Train_Rubber.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.9/site-packages/jax/_src/lib/__init__.py:33: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n",
      "  warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n",
      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n",
      "/opt/homebrew/lib/python3.9/site-packages/jax/experimental/optimizers.py:28: FutureWarning: jax.experimental.optimizers is deprecated, import jax.example_libraries.optimizers instead\n",
      "  warnings.warn('jax.experimental.optimizers is deprecated, '\n"
     ]
    }
   ],
   "source": [
    "import jax.numpy as np\n",
    "import numpy as onp\n",
    "from jax import random, grad, jit, vmap\n",
    "from functools import partial\n",
    "key = random.PRNGKey(0)\n",
    "from jax.experimental import optimizers\n",
    "from NODE_ICNN_CANN_MF_fns import *\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from jax.config import config\n",
    "import pandas as pd\n",
    "config.update(\"jax_enable_x64\", True)\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train NODE, CANN, ICNN, MFNN, directly against rubber stress data\n",
    "\n",
    "Train directly against the stress data from the three rubber datasets.\n",
    "Note. I wanted to do these four different schemes. the last one I wanted to do was this MF scheme where I would combine a CANN with a NODE but that doesnt seem necessary in that the CANN and the NODE alone can do just fine for rubber data. So at least for rubber doesnt seem necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CANN_model():\n",
    "    def __init__(self, params_I1, params_I2,normalization):\n",
    "        self.params_I1 = params_I1\n",
    "        self.params_I2 = params_I2\n",
    "        self.normalization = normalization\n",
    "    # Psi1\n",
    "    def Psi1norm(self, I1norm):\n",
    "        # Note: I1norm = (I1-3)/normalization\n",
    "        return CANN_dpsidInorm(I1norm,self.params_I1)[:,0]/normalization[0]\n",
    "    # Psi2\n",
    "    def Psi2norm(self, I2norm):\n",
    "        # Note: I2norm = (I2-3)/normalization\n",
    "        return CANN_dpsidInorm(I2norm,self.params_I2)[:,0]/normalization[1]\n",
    "\n",
    "# play with ICNN a bit, how do we get that one to work\n",
    "class ICNN_model():\n",
    "    def __init__(self, params_I1, params_I2,normalization):\n",
    "        self.params_I1 = params_I1\n",
    "        self.params_I2 = params_I2\n",
    "        self.normalization = normalization\n",
    "    # Psi1\n",
    "    # note: for ICNN the prediction is the function not the gradient\n",
    "    # but the P11_UT, P11_ET, etc expects the model to output the derivative\n",
    "    def Psi1norm(self, I1norm):\n",
    "        # Note: I1norm = (I1-3)/normalization\n",
    "        f1 = lambda x: icnn_forwardpass(x, self.params_I1)[0]\n",
    "        #normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "        df1 = grad(f1)\n",
    "        return vmap(df1)(I1norm[:,None])[:,0]/self.normalization[0]\n",
    "    # Psi2\n",
    "    # note: for ICNN the prediction is the function not the gradient\n",
    "    def Psi2norm(self, I2norm):\n",
    "        # Note: I2norm = (I2-3)/normalization\n",
    "        f2 = lambda x: icnn_forwardpass(x, self.params_I2)[0]\n",
    "        #normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "        df2 = grad(f2)\n",
    "        return vmap(df2)(I2norm[:,None])[:,0]/self.normalization[1]\n",
    "        \n",
    "## NODE model outputs normalized strain energy given normalized invariants\n",
    "class NODE_model():\n",
    "    def __init__(self, params_I1, params_I2):\n",
    "        self.params_I1 = params_I1\n",
    "        self.params_I2 = params_I2\n",
    "    def Psi1norm(self, I1norm):\n",
    "        # Note: I1norm = (I1-3)/normalization\n",
    "        return NODE_posb_vmap(I1norm, self.params_I1)\n",
    "    def Psi2norm(self, I2norm):\n",
    "        # Note: I2norm = (I2-3)/normalization\n",
    "        return NODE_posb_vmap(I2norm, self.params_I2)\n",
    "\n",
    "def plotstresses(x_gt,y_gt,x_pr,y_pr):\n",
    "    fig, ax = plt.subplots(1,3,figsize=(12,4))\n",
    "    labels = ['UT', 'ET', 'PS']\n",
    "    for axi, x_gti, y_gti, x_pri, y_pri, label in zip(ax, x_gt, y_gt, x_pr, y_pr, labels):\n",
    "        axi.plot(x_gti, y_gti, 'k.')\n",
    "        axi.plot(x_pri, y_pri)\n",
    "        axi.set_title(label)\n",
    "        axi.set_xlabel(r'Stretch $\\lambda [-]$')\n",
    "        axi.set_ylabel(r'Nominal stress $P_{11} [MPa]$')\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def P11_UT(lamb, model, normalization):\n",
    "    I1_factor = normalization[0]\n",
