diff --git a/README.md b/README.md index e5e37424..878d5f2e 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,19 @@ in understanding the solvation structure of a liquid, this package is for you! Find the documentation on [readthedocs]. +### Installing SolvationAnalysis + +SolvationAnalysis is available on PyPI and can be installed with pip: + +```bash +pip install solvation-analysis +``` + +### Contributing + +Contributions, both issues and PRs, are welcome. If you'd like to contribute, we ask that you +follow the community guidelines outlined in the [MDAnalysis Code of Conduct](https://www.mdanalysis.org/pages/conduct/). + --- #### Acknowledgements diff --git a/docs/tutorials/basics_tutorial.ipynb b/docs/tutorials/basics_tutorial.ipynb index 0acf347f..f235d3bf 100644 --- a/docs/tutorials/basics_tutorial.ipynb +++ b/docs/tutorials/basics_tutorial.ipynb @@ -23,18 +23,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "id": "52522e0f-a2c4-4802-9350-6afb3e33036e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - } - ], + "outputs": [], "source": [ "# imports\n", "import MDAnalysis as mda\n", @@ -58,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "id": "026c729e-eeb6-4f90-8785-fb38a79810d4", "metadata": {}, "outputs": [], @@ -82,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "id": "03ec0dce-8887-471b-8aa3-d3635a5336ed", "metadata": {}, "outputs": [], @@ -105,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "id": "e515cc9f-435a-4318-9092-69b9d90fd601", "metadata": {}, "outputs": [ @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "id": "928e706d-7206-404f-b1ba-41a3897fd194", "metadata": { "tags": [] @@ -153,9 +145,9 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -176,38 +168,32 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "id": "1da344ad-5c88-44da-b0c2-bd6e3ac6fc0c", "metadata": {}, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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" 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\n" 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" 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\n" 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -236,13 +222,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 8, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -264,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "id": "6172b9fc-8c94-4bfe-8b08-b534e30f5e31", "metadata": {}, "outputs": [ @@ -277,12 +263,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -317,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "id": "f274646c-1d88-4bb5-85d2-c219e74cd5dd", "metadata": {}, "outputs": [ @@ -325,14 +309,14 @@ "data": { "text/plain": "{'BN': 4.351020408163265,\n 'FEC': 0.3346938775510204,\n 'PF6': 0.12040816326530612}" }, - "execution_count": 10, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inspect the coordination numbers\n", - "solute.coordination.cn_dict" + "solute.coordination.coordination_numbers" ] }, { @@ -345,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "id": "2c8cfe82-a0e3-4f11-85c7-d4f4f0ad6b54", "metadata": {}, "outputs": [ @@ -353,14 +337,14 @@ "data": { "text/plain": "{'BN': 1.0, 'FEC': 0.2653061224489796, 'PF6': 0.12040816326530612}" }, - "execution_count": 11, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inspect the pairing percentages\n", - "solute.pairing.pairing_dict" + "solute.pairing.solvent_pairing" ] }, { @@ -373,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "id": "ad3867bc-067f-4e33-b468-92a8ea93384f", "metadata": {}, "outputs": [ @@ -382,14 +366,14 @@ "text/plain": "frame 0 1 2 3 4 5 6 \\\nsolvent \nBN 0.434694 0.436735 0.428571 0.432653 0.432653 0.436735 0.434694 \nFEC 0.024490 0.026531 0.030612 0.036735 0.030612 0.048980 0.030612 \nPF6 0.016327 0.012245 0.014286 0.012245 0.012245 0.006122 0.010204 \n\nframe 7 8 9 \nsolvent \nBN 0.444898 0.434694 0.434694 \nFEC 0.036735 0.038776 0.030612 \nPF6 0.014286 0.012245 0.010204 ", "text/html": "
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frame0123456789
