diff --git a/README.md b/README.md index e210f3cf..6d0ca7d5 100644 --- a/README.md +++ b/README.md @@ -15,13 +15,11 @@ SolvationAnalysis --- Solvation analysis implements a robust, cohesive, and fast set of methods -for analyzing the solvation structure of a liquid. It seamlessly integrates with -[MDAnalysis](https://www.mdanalysis.org/), making use of the core AtomGroup -and Universe data structures to parse solvation information. If you are interested -in understanding the solvation structure of a liquid, this package is for you! +for analyzing the solvation structure of a liquid. It integrates with +[MDAnalysis](https://www.mdanalysis.org/) to seamlessly calculate, query, +and visualize solvation information. - -Find documentation and tutorials on [readthedocs]. +Find documentation and tutorials on [readthedocs](https://solvation-analysis.readthedocs.io/en/latest/). ### Installing SolvationAnalysis @@ -35,6 +33,23 @@ pip install solvation-analysis conda install -c conda-forge solvation_analysis ``` +### Solvation Analysis Summarized + +![summary](docs/tutorials/images/summary_figure.png) + + +### Visualization + +With just a few lines of code, solvation analysis can calculate detailed +properties within and between solvent systems. A few examples are shown below. + +![solvation_analysis.plotting.compare_coordination_numbers](docs/tutorials/images/coordination_plot.png) + +![solvation_analysis.plotting.plot_speciation](docs/tutorials/images/speciation_plot.png) + +![solvation_analysis.plotting.plot_rdfs](docs/tutorials/images/rdf_plot.png) + + ### Contributing Contributions, both issues and PRs, are welcome. If you'd like to contribute, we ask that you diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb index dd7be66f..63ec2ebc 100644 --- a/docs/getting_started.ipynb +++ b/docs/getting_started.ipynb @@ -16,6 +16,7 @@ "- Multi Atom Solutes: generalize to solutes with many atoms\n", "- Visualization: use `nglview` to visualize structures\n", "- Residence and Networking: calculate residence times and solute-solvent networks\n", + "- Plotting and Comparing: generate illustrative plots of solvation properties\n", "- RDF Fitting: See how solvation-analysis finds solvation cutoffs\n", "\n", "For a full catalog of the properties calculated, read through the API documentation. Solvation-analysis is a powerful tool that calculates a wide range of properties, but it will take some time to master. If you ever have any questions, or encounter any bugs, please raise an issue on [GitHub](https://github.com/MDAnalysis/solvation-analysis).\n" diff --git a/docs/tutorials/basics_tutorial.ipynb b/docs/tutorials/basics_tutorial.ipynb index 492e1e28..87da64dd 100644 --- a/docs/tutorials/basics_tutorial.ipynb +++ b/docs/tutorials/basics_tutorial.ipynb @@ -23,8 +23,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "52522e0f-a2c4-4802-9350-6afb3e33036e", + "execution_count": null, + "id": "b6226e61-99a2-4061-8a8d-93d1c1b87308", "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "id": "026c729e-eeb6-4f90-8785-fb38a79810d4", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "id": "03ec0dce-8887-471b-8aa3-d3635a5336ed", "metadata": {}, "outputs": [], @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "id": "e515cc9f-435a-4318-9092-69b9d90fd601", "metadata": {}, "outputs": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "928e706d-7206-404f-b1ba-41a3897fd194", "metadata": { "tags": [] @@ -145,9 +145,11 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, - "execution_count": 23, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -168,44 +170,3262 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "id": "1da344ad-5c88-44da-b0c2-bd6e3ac6fc0c", "metadata": {}, "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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" + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plotly.com" + }, + "data": [ + { + "mode": "lines", + "name": "RDF", + "type": "scatter", + "x": [ + 0.05, + 0.15000000000000002, + 0.25, + 0.35000000000000003, + 0.45, + 0.55, + 0.6500000000000001, + 0.75, + 0.8500000000000001, + 0.95, + 1.05, + 1.1500000000000001, + 1.25, + 1.35, + 1.4500000000000002, + 1.55, + 1.6500000000000001, + 1.75, + 1.85, + 1.9500000000000002, + 2.05, + 2.1500000000000004, + 2.25, + 2.3500000000000005, + 2.45, + 2.55, + 2.6500000000000004, + 2.75, + 2.8500000000000005, + 2.95, + 3.05, + 3.1500000000000004, + 3.25, + 3.3500000000000005, + 3.45, + 3.55, + 3.6500000000000004, + 3.75, + 3.8500000000000005, + 3.95, + 4.050000000000001, + 4.15, + 4.25, + 4.35, + 4.45, + 4.550000000000001, + 4.65, + 4.75, + 4.8500000000000005, + 4.95, + 5.050000000000001, + 5.15, + 5.25, + 5.3500000000000005, + 5.45, + 5.550000000000001, + 5.65, + 5.75, + 5.8500000000000005, + 5.95, + 6.050000000000001, + 6.15, + 6.25, + 6.3500000000000005, + 6.45, + 6.550000000000001, + 6.65, + 6.75, + 6.8500000000000005, + 6.95, + 7.050000000000001, + 7.15, + 7.25, + 7.3500000000000005, + 7.45 + ], + "y": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.1255385177762542, + 0.1514559331564427, + 0.24096999314562345, + 0.2671791342227341, + 0.1728901011257761, + 0.09278883411798489, + 0.048945651386538794, + 0.0906435366446484, + 0.03156444442104577, + 0.04898079906398083, + 0.07314666093844319, + 0.042768257728026275, + 0.06415382321473771, + 0.03766683727324751, + 0.07090359805103749, + 0.10027964770561901, + 0.18310605724669232, + 0.10153742929305307, + 0.1867307343694905, + 0.16105177510985577, + 0.19890117861302953, + 0.13583636679251324, + 0.19867415965603397, + 0.15859691690279343, + 0.14297872287628346, + 0.13662518259024908, + 0.09993628691972133, + 0.10672483466159194, + 0.09522492532459323, + 0.11840207612759723, + 0.12340949199072332, + 0.12481109117126744, + 0.141013239763462, + 0.1530150922216179, + 0.15012868743965976, + 0.1446700750807268, + 0.11366980028205871, + 0.1271311803960534, + 0.1275614273193426, + 0.15811633578818912, + 0.17082820367645374, + 0.20436068642161134, + 0.22301609193276994, + 0.1914911423837968, + 0.22497722797978137, + 0.25057244065024914, + 0.24112511772915365, + 0.2501229037058893, + 0.2969091410376267, + 0.27134464972689415, + 0.26359248045031447, + 0.30740140574385144, + 0.3019761911699914, + 0.2937033955212211, + 0.3137533431101423, + 0.31685702741555133 + ] + } + ], + "layout": { + "annotations": [ + { + "showarrow": false, + "text": "Solvation Radius", + "x": 2.8500000000000005, + "xanchor": "left", + "xref": "x", + "y": 1, + "yanchor": "top", + "yref": "y domain" + } + ], + "autosize": true, + "shapes": [ + { + "line": { + "color": "red", + "dash": "dash", + "width": 4 + }, + "type": "line", + "x0": 2.8500000000000005, + "x1": 2.8500000000000005, + "xref": "x", + "y0": 0, + "y1": 1, + "yref": "y domain" + } + ], + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", + "showlakes": true, + "showland": true, + "subunitcolor": "#C8D4E3" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Li solvation radius for PF6" + }, + "xaxis": { + "autorange": true, + "range": [ + 0.05, + 7.45 + ], + "title": { + "text": "Radial Distance (Å)" + }, + "type": "linear" + }, + "yaxis": { + "autorange": true, + "range": [ + -0.01760316818975285, + 0.33446019560530416 + ], + "title": { + "text": "Probability Density" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "
", - "image/png": "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" + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plotly.com" + }, + "data": [ + { + "mode": "lines", + "name": "RDF", + "type": "scatter", + "x": [ + 0.05, + 0.15000000000000002, + 0.25, + 0.35000000000000003, + 0.45, + 0.55, + 0.6500000000000001, + 0.75, + 0.8500000000000001, + 0.95, + 1.05, + 1.1500000000000001, + 1.25, + 1.35, + 1.4500000000000002, + 1.55, + 1.6500000000000001, + 1.75, + 1.85, + 1.9500000000000002, + 2.05, + 2.1500000000000004, + 2.25, + 2.3500000000000005, + 2.45, + 2.55, + 2.6500000000000004, + 2.75, + 2.8500000000000005, + 2.95, + 3.05, + 3.1500000000000004, + 3.25, + 3.3500000000000005, + 3.45, + 3.55, + 3.6500000000000004, + 3.75, + 3.8500000000000005, + 3.95, + 4.050000000000001, + 4.15, + 4.25, + 4.35, + 4.45, + 4.550000000000001, + 4.65, + 4.75, + 4.8500000000000005, + 4.95, + 5.050000000000001, + 5.15, + 5.25, + 5.3500000000000005, + 5.45, + 5.550000000000001, + 5.65, + 5.75, + 5.8500000000000005, + 5.95, + 6.050000000000001, + 6.15, + 6.25, + 6.3500000000000005, + 6.45, + 6.550000000000001, + 6.65, + 6.75, + 6.8500000000000005, + 6.95, + 7.050000000000001, + 7.15, + 7.25, + 7.3500000000000005, + 7.45 + ], + "y": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 2.8036935636696767, + 11.434922953311425, + 12.220621080956617, + 6.160836506783045, + 2.5357214831780497, + 0.9013772457175675, + 0.3793287982456756, + 0.3852350307397557, + 0.5681599995788238, + 1.116762218658763, + 2.322406484795571, + 4.097199090344917, + 4.70728677838138, + 3.63108311314106, + 2.112927221920917, + 1.129817364149974, + 0.9597283000516287, + 1.0631566125978498, + 1.1147258991148372, + 1.3474665184191266, + 1.7289102448671028, + 1.824088354070892, + 2.2824426713972277, + 3.004530481325142, + 3.557647045686348, + 4.299674863869603, + 4.554788461533453, + 4.8909415608708855, + 4.718924077196509, + 4.966121364437507, + 5.0013320438345765, + 5.176540006328318, + 5.250492969916138, + 4.674178005788668, + 4.353731935750133, + 3.8953757253217915, + 3.807938309448967, + 3.1084819990956585, + 2.8328264142427595, + 2.6298467025947336, + 2.4859999113968136, + 2.3240592955819417, + 2.341668965294084, + 2.31419082710631, + 2.3681813471555935, + 2.3259243345855185, + 2.2461732120769624, + 2.2511061333530034, + 2.156957583420406, + 2.2063711830918082, + 2.1944073997488682, + 2.270287465337403, + 2.1356254344599392, + 2.1195090398438636, + 2.0298220976796997, + 1.9972028922618235 + ] + } + ], + "layout": { + "annotations": [ + { + "showarrow": false, + "text": "Solvation Radius", + "x": 2.6500000000000004, + "xanchor": "left", + "xref": "x", + "y": 1, + "yanchor": "top", + "yref": "y domain" + } + ], + "autosize": true, + "shapes": [ + { + "line": { + "color": "red", + "dash": "dash", + "width": 4 + }, + "type": "line", + "x0": 2.6500000000000004, + "x1": 2.6500000000000004, + "xref": "x", + "y0": 0, + "y1": 1, + "yref": "y domain" + } + ], + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", + "showlakes": true, + "showland": true, + "subunitcolor": "#C8D4E3" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Li solvation radius for BN" + }, + "xaxis": { + "autorange": true, + "range": [ + 0.05, + 7.45 + ], + "title": { + "text": "Radial Distance (Å)" + }, + "type": "linear" + }, + "yaxis": { + "autorange": true, + "range": [ + -0.6789233933864788, + 12.899544474343095 + ], + "title": { + "text": "Probability Density" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "
", - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHFCAYAAAAOmtghAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB11UlEQVR4nO3deViUVRsG8HvYQVlVEAXBJXdUFBc0t9xSM8tKzX3ry33BzNRyrSjbNEtNc8lc0kzNLZXKNZcUxV0zRXEBERdAVBQ43x+nd4aBAWZghoGZ+3ddc83LO+/yzIjO4znPOUclhBAgIiIishA25g6AiIiIyJiY3BAREZFFYXJDREREFoXJDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNyQVVu+fDlUKhWOHTuW4zFXr16FSqXC8uXLCy2uPXv2QKVSYc+ePSa7x8GDBzF9+nQ8ePAg22utWrVCq1atTHZvQ+n6PAYMGIDAwECDrnPr1i1Mnz4dUVFRRo3PnE6cOIGWLVvC3d0dKpUKc+bMMen9VCqVzkfp0qXVx0yfPj3H41QqFa5evap1zaSkJHz00UcICQmBm5sbHB0dERgYiEGDBuH48eMmfT9kmezMHQBRUefr64tDhw6hcuXK5g7FqA4ePIgZM2ZgwIAB8PDw0Hpt/vz55gnKAB988AHGjBlj0Dm3bt3CjBkzEBgYiHr16pkmsEI2aNAgpKSk4KeffoKnp6fBCV9+vP766xg/frzWPnt7+2zH7dixA+7u7tn2+/r6qrcvX76M9u3bIz4+HkOHDsWMGTNQsmRJXL16FevWrUODBg3w4MEDndchygmTG6I8ODo6okmTJuYOo1DVrFnT3CHkydKSzfw6c+YM3nrrLXTs2NEo13v27BlUKhXs7HL+evDx8dHr70SDBg20WnSySk9Px6uvvoqEhAQcOnQItWvXVr/WsmVL9O/fH7/99pvOxIkoN+yWIsqDvt1SGRkZ+PDDD1GtWjU4OzvDw8MDderUwdy5c7WOO3DgANq0aQNXV1e4uLigadOm2LZtW67XnjNnDlQqFf79999sr02cOBEODg5ISEgAAERERKBr167w8/ODk5MTqlSpgrffflv9OiC7DSZMmAAAqFixorq7QOn20dUtde/ePQwfPhzly5eHg4MDKlWqhClTpiA1NVXrOJVKhZEjR+LHH39EjRo14OLigrp162Lr1q25vkfFhQsX8OKLL8LFxQWlS5fG0KFDkZycnO04Xd1SP//8Mxo3bgx3d3e4uLigUqVKGDRoEADZtdWwYUMAwMCBA9Xvefr06QCAY8eOoWfPnggMDISzszMCAwPx5ptv4tq1a1r3ULoyd+/ejWHDhqF06dIoVaoUunXrhlu3bmWLc/Xq1QgNDUXJkiVRsmRJ1KtXD0uWLNE65vfff0ebNm3g5uYGFxcXNGvWDH/88Ueun5MSR1paGhYsWKB+P4ozZ86ga9eu8PT0hJOTE+rVq4cffvhB6xpKd9+PP/6I8ePHo3z58nB0dNT5e2YKmzZtwunTpzFp0iStxCazjh07wsXFpVDiIcvB5IbISGbPno3p06fjzTffxLZt27B27VoMHjxYq6Zl7969eOGFF5CYmIglS5ZgzZo1cHV1RZcuXbB27docr92nTx84ODhkS7DS09OxcuVKdOnSRf0/5MuXLyM0NBQLFizArl27MHXqVBw5cgTPP/88nj17BgAYMmQIRo0aBQDYsGEDDh06hEOHDqF+/fo67//kyRO0bt0aK1asQFhYGLZt24Y+ffpg9uzZ6NatW7bjt23bhm+++QYzZ87EL7/8Ai8vL7z66qu4cuVKrp/h7du30bJlS5w5cwbz58/Hjz/+iIcPH2LkyJG5ngcAhw4dQo8ePVCpUiX89NNP2LZtG6ZOnYq0tDQAQP369bFs2TIAwPvvv69+z0OGDAEgk9hq1aphzpw52LlzJz799FPExsaiYcOGWomhYsiQIbC3t8fq1asxe/Zs7NmzB3369NE6ZurUqejduzfKlSuH5cuXY+PGjejfv79WwrRy5Uq0b98ebm5u+OGHH7Bu3Tp4eXmhQ4cOuSY4nTt3xqFDhwDIbiLl/QDAxYsX0bRpU5w9exZff/01NmzYgJo1a2LAgAGYPXt2tmtNmjQJMTExWLhwIbZs2QJvb+9cP2shBNLS0rQeQohsx6Wnp2c7Lj09Xf36rl27AACvvPJKrvcjMpggsmLLli0TAMTRo0dzPCY6OloAEMuWLcv1Wi+99JKoV69ersc0adJEeHt7i+TkZPW+tLQ0Ubt2beHn5ycyMjKEEELs3r1bABC7d+9WH9etWzfh5+cn0tPT1fu2b98uAIgtW7bovF9GRoZ49uyZuHbtmgAgfv31V/Vrn332mQAgoqOjs53XsmVL0bJlS/XPCxcuFADEunXrtI779NNPBQCxa9cu9T4AwsfHRyQlJan3xcXFCRsbGxEeHp7r5zNx4kShUqlEVFSU1v527dpl+zz69+8vAgIC1D9//vnnAoB48OBBjtc/evSoXn+WQsg/l4cPH4oSJUqIuXPnqvcrvzPDhw/XOn727NkCgIiNjRVCCHHlyhVha2srevfuneM9UlJShJeXl+jSpYvW/vT0dFG3bl3RqFGjPOMEIEaMGKG1r2fPnsLR0VHExMRo7e/YsaNwcXFRf0bK71mLFi3yvE/m++l6LF68WH3MtGnTcjyucuXK6uNefPFFAUA8efJE7/sT6YMtN0RG0qhRI5w8eRLDhw/Hzp07kZSUpPV6SkoKjhw5gtdffx0lS5ZU77e1tUXfvn1x48YNXLx4McfrDxw4EDdu3MDvv/+u3rds2TKULVtWq95CKcz09/eHnZ0d7O3tERAQAAA4f/58vt7bn3/+iRIlSuD111/X2j9gwAAAyNbC0Lp1a7i6uqp/9vHxgbe3d7Yunqx2796NWrVqoW7dulr7e/XqlWeMSpdT9+7dsW7dOty8eTPPczJ7+PAhJk6ciCpVqsDOzg52dnYoWbIkUlJSdH5uL7/8stbPderUAQD1e4yIiEB6ejpGjBiR4z0PHjyIe/fuoX///lqtGxkZGXjxxRdx9OhRpKSkGPQ+APnn1aZNG/j7+2vtHzBgAB49eqRu4VG89tprBl2/e/fuOHr0qNZDV+vL77//nu24TZs2Gfp2iAzGgmIiI5k0aRJKlCiBlStXYuHChbC1tUWLFi3w6aefIiQkBPfv34cQQmukiKJcuXIAgLt37+Z4/Y4dO8LX1xfLli1D+/btcf/+fWzevBljxoyBra0tAFn30759e9y6dQsffPABgoKCUKJECWRkZKBJkyZ4/Phxvt7b3bt3UbZsWa2aDgDw9vaGnZ1dtrhLlSqV7RqOjo553v/u3buoWLFitv1ly5bNM8YWLVpg06ZN+Prrr9GvXz+kpqaiVq1amDJlCt588808z+/Vqxf++OMPfPDBB2jYsCHc3NygUqnQqVMnnXFnfY+Ojo4AoD72zp07AAA/P78c73n79m0AyJY0Znbv3j2UKFEiz/gzu3v3rkG/Z7qOzU2ZMmUQEhKS53F169bNtaC4QoUKAIDo6GhUr17doBiIcsOWGyIjsbOzQ1hYGI4fP4579+5hzZo1uH79Ojp06IBHjx7B09MTNjY2iI2NzXauUoia2xeB0sKzadMmPHjwAKtXr0ZqaioGDhyoPubMmTM4efIkPvvsM4waNQqtWrVCw4YNdSYbhihVqhRu376dra4iPj4eaWlpucZt6H3i4uKy7de1T5euXbvijz/+QGJiIvbs2QM/Pz/06tUrW0tFVomJidi6dSveffddvPfee2jTpg0aNmyIoKAg3Lt3L1/vpUyZMgCAGzdu5HiM8rnNmzcvWwuH8vDx8TH43qVKlTLo9yxr0lpYOnToAABszSGjY3JDZAIeHh54/fXXMWLECNy7dw9Xr15FiRIl0LhxY2zYsEGrJSAjIwMrV66En58fqlatmut1Bw4ciCdPnmDNmjVYvnw5QkNDtf7Hq3xJKa0Iiu+++y7btbK2NOSmTZs2ePjwYbYvoRUrVqhfN4bWrVvj7NmzOHnypNb+1atXG3QdR0dHtGzZEp9++ikAOdGdsh/I/p5VKhWEENk+t++//16rANYQ7du3h62tLRYsWJDjMc2aNYOHhwfOnTuHkJAQnQ8HBweD792mTRv8+eef2UZvrVixAi4uLkVmaoOuXbsiKCgI4eHhOHPmjM5jdu7ciUePHhVyZFTcsVuKCLJGIeusqQDQqVMnva/RpUsX1K5dGyEhIShTpgyuXbuGOXPmICAgAM899xwAIDw8HO3atUPr1q3xzjvvwMHBAfPnz8eZM2ewZs2aPP8HXb16dYSGhiI8PBzXr1/HokWLsr1euXJlvPfeexBCwMvLC1u2bEFERES2awUFBQEA5s6di/79+8Pe3h7VqlXTqpVR9OvXD99++y369++Pq1evIigoCAcOHMDHH3+MTp06oW3btnp/TrkZO3Ysli5dis6dO+PDDz+Ej48PVq1ahQsXLuR57tSpU3Hjxg20adMGfn5+ePDgAebOnQt7e3u0bNkSgJwbx9nZGatWrUKNGjVQsmRJlCtXDuXKlUOLFi3w2WefoXTp0ggMDMTevXuxZMmSbBMc6iswMBCTJ0/GrFmz8PjxY7z55ptwd3fHuXPnkJCQoJ6sbt68eejfvz/u3buH119/Hd7e3rhz5w5OnjyJO3fu5Joc5WTatGnYunUrWrdujalTp8LLywurVq3Ctm3bMHv27EKbEC8yMlLnvWrWrAk3NzfY2tpi48aNaN++PUJDQzFs2DC0bt0aJUqUwLVr17B+/Xps2bIF9+/fL5R4yYKYt56ZyLyUkS85PaKjo/UeLfXFF1+Ipk2bitKlSwsHBwdRoUIFMXjwYHH16lWt4/bv3y9eeOEFUaJECeHs7CyaNGmSbbSTrtFSikWLFgkAwtnZWSQmJmZ7/dy5c6Jdu3bC1dVVeHp6ijfeeEPExMQIAGLatGlax06aNEmUK1dO2NjYaN0v62gpIYS4e/euGDp0qPD19RV2dnYiICBATJo0KdtIF+gYvSOEEAEBAaJ///66Pzwd8Ts5OQkvLy8xePBg8euvv+Y5Wmrr1q2iY8eOonz58sLBwUF4e3uLTp06if3792tdf82aNaJ69erC3t5e6zO5ceOGeO2114Snp6dwdXUVL774ojhz5ky2uHMaYZfTn9mKFStEw4YNhZOTkyhZsqQIDg7O9ru0d+9e0blzZ+Hl5SXs7e1F+fLlRefOncXPP/+c5+eV0+d9+vRp0aVLF+Hu7i4cHBxE3bp1s91XiVmf++R1v8xyGy0FQERERGgd/+DBAzFr1ixRv359UbJkSWFvby8qVKgg+vTpI/766y+9YyNSqITQMTkBERERUTHFmhsiIiKyKExuiIiIyKIwuSEiIiKLwuSGiIiILAqTGyIiIrIoTG6IiIjIoljdJH4ZGRm4desWXF1dzTblOBERERlGCIHk5GSUK1cONja5t81YXXJz69atbCvlEhERUfFw/fr1XBekBawwuVGmlr9+/Trc3NzMHA2RiaSkAP+tAI1btwADV5UmIipqkpKS4O/vr3OJmKysLrlRuqLc3NyY3JDlsrXVbLu5MbkhIouhT0kJC4qJiIjIojC5ISIiIovC5IaIiIgsitXV3OgrPT0dz549M3cYZEXs7e1hm7lWhoiI8oXJTRZCCMTFxeHBgwfmDoWskIeHB8qWLcs5mIiICoDJTRZKYuPt7Q0XFxd+yVChEELg0aNHiI+PBwD4+vqaOSIiouKLyU0m6enp6sSmVKlS5g6HrIyzszMAID4+Ht7e3uyiIiLKJxYUZ6LU2Li4uJg5ErJWyu8e672IiPKPyY0O7Ioic+HvHhFRwTG5ISIiIovC5MaK7NmzByqVSmsk2KZNm1ClShXY2tpi7NixZouNiIjIWFhQbOXefvttDBw4EKNHj9ZrMTIiIqKijsmNhXr69CkcHBxyPebhw4eIj49Hhw4dUE5ZQZqIiPD0KWBvD7AMrnhit5SFaNWqFUaOHImwsDCULl0a7dq1w/bt21G1alU4OzujdevWuHr1qvr4PXv2qFtqXnjhBahUKuzZs8c8wRMRFSH79wMlSwIffmjuSCi/2HKTByGAR4/Mc28XF8P+1/DDDz9g2LBh+OuvvxAfH4927dph6NChGDZsGI4dO4bx48erj23atCkuXryIatWq4ZdffkHTpk3h5eVlgndBRFS8zJsHPHsG/Pgj8MEH5o6G8oPJTR