    "    I2_factor = normalization[1]\n",
    "    Psi1_factor = normalization[2]\n",
    "    Psi2_factor = normalization[3]\n",
    "    I1 = lamb**2 + 2/lamb\n",
    "    I2 = 2*lamb + 1/lamb**2\n",
    "    I1norm = (I1-3)/I1_factor\n",
    "    I2norm = (I2-3)/I2_factor\n",
    "    Psi1 = model.Psi1norm(I1norm)*Psi1_factor\n",
    "    Psi2 = model.Psi2norm(I2norm)*Psi2_factor\n",
    "    return 2*(Psi1 + Psi2/lamb)*(lamb-1/lamb**2)\n",
    "def P11_ET(lamb, model, normalization):\n",
    "    I1_factor = normalization[0]\n",
    "    I2_factor = normalization[1]\n",
    "    Psi1_factor = normalization[2]\n",
    "    Psi2_factor = normalization[3]\n",
    "    I1 = 2*lamb**2 + 1/lamb**4\n",
    "    I2 = lamb**4 + 2/lamb**2\n",
    "    I1norm = (I1-3)/I1_factor\n",
    "    I2norm = (I2-3)/I2_factor\n",
    "    Psi1 = model.Psi1norm(I1norm)*Psi1_factor\n",
    "    Psi2 = model.Psi2norm(I2norm)*Psi2_factor\n",
    "    return 2*(Psi1 + Psi2*lamb**2)*(lamb-1/lamb**5)\n",
    "def P11_PS(lamb, model, normalization):\n",
    "    I1_factor = normalization[0]\n",
    "    I2_factor = normalization[1]\n",
    "    Psi1_factor = normalization[2]\n",
    "    Psi2_factor = normalization[3]\n",
    "    I1 =  lamb**2 + 1/lamb**2 + 1\n",
    "    I2 =  lamb**2 + 1/lamb**2 + 1\n",
    "    I1norm = (I1-3)/I1_factor\n",
    "    I2norm = (I2-3)/I2_factor\n",
    "    Psi1 = model.Psi1norm(I1norm)*Psi1_factor\n",
    "    Psi2 = model.Psi2norm(I2norm)*Psi2_factor\n",
    "    return 2*(Psi1+Psi2)*(lamb-1/lamb**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Eval invariants for the three types of deformation\n",
    "def evalI1_UT(lam):\n",
    "    return lam**2 + (2/lam)\n",
    "def evalI2_UT(lam):\n",
    "    return 2*lam + (1/lam**2)\n",
    "\n",
    "def evalI1_ET(lam):\n",
    "    return 2*lam**2 +1/lam**4\n",
    "def evalI2_ET(lam):\n",
    "    return lam**4 + 2/lam**2\n",
    "\n",
    "def evalI1_PS(lam):\n",
    "    return lam**2 + 1/lam**2 + 1\n",
    "def evalI2_PS(lam):\n",
    "    return lam**2 + 1/lam**2 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data\n",
    "UTdata = pd.read_csv('Data/UT20.csv')\n",
    "ETdata = pd.read_csv('Data/ET20.csv')\n",
    "PSdata = pd.read_csv('Data/PS20.csv')\n",
    "# stack into single array \n",
    "P11_data = np.hstack([UTdata['P11'].to_numpy(),ETdata['P11'].to_numpy(),PSdata['P11'].to_numpy()])\n",
    "F11_data = np.hstack([UTdata['F11'].to_numpy(),ETdata['F11'].to_numpy(),PSdata['F11'].to_numpy()])\n",
    "# indices for the three data sets\n",
    "indET = len(UTdata['P11'])\n",
    "indPS = indET + len(ETdata['P11'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum I1 and I2 for different cases:\n",
    "UT: 60, 15\n",
    "ET: 40, 392\n",
    "PS: 26, 26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "I1_factor = 30\n",
    "Psi1_factor = 0.3\n",
    "I2_factor = 250\n",
    "Psi2_factor = 0.001\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "\n",
    "@partial(jit, static_argnums=(2,))\n",
    "def loss_P11_all(params, F11_data, mdlnumber):\n",
    "    lamUT = F11_data[0:indET]\n",
    "    lamET = F11_data[indET:indPS]\n",
    "    lamPS = F11_data[indPS:]\n",
    "    params_I1 = params[0]\n",
    "    params_I2 = params[1]\n",
    "    if mdlnumber == 1:\n",
    "        model = CANN_model(params_I1,params_I2,normalization)\n",
    "    elif mdlnumber == 2:\n",
    "        model = ICNN_model(params_I1,params_I2,normalization)\n",
    "    else:\n",
    "        model = NODE_model(params_I1,params_I2)\n",
    "    P11UT_pr = P11_UT(lamUT, model, normalization)\n",
    "    P11ET_pr = P11_ET(lamET, model, normalization)\n",
    "    P11PS_pr = P11_PS(lamPS, model, normalization)    \n",
    "    return np.mean((P11UT_pr-P11_data[0:indET])**2)+np.mean((P11ET_pr-P11_data[indET:indPS])**2)+np.mean((P11PS_pr-P11_data[indPS:])**2)\n",
    "\n",
    "@partial(jit, static_argnums=(2,))\n",
    "def loss_P11_UT(params, F11_data, mdlnumber):\n",
    "    lamUT = F11_data[0:indET]\n",
    "    params_I1 = params[0]\n",
    "    params_I2 = params[1]\n",
    "    if mdlnumber == 1:\n",
    "        model = CANN_model(params_I1,params_I2,normalization)\n",
    "    elif mdlnumber == 2:\n",
    "        model = ICNN_model(params_I1,params_I2,normalization)\n",
    "    else:\n",
    "        model = NODE_model(params_I1,params_I2)\n",
    "    P11UT_pr = P11_UT(lamUT, model, normalization)\n",
    "    return np.mean((P11UT_pr-P11_data[0:indET])**2)\n",
    "\n",
    "@partial(jit, static_argnums=(2,))\n",
    "def loss_P11_ET(params, F11_data, mdlnumber):\n",
    "    lamET = F11_data[indET:indPS]\n",
    "    params_I1 = params[0]\n",
    "    params_I2 = params[1]\n",