solvent
BN0.4346940.4367350.4285710.4326530.4326530.4367350.4346940.4448980.4346940.434694
FEC0.0244900.0265310.0306120.0367350.0306120.0489800.0306120.0367350.0387760.030612
PF60.0163270.0122450.0142860.0122450.0122450.0061220.0102040.0142860.0122450.010204
\n
" }, - "execution_count": 12, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inspect coordination numbers by frame\n", - "solute.coordination.cn_by_frame" + "solute.coordination.coordination_numbers_by_frame" ] }, { @@ -418,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "id": "15fe843d-03df-4b59-ab20-1d81abf97875", "metadata": {}, "outputs": [ @@ -427,7 +411,7 @@ "text/plain": " BN FEC PF6 count\n0 5 0 0 0.359184\n1 4 0 0 0.257143\n2 4 1 0 0.138776\n3 4 0 1 0.085714\n4 5 1 0 0.048980\n5 4 2 0 0.030612\n6 3 2 0 0.024490\n7 3 0 1 0.022449", "text/html": "
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BNFECPF6count
05000.359184
14000.257143
24100.138776
34010.085714
45100.048980
54200.030612
63200.024490
73010.022449
\n
" }, - "execution_count": 13, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -446,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "98db8743-b0b5-4f63-9d8e-bf74583b1b21", "metadata": {}, "outputs": [ @@ -454,7 +438,7 @@ "data": { "text/plain": "0.516326530612245" }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "id": "06ccfc96-181f-4d50-bb44-c50bf6923fed", "metadata": {}, "outputs": [ @@ -474,7 +458,7 @@ "data": { "text/plain": "0.08979591836734693" }, - "execution_count": 15, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "id": "d943842a-b58d-4cbd-9649-1e60295cf2f9", "metadata": { "tags": [] @@ -505,7 +489,7 @@ "text/plain": "solvent BN FEC PF6\nframe solute_ix \n0 655 4 0 1\n 667 4 0 1\n 670 4 0 1\n 683 4 0 1\n 690 4 0 1\n 693 4 0 1\n1 667 4 0 1\n 668 4 0 1\n 670 4 0 1\n 671 4 0 1\n 683 4 0 1\n 693 4 0 1", "text/html": "
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solventBNFECPF6
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" }, - "execution_count": 16, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 34, "id": "554278b5-8664-41c1-8594-3702c600d48f", "metadata": {}, "outputs": [ @@ -534,7 +518,7 @@ "text/plain": " distance solute solvent solvent_ix\nsolute_atom_ix solvent_atom_ix \n7005 3700 2.047750 Li BN 308\n 568 2.129007 Li BN 47\n 412 2.147081 Li BN 34\n 892 2.294223 Li BN 74\n 6755 2.435834 Li PF6 603", "text/html": "
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distancesolutesolventsolvent_ix
solute_atom_ixsolvent_atom_ix
700537002.047750LiBN308
5682.129007LiBN47
4122.147081LiBN34
8922.294223LiBN74
67552.435834LiPF6603
\n
" }, - "execution_count": 17, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -554,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 35, "id": "31f2f907-dbdb-412e-922b-d4f1ea7912ac", "metadata": {}, "outputs": [ @@ -562,7 +546,7 @@ "data": { "text/plain": "" }, - "execution_count": 18, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -584,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "id": "cf10b57b-079d-4068-a2a2-c1712423a905", "metadata": {}, "outputs": [ @@ -593,7 +577,7 @@ "text/plain": " distance solute solvent \\\nframe solute_ix solute_atom_ix solvent_atom_ix \n0 649 6733 4168 2.103129 Li BN \n 2308 2.127130 Li BN \n 6110 2.176079 Li FEC \n 1312 2.316887 Li BN \n 2608 2.376575 Li BN \n... ... ... ... \n9 697 7117 652 2.018652 Li BN \n 4000 2.092055 Li BN \n 1468 2.148709 Li BN \n 3328 2.184715 Li BN \n 1804 2.371709 Li BN \n\n solvent_ix \nframe solute_ix solute_atom_ix solvent_atom_ix \n0 649 6733 4168 347 \n 2308 192 \n 6110 538 \n 1312 109 \n 2608 217 \n... ... \n9 697 7117 652 54 \n 4000 333 \n 1468 122 \n 3328 277 \n 1804 150 \n\n[2355 rows x 4 columns]", "text/html": "
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distancesolutesolventsolvent_ix
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0649673341682.103129LiBN347
23082.127130LiBN192
61102.176079LiFEC538
13122.316887LiBN109
26082.376575LiBN217
........................
