wfnsympy module
Project description
WFNSYM
This software calculates continuous symmetry measures (CSM) of the electronic wave function of molecules
Installation instructions
1a. Requirements
- LAPACK & Blas libraries
- Fortran77 compiler (g77/ifort/gfortran)
- cmake 2.6+
1b. Additional requirements for python module
- Python 3.5+
- numpy 1.x (not compatible with numpy 2.x!)
- scipy
- C compiler
2a. Install as standalone binary
./configure (see --help for available options)
cd build
make install
2b. Install as a python module
cd python
python setup.py install --user
b. Install from pip (pre-compiled versions available for python 3.6-3.10)
pip install wfnsympy
Simple python API
from wfnsympy import WfnSympy
basis = {'name': 'STO-3G',
'primitive_type': 'gaussian',
'atoms': [{'symbol': 'O',
'shells': [{'shell_type': 's',
'p_exponents': [130.70932, 23.808861, 6.4436083],
'con_coefficients': [0.154328969, 0.535328136, 0.444634536],
'p_con_coefficients': [0.0, 0.0, 0.0]},
{'shell_type': 'sp',
'p_exponents': [5.0331513, 1.1695961, 0.380389],
'con_coefficients': [-0.0999672287, 0.399512825, 0.700115461],
'p_con_coefficients': [0.155916268, 0.607683714, 0.391957386]}]},
{'symbol': 'H',
'shells': [{'shell_type': 's',
'p_exponents': [3.42525091, 0.62391373, 0.1688554],
'con_coefficients': [0.154328971, 0.535328142, 0.444634542],
'p_con_coefficients': [0.0, 0.0, 0.0]}]},
{'symbol': 'H',
'shells': [{'shell_type': 's',
'p_exponents': [3.42525091, 0.62391373, 0.1688554],
'con_coefficients': [0.154328971, 0.535328142, 0.444634542],
'p_con_coefficients': [0.0, 0.0, 0.0]}]}]}
mo_coefficients = [[ 0.9942164, 0.0258468, 0.0000000, 0.0000000,-0.0041640,-0.0055837,-0.0055837],
[ 0.2337666,-0.8444565, 0.0000000, 0.0000000, 0.1228297,-0.1555932,-0.1555932],
[ 0.0000000, 0.0000000, 0.6126923, 0.0000000, 0.0000000,-0.4492216, 0.4492216],
[-0.1040333, 0.5381536, 0.0000000, 0.0000000, 0.7558802,-0.2951071,-0.2951071],
[ 0.0000000, 0.0000000, 0.0000000,-1.0000000, 0.0000000, 0.0000000, 0.0000000],
[-0.1258185, 0.8201209, 0.0000000, 0.0000000,-0.7635388,-0.7691551,-0.7691551],
[ 0.0000000, 0.0000000, 0.9598001, 0.0000000, 0.0000000, 0.8146297,-0.8146297]]
wf_results = WfnSympy(coordinates=[[ 0.00000000, 0.00000000, -0.04280085],
[-0.75810741, 0.00000000, -0.67859957],
[ 0.75810741, 0.00000000, -0.67859957]],
symbols=['O', 'H', 'H'],
basis=basis,
alpha_mo_coeff=mo_coefficients,
group='C2v')
wf_results.print_CSM()
wf_results.print_ideal_group_table()
wf_results.print_overlap_mo_alpha()
wf_results.print_overlap_wf()
Authors
This software has been developed by David Casanova
Python module by Abel Carreras & Efrem Bernuz
The theoretical background implemented in this software is described in:
Casanova D, Alemany P. Phys Chem Chem Phys. 2010;12(47):15523–9.
Casanova D, Alemany P, Falceto A, Carreras A, Alvarez S. J Comput Chem 2013;34(15):1321–31.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file wfnsympy-0.4.2.tar.gz.