    "    if mdlnumber == 1:\n",
    "        model = CANN_model(params_I1,params_I2,normalization)\n",
    "    elif mdlnumber == 2:\n",
    "        model = ICNN_model(params_I1,params_I2,normalization)\n",
    "    else:\n",
    "        model = NODE_model(params_I1,params_I2)\n",
    "    P11ET_pr = P11_ET(lamET, model, normalization)\n",
    "    return np.mean((P11ET_pr-P11_data[indET:indPS])**2)\n",
    "\n",
    "@partial(jit, static_argnums=(2,))\n",
    "def loss_P11_PS(params, F11_data, mdlnumber):\n",
    "    lamPS = F11_data[indPS:]\n",
    "    params_I1 = params[0]\n",
    "    params_I2 = params[1]\n",
    "    if mdlnumber == 1:\n",
    "        model = CANN_model(params_I1,params_I2,normalization)\n",
    "    elif mdlnumber == 2:\n",
    "        model = ICNN_model(params_I1,params_I2,normalization)\n",
    "    else:\n",
    "        model = NODE_model(params_I1,params_I2)\n",
    "    P11PS_pr = P11_PS(lamPS, model, normalization)    \n",
    "    return np.mean((P11PS_pr-P11_data[indPS:])**2)\n",
    "\n",
    "\n",
    "@partial(jit, static_argnums=(0,1,))\n",
    "def step_jp(loss, mdlnumber, i, opt_state, X_batch):\n",
    "    params = get_params(opt_state)\n",
    "    g = grad(loss)(params, X_batch, mdlnumber)\n",
    "    return opt_update(i, g, opt_state)\n",
    "\n",
    "def train_jp(loss, mdlnumber, X, opt_state, key, nIter = 10000, print_freq = 1000):\n",
    "    train_loss = []\n",
    "    val_loss = []\n",
    "    for it in range(nIter):\n",
    "        opt_state = step_jp(loss, mdlnumber, it, opt_state, X)         \n",
    "        if (it+1)% print_freq == 0:\n",
    "            params = get_params(opt_state)\n",
    "            train_loss_value = loss(params, X, mdlnumber)\n",
    "            train_loss.append(train_loss_value)\n",
    "            to_print = \"it %i, train loss = %e\" % (it+1, train_loss_value)\n",
    "            print(to_print)\n",
    "    return get_params(opt_state), train_loss, val_loss\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using layers = [1,2,3,1] for node and [1,4,1] for icnn\n",
    "n_params_cann = 2*12 = 24\n",
    "n_params_icnn = 2*(4 + 8) = 24\n",
    "n_params_node = 2*(2 + 6 + 3) = 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_node(key, layers = [1,5,5,1]):\n",
    "    params_I1 = init_params_posb(layers, key) \n",
    "    params_I2 = init_params_posb(layers, key)\n",
    "    return [params_I1,params_I2]\n",
    "\n",
    "def init_icnn(key, layers = [1,3,4,1]):\n",
    "    params_I1 = init_params_icnn(layers, key)\n",
    "    params_I2 = init_params_icnn(layers, key)\n",
    "    return [params_I1,params_I2]\n",
    "\n",
    "def init_cann(key, layers = None):\n",
    "    params_I1 = init_params_cann(key)\n",
    "    params_I2 = init_params_cann(key)\n",
    "    return [params_I1,params_I2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NODE fits\n",
    "\n",
    "### Fit all three tests at the same time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 5.585331e+00\n",
      "it 2000, train loss = 4.111750e-01\n",
      "it 3000, train loss = 1.728923e-01\n",
      "it 4000, train loss = 1.308042e-01\n",
      "it 5000, train loss = 9.964674e-02\n",
      "it 6000, train loss = 8.227019e-02\n",
      "it 7000, train loss = 7.528298e-02\n",
      "it 8000, train loss = 7.222669e-02\n",
      "it 9000, train loss = 6.889756e-02\n",
      "it 10000, train loss = 6.419773e-02\n",
      "it 11000, train loss = 5.835025e-02\n",
      "it 12000, train loss = 5.193269e-02\n",
      "it 13000, train loss = 4.550062e-02\n",
      "it 14000, train loss = 3.955682e-02\n",
      "it 15000, train loss = 3.444139e-02\n",
      "it 16000, train loss = 3.027742e-02\n",
      "it 17000, train loss = 2.700249e-02\n",
      "it 18000, train loss = 2.449868e-02\n",
      "it 19000, train loss = 2.263605e-02\n",
      "it 20000, train loss = 2.128555e-02\n",
      "it 21000, train loss = 2.033289e-02\n",
      "it 22000, train loss = 1.969079e-02\n",
      "it 23000, train loss = 1.930204e-02\n",
      "it 24000, train loss = 1.908022e-02\n",
      "it 25000, train loss = 1.895053e-02\n",
      "it 26000, train loss = 1.886870e-02\n",
      "it 27000, train loss = 1.881394e-02\n",
      "it 28000, train loss = 1.877543e-02\n",
      "it 29000, train loss = 1.874756e-02\n",
      "it 30000, train loss = 1.872709e-02\n",
      "it 31000, train loss = 1.871162e-02\n",
      "it 32000, train loss = 1.869965e-02\n",
      "it 33000, train loss = 1.869021e-02\n",
      "it 34000, train loss = 1.868263e-02\n",
      "it 35000, train loss = 1.867643e-02\n",
      "it 36000, train loss = 1.867208e-02\n",
      "it 37000, train loss = 1.866708e-02\n",