969771176522.018652LiBN54
40002.092055LiBN333
14682.148709LiBN122
33282.184715LiBN277
18042.371709LiBN150
\n

2355 rows × 4 columns

\n
" }, - "execution_count": 19, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/tutorials/clustering_and_residence_tutorial.ipynb b/docs/tutorials/clustering_and_residence_tutorial.ipynb index 27985820..84eec72d 100644 --- a/docs/tutorials/clustering_and_residence_tutorial.ipynb +++ b/docs/tutorials/clustering_and_residence_tutorial.ipynb @@ -37,12 +37,11 @@ "import MDAnalysis as mda\n", "from solvation_analysis.solute import Solute\n", "\n", - "# define paths to data\n", - "data = \"../../solvation_analysis/tests/data/bn_fec_data/bn_fec.data\"\n", - "traj = \"../../solvation_analysis/tests/data/bn_fec_data/bn_fec_short_unwrap.dcd\"\n", + "# we will use a trajectory supplied by the package\n", + "from solvation_analysis.tests import datafiles\n", "\n", "# instantiate Universe\n", - "u = mda.Universe(data, traj)\n", + "u = mda.Universe(datafiles.bn_fec_data, datafiles.bn_fec_dcd_unwrap)\n", "\n", "# define solute AtomGroup\n", "li_atoms = u.atoms.select_atoms(\"type 22\")\n", diff --git a/docs/tutorials/multi_atom_solutes.ipynb b/docs/tutorials/multi_atom_solutes.ipynb index 925a3edd..fc1d2e89 100644 --- a/docs/tutorials/multi_atom_solutes.ipynb +++ b/docs/tutorials/multi_atom_solutes.ipynb @@ -22,12 +22,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "is_executing": true - } - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "import MDAnalysis as mda\n", @@ -52,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -70,15 +66,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "text/plain": "", - "image/png": 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\n" 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" }, - "execution_count": 3, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -98,15 +94,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "text/plain": "", - "image/png": 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\n" 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" }, - "execution_count": 5, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -146,13 +142,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "outputs": [ { "data": { - "text/plain": "{'solute_0': ,\n 'solute_1': ,\n 'solute_2': ,\n 'solute_3': ,\n 'ketone_O': ,\n 'alcohol_O': ,\n 'solute_6': ,\n 'solute_7': ,\n 'solute_8': ,\n 'solute_9': ,\n 'solute_10': ,\n 'alcohol_H': }" + "text/plain": "{'solute_0': ,\n 'solute_1': ,\n 'solute_2': ,\n 'solute_3': ,\n 'ketone_O': ,\n 'alcohol_O': ,\n 'solute_6': ,\n 'solute_7': ,\n 'solute_8': ,\n 'solute_9': ,\n 'solute_10': ,\n 'alcohol_H': }" }, - "execution_count": 6, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "outputs": [ { "name": "stdout", @@ -183,7 +179,7 @@ "text": [ "ketone solvents are: {'IBA': , 'H2O': }\n", "ketone solute name is: ketone_O\n", - "ketone solute: atom_solutes {'ketone_O': }\n" + "ketone solute: atom_solutes {'ketone_O': }\n" ] } ], @@ -211,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "outputs": [ { "name": "stdout", @@ -269,14 +265,14 @@ "text/plain": " H2O IBA count\n0 1 0 0.1425\n1 2 0 0.1425\n2 1 1 0.1175\n3 0 1 0.1050\n4 0 2 0.1050\n5 2 1 0.0900\n6 3 0 0.0675\n7 1 2 0.0625\n8 3 1 0.0175\n9 4 0 0.0175", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
H2OIBAcount
0100.1425
1200.1425
2110.1175
3010.1050
4020.1050
5210.0900
6300.0675
7120.0625
8310.0175
9400.0175
\n
" }, - "execution_count": 10, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print('Coordination Dictionary:', solute.coordination.cn_dict)\n", - "print('Pairing Dictionary: ', solute.pairing.pairing_dict)\n", + "print('Coordination Dictionary:', solute.coordination.coordination_numbers)\n", + "print('Pairing Dictionary: ', solute.pairing.solvent_pairing)\n", "solute.speciation.speciation_fraction.head(10)" ], "metadata": { @@ -294,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -310,15 +306,15 @@ "text/plain": " H2O IBA count\n0 1 0 0.3075\n1 0 1 0.2425\n2 2 0 0.1175\n3 1 1 0.0575\n4 0 2 0.0150\n5 1 2 0.0025\n6 2 1 0.0025\n7 3 0 0.0025", "text/html": "
