chembl/fpsim2

Simple package for fast molecular similarity searches

FPSim2: Simple package for fast molecular similarity searches

FPSim2 is a small NumPy centric Python/C++ RDKit based package to run fast compound similarity searches. FPSim2 performs better with high search thresholds (>=0.7). Currently used in the ChEMBL interface.

Highlights:

• Using CPU POPCNT instruction
• Bounds for sublinear speedups from 10.1021/ci600358f
• A compressed file format with optimised read speed based in PyTables and BLOSC
• Fast multicore CPU and GPU similarity searches
• In memory and on disk search modes
• Distance matrix calculation

Installation

pip install fpsim2

or

conda install -c conda-forge fpsim2

Documentation

Documentation is available at https://chembl.github.io/FPSim2/

Trying it online

To try out FPSim2 interactively in your web browser, just click on the binder icon

fpsim2's Issues

FPSim only supports integer ids for molecules

Hello all:
I installed the latest version of FPSim2 on Centos 7 and tried to create a db.
However, I am not able to use the vendor's provided name for mol_id_prop.
Is it possible to allow strings in id?
Best,
Markus

substructure SMARTS matching

Hi,
I am wondering if FPSim2 supports substructure search by SMARTS patterns ?

Thanks

Feature: create_db_file() should also accept iterables of RDKitMol objects

I've started wrapping FPSim2 within a system where I have already validated my data and created RDKit mol objects from it. With the current implementation of FPSim2, initialising the DB would mean recreating the objects since create_db_file() can accept an iterable but will always try and create an RDKit mol (rdmol = load_molecule(mol_string)). With a simple change, create_db_file() could accept a parameter input_type='rdkit_mol' and load the molecules directly. It would be useful to speed up the initialisation of DBs.

add sqla query mol supplier

so it will be possible to create a fp file/update it from a db connection

Ochiai/Cosine similarity

Hi! If I were to implement the querying by Ochiai/Cosine similarity measure (as described by equation 48 here), would you be interested in merging it? I will probably implement it in my fork anyways. :))

Multiple query support

Is there a way multiple queries can be passed inside the engine (without a for loop) and parallelised inside.

Faced an Error while running with n_workers=70

File "/opt/conda/lib/python3.7/site-packages/FPSim2/FPSim2.py", line 193, in _parallel_run
c_indexes = [[x, x + chunk_size] for x in range(i_start, i_end, chunk_size)]
ValueError: range() arg 3 must not be zero

error install

I try to install via conda

conda install -c efelix fpsim2
but got this error

UnsatisfiableError: The following specifications were found to be in conflict:
  - fpsim2

what should i do?

Conda cant f

Hi,
I tried running conda install -c conda-forget fpmsim2

but I get an error saying that fpmsim2 cant be found from current channels.
Current channels:   - https://conda.anaconda.org/conda-forge/win-64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://repo.anaconda.com/pkgs/main/win-64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/win-64
  - https://repo.anaconda.com/pkgs/r/noarch
  - https://repo.anaconda.com/pkgs/msys2/win-64
  - https://repo.anaconda.com/pkgs/msys2/noarch

similarity matrix id don't match smi ids.

Hello, I really like the package but recently started to play around with the similarity matrix, which I've change into a edge list using networkx. However I've been finding that the id in the edge list don't make sense and the ids have got scrambled somewhere along the line... for example two molecules that are meant to be identical in the edge list are very clearly different if I look up the id in the smiles file that the .h5 file was generated from.

References for substructure search methodology

Could you please recommend any references on the use of the approximate substructure search that is implemented in FPSim2, based on the Tversky metric and on the RDKit Pattern Fingerprint?

I read both the FPSim2 documentation and the RDKit documentation on the pattern fingerprint, but I haven't found any reference. Am I missing something?

I would like references explaining how this approximate method compares to full substructure searches.
In particular, I am interested in knowing:

• is stereochemistry considered in any way in the approximate method?
• is the approximate method more biased to false positives or false negatives? The set produced by an approximate substructure search will mostly include compounds that are not real substructure matches, and should be discarded by an additional full substructure search? Or will the approximate search exclude a lot of compounds that a full search would have retrieved?

