# gmxpy
[](https://badge.fury.io/py/gmxpy)
[](https://badge.fury.io/py/gmxpy)
[](https://img.shields.io/pypi/l/ansicolortags.svg)
Author: Goosang Yu
Contact: gsyu93@gmali.com
Wrapping GROMACS by python script for me
Since 2023. 07. 12.
Tested GROMCAS ver. 2023.1
## Installation
```python
pip install gmxpy
```
*This package does not include GROMACS. You need to install GROMCAS separately. Also, gmxpy is currently being developed based on GROMACS version 2023.1. If you use a version that is too old, it may not be compatible.*
## You don need to use XMGRACE anymore!
GROMACS by default generates graphs of data in the form of xmgrace files (.xvg). Xmgrace produces visually appealing plots, but it can be cumbersome to handle in different languages or operating systems. The most important thing is that I am not familiar with it.
One of the functions included in gmxpy, called 'xvg2df', converts it into a much simpler DataFrame format.
```python
import gmxpy as gmx
df_xvg = gmx.xvg2df('interaction_energy.xvg')
df_xvg()
```
| | Coul-SR:Protein-JZ4 | LJ-SR:Protein-JZ4 |
| --------- | ------------------- | ----------------- |
| Time (ps) | | |
| 0 | \-15.2106 | \-98.9382 |
| 10 | \-15.5369 | \-108.834 |
| 20 | \-26.0345 | \-105.193 |
| 30 | \-13.2364 | \-108.948 |
| 40 | \-13.0772 | \-109.427 |
With just a little additional effort, it can be conveniently plotted and visualized as a graph. I have freely chosen the colors that I personally like.
```python
df_xvg().plot()
```
## Make dataframe from gmx_MMPBSA results file
When you done your MMPB(GB)SA by [gmx_MMPBSA](https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA), you will get data file containing decomposition results (DECOMP_MMPBSA_YOURSAMPLE.dat)
```
| Run on Fri Oct 27 19:17:14 2023\n
| GB non-polar solvation energies calculated with gbsa=2
idecomp = 3: Pairwise decomp adding 1-4 interactions to Internal.
Energy Decomposition Analysis (All units kcal/mol): Generalized Born model
DELTAS:
Total Energy Decomposition:
Resid 1,Resid 2,Internal,,,van der Waals,,,Electrostatic,,,Polar Solvation,,,Non-Polar Solv.,,,TOTAL,,
,,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean
R:A:ARG:351,R:A:ARG:351,0.0,0.0,0.0,0.0,0.5897586551665688,0.015222429700453965,0.0,6.785105251173467,0.1751322965612053,2.9772637441705534,3.1993571192946018,0.08257952368898057,1.0425542756210526,0.6088115226883254,0.01571420872548597,4.019818019791606,7.549305000315245,0.19485727534109232
R:A:ARG:351,R:A:ASN:355,0.0,0.0,0.0,0.0,0.1863456749011414,0.0048098216301125886,0.0,0.6239917901458101,0.016106030960193803,0.01945755229846769,0.6328086828777381,0.016333606305824484,0.0033432117553630915,0.135970829538314,0.0035095820567055645,0.022800764053830778,0.9181641249441898,0.023698997416992967
...
```
This file need somethigng arrange for analysis... so I make function ```make_decomp_df``` for this.
```python
from gmxpy.analysis import make_decomp_df
df_decomp = make_decomp_df('DECOMP_MMPBSA_8SSN_K294E')
df_decomp
```
| Resid 1 | Resid 2 | Internal_Avg. | Internal_Std. Dev. | Internal_Std. Err. of Mean | van der Waals_Avg. | van der Waals_Std. Dev. | van der Waals_Std. Err. of Mean | Electrostatic_Avg. | Electrostatic_Std. Dev. | Electrostatic_Std. Err. of Mean | Polar Solvation_Avg. | Polar Solvation_Std. Dev. | Polar Solvation_Std. Err. of Mean | Non-Polar Solv._Avg. | Non-Polar Solv._Std. Dev. | Non-Polar Solv._Std. Err. of Mean | TOTAL_Avg. | TOTAL_Std. Dev. | TOTAL_Std. Err. of Mean |