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" }, - "execution_count": 11, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ketone = solute.atom_solutes['ketone_O']\n", - "print('Coordination Dictionary:', ketone.coordination.cn_dict)\n", - "print('Pairing Dictionary: ', ketone.pairing.pairing_dict)\n", + "print('Coordination Dictionary:', ketone.coordination.coordination_numbers)\n", + "print('Pairing Dictionary: ', ketone.pairing.solvent_pairing)\n", "ketone.speciation.speciation_fraction.head(10)" ] }, diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 264fa8e4..3acf4a92 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -102,6 +102,23 @@ @software{pandas:2020 url = {https://doi.org/10.5281/zenodo.3509134}, } +@article{nglview:2018, + author = {Nguyen, Hai and Case, David A and Rose, Alexander S}, + title = "{NGLview–interactive molecular graphics for Jupyter notebooks}", + journal = {Bioinformatics}, + volume = {34}, + number = {7}, + pages = {1241-1242}, + year = {2017}, + month = {12}, + issn = {1367-4803}, + doi = {10.1093/bioinformatics/btx789}, + url = {https://doi.org/10.1093/bioinformatics/btx789}, + eprint = {https://academic.oup.com/bioinformatics/article-pdf/34/7/1241/48914829/bioinformatics\_34\_7\_1241.pdf}, +} + + + @article{Hou:2019, title = {The influence of FEC on the solvation structure and reduction reaction of LiPF6/EC electrolytes and its implication for solid electrolyte interphase formation}, author = {Hou, Tingzheng and Yang, Guang and Rajput, Nav Nidhi and Self, Julian and Park, Sang-Won and Nanda, Jagjit and Persson, Kristin A.}, diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 582c8396..1cc8ceb3 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -77,8 +77,9 @@ coordination numbers, solute-solvent pairing, and solute speciation, SolvationAnalysis uses tools from the SciPy ecosystem [@numpy:2020] [@scipy:2020] to implement analyses of network formation [@Xie:2023] and residence times [@Self:2019], summarized in \autoref{fig:summary}. To make visualization fast, -the package includes a robust set of plotting tools built -on top of `Matplotlib` and `Plotly` [@matplotlib:2007] [@plotly:2015]. +the package includes a robust set of plotting tools built on top of `Matplotlib` and +`Plotly` [@matplotlib:2007] [@plotly:2015]. Paired with nglview [@nglview:2018], both +exploration and 3d visualization can be done in a Jupyter notebook. A full set of tutorials based on state-of-the-art battery electrolytes [@Hou:2019] [@Dong-Joo:2022] are also included to familiarize new researchers with solvation structure analysis. Together, these features allow for diff --git a/setup.py b/setup.py index 20c318c2..858fc96e 100644 --- a/setup.py +++ b/setup.py @@ -46,11 +46,11 @@ install_requires=[ 'numpy>=1.20.0', - 'mdanalysis>=2.0.0b0', + 'mdanalysis>=2.0.0', 'pandas', 'matplotlib', 'scipy', - 'statsmodels', + 'statsmodels', 'plotly', 'rdkit' ], diff --git a/solvation_analysis/coordination.py b/solvation_analysis/coordination.py index 8b244d60..4f8aa29b 100644 --- a/solvation_analysis/coordination.py +++ b/solvation_analysis/coordination.py @@ -45,16 +45,6 @@ class Coordination: n_solutes : int The number of solutes in solvation_data. - Attributes - ---------- - cn_dict : dict of {str: float} - a dictionary where keys are residue names (str) and values are the - mean coordination number of that residue (float). - cn_by_frame : pd.DataFrame - a DataFrame of the mean coordination number of in each frame of the trajectory. - coordinating_atoms : pd.DataFrame - fraction of each atom_type participating in solvation, calculated for each solvent. - Examples -------- @@ -63,7 +53,7 @@ class Coordination: # first define Li, BN, and FEC AtomGroups >>> solute = Solute(Li, {'BN': BN, 'FEC': FEC, 'PF6': PF6}) >>> solute.run() - >>> solute.coordination.cn_dict + >>> solute.coordination.coordination_numbers {'BN': 4.328, 'FEC': 0.253, 'PF6': 0.128} """ @@ -72,9 +62,9 @@ def __init__(self, solvation_data, n_frames, n_solutes, atom_group): self.solvation_data = solvation_data self.n_frames = n_frames self.n_solutes = n_solutes - self.cn_dict, self.cn_by_frame = self._mean_cn() + self._cn_dict, self._cn_dict_by_frame = self._mean_cn() self.atom_group = atom_group - self.coordinating_atoms = self._calculate_coordinating_atoms() + self._coordinating_atoms = self._calculate_coordinating_atoms() @staticmethod def from_solute(solute): @@ -132,3 +122,26 @@ def _calculate_coordinating_atoms(self, tol=0.005): .set_index(ATOM_TYPE, append=True) ) return type_fractions[type_fractions[FRACTION] > tol] + + @property + def coordination_numbers(self): + """ + A dictionary where keys are residue names (str) and values are the + mean coordination number of that residue (float). + """ + return self._cn_dict + + @property + def coordination_numbers_by_frame(self): + """ + A DataFrame of the mean coordination number of in each frame of the trajectory. + """ + return self._cn_dict_by_frame + + @property + def coordinating_atoms(self): + """ + Fraction of each atom_type participating in solvation, calculated for each solvent. + """ + return self._coordinating_atoms + diff --git a/solvation_analysis/networking.py b/solvation_analysis/networking.py index 1a50821c..b6a2146f 100644 --- a/solvation_analysis/networking.py +++ b/solvation_analysis/networking.py @@ -52,30 +52,6 @@ class Networking: res_name_map : pd.Series a mapping between residue indices and the solute & solvent names in a Solute. - Attributes - ---------- - network_df : pd.DataFrame - the dataframe containing all networking data. the indices are the frame and - network index, respectively. the columns are the solvent_name and res_ix. - network_sizes : pd.DataFrame - a dataframe of network sizes. the index is the frame. the column headers - are network sizes, or the number of solutes + solvents in the network, so - the columns might be [2, 3, 4, ...]