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https://github.com/dgasmith/gau2grid
Fast computation of a gaussian and its derivative on a grid.
https://github.com/dgasmith/gau2grid
c collocations grid numpy python
Last synced: 13 days ago
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Fast computation of a gaussian and its derivative on a grid.
- Host: GitHub
- URL: https://github.com/dgasmith/gau2grid
- Owner: dgasmith
- License: bsd-3-clause
- Created: 2017-11-01T19:39:46.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-05-05T19:20:15.000Z (6 months ago)
- Last Synced: 2024-10-15T01:24:18.025Z (25 days ago)
- Topics: c, collocations, grid, numpy, python
- Language: Python
- Homepage: https://gau2grid.readthedocs.io/en/latest/
- Size: 907 KB
- Stars: 29
- Watchers: 6
- Forks: 15
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# gau2grid
A collocation code for computing gaussians on a grid of the form:
```
out_Lp = x^l y^m z^n \sum_i coeff_i e^(exponent_i * (|center - p|)^2)
```
Where the returned matrix dimension are the angular momentum (L) by number of requested points (p).
```python
import gau2grid
import numpy as np
# Build coordinates along the Z axis
>>> xyz = np.zeros((3, 5))
>>> xyz[2] = np.arange(5)
# Compute a 's' gaussian with a scaling and exponent of one at the origin
>>> ret = gau2grid.collocation(xyz, 0, [1], [1], [0, 0, 0])
>>> print(ret["PHI"])
[[ 1.00000e+00 3.67879e-01 1.83156e-02 1.23409e-04 1.12535e-07]]
# Compute a 'p' gaussian with a scaling and exponent of one at the origin
>>> ret = gau2grid.collocation(xyz, 1, [1], [1], [0, 0, 0], spherical=False)
>>> print(ret["PHI"])
[[ 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00]
[ 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00]
[ 0.00000e+00 3.67879e-01 3.66312e-02 3.70229e-04 4.50140e-07]]
# Note that the X and Y components are zero as they are orthogonal to our Z vector.
```
The returned matrix can be in either cartesian or regular solid harmonics. There are currently
three algorithms in which to compute these collocation matrices:
- Optimize C: A autogenerated C library that optimizes for cache,
vectorization, and sparsity. Fastest, requires compilation, found at
`gau2grid.collocation`.
- Optimized/Generated NumPy: A exploratory tool to
examine the sparsity in the gaussians. No compilation required, found at
`gau2grid.np_gen.collocation`.
- NumPy Reference: A simple NumPy-based loop
code. No compilation required, found at `gau2grid.ref.collocation`.
See the [documentation](https://gau2grid.readthedocs.io/en/latest/?badge=latest) for more information!
## Building Gau2Grid
The C library is built with CMake and has C no required dependancies other than
the standard library. A CMake and build example can found below:
```bash
cmake -H. -Bobjdir
cd objdir; make -j2
```
Several common CMake options are as follow:
- `-DPYTHON_EXECUTABLE` - Path to the desired Python executable
- `-DMAX_AM` - Maximum angular momentum to compile to, default 6
- `-DCMAKE_INSTALL_PREFIX` - Installation directory
## Python installation
The gau2grid program (without the optimized C library) can be installed using
the canonical `setup.py` script,
```
python setup.py install
```
# Authors
This code was inspired by a number of folks and quite a few provided excellent advice.
- Daniel G. A. Smith - Code author
- Rob M. Parrish - Author of the Psi4 section which contains the original equations
- Lori A. Burns - CMake, building, and library linking
- Andy C. Simmonett - RSH coefficients
- Ben Pritchard - Generator and vectorization recommendations