File metadata
- Download URL: wfnsympy-0.4.2.tar.gz
- Upload date:
- Size: 38.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
43274401d4e2783c425ad33f4f38793b737683acc1f10a30a9ac4eceaac74b78
|
|
| MD5 |
64c98a9fd42fbdbebbf73cc4f572be0e
|
|
| BLAKE2b-256 |
d6d855a41398513802082fb6319bfd44a64eac7eb9232c9be1501b40b800b19c
|
File details
Details for the file wfnsympy-0.4.2-cp312-cp312-win_amd64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp312-cp312-win_amd64.whl
- Upload date:
- Size: 394.3 kB
- Tags: CPython 3.12, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
08fc39a52855e573006a2ea9cc9e5bd13a80af45db267cb41a245817d0987f5c
|
|
| MD5 |
24855a9e4ba237e3a4f0bfe7e5e0dc82
|
|
| BLAKE2b-256 |
8f585720aabe5d103c7bb8502fb8d2f4acec6b7f81b70df371237b00304ea12e
|
File details
Details for the file wfnsympy-0.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
- Upload date:
- Size: 4.5 MB
- Tags: CPython 3.12, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
de10082a9e04c3f2f21a3832cf89f1a1b4140fd17cad447643e4771f65fa06b9
|
|
| MD5 |
e43f1b1b43fc5588e82b24b2a1cf66c5
|
|
| BLAKE2b-256 |
76f3bd5a114d32fb4a0b4c8bdd17f5a586c3c24c69cd8d2847f283d5ad1b05d7
|
File details
Details for the file wfnsympy-0.4.2-cp312-cp312-macosx_11_0_arm64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp312-cp312-macosx_11_0_arm64.whl
- Upload date:
- Size: 488.2 kB
- Tags: CPython 3.12, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9a6c89e695adfadd46bcd3030d2197136e4d7a25798c1f7b7ad790cd39b9f084
|
|
| MD5 |
c31546221f4d75df203fa22d5349d1c6
|
|
| BLAKE2b-256 |
b19fe029a1c9e8fc1b2d18c82615136cdf0961711175c0ea0d7ddd9d0b7edbd9
|
File details
Details for the file wfnsympy-0.4.2-cp311-cp311-win_amd64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp311-cp311-win_amd64.whl
- Upload date:
- Size: 394.3 kB
- Tags: CPython 3.11, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5e86565780329cde32dae8da92e0741e625140fd40c497d18ab0c53e87fd2640
|
|
| MD5 |
6c9dcdd1d347ebbf4ae4a59f50ba6e9c
|
|
| BLAKE2b-256 |
e86eb18b5b25fb96a828a3e4bc77c8b9da3bd1018d5b5e2a50d1b05010c81924
|
File details
Details for the file wfnsympy-0.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
- Upload date:
- Size: 4.5 MB
- Tags: CPython 3.11, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
dd8365322f7d10e263995755a6f37ba5b109d82add33a7fa7a6f435d580af829
|
|
| MD5 |
ed83515dd1557e42235d5b5336a5f218
|
|
| BLAKE2b-256 |
7bf690b04c1184daa0c3678e2763a159924da6feafc2e7cdf9c0a31db9c191df
|
File details
Details for the file wfnsympy-0.4.2-cp311-cp311-macosx_11_0_arm64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp311-cp311-macosx_11_0_arm64.whl
- Upload date:
- Size: 329.0 kB
- Tags: CPython 3.11, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
34b85f33860ba75ff3d01dd2c2ad5cf723a6558f2aa5388adc04cb6e13da83fa
|
|
| MD5 |
7898160bef278118c36a05e7e80b2c85
|
|
| BLAKE2b-256 |
339109e02b08b35b212eb00cac81487169a14530c25cab100de3911a1c5a6c22
|
File details
Details for the file wfnsympy-0.4.2-cp310-cp310-win_amd64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp310-cp310-win_amd64.whl
- Upload date:
- Size: 394.3 kB
- Tags: CPython 3.10, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c5c3b4a81008d82d88c4cd90732c9ffbba820a155deaf98802c915044a907a90
|
|
| MD5 |
b650580ac71bdbd321e97d0fb63bbbb7
|
|
| BLAKE2b-256 |
5858a957349e174d075b02d3f2b5ad2a2d0b9ad9e4856988a96b3f7d3af20dc0
|
File details
Details for the file wfnsympy-0.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
- Upload date:
- Size: 4.5 MB
- Tags: CPython 3.10, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
39a2f177f7f72bac889a123496dfb3e924b137220b59ec93249e92235830921b
|
|
| MD5 |
2c5675823180c566a7ec89b71444bca5
|
|
| BLAKE2b-256 |
947ab7e88db23e3c04e17086bb9b8c7f86776fbbc7ec11c4bdfb94183d712e68
|
File details
Details for the file wfnsympy-0.4.2-cp310-cp310-macosx_11_0_arm64.whl.
File metadata
- Download URL: wfnsympy-0.4.2-cp310-cp310-macosx_11_0_arm64.whl
- Upload date:
- Size: 171.3 kB
- Tags: CPython 3.10, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.25
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
dfb7c9b6039dc00d56d1b60eadd015dd6c11162f136c22aaf82eca2ee322f638
|
|
| MD5 |
75ca24a124f44ef1cad583deca7e7e63
|
|
| BLAKE2b-256 |
5763138c33572b1ae368dd39717fb1b9860faa450907e4a9aaf4b60c172a5fe3
|