dim2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import sys\n",
    "import numpy as np\n",
    "from scr.lattice import Lattice\n",
    "from scr.random_walk import RandomWalk\n",
    "from scr.simulation import Simulation\n",
    "from scr.utils import make_video\n",
    "from tqdm import tqdm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimension 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "simu_name = \"plot_dim2\"\n",
    "os.mkdir(simu_name)\n",
    "os.mkdir(simu_name + \"/frames\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 134\n",
    "lat = Lattice(dim=2,L=L)\n",
    "rw = RandomWalk(x=np.array([[L/2,L/2]]))\n",
    "simu = Simulation(rw,lat,N_traj_plot=0)\n",
    "t_iter = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Show mechanism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 80/80 [00:03<00:00, 24.88it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simu.lat.set_title(\"Close look\")\n",
    "simu.lat.set_zoom(10)\n",
    "simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "\n",
    "simu.N_nbgh_plot = 1\n",
    "# simu.N_traj_plot = 1\n",
    "# simu.trajs_plot[0].set(alpha=0.0)\n",
    "\n",
    "arrow_east = simu.lat.ax.arrow(simu.rw.x[0,0]+0.1,simu.rw.x[0,1],1.0,0.0,color='k',length_includes_head=True,width=0.08,head_length=0.24)\n",
    "arrow_north = simu.lat.ax.arrow(simu.rw.x[0,0],simu.rw.x[0,1]+0.1,0.0,1.0,color='k',length_includes_head=True,width=0.08,head_length=0.24)\n",
    "arrow_west = simu.lat.ax.arrow(simu.rw.x[0,0]-0.1,simu.rw.x[0,1],-1.0,0.0,color='k',length_includes_head=True,width=0.08,head_length=0.24)\n",
    "arrow_south = simu.lat.ax.arrow(simu.rw.x[0,0],simu.rw.x[0,1]-0.1,0.0,-1.0,color='k',length_includes_head=True,width=0.08,head_length=0.24)\n",
    "\n",
    "simu.lat.fig\n",
    "\n",
    "for _ in tqdm(range(80)):\n",
    "    t_iter +=1\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "    \n",
    "arrow_east.remove()\n",
    "arrow_north.remove()\n",
    "arrow_west.remove()\n",
    "arrow_south.remove()\n",
    "\n",
    "simu.N_nbgh_plot = 0\n",
    "simu.extra[\"nbgh\"].set(alpha=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 250/250 [00:09<00:00, 25.21it/s]\n"
     ]
    }
   ],
   "source": [
    "simu.lat.set_title(\"Run the simulation\")\n",
    "\n",
    "simu.rw.rate = 3.0\n",
    "\n",
    "for _ in tqdm(range(250)):\n",
    "    simu.update()\n",
    "    t_iter +=1\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Pause: show trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:01<00:00, 25.41it/s]\n",
      "100%|██████████| 250/250 [00:10<00:00, 24.97it/s]\n"
     ]
    }
   ],
   "source": [
    "simu.lat.set_title(\"Now show the trajectory\")\n",
    "\n",
    "# simu.trajs_plot[0].set(alpha=1.0)\n",
    "simu.N_traj_plot = 1\n",
    "simu.trajs_plot = simu._Simulation__plot_traj()\n",
    "simu.traj = [simu.rw.x.copy()]\n",
    "    \n",
    "for _ in tqdm(range(40)):\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "    t_iter += 1\n",
    "    \n",
    "for _ in tqdm(range(250)):\n",
    "    simu.update()\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "    t_iter += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Pause: dezoom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 124/124 [00:05<00:00, 23.44it/s]\n",
      "100%|██████████| 60/60 [00:02<00:00, 22.23it/s]\n"
     ]
    }
   ],
   "source": [
    "simu.lat.set_title(\"Pause: zoom out\")\n",
    "\n",
    "lw = 1.0\n",
    "for k in tqdm(range(L-10)):\n",
    "    simu.lat.set_zoom(10+k)\n",
    "    simu.lat.set_lines_param(linewidth=lw/(k+1))\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "    t_iter += 1\n",
    "    \n",
    "for _ in tqdm(range(60)):\n",
    "    simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "    t_iter += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Speed up and run the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:32<00:00, 310.80it/s]\n"
     ]
    }
   ],
   "source": [
    "simu.lat.set_title(\"Speed up\")\n",
    "\n",
    "simu.rw.rate = 30.0\n",
    "simu.trajs_plot[0].set(linewidth=1.6)\n",
    "for k in tqdm(range(10000)):\n",
    "    simu.update(update_nbgh=True,update_traj=(k%15==0))\n",
    "    if k%15==0:\n",
    "        simu.lat.fig.savefig(simu_name + \"/frames/\" + f\"t_{t_iter}.png\")\n",
    "        t_iter +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_video(simu_name=simu_name,video_name=simu_name)"
   ]
  }
 ],
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