jupyter-console==6.6.3 jupyter-events==0.10.0 jupyter-lsp==2.2.5 jupyter_client==8.6.0 jupyter_core==5.5.1 jupyter_server==2.14.2 jupyter_server_terminals==0.5.3 jupyterlab==4.2.5 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.5 lazy_loader==0.3 lightning-utilities==0.9.0 lmfit==1.2.2 locket==1.0.0 magicgui==0.8.1 markdown-it-py==3.0.0 MarkupSafe==2.1.3 matplotlib @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-suite_1610582847402/work matplotlib-inline==0.1.6 mdurl==0.1.2 mistune==3.0.2 monai==1.3.0 mpmath==1.3.0 mrcfile==1.4.3 multidict==6.0.4 multiprocess==0.70.16 mypy-extensions==1.0.0 napari==0.4.18 napari-console==0.0.9 napari-em-reader==0.1.0 napari-mrcfile-reader==0.2.0 -e git+https://gitlab.mpcdf.mpg.de/mpibr/scic/napari-particle-extraction.git@c1d09b4a2832d2aee52934ba44d6739ee89f0034#egg=napari_particle_extraction napari-plugin-engine==0.2.0 napari-plugin-manager==0.1.0a2 napari-svg==0.1.10 nbclient==0.10.0 nbconvert==7.16.4 nbformat==5.10.4 nest-asyncio==1.5.8 networkx==3.2.1 nibabel==5.2.1 notebook==7.2.2 notebook_shim==0.2.4 npe2==0.7.3 numpy==1.26.0 numpydoc==1.5.0 nvidia-cublas-cu12==12.1.3.1 nvidia-cuda-cupti-cu12==12.1.105 nvidia-cuda-nvrtc-cu12==12.1.105 nvidia-cuda-runtime-cu12==12.1.105 nvidia-cudnn-cu12==8.9.2.26 nvidia-cufft-cu12==11.0.2.54 nvidia-curand-cu12==10.3.2.106 nvidia-cusolver-cu12==11.4.5.107 nvidia-cusparse-cu12==12.1.0.106 nvidia-nccl-cu12==2.18.1 nvidia-nvjitlink-cu12==12.3.101 nvidia-nvtx-cu12==12.1.105 opencv-python==4.8.1.78 overrides==7.7.0 packaging @ file:///croot/packaging_1720101850331/work pandas==2.1.3 pandocfilters==1.5.1 parso==0.8.3 partd==1.4.1 pathlib==1.0.1 pathos==0.3.2 patsy==0.5.6 peakdetect==1.1 pexpect==4.9.0 Pillow==10.0.1 Pint==0.23 platformdirs==4.1.0 pluggy==1.5.0 ply==3.11 pooch==1.8.0 pox==0.3.4 ppft==1.7.6.8 prometheus_client==0.20.0 prompt-toolkit==3.0.43 psutil==5.9.7 psygnal==0.9.5 ptyprocess==0.7.0 pure-eval==0.2.2 pycairo==1.23.0 pyconify==0.1.6 pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work pycurv @ git+https://github.com/kalemaria/pycurv.git@6dd72f64455b3c8abfa0a152ab54894a27043080 pydantic==1.10.13 pydantic-compat==0.1.2 Pygments==2.16.1 PyGObject==3.48.2 pymeshlab==2023.12.post1 PyOpenGL==3.1.7 pyparsing @ file:///opt/conda/conda-bld/pyparsing_1661452539315/work pyproject_hooks==1.0.0 PyQt5==5.15.10 PyQt5-Qt5==5.15.2 PyQt5-sip==12.13.0 pyseg==0.0.9 pytest==8.3.2 python-dateutil==2.8.2 python-json-logger==2.0.7 pyto @ git+https://github.com/vladanl/Pyto.git@5cd9c556fee69a270dae5257c7dc78288c934a2c pytorch-lightning==2.1.2 pytz==2023.3.post1 PyYAML==6.0.1 pyzmq==25.1.2 qtconsole==5.5.1 QtPy==2.4.1 referencing==0.32.0 requests==2.32.3 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rich==13.7.0 rpds-py==0.15.2 Rtree==1.3.0 scikit-image==0.22.0 scikit-learn==1.5.1 scipy==1.11.3 Send2Trash==1.8.3 shapely==2.0.6 shellingham==1.5.4 SimpleITK==2.3.1 sip @ file:///croot/sip_1698675935381/work six @ file:///tmp/build/80754af9/six_1644875935023/work sniffio==1.3.1 snowballstemmer==2.2.0 soupsieve==2.6 Sphinx==4.5.0 sphinxcontrib-applehelp==1.0.4 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 stack-data==0.6.3 statsmodels==0.14.2 