| ----------- | ----------- | ------------- | ------------------ | -------------------------- | ------------------ | ----------------------- | ------------------------------- | ------------------ | ----------------------- | ------------------------------- | -------------------- | ------------------------- | --------------------------------- | -------------------- | ------------------------- | --------------------------------- | ---------- | --------------- | ----------------------- |
| R:A:ARG:351 | R:A:ARG:351 | 0 | 0 | 0 | 0 | 0.589759 | 0.015222 | 0 | 6.785105 | 0.175132 | 2.977264 | 3.199357 | 0.08258 | 1.042554 | 0.608812 | 0.015714 | 4.019818 | 7.549305 | 0.194857 |
| R:A:ARG:351 | R:A:ASN:355 | 0 | 0 | 0 | 0 | 0.186346 | 0.00481 | 0 | 0.623992 | 0.016106 | 0.019458 | 0.632809 | 0.016334 | 0.003343 | 0.135971 | 0.00351 | 0.022801 | 0.918164 | 0.023699 |
| R:A:ARG:351 | R:A:ALA:356 | 0 | 0 | 0 | 0 | 0.016111 | 0.000416 | 0 | 0.220186 | 0.005683 | \-0.00927 | 0.230623 | 0.005953 | 0.000006 | 0.001666 | 0.000043 | \-0.00926 | 0.319267 | 0.008241 |
| R:A:ARG:351 | R:A:VAL:357 | 0 | 0 | 0 | 0 | 0.003677 | 0.000095 | 0 | 0.114568 | 0.002957 | \-0.00283 | 0.11502 | 0.002969 | 0 | 0 | 0 | \-0.00283 | 0.162385 | 0.004191 |
| R:A:ARG:351 | R:A:LEU:359 | 0 | 0 | 0 | 0 | 0.022395 | 0.000578 | 0 | 0.277392 | 0.00716 | \-0.02104 | 0.290559 | 0.0075 | 0.000031 | 0.003792 | 0.000098 | \-0.02101 | 0.402351 | 0.010385 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| L:B:AY7:527 | R:A:PHE:516 | 0 | 0 | 0 | \-0.20527 | 0.107602 | 0.002777 | \-0.03828 | 0.02827 | 0.00073 | 0.077929 | 0.030734 | 0.000793 | \-0.05729 | 0.05616 | 0.00145 | \-0.22291 | 0.128359 | 0.003313 |
| L:B:AY7:527 | R:A:ILE:521 | 0 | 0 | 0 | \-0.68336 | 0.218584 | 0.005642 | \-0.17155 | 0.131663 | 0.003398 | 0.199371 | 0.118569 | 0.00306 | \-0.51561 | 0.13099 | 0.003381 | \-1.17114 | 0.310372 | 0.008011 |
| L:B:AY7:527 | R:A:SER:522 | 0 | 0 | 0 | \-0.09544 | 0.055133 | 0.001423 | \-0.08589 | 0.123155 | 0.003179 | 0.102106 | 0.116548 | 0.003008 | \-0.00409 | 0.02269 | 0.000586 | \-0.08332 | 0.179736 | 0.004639 |
| L:B:AY7:527 | R:A:VAL:525 | 0 | 0 | 0 | \-1.6084 | 0.812559 | 0.020973 | \-0.48198 | 0.961192 | 0.02481 | 0.332301 | 0.442281 | 0.011416 | \-1.14992 | 0.584504 | 0.015087 | \-2.90801 | 1.456502 | 0.037594 |
| L:B:AY7:527 | L:B:AY7:527 | 0 | 0 | 0 | 0 | 2.464959 | 0.063624 | 0 | 2.834387 | 0.073159 | 14.78321 | 1.878281 | 0.048481 | 9.8872 | 0.649812 | 0.016772 | 24.67041 | 4.249702 | 0.10969 |
Raw data
{
"_id": null,
"home_page": null,
"name": "gmxpy",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "analysis,gromacs,molecular dynamics,simulation",
"author": null,
"author_email": "Goosang Yu <gsyu93@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/63/52/be0118fc5bebb5c7e3fcecb75227d1c14ed880c68f5c2bb21b63df94689a/gmxpy-0.0.4.tar.gz",
"platform": null,
"description": "# gmxpy\n\n[](https://badge.fury.io/py/gmxpy) \n[](https://badge.fury.io/py/gmxpy)\n[](https://img.shields.io/pypi/l/ansicolortags.svg) \n\n\nAuthor: Goosang Yu \nContact: gsyu93@gmali.com \n\nWrapping GROMACS by python script for me \nSince 2023. 07. 12. \nTested GROMCAS ver. 2023.1\n\n## Installation\n\n```python\npip install gmxpy\n```\n*This package does not include GROMACS. You need to install GROMCAS separately. Also, gmxpy is currently being developed based on GROMACS version 2023.1. If you use a version that is too old, it may not be compatible.*\n\n## You don need to use XMGRACE anymore!\nGROMACS by default generates graphs of data in the form of xmgrace files (.xvg). Xmgrace produces visually appealing plots, but it can be cumbersome to handle in different languages or operating systems. The most important thing is that I am not familiar with it.