I would be grateful if you could provide any reference!

parallelise fp file creation

this should save some time when creating big fp files

Import Error

Hi,

I have recently install the FPSim2 but unable to load the library. This is the following error.

import FPSim2

Here is my output.

--------------------------------------------------------------------------
ImportError                              Traceback (most recent call last)
<ipython-input-1-0ac5481f3631> in <module>
----> 1 import FPSim2

~\Anaconda3\envs\similarity_search\lib\site-packages\FPSim2\__init__.py in <module>
----> 1 from .FPSim2 import FPSim2Engine

~\Anaconda3\envs\similarity_search\lib\site-packages\FPSim2\FPSim2.py in <module>
      1 import concurrent.futures as cf
      2 import numpy as np
----> 3 import tables as tb
      4 from .io import S_INDEXS, load_fps, load_query, get_bounds_range
      5 from .FPSim2lib import _similarity_search, _substructure_search, sort_results

~\Anaconda3\envs\similarity_search\lib\site-packages\tables\__init__.py in <module>
     97 
     98 # Necessary imports to get versions stored on the cython extension
---> 99 from .utilsextension import (
    100     get_pytables_version, get_hdf5_version, blosc_compressor_list,
    101     blosc_compcode_to_compname_ as blosc_compcode_to_compname,

ImportError: DLL load failed: The specified module could not be found.

This is what I have installed in the environment.

