{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Welcome to the EPISOL Colab Playground!\n",
        "\n"
      ],
      "metadata": {
        "id": "2UZUSEe9RzKv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "JV402f06Qs2I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 3D reference interaction site model (3DRISM) provides an efficient grid-based solvation model to compute the structural and thermodynamic properties of solutes emersed in solvent. However, this solvent need not be aqueous, and can hypothetically be *any* molecule. In this notebook we will walk through a high-throughput calculation on the solvation free energy of a test set of small molecules, emersed in an organic solvent.  \n",
        "\n",
        "* **goals**:\n",
        "  * generate correlations for an organic solvent using 1DRISM\n",
        "  * Generate the coordinate and topology files for our test set using RDKit and openFF\n",
        "  * Perform high-throughput 3DRISM calculations to determine solvation free energy of 100 electrolyte molecules in the test set\n",
        "  * Be able to perform similar calculations on your own molecules\n",
        "    * use the colab-notebook to download and run calculations on your own molecules"
      ],
      "metadata": {
        "id": "2JyzyqCTrO2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ##Download and Install Episol\n",
        "#@markdown ($\\approx 2$min) Stable as of 07/01/25 eprism v1.2.6\n",
        "%%capture\n",
        "import subprocess\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "#%cd ../home/\n",
        "%cd $HOME/\n",
        "%mkdir episol\n",
        "%cd episol\n",
        "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/fftw/fftw-3.3.8.tar.gz\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!echo \"downloaded fftw files\"\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!tar -xzf fftw-3.3.8.tar.gz\n",
        "%cd fftw-3.3.8/\n",
        "#!./configure --prefix=/home/fftw-3.3.8\n",
        "!./configure --prefix=$HOME/episol/fftw-3.3.8\n",
        "!make\n",
        "!make install\n",
        "%cd ../\n",
        "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/kernel/release.tar.gz\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!echo \"downloaded Episol files\"\n",
        "!echo \"+++++++++++++++++++\"\n",
        "!tar -xzf release.tar.gz\n",
        "%cd release/\n",
        "#!./configure --with-fftw=/home/fftw-3.3.8\n",
        "!./configure --with-fftw=$HOME/episol/fftw-3.3.8\n",
        "!make\n",
        "!make install\n",
        "#%cd /content\n",
        "########################### WRAPEPR\n",
        "import subprocess\n",
        "import os\n",
        "import threading\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install episol\n"
      ],
      "metadata": {
        "id": "e9Zwmfx1MLyE",
        "cellView": "form"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install some python packages for topology generation\n",
        "#@markdown ($\\approx$10sec)\n",
        "\n",
        "#@markdown This will prompt a restart in our colab session, this is necessary, just keep moving\n",
        "\n",
        "#@markdown (if you are using the notebook offline this wont be necessary, as presumably you'll have your own forcefield to generate topologies)\n",
        "########################################\n",
        "#        FOR COLAB USERS ONLY          #\n",
        "#---------------------------------------#\n",
        "# if you are running locally you dont need\n",
        "# condacolab. Just use your local conda dist\n",
        "########################################\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "########################################\n",
        "#!conda update conda\n",
        "#!conda install --yes -c conda-forge  python=3.11 numpy=1.26.4 openmm pdbfixer parmed mdanalysis py3dmol rdkit openff-toolkit\n",
        "#!conda install -y -c conda-forge numpy=1.26.4 openmm=8.3.1 python={PYTHON_VERSION} pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 libgcc\n",
        "#!conda install -y -c conda-forge python=3.12 numpy=1.26.4 openmm=8.3.1 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 torchvision\n",
        "#openmm pdbfixer parmed mdanalysis py3dmol rdkitconda install libgcc"
      ],
      "metadata": {
        "id": "Pz6E85eEOsUO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8f71300-bb96-4a3f-edd4-a00c65bec056",
        "cellView": "form"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/download/24.11.2-1_colab/Miniforge3-colab-24.11.2-1_colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:15\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!python -m ensurepip --upgrade\n",
        "!python3.12 -m pip install --upgrade setuptools\n",
        "#!pip3 install scikit-learn\n",
        "!conda install -y -c conda-forge python=3.12 numpy=1.26.4 openmm=8.3.1 pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 torchvision pymol-open-source #ambertools"
      ],
      "metadata": {