\n\nOne of the functions included in gmxpy, called 'xvg2df', converts it into a much simpler DataFrame format.\n\n```python\nimport gmxpy as gmx\n\ndf_xvg = gmx.xvg2df('interaction_energy.xvg')\ndf_xvg()\n```\n| | Coul-SR:Protein-JZ4 | LJ-SR:Protein-JZ4 |\n| --------- | ------------------- | ----------------- |\n| Time (ps) | | |\n| 0 | \\-15.2106 | \\-98.9382 |\n| 10 | \\-15.5369 | \\-108.834 |\n| 20 | \\-26.0345 | \\-105.193 |\n| 30 | \\-13.2364 | \\-108.948 |\n| 40 | \\-13.0772 | \\-109.427 |\n\nWith just a little additional effort, it can be conveniently plotted and visualized as a graph. I have freely chosen the colors that I personally like.\n\n```python\ndf_xvg().plot()\n```\n\n\n## Make dataframe from gmx_MMPBSA results file\nWhen you done your MMPB(GB)SA by [gmx_MMPBSA](https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA), you will get data file containing decomposition results (DECOMP_MMPBSA_YOURSAMPLE.dat)\n\n```\n| Run on Fri Oct 27 19:17:14 2023\\n\n| GB non-polar solvation energies calculated with gbsa=2\nidecomp = 3: Pairwise decomp adding 1-4 interactions to Internal.\nEnergy Decomposition Analysis (All units kcal/mol): Generalized Born model\n\nDELTAS:\nTotal Energy Decomposition:\nResid 1,Resid 2,Internal,,,van der Waals,,,Electrostatic,,,Polar Solvation,,,Non-Polar Solv.,,,TOTAL,,\n,,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean,Avg.,Std. Dev.,Std. Err. of Mean\nR:A:ARG:351,R:A:ARG:351,0.0,0.0,0.0,0.0,0.5897586551665688,0.015222429700453965,0.0,6.785105251173467,0.1751322965612053,2.9772637441705534,3.1993571192946018,0.08257952368898057,1.0425542756210526,0.6088115226883254,0.01571420872548597,4.019818019791606,7.549305000315245,0.19485727534109232\nR:A:ARG:351,R:A:ASN:355,0.0,0.0,0.0,0.0,0.1863456749011414,0.0048098216301125886,0.0,0.6239917901458101,0.016106030960193803,0.01945755229846769,0.6328086828777381,0.016333606305824484,0.0033432117553630915,0.135970829538314,0.0035095820567055645,0.022800764053830778,0.9181641249441898,0.023698997416992967\n...\n```\n\nThis file need somethigng arrange for analysis... so I make function ```make_decomp_df``` for this.\n\n```python\nfrom gmxpy.analysis import make_decomp_df\n\ndf_decomp = make_decomp_df('DECOMP_MMPBSA_8SSN_K294E')\n\ndf_decomp\n```\n| Resid 1 | Resid 2 | Internal_Avg. | Internal_Std. Dev. | Internal_Std. Err. of Mean | van der Waals_Avg. | van der Waals_Std. Dev. | van der Waals_Std. Err. of Mean | Electrostatic_Avg. | Electrostatic_Std. Dev. | Electrostatic_Std. Err. of Mean | Polar Solvation_Avg. | Polar Solvation_Std. Dev. | Polar Solvation_Std. Err. of Mean | Non-Polar Solv._Avg. | Non-Polar Solv._Std. Dev. | Non-Polar Solv._Std. Err. of Mean | TOTAL_Avg. | TOTAL_Std. Dev. | TOTAL_Std. Err. of Mean |\n| ----------- | ----------- | ------------- | ------------------ | -------------------------- | ------------------ | ----------------------- | ------------------------------- | ------------------ | ----------------------- | ------------------------------- | -------------------- | ------------------------- | --------------------------------- | -------------------- | ------------------------- | --------------------------------- | ---------- | --------------- | ----------------------- |\n| R:A:ARG:351 | R:A:ARG:351 | 0 | 0 | 0 | 0 | 0.589759 | 0.015222 | 0 | 6.785105 | 0.175132 | 2.977264 | 3.199357 | 0.08258 | 1.042554 | 0.608812 | 0.015714 | 4.019818 | 7.549305 | 0.194857 |\n| R:A:ARG:351 | R:A:ASN:355 | 0 | 0 | 0 | 0 | 0.186346 | 0.00481 | 0 | 0.623992 | 0.016106 | 0.019458 | 0.632809 | 0.016334 | 0.003343 | 0.135971 | 0.00351 | 0.022801 | 0.918164 | 0.023699 |\n| R:A:ARG:351 | R:A:ALA:356 | 0 | 0 | 0 | 0 | 0.016111 | 0.000416 | 0 | 0.220186 | 0.005683 | \\-0.00927 | 0.230623 | 0.005953 | 0.000006 | 0.001666 | 0.000043 | \\-0.00926 | 0.319267 | 0.008241 |\n| R:A:ARG:351 | R:A:VAL:357 | 0 | 0 | 0 | 0 | 0.003677 | 0.000095 | 0 | 0.114568 | 0.002957 | \\-0.00283 | 0.11502 | 0.002969 | 0 | 0 | 0 | \\-0.00283 | 0.162385 | 0.004191 |\n| R:A:ARG:351 | R:A:LEU:359 | 0 | 0 | 0 | 0 | 0.022395 | 0.000578 | 0 | 0.277392 | 0.00716 | \\-0.02104 | 0.290559 | 0.0075 | 0.000031 | 0.003792 | 0.000098 | \\-0.02101 | 0.402351 | 0.010385 |\n| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n| L:B:AY7:527 | R:A:PHE:516 | 0 | 0 | 0 | \\-0.20527 | 0.107602 | 0.002777 | \\-0.03828 | 0.02827 | 0.00073 | 0.077929 | 0.030734 | 0.000793 | \\-0.05729 | 0.05616 | 0.00145 | \\-0.22291 | 0.128359 | 0.003313 |\n| L:B:AY7:527 | R:A:ILE:521 | 0 | 0 | 0 | \\-0.68336 | 0.218584 | 0.005642 | \\-0.17155 | 0.131663 | 0.003398 | 0.199371 | 0.118569 | 0.00306 | \\-0.51561 | 0.13099 | 0.003381 | \\-1.17114 | 0.310372 | 0.008011 |\n| L:B:AY7:527 | R:A:SER:522 | 0 | 0 | 0 | \\-0.09544 | 0.055133 | 0.001423 | \\-0.08589 | 0.123155 | 0.003179 | 0.102106 | 0.116548 | 0.003008 | \\-0.00409 | 0.02269 | 0.000586 | \\-0.08332 | 0.179736 | 0.004639 |\n| L:B:AY7:527 | R:A:VAL:525 | 0 | 0 | 0 | \\-1.6084 | 0.812559 | 0.020973 | \\-0.48198 | 0.961192 | 0.02481 | 0.332301 | 0.442281 | 0.011416 | \\-1.14992 | 0.584504 | 0.015087 | \\-2.90801 | 1.456502 | 0.037594 |\n| L:B:AY7:527 | L:B:AY7:527 | 0 | 0 | 0 | 0 | 2.464959 | 0.063624 | 0 | 2.834387 | 0.073159 | 14.78321 | 1.878281 | 0.048481 | 9.8872 | 0.649812 | 0.016772 | 24.67041 | 4.249702 | 0.10969 |\n\n",
"bugtrack_url": null,
"license": null,
"summary": "Wrapping GROMACS by Python for me",
"version": "0.0.4",
"project_urls": {
"Homepage": "https://github.com/Goosang-Yu/gmxpy",
"Repository": "https://github.com/Goosang-Yu/gmxpy",
"Source": "https://github.com/Goosang-Yu/gmxpy",
"Tracker": "https://github.com/Goosang-Yu/gmxpy/issues"
},
"split_keywords": [
"analysis",
"gromacs",
"molecular dynamics",
"simulation"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "0e2f3766421a0259d004c31fc88c1b2df679febd537de954a2ad4874ea923bc6",
"md5": "caac2036da676bba62341c3775499f9c",
"sha256": "1000a47c76c260e348a9e4cb37029dc44a52bc98201a74597b534858f9d6dd59"
},
"downloads": -1,
"filename": "gmxpy-0.0.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "caac2036da676bba62341c3775499f9c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 8390,
"upload_time": "2023-10-27T16:01:28",
"upload_time_iso_8601": "2023-10-27T16:01:28.569414Z",
"url": "https://files.pythonhosted.org/packages/0e/2f/3766421a0259d004c31fc88c1b2df679febd537de954a2ad4874ea923bc6/gmxpy-0.0.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "6352be0118fc5bebb5c7e3fcecb75227d1c14ed880c68f5c2bb21b63df94689a",
"md5": "995a4276602997535340e0826a312553",
"sha256": "267e6c2067b266fbb3a583c40b6926ba9d92dd85a29e5275e16401d7deccc54a"
},
"downloads": -1,
"filename": "gmxpy-0.0.4.tar.gz",
"has_sig": false,
"md5_digest": "995a4276602997535340e0826a312553",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 7914,
"upload_time": "2023-10-27T16:01:30",
"upload_time_iso_8601": "2023-10-27T16:01:30.019975Z",
"url": "https://files.pythonhosted.org/packages/63/52/be0118fc5bebb5c7e3fcecb75227d1c14ed880c68f5c2bb21b63df94689a/gmxpy-0.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-27 16:01:30",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Goosang-Yu",
"github_project": "gmxpy",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "gmxpy"
}
JSON