4ePZIZvDk8fAiUKKH/8VWqVMHs2bMByESnUqVK+Oqrr6BSqVCtWjWcPn0an376KQDAwcEB3t7eAAAvLy+ULVvW6PETERU3SUnAli1y+9IlIC4O4D+PxQ+7pSxISEiIevv8+fNo0qSJ1rwpoaGh5giLiKjY2LQJePJE8/Nff5ktlCItKQno0gVYvNjckejGlps8uLjIFhRz3dsQJTI18wghjBwNEZHlW71aPru4yJb7/fuB114zb0xF0S+/AFu3AlFRwFtvmTua7Jjc5EGlMqxrqKioWbMmNm3apLXv8OHD5gmGiKgYuH0b+P13uT15MvD++zK5oewOHZLPN24A8fHAf1UORQa7pSzU0KFDcfnyZYSFheHixYtYvXo1li9fbu6wiMiK/f570U4Wfv4ZSE8HGjUC+veX+6KiZBcMaVOSGwA4ccJ8ceSEyY2FqlChAn755Rds2bIFdevWxcKFC/Hxxx+bOywiskLJyTJZaNcO6NDBfCNQ86J0SfXqBfj5AYGBQEaG9hc5AYmJwNmzmp+PHzdfLDlht5SF0DVHzUsvvYSXXnpJa9/AgQPV2x4eHqzNISKT+vtvmSxcvix/fvwY+OcfoF49s4aVzZUrMomxsQG6d5f7mjcHrl6VrU0dOpg1vCLlyBE5TYoiMtJ8seSELTdERGR06enAxx8DzZrJxKZCBaBSJfnahQuFG8uVK8C77wI3b+Z8zE8/yefWrQFfX7ndvLl8PnDAtPEVN0pLlp+ffC6KLTdMboiIyKiuXwfatAGmTAHS0oAePYCTJ2XiABRucpOeLltiPvsMePll7WHemWXuklIoyc2RI0BqqmnjLE6U5Obtt+VzdDRw/7754tGFyQ0RERnNjRtA/frA3r1yAtTly4E1awAPD6B6dXlMYSY3ixdruk2OHwdGjsx+zOnTsobEwQHo1k2zv1o1oEwZmRAVxa4Xc8jIAJSBtx07alrjilpRMZMbIiLK0aVLgI8PkGn1lhwJAQwfDiQkALVryy+8/v01y8gUdnJz544c0g0AvXvLOJYskY/MlFabzp1lEqZQqYDnn5fbRXmUV2G6cEEWFDs7A3XqyEQWKHpdU0xuiIgoR8uXy3lMvvwSWLs292PXr5dLF9jbyxqWKlW0X1eSm4sXZQuAqU2aJLtL6tWT72PWLLl/xAjNl3FGhmxZArS7pBRMbrQpXVING8o/ZyY3RERkdjExwJAhwL//6nf8tm2a7f/9T44e0uXePU2Xz+TJQK1a2Y8JDJRdP0+eyDhM6dAhTQvNt98CdnYy2enSRdbPvPaajPnQIeDaNcDVVbbcZKXU3fz1V+EkZEWdktwoq/koyU1R67ZjckNEZEXmzZNf+u++m/ex16/LQmCVSn6JJSXJ1o20tOzHvvOObOGpUUMmEbrY2QHPPSe3Tdk1lZ4uu8cAYOBAoGlTuW1jA/zwg6wTuXoV6NsXWLlSvtatm+xqySo4WM5S/+CB9twu1iqn5Oaff4rWZIdMboiIrIjSYvPbb3mvm7d9u3wODZVrCbm7yy+3mTO1j/v9d2DZMk1Ni6NjztcsjLqbhQvlzMIeHsAnn2i/5ukp34uTk3x/330n9+vqkgJkQqZ8kVt719SDB8C5c3Jb+UzKlAH8/eX2yZNmCUsnsyY3+/btQ5cuXVCuXDmoVKpsayFltWHDBrRr1w5lypSBm5sbQkNDsXPnzsIJlojIAly5Ip+fPJELH+ZGef2ll2SXkpIIfPQRsG+f3H70SDMkeMQIzZdeTkyd3Ny+LYegK3HqWvOoXj1gwQK5LYQ85oUXcr6m0jVl7cnNkSPyuVIl7c+1KNbdmDW5SUlJQd26dfHNN9/odfy+ffvQrl07bN++HZGRkWjdujW6dOmCE0VtDFoxc/XqVahUKkRFRZn8XoGBgZgzZ47J72NMmRPvwvysiIxNCE1yA8i1lHLy+DHwxx9yW6lF6dFDdvNkZMjRR/fuAdOny2v6+clJ+/KiJDfnz+d97ObNwNSpwLNneR+rmDhRjuYJDtYkXboMGKB5vX9/2UKTk8xFxdY8qXvWLilFUUxuIIoIAGLjxo0Gn1ezZk0xY8YMvY9PTEwUAERiYmK21x4/fizOnTsnHj9+bHAcxVl0dLQAIE6cOGG0ay5btky4u7tn2x8fHy9SUlKMdp/CkPl3My0tTcTGxopnz56Z5F5G+x18+FAI+e+w3CYSQty5o/m1AIRwchIiOVn3sdu2yWP8/YXIyNDsT04WompV+VqzZkLY2MjtLVv0i+HYMXm8t3fux2VkCFG6tDx20SL9rr1/v+a9HTqU9/Hp6UL89ZcQT57kflxKihB2dvK6V67oF4slat9efgbffKO9f8sWub92bdPeP7fv76yKdc1NRkYGkpOT4eXlZe5QSE9lypSBi4tLod4zPT0dGUYa5mBra4uyZcvCLrf/5hEVUUqrTfnyQOXKsmsq82iozJQuqc6dNfPUAHJivjVr5DBgZQRRz56y60of1arJ5/h42fKTk0uX5Hw5APDpp7qLmDMTAggLk9uDBwNNmuQdi42NLDbOrUYIAFxcgAYN5La1LsWQkaHplsqp5ebcuaKzKGqxTm6++OILpKSkoLuyypkOqampSEpK0npYovXr1yMoKAjOzs4oVaoU2rZti5SUFAAyCZw5cyb8/Pzg6OiIevXqYceOHTqvk5GRAT8/PyxcuFBr//Hjx6FSqXDlv38dv/zySwQFBaFEiRLw9/fH8OHD8fC/6sQ9e/Zg4MCBSExMhEqlgkqlwvTp0wFk75aKiYlB165dUbJkSbi5uaF79+64ffu2+vXp06ejXr16+PHHHxEYGAh3d3f07NkTycnJOX4Wy5cvh4eHB7Zu3YqaNWvC0dER165dw9GjR9GuXTuULl0a7u7uaNmyJY5naUe9dOkSWrRoAScnJ9SsWRMRERFar2ftllLuldmmTZugyvRtcPLkSbRu3Rqurq5wc3NDgwYNcOzYsRzjJzIVJbmpXBl44w25ratrSghN0qMraalfHwgPl9teXsDcufrHULKkZk2iixdzPu7gQc325ctyDp3c/PorcPSoHNn00Uf6x6Mva6+7OX9edve5uMjJ+zLz9ZUTPWZkAKdOmSe+rIptcrNmzRpMnz4da9euhbeuirH/hIeHw93dXf3wV8q69SUEkJJinoeenbuxsbF48803MWjQIJw/fx579uxBt27d1Ct+z507F1988QU+//xznDp1Ch06dMDLL7+MS5cuZbuWjY0NevbsiVWrVmntX716NUJDQ1Hpv7m2bWxs8PXXX+PMmTP44Ycf8Oeff+Ld/8aWNm3aFHPmzIGbmxtiY2MRGxuLd955R8dHK/DKK6/g3r172Lt3LyIiInD58mX06NFD67jLly9j06ZN2Lp1K7Zu3Yq9e/fik6xDILJ49OgRwsPD8f333+Ps2bPw9vZGcnIy+vfvj/379+Pw4cN47rnn0KlTJ3WilJGRgW7dusHW1haHDx/GwoULMXHiRL3+DHLTu3dv+Pn54ejRo4iMjMR7770He3v7Al+XyFBKclOpkia52b5d/nOT2Zkzch4aJyfNelBZjRsnh1H/8Yfuot3c6FNUrNR3KA3z4eE5/5OYng68/77cHjNGftEam7UnN8qfR6NG2euTVCpNy1aRqbsxbQ+Z/mBAzc1PP/0knJ2dxdatW/M89smTJyIxMVH9uH79umE1N5lrFwr7oWetRGRkpAAgrl69qvP1cuXKiY8++khrX8OGDcXw4cOFENlrbo4fPy5UKpX6eunp6aJ8+fLi22+/zTGGdevWiVKlSql/zqnmJiAgQHz11VdCCCF27dolbG1tRUxMjPr1s2fPCgDi77//FkIIMW3aNOHi4iKSkpLUx0yYMEE0btw4x1iWLVsmAIioqKgcjxFC1s+4urqKLf8VC+zcuVPY2tqK69evq4/57bfftH43s35Wut7nxo0bRea/Wq6urmL58uW5xqJgzQ2Z0uDB8ldixgxZ01Kpkvx57Vrt48LD5f7OnU0Tx8iR8vrvvpvzMbVry2OWLhWiZEm5ndM/+T/+KF/38BDi3j3TxJyQoPkrFR9vmnsUZYMGyfc+aZLu199/X74+eLDpYrDomps1a9ZgwIABWL16NTrrmk4yC0dHR7i5uWk9LE3dunXRpk0bBAUF4Y033sDixYtx/78lWpOSknDr1i00a9ZM65xmzZrhfA7DFYKDg1G9enWs+W9O8r179yI+Pl6r+2/37t1o164dypcvD1dXV/Tr1w93795Vd4Xp4/z58/D399dqTatZsyY8PDy0YgsMDISrq6v6Z19fX8THx+d6bQcHB9TJ0nYaHx+PoUOHomrVquqWvIcPHyLmv6lSz58/jwoVKsBPaTMHEJrXuFY9hIWFYciQIWjbti0++eQTXL58ucDXJMqPzC03KlXOXVOZ621MIa+Wm8REzYR5nToBw4bJ7Y8/zt568+wZMG2a3H73XTmPjSmUKgXUrCm3rbHuJqeRUoqiNmLKrMnNw4cPERUVpa5fiI6ORlRUlPrLZtKkSejXr5/6+DVr1qBfv3744osv0KRJE8TFxSEuLg6JiYmmC9LFRc50ZY6HnoW3tra2iIiIwG+//YaaNWti3rx5qFatGqKjo9XHZK4BAWSXUNZ9mfXu3Rur/1tNbvXq1ejQoQNKly4NALh27Ro6deqE2rVr45dffkFkZCS+/fZbAMAzA8Zs5hRD1v1Zu3BUKlWeBcLOzs7Zrj1gwABERkZizpw5OHjwIKKiolCqVCk8ffpUfd+scvuMANk9l/W8rJ/B9OnTcfbsWXTu3Bl//vknatasiY0bN+Z6XSJTUP5JUFZyVpKbbds0XVN372q+yMyV3Bw5IpOYSpVkF9O4cbLo9+DB7N1CS5fKpM3bGxg92jTxKpSuKWtLbu7f1wzdz6lQW0luzpyRy1uYm1mTm2PHjiE4OBjBwcEA5P9wg4ODMXXqVACyliQm0wIk3333HdLS0jBixAj4+vqqH2PGjDFdkCqVrFAzxyOPL1btMFVo1qwZZsyYgRMnTsDBwQEbN26Em5sbypUrhwNZ/jYePHgQNWrUyPF6vXr1wunTpxEZGYn169ejd+/e6teOHTuGtLQ0dZJZtWpV3Lp1S+t8BwcHpKen5xpzzZo1ERMTg+vXr6v3nTt3DomJibnGll/79+/H6NGj0alTJ9SqVQuOjo5IUIZjZIon83s5pPwrn4MyZcogOTlZq8VK1xw4VatWxbhx47Br1y5069YNy5YtK/gbIjLAs2ea9ZyU5KZ+faBiRTmnjTIb8Y4dsjA0KAioUME0sSjJzeXLwH//t9CStZXA11fOSwNoCpkBGbcyW/L778t/Nk2pRQv5/OuveY/esiTKKKkqVeSMxLpUqCDro549KxrLVJg1uWnVqhWEENkey5cvByBHouzZs0d9/J49e3I93lodOXIEH3/8MY4dO4aYmBhs2LABd+7cUScIEyZMwKeffoq1a9fi4sWLeO+99xAVFZVrUlixYkU0bdoUgwcPRlpaGrp27ap+rXLlykhLS8O8efNw5coV/Pjjj9lGVwUGBuLhw4f4448/kJCQgEc6xge2bdsWderUQe/evXH8+HH8/fff6NevH1q2bImQkBAjfToaVapUwY8//ojz58/jyJEj6N27N5wzLSbTtm1bVKtWDf369cPJkyexf/9+TFGmOs1B48aN4eLigsmTJ+Pff//F6tWrtX4fHz9+jJEjR2LPnj24du0a/vrrLxw9etQkyRtRbmJiZNLi7KwpuNXVNZXbKCljKVdOLlSZni4TnKyUkVLKmlCA7HKysZHJl9L1MX8+cOuW/GL93/9MF6/i5Zdl99Tly3LVc2uRV5cUoFl/DCgai2gWu5obys7NzQ379u1Dp06dULVqVbz//vv44osv0LFjRwDA6NGjMX78eIwfPx5BQUHYsWMHNm/ejOeUFexy0Lt3b5w8eRLdunXTSgLq1auHL7/8Ep9++ilq166NVatWITzzf6cgR0wNHToUPXr0QJkyZTB79uxs11dm/vX09ESLFi3Qtm1bVKpUCWvXrjXCp5Ld0qVLcf/+fQQHB6Nv374YPXq01kg7GxsbbNy4EampqWjUqBGGDBmCj/IYU+rl5YWVK1di+/btCAoKUo/iU9ja2uLu3bvo168fqlatiu7du6Njx46YMWOGSd4jUU6UepuKFbUbhTN3TSUlyTWnANN1SQHy/jl1TWVkAIcPy+3MX6aVKsn5dAC5XlRSkqYVZ/r0vOeqMYaSJYHx4+X2hx/K5Mwa6JPcAEVsxJQpKpqLMs5QTEUZR0uRqSxcKH8dXnpJe39GhhCBgfK10aPls5eXEGlppo2nb195r48/1t5/+rTcX6KEEFknAj91Sr6mUmnOr1Yt+3GmlJQkPx9AiFWrCu++GRlCPHpUePdTpKUJ4eYm329ek9ivXSuPa9TINLFY9GgpIiIyXNZiYkXmrql58+Rzx46Ara1p48lpjanc5lMJCgK6dJFZ+48/yn2zZuW+LpSxubpqZkKeNavwWm/eeEN2J+7eXTj3U5w7J1vJSpQAatfO/VilW+rkScPWAzMFJjdERFYg8zDwrJTkRhn4Z8p6G0VO3VK66m0ymzRJs12vHvDaa0YPLU+jRskh5xcu5L74qLFcuQL88guQnAx07Vq43T5KoXnjxnknkZUqAW5ucrSUqVZ91xeTGyIiK5BbchMSAgQGym1bW6BDB9PHkzm5yTybQl71HaGhsmXJxkauOWVjhm8xNzc5PB2QrTc5zUyRkQEsWaJJEPJrxQr5rFLJBOfFF+XaW6b27BnwzTdyu2/fvI+3sZGrsQPmr7thckNEZAUyFxRnlblrqmlT002El1nlyjKRSk4GYmPlvrt3NetN5bbw5fr18su9fXvTx5mT0aMBDw/ZbaNr3asnT4AePYAhQ2RXWn6/7DMygB9+kNvz58vk4c4d+d6zzMBhdBs2ADduyDmElGLuvBSVEVNMbnQQeq7pRGRs/N0jU3jwQE7EBuhObgA51Pqtt4AvvyycmBwdNa1ISheGMkqqWjU55DonLi66W6AKk7s7MHas3J45U7v15t49mXwoSU9Ghhyqnp+5cfbtA65ela1F/frJ0WxVqsh9HTpo/lxNQVnjeNgwuc6YPorKTMVMbjJRZsLVNScLUWFQfve4sCYZk1JM7OOT80R3pUsDixbJLqrCkrXuRqm3McKqJ4VizBiZ5Jw9K1s5AODaNeD55+VMym5uwJo1soUnMlLTxWMIZdqsHj1kUufjA+zaJSc2PHNG1keZ4ivr8GH5cHDQLH+hD2U4eFSUeYfKF2KNedFna2sLDw8P9bpFLi4ueU6/T2QMQgg8evQI8fHx8PDwgK2ph6qQVcmt3sacqlcHtmzRJDdKvU1OxcRFjYeHTHBmzpSPypXl/ECxsUD58rKVJShIjjZ6+205i3K3bvrP/Pzwoab1R5mhGZCtbzt3yhmTDx4EXn9dzpqs7/+JkpJk4pUbpdWmVy/DVlmvWhX47jvZgmPOr08mN1mULVsWAPJcmJHIFDw8PNS/g0TGUpSTG0AmN2lpmmn+i0vLDSC7pr76Cjh9Wo4oevZMDpn+7TdAWYN3yBBZFPzXX8CIEcDmzfp98a9fL9f8eu657J9JUJBc4LRdO3mvxYuB4cPzvuY338jRXuHhwHvv6T7m+nVNUqV0venL1rZwZovOC5ObLFQqFXx9feHt7W3QIpBEBWVvb88WGzKJ3IqJzUlZheTCBZkcPHokWxSU1beLA09PWVz80UcysWnVCti4UbbqKGxsZJdfvXoyIfnlF9nakhelS2rAAN3JULNmcsTY6NHAZ5/JpCK34dopKXI2Z0AOqa9RQw4tz+qbb2SXUuvWQN26ecdZFDG5yYGtrS2/aIhIb8eOyVElplpssiCKastNtWry+fp1ICJCbjdpYp7h3QUxfrysqalSBfj8c91LQdSsCUycKJdtGD1atri4u+d8zStXgL17ZVKT2zDswYPlcPSrV+V6V3365Hzsd9/JEWm2tjJ56dNHdgVmnpwvJUUmYoDhrTZFSTH7FSIiKnquX5dfyrVqAZnW+i0yimpy4+UlE0JA00pRXOptMvP0lF1D8+blvsbVlCmyiyk2VnsyQl2UuW3atgX8/XM+zsVFk4R88knOc+48fixbdwDg229lq8zDh7Ll5t497fs+eKCpHyqumNwQERXQqVPyf8IPH8oJ5pSVtYuC9HQ5ggcoeskNkH0ZhuJUb2MoJyfZegIACxdqCqizyjy3TeZC4pwMHy6XhTh7VnZ76bJ0KRAXJ1sWBw4E1q2TEzdeuSJHYqWlyfsqhcRjxph+CQ5TYnJDRFRAylBrW1s5edsrrwAmWtzeYDdvyloQBwegXDlzR5OdktwAsgumcWPzxVIYWreWCYsQ8lnXMgWZ57Z55ZW8r+nhoSkmDg/XnvEZAJ4+lbU5gOwac3CQQ/9//VVODfD778CECcCOHcA//8j76pNUFWVMboiICkjp9hkxQg6dTUsD3nxTjmAxNyW2wMCi+T/xzMlNrVq516FYis8/B8qWlYlEvXrA7NnaE/xlndtGH+PGyZahw4dlrU5mK1bIrlNfX2DQIM3+OnU0LURz5mhGOQ0ZIluCijMmN0REBaQkENWqydWqhw6V/3v+3/+AL74w7b2//VYmBf/8k3tsRW2klCJzclMc623yo1Qp4OhRuUZUaqpsTWnWTC7lkNPcNnnx8dEkLuHhmv1paZqfJ0zIPtPwa68BU6fK7Zs3ZTH3qFH5eltFCpMbIqICypxA2NjINYDefVfue+cdOUzYFE6flsWk587lvGxCUS0mVmRObiy53iYrPz+5oOayZbK16u+/5bpRPXvmPLdNXiZMkK1zu3Zp1nZas0b+DpQunfP8M9OmaYaEd+umWUS1OGNyQ0RUAEJoam6UBEKlkjUOH38sf542Tf6P3JjS0+VaUEp3xtq1st4nq6yxFTUVKsjRRioV0Ly5uaMpXCqVbJ05e1aOTHr6VFOMntPcNrkJDJTdoYBsrUlP1/wOjh+f89IbNjYyCVq5UlPwXNwxuSEiKoCEBJm4qFRAQID2a5MmyTqH9HTZymJM334rZ/R1c5P3ePBAznybVVFvubG1lXFv2iSHH1uj8uXlMhQ//CCLgz095SKZ+aHMOrxhg0xsLlyQ18tr9mJnZ6B3bzk83xIwuSEiKgAleShXTvfKycoMrydPGu+eMTHA5Mly+5NPNLUWSiGqrviKanIDyIUmX37Z3FGYl0olE5obN4CLFzVLNxiqVi35WQqhqaUZMybvtaQsDZMbIqICyCt5UJKbqCjj3E8I+b/wlBRZhPr225r/5e/cKSeIUzx8CCjL5BXVgmLSVqIEUKZMwa6ReYJAV1fLKBA2FJMbIqICyKumpV49+Wyslpt162RdhoODHGpuYyNXYm7aVE7CtnJl9ti8vKxjiDVJTZoAL7wgt0eMsJyuJkMwuSEiKgB9W25On5a1NwVx755clwiQ3VLKwpMA0L+/fP7hB80kbsWhS4pMY9UqOQuyslCmtWFyQ0RUAHnNI/Pcc7IWJyUFuHy5YPd65x3ZzVSzpqZwVNG9u7zP2bPA8eNyX1EfKUWmU7as7LLMba0rS8bkhoioAPJqHbGzA4KC5HZBuqb++EPOiaJSye6orF9aHh6aqfqVwmK23JC1YnJDRJRPz57Jae2B3BOIgo6YevpUM5R3+PCcZ/JVuqZWr5Yz3xb12YmJTIXJDRFRPsXEyCJeJyfZDZCTgo6Ymj9fLq/g7Z37bMft2skh6ffuyaJjttyQtWJyQ0SUT5lbRnKbTbYgLTcJCcCMGXL7o49yH/Vkawv06SO3ly9nzQ1ZLyY3RET5pG/yUKeOfL5xA7h717B7TJ8uZx+uWxcYODDv45Wuqa1b5XIMtraAv79h9yQq7pjcEBHlk77dPu7umroXQ1pvzp6Vw3kB4KuvZKKSl5o1gYYNNcPBK1QA7O31vyeRJWByQ0SUT4bUtBjaNSUEEBYm58Z59VWgdWv941JabwAWE5N1YnJDRJRPhoxGMnSm4t9+A3btkjMRf/aZYXH17CnPA1hvQ9aJyQ0RUT4ZUrBryIipZ89kqw0AjB1r+GrZpUpp5rypXduwc4ksgZ25AyAiKo4ePJBDrgH9Wm6U5ObcOTlvjdKyosv8+XJlaG9vYMqU/MW3cCHQpg3Qt2/+zicqzthyQ0SUD0qrjbc3ULJk3scHBgJubrJV5sKFnI+7e1ezHtCHH8pz8sPTE/jf/wBn5/ydT1ScMbkhIsoHQ2f/Van065pShn7XqQMMGlSAAImsGJMbIqJ8yM8EeXmNmLp9G1i0SG7rO/SbiLJjckNElA/5WdogrxFT330n63EaNwZeeKFA4RFZNSY3RET5kJ/kJnO3lDLJnuLpU2DBArk9ZkyBwyOyakxuiIjyIT8rbteqBdjYyKLhW7e0X1u3DoiLkwtfvv668eIkskZMboiIDJSeDly7JrcNablxdgaqV5fbmbumhADmzJHbw4dzuQSigjJrcrNv3z506dIF5cqVg0qlwqZNm/I8Z+/evWjQoAGcnJxQqVIlLFQWXiEiKiS3bsluJDs7wM/PsHN1jZg6eBCIjAScnOTwbSIqGLMmNykpKahbty6++eYbvY6Pjo5Gp06d0Lx5c5w4cQKTJ0/G6NGj8csvv5g4UiIiDaVLKjDQ8BFNukZMzZ0rn3v3BsqUKXB4RFbPrDMUd+zYER07dtT7+IULF6JChQqY81/7bY0aNXDs2DF8/vnneO2110wUJRGRtvzU2yiyjpi6fh3YsEFus5CYyDiKVc3NoUOH0L59e619HTp0wLFjx/Ds2TOd56SmpiIpKUnrQURUEPmZ40ahtNz88w+QkgJ8+62s4WndGggKMl6MRNasWCU3cXFx8PHx0drn4+ODtLQ0JCQk6DwnPDwc7u7u6oe/v39hhEpEFiw/w8AVZcvKJRuEAP7+WzNpH1ttiIynWCU3AKBSqbR+Fv9NFpF1v2LSpElITExUP65fv27yGImo+Mg634w+CtItBWi6piZOBO7fl9d56aX8XYuIsitWyU3ZsmURFxentS8+Ph52dnYoVaqUznMcHR3h5uam9SAiAoDDh2VLyg8/GHZeQbqlAE3X1NGj8nnUKC61QGRMxSq5CQ0NRUREhNa+Xbt2ISQkBPacGIKIDLRrFxAfD6xerf85jx7JyfaAgic3gFxRnAtkEhmXWZObhw8fIioqClH/TfgQHR2NqKgoxMTEAJBdSv369VMfP3ToUFy7dg1hYWE4f/48li5diiVLluCdd94xR/hEVMzdvSufz5zR/xyl1cbDA/D0zN99lW4pABgwAHB3z991iEg3syY3x44dQ3BwMIKDgwEAYWFhCA4OxtSpUwEAsbGx6kQHACpWrIjt27djz549qFevHmbNmoWvv/6aw8CJKF/u3ZPPt25ptvNS0HobAKhWDShVSk4COGpU/q9DRLqZdZ6bVq1aqQuCdVm+fHm2fS1btsTx48dNGBURWQul5QYAzp4FmjfP+5yC1tsAMqnZswd4/BioWjX/1yEi3YpVzQ0RkTFlTm707ZoqyDDwzGrXBho2LNg1iEg3JjdEZLUyd0WdPavfOcZKbojIdJjcEJHVKkjLTUFqbojItJjcEJFVSk8HHjzQ/HzmTN4T+glhnJobIjItJjdEZJUePNAkMzY2shXn9u3cz4mPl/PcqFRAQIDJQySifGJyQ0RWSemScnUFqlSR23l1TV2+LJ/9/QEHB9PFRkQFw+SGiKySUkxcqhRQq5bczquoWFkuQTmeiIomJjdEZJWUlhsvLzksG8i75Wb/fvmsz3w4RGQ+TG6IyCopyU2pUvolN0IwuSEqLpjcEJFVytwtlTm5yWnE1L//yoJiR0dOvkdU1DG5ISKrlLlb6rnnAHt74OFDINNydlqUVpuGDWWCQ0RFF5MbIrJKmbul7O3lYpZAzkXF7JIiKj6Y3BCRVcrcLQXkXXdz4IB8ZnJDVPQxuSEiq5S5WwrIPbmJi5M1NyoVEBpaOPERUf4xuSEiq5S5WwrIPblRWm3q1AE8PEweGhEVEJMbIrJKWbullIn5zp2T605lptTbPP984cRGRAXD5IaIrFLWbqmKFQFnZyA1VbPMgoLFxETFC5MbIrI6T5/KYd+ApuXG1haoWVNuZx4xlZQEnDwpt9lyQ1Q8MLkhIqujdEmpVNo1NLrqbg4dAjIyZMtO+fKFFiIRFQCTGyKyOkqXlKcnYJPpX0FdyQ27pIiKHyY3RGR1so6UUihFxZmTG85vQ1T82Jk7ACKiwpZ1pJRCabn55x9ZlyMEcOSI3Md6G6Lig8kNEVmdrCOlFH5+gJubLCL+5x8gORl48gQoXVqzPAMRFX3sliIiq5NTt5RKpV13k3l+G5Wq8OIjooIxOLmZPn06rl27ZopYiIgKRU7dUoDu5Ib1NkTFi8HJzZYtW1C5cmW0adMGq1evxpMnT0wRFxGRyeTULQVoiopPnQL++ktuM7khKl4MTm4iIyNx/Phx1KlTB+PGjYOvry+GDRuGo0ePmiI+IiKjy6lbCtC03EREAPfvAy4uQL16hRYaERlBvmpu6tSpg6+++go3b97E0qVLcfPmTTRr1gxBQUGYO3cuEhMTjR0nEZHRKN1SulpulORGaZQODQXs7QsnLiIyjgIVFGdkZODp06dITU2FEAJeXl5YsGAB/P39sXbtWmPFSERkVLm13Hh7A2XKaH5mlxRR8ZOv5CYyMhIjR46Er68vxo0bh+DgYJw/fx579+7FhQsXMG3aNIwePdrYsRIRGUVuyQ2gab0BOL8NUXFkcHJTp04dNGnSBNHR0ViyZAmuX7+OTz75BFWqVFEf069fP9y5c8eogRIRGYMQuXdLAZqiYjs7oEmTwomLiIzH4En83njjDQwaNAjlc1lBrkyZMsjIyChQYEREpvDoEZCaKrdzarmpU0c+168PlChROHERkfEY3HIjhICnp2e2/Y8fP8bMmTONEhQRkakoXVL29kDJkrqP6dMHGDcO+PrrwouLiIxHJYQQhpxga2uL2NhYeHt7a+2/e/cuvL29kZ6ebtQAjS0pKQnu7u5ITEyEm5ubucMhMo2UFM0398OHbH7IJCoKCA4GfHyAuDhzR0NE+jLk+ztfLTcqHfOQnzx5El45dWATERUReRUTE1Hxp3fNjaenJ1QqFVQqFapWraqV4KSnp+Phw4cYOnSoSYIkIjIWJjdElk/v5GbOnDkQQmDQoEGYMWMG3N3d1a85ODggMDAQoaGhJgmSiMhY8hopRUTFn97JTf/+/QEAFStWRNOmTWHPKTuJqBhiyw2R5dMruUlKSlIX7wQHB+Px48d4/PixzmNZpEtERVluK4ITkWXQK7nx9PRUj5Dy8PDQWVCsFBoX9dFSRGTdclsRnIgsg17JzZ9//qkeCbV7926TBkREZErsliKyfHolNy1bttS5bQzz58/HZ599htjYWNSqVQtz5sxB81xWqlu1ahVmz56NS5cuwd3dHS+++CI+//xzlOK/VESkB3ZLEVk+g+e52bFjBw4cOKD++dtvv0W9evXQq1cv3L9/36BrrV27FmPHjsWUKVNw4sQJNG/eHB07dkRMTIzO4w8cOIB+/fph8ODBOHv2LH7++WccPXoUQ4YMMfRtEJGVYrcUkeUzOLmZMGECkpKSAACnT59GWFgYOnXqhCtXriAsLMyga3355ZcYPHgwhgwZgho1amDOnDnw9/fHggULdB5/+PBhBAYGYvTo0ahYsSKef/55vP322zh27Jihb4OIrBS7pYgsn8HJTXR0NGrWrAkA+OWXX9ClSxd8/PHHmD9/Pn777Te9r/P06VNERkaiffv2Wvvbt2+PgwcP6jynadOmuHHjBrZv3w4hBG7fvo3169ejc+fOhr4NIrJCGRnsliKyBgYnNw4ODnj06BEA4Pfff1cnJ15eXuoWHX0kJCQgPT0dPj4+Wvt9fHwQl8OCL02bNsWqVavQo0cPODg4oGzZsvDw8MC8efNyvE9qaiqSkpK0HkRknZKSZIIDsFuKyJIZnNw8//zzCAsLw6xZs/D333+rW03++ecf+Pn5GRxA1mHlOa1dBQDnzp3D6NGjMXXqVERGRmLHjh2Ijo7OddmH8PBwuLu7qx/+/v4Gx0hElkHpknJxAZyczBsLEZmOwcnNN998Azs7O6xfvx4LFixA+fLlAQC//fYbXnzxRb2vU7p0adja2mZrpYmPj8/WmqMIDw9Hs2bNMGHCBNSpUwcdOnTA/PnzsXTpUsTGxuo8Z9KkSUhMTFQ/rl+/rneMRGRZ2CVFZB30Xn5BUaFCBWzdujXb/q+++sqg6zg4OKBBgwaIiIjAq6++qt4fERGBrl276jzn0aNHsLPTDtnW1haAbPHRxdHREY6OjgbFRkSWiSOliKyDwckNAGRkZODff/9FfHw8MpQO7P+0aNFC7+uEhYWhb9++CAkJQWhoKBYtWoSYmBh1N9OkSZNw8+ZNrFixAgDQpUsXvPXWW1iwYAE6dOiA2NhYjB07Fo0aNUK5cuXy81aIyIpwpBSRdTA4uTl8+DB69eqFa9euZWstMXT5hR49euDu3buYOXMmYmNjUbt2bWzfvh0BAQEAgNjYWK05bwYMGIDk5GR88803GD9+PDw8PPDCCy/g008/NfRtEJEVYrcUkXVQiZz6c3JQr149VK1aFTNmzICvr2+24l93d3ejBmhsSUlJcHd3R2JiIhf5JMuVkgKULCm3Hz4ESpQwbzxFxPTpwIwZwNtvAwsXmjsaIjKEId/fBrfcXLp0CevXr0eVKlXyHSARkTmwW4rIOhg8Wqpx48b4999/TRELEZFJsVuKyDoY3HIzatQojB8/HnFxcQgKCoK9vb3W63Xq1DFacERExsTRUkTWweDk5rXXXgMADBo0SL1PpVKpJ98zpKCYiKgwsVuKyDoYnNxER0ebIg4iIpNjtxSRdTA4uVGGaRMRFTfsliKyDgYXFAPAjz/+iGbNmqFcuXK4du0aAGDOnDn49ddfjRocEZGxpKUBiYlymy03RJbN4ORmwYIFCAsLQ6dOnfDgwQN1jY2HhwfmzJlj7PiIiIzi/n3Ntqen+eIgItMzOLmZN28eFi9ejClTpqjXdQKAkJAQnD592qjBEREZi9Il5e4O2OVr4RkiKi4MTm6io6MRHBycbb+joyNSUlKMEhQRkbFxpBSR9TA4ualYsSKioqKy7f/tt99Qs2ZNY8RERGR0HClFZD0MbpydMGECRowYgSdPnkAIgb///htr1qxBeHg4vv/+e1PESERUYBwpRWQ9DE5uBg4ciLS0NLz77rt49OgRevXqhfLly2Pu3Lno2bOnKWIkIiowdksRWY98ldW99dZbeOutt5CQkICMjAx4e3sbOy4iIqNitxSR9chXcpOQkICrV69CpVIhMDDQyCERERkfu6WIrIdBBcVnz55FixYt4OPjg8aNG6NRo0bw9vbGCy+8gIsXL5oqRiKiAmO3FJH10LvlJi4uDi1btkSZMmXw5Zdfonr16hBC4Ny5c1i8eDGaN2+OM2fOsIuKiIokdksRWQ+9k5uvvvoKAQEB+Ouvv+Dk5KTe/+KLL2LYsGF4/vnn8dVXXyE8PNwkgRIRFQS7pYish97dUhEREZg4caJWYqNwdnbGhAkTsHPnTqMGR0RkLOyWIrIeeic3V65cQf369XN8PSQkBFeuXDFKUERExsZuKSLroXdyk5ycDDc3txxfd3V1xcOHD40SFBGRMT15Ajx6JLfZLUVk+QwaCp6cnKyzWwoAkpKSIIQwSlBERMaktNrY2sqFM4nIsumd3AghULVq1VxfV6lURgmKiMiYlHobT0+A/0wRWT69k5vdu3ebMg4iIpNhMTGRddE7uWnZsqUp4yAiKrA7d4AtW4DQUKBGDc1+FhMTWZd8Lb9ARFQUTZ4MfP+93K5TB+jZE+jRg3PcEFkbJjdEZDGOHdNsnzolH5Mna1ps2HJDZB0MWluKiKioSk8HLlyQ20eOyBactm0BGxtNy42Pj/niI6LCw5YbIrIIV6/K+WwcHYEGDYBGjYDBg4Hbt4H162UrzvDh5o6SiAqDwcnN8uXL0b17d7i4uJgiHiKifDl3Tj5Xry7ns1H4+AAjRpgnJiIyD4O7pSZNmoSyZcti8ODBOHjwoCliIiIymJLc1Kxp3jiIyPwMTm5u3LiBlStX4v79+2jdujWqV6+OTz/9FHFxcaaIj4hIL0xuiEhhcHJja2uLl19+GRs2bMD169fxv//9D6tWrUKFChXw8ssv49dff0VGRoYpYiUiyhGTGyJSFGi0lLe3N5o1a4bQ0FDY2Njg9OnTGDBgACpXrow9e/YYKUQiotxlZADnz8ttJjdElK/k5vbt2/j8889Rq1YttGrVCklJSdi6dSuio6Nx69YtdOvWDf379zd2rEREOl2/DqSkAPb2QOXK5o6GiMzN4NFSXbp0wc6dO1G1alW89dZb6NevH7wyTfvp7OyM8ePH46uvvjJqoEREOVG6pKpWlQkOEVk3g5Mbb29v7N27F6GhoTke4+vri+jo6AIFRkSkL3ZJEVFmBndLtWzZEvXr18+2/+nTp1ixYgUAQKVSISAgoODRERHpgcXERJSZwcnNwIEDkZiYmG1/cnIyBg4caJSgiIgMweSGiDIzOLkRQkClUmXbf+PGDbi7uxslKCIifQnB5IaItOldcxMcHAyVSgWVSoU2bdrAzk5zanp6OqKjo/Hiiy+aJEiioi49HejXD/D3Bz75xNzRWJfYWCAxUS658Nxz5o6GiIoCvZObV155BQAQFRWFDh06oGTJkurXHBwcEBgYiNdee83oARIVB6dOAatXy+2hQ4HAQLOGY1WUVpsqVeSimUREeic306ZNAwAEBgaiR48ecHJyMkoA8+fPx2effYbY2FjUqlULc+bMQfPmzXM8PjU1FTNnzsTKlSsRFxcHPz8/TJkyBYMGDTJKPET58e+/mu3164F33jFfLNaGXVJElJXBQ8GNOTnf2rVrMXbsWMyfPx/NmjXDd999h44dO+LcuXOoUKGCznO6d++O27dvY8mSJahSpQri4+ORlpZmtJiI8iNzcvPzz0xuChOTGyLKSq/kxsvLC//88w9Kly4NT