        "id": "KSSOftdLvj7_"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title import our download packages\n",
        "%%capture\n",
        "import py3Dmol\n",
        "\n",
        "def MolTo3DView(mol, size=(300, 300), style=\"stick\", surface=False, opacity=0.5):\n",
        "    \"\"\"\n",
        "    https://birdlet.github.io/2019/10/02/py3dmol_example/\n",
        "    Draw molecule in 3D\n",
        "\n",
        "    Args:\n",
        "    ----\n",
        "        mol: rdMol, molecule to show\n",
        "        size: tuple(int, int), canvas size\n",
        "        style: str, type of drawing molecule\n",
        "               style can be 'line', 'stick', 'sphere', 'carton'\n",
        "        surface, bool, display SAS\n",
        "        opacity, float, opacity of surface, range 0.0-1.0\n",
        "    Return:\n",
        "    ----\n",
        "        viewer: py3Dmol.view, a class for constructing embedded 3Dmol.js views in ipython notebooks.\n",
        "    \"\"\"\n",
        "    assert style in ('line', 'stick', 'sphere', 'carton')\n",
        "    mblock = Chem.MolToMolBlock(mol)\n",
        "    viewer = py3Dmol.view(width=size[0], height=size[1])\n",
        "    viewer.addModel(mblock, 'mol')\n",
        "    viewer.setStyle({style:{}})\n",
        "    if surface:\n",
        "        viewer.addSurface(py3Dmol.SAS, {'opacity': opacity})\n",
        "    viewer.zoomTo()\n",
        "    return viewer\n",
        "def smi2conf(smiles):\n",
        "    '''Convert SMILES to rdkit.Mol with 3D coordinates'''\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    if mol is not None:\n",
        "        mol = Chem.AddHs(mol)\n",
        "        AllChem.EmbedMolecule(mol)\n",
        "        AllChem.MMFFOptimizeMolecule(mol, maxIters=200)\n",
        "        return mol\n",
        "    else:\n",
        "        return None\n",
        "#free_energy(\"EXP\")\n",
        "!python -m ensurepip --upgrade # since we are using python 3.12 some pkg utils are now obsolete\n",
        "# after conda-initiate restart colab resets pip\n",
        "import matplotlib.pyplot as plt\n",
        "import openmm as mm\n",
        "from   openmm import app\n",
        "from   openmm.unit import *\n",
        "import py3Dmol as pymol\n",
        "import MDAnalysis as md\n",
        "import parmed as chem\n",
        "from openff.toolkit.topology import Molecule, Topology\n",
        "import numpy as np\n",
        "from MDAnalysis.transformations import center_in_box\n",
        "# from episol import epipy\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from openff.toolkit.topology import Molecule\n",
        "from openff.toolkit.utils import get_data_file_path\n",
        "from openff.toolkit.typing.engines.smirnoff import ForceField\n",
        "from openff.interchange import Interchange\n",
        "##########################################\n",
        "plt.rcParams.update({'font.size': 15})\n",
        "plt.rcParams.update({'axes.linewidth':3})\n",
        "plt.rcParams['image.cmap'] = 'turbo'\n",
        "##########################################\n",
        "# FIX IMPORTER ERROR\n",
        "!python -m ensurepip --upgrade\n",
        "!uv pip3 install episol\n",
        "!python3.12 -m pip install --upgrade setuptools\n",
        "%cd /content/"
      ],
      "metadata": {
        "id": "rjpO1aNMD_89",
        "cellView": "form"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Getting Started"
      ],
      "metadata": {
        "id": "FPI8DegtS21r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To determine solvation density and thermodynamics, we must solve the 3DRISM equation"
      ],
      "metadata": {
        "id": "tOzUB2a4QuJp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$$$$\n",
        "$$h(r)_i^{\\rm sol.-solv.} = \\rho\\sum_j\\int c(r)_j^{\\rm sol.-solv.}\\chi^{\\rm solv.-solv.}d\\mathbf{r}$$"
      ],
      "metadata": {
        "id": "ajoy1CuKNJp-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using a closure relation"
      ],
      "metadata": {
        "id": "RLfDVCNIQ3iO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$$$$\n",
        "$$\n",
        "h(r)_i^{\\rm sol.-solv.} \\equiv \\exp{\\left( - U(r)_i^{\\rm sol.-solv.} + h(r)_i^{\\rm sol.-solv.}+ c(r)_i^{\\rm sol.-solv.} + \\mathfrak{B}\\right)}\n",
        "$$\n",
        "\n",
        "$$$$"
      ],
      "metadata": {
        "id": "nZGk5jF5StW8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given that we have two unknowns $h$ and $c$ we must solve in an iterative self-consisten manner. however, upon closer inspection, we have a third unkown, $\\chi$, our solvent solvent susceptability. This contains our bulk solvent correlations. We need these values from MD (costly, but accurate) or from 1DRISM which is cheap, and accurate if our molecule is small and rigid (water, CO$_2$, etc.)\n",
        "\n",
        "Previously we have used water or ions as solvent, but there is no reason why we can't use a non-aqueous  solvent. The calculation that follows is identical, however, we first need $\\chi$."