      "it 38000, train loss = 1.866343e-02\n",
      "it 39000, train loss = 1.866028e-02\n",
      "it 40000, train loss = 1.865752e-02\n",
      "it 41000, train loss = 1.865508e-02\n",
      "it 42000, train loss = 1.865290e-02\n",
      "it 43000, train loss = 1.865093e-02\n",
      "it 44000, train loss = 1.864914e-02\n",
      "it 45000, train loss = 1.864781e-02\n",
      "it 46000, train loss = 1.864603e-02\n",
      "it 47000, train loss = 1.864466e-02\n",
      "it 48000, train loss = 1.864339e-02\n",
      "it 49000, train loss = 1.864221e-02\n",
      "it 50000, train loss = 1.864112e-02\n"
     ]
    }
   ],
   "source": [
    "params_all = init_node(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 2.e-4\n",
    "opt_state = opt_init(params_all)\n",
    "\n",
    "mdlnumber = 3 #NODE\n",
    "params_all, train_loss, val_loss = train_jp(loss_P11_all, mdlnumber, F11_data, opt_state, key, nIter = 50000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = NODE_model(params_all[0], params_all[1])\n",
    "P11_NN_UT = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT, P11_NN_ET, P11_NN_PS])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/NODE_params_all.npy', 'wb') as f:\n",
    "    pickle.dump(params_all, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NODE training with one set of data and comparing to the others "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 7.272730e-02\n",
      "it 2000, train loss = 4.017158e-02\n",
      "it 3000, train loss = 3.492846e-02\n",
      "it 4000, train loss = 3.251616e-02\n",
      "it 5000, train loss = 3.170074e-02\n",
      "it 6000, train loss = 3.076195e-02\n",
      "it 7000, train loss = 2.924254e-02\n",
      "it 8000, train loss = 2.741605e-02\n",
      "it 9000, train loss = 2.538205e-02\n",
      "it 10000, train loss = 2.302985e-02\n",
      "it 11000, train loss = 2.020118e-02\n",
      "it 12000, train loss = 1.684912e-02\n",
      "it 13000, train loss = 1.370411e-02\n",
      "it 14000, train loss = 1.023368e-02\n",
      "it 15000, train loss = 6.889880e-03\n",
      "it 16000, train loss = 5.303637e-03\n",
      "it 17000, train loss = 4.528757e-03\n",
      "it 18000, train loss = 4.079923e-03\n",
      "it 19000, train loss = 3.797400e-03\n",
      "it 20000, train loss = 3.603793e-03\n",
      "it 21000, train loss = 3.465828e-03\n",
      "it 22000, train loss = 3.360338e-03\n",
      "it 23000, train loss = 3.278122e-03\n",
      "it 24000, train loss = 3.209995e-03\n",
      "it 25000, train loss = 3.153007e-03\n",
      "it 26000, train loss = 3.104119e-03\n",
      "it 27000, train loss = 3.061661e-03\n",
      "it 28000, train loss = 3.023903e-03\n",
      "it 29000, train loss = 2.990949e-03\n",
      "it 30000, train loss = 2.961226e-03\n",
      "it 31000, train loss = 2.934695e-03\n",
      "it 32000, train loss = 2.910335e-03\n",
      "it 33000, train loss = 2.887543e-03\n",
      "it 34000, train loss = 2.865525e-03\n",
      "it 35000, train loss = 2.862004e-03\n",
      "it 36000, train loss = 2.824901e-03\n",
      "it 37000, train loss = 2.806141e-03\n",
      "it 38000, train loss = 2.788159e-03\n",
      "it 39000, train loss = 2.770791e-03\n",
      "it 40000, train loss = 2.753816e-03\n",
      "it 41000, train loss = 2.737305e-03\n",
      "it 42000, train loss = 2.720956e-03\n",
      "it 43000, train loss = 2.704539e-03\n",
      "it 44000, train loss = 2.687762e-03\n",
      "it 45000, train loss = 2.670200e-03\n",
      "it 46000, train loss = 2.651289e-03\n",
      "it 47000, train loss = 2.630190e-03\n",
      "it 48000, train loss = 2.605609e-03\n",
      "it 49000, train loss = 2.575560e-03\n",
      "it 50000, train loss = 2.537361e-03\n",
      "it 51000, train loss = 2.488680e-03\n",
      "it 52000, train loss = 2.429176e-03\n",
      "it 53000, train loss = 2.364579e-03\n",
      "it 54000, train loss = 2.301994e-03\n",
      "it 55000, train loss = 2.244134e-03\n",
      "it 56000, train loss = 2.188468e-03\n",
      "it 57000, train loss = 2.131639e-03\n",
      "it 58000, train loss = 2.071195e-03\n",
      "it 59000, train loss = 2.009409e-03\n",
      "it 60000, train loss = 1.955088e-03\n",
      "it 61000, train loss = 1.917621e-03\n",
      "it 62000, train loss = 1.895745e-03\n",
      "it 63000, train loss = 1.887843e-03\n",
      "it 64000, train loss = 1.869791e-03\n",
      "it 65000, train loss = 1.859609e-03\n",
      "it 66000, train loss = 1.850372e-03\n",
      "it 67000, train loss = 1.841929e-03\n",
      "it 68000, train loss = 1.834087e-03\n",
      "it 69000, train loss = 1.826719e-03\n",
      "it 70000, train loss = 1.819683e-03\n",
      "it 71000, train loss = 1.812839e-03\n",
