{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Conveyor Belt Thermodynamic Integration with Ensembler\n",
    "In this notebook, we give examples how to run conveyor belt simulations with Ensembler. \n",
    "\n",
    "Maintainers: [@SchroederB](https://https://github.com/SchroederB), [@linkerst](https://https://github.com/linkerst), [@dfhahn](https://https://github.com/dfhahn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading Ensembler and necessary external packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append(os.getcwd()+\"/..\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib import colorbar\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "import ensembler.potentials.OneD as pot\n",
    "import ensembler.system.perturbed_system as system\n",
    "import ensembler.ensemble.replicas_dynamic_parameters as cvb\n",
    "from ensembler.samplers import stochastic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Example\n",
    "The following is an interactive example, which explains the concept of the conveyor belt algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb4905137bc543789a3e0d02640bd354",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AppLayout(children=(VBox(children=(Play(value=0, description='rotate', max=360), HBox(children=(IntSlider(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ensembler.visualisation.interactive_plots import interactive_conveyor_belt\n",
    "\n",
    "iwidget = interactive_conveyor_belt()\n",
    "iwidget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a conveyor belt object\n",
    "For building a `conveyorBelt` object, you first have to build a `system`, which will be used as a template for the replicas. The `system` itself needs to to be initialized with a potential and an integrator. For details, see the Tutorial Simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = stochastic.metropolisMonteCarloIntegrator()\n",
    "potential = pot.linearCoupledPotentials(Va = pot.harmonicOscillatorPotential(k=1, x_shift=0.0),\n",
    "    Vb = pot.harmonicOscillatorPotential(k=2, x_shift=2.0))\n",
    "sys = system.perturbedSystem(potential=potential , \n",
    "                              sampler=sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Additional the number of replicas `n_replicas` and the initial capital Lambda value needs to be specified. The latter can usually be set to 0. An additional (optional) argument is the `build` variable, which will be discussed later.\n",
    "\n",
    "The output shows the current state of the conveyor belt as a table with the replica ID, the corresponding lambda value and the energy of the replica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i    lambda_i  E_i       \n",
       "-------------------------\n",
       "    0      0.00    32.975\n",
       "    1      0.50    32.975\n",
       "    2      1.00    32.975\n",
       "    3      0.50    32.975"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble = cvb.conveyorBelt(capital_lambda=0.0,\n",
    "                            n_replicas=4,\n",
    "                            system=sys)\n",
    "ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Start a conveyor belt simulation \n",
    "The conveyor belt is simulated by using its member function `simulate` with the number of steps as argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14271dff9b23471ca713a49a5e1d1540",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Trials: ', style=ProgressStyle(description_width='initial…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "steps = 100\n",
    "ensemble.simulate(steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The trajectories of the ensemble `cvb_traj` and the single systems `sys_trajs` can be retrieved by using its member function `get_trajs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "(cvb_traj, systrajs) = ensemble.get_trajs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The ensemble trajectory is a `pandas.DataFrame` object with the following columns:\n",
    "- **Step**: index of frame (starting from 1)\n",
    "- **capital_lambda**: the capital lambda of the frame\n",
    "- **TotE**: the current total energy of the ensemble\n",
    "- **biasE**: the current bias energy\n",
    "- **doAccept**: information whether the Monte Carlo step before the frame was accepted or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "   Step  capital_lambda        TotE  biasE  doAccept\n",
       "0     0        0.000000  131.899937    0.0      True\n",
       "1     0        5.539295  131.899937    0.0      True\n",
       "2     1        5.249743   80.759163    0.0      True\n",
       "3     2        5.748955   41.452370    0.0      True\n",
       "4     3        6.149164   26.454378    0.0      True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvb_traj.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The system trajectory object `systrajs` is a `list` of `pandas.DataFrame` objects (one per replica) with the following columns:\n",
    "- **position**: (spatial) position of particle\n",
    "- **temperature**: temperature\n",
    "- **total_system_energy**: the current total energy of the replica\n",
    "- **total_potential_energy**: the current potential energy of the replica\n",
    "- **total_kinetic_energy**: the current kinetic energy of the replica\n",
    "- **dhdpos**: the derivative of the hamiltonian with repect to the position (negative of the force)\n",
    "- **velocity**: velocity of the particle\n",
    "- **lam**: lambda value of the particle\n",
    "- **dhdlam**: Hamiltonian derivative with respect to lambda\n",
    "The kinetic energy and the velocity are `NaN` in the following, becauce this example uses the stochastic `metropolisMonteCarloIntegrator`, which does not calculate velocities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>temperature</th>\n",
       "      <th>total_system_energy</th>\n",
       "      <th>total_potential_energy</th>\n",