      ],
      "metadata": {
        "id": "Tax0rBW0ridA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, solvent-solvent correlations can be acquired with MD or with 1DRISM.\n",
        "Here, we will use 1DRISM  to generate correlations. Then, feed these into epipy. lets start with an example solvent: Carbon Dioxide"
      ],
      "metadata": {
        "id": "OxhknOidS6e_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "CO$_2$  is a nice example, as it is generally easier to converge if\n",
        "  * Small number of sites\n",
        "  * Low dielectric constant\n",
        "  * uncharged\n",
        "  * high temp"
      ],
      "metadata": {
        "id": "pmKq4FAx-5sT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we have previously prepared proper calculations and input files, lets download them"
      ],
      "metadata": {
        "id": "pUbrGGhmvkis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/co2.gvv\n",
        "!wget https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/co2.sol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSVJcaKXpepX",
        "outputId": "4366f094-1106-478c-b724-1ab863553bd6"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2026-05-12 19:26:25--  https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/co2.gvv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1982792 (1.9M) [text/plain]\n",
            "Saving to: ‘co2.gvv’\n",
            "\n",
            "\rco2.gvv               0%[                    ]       0  --.-KB/s               \rco2.gvv             100%[===================>]   1.89M  --.-KB/s    in 0.04s   \n",
            "\n",
            "2026-05-12 19:26:26 (49.6 MB/s) - ‘co2.gvv’ saved [1982792/1982792]\n",
            "\n",
            "--2026-05-12 19:26:26--  https://raw.githubusercontent.com/p-swanson/solvents/refs/heads/main/organic/co2.sol\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 612 [text/plain]\n",
            "Saving to: ‘co2.sol’\n",
            "\n",
            "co2.sol             100%[===================>]     612  --.-KB/s    in 0s      \n",
            "\n",
            "2026-05-12 19:26:26 (32.3 MB/s) - ‘co2.sol’ saved [612/612]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CO2"
      ],
      "metadata": {
        "id": "8N1N6jmrCDEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from episol import epipy"
      ],
      "metadata": {
        "id": "uBtFNW2aw5SX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c17e2a94-ef13-45d4-f693-941e50b6a819"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All Checks complete\n",
            "Good to go!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Co2 is commonly used for caffiene extraction\n",
        "* lets see our solvent distribution around a caffiene molecule"
      ],
      "metadata": {
        "id": "M96MJ8pUEowY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smi = \"CN1C=NC2=C1C(=O)N(C(=O)N2C)C\" # caffiene\n",
        "mol = Chem.MolFromSmiles(smi)\n",
        "mol\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "Aj7z-lT4x1dL",
        "outputId": "cbb30546-9833-42a3-e35a-3959b96cf860"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7c814eeeed50>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* add hydrogens and optimize our caffiene"
      ],
      "metadata": {
        "id": "3Lu3x5ULFbKL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mol = Chem.rdmolops.AddHs(mol,addCoords=True)\n",
        "AllChem.EmbedMolecule(mol)\n",
        "AllChem.UFFOptimizeMolecule(mol)\n",
        "MolTo3DView(mol).show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "t602ZiVFyULH",
        "outputId": "dde320ba-8753-486e-c9e4-bd673cbcede9"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "viewer_17786139320774646 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17786139320774646\"),{backgroundColor:\"white\"});\n",
              "viewer_17786139320774646.zoomTo();\n",