      "it 72000, train loss = 1.806076e-03\n",
      "it 73000, train loss = 1.799312e-03\n",
      "it 74000, train loss = 1.792522e-03\n",
      "it 75000, train loss = 1.785690e-03\n",
      "it 76000, train loss = 1.778986e-03\n",
      "it 77000, train loss = 1.772528e-03\n",
      "it 78000, train loss = 1.766564e-03\n",
      "it 79000, train loss = 1.761273e-03\n",
      "it 80000, train loss = 1.756752e-03\n",
      "it 81000, train loss = 1.753027e-03\n",
      "it 82000, train loss = 1.749821e-03\n",
      "it 83000, train loss = 1.746992e-03\n",
      "it 84000, train loss = 1.744401e-03\n",
      "it 85000, train loss = 1.741941e-03\n",
      "it 86000, train loss = 1.740443e-03\n",
      "it 87000, train loss = 1.737297e-03\n",
      "it 88000, train loss = 1.735019e-03\n",
      "it 89000, train loss = 1.732785e-03\n",
      "it 90000, train loss = 1.730576e-03\n",
      "it 91000, train loss = 1.728391e-03\n",
      "it 92000, train loss = 1.726220e-03\n",
      "it 93000, train loss = 1.724059e-03\n",
      "it 94000, train loss = 1.749283e-03\n",
      "it 95000, train loss = 1.720557e-03\n",
      "it 96000, train loss = 1.717849e-03\n",
      "it 97000, train loss = 1.715701e-03\n",
      "it 98000, train loss = 1.713661e-03\n",
      "it 99000, train loss = 1.711635e-03\n",
      "it 100000, train loss = 1.709623e-03\n"
     ]
    }
   ],
   "source": [
    "# train with uniaxial only\n",
    "params_UT = init_node(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(5.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_UT)\n",
    "mdlnumber = 3 #NODE\n",
    "params_UT, train_loss, val_loss = train_jp(loss_P11_UT, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = NODE_model(params_UT[0], params_UT[1])\n",
    "P11_NN_UT_u = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_u = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_u = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_u, P11_NN_ET_u, P11_NN_PS_u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/NODE_params_UT.npy', 'wb') as f:\n",
    "    pickle.dump(params_UT, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 5.448811e-01\n",
      "it 2000, train loss = 4.745159e-02\n",
      "it 3000, train loss = 3.288570e-02\n",
      "it 4000, train loss = 2.635361e-02\n",
      "it 5000, train loss = 1.974330e-02\n",
      "it 6000, train loss = 1.435946e-02\n",
      "it 7000, train loss = 1.057199e-02\n",
      "it 8000, train loss = 7.834639e-03\n",
      "it 9000, train loss = 5.688195e-03\n",
      "it 10000, train loss = 3.952568e-03\n",
      "it 11000, train loss = 2.650018e-03\n",
      "it 12000, train loss = 1.834617e-03\n",
      "it 13000, train loss = 1.409608e-03\n",
      "it 14000, train loss = 1.131430e-03\n",
      "it 15000, train loss = 8.619620e-04\n",
      "it 16000, train loss = 6.474750e-04\n",
      "it 17000, train loss = 4.940890e-04\n",
      "it 18000, train loss = 3.914339e-04\n",
      "it 19000, train loss = 3.273921e-04\n",
      "it 20000, train loss = 2.910934e-04\n",
      "it 21000, train loss = 2.732010e-04\n",
      "it 22000, train loss = 2.660084e-04\n",
      "it 23000, train loss = 2.616540e-04\n",
      "it 24000, train loss = 2.500025e-04\n",
      "it 25000, train loss = 2.382116e-04\n",
      "it 26000, train loss = 2.283689e-04\n",
      "it 27000, train loss = 2.204119e-04\n",
      "it 28000, train loss = 2.138934e-04\n",
      "it 29000, train loss = 2.084305e-04\n",
      "it 30000, train loss = 2.037469e-04\n",
      "it 31000, train loss = 1.996973e-04\n",
      "it 32000, train loss = 1.961500e-04\n",
      "it 33000, train loss = 1.929840e-04\n",
      "it 34000, train loss = 1.901230e-04\n",
      "it 35000, train loss = 1.875318e-04\n",
      "it 36000, train loss = 1.851668e-04\n",
      "it 37000, train loss = 1.788190e-04\n",
      "it 38000, train loss = 1.767048e-04\n",
      "it 39000, train loss = 1.766029e-04\n",
      "it 40000, train loss = 1.769462e-04\n",
      "it 41000, train loss = 1.767745e-04\n",
      "it 42000, train loss = 1.765680e-04\n",
      "it 43000, train loss = 1.759013e-04\n",
      "it 44000, train loss = 1.764337e-04\n",
      "it 45000, train loss = 1.863139e-04\n",
      "it 46000, train loss = 1.770109e-04\n",
      "it 47000, train loss = 1.750062e-04\n",
      "it 48000, train loss = 1.759135e-04\n",
      "it 49000, train loss = 1.857573e-04\n",
      "it 50000, train loss = 1.746868e-04\n",
      "it 51000, train loss = 1.853273e-04\n",
      "it 52000, train loss = 1.762422e-04\n",
      "it 53000, train loss = 1.759503e-04\n",
      "it 54000, train loss = 1.763395e-04\n",
      "it 55000, train loss = 1.755543e-04\n",
      "it 56000, train loss = 1.739744e-04\n",