#
# Name                    Version                   Build  Channel
argon2-cffi               20.1.0           py37he774522_1    anaconda
async_generator           1.10             py37h28b3542_0    anaconda
attrs                     20.2.0                     py_0    anaconda
backcall                  0.2.0                      py_0    anaconda
blas                      1.0                         mkl  
bleach                    3.2.1                      py_0    anaconda
blosc                     1.21.0               h19a0ad4_0  
bottleneck                1.3.2            py37h2a96729_1  
bzip2                     1.0.8                he774522_0  
ca-certificates           2020.10.14                    0    anaconda
cairo                     1.14.12              hf171d8a_3  
certifi                   2020.6.20                py37_0    anaconda
cffi                      1.14.3           py37h7a1dbc1_0    anaconda
colorama                  0.4.4                      py_0    anaconda
decorator                 4.4.2                      py_0    anaconda
defusedxml                0.6.0                      py_0    anaconda
entrypoints               0.3                      py37_0    anaconda
fpsim2                    0.1.5                    py37_0    efelix
freetype                  2.10.4               hd328e21_0  
greenlet                  1.1.1            py37hd77b12b_0  
hdf5                      1.10.4               h7ebc959_0  
icc_rt                    2019.0.0             h0cc432a_1  
icu                       58.2                 ha925a31_3  
importlib-metadata        3.10.0           py37haa95532_0  
intel-openmp              2021.3.0          haa95532_3372  
ipykernel                 5.3.4            py37h5ca1d4c_0    anaconda
ipython                   7.18.1           py37h5ca1d4c_0    anaconda
ipython_genutils          0.2.0                    py37_0    anaconda
ipywidgets                7.5.1                      py_1    anaconda
jedi                      0.17.2                   py37_0    anaconda
jinja2                    2.11.2                     py_0    anaconda
jpeg                      9b                   hb83a4c4_2  
jsonschema                3.0.2                    py37_0    anaconda
jupyter                   1.0.0                    py37_7    anaconda
jupyter_client            6.1.7                      py_0    anaconda
jupyter_console           6.2.0                      py_0    anaconda
jupyter_core              4.6.3                    py37_0    anaconda
jupyterlab_pygments       0.1.2                      py_0    anaconda
libboost                  1.67.0               hd9e427e_4  
libpng                    1.6.37               h2a8f88b_0  
libsodium                 1.0.18               h62dcd97_0    anaconda
libtiff                   4.2.0                hd0e1b90_0  
lz4-c                     1.9.3                h2bbff1b_1  
lzo                       2.10                 he774522_2  
m2w64-gcc-libgfortran     5.3.0                         6  
m2w64-gcc-libs            5.3.0                         7  
m2w64-gcc-libs-core       5.3.0                         7  
m2w64-gmp                 6.1.0                         2  
m2w64-libwinpthread-git   5.0.0.4634.697f757               2  
markupsafe                1.1.1            py37hfa6e2cd_1    anaconda
mistune                   0.8.4           py37hfa6e2cd_1001    anaconda
mkl                       2021.3.0           haa95532_524  
mkl-service               2.4.0            py37h2bbff1b_0  
mkl_fft                   1.3.0            py37h277e83a_2  
mkl_random                1.2.2            py37hf11a4ad_0  
mock                      4.0.3              pyhd3eb1b0_0  
msys2-conda-epoch         20160418                      1  
nbclient                  0.5.1                      py_0    anaconda
nbconvert                 6.0.7                    py37_0    anaconda
nbformat                  5.0.8                      py_0    anaconda
nest-asyncio              1.4.1                      py_0    anaconda
notebook                  6.1.4                    py37_0    anaconda
numexpr                   2.7.3            py37hb80d3ca_1  
numpy                     1.20.3           py37ha4e8547_0  
numpy-base                1.20.3           py37hc2deb75_0  
olefile                   0.46                     py37_0  
openssl                   1.1.1k               h2bbff1b_0  
packaging                 20.4                       py_0    anaconda
pandas                    1.3.1            py37h6214cd6_0  
pandoc                    2.11                 h9490d1a_0    anaconda
pandocfilters             1.4.2                    py37_1    anaconda
parso                     0.7.0                      py_0    anaconda
pickleshare               0.7.5                 py37_1001    anaconda
pillow                    8.3.1            py37h4fa10fc_0  
pip                       21.2.2           py37haa95532_0  
pixman                    0.40.0               he774522_0  
prometheus_client         0.8.0                      py_0    anaconda
prompt-toolkit            3.0.8                      py_0    anaconda
prompt_toolkit            3.0.8                         0    anaconda
py-boost                  1.67.0           py37h8300f20_4  
pycparser                 2.20                       py_2    anaconda
pygments                  2.7.1                      py_0    anaconda
pyparsing                 2.4.7                      py_0    anaconda
pyqt                      5.9.2            py37ha878b3d_0    anaconda
pyrsistent                0.17.3           py37he774522_0    anaconda
pytables                  3.6.1            py37h1da0976_0    anaconda
python                    3.7.11               h6244533_0  
python-dateutil           2.8.2              pyhd3eb1b0_0  
pytz                      2021.1             pyhd3eb1b0_0  
pywin32                   227              py37he774522_1    anaconda
pywinpty                  0.5.7                    py37_0    anaconda
pyzmq                     19.0.2           py37ha925a31_1    anaconda
qt                        5.9.7            vc14h73c81de_0  [vc14]  anaconda
qtconsole                 4.7.7                      py_0    anaconda
qtpy                      1.9.0                      py_0    anaconda
rdkit                     2020.09.1.0      py37h3d1ada6_1    rdkit
send2trash                1.5.0                    py37_0    anaconda
setuptools                52.0.0           py37haa95532_0  
sip                       4.19.24          py37ha925a31_0    anaconda
six                       1.16.0             pyhd3eb1b0_0  
sqlalchemy                1.4.22           py37h2bbff1b_0  
sqlite                    3.36.0               h2bbff1b_0  
terminado                 0.9.1                    py37_0    anaconda
testpath                  0.4.4                      py_0    anaconda
tk                        8.6.10               he774522_0  
tornado                   6.0.4            py37he774522_1    anaconda
traitlets                 5.0.5                      py_0    anaconda
typing_extensions         3.10.0.0           pyh06a4308_0  
vc                        14.2                 h21ff451_1  
vs2015_runtime            14.27.29016          h5e58377_2  
wcwidth                   0.2.5                      py_0    anaconda
webencodings              0.5.1                    py37_1    anaconda
wheel                     0.37.0             pyhd3eb1b0_0  
widgetsnbextension        3.5.1                    py37_0    anaconda
wincertstore              0.2                      py37_0  
winpty                    0.4.3                         4    anaconda
xz                        5.2.5                h62dcd97_0  
zeromq                    4.3.2                ha925a31_3    anaconda
zipp                      3.5.0              pyhd3eb1b0_0  
zlib                      1.2.11               h62dcd97_4  
zstd                      1.4.9                h19a0ad4_0  

Similarity Search with Bitvector rather than SMILES as Query?