              "\tviewer_17786139320774646.addModel(\"\\n     RDKit          3D\\n\\n 24 25  0  0  0  0  0  0  0  0999 V2000\\n    3.3183    0.3530   -0.0047 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1035   -0.4389   -0.1688 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.0239   -1.7646   -0.4410 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.7457   -2.2190   -0.5164 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.0184   -1.1084   -0.2793 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.8238   -0.0427   -0.0681 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2826    1.2027    0.2223 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.0339    2.1926    0.4400 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.0880    1.3207    0.2743 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.8912    0.2077    0.0838 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.1453    0.3120    0.1999 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.3381   -1.0170   -0.2242 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1793   -2.2076   -0.4048 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.6992    2.6160    0.6159 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.3224    1.1890   -0.7352 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2177   -0.2752   -0.1791 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.3628    0.7608    1.0268 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8874   -2.4022   -0.5804 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.6605   -2.9806   -1.0111 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.4343   -2.6366    0.5870 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.1156   -1.9477   -0.9433 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.8411    2.6820    1.7152 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.6818    2.7398    0.1124 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.0660    3.4643    0.2789 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  2  0\\n  4  5  1  0\\n  5  6  2  0\\n  6  7  1  0\\n  7  8  2  0\\n  7  9  1  0\\n  9 10  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n  9 14  1  0\\n  6  2  1  0\\n 12  5  1  0\\n  1 15  1  0\\n  1 16  1  0\\n  1 17  1  0\\n  3 18  1  0\\n 13 19  1  0\\n 13 20  1  0\\n 13 21  1  0\\n 14 22  1  0\\n 14 23  1  0\\n 14 24  1  0\\nM  END\\n\",\"mol\");\n",
              "\tviewer_17786139320774646.setStyle({\"stick\": {}});\n",
              "\tviewer_17786139320774646.zoomTo();\n",
              "viewer_17786139320774646.render();\n",
              "});\n",
              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* now, lets write out this fixed caffiene"
      ],
      "metadata": {
        "id": "BaPD2CarFgYn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "OUTNAME=\"caffiene\"\n",
        "tmp_u = md.Universe(mol)\n",
        "coords = tmp_u.atoms.positions\n",
        "\n",
        "buffer = 10  # convert to A\n",
        "#\n",
        "box_x = np.ceil(np.abs(np.max((coords[:,0]))-np.min((coords[:,0])))+buffer)\n",
        "box_y = np.ceil(np.abs(np.max((coords[:,1]))-np.min((coords[:,1])))+buffer)\n",
        "box_z = np.ceil(np.abs(np.max((coords[:,2]))-np.min((coords[:,2])))+buffer)\n",
        "tmp_u.dimensions = [box_x,box_y,box_z,90,90,90]\n",
        "trans = center_in_box(tmp_u.atoms,center='geometry')\n",
        "\n",
        "tmp_u.trajectory.add_transformations(trans)\n",
        "tmp_u.atoms.write(f'{OUTNAME}.pdb')\n",
        "tmp_u.atoms.write(f'{OUTNAME}.gro')\n",
        "# CREATE TOP\n",
        "from_rdmol = Molecule.from_rdkit(mol,allow_undefined_stereo=True)\n",
        "topology = from_rdmol.to_topology()\n",
        "# we will use openFF to assign SMIRNOFF parameters\n",
        "sage = ForceField(\"openff-2.0.0.offxml\")\n",
        "interchange = Interchange.from_smirnoff(force_field=sage, topology=topology)\n",
        "# just pick the first conformer\n",