      "it 57000, train loss = 1.737673e-04\n",
      "it 58000, train loss = 1.736525e-04\n",
      "it 59000, train loss = 1.743425e-04\n",
      "it 60000, train loss = 1.739305e-04\n",
      "it 61000, train loss = 1.762267e-04\n",
      "it 62000, train loss = 1.732637e-04\n",
      "it 63000, train loss = 1.735828e-04\n",
      "it 64000, train loss = 1.737719e-04\n",
      "it 65000, train loss = 1.728738e-04\n",
      "it 66000, train loss = 1.743762e-04\n",
      "it 67000, train loss = 1.731013e-04\n",
      "it 68000, train loss = 1.725142e-04\n",
      "it 69000, train loss = 1.735816e-04\n",
      "it 70000, train loss = 1.734316e-04\n",
      "it 71000, train loss = 1.737588e-04\n",
      "it 72000, train loss = 1.721460e-04\n",
      "it 73000, train loss = 1.721458e-04\n",
      "it 74000, train loss = 1.725138e-04\n",
      "it 75000, train loss = 1.741442e-04\n",
      "it 76000, train loss = 1.832435e-04\n",
      "it 77000, train loss = 1.717282e-04\n",
      "it 78000, train loss = 1.813069e-04\n",
      "it 79000, train loss = 1.736221e-04\n",
      "it 80000, train loss = 1.723781e-04\n",
      "it 81000, train loss = 1.711141e-04\n",
      "it 82000, train loss = 1.713610e-04\n",
      "it 83000, train loss = 1.723217e-04\n",
      "it 84000, train loss = 1.711470e-04\n",
      "it 85000, train loss = 1.819352e-04\n",
      "it 86000, train loss = 1.809978e-04\n",
      "it 87000, train loss = 1.707413e-04\n",
      "it 88000, train loss = 1.709463e-04\n",
      "it 89000, train loss = 1.703645e-04\n",
      "it 90000, train loss = 1.781174e-04\n",
      "it 91000, train loss = 1.705684e-04\n",
      "it 92000, train loss = 1.725667e-04\n",
      "it 93000, train loss = 1.777931e-04\n",
      "it 94000, train loss = 1.715653e-04\n",
      "it 95000, train loss = 1.776167e-04\n",
      "it 96000, train loss = 1.698975e-04\n",
      "it 97000, train loss = 1.708898e-04\n",
      "it 98000, train loss = 1.693084e-04\n",
      "it 99000, train loss = 1.695000e-04\n",
      "it 100000, train loss = 1.706994e-04\n"
     ]
    }
   ],
   "source": [
    "# train with equibiaxial only, \n",
    "params_ET = init_node(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_ET)\n",
    "mdlnumber = 3\n",
    "params_ET, train_loss, val_loss = train_jp(loss_P11_ET, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = NODE_model(params_ET[0], params_ET[1])\n",
    "P11_NN_UT_e = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_e = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_e = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_e, P11_NN_ET_e, P11_NN_PS_e])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/NODE_params_ET.npy', 'wb') as f:\n",
    "    pickle.dump(params_ET, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 4.510426e-01\n",
      "it 2000, train loss = 2.592479e-02\n",
      "it 3000, train loss = 1.638121e-02\n",
      "it 4000, train loss = 1.542358e-02\n",
      "it 5000, train loss = 1.410213e-02\n",
      "it 6000, train loss = 1.243106e-02\n",
      "it 7000, train loss = 1.055699e-02\n",
      "it 8000, train loss = 8.663393e-03\n",
      "it 9000, train loss = 6.865657e-03\n",
      "it 10000, train loss = 5.205768e-03\n",
      "it 11000, train loss = 3.759231e-03\n",
      "it 12000, train loss = 2.658937e-03\n",
      "it 13000, train loss = 1.915095e-03\n",
      "it 14000, train loss = 1.465289e-03\n",
      "it 15000, train loss = 1.224767e-03\n",
      "it 16000, train loss = 1.108421e-03\n",
      "it 17000, train loss = 1.041394e-03\n",
      "it 18000, train loss = 9.630906e-04\n",
      "it 19000, train loss = 8.781184e-04\n",
      "it 20000, train loss = 8.030605e-04\n",
      "it 21000, train loss = 7.394615e-04\n",
      "it 22000, train loss = 6.865123e-04\n",
      "it 23000, train loss = 6.450016e-04\n",
      "it 24000, train loss = 6.138009e-04\n",
      "it 25000, train loss = 5.899172e-04\n",
      "it 26000, train loss = 5.705848e-04\n",
      "it 27000, train loss = 5.538251e-04\n",
      "it 28000, train loss = 5.388299e-04\n",
      "it 29000, train loss = 5.256788e-04\n",
      "it 30000, train loss = 5.148748e-04\n",
      "it 31000, train loss = 5.078132e-04\n",
      "it 32000, train loss = 5.047561e-04\n",
      "it 33000, train loss = 5.040532e-04\n",
      "it 34000, train loss = 5.039627e-04\n",
      "it 35000, train loss = 5.039181e-04\n",
      "it 36000, train loss = 5.038998e-04\n",
      "it 37000, train loss = 5.038848e-04\n",
      "it 38000, train loss = 5.038714e-04\n",
      "it 39000, train loss = 5.038588e-04\n",
      "it 40000, train loss = 5.038465e-04\n",
      "it 41000, train loss = 5.038343e-04\n",
      "it 42000, train loss = 5.038219e-04\n",