superqt==0.6.1 sympy==1.12 terminado==0.18.1 threadpoolctl==3.2.0 tifffile==2023.9.26 tinycss2==1.3.0 toml @ file:///tmp/build/80754af9/toml_1616166611790/work tomli==2.0.1 tomli_w==1.0.0 torch==2.1.1 torchmetrics==1.2.0 tornado==6.4 tqdm==4.66.5 traitlets==5.14.0 trimesh==4.4.9 triton==2.1.0 typer==0.9.0 types-python-dateutil==2.9.0.20240821 typing_extensions==4.8.0 tzdata==2023.3 uncertainties==3.1.7 uri-template==1.3.0 urllib3==2.2.2 vispy==0.12.2 vtk==9.3.1 wcwidth==0.2.12 webcolors==24.8.0 webencodings==0.5.1 websocket-client==1.8.0 wget==3.2 widgetsnbextension==4.0.13 wrapt==1.16.0 xarray==2023.12.0 yarl==1.9.2 zipp==3.17.0 zstandard @ file:///croot/zstandard_1677013143055/workconda list_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _x86_64-microarch-level 1 2_x86_64 conda-forge alsa-lib 1.2.12 h4ab18f5_0 conda-forge anyio 4.4.0 pypi_0 pypi argon2-cffi 23.1.0 pypi_0 pypi argon2-cffi-bindings 21.2.0 pypi_0 pypi arrow 1.3.0 pypi_0 pypi async-lru 2.0.4 pypi_0 pypi at-spi2-atk 2.38.0 h0630a04_3 conda-forge at-spi2-core 2.40.3 h0630a04_0 conda-forge atk-1.0 2.38.0 h04ea711_2 conda-forge attr 2.5.1 h166bdaf_1 conda-forge beautifulsoup4 4.12.3 pypi_0 pypi blas 1.0 openblas bleach 6.1.0 pypi_0 pypi bzip2 1.0.8 h5eee18b_6 ca-certificates 2024.7.2 h06a4308_0 cairo 1.18.0 hbb29018_2 conda-forge cairomm-1.16 1.16.2 h7e731d7_1 conda-forge certifi 2024.7.4 py39h06a4308_0 cffi 1.16.0 py39h5eee18b_1 charset-normalizer 3.3.2 pypi_0 pypi cycler 0.11.0 pyhd3eb1b0_0 dbus 1.13.18 hb2f20db_0 defusedxml 0.7.1 pypi_0 pypi dill 0.3.8 pypi_0 pypi doit 0.36.0 pypi_0 pypi epoxy 1.5.10 h166bdaf_1 conda-forge expat 2.6.2 h6a678d5_0 fastjsonschema 2.20.0 pypi_0 pypi font-ttf-dejavu-sans-mono 2.37 hd3eb1b0_0 font-ttf-inconsolata 2.001 hcb22688_0 font-ttf-source-code-pro 2.030 hd3eb1b0_0 font-ttf-ubuntu 0.83 h8b1ccd4_0 fontconfig 2.14.2 h14ed4e7_0 conda-forge fonts-anaconda 1 h8fa9717_0 fonts-conda-ecosystem 1 hd3eb1b0_0 fqdn 1.5.1 pypi_0 pypi freetype 2.12.1 h267a509_2 conda-forge fribidi 1.0.10 h7b6447c_0 gdk-pixbuf 2.42.12 hb9ae30d_0 conda-forge gettext 0.22.5 h59595ed_2 conda-forge gettext-tools 0.22.5 h59595ed_2 conda-forge glib 2.80.3 h8a4344b_1 conda-forge glib-tools 2.80.3 h73ef956_1 conda-forge gmp 6.3.0 hac33072_2 conda-forge graph-tool 2.76 py39hf2e61fb_100 conda-forge graph-tool-base 2.76 py39h45f8f1b_100 conda-forge graphite2 1.3.14 h295c915_1 gst-plugins-base 1.24.6 hbaaba92_0 conda-forge gstreamer 1.24.6 haf2f30d_0 conda-forge gtk3 3.24.43 hd6cba2e_1 conda-forge h11 0.14.0 pypi_0 pypi harfbuzz 9.0.0 hfac3d4d_0 conda-forge hicolor-icon-theme 0.17 h06a4308_2 httpcore 1.0.5 pypi_0 pypi httpx 0.27.2 pypi_0 pypi icu 73.2 h59595ed_0 conda-forge idna 3.8 pypi_0 pypi iniconfig 2.0.0 pypi_0 pypi ipywidgets 8.1.5 pypi_0 pypi isoduration 20.11.0 pypi_0 pypi joblib 1.4.2 pypi_0 pypi json5 0.9.25 pypi_0 pypi jsonpointer 3.0.0 pypi_0 pypi jupyter 1.0.0 pypi_0 pypi jupyter-console 6.6.3 pypi_0 pypi jupyter-events 0.10.0 pypi_0 pypi jupyter-lsp 2.2.5 pypi_0 pypi jupyter-server 2.14.2 pypi_0 pypi jupyter-server-terminals 0.5.3 pypi_0 pypi jupyterlab 4.2.5 pypi_0 pypi jupyterlab-pygments 0.3.0 pypi_0 pypi jupyterlab-server 2.27.3 pypi_0 pypi