lorenz_data_generation.ipynb

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     "data": {
      "text/plain": [
       "'Varying all 3: b,c,h'"
      ]
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     "execution_count": 31,
     "metadata": {},
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    }
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   "source": [
    "\"\"\"Varying all 3: b,c,h\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from L96 import *\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "np.random.seed(123)\n",
    "sns.set_style('darkgrid')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
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   "execution_count": 33,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Parameters'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Parameters\"\"\"\n",
    "\n",
    "# b = 10\n",
    "# c = 10\n",
    "# h = 1\n",
    "\n",
    "# b_ = np.random.uniform(5,15,n_iter)\n",
    "# c_ = np.random.uniform(5,15,n_iter)\n",
    "# h_ = np.random.uniform(0,2,n_iter)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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   "source": [
    "# fig, ax = plt.subplots(1, 3, figsize=(12,6))\n",
    "\n",
    "# sns.distplot(h_, hist=False, ax=ax[0]).set_title('h')\n",
    "# sns.distplot(b_, hist=False, ax=ax[1]).set_title('b')\n",
    "# sns.distplot(c_, hist=False, ax=ax[2]).set_title('c')\n",
    "\n",
    "# plt.suptitle('True Distributions', fontsize=14)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Two-Level Lorenz 96\"\"\"\n",
    "\n",
    "K = 8  # index for slow variables (total=4)\n",
    "J = 8 # index for fast variables (total=16)\n",
    "F = 10  # forcing\n",
    "\n",
    "n_iter = 500 #number of different parameter simulations\n",
    "t = 21\n",
    "dt = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Load IC and Parameters\"\"\"\n",
    "\n",
    "infile = open(\"ic_params.pkl\", \"rb\" )\n",
    "ic_params = pickle.load(infile)\n",
    "infile.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,)\n",
      "(8,)\n",
      "(64,)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Extract IC and Parameters\"\"\"\n",
    "\"\"\"Note: ICs have to be generated again due to different shape\"\"\"\n",
    "\n",
    "b_ = ic_params[0]\n",
    "c_ = ic_params[1]\n",
    "h_ = ic_params[2]\n",
    "print(h_.shape)\n",
    "\n",
    "\"Initial Conditions\"\n",
    "\n",
    "X0 = np.random.randn(K)\n",
    "Y0 = np.random.randn(J*K)\n",
    "\n",
    "print(X0.shape)\n",
    "print(Y0.shape)\n"
   ]
  },
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      "\n",
      "n_iter:  197\n"
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     "output_type": "stream",
     "text": [
      "\n",
      "n_iter:  198\n"
     ]
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       "model_id": "c3b922f59cc1469fabea5434e18f8a09",
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       "version_minor": 0
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       "HBox(children=(IntProgress(value=0, max=21000), HTML(value='')))"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "n_iter:  199\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cf939fc8fb54a8aa9a9aeddd5fd0e1e",
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     "output_type": "stream",
     "text": [
      "\n",
      "n_iter:  200\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 200 is out of bounds for axis 0 with size 200",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 200 is out of bounds for axis 0 with size 200"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "out = []\n",
    "X_ = []\n",
    "Y_ = []\n",
    "\n",
    "for i in range(n_iter):\n",
    "    print('n_iter: ', i)\n",
    "    h = h_[i] \n",
    "    c = c_[i] \n",
    "    b = b_[i]\n",
    "    l96_ref = L96TwoLevel(K=K, J=J, X_init=X0, Y_init=Y0, h=h, c=c, b=b, save_dt=dt, noYhist=False)\n",
    "    l96_ref.iterate(t)\n",
    "    \n",
    "    out.append(l96_ref.history)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-c77efd593ffc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mX_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mY_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "X_ = []\n",
    "Y_ = []\n",
    "\n",
    "for i in range(n_iter):\n",
    "    X_.append((out[i]).X.values)\n",
    "    Y_.append((out[i]).Y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"Pickle Output Data\"\"\"\n",
    "\n",
    "output = [X_, Y_, b_, c_, h_, X0, Y0]\n",
    "\n",
    "outfile = open(\"data_generated/old/F_10_i_500_T_51_dt_0.005_X_8.pkl\", \"wb\" )\n",
    "pickle.dump(output, outfile)\n",
    "outfile.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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