      "it 43000, train loss = 5.038073e-04\n",
      "it 44000, train loss = 5.037849e-04\n",
      "it 45000, train loss = 5.037415e-04\n",
      "it 46000, train loss = 5.036820e-04\n",
      "it 47000, train loss = 5.036263e-04\n",
      "it 48000, train loss = 5.035803e-04\n",
      "it 49000, train loss = 5.035428e-04\n",
      "it 50000, train loss = 5.035113e-04\n",
      "it 51000, train loss = 5.034835e-04\n",
      "it 52000, train loss = 5.034582e-04\n",
      "it 53000, train loss = 5.034340e-04\n",
      "it 54000, train loss = 5.034102e-04\n",
      "it 55000, train loss = 5.033857e-04\n",
      "it 56000, train loss = 5.033604e-04\n",
      "it 57000, train loss = 5.033309e-04\n",
      "it 58000, train loss = 5.032974e-04\n",
      "it 59000, train loss = 5.033594e-04\n",
      "it 60000, train loss = 5.032043e-04\n",
      "it 61000, train loss = 5.031357e-04\n",
      "it 62000, train loss = 5.030434e-04\n",
      "it 63000, train loss = 5.029169e-04\n",
      "it 64000, train loss = 5.027552e-04\n",
      "it 65000, train loss = 5.025233e-04\n",
      "it 66000, train loss = 5.022467e-04\n",
      "it 67000, train loss = 5.019210e-04\n",
      "it 68000, train loss = 5.015619e-04\n",
      "it 69000, train loss = 5.011935e-04\n",
      "it 70000, train loss = 5.008056e-04\n",
      "it 71000, train loss = 5.004244e-04\n",
      "it 72000, train loss = 5.000388e-04\n",
      "it 73000, train loss = 4.996421e-04\n",
      "it 74000, train loss = 4.992226e-04\n",
      "it 75000, train loss = 4.987418e-04\n",
      "it 76000, train loss = 4.982002e-04\n",
      "it 77000, train loss = 4.975966e-04\n",
      "it 78000, train loss = 4.968844e-04\n",
      "it 79000, train loss = 4.959859e-04\n",
      "it 80000, train loss = 4.947805e-04\n",
      "it 81000, train loss = 4.931590e-04\n",
      "it 82000, train loss = 4.914813e-04\n",
      "it 83000, train loss = 4.904555e-04\n",
      "it 84000, train loss = 4.899029e-04\n",
      "it 85000, train loss = 4.895847e-04\n",
      "it 86000, train loss = 4.893946e-04\n",
      "it 87000, train loss = 4.892807e-04\n",
      "it 88000, train loss = 4.892111e-04\n",
      "it 89000, train loss = 4.891684e-04\n",
      "it 90000, train loss = 4.891421e-04\n",
      "it 91000, train loss = 4.891259e-04\n",
      "it 92000, train loss = 4.891157e-04\n",
      "it 93000, train loss = 4.891150e-04\n",
      "it 94000, train loss = 4.891047e-04\n",
      "it 95000, train loss = 4.891011e-04\n",
      "it 96000, train loss = 4.890984e-04\n",
      "it 97000, train loss = 4.890958e-04\n",
      "it 98000, train loss = 4.890920e-04\n",
      "it 99000, train loss = 4.890801e-04\n",
      "it 100000, train loss = 4.890502e-04\n"
     ]
    }
   ],
   "source": [
    "# train with pure shear only, \n",
    "params_PS = init_node(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_PS)\n",
    "mdlnumber = 3\n",
    "params_PS, train_loss, val_loss = train_jp(loss_P11_PS, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = NODE_model(params_PS[0], params_PS[1])\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/NODE_params_PS.npy', 'wb') as f:\n",
    "    pickle.dump(params_PS, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train ICNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 9.417284e+00\n",
      "it 2000, train loss = 6.662537e+00\n",
      "it 3000, train loss = 7.045945e-01\n",
      "it 4000, train loss = 3.098629e-01\n",
      "it 5000, train loss = 2.589815e-01\n",
      "it 6000, train loss = 2.280548e-01\n",
      "it 7000, train loss = 1.949669e-01\n",
      "it 8000, train loss = 1.646976e-01\n",
      "it 9000, train loss = 1.413283e-01\n",
      "it 10000, train loss = 1.259102e-01\n",
      "it 11000, train loss = 1.166755e-01\n",
      "it 12000, train loss = 1.098154e-01\n",
      "it 13000, train loss = 1.024651e-01\n",
      "it 14000, train loss = 9.409273e-02\n",
      "it 15000, train loss = 8.447841e-02\n",
      "it 16000, train loss = 7.337204e-02\n",
      "it 17000, train loss = 5.966641e-02\n",
      "it 18000, train loss = 4.357409e-02\n",
      "it 19000, train loss = 2.982296e-02\n",
      "it 20000, train loss = 2.042897e-02\n",
      "it 21000, train loss = 1.490873e-02\n",
      "it 22000, train loss = 1.228063e-02\n",
      "it 23000, train loss = 1.124890e-02\n",
      "it 24000, train loss = 1.092930e-02\n",
      "it 25000, train loss = 1.081056e-02\n",
      "it 26000, train loss = 1.071496e-02\n",
      "it 27000, train loss = 1.062868e-02\n",
      "it 28000, train loss = 1.055427e-02\n",
      "it 29000, train loss = 1.049206e-02\n",
      "it 30000, train loss = 1.043986e-02\n",
      "it 31000, train loss = 1.039488e-02\n",