. the values in each column are the - number of networks with that size in each frame. - solute_status : dict of {str: float} - a dictionary where the keys are the "status" of the solute and the values - are the fraction of solute with that status, averaged over all frames. - "isolated" means that the solute not coordinated with any of the networking - solvents, network size is 1. - "paired" means the solute and is coordinated with a single networking - solvent and that solvent is not coordinated to any other solutes, network - size is 2. - "networked" means that the solute is coordinated to more than one solvent - or its solvent is coordinated to more than one solute, network size >= 3. - solute_status_by_frame : pd.DataFrame - as described above, except organized into a dataframe where each - row is a unique frame and the columns are "isolated", "paired", and "networked". - Examples -------- .. code-block:: python @@ -95,10 +71,10 @@ def __init__(self, solvents, solvation_data, solute_res_ix, res_name_map): self.solute_res_ix = solute_res_ix self.res_name_map = res_name_map self.n_solute = len(solute_res_ix) - self.network_df = self._generate_networks() - self.network_sizes = self._calculate_network_sizes() - self.solute_status, self.solute_status_by_frame = self._calculate_solute_status() - self.solute_status = self.solute_status.to_dict() + self._network_df = self._generate_networks() + self._network_sizes = self._calculate_network_sizes() + self._solute_status, self._solute_status_by_frame = self._calculate_solute_status() + self._solute_status = self._solute_status.to_dict() @staticmethod def from_solute(solute, solvents): @@ -239,3 +215,44 @@ def get_network_res_ix(self, network_index, frame): """ res_ix = self.network_df.loc[pd.IndexSlice[frame, network_index], SOLVENT_IX].values return res_ix.astype(int) + + @property + def network_df(self): + """ + The dataframe containing all networking data. the indices are the frame and + network index, respectively. the columns are the solvent_name and res_ix. + """ + return self._network_df + + @property + def network_sizes(self): + """ + A dataframe of network sizes. the index is the frame. the column headers + are network sizes, or the number of solutes + solvents in the network, so + the columns might be [2, 3, 4, ...]. the values in each column are the + number of networks with that size in each frame. + """ + return self._network_sizes + + @property + def solute_status(self): + """ + A dictionary where the keys are the "status" of the solute and the values + are the fraction of solute with that status, averaged over all frames. + "isolated" means that the solute not coordinated with any of the networking + solvents, network size is 1. + "paired" means the solute and is coordinated with a single networking + solvent and that solvent is not coordinated to any other solutes, network + size is 2. + "networked" means that the solute is coordinated to more than one solvent + or its solvent is coordinated to more than one solute, network size >= 3. + """ + return self._solute_status + + @property + def solute_status_by_frame(self): + """ + As described above, except organized into a dataframe where each + row is a unique frame and the columns are "isolated", "paired", and "networked". + """ + return self._solute_status_by_frame diff --git a/solvation_analysis/pairing.py b/solvation_analysis/pairing.py index 549ecd42..3a93d912 100644 --- a/solvation_analysis/pairing.py +++ b/solvation_analysis/pairing.py @@ -44,25 +44,6 @@ class Pairing: n_solvents : dict of {str: int} The number of each kind of solvent. - Attributes - ---------- - pairing_dict : dict of {str: float} - a dictionary where keys are residue names (str) and values are the - fraction of solutes that contain that residue (float). - pairing_by_frame : pd.DataFrame - a dictionary tracking the mean fraction of each residue across frames. - fraction_free_solvents : dict of {str: float} - a dictionary containing the fraction of each solvent that is free. e.g. - not coordinated to a solute. - diluent_dict : dict of {str: float} - the fraction of the diluent constituted by each solvent. The diluent is - defined as everything that is not coordinated with the solute. - diluent_by_frame : pd.DataFrame - a DataFrame of the diluent composition in each frame of the trajectory. - diluent_counts : pd.DataFrame - a DataFrame of the raw solvent counts in the diluent in each frame of the trajectory. - - Examples -------- @@ -71,7 +52,7 @@ class Pairing: # first define Li, BN, and FEC AtomGroups >>> solute = Solute(Li, {'BN': BN, 'FEC': FEC, 'PF6': PF6}) >>> solute.run() - >>> solute.pairing.pairing_dict + >>> solute.pairing.solvent_pairing {'BN': 1.0, 'FEC': 0.210, 'PF6': 0.120} """ @@ -80,9 +61,9 @@ def __init__(self, solvation_data, n_frames, n_solutes, n_solvents): self.n_frames = n_frames self.n_solutes = n_solutes self.solvent_counts = n_solvents - self.pairing_dict, self.pairing_by_frame = self._fraction_coordinated() - self.fraction_free_solvents = self._fraction_free_solvent() - self.diluent_dict, self.diluent_by_frame, self.diluent_counts = self._diluent_composition() + self._solvent_pairing, self._pairing_by_frame = self._fraction_coordinated() + self._fraction_free_solvents = self._fraction_free_solvent() + self._diluent_composition, self._diluent_composition_by_frame, self._diluent_counts = self._diluent_composition() @staticmethod def from_solute(solute): @@ -135,3 +116,47 @@ def _diluent_composition(self): diluent_dict = diluent_by_frame.mean(axis=1).to_dict() return diluent_dict, diluent_by_frame, diluent_counts + @property + def solvent_pairing(self): + """ + A dictionary where keys are residue names (str) and values are the + fraction of solutes that contain that residue (float). + """ + return self._solvent_pairing + + @property + def pairing_by_frame(self): + """ + A pd.Dataframe tracking the mean fraction of each residue across frames. + """ + return self._pairing_by_frame + + @property + def fraction_free_solvents(self): + """ + A dictionary containing the fraction of each solvent that is free. e.g. + not coordinated to a solute. + """ + return self._fraction_free_solvents + + @property + def diluent_composition(self): + """ + The fraction of the diluent constituted by each solvent. The diluent is + defined as everything that is not coordinated with the solute. + """ + return self._diluent_composition + + @property + def diluent_composition_by_frame(self): + """ + A DataFrame of the diluent composition in each frame of the trajectory. + """ + return self._diluent_composition_by_frame + + @property + def diluent_counts(self): + """ + A DataFrame of the raw solvent counts in the diluent in each frame of the trajectory. + """ + return self._diluent_counts diff --git a/solvation_analysis/plotting.py b/solvation_analysis/plotting.py index 5c08eb14..6729ab81 100644 --- a/solvation_analysis/plotting.py +++ b/solvation_analysis/plotting.py @@ -243,7 +243,7 @@ def compare_func( ) return fig - arguments_docstring = """ + arguments_docstring = """ property_dict : dict of {str: dict} a dictionary with the solution name as keys and a dict of {str: float} as values, where each key @@ -279,7 +279,7 @@ def compare_func( compare_pairing = _compare_function_generator( "pairing", - "pairing_dict", + "solvent_pairing", "Fractional Pairing of Solvents", "Compare the solute-solvent pairing.", ) @@ -295,7 +295,7 @@ def compare_func( compare_diluent = _compare_function_generator( "pairing", - "diluent_dict", + "diluent_composition", "Diluent Composition of Solutes", "Compare the diluent composition.", ) @@ -303,7 +303,7 @@ def compare_func( compare_coordination_numbers = _compare_function_generator( "coordination", - "cn_dict", + "coordination_numbers", "Coordination Numbers of Solvents", "Compare the coordination numbers.", ) diff --git a/solvation_analysis/residence.py b/solvation_analysis/residence.py index 708b2d00..8661dae9 100644 --- a/solvation_analysis/residence.py +++ b/solvation_analysis/residence.py @@ -71,20 +71,6 @@ class Residence: The spacing of frames in solvation_data. This should be equal to solute.step. - Attributes - ---------- - residence_times_cutoff : dict of {str: float} - a dictionary where keys are residue names and values are the - residence times of the that residue on the solute, calculated - with the 1/e cutoff method. - residence_times_fit : dict of {str: float} - a dictionary where keys are residue names and values are the - residence times of the that residue on the solute, calculated - with the exponential fit method. - fit_parameters : pd.DataFrame - a dictionary where keys are residue names and values are the - arameters for the exponential fit to the autocorrelation function. - Examples -------- @@ -99,10 +85,10 @@ class Residence: def __init__(self, solvation_data, step): self.solvation_data = solvation_data - self.auto_covariances = self._calculate_auto_covariance_dict() - self.residence_times_cutoff = self._calculate_residence_times_with_cutoff(self.auto_covariances, step) - self.residence_times_fit, self.fit_parameters = self._calculate_residence_times_with_fit( - self.auto_covariances, + self._auto_covariances = self._calculate_auto_covariance_dict() + self._residence_times_cutoff = self._calculate_residence_times_with_cutoff(self._auto_covariances, step) + self._residence_times_fit, self._fit_parameters = self._calculate_residence_times_with_fit( + self._auto_covariances, step ) @@ -259,3 +245,37 @@ def _calculate_auto_covariance(adjacency_matrix): auto_covariance = np.mean(np.concatenate(auto_covariances, axis=1), axis=1) return auto_covariance + + @property + def auto_covariances(self): + """ + A dictionary where keys are residue names and values are the + autocovariance of the that residue on the solute. + """ + return self._auto_covariances + + @property + def residence_times_cutoff(self): + """ + A dictionary where keys are residue names and values are the + residence times of the that residue on the solute, calculated + with the 1/e cutoff method. + """ + return self._residence_times_cutoff + + @property + def residence_times_fit(self): + """ + A dictionary where keys are residue names and values are the + residence times of the that residue on the solute, calculated + with the exponential fit method. + """ + return self._residence_times_fit + + @property + def fit_parameters(self): + """ + A dictionary where keys are residue names and values are the + arameters for the exponential fit to the autocorrelation function. + """ + return self._fit_parameters diff --git a/solvation_analysis/speciation.py b/solvation_analysis/speciation.py index 47382f7c..c07f4050 100644 --- a/solvation_analysis/speciation.py +++ b/solvation_analysis/speciation.py @@ -51,33 +51,14 @@ class Speciation: The number of frames in solvation_data. n_solutes : int The number of solutes in solvation_data. - - Attributes - ---------- - speciation : pandas.DataFrame - a dataframe containing the speciation of every li ion at - every trajectory frame. Indexed by frame and solute numbers. - Columns are the solvent molecules and values are the number - of solvent in the shell. - speciation_fraction : pandas.DataFrame - the fraction of shells of each type. Columns are the solvent - molecules and and values are the number of solvent in the shell. - The final column is the fraction of total shell of that - particular composition. - co_occurrence : pandas.DataFrame - The actual co-occurrence of solvents divided by the expected co-occurrence. - In other words, given one molecule of solvent i in the shell, what is the - probability of finding a solvent j relative to choosing a solvent at random - from the pool of all coordinated solvents. This matrix will - likely not be symmetric. """ def __init__(self, solvation_data, n_frames, n_solutes): self.solvation_data = solvation_data self.n_frames = n_frames self.n_solutes = n_solutes - self.speciation_data, self.speciation_fraction = self._compute_speciation() - self.co_occurrence = self._solvent_co_occurrence() + self._speciation_df, self._speciation_fraction = self._compute_speciation() + self._solvent_co_occurrence = self._solvent_co_occurrence() @staticmethod def from_solute(solute): @@ -224,9 +205,10 @@ def plot_co_occurrence(self): ax : matplotlib.Axes """ + # TODO: rewrite in plotly and move this to the plotting module solvent_names = self.speciation_data.columns.values fig, ax = plt.subplots() - im = ax.imshow(self.co_occurrence) + im = ax.imshow(self.solvent_co_occurrence) # We want to show all ticks... ax.set_xticks(np.arange(len(solvent_names))) ax.set_yticks(np.arange(len(solvent_names))) @@ -242,7 +224,7 @@ def plot_co_occurrence(self): # Loop over data dimensions and create text annotations. for i in range(len(solvent_names)): for j in range(len(solvent_names)): - ax.text(j, i, round(self.co_occurrence.iloc[i, j], 2), + ax.text(j, i, round(self.solvent_co_occurrence.iloc[i, j], 2), horizontalalignment="center", verticalalignment="center", color="black", @@ -250,3 +232,34 @@ def plot_co_occurrence(self): ) fig.tight_layout() return fig, ax + + @property + def speciation_data(self): + """ + A dataframe containing the speciation of every solute at + every trajectory frame. Indexed by frame and solute numbers. + Columns are the solvent molecules and values are the number + of solvent in the shell. + """ + return self._speciation_df + + @property + def speciation_fraction(self): + """ + The fraction of shells of each type. Columns are the solvent + molecules and values are the number of solvent in the shell. + The final column is the fraction of total shell of that + particular composition. + """ + return self._speciation_fraction + + @property + def solvent_co_occurrence(self): + """ + The actual co-occurrence of solvents divided by the expected co-occurrence. + In other words, given one molecule of solvent i in the shell, what is the + probability of finding a solvent j relative to choosing a solvent at random + from the pool of all coordinated solvents. This matrix will + likely not be symmetric. + """ + return self._solvent_co_occurrence diff --git a/solvation_analysis/tests/test_coordination.py