Hi I was just wondering how one would go about performing a similarity search, when the queries Smiles is not known. So I only have the array of bits. As long as the length of the array is the same as the `molecules I want to screen through this should work, right?

In you code I find it difficult to find where exactly the conversion from Smiles string to Vector takes place. Bc I guess that's where I would have to start.

Create FP database using multi-threads (parallel)

I want to create FP database about >1B compounds.
Currently, FPSim2 only uses a single thread.

So, Is it possible to enable parallel processing?
Can I use a package like multithreads provided in python?

How to cite.

Hi Guys,

I was just wondering on how you would like FPSim2 to be cited?

Problem while creating db file for MACCSKeys

When trying to create db file with "create_db_file('chembl_26.sdf', 'chembl_26_maccs.h5', 'MACCSKeys', mol_id_prop='chembl_id')" I get the following error:

Traceback (most recent call last):
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/numpy/core/records.py", line 690, in fromrecords
retval = sb.array(recList, dtype=descr)
ValueError: could not assign tuple of length 5 to structure with 4 fields.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/tables/table.py", line 2221, in append
wbufRA = numpy.rec.array(rows, dtype=self._v_dtype)
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/numpy/core/records.py", line 854, in array
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/numpy/core/records.py", line 700, in fromrecords
_array[k] = tuple(recList[k])
ValueError: could not assign tuple of length 5 to structure with 4 fields.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "", line 1, in
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/FPSim2/io/FPSim2_io.py", line 429, in create_db_file
fps_table.append(fps)
File "/home/benjamin/anaconda3/envs/pure-rdkit-env/lib/python3.6/site-packages/tables/table.py", line 2225, in append
"The error was: <%s>" % (str(self), exc))
ValueError: rows parameter cannot be converted into a recarray object compliant with table '/fps (Table(0,), shuffle, blosc(5)) 'Table storing fps''. The error was: <could not assign tuple of length 5 to structure with 4 fields.>

Do I have to add additional parameters in the create_db_file() command or am I missing something else?

NVRTCError

Hi,

When I perform the scripts:

from FPSim2 import FPSim2CudaEngine

fp_filename = 'chembl_27.h5'
fpce = FPSim2CudaEngine(fp_filename)

query = 'CC(=O)Oc1ccccc1C(=O)O'
results = fpce.similarity(query, 0.7)

I get the error in the follow:

NVRTCError Traceback (most recent call last)
/home/anaconda3/lib/python3.7/site-packages/cupy/cuda/compiler.py in compile(self, options)
445 try:
--> 446 nvrtc.compileProgram(self.ptr, options)
447 return nvrtc.getPTX(self.ptr)

cupy/cuda/nvrtc.pyx in cupy.cuda.nvrtc.compileProgram()

cupy/cuda/nvrtc.pyx in cupy.cuda.nvrtc.compileProgram()

cupy/cuda/nvrtc.pyx in cupy.cuda.nvrtc.check_status()

NVRTCError: NVRTC_ERROR_COMPILATION (6)

During handling of the above exception, another exception occurred:

CompileException Traceback (most recent call last)
in
1 query = 'CC(=O)Oc1ccccc1C(=O)O'
----> 2 results = fpce.similarity(query, 0.7)

/home/anaconda3/lib/python3.7/site-packages/FPSim2/FPSim2Cuda.py in similarity(self, query_string, threshold)
226 """
227 if self.kernel == "raw":
--> 228 ids, sims = self._raw_kernel_search(query_string, threshold)
229 elif self.kernel == "element_wise":
230 ids, sims = self._element_wise_search(query_string, threshold)

/home/anaconda3/lib/python3.7/site-packages/FPSim2/FPSim2Cuda.py in _raw_kernel_search(self, query_string, threshold)
176
177 # copy query and threshold to GPU
--> 178 cuda_threshold = cp.asarray(threshold, dtype="f4")
179 query = cp.asarray(np_query[1:-1])
180 popcount = cp.asarray(np_query[-1])

