{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ca17f2a",
   "metadata": {},
   "source": [
    "# Create ICs on Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5970fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from models.multiresolution_flow_3d import *\n",
    "from models.trainer import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff1b4d5",
   "metadata": {},
   "source": [
    "# Setup the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b06a811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainer model: tm\n",
      "load initial state\n"
     ]
    }
   ],
   "source": [
    "nlevel=5\n",
    "\n",
    "shape = (2**nlevel,2**nlevel,2**nlevel)\n",
    "\n",
    "model = multi_scale_model(nlevel=nlevel)\n",
    "\n",
    "tm = trainer(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5f1e83",
   "metadata": {},
   "source": [
    "# test training with white noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bdfe2a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train model...\n",
      "Training done\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in np.arange(1000):\n",
    "    x_train = np.random.normal(0,0.01,shape)+3.1415\n",
    "    tm.train_single(x_train, silent=True)\n",
    "tm.transfer(silent=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65a41e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006170461447273722 1.006313544079366\n"
     ]
    },
    {
     "data": {
      "image/png": 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qiKEvSR0x9CcoyXuSVJKTJt2WSUjy4STfTPJgks8nWTvpNo1TkrOTPJpkNskVk27PJCTZkOSOJA8neSjJ5ZNu06QkWZPk/iRfWM7zGPoTkmQD8Abge5NuywTtBk6rqlcB3wKunHB7xmZoSZJzgFOBi5OcOtlWTcR+4D1VdSpwBnBZp9cB4HLgkeU+iaE/OdcA7wW6vZNeVV+pqv1t9y4Gz2304hdLklTVz4ADS5J0paqeqKr72vZPGITeusm2avySrAfeBFy33Ocy9CcgyVZgX1V9fdJtWUHeAXxp0o0Yo3XAnqH9vXQYdsOSbAReDdw94aZMwkcZdAJ/vtwnWnHLMKwWSf4N+I15Dn0AeD+DoZ1V73DXoapubXU+wODX/BvH2TatHEleAnwWeFdV/XjS7RmnJOcBT1XVvUnOWu7zGfrLpKpeP195kt8FNgFfTwKDIY37kpxeVU+OsYljcajrcECStwPnAVuqr4dGXJKkSfICBoF/Y1V9btLtmYAzgTcnORd4IfDrSf6lqv5kOU7mw1kTluS7wHRVdbfCYPuDOh8B/qCq5ibdnnFKchyDm9dbGIT9PcAf9faEegY9n53A01X1rgk3Z+JaT/+vquq85TqHY/qapL8Hfg3YneSBJP846QaNS7uBfWBJkkeAm3sL/OZM4K3A69rPwAOtx6tlYk9fkjpiT1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI78H+SbaGw0YK3IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = model.generate()\n",
    "print(np.mean(x[nlevel].flatten()),np.std(x[nlevel].flatten()))\n",
    "\n",
    "\n",
    "plt.hist(x[nlevel].flatten(),bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be69ddd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}