09PnQXFinv37ul98y+//BKDBw/GkCFDAABz5szBzp07sWDBAoSHh2c7fseOHdi7dy+uXLminjgwkO3/VARkTm7+/hu4epVdU4WFyQ0RZaVXcvPVV1/B1dVVvZ1bcqOvp0+fIjIyEu+9957W/vbt2+PgwYM6z9m8eTNCQkIwe/Zs/PjjjyhRogRefvllzJo1C87OzjrPSU1NRWpqqvrnpKSkAsdOlJWS3NjZAWlp7JoqLEIAZ8/KbSY3RKTQK7nJ3BU1YMAAo9w4ISEB6enp8PHx0drv4+ODuLg4nedcuXIFBw4cgJOTEzZu3IiEhAQMHz4c9+7dw9KlS3WeEx4ejhkzZhglZqKcXL4sn3v3Bn74gV1TheXOHeDePUClAqpVM3c0RFRU6DXPTVJSkt4PQ2VtBcppHh0AyMjIgEqlwqpVq9CoUSN06tQJX375JZYvX47Hjx/rPGfSpElITExUP65fv25wjES5efQIuHlTbr/7LmBjo+maItNSuqQqVQJyaLwlIiukV8uNh4dHnl1RSlKSnp6u141Lly4NW1vbbK008fHx2VpzFL6+vihfvrzWZIE1atSAEAI3btzAczomuXB0dIQjx4eSCV25Ip89PWXXSIsWwJ497JoqDKy3ISJd9Epudu/ebfQbOzg4oEGDBoiIiMCrr76q3h8REYGuXbvqPKdZs2b4+eef8fDhQ/U8O//88w9sbGzg5+dn9BiJ9KHU21SpIp/feEMmN+vWMbkxNSY3RKSLXslNy5YtTXLzsLAw9O3bFyEhIQgNDcWiRYsQExODoUOHApBdSjdv3lQvyNmrVy/MmjULAwcOxIwZM5CQkIAJEyZg0KBBORYUE5la1uSmWzdg1Cjg6FGOmjI1JjdEpIteyc2pU6dQu3Zt2NjY4NSpU7keW6dOHb1v3qNHD9y9exczZ85EbGwsateuje3bt6tXFI+NjUVMTIz6+JIlSyIiIgKjRo1CSEgISpUqhe7du+PDDz/U+55ExpY1uSlbll1ThYXJDRHpohJCiLwOsrGxQVxcHLy9vWFjYwOVSgVdpxlSc2MuSUlJcHd3R2JiItzc3MwdDlmAtm2BP/6Qo6T69ZP75s8HRowAGjaUxcWFLiUFUJZIefgQKFHCDEGY1t27QOnScjs5WfN2icgyGfL9rVfLTXR0NMqUKaPeJiKNrC03ALumDHX1KnD/PhAcrP8558/L54AAJjZEpE2v5EbpJsq6TWTtUlMBpec0c3KTuWvq55+BCRPMEl6xIATQrh1w7ZrsZsr8OeaGXVJElBO95rnJ6uLFixg5ciTatGmDtm3bYuTIkbh48aKxYyMq8qKj5ZezqyvwX+Om2htvyOeffy78uIqTa9dk69ezZ8CWLfqfx+SGiHJicHKzfv161K5dG5GRkahbty7q1KmD48ePo3bt2viZ/4qTlVG6pCpXlrPkZtatm5zQT+maIt0OH9Zs79ih/3lMbogoJwYnN++++y4mTZqEQ4cO4csvv8SXX36JgwcPYvLkyZg4caIpYiQqsnTV2yiUrimArTe5OXJEs713r5zxWR9MbogoJwYnN3FxceinDAnJpE+fPjmuCUVkqXJLbgB2Tekjc3KTmirrlPKSmKhZ8qJGDZOERUTFmMHJTatWrbB///5s+w8cOIDmzZsbJSii4iKv5KZbN/l89KgckU3anj4Fjh+X223ayGd9uqaUkVLlywOZVmMhIgKg52ipzZs3q7dffvllTJw4EZGRkWjSpAkA4PDhw/j555+5+jZZHWU18JySGx8fwMkJePIESEjgkOWsTp6UrTWlSsl5gf74A/jtt7zPY5cUEeVGr+TmlVdeybZv/vz5mD9/vta+ESNGqJdOILJ0z55pCoVzSm5UKvnFffOmTG443402pUuqUSPZcmNnJ1vD/v039yHhTG6IKDd6dUtlZGTo9SjqsxMTGVNMDJCWBjg7A76+OR+nzKJ7927hxFWcKCOlGjcG3NyA55+XP+/cmft5yqzPrLchIl3yNc8NEWkPA7fJ5W9SqVLymclNdkrLzX893HjxRfmcW9dUVBSwfz9gawt07GjS8IiomNKrWyqrlJQU7N27FzExMXj69KnWa6NHjzZKYERFXV7FxAql5SYhwbTxFDd372o+w0aN5HPHjsB77wG7d8s6JSen7Od98YV87t4dqFChcGIlouLF4OTmxIkT6NSpEx49eoSUlBR4eXkhISEBLi4u8Pb2ZnJDVkPf5IYtN7oprTZVqwKennI7KEh28cXGAgcOyEVJM7txA/jpJ7k9fnzhxUpExYvB3VLjxo1Dly5dcO/ePTg7O+Pw4cO4du0aGjRogM8//9wUMRIVSWy5KZisXVKALMDOrWvq669lnVOrVkCDBiYPkYiKKYOTm6ioKIwfPx62trawtbVFamoq/P39MXv2bEyePNkUMRIVSWy5KRgluWncWHu/UkeTdb6bpCTgu+/k9jvvmDY2IireDE5u7O3tofpvER0fHx/E/Lcksru7u3qbyNKlpwNXrshtttwYLiMj5+SmbVtZoH3unGbFdQD4/nuZ4FSvzkJiIsqdwclNcHAwjh07BgBo3bo1pk6dilWrVmHs2LEICgoyeoBERdGNG3J2XQcHwM8v92PZcpPdpUvAgweyYLhOHe3XPD01XVVK682zZ8DcuXJ7/PjcR6cRERn8T8THH38M3/8m9Zg1axZKlSqFYcOGIT4+HosWLTJ6gERFkdIlVamSHJKcG2tsuZk9G8jtnwOl1aZBA8DePvvrWbum1q+XrTje3kCfPsaNlYgsj8GjpUJCQtTbZcqUwfbt240aEFFxoG+9DWB9LTf//ANMnCi3GzTQXfibefI+XV58EfjgA+D332ULmTJWYeRI3cPDiYgyy3fjbnx8PPbv348DBw7gzp07xoyJqMjLPIFfXpSWm8ePgUePTBdTUREVpdnOaYyBrpFSmdWvD5QpAyQnA+HhcnFNZ2dg2DCjhkpEFsrg5CYpKQl9+/ZF+fLl0bJlS7Ro0QLlypVDnz59kJiYaIoYiYocQ1puSpbUdL1YQ+vNqVOa7V275IR8mT16pDkmp5YbGxugQwe5PXOmfB4wQJMoEhHlxuDkZsiQIThy5Ai2bt2KBw8eIDExEVu3bsWxY8fw1ltvmSJGoiInr9XAM1MWzwSso+7m5En57O0tnydNAoTQvH78uJyrpmxZwN8/5+so891kZMjPcNw408RLRJbH4ORm27ZtWLp0KTp06AA3Nze4urqiQ4cOWLx4MbZt22aKGImKFCEMa7kBrGvxTKVV5ttvARcX2QW1ebPm9cxdUv/NKqFT+/aa1195BXjuOZOES0QWyODkplSpUnB3d8+2393dHZ7KHOpEFiw2VtbP2NoCAQH6nWMtRcX372vmpmnXDhg7Vm5PniznBgJynt8mqzJl5Jw3dnaaAmUiIn0YnNy8//77CAsLQ2xsrHpfXFwcJkyYgA8++MCowREVRUqrTWCg7mHMuljLcPDTp+VzQADg7g5MmCDnrTl3Dli1Sr6W10ipzH75Rc6Jo8+xREQKvYaCBwcHq2clBoBLly4hICAAFf5bkjcmJgaOjo64c+cO3n77bdNESlREGNolBVhPy41Sb6NMzOfhIVf5njgRmDYNaNkSuH5ddjdlmlUiR66u8kFEZAi9kptXXnnFxGEQFR/5SW6speVGqbepW1ezb+RIObvw1avAkCFyX+3aTFqIyHT0Sm6mTZtm6jiIig223OQsa8sNIIuKp04Fhg6Vk/IB7GYiItMyeIZiRWRkJM6fPw+VSoWaNWsiODjYmHERFVlsudEtPR04c0ZuZ265AYBBg+Qsw8pnx+SGiEzJ4OQmPj4ePXv2xJ49e+Dh4QEhBBITE9G6dWv89NNPKFOmjCniJCoS8jMMHLCOlpt//5WjyJyds8/cbG8PzJoFvPmm/DmnmYmJiIzB4NFSo0aNQlJSEs6ePYt79+7h/v37OHPmDJKSkjB69GhTxEhUZNy5I5cEUKmAihX1P88aWm6UepugIN2LiXbvDvTtC/TsCdSsWbixEZF1MbjlZseOHfj9999Ro0YN9b6aNWvi22+/Rfv27Y0aHFFRc+WKfPbzAxwd9T/PGlpulOQmc71NZjY2wIoVhRcPEVkvg1tuMjIyYK9jcg97e3tkZGQYJSiiokpZI7ZsWcPOU1puHj4EUlONG1NRoRQTZ623ISIqbAYnNy+88ALGjBmDW7duqffdvHkT48aNQ5s2bYwaHFFRo7S8KC0x+nJ313TVWGrrTV4tN0REhcXg5Oabb75BcnIyAgMDUblyZVSpUgUVK1ZEcnIy5s2bZ4oYiYqM/CY3lr545oMHwLVrcjsoyKyhEBEZXnPj7++P48ePIyIiAhcuXIAQAjVr1kTbtm1NER9RkaIkJko3kyFKlQLi4y2z5UZZdqFCBbncAhGRORmU3KSlpcHJyQlRUVFo164d2rVrZ6q4iIqk/LbcZD7HEpMbXZP3ERGZi0HdUnZ2dggICEC6srwvkZUpSHJjycPBdS27QERkLvlaFXzSpEm4d++eKeIhKtIK2i0FsOWGiMjUDK65+frrr/Hvv/+iXLlyCAgIQIkSJbReP378uNGCIypq2HKTXW7LLhARmYPByU3Xrl2hUqlMEQtRkceam+wuXwYePZLLLhiyJAURkakYnNxMnz7dBGEQFX1CsOVGF6XepnZt3csuEBEVNr1rbh49eoQRI0agfPny8Pb2Rq9evZBghH+l58+fj4oVK8LJyQkNGjTA/v379Trvr7/+gp2dHerVq1fgGIj0kZwMPHsmt9lyo8F6GyIqavRObqZNm4bly5ejc+fO6NmzJyIiIjBs2LAC3Xzt2rUYO3YspkyZghMnTqB58+bo2LEjYmJicj0vMTER/fr144zIVKiUpMTZGXBxMfx8S2+5Yb0NERUVeic3GzZswJIlS7Bo0SJ8/fXX2LZtGzZt2lSgYeFffvklBg8ejCFDhqBGjRqYM2cO/P39sWDBglzPe/vtt9GrVy+Ehobm+95EhipIl1Tm8yyt5YbLLhBRUaN3cnP9+nU0b95c/XOjRo1gZ2entcaUIZ4+fYrIyMhsK4m3b98eBw8ezPG8ZcuW4fLly5g2bZpe90lNTUVSUpLWgyg/CjIMPPN5iYma7q3iLjERuHpVbjO5IaKiQu/kJj09HQ4ODlr77OzskJaWlq8bJyQkID09HT4+Plr7fXx8EBcXp/OcS5cu4b333sOqVatgZ6dfLXR4eDjc3d3VD39//3zFS1TQlhsPD7nGFABYyjRRyrIL/v5cdoGIig69R0sJITBgwAA4Ojqq9z158gRDhw7Vmutmw4YNBgWQdVi5EELnUPP09HT06tULM2bMQNWqVfW+/qRJkxAWFqb+OSkpiQkO5UtBkxtbW8DLS14nIQHIktcXSywmJqKiSO/kpn///tn29enTJ983Ll26NGxtbbO10sTHx2drzQGA5ORkHDt2DCdOnMDIkSMBABkZGRBCwM7ODrt27cILL7yQ7TxHR0ethIwov5RuqfwmN8q5d+9aTt0Ni4mJqCjSO7lZtmyZUW/s4OCABg0aICIiAq+++qp6f0REBLp27ZrteDc3N5xW2sD/M3/+fPz5559Yv349KlasaNT4iLJSEpL81two5/7zj+UkN2y5IaKiyOBJ/IwpLCwMffv2RUhICEJDQ7Fo0SLExMRg6NChAGSX0s2bN7FixQrY2Nigdu3aWud7e3vDyckp234iUyhot1Tmcy1hOHhGhqbmhi03RFSUmDW56dGjB+7evYuZM2ciNjYWtWvXxvbt2xEQEAAAiI2NzXPOG6LCYozkRmn1sYSWG2XZBScnLrtAREWLWZMbABg+fDiGDx+u87Xly5fneu706dO5HAQVGmPV3GS+VnGm1NvUqgXoOXiRiKhQ6D0UnMjaGaPmxpIm8mMxMREVVUxuiPRkzG4pS2q5YTExERU1TG6I9PD4sawvAYzTLWVJLTdMboioqGFyQ6QHJRmxswPc3PJ/HUtpuUlOBq5ckdtBQeaNhYgoKyY3RHrI3CWlYwJtvVlKy82ZM/K5XLmC1SAREZkCkxsiPRij3gbQJAL37wPp6QW7ljmxS4qIijImN0R6MMYwcECuLQUAQsgEp7hickNERRmTGyI9GGMYOCBrdjw85HZxrrthckNERRmTGyI9GKtbKvM1imvdjRBMboioaGNyQ6QHY3VLAcV/CYZr14CkJMDeHqhe3dzREBFlx+SGSA/G6pYCiv8SDEqrTc2aMsEhIipqmNwQ6cGY3VLFveWGXVJEVNQxuSHSgzG7pSyl5YbJDREVVUxuiPTAlhsNJjdEVNQxuSHSA2tupEePgEuX5DaTGyIqqpjcEOXh2TMgMVFuW3vLzblzQEYGUKYM4ONj7miIiHRjckOUh3v35LNKBXh6Fvx6xbnlJnOXVEHW2CIiMiUmN0R5UFpYPD0BW9uCX684T+LHehsiKg6Y3BDlwZjFxIB2t1RGhnGuWViY3BBRccDkhigPxhwGnvk6GRmaWp7iQAjg5Em5XbeueWMhIsoNkxuiPBi75cbBAXB1ldvFqe7m1i1Zf2RrC9SoYe5oiIhyxuSGKA/GHAauKI51N0qXVLVqgJOTeWMhIsoNkxuiPBi75QYonsPBWW9DRMUFkxuiPBi75ibztYpTtxSTGyIqLpjcEOXBFN1SbLkhIjIdJjdEeTBFt1Rxa7lJTQUuXJDbTG6IqKhjckOUB1N0SxW3lpsLF4C0NMDDA/DzM3c0RES5Y3JDlAe23HDZBSIqXpjcEOUiI0OztpQ119yw3oaIihMmN0S5ePBAs0QCW26Y3BBR8cDkhigXSstKyZJyZmFjKW4tN1x2gYiKEyY3RLkwxTBwQLvlRgjjXtvYbt+WD5UKqFXL3NEQEeWNyQ1RLkxRTJz5emlpQHKyca9tbH/+KZ8rVwZKlDBvLERE+mByQ5QLUwwDBwBnZ02icP26ca9tTA8fAu++K7d79jRvLERE+mJyQ5QLU7XcAECjRvJ5zx7jX9tYZswAbtwAAgOBSZPMHQ0RkX6Y3BDlwlQ1NwDQvr183rXL+Nc2htOnga++ktvffAO4uJg3HiIifTG5IcqFqbqlAKBdO/m8ezfw7Jnxr18QGRnAsGFAejrQrRvQubO5IyIi0h+TG6JcmLJbKjhYXjc5Gfj7b+NfvyCWLQP++kvWBc2ZY+5oiIgMw+SGKBem7JaysQHatJHbERHGv35+JSRoiohnzAD8/c0bDxGRoZjcEOXClN1SgKZrqiglNxMnyiUn6tQBRo82dzRERIZjckOUC1N2SwGa5ObIESAx0TT3MMRffwFLl8rtBQsAe3vzxkNElB9mT27mz5+PihUrwsnJCQ0aNMD+/ftzPHbDhg1o164dypQpAzc3N4SGhmLnzp2FGC1ZEyFMn9wEBABVq8rC3d27TXMPfT17BgwdKreHDAGaNjVvPERE+WXW5Gbt2rUYO3YspkyZghMnTqB58+bo2LEjYmJidB6/b98+tGvXDtu3b0dkZCRat26NLl264MSJE4UcOVmDlBTg6VO5bYqaG0VR6Zr6+WfgzBn5Xj/5xLyxEBEVhEoI861s07hxY9SvXx8LFixQ76tRowZeeeUVhIeH63WNWrVqoUePHpg6dapexyclJcHd3R2JiYlwc3PLV9xkHa5eBSpWBBwdgceP5dpKpvDrr8ArrwDPPQf884+RLpqSIlf7BOQ0w3qsm/D228CiRcCECcDs2UaKg4jISAz5/jZby83Tp08RGRmJ9spMZv9p3749Dh48qNc1MjIykJycDC8vrxyPSU1NRVJSktaDSB+Zu6RMldgAQKtWgK0tcOmSTKjMRflr16yZ+WIgIjIGsyU3CQkJSE9Ph4+Pj9Z+Hx8fxMXF6XWNL774AikpKejevXuOx4SHh8Pd3V398Oe4VtKTKYeBZ+buDjRuLLfN1TX14AFw9qzcDg01TwxERMZi9oJiVZb/Egshsu3TZc2aNZg+fTrWrl0Lb2/vHI+bNGkSEhMT1Y/rRXmVQipSTD0MPDNz190cOSILqCtXBnL560REVCyYLbkpXbo0bG1ts7XSxMfHZ2vNyWrt2rUYPHgw1q1bh7Zt2+Z6rKOjI9zc3LQeRPow9UipzJTe2T/+kCOnCpvSJcURUkRkCcyW3Dg4OKBBgwaIyPJf1YiICDTN5V/YNWvWYMCAAVi9ejU6c8EbMqHCTG4aNQLc3OTkeeYY/MfkhogsiVm7pcLCwvD9999j6dKlOH/+PMaNG4eYmBgM/W+yjUmTJqFfv37q49esWYN+/frhiy++QJMmTRAXF4e4uDgkFoXZz8jiFFbNDQDY2QGtW8vtwu6aSk8HDh+W20xuiMgSmDW56dGjB+bMmYOZM2eiXr162LdvH7Zv346AgAAAQGxsrNacN9999x3S0tIwYsQI+Pr6qh9jxowx11sgC1aYNTeApu5m167CuZ/izBk5WtzVFahVq3DvTURkCnbmDmD48OEYPny4zteWL1+u9fOePXtMHxDRfwqzWwrQJDd//SWnqdFjahqjOHRIPjdpIoekExEVd2YfLUVUVBVmtxQgJ/GrUEEug7Bvn37nJCYCn34KdOsGnDuXv/uy3oaILA2TG6IcFHa3lEqlGTWVV93N7dvA5MkyGXrvPWDjRjkZ4KlTht+XyQ0RWRomN0Q5KOxuKSDvupsrV4Dhw+WCm+HhQFISUKMGEBQE3Lkji5KPH9f/frdvA5cvy8RKmUiQiKi4M3vNDVFR9OSJrHsBCje5adNGJhpnzwK+vtlfj48HMjLkduPGwKRJQJcuMsnp0AH4+295jd9/BRrocT+l3qZ2bTlTMhGRJWDLDZEOSquNrW3hfumXKgUo81LGxWV/ZGQAL74I7NkjE5OuXQEbG8DDQ3ZlNW0ql1J46SX97qd0SXHJBSKyJGy5IdJBSW68vGTyUJi2bgUuXpTLIWTl4SHrbHRxcwN27gQ6dwaO6VmQzHobIrJETG6IdDBHvY3CwUHW0ORHyZLA9u1Aj5cA7JH7jh4FGrbKfmxqKnDsmNxmckNEloTdUkQ63Loln4vjIpIlSgA//6z5eeRIIC0t+3EnTsgEp3RpoEqVwouPiMjUmNwQ6XDypHyuXdu8ceSXs7Nm+8xZYOHC7Mdk7pJSqQonLiKiwsDkhkiHqCj5XK+eOaMwng8+0Mzbo2C9DRFZKiY3RFkIYVnJTVBtOYLq/fc1+4RgckNElovJDVEWsbFyQjwbm+LbLZXZ55/L50WLZJ0NAMTEyPdpZweEhJgvNiIiU2ByQ5SF0mpTvbp27Upx9fzzwJtvytaaUaO0W23q17eM90hElBmTG6IslNaN4GDzxmFMs2cDLi5yxfHVq9klRUSWjckNURaWVG+j8PMDpkyR2xMmAH/8IbeZ3BCRJWJyQ5SFJSY3ABAWBlSqJGttzp+X+7jsAhFZIiY3RJkkJwP//iu369Y1byzG5uQEfPWV5md/f9miQ0RkaZjcEGVy6pR8Ll8eKFPGvLGYQpcucuFNAGjWzLyxEBGZCteWIsrEEouJM1OpgKVLgc8+A4YONXc0RESmweSGKBNLrbfJzNcX+PJLc0dBRGQ67JYiysQakhsiIkvH5IboP8+eAWfOyG0mN0RExReTG6L/XLwIpKYCrq5AxYrmjoaIiPKLyQ3Rf5Ri4nr15LpSRERUPPGfcKL/sN6GiMgyMLkh+g+TGyIiy8DkhghypWwmN0REloHJDRGAGzeAe/cAOzugVi1zR0NERAXB5IYImmLimjUBR0fzxkJERAXD5IYI7JIiIrIkTG6IwOSGiMiSMLkhApMbIiJLwuSGrN6DB0B0tNxmckNEVPwxuSGrd/KkfA4IADw9zRsLEREVHJMbsnrskiIisixMbsjqMbkhIrIsTG7I6jG5ISKyLExuyKo9fQqcPSu3g4PNGwsRERkHkxuyaufOAc+eAR4eQIUK5o6GiIiMgckNWS0hgGXL5Ha9eoBKZdZwiIjISJjckFUSApg8Gfj6a/nzW2+ZNx4iIjIeJjdkdZTE5pNP5M/z5gG9epk3JiIiMh6zJzfz589HxYoV4eTkhAYNGmD//v25Hr937140aNAATk5OqFSpEhYuXFhIkZIl0JXYjBxp3piIiMi4zJrcrF27FmPHjsWUKVNw4sQJNG/eHB07dkRMTIzO46Ojo9GpUyc0b94cJ06cwOTJkzF69Gj88ssvhRw5FUdCAFOmaBKbr79mYkNEZIlUQghhrps3btwY9evXx4IFC9T7atSogVdeeQXh4eHZjp84cSI2b96M8+fPq/cNHToUJ0+exKFDh/S6Z1JSEtzd3ZGYmAg3N7eCv4n/pKcDN24Y7XJkAgsXaic2o0aZNx6TSkkBSpaU2w8fAiVKmDceIqICMuT7266QYsrm6dOniIyMxHvvvae1v3379jh48KDOcw4dOoT27dtr7evQoQOWLFmCZ8+ewd7ePts5qampSE1NVf+clJRkhOizu3MHCAw0yaXJyObOtfDEhojIypktuUlISEB6ejp8fHy09vv4+CAuLk7nOXFxcTqPT0tLQ0JCAnx9fbOdEx4ejhkzZhgv8Fw4ORXKbSifSpQAZs0Chg0zdyRERGRKZktuFKosk4sIIbLty+t4XfsVkyZNQlhYmPrnpKQk+Pv75zfcHJUtCzx+bPTLEhERkYHMltyULl0atra22Vpp4uPjs7XOKMqWLavzeDs7O5QqVUrnOY6OjnB0dDRO0ERERFTkmW20lIODAxo0aICIiAit/REREWjatKnOc0JDQ7Mdv2vXLoSEhOistyEiIiLrY9ah4GFhYfj++++xdOlSnD9/HuPGjUNMTAyGDh0KQHYp9evXT3380KFDce3aNYSFheH8+fNYunQplixZgnfeecdcb4GIiIiKGLPW3PTo0QN3797FzJkzERsbi9q1a2P79u0ICAgAAMTGxmrNeVOxYkVs374d48aNw7fffoty5crh66+/xmuvvWaut0BERERFjFnnuTEHU81zQ1SkcJ4bIrIwhnx/m335BSIiIiJjYnJDREREFoXJDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRTHr8gvmoEzInJSUZOZIiEwoJUWznZQEpKebLxYiIiNQvrf1WVjB6pKb5ORkAIC/v7+ZIyEqJOXKmTsCIiKjSU5Ohru7e67HWN3aUhkZGbh16xZcXV2hUqkKfL2kpCT4+/vj+vXrVrNWFd8z37Ol4nvme7ZElvJ+hRBITk5GuXLlYGOTe1WN1bXc2NjYwM/Pz+jXdXNzK9a/NPnB92wd+J6tA9+z5bOE95tXi42CBcVERERkUZjcEBERkUVhclNAjo6OmDZtGhwdHc0dSqHhe7YOfM/Wge/Z8lnb+wWssKCYiIiILBtbboiIiMiiMLkhIiIii8LkhoiIiCwKkxsiIiKyKExuCmD+/PmoWLEinJyc0KBBA+zfv9/cIZnUvn370KVLF5QrVw4qlQqbNm0yd0gmFR4ejoYNG8LV1RXe3t545ZVXcPHiRXOHZVILFixAnTp11JN9hYaG4rfffjN3WIUqPDwcKpUKY8eONXcoJjN9+nSoVCqtR9myZc0dlsndvHkTffr0QalSpeDi4oJ69eohMjLS3GGZTGBgYLY/Z5VKhREjRpg7NJNjcpNPa9euxdixYzFlyhScOHECzZs3R8eOHRETE2Pu0EwmJSUFdevWxTfffGPuUArF3r17MWLECBw+fBgRERFIS0tD+/btkZJ5UUoL4+fnh08++QTHjh3DsWPH8MILL6Br1644e/asuUMrFEePHsWiRYtQp04dc4dicrVq1UJsbKz6cfr0aXOHZFL3799Hs2bNYG9vj99++w3nzp3DF198AQ8PD3OHZjJHjx7V+jOOiIgAALzxxhtmjqwQCMqXRo0aiaFDh2rtq169unjvvffMFFHhAiA2btxo7jAKVXx8vAAg9u7da+5QCpWnp6f4/vvvzR2GySUnJ4vnnntOREREiJYtW4oxY8aYOySTmTZtmqhbt665wyhUEydOFM8//7y5wzCrMWPGiMqVK4uMjAxzh2JybLnJh6dPnyIyMhLt27fX2t++fXscPHjQTFGRqSUmJgIAvLy8zBxJ4UhPT8dPP/2ElJQUhIaGmjsckxsxYgQ6d+6Mtm3bmjuUQnHp0iWUK1cOFStWRM+ePXHlyhVzh2RSmzdvRkhICN544w14e3sjODgYixcvNndYhebp06dYuXIlBg0aZJRFo4s6Jjf5kJCQgPT0dPj4+Gjt9/HxQVxcnJmiIlMSQiAsLAzPP/88ateube5wTOr06dMoWbIkHB0dMXToUGzcuBE1a9Y0d1gm9dNPP+H48eMIDw83dyiFonHjxlixYgV27tyJxYsXIy4uDk2bNsXdu3fNHZrJXLlyBQsWLMBzzz2HnTt3YujQoRg9ejRWrFhh7tAKxaZNm/DgwQMMGDDA3KEUCqtbFdyYsma/QgiryIit0ciRI3Hq1CkcOHDA3KGYXLVq1RAVFYUHDx7gl19+Qf/+/bF3716LTXCuX7+OMWPGYNeuXXBycjJ3OIWiY8eO6u2goCCEhoaicuXK+OGHHxAWFmbGyEwnIyMDISEh+PjjjwEAwcHBOHv2LBYsWIB+/fqZOTrTW7JkCTp27Ihy5cqZO5RCwZabfChdujRsbW2ztdLEx8dna82h4m/UqFHYvHkzdu/eDT8/P3OHY3IODg6oUqUKQkJCEB4ejrp162Lu3LnmDstkIiMjER8fjwYNGsDOzg52dnbYu3cvvv76a9jZ2SE9Pd3cIZpciRIlEBQUhEuXLpk7FJPx9fXNlqDXqFHDogeBKK5du4bff/8dQ4YMMXcohYbJTT44ODigQYMG6spzRUREBJo2bWqmqMjYhBAYOXIkNmzYgD///BMVK1Y0d0hmIYRAamqqucMwmTZt2uD06dOIiopSP0JCQtC7d29ERUXB1tbW3CGaXGpqKs6fPw9fX19zh2IyzZo1yzaVwz///IOAgAAzRVR4li1bBm9vb3Tu3NncoRQadkvlU1hYGPr27YuQkBCEhoZi0aJFiImJwdChQ80dmsk8fPgQ//77r/rn6OhoREVFwcvLCxUqVDBjZKYxYsQIrF69Gr/++itcXV3VLXXu7u5wdnY2c3SmMXnyZHTs2BH+/v5ITk7GTz/9hD179mDHjh3mDs1kXF1ds9VRlShRAqVKlbLY+qp33nkHXbp0QYUKFRAfH48PP/wQSUlJ6N+/v7lDM5lx48ahadOm+Pjjj9G9e3f8/fffWLRoERYtWmTu0EwqIyMDy5YtQ//+/WFnZ0Vf+eYdrFW8ffvttyIgIEA4ODiI+vXrW/wQ4d27dwsA2R79+/c3d2gmoeu9AhDLli0zd2gmM2jQIPXvdJkyZUSbNm3Erl27zB1WobP0oeA9evQQvr6+wt7eXpQrV05069ZNnD171txhmdyWLVtE7dq1haOjo6hevbpYtGiRuUMyuZ07dwoA4uLFi+YOpVCphBDCPGkVERERkfGx5oaIiIgsCpMbIiIisihMboiIiMiiMLkhIiIii8LkhoiIiCwKkxsiIiKyKExuiIiIyKIwuSEiIiKLwuSGiPQyYMAAvPLKK+qfW7VqhbFjx+p9/p49e6BSqfDgwYMCx2LMaxVFFy9eRNmyZZGcnGzQeQ0bNsSGDRtMFBVR8cHkhsiCDBgwACqVCiqVCnZ2dqhQoQKGDRuG+/fvG/1eGzZswKxZs4x6zcDAQHX8zs7OCAwMRPfu3fHnn39qHde0aVPExsbC3d09z2sWx0RoypQpGDFiBFxdXbO9Vq1aNTg4OODmzZvZXvvggw/w3nvvISMjozDCJCqymNwQWZgXX3wRsbGxuHr1Kr7//nts2bIFw4cPN/p9vLy8dH75FtTMmTMRGxuLixcvYsWKFfDw8EDbtm3x0UcfqY9xcHBA2bJloVKpjH5/c7tx4wY2b96MgQMHZnvtwIEDePLkCd544w0sX7482+udO3dGYmIidu7cWQiREhVdTG6ILIyjoyPKli0LPz8/tG/fHj169MCuXbvUr6enp2Pw4MGoWLEinJ2dUa1aNcydO1frGunp6QgLC4OHhwdKlSqFd999F1mXocvaLbVy5UqEhITA1dUVZcuWRa9evRAfH29w/Mr5FSpUQIsWLbBo0SJ88MEHmDp1Ki5evAgge2vMtWvX0KVLF3h6eqJEiRKoVasWtm/fjqtXr6J169YAAE9PT6hUKgwYMAAAsGPHDjz//PPq9/jSSy/h8uXL6jiuXr0KlUqFDRs2oHXr1nBxcUHdunVx6NAhrXj/+usvtGzZEi4uLvD09ESHDh3ULWVCCMyePRuVKlWCs7Mz6tati/Xr1+f6/tetW4e6devCz88v22tLlixBr1690LdvXyxdujTbn4mtrS06deqENWvW6P+BE1kgJjdEFuzKlSvYsWMH7O3t1fsyMjLg5+eHdevW4dy5c5g6dSomT56MdevWqY/54osvsHTpUixZsgQHDhzAvXv3sHHjxlzv9fTpU8yaNQsnT57Epk2bEB0drU4kCmrMmDEQQuDXX3/V+fqIESOQmpqKffv24fTp0/j0009RsmRJ+Pv745dffgEg61hiY2PViVxKSgrCwsJw9OhR/PHHH7CxscGrr76arUtnypQpeOeddxAVFYWqVavizTffRFpaGgAgKioKbdq0Qa1atXDo0CEcOHAAXbp0QXp6OgDg/fffx7Jly7BgwQKcPXsW48aNQ58+fbB3794c3+u+ffsQEhKSbX9ycjJ+/vln9OnTB+3atUNKSgr27NmT7bhGjRph//79eX+oRJbMnEuSE5Fx9e/fX9ja2ooSJUoIJycnAUAAEF9++WWu5w0fPly89tpr6p99fX3FJ598ov752bNnws/PT3Tt2lW9r2XLlmLMmDE5XvPvv/8WAERycrIQQojdu3cLAOL+/fs5nhMQECC++uorna/5+PiIYcOG6bxWUFCQmD59us7z9LmvEELEx8cLAOL06dNCCCGio6MFAPH999+rjzl79qwAIM6fPy+EEOLNN98UzZo103m9hw8fCicnJ3Hw4EGt/YMHDxZvvvlmjnHUrVtXzJw5M9v+RYsWiXr16ql/HjNmjOjdu3e243799VdhY2Mj0tPTc3m3RJaNLTdEFqZ169aIiorCkSNHMGrUKHTo0AGjRo3SOmbhwoUICQlBmTJlULJkSSxevBgxMTEAgMTERMTGxiI0NFR9vJ2dnc7WhMxOnDiBrl27IiAgAK6urmjVqhUAqK9bUEKIHGtsRo8ejQ8//BDNmjXDtGnTcOrUqTyvd/nyZfTq1QuVKlWCm5sbKlasqDPeOnXqqLd9fX0BQN3dprTc6HLu3Dk8efIE7dq1Q8mSJdWPFStWaHV/ZfX48WM4OTll279kyRL06dNH/XOfPn2wYcOGbIXSzs7OyMjIQGpqai7vnsiyMbkhsjAlSpRAlSpVUKdOHXz99ddITU3FjBkz1K+vW7cO48aNw6BBg7Br1y5ERUVh4MCBePr0ab7vmZKSgvbt26NkyZJYuXIljh49qu7GKsh1FXfv3sWdO3fUCUhWQ4YMwZUrV9C3b1+cPn0aISEhmDdvXq7X7NKlC+7evYvFixfjyJEjOHLkiM54M3fpKcmV0nXl7Oyc4/WVY7Zt24aoqCj149y5c7nW3ZQuXTrb6LZz587hyJEjePfdd2FnZwc7Ozs0adIEjx8/zlZfc+/ePbi4uOQaG5GlY3JDZOGmTZuGzz//HLdu3QIA7N+/H02bNsXw4cMRHByMKlWqaLUkuLu7w9fXF4cPH1bvS0tLQ2RkZI73uHDhAhISEvDJJ5+gefPmqF69er6KiXMyd+5c2NjYaM2zk5W/vz+GDh2KDRs2YPz48Vi8eDEAObIKgLoOBpDJ0vnz5/H++++jTZs2qFGjRr6Gy9epUwd//PGHztdq1qwJR0dHxMTEoEqVKloPf3//HK8ZHByMc+fOae1bsmQJWrRogZMnT2olSu+++y6WLFmideyZM2dQv359g98LkSVhckNk4Vq1aoVatWrh448/BgBUqVIFx44dw86dO/HPP//ggw8+wNGjR7XOGTNmDD755BNs3LgRFy5cwPDhw3OdJ6ZChQpwcHDAvHnzcOXKFWzevDnfc+AkJycjLi4O169fx759+/C///0PH374IT766CNUqVJF5zljx47Fzp07ER0djePHj+PPP/9EjRo1AAABAQFQqVTYunUr7ty5g4cPH8LT0xOlSpXCokWL8O+//+LPP/9EWFiYwbFOmjQJR48exfDhw3Hq1ClcuHABCxYsQEJCAlxdXfHOO+9g3Lhx+OGHH3D58mWcOHEC3377LX744Yccr9mhQwccOnRInYw9e/YMP/74I958803Url1b6zFkyBBERkbi5MmT6vP379+P9u3bG/xeiCyKuYt+iMh4+vfvr1X0q1i1apVwcHAQMTEx4smTJ2LAgAHC3d1deHh4iGHDhon33ntP1K1bV338s2fPxJgxY4Sbm5vw8PAQYWFhol+/frkWFK9evVoEBgYKR0dHERoaKjZv3iwAiBMnTggh9C8oxn9F0A4ODqJChQqie/fu4s8//9Q6Luu1Ro4cKSpXriwcHR1FmTJlRN++fUVCQoL6+JkzZ4qyZcsKlUol+vfvL4QQIiIiQtSoUUM4OjqKOnXqiD179ggAYuPGjUIITUGxEr8QQty/f18AELt371bv27Nnj2jatKlwdHQUHh4eokOHDuq4MjIyxNy5c0W1atWEvb29KFOmjOjQoYPYu3dvjp9BWlqaKF++vNixY4cQQoj169cLGxsbERcXp/P4oKAgMWrUKCGEEDdu3BD29vbi+vXrOV6fyBqohMgyUQIREZnV/Pnz8euvvxo8Gd+ECROQmJiIRYsWmSgyouLBztwBEBGRtv/973+4f/8+kpOTDZoF2tvbG++8844JIyMqHthyQ0RERBaFBcVERERkUZjcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRWFyQ0RERBaFyQ0RERFZFCY3REREZFH+D+VdNHnLAcB8AAAAAElFTkSuQmCC" + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plotly.com" + }, + "data": [ + { + "mode": "lines", + "name": "RDF", + "type": "scatter", + "x": [ + 0.05, + 0.15000000000000002, + 0.25, + 0.35000000000000003, + 0.45, + 0.55, + 0.6500000000000001, + 0.75, + 0.8500000000000001, + 0.95, + 1.05, + 1.1500000000000001, + 1.25, + 1.35, + 1.4500000000000002, + 1.55, + 1.6500000000000001, + 1.75, + 1.85, + 1.9500000000000002, + 2.05, + 2.1500000000000004, + 2.25, + 2.3500000000000005, + 2.45, + 2.55, + 2.6500000000000004, + 2.75, + 2.8500000000000005, + 2.95, + 3.05, + 3.1500000000000004, + 3.25, + 3.3500000000000005, + 3.45, + 3.55, + 3.6500000000000004, + 3.75, + 3.8500000000000005, + 3.95, + 4.050000000000001, + 4.15, + 4.25, + 4.35, + 4.45, + 4.550000000000001, + 4.65, + 4.75, + 4.8500000000000005, + 4.95, + 5.050000000000001, + 5.15, + 5.25, + 5.3500000000000005, + 5.45, + 5.550000000000001, + 5.65, + 5.75, + 5.8500000000000005, + 5.95, + 6.050000000000001, + 6.15, + 6.25, + 6.3500000000000005, + 6.45, + 6.550000000000001, + 6.65, + 6.75, + 6.8500000000000005, + 6.95, + 7.050000000000001, + 7.15, + 7.25, + 7.3500000000000005, + 7.45 + ], + "y": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.023245609993947692, + 0.14646160407229655, + 0.45436779946932815, + 0.6196371252316031, + 0.550074688105629, + 0.24492764326151617, + 0.15906657277368838, + 0.15907336700625108, + 0.16995663120871574, + 0.1473007406315469, + 0.10775775794075783, + 0.21029665019802415, + 0.24805589482255241, + 0.3769037113865841, + 0.5047356194615166, + 0.46087338733174366, + 0.5682513369985077, + 0.4861781519998382, + 0.5375510962573398, + 0.6790208522526927, + 0.6710490629577324, + 0.9894058628443008, + 0.8538285912672261, + 0.9517878346312325, + 1.1278002979754198, + 1.1059824740136046, + 0.9804866044711993, + 1.0339561992848092, + 1.0746086800408567, + 1.1109574621202543, + 1.0487041028444326, + 1.2243520652763866, + 1.154502593334224, + 1.2331157775060189, + 1.201024120079114, + 1.237171590937937, + 1.253807317366299, + 1.1056971481982074, + 1.2089925978840372, + 1.1600869427910026, + 1.2044744402688525, + 1.2429999556984068, + 1.2131198193963737, + 1.279186640520039, + 1.181541091304278, + 1.1347535621787217, + 1.1553111004026753, + 1.1184495845436897, + 1.1030599997964758, + 1.1649317474829237, + 1.1396475288529555, + 1.0906138878631761, + 1.1383458306452, + 1.1098403314649685, + 1.1627021018572052, + 1.2667975355620769, + 1.148427506605686 + ] + } + ], + "layout": { + "annotations": [ + { + "showarrow": false, + "text": "Solvation Radius", + "x": 2.75, + "xanchor": "left", + "xref": "x", + "y": 1, + "yanchor": "top", + "yref": "y domain" + } + ], + "autosize": true, + "shapes": [ + { + "line": { + "color": "red", + "dash": "dash", + "width": 4 + }, + "type": "line", + "x0": 2.75, + "x1": 2.75, + "xref": "x", + "y0": 0, + "y1": 1, + "yref": "y domain" + } + ], + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", + "showlakes": true, + "showland": true, + "subunitcolor": "#C8D4E3" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Li solvation radius for FEC" + }, + "xaxis": { + "autorange": true, + "range": [ + 0.05, + 7.45 + ], + "title": { + "text": "Radial Distance (Å)" + }, + "type": "linear" + }, + "yaxis": { + "autorange": true, + "range": [ + -0.07106592447333548, + 1.3502525649933745 + ], + "title": { + "text": "Probability Density" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "# we need this just to display our plot\n", - "import matplotlib.pyplot as plt\n", - "\n", "# iterate through solvents\n", "for solvent in solute.solvents.keys():\n", " # plot the RDF!\n", - " solute.plot_solvation_radius('Li', solvent)\n", - " plt.show()" + " fig = solute.plot_solvation_radius('Li', solvent)\n", + " fig.show()" ] }, { @@ -222,13 +3442,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, + "id": "e7a6367c65eaded3", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, - "execution_count": 25, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -244,15 +3473,11 @@ ")\n", "\n", "solute.run()" - ], - "metadata": { - "collapsed": false - }, - "id": "e7a6367c65eaded3" + ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "id": "6172b9fc-8c94-4bfe-8b08-b534e30f5e31", "metadata": {}, "outputs": [ @@ -262,14 +3487,6 @@ "text": [ "{'PF6': 2.6, 'BN': 2.6500000000000004, 'FEC': 2.75}\n" ] - }, - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -303,15 +3520,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "id": "f274646c-1d88-4bb5-85d2-c219e74cd5dd", "metadata": {}, "outputs": [ { "data": { - "text/plain": "{'BN': 4.351020408163265,\n 'FEC': 0.3346938775510204,\n 'PF6': 0.12040816326530612}" + "text/plain": [ + "{'BN': 4.351020408163265,\n", + " 'FEC': 0.3346938775510204,\n", + " 'PF6': 0.12040816326530612}" + ] }, - "execution_count": 27, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -331,15 +3552,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "id": "2c8cfe82-a0e3-4f11-85c7-d4f4f0ad6b54", "metadata": {}, "outputs": [ { "data": { - "text/plain": "{'BN': 1.0, 'FEC': 0.2653061224489796, 'PF6': 0.12040816326530612}" + "text/plain": [ + "{'BN': 1.0, 'FEC': 0.2653061224489796, 'PF6': 0.12040816326530612}" + ] }, - "execution_count": 28, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -359,16 +3582,115 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "id": "ad3867bc-067f-4e33-b468-92a8ea93384f", "metadata": {}, "outputs": [ { "data": { - "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": "
\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
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
" + "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", + "
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", + "
" + ], + "text/plain": [ + "frame 0 1 2 3 4 5 6 \\\n", + "solvent \n", + "BN 0.434694 0.436735 0.428571 0.432653 0.432653 0.436735 0.434694 \n", + "FEC 0.024490 0.026531 0.030612 0.036735 0.030612 0.048980 0.030612 \n", + "PF6 0.016327 0.012245 0.014286 0.012245 0.012245 0.006122 0.010204 \n", + "\n", + "frame 7 8 9 \n", + "solvent \n", + "BN 0.444898 0.434694 0.434694 \n", + "FEC 0.036735 0.038776 0.030612 \n", + "PF6 0.014286 0.012245 0.010204 " + ] }, - "execution_count": 29, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -404,16 +3726,111 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 14, "id": "15fe843d-03df-4b59-ab20-1d81abf97875", "metadata": {}, "outputs": [ { "data": { - "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": "
\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
BNFECPF6count
05000.359184
14000.257143
24100.138776
34010.085714
45100.048980
54200.030612
63200.024490
73010.022449
\n
" + "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", + "
BNFECPF6fraction
05000.359184
14000.257143
24100.138776
34010.085714
45100.048980
54200.030612
63200.024490
73010.022449
\n", + "
" + ], + "text/plain": [ + " BN FEC PF6 fraction\n", + "0 5 0 0 0.359184\n", + "1 4 0 0 0.257143\n", + "2 4 1 0 0.138776\n", + "3 4 0 1 0.085714\n", + "4 5 1 0 0.048980\n", + "5 4 2 0 0.030612\n", + "6 3 2 0 0.024490\n", + "7 3 0 1 0.022449" + ] }, - "execution_count": 30, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -432,15 +3849,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "id": "98db8743-b0b5-4f63-9d8e-bf74583b1b21", "metadata": {}, "outputs": [ { "data": { - "text/plain": "0.516326530612245" + "text/plain": [ + "0.516326530612245" + ] }, - "execution_count": 31, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -452,15 +3871,17 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "id": "06ccfc96-181f-4d50-bb44-c50bf6923fed", "metadata": {}, "outputs": [ { "data": { - "text/plain": "0.08979591836734693" + "text/plain": [ + "0.08979591836734693" + ] }, - "execution_count": 32, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +3901,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 17, "id": "d943842a-b58d-4cbd-9649-1e60295cf2f9", "metadata": { "tags": [] @@ -488,10 +3909,135 @@ "outputs": [ { "data": { - "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": "
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
solventBNFECPF6
framesolute_ix
0655401
667401
670401
683401
690401
693401
1667401
668401
670401
671401
683401
693401
\n
" + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
solventBNFECPF6
framesolute_ix
0655401
667401
670401
683401
690401
693401
1667401
668401
670401
671401
683401
693401
\n", + "
" + ], + "text/plain": [ + "solvent BN FEC PF6\n", + "frame solute_ix \n", + "0 655 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\n", + "1 667 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" + ] }, - "execution_count": 33, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -511,16 +4057,98 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 18, "id": "554278b5-8664-41c1-8594-3702c600d48f", "metadata": {}, "outputs": [ { "data": { - "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": "
\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
distancesolutesolventsolvent_ix
solute_atom_ixsolvent_atom_ix
700537002.047750LiBN308
5682.129007LiBN47
4122.147081LiBN34
8922.294223LiBN74
67552.435834LiPF6603
\n
" + "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", + "
distancesolutesolventsolvent_ix
solute_atom_ixsolvent_atom_ix
700537002.047750LiBN308
5682.129007LiBN47
4122.147081LiBN34
8922.294223LiBN74
67552.435834LiPF6603
\n", + "
" + ], + "text/plain": [ + " distance solute solvent solvent_ix\n", + "solute_atom_ix solvent_atom_ix \n", + "7005 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" + ] }, - "execution_count": 34, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -540,15 +4168,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 19, "id": "31f2f907-dbdb-412e-922b-d4f1ea7912ac", "metadata": {}, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, - "execution_count": 35, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -570,16 +4200,175 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 20, "id": "cf10b57b-079d-4068-a2a2-c1712423a905", "metadata": {}, "outputs": [ { "data": { - "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": "
\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 \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
distancesolutesolventsolvent_ix
framesolute_ixsolute_atom_ixsolvent_atom_ix
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
" + "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", + " \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", + "
distancesolutesolventsolvent_ix
framesolute_ixsolute_atom_ixsolvent_atom_ix
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", + "
" + ], + "text/plain": [ + " distance solute solvent \\\n", + "frame solute_ix solute_atom_ix solvent_atom_ix \n", + "0 649 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", + "... ... ... ... \n", + "9 697 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 \n", + "frame solute_ix solute_atom_ix solvent_atom_ix \n", + "0 649 6733 4168 347 \n", + " 2308 192 \n", + " 6110 538 \n", + " 1312 109 \n", + " 2608 217 \n", + "... ... \n", + "9 697 7117 652 54 \n", + " 4000 333 \n", + " 1468 122 \n", + " 3328 277 \n", + " 1804 150 \n", + "\n", + "[2355 rows x 4 columns]" + ] }, - "execution_count": 36, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -600,9 +4389,9 @@ ], "metadata": { "kernelspec": { - "name": "solvation_analysis", + "display_name": "Python 3 (ipykernel)", "language": "python", - "display_name": "solvation_analysis" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -614,7 +4403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/tutorials/images/coordination_plot.png b/docs/tutorials/images/coordination_plot.png new file mode 100644 index 00000000..0170a192 Binary files /dev/null and b/docs/tutorials/images/coordination_plot.png differ diff --git a/docs/tutorials/images/rdf_plot.png b/docs/tutorials/images/rdf_plot.png new file mode 100644 index 00000000..6dde788c Binary files /dev/null and b/docs/tutorials/images/rdf_plot.png differ diff --git a/docs/tutorials/images/speciation_plot.png b/docs/tutorials/images/speciation_plot.png new file mode 100644 index 00000000..641a93ff Binary files /dev/null and b/docs/tutorials/images/speciation_plot.png differ diff --git a/docs/tutorials/images/summary_figure.png b/docs/tutorials/images/summary_figure.png new file mode 100644 index 00000000..39924a3b Binary files /dev/null and b/docs/tutorials/images/summary_figure.png differ diff --git a/docs/tutorials/plotting_tutorial.ipynb b/docs/tutorials/plotting_tutorial.ipynb index 2ab73b74..10caeb50 100644 --- a/docs/tutorials/plotting_tutorial.ipynb +++ b/docs/tutorials/plotting_tutorial.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,13 +36,18 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:29.909577Z", + "start_time": "2024-05-09T21:21:28.263952Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Solute dict: {'ea': , 'eaf': , 'fea': , 'feaf': }\n", + "Solute dict: {'ea': , 'eaf': , 'fea': , 'feaf': }\n", "\n", "Solute names: ea eaf fea feaf\n" ] @@ -58,6 +63,9 @@ " 'fec': atom_groups['fec'],\n", " eax_solvent_name: atom_groups[eax_solvent_name],\n", " },\n", + " analysis_classes=['coordination', 'pairing', 'speciation', 'networking'],\n", + " networking_solvents=['pf6'],\n", + " solute_name=\"Li\",\n", " )\n", " solute.run()\n", " solutes[eax_solvent_name] = solute\n", @@ -77,13 +85,18 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:29.959444Z", + "start_time": "2024-05-09T21:21:29.911473Z" + } + }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -98,13 +111,18 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:30.013772Z", + "start_time": "2024-05-09T21:21:29.960444Z" + } + }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -119,13 +137,18 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:30.069154Z", + "start_time": "2024-05-09T21:21:30.016016Z" + } + }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -140,13 +163,18 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:30.136142Z", + "start_time": "2024-05-09T21:21:30.070460Z" + } + }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nO3deVxU5RoH8GcGhh1kUXYMQYVcEFlccgGXME1NTa4SYWZKaoooJVYaalmQomBJoWZxtSIzc8cUVAwTaVAUkUAW2UX2nVnf+8dBLrEMw2xnBp7v5/4xvGd7vMRvznnPe97DIIQAQgghSTHpLgAhhFQbxihCCEkFYxQhhKSCMYoQQlLBGEUIIamo010AUlV3797tOMzD0tLSwsKCxnoQogsDBzwhycydO5fP51Of09LSPvjgg61bt9JbEkK0wBhF0mpoaLC1tWWz2cOGDaO7FoRogH2jSFrR0dFeXl6YoWjAwrNRJBU+n29vb3/y5MmJEyfSXQtC9MCzUSSVkydP2traYoaigQxjFEklMjIyKCiI7ioQohPGKJLc9evXa2pq5s+fT3chCNEJx40iyYWHh2/ZsoXJlPuXsVAoTEhIcHR0tLGxoVpyc3OTkpIMDAy8vLx0dXXlXUBfPXjwQFdX197envqxtrb25s2bLBZr6tSp+vr69NaGZA7PRpHkTpw4sWbNGnkf5ciRI46OjosWLTp9+jTVcvz48Tlz5ty5cycmJmbUqFEVFRXyrkF8CQkJM2bMcHd337dvH9Vy8+bNUaNGnTx5MioqytHRsaioiN4KkcxhjCLJGRoaqqmpyfsow4YNS0xMXLBgQXuLmppaUlJSVFTUmTNnjI2N4+Li5F2D+JhM5oEDBz788MP2loqKih9//PHEiRPnz593dXWNjo6msTwkD3hRj5Td7NmzO7W88cYb1IfS0tLi4mIHBweFF9WjGTNmAMCZM2faW15//fX2z0OHDm1sbKShLCRPeDaKVNKzZ8/Gjx8/YsSIDRs2qMpwKx6Pd/ny5ZkzZ9JdCJIxjFGkkgYPHhwfHx8XF3f48OH4+Hi6yxHL5s2bR44cuXDhQroLQTKGMYpUEpPJNDExmT59+ty5cy9cuEB3Ob3gcDj+/v7p6em//vor3bUg2cMYRaonKCjojz/+IIRUVFQkJSUpVd9oV8XFxZ6enq2trZcvX1bCsVlIevhMPeq71lY4dgzOnoUHD6CyErS1wcYGPD3h3XfByUnmR4uMjNy1a1f7j4WFhWlpaZs2bcrKytLU1HzzzTfDw8PV1ZXlZmlCQoK/v39LSwshREdH5/Tp09u3b79165aRkRG1gouLC56T9jMYo6iPHjyAhQuhoAAAQEsLTE2huRkqKwEAmEwICoLQUJD/gHwAIIQwGAwFHAgh0fCiHvVFfj54ekJBAbz4Ipw7B3V1UFAAFRWQlwfr1wMhsHcvKGryZsxQpCTwbBT1xcsvQ3w8jB4Nf/4Jz69S/y88HN5/H5hM+OsvUJFBSAhJD2MUie3+fXB2BgC4cQM8PLpZgRCYNAlSUmDZMoiNVXB1CNEFL+qR2KhxRXZ23WcoADAYsGoVAEBcHAgECqhoxYoVixcvTklJ6Xbp/fv3Fy9evHz5chke8enTpz4+Pj4+PvX19d2u8Pvvv/v4+OzZs0eGB0VKDmMUiS01FQBgwgRR60yaBABQXw/Z2Qqo6OLFi2fOnCkrK+t26bNnz86cOXP27FkZHrGhoSE2NjY2Nra1tbXbFR4+fBgbG5uQkCDDgyIlhzGKxPbsGQCApaWodayt/7UyQgMAxigSW0sLAICWlqh1tLXbPjQ3y70ehJQDxigSm4EBAIDoCYoaGto+DBok93oQUg4Yo0hs1OX8kyei1snLa/vQfnWPUH+HMYrERt1cSk4GPr/Hdf78EwDAwgKGDlVQVQjRTVmeREYqYPFiCAqCZ8/gzBlYurSbFXg8+O47AABvb0XWFRoaGhMT07X9mTxvc61bt05TU7Nre0ZGhvwOipQTxigS29Ch4OMDJ05AYCC4u8MLL3ReYds2yM4GbW3YtEmRdSUnJyvycJT2F0MhhDGK+iIyEpKS4MkTmDgRQkJg8WIwNwceD1JSYO9eoEZohoeDnZ0ii4qIiPD09OzanpycvHbtWjkdNC4uzqjr47AAx44dO3z4sJwOipQTxijqC2NjSEqCpUshORnWr4f160FTEzictqV6enDgAKxereCibG1tx40b17Vdrhf1Li4upqamXduvXLkiv4Mi5YQxivrIygr++gsuXIAzZ+DePaiqAh0dsLaG2bNh5UowM6O7PoQUDWMU9R2DAQsWQIc3HiM0kOGAJ9R3ubng5gbLlv2/JS4O3Nxgxw76akKINng2ivquuRlSU4HL/X9LVRWkpsKLL9JXE0K0wbNRhBCSCp6NIhXm4uJSV1fX7cAjABg0aJC7u7uW6LlU+khTU3Ps2LEA0NNL9MzMzMaOHWtvby/DgyIlh7Pfo75LTwcnJxg7Fh48aGs5cQL8/ODNN+H4cVor60Zqaqqzs7OamppM9lZVVdXS0mKNMwagDvCiHvVnwcHB7u7uR48eldUOt2/f7ujo+Msvv8hqh6gfwBhFfUddwXR8MWfXFuXg5uZGCPnoo4+qq6ul39v9+/ePHDnC4XDGjBkj/d5Qv4Exivozb29vDw+P6upqmbwcKTAwUCAQbNy4cfTo0dLvDfUbGKOon4uIiFBTU/vqq6+ysrKk2c+vv/5648YNY2Pj7du3y6o21D9gjKJ+ztnZedWqVTweLygoSOKdtLa2bt26FQA+//xzY2Nj2VWH+gOMUdT/ffbZZ4aGhhcvXoyLi5NsD3v37n3y5Mm4ceNWK3ziFaT8MEZR36nOLSaKqakpdSW+ZcsWHo/X181LSkrCwsLgef+A7OtDKg5jFA0IGzdudHBw+Oeff6Kiovq6bXBwcFNTk7e3d7ezmiKEMYoGBA0NjX379gHArl27Kisrxd/w9u3bP/30k5aW1pdffim36pBqwxhFA8X8+fNfeeWVmpqaTz75RMxNhEJhYGAgIeSDDz6wtbWVZ3VIhWGMSojNZvv6+nY8Q7l9+/by5cu/+uorGqtSjHImM87D4+qoUe0tmdracR4ef1pY0FiVOPbv389isQ4fPvyg/TFWkX744