jupyterlab-widgets 3.0.13 pypi_0 pypi keyutils 1.6.1 h166bdaf_0 conda-forge kiwisolver 1.4.4 py39h6a678d5_0 krb5 1.21.3 h659f571_0 conda-forge lame 3.100 h7b6447c_0 lcms2 2.16 hb7c19ff_0 conda-forge ld_impl_linux-64 2.38 h1181459_1 lerc 4.0.0 h27087fc_0 conda-forge libasprintf 0.22.5 h661eb56_2 conda-forge libasprintf-devel 0.22.5 h661eb56_2 conda-forge libboost 1.84.0 hba137d9_3 conda-forge libboost-python 1.84.0 py39h1eb36c5_5 conda-forge libcap 2.69 h0f662aa_0 conda-forge libclang-cpp15 15.0.7 default_h127d8a8_5 conda-forge libclang13 18.1.8 default_h9def88c_1 conda-forge libcups 2.3.3 h4637d8d_4 conda-forge libdeflate 1.21 h4bc722e_0 conda-forge libedit 3.1.20230828 h5eee18b_0 libevent 2.1.12 hdbd6064_1 libexpat 2.6.2 h59595ed_0 conda-forge libffi 3.4.4 h6a678d5_1 libflac 1.4.3 h59595ed_0 conda-forge libgcc-ng 14.1.0 h77fa898_0 conda-forge libgcrypt 1.11.0 h4ab18f5_1 conda-forge libgettextpo 0.22.5 h59595ed_2 conda-forge libgettextpo-devel 0.22.5 h59595ed_2 conda-forge libgfortran-ng 11.2.0 h00389a5_1 libgfortran5 11.2.0 h1234567_1 libgirepository 1.80.1 h003a4f0_0 conda-forge libglib 2.80.3 h8a4344b_1 conda-forge libgomp 14.1.0 h77fa898_0 conda-forge libgpg-error 1.50 h4f305b6_0 conda-forge libiconv 1.17 hd590300_2 conda-forge libjpeg-turbo 3.0.3 h5eee18b_0 libllvm15 15.0.7 hb3ce162_4 conda-forge libllvm18 18.1.8 h8b73ec9_1 conda-forge libnsl 2.0.1 hd590300_0 conda-forge libogg 1.3.5 h27cfd23_1 libopenblas 0.3.21 h043d6bf_0 libopus 1.3.1 h7b6447c_0 libpng 1.6.43 h2797004_0 conda-forge libpq 16.4 h482b261_0 conda-forge librsvg 2.58.2 h9564881_1 conda-forge libsndfile 1.2.2 hc60ed4a_1 conda-forge libsqlite 3.46.0 hde9e2c9_0 conda-forge libstdcxx-ng 14.1.0 hc0a3c3a_0 conda-forge libsystemd0 255 h3516f8a_1 conda-forge libtiff 4.6.0 h46a8edc_4 conda-forge libuuid 2.38.1 h0b41bf4_0 conda-forge libvorbis 1.3.7 h7b6447c_0 libwebp-base 1.4.0 hd590300_0 conda-forge libxcb 1.16 hd590300_0 conda-forge libxcrypt 4.4.36 hd590300_1 conda-forge libxkbcommon 1.7.0 h2c5496b_1 conda-forge libxml2 2.12.7 h4c95cb1_3 conda-forge libzlib 1.3.1 h4ab18f5_1 conda-forge lz4-c 1.9.4 h6a678d5_1 matplotlib 3.3.3 py39hf3d152e_0 conda-forge matplotlib-base 3.3.3 py39h2fa2bec_0 conda-forge mistune 3.0.2 pypi_0 pypi mpg123 1.32.6 h59595ed_0 conda-forge multiprocess 0.70.16 pypi_0 pypi mysql-common 8.3.0 h70512c7_5 conda-forge mysql-libs 8.3.0 ha479ceb_5 conda-forge nbclient 0.10.0 pypi_0 pypi nbconvert 7.16.4 pypi_0 pypi nbformat 5.10.4 pypi_0 pypi ncurses 6.5 h59595ed_0 conda-forge nibabel 5.2.1 pypi_0 pypi notebook 7.2.2 pypi_0 pypi notebook-shim 0.2.4 pypi_0 pypi nspr 4.35 h6a678d5_0 nss 3.103 h593d115_0 conda-forge numpy 1.26.4 py39heeff2f4_0 numpy-base 1.26.4 py39h8a23956_0 openjpeg 2.5.2 he7f1fd0_0 openssl 3.3.1 h4bc722e_2 conda-forge overrides 7.7.0 pypi_0 pypi packaging 24.1 py39h06a4308_0 pandas 2.2.2 py39hfc16268_1 conda-forge pandocfilters 1.5.1 pypi_0 pypi pango 1.54.0 h4c5309f_1 conda-forge pathlib 1.0.1 py39hf3d152e_7 conda-forge pathos 0.3.2 pypi_0 pypi patsy 0.5.6 pypi_0 pypi pcre2 10.44 h0f59acf_0 conda-forge pillow 10.4.0 py39h16a7006_0 conda-forge pip 24.2 pyhd8ed1ab_0 conda-forge pixman 0.43.2 h59595ed_0 conda-forge pluggy 1.5.0 