/home/anaconda3/lib/python3.7/site-packages/cupy/creation/from_data.py in asarray(a, dtype, order)
66
67 """
---> 68 return core.array(a, dtype, False, order)
69
70

cupy/core/core.pyx in cupy.core.core.array()

cupy/core/core.pyx in cupy.core.core.array()

cupy/core/core.pyx in cupy.core.core._send_object_to_gpu()

cupy/core/core.pyx in cupy.core.core.ndarray.fill()

cupy/core/_kernel.pyx in cupy.core._kernel.ElementwiseKernel.call()

cupy/core/_kernel.pyx in cupy.core._kernel.ElementwiseKernel._get_elementwise_kernel()

cupy/core/_kernel.pyx in cupy.core._kernel._get_elementwise_kernel()

cupy/core/_kernel.pyx in cupy.core._kernel._get_simple_elementwise_kernel()

cupy/core/_kernel.pyx in cupy.core._kernel._get_simple_elementwise_kernel()

cupy/core/carray.pxi in cupy.core.core.compile_with_cache()

/home/anaconda3/lib/python3.7/site-packages/cupy/cuda/compiler.py in compile_with_cache(source, options, arch, cache_dir, extra_source, backend)
293 else:
294 return _compile_with_cache_cuda(source, options, arch, cache_dir,
--> 295 extra_source, backend)
296
297

/home/anaconda3/lib/python3.7/site-packages/cupy/cuda/compiler.py in _compile_with_cache_cuda(source, options, arch, cache_dir, extra_source, backend)
345
346 if backend == 'nvrtc':
--> 347 ptx = compile_using_nvrtc(source, options, arch, name + '.cu')
348 ls = function.LinkState()
349 ls.add_ptr_data(ptx, 'cupy.ptx')

/home/anaconda3/lib/python3.7/site-packages/cupy/cuda/compiler.py in compile_using_nvrtc(source, options, arch, filename)
153 prog = _NVRTCProgram(source, cu_path)
154 try:
--> 155 ptx = prog.compile(options)
156 except CompileException as e:
157 dump = _get_bool_env_variable(

/home/anaconda3/lib/python3.7/site-packages/cupy/cuda/compiler.py in compile(self, options)
448 except nvrtc.NVRTCError:
449 log = nvrtc.getProgramLog(self.ptr)
--> 450 raise CompileException(log, self.src, self.name, options, 'nvrtc')
451
452

CompileException: /usr/local/cuda-9.0/include/cuda_fp16.h(133): error: cannot overload functions distinguished by return type alone

/usr/local/cuda-9.0/include/cuda_fp16.hpp(648): error: cannot overload functions distinguished by return type alone

2 errors detected in the compilation of "/tmp/tmpy9zye_jr/41cdaa87a74069ee5ed1584c4bbf0719_2.cubin.cu".

I have installed the Cupy 7.7 and Cuda 9.0, and set the cuda environment as
export CUDA_PATH=/usr/local/cuda-9.0
export LD_LIBRARY_PATH=$CUDA_PATH/lib64:$LD_LIBRARY_PATH

Would need your help to solve the error in the above, many thanks.

Best regards

Jiyuan

Stereochemistry (tetrahedral, double-bond) options?

Hi,

Thank you for this software!

As presented in the documentation, we're using Morgan fingerprints for similarity searches and RDKPatternFingerprint's for substructure searches. When calculated as in the documentation examples, neither seems to consider stereochemistry (tetrahedral or double-bond).

Are there options for either/both so that stereochemistry is considered?

Thank you for any guidance.

Best regards,

Andrew

Tversky similarity

Could you add the Tversky similarity and the choice of alpha and beta coefficients in arguments ?
I’m actually doing this using RDKit and jupyter notebooks, but with FPSim 2 it would be faster and take less RAM.

FPSim2Cuda.py - "list index out of range"

Hey I am running a for-loop over multiple queries
However, for some Queries the FPSIM2Cuda.py throws the Error List index out of errors

And it points to this line:
subset_size = int(fp_range[1] - fp_range[0])

For other queries is works just fine.
Do you know what could cause the error

don't store 1.0 for all molecules in substructure search

because it was first written as a prototype for similarity search fpsim is storing the coefficients and it's not needed when doing sss

Backwards incompatibility with SQLAlchemy <2.0

Starting with 0.4.3, the package is now incompatible with many other packages which still use SQLAlchemy 1. Would it be possible to revert the requirement in setup.py from "sqlalchemy>=2.0" to "sqlalchemy>=1.4.45", or are the changes from #94 onward truly incompatible with 1.4.x?