        "interchange.positions = from_rdmol.conformers[0]\n",
        "#openmm_system = interchange.to_gromacs('out')\n",
        "openmm_system = interchange.to_openmm()\n",
        "#os.remove(\"out.top\")\n",
        "interchange.to_top(f\"{OUTNAME}.top\")\n",
        "t = chem.load_file(f\"{OUTNAME}.top\")\n",
        "t.positions = tmp_u.atoms.positions\n",
        "t.save(f\"{OUTNAME}.parm7\",overwrite=True)\n",
        "t.save(f\"{OUTNAME}.rst7\",overwrite=True)"
      ],
      "metadata": {
        "id": "sZMrIstAyIjO"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# WITH EPIPY\n",
        "ec = epipy(\"caffiene.gro\",\"caffiene.top\",gen_idc=True)\n",
        "ec.closure = \"KH\"\n",
        "ec.err_tol = 1e-12\n",
        "ec.ndiis = 15\n",
        "# OLD WORKS: ec.solvent(\"out_1.sol\")\n",
        "#ec.solvent(\"load_top_test.sol\")\n",
        "ec.solvent(\"co2.sol\")\n",
        "ec.solvent_path = \"./\"\n",
        "ec.report(f\"caffiene_{ec.closure}\")\n",
        "ec.rism(step=2000,resolution=0.25)\n",
        "ec.kernel(nt=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tu8t1ZjdwO-z",
        "outputId": "8b43f460-6736-4b60-a8c1-f98340cddc8f"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converted caffiene.top to caffiene.solute\n",
            "generated idc-enabled solute file to: idc_caffiene.solute\n",
            "Calculation finished in 332 steps \n",
            "err_tol: 1e-12 actual: 8.81632e-13 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* now, lets extract our distribution"
      ],
      "metadata": {
        "id": "e9T82-M0qS6y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t = ec.reader(\"guv_caffiene_KH.txt\",atom_to_select=3)\n",
        "t.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDg2lc4Wqyid",
        "outputId": "dcda9116-75c7-4285-8e0d-7a222c4cfd4e"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72, 68, 52)"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "guv = ec.select_grid()"
      ],
      "metadata": {
        "id": "psTLAhkb0JBI"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SLICE = 30\n",
        "#C = gridData.Grid(\"g.C0.1.dx\")\n",
        "#O1 = gridData.Grid(\"g.O1.1.dx\")\n",
        "#O2 = gridData.Grid(\"g.O2.1.dx\")\n",
        "fig,axs = plt.subplots()#1,2,figsize=(10,5))\n",
        "p = axs.pcolormesh(guv[SLICE],cmap='bwr')\n",
        "fig.colorbar(p,ax=axs,label=\"$g(r)$\")\n",
        "axs.set_title(\"CO$_2$ Oxygen\")\n",
        "\n",
        "axs.set_axis_off()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "-XJebPRC0Xnz",
        "outputId": "94e0fb0c-7209-44aa-cea2-bd068ad582e9"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we can also select the density of each atom site.\n",
        "* since water is the default solvent, our selection names wont work here, so we need to pass the atom site we wish to use, here the 2nd site is carbon"
      ],
      "metadata": {
        "id": "ucXQiON6rb6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# extract the guv file and select our column\n",
        "t = ec.reader(\"guv_caffiene_KH.txt\",atom_to_select=-1)\n",
        "t.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U33uBx1GrZn-",
        "outputId": "449582bd-bf75-457f-9e79-a9b3ec5337bc"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72, 68, 52)"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SLICE = 30\n",
        "#C = gridData.Grid(\"g.C0.1.dx\")\n",
        "#O1 = gridData.Grid(\"g.O1.1.dx\")\n",
        "#O2 = gridData.Grid(\"g.O2.1.dx\")\n",
        "fig,axs = plt.subplots()#1,2,figsize=(10,5))\n",
        "p = axs.pcolormesh(t[SLICE],cmap='bwr')\n",
        "fig.colorbar(p,ax=axs,label=\"$g(r)$\")\n",
        "axs.set_title(\"CO$_2$ Carbon\")\n",
        "\n",
        "axs.set_axis_off()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "GW2jyf8Frbht",
        "outputId": "5d169a99-52f6-4e82-d12c-3db3473812ff"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}