       "      <th>total_kinetic_energy</th>\n",
       "      <th>dhdpos</th>\n",
       "      <th>velocity</th>\n",
       "      <th>lam</th>\n",
       "      <th>dhdlam</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>298.0</td>\n",
       "      <td>0.344227</td>\n",
       "      <td>0.344227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.8779506675835974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042660</td>\n",
       "      <td>5.839644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2002660837131477</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.395400</td>\n",
       "      <td>0.395400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6364005000521428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116604</td>\n",
       "      <td>3.218989</td>\n",
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      ],
      "text/plain": [
       "              position  temperature  total_system_energy  \\\n",
       "0   -3.478970704571898        298.0            11.726825   \n",
       "1   0.7551488977681826        298.0             0.701098   \n",
       "2  0.44181625124460233        298.0             0.493877   \n",
       "3  -0.4361344163389951        298.0             0.344227   \n",
       "4   0.2002660837131477        298.0             0.395400   \n",
       "\n",
       "   total_potential_energy  total_kinetic_energy               dhdpos  \\\n",
       "0               11.726825                   NaN   1.7510607172656856   \n",
       "1                0.701098                   NaN    4.234119602340081   \n",
       "2                0.493877                   NaN  -0.3133326465235803   \n",
       "3                0.344227                   NaN  -0.8779506675835974   \n",
       "4                0.395400                   NaN   0.6364005000521428   \n",
       "\n",
       "   velocity       lam     dhdlam  \n",
       "0       NaN  0.236788  23.967501  \n",
       "1       NaN  0.328955   1.264529  \n",
       "2       NaN  0.170051   2.330336  \n",
       "3       NaN  0.042660   5.839644  \n",
       "4       NaN  0.116604   3.218989  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "systrajs[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Visualizations of the trajectories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph shows exemplarious the trajectories and histograms of the lambda variables of the single replicas. Other of the above mentioned variables can be plottet in the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10,4), sharey=True)\n",
    "for i in systrajs:\n",
    "    ax[0].plot(systrajs[i].index, systrajs[i].lam, label=f\"{i}\")\n",
    "    h1=np.histogram(systrajs[i].lam, bins=50, density=1)\n",
    "    ax[1].plot( h1[0], (h1[1][:-1]+h1[1][1:])/2)\n",
    "ax[0].set_xlabel(\"time step\")\n",
    "ax[1].set_xlabel(\"probability density\")\n",
    "ax[0].set_ylabel(\"$\\lambda$\")\n",
    "ax[1].set_xlim(0, 1.5)\n",
    "out = plt.suptitle(\"$\\lambda$ trajectories and histograms of the replicas\", x=.6, y=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple analysis of the conveyor belt simulation\n",
    "First, we simulate the conveyor belt even longer to get more statistics. Then, we sort the combined dhdlam trajectories from all replicas according to the associated lambda value. Then, we calculate the average per bin, which gives you the `<dhdlam>`(lam) estimate at point `lam`. In the following we use `nbins=100`, but you can adapt it according to your sampled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b59d583a118140b1a46cced11c82c910",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Trials: ', max=900.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble.simulate(900)\n",
    "(cvb_traj, systrajs) = ensemble.get_trajs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nbins=100\n",
    "bins=np.zeros(nbins)\n",
    "dhdlbins=np.zeros(nbins)\n",
    "for i in systrajs:\n",
    "    for j in range(systrajs[i].shape[0]):\n",
    "        index=int(np.floor(systrajs[i].lam[j]*nbins))\n",
    "        if index == nbins:\n",
    "            index=nbins-1\n",
    "        bins[index]+=1\n",
    "        dhdlbins[index]+=systrajs[i].dhdlam[j]\n",
    "# avoid division by zero by setting elements == 0 to 1\n",
    "bins = np.where(bins, bins, 1)\n",
    "dhdlbins/=bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotted `<dhdlam>(lam)` curve is similar to the curves you get from TI simulations, but is usually base on more lambda points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "plt.plot(np.linspace(0,1,nbins), dhdlbins)\n",
    "plt.xlabel(\"$\\lambda$\")\n",
    "plt.ylabel(\"$\\partial H / \\partial \\lambda$\")\n",
    "out = plt.suptitle(\"$\\partial H / \\partial \\lambda (\\lambda)$ curve\", x=.6, y=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The integral of this curve gives the free energy estimate. As you have many points along $\\lambda$, you can use the rectangular integration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "integral=np.sum(dhdlbins)*1.0/nbins\n",
    "print(f'Delta G = {integral:.2f} kJ/mol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This value agrees well to the analytical value, which is calculated below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#analytical\n",
    "u=1.66053886e-27\n",
    "NA=6.0221415e23\n",
    "hbar=1.054571800e-34*1e12*1e-3*NA  #kJ/mol*ps\n",
    "R=0.00831446 #kJ/mol/K\n",
    "mu=0.5  #u\n",
    "T=298.0  #K\n",
    "fc1=1  #kJ/nm^2/mol\n",
    "fc2=2.0 #kJ/nm^2/mol\n",
    "omega1=np.sqrt(fc1/mu)\n",
    "omega2=np.sqrt(fc2/mu)\n",
    "alpha1=hbar*np.sqrt(fc1/mu)/(R*T)\n",
    "alpha2=hbar*np.sqrt(fc2/mu)/(R*T)\n",
    "Z1=np.exp(-alpha1/2.0)/(1-np.exp(-alpha1))\n",
    "Z2=np.exp(-alpha2/2.0)/(1-np.exp(-alpha2))\n",
    "DF=-R*T*np.log(Z2/Z1)\n",
    "print(f'Analytical value = {DF:.2f} kJ/mol')"
   ]
  }
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