PyTables pip install with python 3.11 fails on macOS M1

As explained in this stackoverflow post the following error is raised with pip install:

$ python -m pip install tables                                                                                                                                                                                                        ─╯
Collecting tables
  Using cached tables-3.8.0.tar.gz (8.0 MB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [51 lines of output]
      <string>:19: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html

      Error compiling Cython file:
      ------------------------------------------------------------
      ...


      def _dump_h5_backtrace():
          cdef object bt = []

          if H5Ewalk(H5E_DEFAULT, H5E_WALK_DOWNWARD, e_walk_cb, <void*>bt) < 0:
                                                     ^
      ------------------------------------------------------------

      tables/utilsextension.pyx:384:47: Cannot assign type 'herr_t (unsigned int, const H5E_error_t *, void *) except? -1 nogil' to 'H5E_walk_t'
      cpuinfo failed, assuming no CPU features: 'flags'
      * Using Python 3.11.4 (main, Jul 27 2023, 14:26:12) [Clang 14.0.3 (clang-1403.0.22.14.1)]
      * Found cython 3.0.0
      * USE_PKGCONFIG: False
      * Found HDF5 headers at ``/opt/homebrew/opt/hdf5/include``, library at ``/opt/homebrew/opt/hdf5/lib``.
      * Found LZO 2 headers at ``/opt/homebrew/Cellar/lzo/2.10/include``, the library is located in the standard system search dirs.
      * Skipping detection of LZO 1 since LZO 2 has already been found.
      * Found bzip2 headers at ``/opt/homebrew/opt/bzip2/include``, library at ``/opt/homebrew/opt/bzip2/lib``.
      * Found blosc headers at ``/opt/homebrew/opt/c-blosc/include``, library at ``/opt/homebrew/opt/c-blosc/lib``.
      * Found blosc2 headers at ``/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/include``, library at ``/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib``.
        * Copying blosc2 runtime library to 'tables' dir because it was not found in standard locations
      Compiling tables/utilsextension.pyx because it changed.
      [1/1] Cythonizing tables/utilsextension.pyx
      Traceback (most recent call last):
        File "/Users/mario.theuermann/.pyenv/versions/i4SEE-sandbox/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 353, in <module>
          main()
        File "/Users/mario.theuermann/.pyenv/versions/i4SEE-sandbox/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 335, in main
          json_out['return_val'] = hook(**hook_input['kwargs'])
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/Users/mario.theuermann/.pyenv/versions/i4SEE-sandbox/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 118, in get_requires_for_build_wheel
          return hook(config_settings)
                 ^^^^^^^^^^^^^^^^^^^^^
        File "/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 341, in get_requires_for_build_wheel
          return self._get_build_requires(config_settings, requirements=['wheel'])
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 323, in _get_build_requires
          self.run_setup()
        File "/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 338, in run_setup
          exec(code, locals())
        File "<string>", line 928, in <module>
        File "<string>", line 923, in get_cython_extfiles
        File "/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib/python3.11/site-packages/Cython/Build/Dependencies.py", line 1134, in cythonize
          cythonize_one(*args)
        File "/private/var/folders/h2/tcw923v140sbnqcz0p0xp3y80000gp/T/pip-build-env-n4mpp65u/overlay/lib/python3.11/site-packages/Cython/Build/Dependencies.py", line 1301, in cythonize_one
          raise CompileError(None, pyx_file)
      Cython.Compiler.Errors.CompileError: tables/utilsextension.pyx
      [end of output]

FPSim2CudaEngine TypeError: missing positional argument: 'pg_schema'

FPSim2CudaEngine raises an error immediately. See the code below (in interactive Python)

>>> from FPSim2 import FPSim2CudaEngine
>>> fpe = FPSim2CudaEngine(H5DB_FILENAME)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/conda/envs/fpsim2/lib/python3.9/site-packages/FPSim2/FPSim2Cuda.py", line 99, in __init__
    super(FPSim2CudaEngine, self).__init__(
TypeError: __init__() missing 1 required positional argument: 'pg_schema'