pypi_0 pypi ply 3.11 py39h06a4308_0 pox 0.3.4 pypi_0 pypi ppft 1.7.6.8 pypi_0 pypi prometheus-client 0.20.0 pypi_0 pypi pthread-stubs 0.3 h0ce48e5_1 pulseaudio-client 17.0 hb77b528_0 conda-forge pybind11-abi 4 hd3eb1b0_1 pycairo 1.23.0 py39hd1222b9_0 pycparser 2.21 pyhd3eb1b0_0 pycurv 2.0.0 pypi_0 pypi pygobject 3.48.2 py39hb25b1be_0 conda-forge pymeshlab 2023.12.post1 pypi_0 pypi pyparsing 3.0.9 py39h06a4308_0 pyqt 5.15.9 py39h52134e7_5 conda-forge pyqt5-sip 12.12.2 py39h3d6467e_5 conda-forge pyseg 0.0.9 pypi_0 pypi pytest 8.3.2 pypi_0 pypi python 3.9.19 h0755675_0_cpython conda-forge python-dateutil 2.9.0post0 py39h06a4308_2 python-json-logger 2.0.7 pypi_0 pypi python-tzdata 2023.3 pyhd3eb1b0_0 python_abi 3.9 4_cp39 conda-forge pyto 1.9.2 pypi_0 pypi pytz 2024.1 py39h06a4308_0 qt-main 5.15.8 h320f8da_24 conda-forge readline 8.2 h5eee18b_0 requests 2.32.3 pypi_0 pypi rfc3339-validator 0.1.4 pypi_0 pypi rfc3986-validator 0.1.1 pypi_0 pypi scikit-learn 1.5.1 pypi_0 pypi scipy 1.13.1 py39heeff2f4_0 send2trash 1.8.3 pypi_0 pypi setuptools 72.1.0 py39h06a4308_0 sigcpp-3.0 3.6.0 h59595ed_0 conda-forge sip 6.7.12 py39h6a678d5_0 six 1.16.0 pyhd3eb1b0_1 sniffio 1.3.1 pypi_0 pypi soupsieve 2.6 pypi_0 pypi sparsehash 2.0.4 hcb278e6_1 conda-forge statsmodels 0.14.2 pypi_0 pypi terminado 0.18.1 pypi_0 pypi tinycss2 1.3.0 pypi_0 pypi tk 8.6.13 noxft_h4845f30_101 conda-forge toml 0.10.2 pyhd3eb1b0_0 tomli 2.0.1 py39h06a4308_0 tornado 6.4.1 py39h5eee18b_0 tqdm 4.66.5 pypi_0 pypi types-python-dateutil 2.9.0.20240821 pypi_0 pypi tzdata 2024a h04d1e81_0 uri-template 1.3.0 pypi_0 pypi urllib3 2.2.2 pypi_0 pypi vtk 9.3.1 pypi_0 pypi wayland 1.23.0 h5291e77_0 conda-forge webcolors 24.8.0 pypi_0 pypi webencodings 0.5.1 pypi_0 pypi websocket-client 1.8.0 pypi_0 pypi wheel 0.43.0 py39h06a4308_0 widgetsnbextension 4.0.13 pypi_0 pypi xcb-util 0.4.1 hb711507_2 conda-forge xcb-util-image 0.4.0 hb711507_2 conda-forge xcb-util-keysyms 0.4.1 hb711507_0 conda-forge xcb-util-renderutil 0.3.10 hb711507_0 conda-forge xcb-util-wm 0.4.2 hb711507_0 conda-forge xkeyboard-config 2.42 h4ab18f5_0 conda-forge xorg-compositeproto 0.4.2 h7f98852_1001 conda-forge xorg-damageproto 1.2.1 h7f98852_1002 conda-forge xorg-fixesproto 5.0 h7f98852_1002 conda-forge xorg-inputproto 2.3.2 h7f98852_1002 conda-forge xorg-kbproto 1.0.7 h7f98852_1002 conda-forge xorg-libice 1.1.1 hd590300_0 conda-forge xorg-libsm 1.2.4 h7391055_0 conda-forge xorg-libx11 1.8.9 hb711507_1 conda-forge xorg-libxau 1.0.11 hd590300_0 conda-forge xorg-libxcomposite 0.4.6 h0b41bf4_1 conda-forge xorg-libxcursor 1.2.0 h0b41bf4_1 conda-forge xorg-libxdamage 1.1.5 h7f98852_1 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xorg-libxext 1.3.4 h0b41bf4_2 conda-forge xorg-libxfixes 5.0.3 h7f98852_1004 conda-forge xorg-libxi 1.7.10 h4bc722e_1 conda-forge xorg-libxinerama 1.1.5 h27087fc_0 conda-forge xorg-libxrandr 1.5.2 h7f98852_1 conda-forge xorg-libxrender 0.9.11 hd590300_0 conda-forge xorg-libxtst 1.2.5 h4bc722e_0 conda-forge xorg-libxxf86vm 1.1.5 h4bc722e_1 conda-forge xorg-randrproto 1.5.0 h7f98852_1001 conda-forge xorg-recordproto 1.14.2 h7f98852_1002 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-util-macros 1.19.0 h27cfd23_2 xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h27cfd23_1007 xz 5.4.6 h5eee18b_1 zlib 1.3.1 h4ab18f5_1 conda-forge zstandard 0.19.0 py39h5eee18b_0 zstd 1.5.6 ha6fb4c9_0 conda-forgeThanks a lot! |