b/solvation_analysis/tests/test_coordination.py index 0814f670..1465b2ac 100644 --- a/solvation_analysis/tests/test_coordination.py +++ b/solvation_analysis/tests/test_coordination.py @@ -6,7 +6,7 @@ def test_coordination_from_solute(run_solute): coordination = Coordination.from_solute(run_solute) - assert len(coordination.cn_dict) == 3 + assert len(coordination.coordination_numbers) == 3 @pytest.mark.parametrize( @@ -20,8 +20,8 @@ def test_coordination_from_solute(run_solute): def test_coordination(name, cn, solvation_data, run_solute): atoms = run_solute.u.atoms coordination = Coordination(solvation_data, 10, 49, atoms) - np.testing.assert_allclose(cn, coordination.cn_dict[name], atol=0.05) - assert len(coordination.cn_by_frame) == 3 + np.testing.assert_allclose(cn, coordination.coordination_numbers[name], atol=0.05) + assert len(coordination.coordination_numbers_by_frame) == 3 @pytest.mark.parametrize( @@ -35,7 +35,7 @@ def test_coordination(name, cn, solvation_data, run_solute): def test_coordinating_atoms(name, atom_type, fraction, solvation_data, run_solute): atoms = run_solute.u.atoms coordination = Coordination(solvation_data, 10, 49, atoms) - calculated_fraction = coordination.coordinating_atoms.loc[(name, atom_type)] + calculated_fraction = coordination._coordinating_atoms.loc[(name, atom_type)] np.testing.assert_allclose(fraction, calculated_fraction, atol=0.05) diff --git a/solvation_analysis/tests/test_pairing.py b/solvation_analysis/tests/test_pairing.py index 41cbe467..ae5141e3 100644 --- a/solvation_analysis/tests/test_pairing.py +++ b/solvation_analysis/tests/test_pairing.py @@ -7,7 +7,7 @@ def test_pairing_from_solute(run_solute): pairing = Pairing.from_solute(run_solute) - assert len(pairing.pairing_dict) == 3 + assert len(pairing.solvent_pairing) == 3 assert len(pairing.fraction_free_solvents) == 3 @@ -21,7 +21,7 @@ def test_pairing_from_solute(run_solute): ) def test_pairing_dict(name, fraction, solvation_data): pairing = Pairing(solvation_data, 10, 49, {'fec': 237, 'bn': 363, 'pf6': 49}) - np.testing.assert_allclose(fraction, pairing.pairing_dict[name], atol=0.05) + np.testing.assert_allclose(fraction, pairing.solvent_pairing[name], atol=0.05) assert len(pairing.pairing_by_frame) == 3 @@ -48,6 +48,6 @@ def test_pairing_participating(name, fraction, solvation_data): ) def test_diluent_composition(name, diluent_fraction, solvation_data): pairing = Pairing(solvation_data, 10, 49, {'fec': 237, 'bn': 363, 'pf6': 49}) - np.testing.assert_allclose(diluent_fraction, pairing.diluent_dict[name], atol=0.05) - np.testing.assert_allclose(sum(pairing.diluent_dict.values()), 1, atol=0.05) + np.testing.assert_allclose(diluent_fraction, pairing.diluent_composition[name], atol=0.05) + np.testing.assert_allclose(sum(pairing.diluent_composition.values()), 1, atol=0.05) diff --git a/solvation_analysis/tests/test_plotting.py b/solvation_analysis/tests/test_plotting.py index ea507e9b..71c54b1f 100644 --- a/solvation_analysis/tests/test_plotting.py +++ b/solvation_analysis/tests/test_plotting.py @@ -298,7 +298,7 @@ def test_compare_residence_times(eax_solutes): def test_compare_generic(eax_solutes): compare = _compare_function_generator( - "pairing", "pairing_dict", "hello", "This is a function" + "pairing", "solvent_pairing", "hello", "This is a function" ) fig = compare( eax_solutes, diff --git a/solvation_analysis/tests/test_solute.py b/solvation_analysis/tests/test_solute.py index a431dbe3..f27dde44 100644 --- a/solvation_analysis/tests/test_solute.py +++ b/solvation_analysis/tests/test_solute.py @@ -175,7 +175,7 @@ def test_speciation_find_shells(shell, n_shells, run_solute): ) def test_coordination_numbers(name, cn, run_solute): # duplicated to test in solute - coord_dict = run_solute.coordination.cn_dict + coord_dict = run_solute.coordination.coordination_numbers np.testing.assert_allclose(cn, coord_dict[name], atol=0.05) @@ -189,7 +189,7 @@ def test_coordination_numbers(name, cn, run_solute): ) def test_pairing(name, fraction, run_solute): # duplicated to test in solute - pairing_dict = run_solute.pairing.pairing_dict + pairing_dict = run_solute.pairing.solvent_pairing np.testing.assert_allclose([fraction], pairing_dict[name], atol=0.05) diff --git a/solvation_analysis/tests/test_speciation.py b/solvation_analysis/tests/test_speciation.py index 5ff1f89e..6ba5b639 100644 --- a/solvation_analysis/tests/test_speciation.py +++ b/solvation_analysis/tests/test_speciation.py @@ -49,7 +49,7 @@ def test_speciation_find_shells(shell, n_shells, solvation_data): ) def test_speciation_correlation(solvent_one, solvent_two, correlation, solvation_data): speciation = Speciation(solvation_data, 10, 49) - df = speciation.co_occurrence + df = speciation.solvent_co_occurrence np.testing.assert_allclose(df[solvent_one][solvent_two], correlation, atol=0.05)