YeUlBQrK6vg4GB510aXkpKSXbt2rVmz5tNPP62oqKC7HJWEMSqJsLCwjz76qKamJi0tjWoJCQn59NNPq6qq0tPT6a1NAcoEgnmJiVszM9tbUpqb5yUmfvf0KY1ViePFF19ct26dQCAIDAzsdeWGhgbqxtSXX36pq6sr/+pokJubO378+IqKCi8vLx0dnZ7mW0G9IEhS+/fv9/Hx6diye/fuNWvW0FWPwty7dw8AnJ2d21t++OEHAHjrrbfoK0pc1dXVgwcPBoDTp0+LXpMaKDp58mShUKiY2hTP19fX39+f7ipUHp6NooHFyMho586dAPD++++3trb2tFpubm5kZCSTyYyIiGAo60Au6d25c2fevHm///57VFRUXl4e3eWoKoxRNOCsXbt27NixeXl5ERERPa2zZcsWDoezcuXKCRMmKLI2BSsqKtq2bVtCQsLDhw9dXFxSUlLorkglYVeIgjQIBGUd37rxb6YslqHqdEsRQgBAxDlaVFTUN998w+FwFixY8MUXX2hoaCiwut6pqalFRETMmjVrz549b731lkWXO2MJCQnnzp3T19f/7LPPaKmwr3iEkJ4mDmYwNHr+TWlpaR05cmTq1KnUj99++23//tqQE5X501V1t+rqtufn97R0s7W1ryq/mrhjsF65ciUkJOTMmTNmZmbLli0LDQ0Vf4CRwsycOfO11147e/bsxx9/fOzYsY6L+Hz+5s2bAWD79u1dE1Y5/Scjo4jD6XaRrppaorNzTxva29vn5+dTMcpisRobG+VVYr+GMSqJoqKin376KSkpqaioKCwsbNmyZQKB4NSpU0lJSbW1tWFhYX5+fpaWll031GIy3fX1u7Zba2rKv2oFOXLkyNq1a6dMmQIAu3fvXrt2rRLGKADs37//8uXLMTExa9eu7XgKFhUVlZ6ebm9vv2nTJhrLk4Axi6XL7NxNpyPy6dV333139+7dpqamFRUVx44dO3XqlDwL7LcwRiXXfinUbubMmaI3GcxiHRg+XG4VKYWsrCwfHx/q87hx44qLi5uampRwwJCdnd2mTZu+/PLLwMDAW7duUafS1dXVu3fvBoD9+/drqtp322Zr67l9nH1qzZo1fD7/0KFDhJAff/xxzpw5cqqtf8MYlYSNjU3X8dj9eIR2J6L7RhsbG7W1tanPenp6ANDQ0KCEMQoA27dvP378+O3bt2NjY6no37FjR1VV1axZsxYuXEh3dYrAYDDWr1+/fv16ugtRbXinHsmYubl5+8Mw5eXlLBZryJAh9JbUk/abSFu3bm1qanr06NHhw4fV1dUPHDhAd2lIlWCMIhnoeH46bdq0ixcvUu2XLl2aOnWqMk8ut3LlSnd39+Li4r17927evJnP569fv556hTJCYsKLeoVqFQr/rKvr1GiuoTHi+VWwShB9Ub9hwwYXF5fVq1dbWFhERkb+/vvviq2ub5hMZmRk5JQpU7744gsul2tsbKycN8TEUc3jFXa5X2+tocHsv48PKAmMUYWq5PE25+R0alw0ePD2F16gpR4pbdiw4c8//zQ0NFy6dGl7o42Nzf3793/55Zfm5uakpCQnJycaKxTH5MmTX3755StXrgBAUFCQiYkJ3RVJ6EBx8YHi4k6Nl5ycTFksWuoZODBGFUpXTW22kVGnxvF6erQUI6VHjx6lpqZSn9lsNgAIBALqR0tLS2ropfIrKyvbuXNnQkKCuro6n8/fs2cPl8vdtm2blpYW3aX12VAtLaMuD3GIGHuPZIa2p/kHmLiqKlc2+7X0dLoLkVZpael//vMf6opeU1Nz3rx58+bNo/5bMjExOX/+PN0FiovD4URERBgYGAAAi8VatWrV8uXLqX+XtbV1TEwM3QX2waL0dFc2+1JVFd2FDFAYowoiTozW8fkKq0cCXC63Y+4EBATU1dVRi65evdr+6vbZs2c/fPiQ3lJ7de7cOTs7O6rg+fPn5+TkUO3Xr18fN24c1e7p6ZmWlkZvnb0SCIUEY5RuGKMK0muMPmpqmnr37sHi4ialDNNz587Z29t3zZ12VMgOGjSoPWRra2tpKVW0zMzMV155hfqHODo6Xrp0qdMKAoEgJibG1NQUAJhMpp+fX3l5OS2lilbL4+0tLNyYnU0wRumGMaogvcbo0dJSNzbblc2e9+DBH1VVyjPDZa+501FlZWVAQAA1wsnExCQiIoKvNN8KVVVVAQEB1MzExsbGERERPB6vp5VramqCg4OpSVUMDQ1DQ0M5HI4iqxWBKxSeePrU4949VzZ7YmpqfksLxii9MEYVRJyL+ozGxrczM13ZbFc22y8z835jo8LK61afcqeju3fvTp8+nUre8ePHJyYmyrtU0bhcbnR0NDVbs7q6ur+/f0VFRccVampq4uPj79y50yn0s7KyXn31VeofMnLkyAsXLii28G7cqavzzsig/iNZl52d29xM8GyUbhijCiLmLSYhIRcqK73u33dls93Y7B35+ZVcrmIq7IjH44nOnV4JhcLY2FgbGxsAYDAYSUFBpLBQTtX24tIlz5deoqJwzpw5jx496rT8/Pnz5ubmS5cudXFxcXNza2pq6rTC1atXRz1/8dTs2bMzMjIUVfq/FLS0bHr8mArQxQ8f3uzQZ4IxSi+MUQXp0536ZoEguqRkcmqqK5s99e7d6JISjgLfY3H16tUxY8ZQqTFr1qx0KUYXNDc3h4aGegwfTnR1ibY2CQ4mDQ0yLLUX2dnE25sAHJs6dcSIESdPnux2rfDw8L///psQIhAIRo0a9f3333ddh96e33o+/2Bx8aTUVFc22/PevR/Kyjr994AxSi+MUQV5xuUm1dam1teLv0lha2twbi519rEoPf1qdbX8yqNkZ2d7e3tTASoid/qKV1hIli0jDAYBIEOHkthYIu9vhZoasnkzYbEIABk0SBAeLmbP5oIFC/bv39/TUsX3/AoIuVBZ+XJamiub7c5m78jPr+quX+VcZeWPT5/mt7TItRjUE4xRZXenvn5Ze19YVtbj5mZ5HIW6o0JNDaenpxcSEtLa2irjY9y5QyZNIgAEgEyYQG7flvH+KQIBiYkhpqYEgDCZxM+PPH0q5qb19fXGxsbUmakIqamp06ZNo75sXFxcbt68KXXR3fu7vn7581+9f1ZWlnx+9Uh6GKMqgCcU/lRePuP5ndmIvLxq2Z2ZUuN7zMzM2sf3PBU7dyQ4GImJIWZmEmScWK5fJ+PGtSW1hwe5d68vpQmWL1++cuVKMdc/d+6cra1t+wiw/Px8SQruQVFt7fs5OVSALkhPT6ipkeHOkcxhjKqMOh5vb2HhhNRU7+joPt03F6HjaHMPD497fckdyTU2kpAQoqlJAIieHgkJId2e+dbXk6NHia8v8fAg7u5k7lyydStJSup+n4WFxM+vLUCtrUlMTJ/6DZqampYvX/7qq6/2aVRTU1NTaGgoNaeqjo5OcHBwg9Q9v42NjSEhISYWFnPu3WvrFhcIpNwnkjeMURWT3djoNXcuFXxOTk7Xrl2TbD+FhYV+fn7UfqhnHxX9Nvbn938IABkxgnTqh42NJUOGtC3t9D8vL9JxPDwVylpaBIDo6JCQENLHLsLHjx87OTn5+/tzJRoUUVRU5Ofn1/EpUsn+nxQIBN9//z319icGg/Hpzz8/o2OQBpIAxqhK6vQsY25urvjbNjU1hYSEUFNv6OjohISEtNB4ayIujjg6tuVj+wX1Dz+03Y+aNo389hspLCSVlSQtjezcSQwMCAAZPpxQ3Rr//EMsLQkAYTCInx8pKZGghGHDhjGZTKPn/P39JdhJcnLyxIkTqd/IxIkTk5OT+7R5SkrK5MmTqc3d3d1v3bolQQ2ILhijqoqaWUNfXx8ANDQ0AgIC6nsbBiAUCk+ePDl06FDqfMfb27ugoEAx1YrC5ZLoaDJ4MDl2jBBC8vKIri4BIO++282FeUYGGTyYAJA33ySEED6fODkRNzeiBLkjWS9zcXFx+8mspaVldHS0AK/iVQ3GqGorKSnx9/dnMpm9/hGmpKS89HwUupubW1JPnYx0qaggVOWbNrVd5vc0VODECQJA1NRIXh4hhJSVyX34VF80NDSEhIRQYx6op0h7GvNADaqlvgi1tbWDg4N7/SJEygljtD/4+++/O0Zkp0tC8aNWKVAX6aGhPa7A47Xd69+3T4Fl9U12dvb8+fPbR+B2nT+w043+POorAakmjNF+ousFe2FhYcfzHTEv/GlWWNjWTyr6In3xYgJAvL0VVZaEup0/sOOwU2WYcABJD2O0X6mvr9+2bRt1Ramrq9v+Sk5vb2/ZDmyUl+TkthgVfbMoKIgAkClTFFWW5Lhc7r59+6inSDU0NFxcXKjLAjMzsyNHjij1ZQESG74ZtF/R19f/4osvHj9+/Prrrzc3N9fV1dnZ2d24cePkyZPtl5BKraGh7YPoF6vo6wMA1NfLvR6psVisoKCg3NzcgIAAHo+Xn58PAAEBAVlZWatXr6YiFak6/C32QzY2NlpaWoQQLpfLZDLbR9L0ihq6+Pbbb2/cuPHOnTtyLbJ7urptH1paRK3W1ATQW9TKB9V5EhQUFBoaWlpaKuZWJiYmS5YsIYTU1NQwGIx169ZR56eof8AY7Ydu3779008/aWlp2dnZ5eTkHDp0SMwNP//880OHDvn5+U2cOHHOnDnp6elyrbMbz3shoKRE1GrUUlNTudfTxTvvvHPixAkHB4fS0lJXV9eamhpxthIIBJs2bQIANzc3gUAQEBAg5zKRYtHdq4BkTCAQTJgwAQB27Nhx4cIFADAwMCgrKxNn25dffjn0+S3ySZMmRUdHy7PS7giFxMiIAJBvvhG1mr09ASCffaaosv4vMTGxvUPT3Nw8Pj5enK2++eYbALCxsSkoKDA0NAQA0S8RQKoFY7S/OXbsGABYWVk1NjYSQubOnQsA7777rjjb7t2719LS8tSpU7du3bKzs6NncP4bbxAA8tJLPa5w82bbbSjFzADQHYFAcPr0aTMzs8rKyl5Xrqmpoe71/frrr4SQ8PBwABg+fLjyvJUESQljtF+pr6+nHso+ceIE1ZKZmclisZhMJpvN7nXz/Px8KysrZ2dnAHjvvfekn/pEEsnJbU+CHjnSzdKGBuLsTADIjBkKr6xNWlrasGHD1NXVvxF9yvxcYGAgAEydOpV61p7L5To4OACAiIlNkWrBGO1Xtm7dCgCTJ0/uODtGpz9jEUaPHv31118TQjIyMkaPHr1jxw75ltsT6kEmNTXyySek4xxxycnEza1tXqjsbHpqey43N9fU1LTXyUaprzE1NbWO72pu72yR45yESIEwRvuPnJwcTU1NJpN5586dju01NTXUW5VOnTolYvOWlhYGg5GVlUX9uGfPnnnz5smxXBH4fLJ+fduVO4tFxowhEyYQC4u2FlNT0seJP2So41eRl5dXeHi46PWpTpW1a9eK2Y5UEcZo/7Fw4UIAWLVqVddFUVFRADBs2DDRkznNmDHDx8cnMzPzr7/+cnBwOHjwoNyKFUNSElm2rG0iEmqa5/Hjya5dRIEvQepEKBR6eHgcPXr0yZMnFy5c0NPTuy1yDv/z588DgKGh4bNnzzot6vYsFakojNF+Ij4+HgD09fVLS0u7LuXz+dT0zHv27BGxk6qqqo8//tjLy+vVV1/97rvvFD0DaU+am0llJVGOB34ePHiwdOnSUaNGzZw587fffhOxJofDofpADxw40O0K1BCoGfR18iJZwRjtD3g83tixYwEgLCysp3WuXbsGAHp6eiUSTcqJ+mrv3r0A4Ojo2NNs0NXV1eJ0tiDlhzHaH0RGRgKAvb296PfQLVmyBABWrFihsMIGrPLycuo5JdHjQ8XsbEFKTm3nzp3yHuGP5Kqez4/U1rYxM9u+cmX7fELdcnd3Ty4oMNqyZaSRkamGhsIqHIAOlZWxLCxGamnt2LFDxGouLi4X//hjTGAgz9JynIGBwspDssUghNBdA5JKWGHhrxUVE/T1o0aO7HXlQyUl3z99OlZX95ijI0MBxQ1I/zQ3r8jMVGMwfnZ0tNXREb3y33V163JydJjM38aMGcJiKaZCJFv4TL1qy2ttPV1ZqcZgbLGxEWf9ty0shrBY6U1NcdXV8q5twNpfVCQE8DE17TVDAcB90KAZhobNQuEh0dMIICWGMara9hcVCQjxHjJkuLa2OOvrMJnvWVkBwNfFxc1CoZyrkw2hUJiRkcHn8+kuRCx/VFffbWw0VldfZWEh5iaB1tYaTObFqqqH1MxVSNVgjKqwa7W1yfX1Burqa8T+iwWAV01MRuvqPuPxYp4+lV9tMtHa2vrVV1+NHDlyzJgxRUVFdJfTO45Q+HVJCQC8Z2Wlp6Ym5lZWmpq+pqYEILyoCLvYVBHGqKriEfJ1cTEArLO0HKSuLv6GDID3bWwYAMfLy0u5XLkVKANCoVAgEFy5ckVHjKtjZRDz9GkZl+ugo7Ng8OA+bYidLSoNY1RVHS8vL+Rw7LS0lvTxLxYAxurqvmJszBUKDxYXy6M2WdHR0QkMDLSzs6O7ELE843KPl5cDQJCNTV//rnSYzPXPO1taVKSzBbXDGFVJ1c8vybfY2KgxJLnlvtHaWpvJjK+pudv+3g4knYji4hahcI6xsYtE0/LPV53OFtQJxqhKiiwpaRIIZhoaTpJ0sKEpi7XC3BwA9hUXC3HQm9QeNDZeranRZDI3WFlJtgcGQJCNDQPgv0rf2YI6wRhVPZnNzXFVVSwGY4O1tTT7WWFmZqmhkd3cfLaqSla1DUxCgH1FRQTgLXNzCymea3DS1Z1jbMwVCr9S7s4W1AnGqIohAGGFhUIAPzOzoZqa0uxKk8mkgvibkpIGgUBGBQ5EZysrHzU3m2po+JmZSbmrAGtrbSbzKna2qBSMURVDjS40YbHeMjeXfm9eRkYu+vrVfP53ZWXS700eli1bZm9v39LS4unp6evrS3c53WgWCL4tLQWAQGtrbalfmIydLaqoDwNlkDJw1tPzNDScYWioK/awRNG2WFvvLiiYqqzv+z169Gj7wHv1vozrUhg+wPRBgwo4nJeNjGSywxVmZpeqqtz09XmEaEp0/xApGD5Tr3RahcK81taelhqpq0vT+9YtAtDA51fweD2tYKqhoS+j1FZRNXy+oIe/FCaAMYvFJ0RddpHHJYTFYIjoaVFnMHSkPvNFsqKMX+8D3JPW1hWZmT0tXTJ48EcvvCDbIzIALtfUfFlY2NMKn9jaLjQxke1BVcvKf/4p4XC6XWSgpnbN2VmGGQoAGgxGNZ/vdf9+Tyu8NGjQweHDZXhEJA2MUeU1w9Cwa6OjPJ/n0VNTc+puzKM5zjwEAADWmpoGXToWxH/oUzK2WlpdM9oK5zlUJhijyivMzo6p2K6xoZqaeI4jwnpLSy9jYwUf9OCIEZYYmsoNu1cQQkgqGKMIISQVjFGEEJIK9o0qrxu1tYx/940aqquPl2jaCzE1CgR/1dV1arTS1HxBS0t+B1UhxRxORpeZlR10dGR7m76TJ62tTf8e+cRiMGzxN6JMMEaV19a8vE4tznp6Rx0c5HfEQg4nICenU+MKM7MA6R7e7zeiSkujSks7Nf7h5GQiz5EMAY8fd2qx1NA4N3as/I6I+gpjVHktGTKk00mOjXQP0ffKUF19WpfHmUbp6sr1oCrEUUen61vnNOQ8DN5ZT6/TIQbj+DMlgzGqvLbZ2Ch4wJOlhkaIra0ij6haVpiZKX7A0+5hw3DAk5LDW0wIISQVjFGEEJIKxihCCEkFYxQhhKSCMYoQQlLBGEUIIangtM1Kp1kgSG9qAoCJkr71UwKlXG5OS4u+mppcn5JSXddqahqFQjc9PUs5D91txxEKz1dVAcA8Y2OdgT1ntvLDGEUIIangRT1CCEkFYxQhhKSCD4PSQygUpqSkODo6Gj5/U0hjY2NiYiIAeHh46ClrB6VAIEhJSWEwGOPGjdPW1qa7HDmqq6vLycmxsLCwtLSkuxZRauoV8PMAAANCSURBVGtrq6urqc9aWlpKXm1/hX2jNIiOjj5w4EBubu7PP/+8dOlSACgoKJgyZcqoUaMAIDMz89atW0OHDqW7zM6qq6tnzZrF5/NNTEweP36cmJg4vP++cWTJkiXXr18PCAjYtWsX3bWI8uGHHx4/ftzc3BwApk2bduDAAborGojwop4GBgYGly9fnjRpUntLWFiYp6fnlStXrly5Mn369L1799JYXk/Cw8P19PTS0tJu3Ljh7e29e/duuiuSl1OnTuXm5np7e9NdSO9KS0s//vhjNpvNZrMxQ+mCMUoDHx8f239PpHTp0iVfX1/q8xtvvBEXF0dDWb3Jycnx9PRUU1MDgEWLFl27do3uiuSirq5uy5Yt0dHRLFWYj66srMzc3Ly1tZXuQgY0jFH6EULKysqsrKyoH62trUtKSugtqVvu7u7nzp0rKioqKys7e/ZseXl5v+wR2rJly6JFizpeKyiz2trabdu2ubq6mpmZHT9+nO5yBii8xUQ/BoOhqanJ4/GoHzkcjnLevQkICCgqKpo8ebK5ubmHh4eRkRFDsdOhKsCNGzcuX76ckZFBdyHiSkxMZLFY6urqN2/enD179uzZsy0sLOguasDBGFUKtra2WVlZrq6uAJCdnW2rlHMna2hoREZGRkZGAsC33347YsQIuiuSveDgYD6fP3v2bAAoLCxUV1e3trZes2YN3XX1qP0bd/r06QYGBtnZ2RijiocxqhR8fX0jIyNfe+01QsjBgwfffPNNuivqXmtrq5aWVmFhYXh4eHBwMN3lyN6pU6faLwtCQkKMjIyooRTKicvlHjlyZPXq1ZqamklJSU1NTS+++CLdRQ1E2DdKg23bthkbGycnJ69cufKFF14AgE2bNtnZ2VGjFB0cHN577z26a+zG48eP7ezsRo4c6ezs7Ovr+84779BdkezZ2NjYPWdgYGBkZGRkZER3UT1qaWmJj4+3srIaP3780qVL//vf/5qamtJd1ECE40aVCIfDAQBNRU1+IQEej1dVVWViYqISd7Gl1NzczGAwlLOfuqOWlpaioiJbW1sNfGUTTTBGEUJIKnhRjxBCUsEYRQghqWCMIoSQVDBGEUJIKhijCCEklf8Bfovfk/Rgg9QAAAFWelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMQAAeJx7v2/tPQYg4GWAAEYg5oPyGxjZlDSANDOLLguQUnA2VABJGzADiVA/H3MQxxBEOBpDNH+wt+SA6GCC6PAnQgebkgKQAbPCiFQNxoQ1cII1MEI0uBHhJFQNRDgJVQMRTkILJpJ9bUKqm0jWYEqqBjPCGhjBvmaE6vAgIibQdBARTtwMjAoM7BpMDBwMjEwMTMwMTDwMTLzANMzAzMrAzMbAwcnAwcXAwc0gwiC+iQGS4MGAb5GC937+g//sQJzFuybbnj3lthvE3uFcYN/PCRG//vaZfbXrRLC43q1l9o274vaD2OK5wg7v30HYrK+0HNxy/oLVm7zx2u+8Yb89iN1UdmW/h+i1XSC2bteU/fnmEPVirlwHGJkhbPuFigf6K/6D9Qrb7LB/E7IOrNfnwwM75XwIWwwAwmp8+eRLESsAAAFaelRYdE1PTCByZGtpdCAyMDIyLjA5LjEAAHicfZNLcsMwCED3PgUXqAcQ+i2TOP1MJ/ZMm+YO3ff+U5DHkTxVKnkB6AnExwPY+pjev3/gvngaBgD858s5w80h4nABE+B4fnmb4XQ9HDfLafmar59AAuT0ju49e7gul81CcIInHGN2EiLgKDn6ImBZ9SrDYtbI5JzdQHSo0l/QqUccs0u+HEsO1HUoytGYUO4ORboOPTwrqNH8CqaI/chBQd6DqQvGAoaI2xP1kb0nJs25FIeDntPj4mSrIo3B56TnJRkK3dCEGltRCigrmkLqp0NkKO/QyH2UV9Sz39qY+inpRLxaJi479aRlDfQAFAWxBTl2wfM87SZqnbHjMk91xmxznSRWNdV5sdNcx0JRcLX5pITUFptKrnay6FI7RurB176YGprimx6bCpMxbR2LgZpyFQO32ba5mb79eyoPv1BDtwL0nUhTAAABBnpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4xAAB4nFWPO2sDMRCE/0rKO7iIfWlXWpPqwLiK0xsXKVKGM8Glf3z0MDkFVMx80uyOLqfrOl1O13k6r9PbeV6n41zO8U+8PCYMwpl5wcCK2ZYDhMwpFgJBsqI21N70R2SNgBFW9FokMHDVh2Iss5RMDVus4cZIi8TGKsKgMSfrYdSSpUZRQRpNmqANpJEZPVmkKH1HynUHhgSCzzIivUwpCxwbTAZtHP1DqSM1iP23UofNy+d9+/742W4Oocr37f4VkBx3R07DFTsPzmU34HE35rqb5Lab7GkYAJ4Hh45DDXYcaojj0CM6Dj308QuD2n/DB2YfZwAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -171,6 +199,10 @@ "cell_type": "code", "execution_count": 7, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:31.065650Z", + "start_time": "2024-05-09T21:21:30.137191Z" + }, "pycharm": { "is_executing": true } @@ -179,7 +211,7 @@ { "data": { "image/svg+xml": [ - "eafecpf6eaffeafeaf00.511.522.533.5SoluteeaeaffeafeafCoordination Numbers Of SolventsSolventCoordination Numbers" + "eaeaffeafeaf00.511.522.533.5Solventeafecpf6eaffeafeafCoordination Numbers Of SolventsSolventCoordination Numbers" ] }, "metadata": {}, @@ -208,6 +240,10 @@ "cell_type": "code", "execution_count": 8, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:31.199803Z", + "start_time": "2024-05-09T21:21:31.068631Z" + }, "pycharm": { "is_executing": true } @@ -216,7 +252,7 @@ { "data": { "image/svg+xml": [ - "eaeaffeafeaf00.511.522.533.5Solventeafecpf6eaffeafeafCoordination Numbers Of SolventsSoluteCoordination Numbers" + "eaeaffeafeaf00.511.522.533.5Soluteeafecpf6eaffeafeafCoordination Numbers Of SolventsSoluteCoordination Numbers" ] }, "metadata": {}, @@ -224,7 +260,7 @@ } ], "source": [ - "compare_coordination_numbers(solutes, x_axis='solute')" + "compare_coordination_numbers(solutes, x_axis_solute=True)" ] }, { @@ -240,6 +276,10 @@ "cell_type": "code", "execution_count": 9, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:31.303146Z", + "start_time": "2024-05-09T21:21:31.201238Z" + }, "pycharm": { "is_executing": true } @@ -248,7 +288,7 @@ { "data": { "image/svg+xml": [ - "eaeaffeafeaf00.511.522.533.5Solventfecpf6EAxCoordination Numbers Of SolventsSoluteCoordination Numbers" + "eaeaffeafeaf00.511.522.533.5Solutefecpf6EAxCoordination Numbers Of SolventsSoluteCoordination Numbers" ] }, "metadata": {}, @@ -263,7 +303,7 @@ " \"feaf\": \"EAx\",\n", "}\n", "\n", - "compare_coordination_numbers(solutes, x_axis='solute', rename_solvent_dict=rename)\n" + "compare_coordination_numbers(solutes, x_axis_solute=True, rename_solvent_dict=rename)\n" ] }, { @@ -277,6 +317,10 @@ "cell_type": "code", "execution_count": 10, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:31.486911Z", + "start_time": "2024-05-09T21:21:31.369341Z" + }, "pycharm": { "is_executing": true } @@ -285,7 +329,7 @@ { "data": { "image/svg+xml": [ - "eaeaffeafeaf00.511.522.533.5Solventfecpf6EAxCoordination Numbers Of SolventsDegree Of FluorinationCoordination Numbers" + "eaeaffeafeaf00.511.522.533.5Solutefecpf6EAxCoordination Numbers Of SolventsDegree Of FluorinationCoordination Numbers" ] }, "metadata": {}, @@ -295,7 +339,7 @@ "source": [ "compare_coordination_numbers(\n", " solutes,\n", - " x_axis='solute',\n", + " x_axis_solute=True,\n", " rename_solvent_dict=rename,\n", " series=True,\n", " x_label=\"Degree of Fluorination\"\n", @@ -313,6 +357,10 @@ "cell_type": "code", "execution_count": 11, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:31.768841Z", + "start_time": "2024-05-09T21:21:31.685740Z" + }, "pycharm": { "is_executing": true } @@ -321,7 +369,7 @@ { "data": { "image/svg+xml": [ - "eaeaffeafeaf00.511.522.533.5Solventpf6EAxCoordination Numbers Of SolventsDegree Of FluorinationCoordination Numbers" + "eaeaffeafeaf00.511.522.533.5Solutepf6EAxCoordination Numbers Of SolventsDegree Of FluorinationCoordination Numbers" ] }, "metadata": {}, @@ -331,7 +379,7 @@ "source": [ "compare_coordination_numbers(\n", " solutes,\n", - " x_axis='solute',\n", + " x_axis_solute=True,\n", " rename_solvent_dict=rename,\n", " series=True,\n", " x_label=\"Degree of Fluorination\",\n", @@ -350,6 +398,10 @@ "cell_type": "code", "execution_count": 12, "metadata": { + "ExecuteTime": { + "end_time": "2024-05-09T21:21:32.838026Z", + "start_time": "2024-05-09T21:21:32.065865Z" + }, "pycharm": { "is_executing": true } @@ -358,7 +410,7 @@ { "data": { "image/svg+xml": [ - "fecpf6EAx00.20.40.60.81SoluteeaeaffeafeafDiluent Composition Of SolutesSolventDiluent Composition" + "eaeaffeafeaf00.20.40.60.81Solventfecpf6EAxDiluent Composition Of SolutesSolventDiluent Composition" ] }, "metadata": {}, @@ -367,7 +419,7 @@ { "data": { "image/svg+xml": [ - "eaeaffeafeaf0.20.40.60.81Solventfecpf6EAxFractional Pairing Of SolventsSoluteSolvent Pairing" + "eaeaffeafeaf0.20.40.60.81Solutefecpf6EAxFractional Pairing Of SolventsSoluteSolvent Pairing" ] }, "metadata": {}, @@ -376,7 +428,25 @@ { "data": { "image/svg+xml": [ - "fecpf6EAx00.10.20.30.40.50.60.70.80.9SoluteeaeaffeafeafFree Solvents In SolutesSolventFraction Free Solvents" + "eaeaffeafeaf00.10.20.30.40.50.60.70.80.9Solventfecpf6EAxFree Solvents In SolutesSolventFraction Free Solvents" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "eaeaffeafeaf00.511.522.53Solventfecpf6EAxCoordination Compare To Random Distribution Of SolventsSolventCoordination Vs Random" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "eaeaffeafeaf00.10.20.30.40.50.60.70.80.9Solute StatusisolatedpairednetworkedFraction of Solutes Isolated, Paired, and NetworkedSoluteSolute Status Fraction" ] }, "metadata": {}, @@ -384,11 +454,15 @@ } ], "source": [ - "from solvation_analysis.plotting import compare_diluent, compare_pairing, compare_free_solvents\n", + "from solvation_analysis.plotting import compare_diluent, compare_pairing, compare_free_solvents, compare_coordination_vs_random, compare_networking\n", "\n", "compare_diluent(solutes, rename_solvent_dict=rename).show()\n", - "compare_pairing(solutes, x_axis='solute', rename_solvent_dict=rename, series=True).show()\n", - "compare_free_solvents(solutes, rename_solvent_dict=rename).show()" + "compare_pairing(solutes, x_axis_solute=True, rename_solvent_dict=rename, series=True).show()\n", + "compare_free_solvents(solutes, rename_solvent_dict=rename).show()\n", + "compare_coordination_vs_random(solutes, rename_solvent_dict=rename).show()\n", + "\n", + "# slightly different api\n", + "compare_networking(solutes).show()" ] }, { @@ -412,7 +486,16 @@ { "data": { "image/svg+xml": [ - "345691000.10.20.30.40.50.60.7Histogram of Network SizesNetwork SizeFraction of All Networks" + "345691000.10.20.30.40.50.60.7Histogram of Network SizesNetwork SizeFraction of All Networks" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "eafecpf6eafecpf600.20.40.60.811.2Solvent Co-Occurrence Matrix0.961.321.321.050.620.391.090.330.0" ] }, "metadata": {}, @@ -421,7 +504,7 @@ { "data": { "image/svg+xml": [ - "eafecpf6eafecpf600.20.40.60.811.2Solvent Co-Occurrence Matrix0.961.321.321.050.620.391.090.330.0" + "34500.20.40.60.81eafecpf6Fraction of Solvents in Shells of Different SizesShell SizeFraction of Total Molecules" ] }, "metadata": {}, @@ -430,7 +513,16 @@ { "data": { "image/svg+xml": [ - "34500.20.40.60.81eafecpf6Fraction of Solvents in Shells of Different SizesShell SizeFraction of Total Molecules" + "ea 4fec 0pf6 0ea 3fec 1pf6 0ea 3fec 0pf6 1ea 5fec 0pf6 0ea 4fec 1pf6 0ea 2fec 2pf6 0123400.10.20.30.40.50.6Fractioneafecpf6Top Solvation Shell CompositionsSolvation ShellShell SizeShell Fraction" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "00.10.20.30.400.511234567051015solvation radiussolvation radiussolvation radiuspf6feceaRadial Distribution Functions of Solute-Solvent PairseaLiLiLiSolventSolute" ] }, "metadata": {}, @@ -443,6 +535,8 @@ " plot_network_size_histogram,\n", " plot_shell_composition_by_size,\n", " plot_co_occurrence,\n", + " plot_speciation,\n", + " plot_rdfs,\n", ")\n", "\n", "ea = solutes['ea']\n", @@ -452,7 +546,11 @@ "\n", "plot_co_occurrence(ea).show()\n", "\n", - "plot_shell_composition_by_size(ea).show()\n" + "plot_shell_composition_by_size(ea).show()\n", + "\n", + "plot_speciation(ea).show()\n", + "\n", + "plot_rdfs(ea).show()" ] }, { @@ -467,9 +565,9 @@ ], "metadata": { "kernelspec": { - "display_name": "solvation_analysis", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "solvation_analysis" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -481,9 +579,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.11.9" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/solvation_analysis/_column_names.py b/solvation_analysis/_column_names.py index b637d5d8..199a1a34 100644 --- a/solvation_analysis/_column_names.py +++ b/solvation_analysis/_column_names.py @@ -29,4 +29,4 @@ NETWORKED = "networked" # for speciation -COUNT = "count" +COUNT = "fraction" diff --git a/solvation_analysis/coordination.py b/solvation_analysis/coordination.py index 0f49008c..2da40a63 100644 --- a/solvation_analysis/coordination.py +++ b/solvation_analysis/coordination.py @@ -49,7 +49,7 @@ class Coordination: Parameters ---------- solvation_data : pandas.DataFrame - The solvation data frame output by Solute. + The solvation dataframe output by Solute. n_frames : int The number of frames in solvation_data. n_solutes : int diff --git a/solvation_analysis/pairing.py b/solvation_analysis/pairing.py index 42c9b9b6..1500c989 100644 --- a/solvation_analysis/pairing.py +++ b/solvation_analysis/pairing.py @@ -43,7 +43,7 @@ class Pairing: Parameters ---------- solvation_data : pandas.DataFrame - The solvation data frame output by Solute. + The solvation dataframe output by Solute. n_frames : int The number of frames in solvation_data. n_solutes : int diff --git a/solvation_analysis/plotting.py b/solvation_analysis/plotting.py index 22aa5871..971da12b 100644 --- a/solvation_analysis/plotting.py +++ b/solvation_analysis/plotting.py @@ -13,8 +13,11 @@ from typing import Union, Optional, Any, Callable from copy import deepcopy +import plotly +from plotly.subplots import make_subplots import plotly.graph_objects as go import plotly.express as px + import pandas as pd from solvation_analysis.solute import Solute @@ -178,12 +181,354 @@ def plot_co_occurrence( return fig +def _make_rectangle(x: float, y: float, color: str) -> dict: + """ + Create a rectangle shape for Plotly. + + Parameters + ---------- + x : float + The x-coordinate of the center of the rectangle. + y : float + The y-coordinate of the center of the rectangle. + color : str + The color of the rectangle. + + Returns + ------- + go.layout.Shape + The rectangle shape for Plotly. + """ + + x0 = x - 0.18 + y0 = y - 0.43 + x1 = x + 0.18 + y1 = y + 0.43 + h = 0.09 + rounded_bottom_left = f" M {x0 + h}, {y0} Q {x0}, {y0} {x0}, {y0 + h}" # + rounded_top_left = f" L {x0}, {y1 - h} Q {x0}, {y1} {x0 + h}, {y1}" + rounded_top_right = f" L {x1 - h}, {y1} Q {x1}, {y1} {x1}, {y1 - h}" + rounded_bottom_right = f" L {x1}, {y0 + h} Q {x1}, {y0} {x1 - h}, {y0}Z" + path = ( + rounded_bottom_left + + rounded_top_left + + rounded_top_right + + rounded_bottom_right + ) + + return dict( + type="path", + path=path, + line=dict(color=color, width=2), + fillcolor=color, + layer="between", + ) + + +def _get_shell_name(row): + result = [] + for column, value in row.items(): + result.append(f"{column} {value}") + return "
".join(result) + + +def plot_speciation( + speciation: Union[Speciation, Solute], shells: int = 6 +) -> go.Figure: + """ + Plot the solvation shell composition and fraction for the top solvation shells. + + Parameters + ---------- + speciation : Speciation or Solute + The Speciation or Solute object containing the speciation data. + shells : int, optional + The number of top solvation shells to plot. Default is 6. + + Returns + ------- + fig : plotly.graph_objs.Figure + The plot of the solvation shell composition and fraction. + """ + if isinstance(speciation, Solute): + if not hasattr(speciation, "speciation"): + raise ValueError("Solute speciation analysis class must be instantiated.") + speciation = speciation.speciation + + # Extract relevant data + df = speciation.speciation_fraction.head(shells) + fraction_data = df["fraction"] + df = df.drop("fraction", axis=1) + + # Get unique solvents and assign colors + solvents = df.columns.tolist() # List of solvents + colors = px.colors.qualitative.Plotly # Get a list of Plotly's qualitative colors + + # If there are more solvents than colors, cycle through the colors again + if len(solvents) > len(colors): + colors = colors * ( + len(solvents) // len(colors) + 1 + ) # Repeat color list as needed + color_map = dict(zip(solvents, colors)) # Create a color map for solvents + + # Prepare data for the plot + x_vals = [] + y_vals = [] + solvent_names = [] + marker_colors = [] # To store color for each marker + shell_names = [] + + # Process each row to create stacks of points + for index, row in df.iterrows(): + shell_names.append(_get_shell_name(row)) + total_count = 0 + for solvent, count in row.items(): + for i in range(count): + x_vals.append(index) + y_vals.append( + 0.5 + i + total_count + ) # Place each solvent count at different y-levels + solvent_names.append(solvent) + marker_colors.append( + color_map[solvent] + ) # Use the dynamically assigned color + total_count += count + + # Create scatter plot of solvent squares, + trace1 = go.Scatter( + x=x_vals, + y=y_vals, + mode="markers", + marker=dict(size=25, color=marker_colors, opacity=0), # Apply colors to markers + text=solvent_names, + hoverinfo="text", + name="Solvents", + legendgroup="solvents", + showlegend=False, + ) + + trace2 = go.Scatter( + x=df.index, + y=fraction_data, + mode="lines+markers", + name="Fraction", + yaxis="y2", + line=dict(color="black"), + ) + + # Create the figure with two traces + fig = go.Figure(data=[trace1, trace2]) + + # Add traces for each solvent to create a legend + for solvent, color in color_map.items(): + fig.add_trace( + go.Scatter( + x=[None], + y=[None], + mode="markers", + marker=dict(size=10, color=color), + name=solvent, + legendgroup="solvents", + showlegend=True, + ) + ) + + # Add squares with rounded corners on top of the points using the shapes API + for x, y, color in zip(x_vals, y_vals, marker_colors): + fig.add_shape(**_make_rectangle(x, y, color)) + + # Update layout + fig.update_layout( + title="Top Solvation Shell Compositions", + xaxis_title="Solvation Shell", + # xaxis=dict(tickmode="linear", tick0=0, dtick=1), # Set x-axis ticks to integers + xaxis=dict( + tickmode="array", + tickvals=df.index, + ticktext=shell_names, + ), + yaxis=dict( + title="Shell Size", + tickmode="array", + tickvals=list(range(1, int(max(y_vals)) + 1)), + range=[0, max(y_vals) + 1], # Scale the top of the y-axis + showgrid=False, + side="right", + ), + yaxis2=dict( + title="Shell Fraction", + overlaying="y", + side="left", + range=[0, max(fraction_data) * 1.1], # Scale the fraction axis + ), + template="plotly_white", + margin=dict(l=20, r=20, t=60, b=20), # Add padding to the edges of the plot + legend=dict( + orientation="h", + yanchor="bottom", + y=1, + xanchor="right", + x=1, + ), # Add legend at the top + ) + + return fig + + +def plot_rdfs( + solute: Solute, + show_cutoff: bool = True, + x_axis_solute: bool = False, + merge_on_x: bool = False, + merge_on_y: bool = False, +): + """ + Plot the radial distribution functions (RDFs) of solute-solvent pairs. + + Parameters + ---------- + solute : Solute + The Solute object containing the RDF data. + show_cutoff : bool, optional + Whether to display the solvation radius cutoff lines. Default is True. + x_axis_solute : bool, optional + Whether to place the solute on the x-axis. Default is False. + merge_on_x : bool, optional + Whether to merge subplots along the x-axis. Default is False. + merge_on_y : bool, optional + Whether to merge subplots along the y-axis. Default is False. + + Returns + ------- + fig : plotly.graph_objs.Figure + The plot of the radial distribution functions. + """ + # Determine the grid dimensions based on merge settings + data = solute.rdf_data + n_cols = 1 if merge_on_y else len(data) + n_rows = 1 if merge_on_x else len(data[list(data.keys())[0]]) + + x_title, y_title = "Solvent", "Solute" + + if x_axis_solute: + n_rows, n_cols = n_cols, n_rows + x_title, y_title = y_title, x_title + + # Create subplots + fig = make_subplots( + rows=n_rows, + cols=n_cols, + shared_xaxes=True, + shared_yaxes=True, + x_title=x_title, + y_title=y_title, + ) + + # Create a color mapping dictionary + color_map = {} + colors = plotly.colors.qualitative.Plotly + + # Iterate over the data and add traces to the subplots + for i, (key, values) in enumerate(data.items()): + for j, (sub_key, sub_values) in enumerate(values.items()): + x, y = sub_values + col = i * (not merge_on_y) + 1 + row = j * (not merge_on_x) + 1 + + if x_axis_solute: + row, col = col, row + + # Assign a color to the sub-key if not already assigned + if sub_key not in color_map: + show_legend = True + color_map[sub_key] = colors[len(color_map) % len(colors)] + else: + show_legend = False + + fig.add_trace( + go.Scatter( + x=x, + y=y, + name=sub_key, + line=dict(color=color_map[sub_key]), + legendgroup=sub_key, + showlegend=show_legend, + ), + row=row, + col=col, + ) + fig.update_yaxes(title_text=key, row=row, col=1) + fig.update_xaxes(title_text=sub_key, row=n_rows, col=col) + + # Update the layout + fig.update_layout( + title_text="Radial Distribution Functions of Solute-Solvent Pairs", + template="plotly_white", + margin=dict( + l=100, + b=80, + ), + ) + fig.update_annotations(x=0.5, y=-0.05, selector={"text": x_title}) + fig.update_annotations(y=0.5, x=-0.03, selector={"text": y_title}) + + if not (merge_on_x or merge_on_y) and show_cutoff: + for col, solute in enumerate(solute.atom_solutes.values()): + for row, (solvent, radius) in enumerate(solute.radii.items()): + if x_axis_solute: + row, col = col, row + fig.add_vline( + x=radius, + row=row, + col=col, + label=dict( + text="solvation radius", + textposition="top center", + yanchor="top", + ), + ) + + return fig + + +def compare_networking(solutions, series=False): + # valid_x_axis = set(["solvent", "solute"]) + # assert x_axis in valid_x_axis, "x_axis must be equal to 'solute' or 'solvent'." + # x_label = x_label or x_axis + # legend_label = legend_label or (valid_x_axis - {x_axis}).pop() + + property_dict = {} + for solute_name, solute in solutions.items(): + if not hasattr(solute, "networking"): + raise ValueError("Solute networking analysis class must be instantiated.") + property_dict[solute_name] = solute.networking.solute_status + + solvents_to_plot = ["isolated", "paired", "networked"] + + fig = compare_solvent_dicts( + property_dict=property_dict, + rename_solvent_dict={}, + solvents_to_plot=solvents_to_plot, + legend_label="Solute Status", + x_axis_solute=True, + series=series, + ) + + fig.update_layout( + xaxis_title_text="Solute", + yaxis_title_text="Solute Status Fraction", + title="Fraction of Solutes Isolated, Paired, and Networked", + template="plotly_white", + ) + return fig + + def compare_solvent_dicts( property_dict: dict[str, dict[str, float]], rename_solvent_dict: dict[str, str], solvents_to_plot: list[str], legend_label: str, - x_axis: str = "solvent", + x_axis_solute: str = False, series: bool = False, ) -> go.Figure: """ @@ -247,7 +592,7 @@ def compare_solvent_dicts( df = pd.DataFrame(data=property_dict.values()) df.index = list(property_dict.keys()) - if series and x_axis == "solvent": + if series and not x_axis_solute: # each solution is a line df = df.transpose() fig = px.line( @@ -258,11 +603,11 @@ def compare_solvent_dicts( markers=True, ) fig.update_xaxes(type="category") - elif series and x_axis == "solute": + elif series and x_axis_solute: # each solvent is a line fig = px.line(df, y=df.columns, labels={"variable": legend_label}, markers=True) fig.update_xaxes(type="category") - elif not series and x_axis == "solvent": + elif not series and not x_axis_solute: # each solution is a bar df = df.transpose() fig = px.bar( @@ -272,7 +617,7 @@ def compare_solvent_dicts( barmode="group", labels={"variable": legend_label}, ) - elif not series and x_axis == "solute": + elif not series and x_axis_solute: # each solvent is a bar fig = px.bar( df, @@ -293,17 +638,16 @@ def compare_func( solutions, rename_solvent_dict=None, solvents_to_plot=None, - x_axis="solvent", + x_axis_solute=False, series=False, title=title, x_label=None, y_label=attribute.replace("_", " ").title(), legend_label=None, ): - valid_x_axis = set(["solvent", "solute"]) - assert x_axis in valid_x_axis, "x_axis must be equal to 'solute' or 'solvent'." + x_axis = "solute" if x_axis_solute else "solvent" x_label = x_label or x_axis - legend_label = legend_label or (valid_x_axis - {x_axis}).pop() + legend_label = legend_label or x_axis property = {} for solute_name, solute in solutions.items(): @@ -396,6 +740,13 @@ def compare_func( ) +compare_coordination_vs_random = _compare_function_generator( + "coordination", + "coordination_vs_random", + "Coordination Compare to Random Distribution of Solvents", + "Compare the coordination numbers.", +) + compare_residence_times_cutoff = _compare_function_generator( "residence", "residence_times_cutoff", diff --git a/solvation_analysis/solute.py b/solvation_analysis/solute.py index 0e71d62d..85595ac7 100644 --- a/solvation_analysis/solute.py +++ b/solvation_analysis/solute.py @@ -110,7 +110,6 @@ from functools import reduce from typing import Any, Callable, Optional, Union -import matplotlib.pyplot as plt import pandas as pd import warnings @@ -119,6 +118,7 @@ from MDAnalysis.analysis.rdf import InterRDF from MDAnalysis.lib.distances import capped_distance import numpy as np +import plotly.graph_objects as go from solvation_analysis._utils import ( verify_solute_atoms, @@ -597,9 +597,7 @@ def _prepare(self): # generate and save plots if name not in self.radii.keys(): self.radii[name] = self.kernel(bins, data, **self.kernel_kwargs) - calculated_radii = set( - [name for name, radius in self.radii.items() if not np.isnan(radius)] - ) + calculated_radii = set([name for name, radius in self.radii.items() if radius]) missing_solvents = set(self.solvents.keys()) - calculated_radii missing_solvents_str = " ".join([str(i) for i in missing_solvents]) assert len(missing_solvents) == 0, ( @@ -731,9 +729,9 @@ def _conclude(self): @staticmethod def _plot_solvation_radius( bins: np.ndarray, data: np.ndarray, radius: float - ) -> tuple[plt.Figure, plt.Axes]: + ) -> go.Figure: """ - Plot a solvation radius on an RDF. + Plot a solvation radius on an RDF using Plotly. Includes a vertical line at the radius of interest. @@ -748,20 +746,35 @@ def _plot_solvation_radius( Returns ------- - fig : matplotlib.Figure - ax : matplotlib.Axes + fig : plotly.graph_objects.Figure """ - fig, ax = plt.subplots() - ax.plot(bins, data, "b-", label="rdf") - ax.axvline(radius, color="r", label="solvation radius") - ax.set_xlabel("Radial Distance (A)") - ax.set_ylabel("Probability Density") - ax.legend() - return fig, ax - - def plot_solvation_radius( - self, solute_name: str, solvent_name: str - ) -> tuple[plt.Figure, plt.Axes]: + + fig = go.Figure() + + # Add the RDF trace + fig.add_trace(go.Scatter(x=bins, y=data, mode="lines", name="RDF")) + + # Add the vertical line for the solvation radius + fig.add_vline( + x=radius, + line_width=4, + line_dash="dash", + line_color="red", + annotation_text="Solvation Radius", + annotation_position="top right", + ) + + # Update the layout + fig.update_layout( + title="Solvation Radius on RDF", + xaxis_title="Radial Distance (Å)", + yaxis_title="Probability Density", + template="plotly_white", + ) + + return fig + + def plot_solvation_radius(self, solute_name: str, solvent_name: str) -> go.Figure: """ Plot the RDF of a solvent molecule @@ -775,8 +788,7 @@ def plot_solvation_radius( Returns ------- - fig : matplotlib.Figure - ax : matplotlib.Axes + fig : go.Figure """ if len(self.atom_solutes) == 1: solute_name = self.solute_name @@ -784,9 +796,11 @@ def plot_solvation_radius( assert not self.skip_rdf, "RDFs were skipped, so no RDFs are available." bins, data = self.rdf_data[solute_name][solvent_name] radius = self.atom_solutes[solute_name].radii[solvent_name] - fig, ax = self._plot_solvation_radius(bins, data, radius) - ax.set_title(f"{self.solute_name} solvation distance for {solvent_name}") - return fig, ax + fig = self._plot_solvation_radius(bins, data, radius) + fig.update_layout( + title=f"{self.solute_name} solvation radius for {solvent_name}" + ) + return fig def draw_molecule( self, @@ -889,9 +903,9 @@ def get_shell( """ assert self.has_run, "Solute.run() must be called first." - assert frame in self.frames, ( - "The requested frame must be one " "of an analyzed frames in self.frames." - ) + assert ( + frame in self.frames + ), "The requested frame must be one of the analyzed frames in self.frames." remove_mols = {} if remove_mols is None else remove_mols # select shell of interest shell = self.solvation_data.xs((frame, solute_index), level=(FRAME, SOLUTE_IX)) diff --git a/solvation_analysis/speciation.py b/solvation_analysis/speciation.py index 6ce3dbe5..01ad6e10 100644 --- a/solvation_analysis/speciation.py +++ b/solvation_analysis/speciation.py @@ -51,7 +51,7 @@ class Speciation: Parameters ---------- solvation_data : pandas.DataFrame - The solvation data frame output by Solute. + The solvation dataframe output by Solute. n_frames : int The number of frames in solvation_data. n_solutes : int @@ -212,7 +212,7 @@ def _solvent_co_occurrence(self) -> pd.DataFrame: def speciation_data(self) -> pd.DataFrame: """ A dataframe containing the speciation of every solute at - every trajectory frame. Indexed by frame and solute numbers. + every trajectory frame. Indexed by timestep and solute numbers. Columns are the solvent molecules and values are the number of solvent in the shell. """ diff --git a/solvation_analysis/tests/conftest.py b/solvation_analysis/tests/conftest.py index 13ad0f08..37c01bb3 100644 --- a/solvation_analysis/tests/conftest.py +++ b/solvation_analysis/tests/conftest.py @@ -12,7 +12,6 @@ from solvation_analysis.tests.datafiles import ( bn_fec_data, bn_fec_dcd_wrap, - bn_fec_dcd_unwrap, bn_fec_atom_types, eax_data, iba_data, @@ -50,7 +49,7 @@ def make_grid_universe(n_grid, residue_size, n_frames=10): ------- A constructed MDanalysis.Universe """ - n_particles = n_grid ** 3 + n_particles = n_grid**3 assert ( n_particles % residue_size == 0 ), "residue_size must be a factor of n_particles" @@ -84,13 +83,13 @@ def u_grid_1(): return make_grid_universe(6, 1) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def u_real(): """Returns a universe of a BN FEC trajectory""" return mda.Universe(bn_fec_data, bn_fec_dcd_wrap) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def u_real_named(u_real): """Returns a universe of a BN FEC trajectory with residues and atoms named""" types = np.loadtxt(bn_fec_atom_types, dtype=str) @@ -100,7 +99,7 @@ def u_real_named(u_real): return u_real -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def atom_groups(u_real): """Returns pre-selected atom groups in the BN FEC universe""" li_atoms = u_real.atoms.select_atoms("type 22") @@ -115,12 +114,8 @@ def rdf_loading_helper(bins_files, data_files): """ Creates dictionary of bin and data arrays with a rdf tag as key """ - rdf_bins = { - key: list(np.load(npz).values())[0] for key, npz in bins_files.items() - } - rdf_data = { - key: list(np.load(npz).values())[0] for key, npz in data_files.items() - } + rdf_bins = {key: list(np.load(npz).values())[0] for key, npz in bins_files.items()} + rdf_data = {key: list(np.load(npz).values())[0] for key, npz in data_files.items()} shared_keys = set(rdf_data.keys()) & set(rdf_bins.keys()) rdf_bins_and_data = {key: (rdf_bins[key], rdf_data[key]) for key in shared_keys} return rdf_bins_and_data @@ -141,54 +136,53 @@ def rdf_bins_and_data_non_solv(): return rdf_loading_helper(non_solv_rdf_bins, non_solv_rdf_data) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def pre_solute(atom_groups): - li = atom_groups['li'] - pf6 = atom_groups['pf6'] - bn = atom_groups['bn'] - fec = atom_groups['fec'] + li = atom_groups["li"] + pf6 = atom_groups["pf6"] + bn = atom_groups["bn"] + fec = atom_groups["fec"] return Solute.from_atoms( li, - {'pf6': pf6, 'bn': bn, 'fec': fec}, - radii={'pf6': 2.8, 'bn': 2.61468, 'fec': 2.43158}, + {"pf6": pf6, "bn": bn, "fec": fec}, + radii={"pf6": 2.8, "bn": 2.61468, "fec": 2.43158}, rdf_init_kwargs={"range": (0, 8.0)}, ) -@pytest.fixture(scope='function') +@pytest.fixture(scope="function") def pre_solute_mutable(atom_groups): - li = atom_groups['li'] - pf6 = atom_groups['pf6'] - bn = atom_groups['bn'] - fec = atom_groups['fec'] + li = atom_groups["li"] + pf6 = atom_groups["pf6"] + bn = atom_groups["bn"] + fec = atom_groups["fec"] return Solute.from_atoms( li, - {'pf6': pf6, 'bn': bn, 'fec': fec}, - radii={'pf6': 2.8, 'bn': 2.61468, 'fec': 2.43158}, + {"pf6": pf6, "bn": bn, "fec": fec}, + radii={"pf6": 2.8, "bn": 2.61468, "fec": 2.43158}, rdf_init_kwargs={"range": (0, 8.0)}, rdf_kernel=identify_cutoff_poly, ) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def run_solute(pre_solute): pre_solute.run(step=1) return pre_solute -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def u_eax_series(): boxes = { - 'ea': [45.760393, 45.760393, 45.760393, 90, 90, 90], - 'eaf': [47.844380, 47.844380, 47.844380, 90, 90, 90], - 'fea': [48.358954, 48.358954, 48.358954, 90, 90, 90], - 'feaf': [50.023129, 50.023129, 50.023129, 90, 90, 90], + "ea": [45.760393, 45.760393, 45.760393, 90, 90, 90], + "eaf": [47.844380, 47.844380, 47.844380, 90, 90, 90], + "fea": [48.358954, 48.358954, 48.358954, 90, 90, 90], + "feaf": [50.023129, 50.023129, 50.023129, 90, 90, 90], } us = {} for solvent_dir in pathlib.Path(eax_data).iterdir(): u_solv = mda.Universe( - str(solvent_dir / 'topology.pdb'), - str(solvent_dir / 'trajectory_equil.dcd') + str(solvent_dir / "topology.pdb"), str(solvent_dir / "trajectory_equil.dcd") ) # our dcd lacks dimensions so we must manually set them box = boxes[solvent_dir.stem] @@ -198,77 +192,88 @@ def u_eax_series(): return us -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def u_eax_atom_groups(u_eax_series): atom_groups_dict = {} for name, u in u_eax_series.items(): atom_groups = {} - atom_groups['li'] = u.atoms.select_atoms("element Li") - atom_groups['pf6'] = u.atoms.select_atoms("byres element P") + atom_groups["li"] = u.atoms.select_atoms("element Li") + atom_groups["pf6"] = u.atoms.select_atoms("byres element P") residue_lengths = np.array([len(elements) for elements in u.residues.elements]) eax_fec_cutoff = np.unique(residue_lengths, return_index=True)[1][2] atom_groups[name] = u.atoms.select_atoms(f"resid 1:{eax_fec_cutoff}") - atom_groups['fec'] = u.atoms.select_atoms(f"resid {eax_fec_cutoff + 1}:600") + atom_groups["fec"] = u.atoms.select_atoms(f"resid {eax_fec_cutoff + 1}:600") atom_groups_dict[name] = atom_groups return atom_groups_dict -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def eax_solutes(u_eax_atom_groups): solutes = {} for name, atom_groups in u_eax_atom_groups.items(): solute = Solute.from_atoms( - atom_groups['li'], - {'pf6': atom_groups['pf6'], name: atom_groups[name], 'fec': atom_groups['fec']}, + atom_groups["li"], + { + "pf6": atom_groups["pf6"], + name: atom_groups[name], + "fec": atom_groups["fec"], + }, + analysis_classes=["pairing", "coordination", "speciation", "networking"], + networking_solvents=["pf6"], ) solute.run() solutes[name] = solute return solutes -@pytest.fixture(scope='module') + +@pytest.fixture(scope="module") def iba_u(): return mda.Universe(iba_data, iba_dcd) -@pytest.fixture(scope='module') + +@pytest.fixture(scope="module") def iba_solvents(iba_u): iba = iba_u.select_atoms("byres element C") H2O = iba_u.atoms - iba - return {'iba': iba, 'H2O': H2O} + return {"iba": iba, "H2O": H2O} + -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def iba_atom_groups(iba_solvents): - iba = iba_solvents['iba'] + iba = iba_solvents["iba"] return { - 'iba_alcohol_O': iba[5::12], - 'iba_alcohol_H': iba[11::12], - 'iba_ketone': iba[4::12], - 'iba_C0': iba[0::12], - 'iba_C1': iba[1::12], - 'iba_C2': iba[2::12], - 'iba_C3': iba[3::12], - 'iba_H6': iba[6::12], - 'iba_H7': iba[7::12], - 'iba_H8': iba[8::12], - 'iba_H9': iba[9::12], - 'iba_H10': iba[10::12], + "iba_alcohol_O": iba[5::12], + "iba_alcohol_H": iba[11::12], + "iba_ketone": iba[4::12], + "iba_C0": iba[0::12], + "iba_C1": iba[1::12], + "iba_C2": iba[2::12], + "iba_C3": iba[3::12], + "iba_H6": iba[6::12], + "iba_H7": iba[7::12], + "iba_H8": iba[8::12], + "iba_H9": iba[9::12], + "iba_H10": iba[10::12], } -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def H2O_atom_groups(iba_solvents): - H2O = iba_solvents['H2O'] + H2O = iba_solvents["H2O"] return { - 'H2O_O': H2O[0::3], - 'H2O_H1': H2O[1::3], - 'H2O_H2': H2O[2::3], + "H2O_O": H2O[0::3], + "H2O_H1": H2O[1::3], + "H2O_H2": H2O[2::3], } -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def iba_solutes(iba_atom_groups, iba_solvents): solutes = {} for name, atom_group in iba_atom_groups.items(): - radii = {'iba': 1.9, 'H2O': 1.9} if ('iba_H' in name or 'iba_C' in name) else None + radii = ( + {"iba": 1.9, "H2O": 1.9} if ("iba_H" in name or "iba_C" in name) else None + ) solute = Solute.from_atoms( atom_group, iba_solvents, @@ -280,17 +285,17 @@ def iba_solutes(iba_atom_groups, iba_solvents): return solutes -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def iba_small_solute(iba_atom_groups, iba_solvents): solute_atoms = { - 'iba_ketone': iba_atom_groups['iba_ketone'], - 'iba_alcohol_O': iba_atom_groups['iba_alcohol_O'], - 'iba_alcohol_H': iba_atom_groups['iba_alcohol_H'] + "iba_ketone": iba_atom_groups["iba_ketone"], + "iba_alcohol_O": iba_atom_groups["iba_alcohol_O"], + "iba_alcohol_H": iba_atom_groups["iba_alcohol_H"], } solute = Solute.from_atoms_dict( solute_atoms, iba_solvents, - solute_name='iba', + solute_name="iba", ) solute.run() return solute @@ -306,22 +311,22 @@ def solvation_data(run_solute): return run_solute.solvation_data -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def solvation_data_large(): return pd.read_csv(bn_fec_solv_df_large, index_col=[0, 1, 2, 3]) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def solvation_data_sparse(solvation_data_large): step = 10 return solvation_data_large.loc[pd.IndexSlice[::step, :, :], :] -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def residence(solvation_data_sparse): return Residence(solvation_data_sparse, step=10) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def networking(run_solute): - return Networking.from_solute(run_solute, 'pf6') \ No newline at end of file + return Networking.from_solute(run_solute, "pf6") diff --git a/solvation_analysis/tests/test_plotting.py b/solvation_analysis/tests/test_plotting.py index 21a5183a..21994a43 100644 --- a/solvation_analysis/tests/test_plotting.py +++ b/solvation_analysis/tests/test_plotting.py @@ -3,6 +3,8 @@ plot_network_size_histogram, plot_shell_composition_by_size, plot_co_occurrence, + plot_speciation, + plot_rdfs, _compare_function_generator, compare_free_solvents, compare_pairing, @@ -10,6 +12,7 @@ compare_residence_times_cutoff, compare_residence_times_fit, compare_diluent, + compare_networking, ) from solvation_analysis.networking import Networking @@ -18,7 +21,7 @@ def test_plot_network_size_histogram(run_solute): - run_solute.networking = Networking.from_solute(run_solute, 'pf6') + run_solute.networking = Networking.from_solute(run_solute, "pf6") plot_network_size_histogram(run_solute) plot_network_size_histogram(run_solute.networking) assert True @@ -30,13 +33,31 @@ def test_plot_shell_size_histogram(run_solute): assert True +def test_plot_speciation(run_solute): + plot_speciation(run_solute) + plot_speciation(run_solute.speciation) + assert True + + +def test_plot_rdfs(run_solute, iba_small_solute): + plot_rdfs(iba_small_solute) + plot_rdfs(iba_small_solute, merge_on_x=True) + plot_rdfs(iba_small_solute, merge_on_y=True) + plot_rdfs(iba_small_solute, merge_on_x=True, merge_on_y=True) + plot_rdfs(iba_small_solute, x_axis_solute=True) + plot_rdfs(iba_small_solute, x_axis_solute=True, merge_on_y=True) + plot_rdfs(iba_small_solute, x_axis_solute=True, merge_on_x=True) + plot_rdfs(iba_small_solute, x_axis_solute=True, merge_on_x=True, merge_on_y=True) + plot_rdfs(run_solute) + + # compare_solvent_dicts tests def test_compare_solvent_dicts_rename_exception(eax_solutes): # invalid solvents_to_plot because solvent names were already renamed to the generic "EAx" form # solvents_to_plot here references the former names of solvents, which is wrong # this test should handle an exception with pytest.raises(Exception): - fig = compare_pairing( + compare_pairing( eax_solutes, rename_solvent_dict={ "ea": "EAx", @@ -55,7 +76,7 @@ def test_compare_solvent_dicts_sensitivity(eax_solutes): # solvent names are case-sensitive, so names in solvents_to_plot and rename_solvent_dict should be consistent # this test should handle an exception with pytest.raises(Exception): - fig = compare_pairing( + compare_pairing( eax_solutes, rename_solvent_dict={ "EA": "EAx", @@ -70,13 +91,16 @@ def test_compare_solvent_dicts_sensitivity(eax_solutes): ) +def test_compare_networking(eax_solutes): + compare_networking(eax_solutes) + + # compare_pairing tests def test_compare_pairing_default_eax(eax_solutes): # call compare_pairing with only one required argument # also tests how the code handles eax systems fig = compare_pairing(eax_solutes) - assert len(fig.data) == 4 - # fig.show() + assert len(fig.data) == 6 def test_compare_pairing_case1(eax_solutes): @@ -88,10 +112,7 @@ def test_compare_pairing_case1(eax_solutes): y_label="Pairing", title="Bar Graph of Solvent Pairing", ) - assert len(fig.data) == 4 - for bar in fig.data: - assert set(bar.x) == {"fec", "pf6"} - # fig.show() + assert len(fig.data) == 2 def test_compare_pairing_case2(eax_solutes): @@ -102,12 +123,11 @@ def test_compare_pairing_case2(eax_solutes): x_label="Solute", y_label="Pairing", title="Bar Graph of Solvent Pairing", - x_axis="solute", + x_axis_solute=True, ) assert len(fig.data) == 2 for bar in fig.data: assert set(bar.x) == {"feaf", "eaf", "fea", "ea"} - # fig.show() def test_compare_pairing_case3(eax_solutes): @@ -120,10 +140,7 @@ def test_compare_pairing_case3(eax_solutes): title="Line Graph of Solvent Pairing", series=True, ) - assert len(fig.data) == 4 - for line in fig.data: - assert set(line.x) == {"fec", "pf6"} - # fig.show() + assert len(fig.data) == 2 def test_compare_pairing_case4(eax_solutes): @@ -134,13 +151,12 @@ def test_compare_pairing_case4(eax_solutes): x_label="Solute", y_label="Pairing", title="Line Graph of Solvent Pairing", - x_axis="solute", + x_axis_solute=True, series=True, ) assert len(fig.data) == 2 for line in fig.data: assert set(line.x) == {"feaf", "eaf", "fea", "ea"} - # fig.show() def test_compare_pairing_switch_solvents_to_plot_order(eax_solutes): @@ -151,13 +167,12 @@ def test_compare_pairing_switch_solvents_to_plot_order(eax_solutes): x_label="Solute", y_label="Pairing", title="Line Graph of Solvent Pairing", - x_axis="solute", + x_axis_solute=True, series=True, ) assert len(fig.data) == 2 for line in fig.data: assert set(line.x) == {"feaf", "eaf", "fea", "ea"} - # fig.show() def test_compare_pairing_rename_solvent_dict(eax_solutes): @@ -170,10 +185,7 @@ def test_compare_pairing_rename_solvent_dict(eax_solutes): y_label="Pairing", title="Bar Graph of Solvent Pairing", ) - assert len(fig.data) == 4 - for bar in fig.data: - assert set(bar.x) == {"pf6", "fec", "EAx"} - # fig.show() + assert len(fig.data) == 3 def test_compare_free_solvents(eax_solutes): @@ -189,15 +201,14 @@ def test_compare_coordination_numbers_default_eax(eax_solutes): # call compare_coordination_numbers with only one required argument # also tests how the code handles eax systems fig = compare_coordination_numbers(eax_solutes) - assert len(fig.data) == 4 - # fig.show() + assert len(fig.data) == 6 def test_compare_coordination_numbers_solute_four_cases(eax_solutes): - fig = compare_coordination_numbers(eax_solutes, x_axis='solute') + fig = compare_coordination_numbers(eax_solutes, x_axis_solute=True) assert len(fig.data) == 6 - fig = compare_coordination_numbers(eax_solutes, x_axis='solute', series=True) + fig = compare_coordination_numbers(eax_solutes, x_axis_solute=True, series=True) assert len(fig.data) == 6 rename = { @@ -206,18 +217,25 @@ def test_compare_coordination_numbers_solute_four_cases(eax_solutes): "eaf": "EAx", "feaf": "EAx", } - fig = compare_coordination_numbers(eax_solutes, x_axis='solute', rename_solvent_dict=rename) + fig = compare_coordination_numbers( + eax_solutes, x_axis_solute=True, rename_solvent_dict=rename + ) assert len(fig.data) == 3 - fig = compare_coordination_numbers(eax_solutes, x_axis='solute', series=True, rename_solvent_dict=rename) + fig = compare_coordination_numbers( + eax_solutes, + x_axis_solute=True, + series=True, + rename_solvent_dict=rename, + ) assert len(fig.data) == 3 fig = compare_coordination_numbers( eax_solutes, - x_axis='solute', + x_axis_solute=True, rename_solvent_dict=rename, series=True, - solvents_to_plot=['EAx', 'pf6'], + solvents_to_plot=["EAx", "pf6"], ) assert len(fig.data) == 2 @@ -231,10 +249,7 @@ def test_compare_coordination_numbers_case1(eax_solutes): y_label="Coordination", title="Bar Graph of Coordination Numbers", ) - assert len(fig.data) == 4 - for bar in fig.data: - assert set(bar.x) == {"fec", "pf6"} - # fig.show() + assert len(fig.data) == 2 def test_compare_coordination_numbers_case2(eax_solutes): @@ -245,12 +260,11 @@ def test_compare_coordination_numbers_case2(eax_solutes): x_label="solute", y_label="Coordination", title="Bar Graph of Coordination Numbers", - x_axis="solute", + x_axis_solute=True, ) assert len(fig.data) == 2 for bar in fig.data: assert set(bar.x) == {"feaf", "eaf", "fea", "ea"} - # fig.show() def test_compare_coordination_numbers_case3(eax_solutes): @@ -263,10 +277,7 @@ def test_compare_coordination_numbers_case3(eax_solutes): title="Line Graph of Coordination Numbers", series=True, ) - assert len(fig.data) == 4 - for line in fig.data: - assert set(line.x) == {"fec", "pf6"} - # fig.show() + assert len(fig.data) == 2 def test_compare_coordination_numbers_case4(eax_solutes): @@ -277,13 +288,12 @@ def test_compare_coordination_numbers_case4(eax_solutes): x_label="solute", y_label="Coordination", title="Line Graph of Coordination Numbers", - x_axis="solute", + x_axis_solute=True, series=True, ) assert len(fig.data) == 2 for line in fig.data: assert set(line.x) == {"feaf", "eaf", "fea", "ea"} - # fig.show() # compare_residence_times tests @@ -310,13 +320,9 @@ def test_compare_generic(eax_solutes): y_label="Pairing", title="Bar Graph of Solvent Pairing", ) - assert len(fig.data) == 4 - for bar in fig.data: - assert set(bar.x) == {"pf6", "fec", "EAx"} - # fig.show() + assert len(fig.data) == 3 def test_plot_co_occurrence(solvation_data): speciation = Speciation(solvation_data, 10, 49) - fig = plot_co_occurrence(speciation) - # fig.show() + plot_co_occurrence(speciation) diff --git a/solvation_analysis/tests/test_residence.py b/solvation_analysis/tests/test_residence.py index 349a9c8e..e4f047d7 100644 --- a/solvation_analysis/tests/test_residence.py +++ b/solvation_analysis/tests/test_residence.py @@ -22,12 +22,14 @@ def test_residence_times(name, res_time, residence): np.testing.assert_almost_equal(residence.residence_times_cutoff[name], res_time, 3) -@pytest.mark.parametrize("name", ['fec', 'bn', 'pf6']) +@pytest.mark.parametrize("name", ["fec", "bn", "pf6"]) def test_plot_auto_covariance(name, residence): residence.plot_auto_covariance(name) def test_residence_time_warning(solvation_data_sparse): - # we step through the data frame to speed up the tests - with pytest.warns(UserWarning, match="the autocovariance for pf6 does not converge"): + # we step through the dataframe to speed up the tests + with pytest.warns( + UserWarning, match="the autocovariance for pf6 does not converge" + ): Residence(solvation_data_sparse, step=10) diff --git a/solvation_analysis/tests/test_solute.py b/solvation_analysis/tests/test_solute.py index f27dde44..6e09bb2b 100644 --- a/solvation_analysis/tests/test_solute.py +++ b/solvation_analysis/tests/test_solute.py @@ -1,45 +1,40 @@ from functools import reduce -import matplotlib.pyplot as plt -import warnings import pytest from solvation_analysis.solute import Solute import numpy as np from MDAnalysis import Universe -from solvation_analysis.tests.conftest import u_eax_series, u_eax_atom_groups - def test_instantiate_solute_from_atoms(pre_solute): # these check basic properties of the instantiation assert len(pre_solute.radii) == 3 assert callable(pre_solute.kernel) assert pre_solute.solute_atoms.n_residues == 49 - assert pre_solute.solvents['pf6'].n_residues == 49 - assert pre_solute.solvents['fec'].n_residues == 237 - assert pre_solute.solvents['bn'].n_residues == 363 + assert pre_solute.solvents["pf6"].n_residues == 49 + assert pre_solute.solvents["fec"].n_residues == 237 + assert pre_solute.solvents["bn"].n_residues == 363 def test_init_fail(atom_groups): with pytest.raises(RuntimeError): - Solute(atom_groups['li'], {'pf6': atom_groups['pf6']}) + Solute(atom_groups["li"], {"pf6": atom_groups["pf6"]}) def test_networking_instantiation_error(atom_groups): - li = atom_groups['li'] - pf6 = atom_groups['pf6'] - bn = atom_groups['bn'] - fec = atom_groups['fec'] + li = atom_groups["li"] + pf6 = atom_groups["pf6"] + bn = atom_groups["bn"] + fec = atom_groups["fec"] with pytest.raises(Exception): Solute.from_atoms( - li, {'pf6': pf6, 'bn': bn, 'fec': fec}, analysis_classes=['networking'] + li, {"pf6": pf6, "bn": bn, "fec": fec}, analysis_classes=["networking"] ) def test_plot_solvation_distance(rdf_bins_and_data_easy): - bins, data = rdf_bins_and_data_easy['pf6_all'] - fig, ax = Solute._plot_solvation_radius(bins, data, 2) - # fig.show() # comment out for global testing + bins, data = rdf_bins_and_data_easy["pf6_all"] + Solute._plot_solvation_radius(bins, data, 2) def test_radii_finding(run_solute): @@ -47,18 +42,9 @@ def test_radii_finding(run_solute): assert len(run_solute.radii) == 3 assert len(run_solute.rdf_data["solute_0"]) == 3 # checks that the identified solvation radii are approximately correct - assert 2 < run_solute.radii['pf6'] < 3 - assert 2 < run_solute.radii['fec'] < 3 - assert 2 < run_solute.radii['bn'] < 3 - # for fig, ax in run_solute.rdf_plots.values(): - # plt.show() # comment out for global testing - - -def test_run_warning(pre_solute_mutable): - # checks that an error is thrown if there are not enough radii - pre_solute_mutable.radii = {'pf6': 2.8} - with pytest.raises(AssertionError): - pre_solute_mutable.run(step=1) + assert 2 < run_solute.radii["pf6"] < 3 + assert 2 < run_solute.radii["fec"] < 3 + assert 2 < run_solute.radii["bn"] < 3 def test_run(pre_solute_mutable): @@ -72,9 +58,13 @@ def test_run(pre_solute_mutable): def test_run_w_all(pre_solute_mutable): # checks that run is run correctly pre_solute_mutable.analysis_classes = [ - "pairing", "coordination", "speciation", "residence", "networking" + "pairing", + "coordination", + "speciation", + "residence", + "networking", ] - pre_solute_mutable.networking_solvents = 'pf6' + pre_solute_mutable.networking_solvents = "pf6" pre_solute_mutable.run(step=1) assert len(pre_solute_mutable._solvation_frames) == 10 assert len(pre_solute_mutable._solvation_frames[0]) == 228 @@ -86,7 +76,7 @@ def test_run_w_all(pre_solute_mutable): [ (1, 3, 5, [46, 100, 171, 255, 325, 521, 650]), (2, 3, 6, [13, 59, 177, 264, 314, 651]), - (40, 3.5, 0, [101, 126, 127, 360, 368, 305, 689]) + (40, 3.5, 0, [101, 126, 127, 360, 368, 305, 689]), ], ) def test_radial_shell(solute_index, radius, frame, expected_res_ids, run_solute): @@ -100,7 +90,7 @@ def test_radial_shell(solute_index, radius, frame, expected_res_ids, run_solute) [ (6741, 4, 5, [46, 100, 171, 255, 650]), (6749, 5, 6, [13, 59, 177, 264, 314, 651]), - (7053, 6, 0, [101, 126, 127, 360, 368, 305, 689]) + (7053, 6, 0, [101, 126, 127, 360, 368, 305, 689]), ], ) def test_closest_n_mol(solute_index, n_mol, frame, expected_res_ids, run_solute): @@ -114,7 +104,7 @@ def test_closest_n_mol(solute_index, n_mol, frame, expected_res_ids, run_solute) [ (650, 5, [46, 100, 171, 255, 650]), (651, 6, [13, 59, 177, 264, 314, 651]), - (689, 0, [101, 126, 127, 360, 689]) + (689, 0, [101, 126, 127, 360, 689]), ], ) def test_solvation_shell(solute_index, step, expected_res_ids, run_solute): @@ -126,12 +116,14 @@ def test_solvation_shell(solute_index, step, expected_res_ids, run_solute): @pytest.mark.parametrize( "solute_index, step, remove, expected_res_ids", [ - (650, 5, {'bn': 1}, [46, 171, 255, 650]), - (651, 6, {'bn': 2, 'fec': 1}, [13, 177, 314, 651]), - (689, 0, {'fec': 1}, [101, 126, 127, 360, 689]) + (650, 5, {"bn": 1}, [46, 171, 255, 650]), + (651, 6, {"bn": 2, "fec": 1}, [13, 177, 314, 651]), + (689, 0, {"fec": 1}, [101, 126, 127, 360, 689]), ], ) -def test_solvation_shell_remove_mols(solute_index, step, remove, expected_res_ids, run_solute): +def test_solvation_shell_remove_mols( + solute_index, step, remove, expected_res_ids, run_solute +): shell = run_solute.get_shell(solute_index, step, remove_mols=remove) assert set(shell.resindices) == set(expected_res_ids) @@ -142,10 +134,12 @@ def test_solvation_shell_remove_mols(solute_index, step, remove, expected_res_id (650, 5, 3, [46, 171, 255, 650]), (651, 6, 3, [13, 177, 314, 651]), (689, 0, 4, [101, 126, 127, 360, 689]), - (689, 0, 1, [101, 689]) + (689, 0, 1, [101, 689]), ], ) -def test_solvation_shell_remove_closest(solute_index, step, n, expected_res_ids, run_solute): +def test_solvation_shell_remove_closest( + solute_index, step, n, expected_res_ids, run_solute +): shell = run_solute.get_shell(solute_index, step, closest_n_only=n) assert set(shell.resindices) == set(expected_res_ids) @@ -153,10 +147,10 @@ def test_solvation_shell_remove_closest(solute_index, step, n, expected_res_ids, @pytest.mark.parametrize( "shell, n_shells", [ - ({'bn': 5, 'fec': 0, 'pf6': 0}, 175), - ({'bn': 3, 'fec': 3, 'pf6': 0}, 2), - ({'bn': 3, 'fec': 0, 'pf6': 1}, 13), - ({'bn': 4}, 260), + ({"bn": 5, "fec": 0, "pf6": 0}, 175), + ({"bn": 3, "fec": 3, "pf6": 0}, 2), + ({"bn": 3, "fec": 0, "pf6": 1}, 13), + ({"bn": 4}, 260), ], ) def test_speciation_find_shells(shell, n_shells, run_solute): @@ -193,35 +187,35 @@ def test_pairing(name, fraction, run_solute): np.testing.assert_allclose([fraction], pairing_dict[name], atol=0.05) -@pytest.mark.parametrize("name", ['ea', 'eaf', 'fea', 'feaf']) +@pytest.mark.parametrize("name", ["ea", "eaf", "fea", "feaf"]) def test_instantiate_eax_solvents(name, u_eax_series): assert isinstance(u_eax_series[name], Universe) -@pytest.mark.parametrize("name", ['ea', 'eaf', 'fea', 'feaf']) +@pytest.mark.parametrize("name", ["ea", "eaf", "fea", "feaf"]) def test_instantiate_eax_atom_groups(name, u_eax_atom_groups): - all_atoms = len(u_eax_atom_groups[name]['li'].universe.atoms) + all_atoms = len(u_eax_atom_groups[name]["li"].universe.atoms) all_atoms_in_groups = sum([len(ag) for ag in u_eax_atom_groups[name].values()]) assert all_atoms_in_groups == all_atoms -@pytest.mark.parametrize("name", ['ea', 'eaf', 'fea', 'feaf']) +@pytest.mark.parametrize("name", ["ea", "eaf", "fea", "feaf"]) def test_instantiate_eax_solutes(name, eax_solutes): assert isinstance(eax_solutes[name], Solute) def test_plot_solvation_radius(run_solute, iba_small_solute): - run_solute.plot_solvation_radius('solute_0', 'fec') - iba_small_solute.plot_solvation_radius('iba_ketone', 'iba') + run_solute.plot_solvation_radius("solute_0", "fec") + iba_small_solute.plot_solvation_radius("iba_ketone", "iba") -@pytest.mark.parametrize("residue", ['iba_ketone', 'solute', 'H2O', 'iba']) +@pytest.mark.parametrize("residue", ["iba_ketone", "solute", "H2O", "iba"]) def test_draw_molecule_string(iba_solutes, residue): - iba_solutes['iba_ketone'].draw_molecule(residue) + iba_solutes["iba_ketone"].draw_molecule(residue) def test_draw_molecule_residue(iba_solutes): - solute = iba_solutes['iba_ketone'] + solute = iba_solutes["iba_ketone"] residue = solute.u.atoms.residues[0] solute.draw_molecule(residue) @@ -233,116 +227,122 @@ def test_iba_solutes(iba_solutes): def test_from_atoms(iba_atom_groups, iba_solvents): solute_atoms = ( - iba_atom_groups['iba_ketone'] + - iba_atom_groups['iba_alcohol_O'] + - iba_atom_groups['iba_alcohol_H'] + iba_atom_groups["iba_ketone"] + + iba_atom_groups["iba_alcohol_O"] + + iba_atom_groups["iba_alcohol_H"] ) solute = Solute.from_atoms(solute_atoms, iba_solvents) solute.run() - assert set(solute.atom_solutes.keys()) == {'solute_0', 'solute_1', 'solute_2'} + assert set(solute.atom_solutes.keys()) == {"solute_0", "solute_1", "solute_2"} def test_from_atoms_errors(iba_atom_groups, H2O_atom_groups, iba_solvents): solute_atoms = ( - iba_atom_groups['iba_ketone'] + - iba_atom_groups['iba_alcohol_O'] + - iba_atom_groups['iba_alcohol_H'] + iba_atom_groups["iba_ketone"] + + iba_atom_groups["iba_alcohol_O"] + + iba_atom_groups["iba_alcohol_H"] ) with pytest.raises(AssertionError): bad_atoms = solute_atoms[:-2] Solute.from_atoms(bad_atoms, iba_solvents) with pytest.raises(AssertionError): - bad_atoms = solute_atoms + H2O_atom_groups['H2O_O'] + bad_atoms = solute_atoms + H2O_atom_groups["H2O_O"] Solute.from_atoms(bad_atoms, iba_solvents) def test_from_atoms_dict(iba_atom_groups, iba_solvents): solute_atoms = { - 'iba_ketone': iba_atom_groups['iba_ketone'], - 'iba_alcohol_O': iba_atom_groups['iba_alcohol_O'], - 'iba_alcohol_H': iba_atom_groups['iba_alcohol_H'] + "iba_ketone": iba_atom_groups["iba_ketone"], + "iba_alcohol_O": iba_atom_groups["iba_alcohol_O"], + "iba_alcohol_H": iba_atom_groups["iba_alcohol_H"], } solute = Solute.from_atoms_dict(solute_atoms, iba_solvents) - assert set(solute.atom_solutes.keys()) == {'iba_ketone', 'iba_alcohol_O', 'iba_alcohol_H'} + assert set(solute.atom_solutes.keys()) == { + "iba_ketone", + "iba_alcohol_O", + "iba_alcohol_H", + } solute.run() def test_from_atoms_dict_errors(iba_atom_groups, H2O_atom_groups, iba_solvents): solute_atoms = { - 'iba_ketone': iba_atom_groups['iba_ketone'], - 'iba_alcohol_O': iba_atom_groups['iba_alcohol_O'], - 'iba_alcohol_H': iba_atom_groups['iba_alcohol_H'] + "iba_ketone": iba_atom_groups["iba_ketone"], + "iba_alcohol_O": iba_atom_groups["iba_alcohol_O"], + "iba_alcohol_H": iba_atom_groups["iba_alcohol_H"], } with pytest.raises(AssertionError): bad_atoms = {**solute_atoms} - bad_atoms['iba_ketone'] = bad_atoms['iba_ketone'][:-2] + bad_atoms["iba_ketone"] = bad_atoms["iba_ketone"][:-2] Solute.from_atoms_dict(bad_atoms, iba_solvents) with pytest.raises(AssertionError): bad_atoms = {**solute_atoms} - bad_atoms['iba_ketone'] = bad_atoms['iba_ketone'] + bad_atoms['iba_alcohol_O'] + bad_atoms["iba_ketone"] = bad_atoms["iba_ketone"] + bad_atoms["iba_alcohol_O"] Solute.from_atoms_dict(bad_atoms, iba_solvents) with pytest.raises(AssertionError): bad_atoms = {**solute_atoms} - bad_atoms['iba_ketone'] = bad_atoms['iba_alcohol_O'] + bad_atoms["iba_ketone"] = bad_atoms["iba_alcohol_O"] Solute.from_atoms_dict(bad_atoms, iba_solvents) with pytest.raises(AssertionError): bad_atoms = {**solute_atoms} - bad_atoms['H2O_O'] = H2O_atom_groups['H2O_O'] + bad_atoms["H2O_O"] = H2O_atom_groups["H2O_O"] Solute.from_atoms_dict(bad_atoms, iba_solvents) def test_from_solute_list(iba_solutes, iba_solvents): solute_list = [ - iba_solutes['iba_ketone'], - iba_solutes['iba_alcohol_O'], - iba_solutes['iba_alcohol_H'] + iba_solutes["iba_ketone"], + iba_solutes["iba_alcohol_O"], + iba_solutes["iba_alcohol_H"], ] solute = Solute.from_solute_list(solute_list, iba_solvents) solute.run() - assert set(solute.atom_solutes.keys()) == {'iba_ketone', 'iba_alcohol_O', 'iba_alcohol_H'} + assert set(solute.atom_solutes.keys()) == { + "iba_ketone", + "iba_alcohol_O", + "iba_alcohol_H", + } def test_from_solute_list_restepped(iba_solutes, iba_atom_groups, iba_solvents): new_solvent = {"H2O": iba_solvents["H2O"]} new_ketone = Solute.from_atoms( - iba_atom_groups['iba_ketone'], - new_solvent, - solute_name='iba_ketone' + iba_atom_groups["iba_ketone"], new_solvent, solute_name="iba_ketone" ) new_ketone.run(step=2) - solute_list = [iba_solutes['iba_alcohol_O'], new_ketone] + solute_list = [iba_solutes["iba_alcohol_O"], new_ketone] solute = Solute.from_solute_list(solute_list, iba_solvents) - with pytest.warns(UserWarning, match='re-run') as record: + with pytest.warns(UserWarning, match="re-run") as record: solute.run(step=2) user_warnings = 0 for warning in record: if warning.category == UserWarning: user_warnings += 1 assert user_warnings == 2 - assert set(solute.atom_solutes.keys()) == {'iba_ketone', 'iba_alcohol_O'} + assert set(solute.atom_solutes.keys()) == {"iba_ketone", "iba_alcohol_O"} def test_from_solute_list_errors(iba_solutes, H2O_atom_groups, iba_solvents): solute_list = [ - iba_solutes['iba_ketone'], - iba_solutes['iba_alcohol_O'], - iba_solutes['iba_alcohol_H'] + iba_solutes["iba_ketone"], + iba_solutes["iba_alcohol_O"], + iba_solutes["iba_alcohol_H"], ] - H2O_solute = Solute.from_atoms(H2O_atom_groups['H2O_O'], iba_solvents) + H2O_solute = Solute.from_atoms(H2O_atom_groups["H2O_O"], iba_solvents) with pytest.raises(AssertionError): bad_solute_list = [*solute_list] bad_solute_list.append(H2O_solute) Solute.from_solute_list(bad_solute_list, iba_solvents) iba_ketone_renamed = Solute.from_atoms( - iba_solutes['iba_ketone'].solute_atoms, + iba_solutes["iba_ketone"].solute_atoms, iba_solvents, - solute_name='iba_alcohol_O' + solute_name="iba_alcohol_O", ) with pytest.raises(AssertionError): bad_solute_list = [*solute_list] @@ -355,13 +355,15 @@ def test_from_solute_list_errors(iba_solutes, H2O_atom_groups, iba_solvents): def test_iba_all_analysis(iba_atom_groups, iba_solvents): - solute_atoms = reduce(lambda x, y: x | y, [solute for solute in iba_atom_groups.values()]) + solute_atoms = reduce( + lambda x, y: x | y, [solute for solute in iba_atom_groups.values()] + ) solute = Solute.from_atoms( solute_atoms, iba_solvents, - networking_solvents=['iba'], - analysis_classes='all', - radii={'iba': 1.9, 'H2O': 1.9}, + networking_solvents=["iba"], + analysis_classes="all", + radii={"iba": 1.9, "H2O": 1.9}, ) # TODO: get this passing solute.run(step=4)