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This is super helpful! I am going to go through the Pycurv tests with different Pyto/Pycurv/Python versions and try to ID an older state that is functional. |
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We have officially found a working system thanks to @hemanthkapa. So far this works on both my system and his - would you all be willing to reinstall from main (not a release) and confirm it resolves on your end? We'll be publishing a container with this dependency set so I'd like to get it right. |
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Hi, sorry for only replying now but I reinstalled and tested the run_pycurv script with 2 meshes today and it works! I no longer get the Segmentation fault error. Many many thanks to both of you! :)) |
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Amazing! I am so so so happy to hear this. I am sorry everyone for the looooong delay on this issue. I've been struggling with the balance of programming myself and managing the lab - which is why I am so happy to have Hemanth on board. I am gonna hold off on closing for the moment till we get the new version validated and released. |
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Hi, I tested it as well on an Centos 8 workstation. I was able to run the pycurv on the meshes, but for some reason it was much slower than the earlier versions that worked. But I am happy that is working. And I think a container might help me to run it on the clusters, so hopefully waiting for the container implementation |
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Wonderful! The speed difference is due to some changes to the way we make our meshes to improve their robustness against quantization/stepping artifacts for thinner segmentations. This improvement is called with the flag "ultrafine", which is enabled by default (since we've switched 100% to that workflow). Container testing is running this week, we should have a published container as early as thursday or as late as mid-next-week. Turns out having someone focused on development really kickstarts things! |
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Closing this now - thanks again everyone. |
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Amazing, thank you for finding a solution to this! Didn't have a chance to try this out yet but will report once I have. |
Hi,
I'm occasionally getting segmentation faults while running
python run_pycurv.py config.yml ${i}.surface.vtpas part of the configurable pipeline. However, the meshes created duringpython segmentation_to_meshes.py config.ymllook good judging by visual inspection.Before going into debugging, I was wondering whether this is would perhaps be a known problem and whether there'd be some go-to parameters / things to look out for for avoiding the seg faults?
Thanks!
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