      "it 32000, train loss = 1.035501e-02\n",
      "it 33000, train loss = 1.031778e-02\n",
      "it 34000, train loss = 1.028083e-02\n",
      "it 35000, train loss = 1.024230e-02\n",
      "it 36000, train loss = 1.020052e-02\n",
      "it 37000, train loss = 1.015370e-02\n",
      "it 38000, train loss = 1.009989e-02\n",
      "it 39000, train loss = 1.003655e-02\n",
      "it 40000, train loss = 9.960797e-03\n",
      "it 41000, train loss = 9.869773e-03\n",
      "it 42000, train loss = 9.761119e-03\n",
      "it 43000, train loss = 9.633441e-03\n",
      "it 44000, train loss = 9.486801e-03\n",
      "it 45000, train loss = 9.324483e-03\n",
      "it 46000, train loss = 9.156916e-03\n",
      "it 47000, train loss = 9.001871e-03\n",
      "it 48000, train loss = 8.869789e-03\n",
      "it 49000, train loss = 8.760089e-03\n",
      "it 50000, train loss = 8.668998e-03\n"
     ]
    }
   ],
   "source": [
    "params_icnn_all = init_icnn(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_icnn_all)\n",
    "mdlnumber = 2\n",
    "params_icnn_all, train_loss, val_loss = train_jp(loss_P11_all, mdlnumber, F11_data, opt_state, key, nIter = 50000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = ICNN_model(params_icnn_all[0], params_icnn_all[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/ICNN_params_all.npy', 'wb') as f:\n",
    "    pickle.dump(params_icnn_all, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 7.391725e+00\n",
      "it 2000, train loss = 5.120892e+00\n",
      "it 3000, train loss = 3.253774e-01\n",
      "it 4000, train loss = 6.841917e-02\n",
      "it 5000, train loss = 6.748908e-02\n",
      "it 6000, train loss = 6.608127e-02\n",
      "it 7000, train loss = 6.391168e-02\n",
      "it 8000, train loss = 5.952445e-02\n",
      "it 9000, train loss = 4.645564e-02\n",
      "it 10000, train loss = 3.519838e-02\n",
      "it 11000, train loss = 2.789429e-02\n",
      "it 12000, train loss = 2.112285e-02\n",
      "it 13000, train loss = 1.703267e-02\n",
      "it 14000, train loss = 1.547484e-02\n",
      "it 15000, train loss = 1.465254e-02\n",
      "it 16000, train loss = 1.361430e-02\n",
      "it 17000, train loss = 1.227456e-02\n",
      "it 18000, train loss = 1.070822e-02\n",
      "it 19000, train loss = 9.059938e-03\n",
      "it 20000, train loss = 7.522836e-03\n",
      "it 21000, train loss = 6.305329e-03\n",
      "it 22000, train loss = 5.454968e-03\n",
      "it 23000, train loss = 4.892109e-03\n",
      "it 24000, train loss = 4.512497e-03\n",
      "it 25000, train loss = 4.225214e-03\n",
      "it 26000, train loss = 3.991726e-03\n",
      "it 27000, train loss = 3.801268e-03\n",
      "it 28000, train loss = 3.645970e-03\n",
      "it 29000, train loss = 3.512813e-03\n",
      "it 30000, train loss = 3.379351e-03\n",
      "it 31000, train loss = 3.207478e-03\n",
      "it 32000, train loss = 2.989102e-03\n",
      "it 33000, train loss = 2.804817e-03\n",
      "it 34000, train loss = 2.682409e-03\n",
      "it 35000, train loss = 2.591256e-03\n",
      "it 36000, train loss = 2.509806e-03\n",
      "it 37000, train loss = 2.430581e-03\n",
      "it 38000, train loss = 2.351746e-03\n",
      "it 39000, train loss = 2.272912e-03\n",
      "it 40000, train loss = 2.193650e-03\n",
      "it 41000, train loss = 2.114470e-03\n",
      "it 42000, train loss = 2.036362e-03\n",
      "it 43000, train loss = 1.961294e-03\n",
      "it 44000, train loss = 1.891413e-03\n",
      "it 45000, train loss = 1.827794e-03\n",
      "it 46000, train loss = 1.771055e-03\n",
      "it 47000, train loss = 1.720361e-03\n",
      "it 48000, train loss = 1.674898e-03\n",
      "it 49000, train loss = 1.633893e-03\n",
      "it 50000, train loss = 1.596612e-03\n",
      "it 51000, train loss = 1.562654e-03\n",
      "it 52000, train loss = 1.531274e-03\n",
      "it 53000, train loss = 1.501703e-03\n",
      "it 54000, train loss = 1.473000e-03\n",
      "it 55000, train loss = 1.444066e-03\n",
      "it 56000, train loss = 1.413969e-03\n",
      "it 57000, train loss = 1.383213e-03\n",
      "it 58000, train loss = 1.352991e-03\n",
      "it 59000, train loss = 1.323795e-03\n",
      "it 60000, train loss = 1.295450e-03\n",
      "it 61000, train loss = 1.268202e-03\n",
      "it 62000, train loss = 1.241019e-03\n",
      "it 63000, train loss = 1.214797e-03\n",
      "it 64000, train loss = 1.189366e-03\n",
      "it 65000, train loss = 1.164901e-03\n",
      "it 66000, train loss = 1.141538e-03\n",
      "it 67000, train loss = 1.119384e-03\n",
      "it 68000, train loss = 1.098576e-03\n",
      "it 69000, train loss = 1.079089e-03\n",
      "it 70000, train loss = 1.060957e-03\n",
      "it 71000, train loss = 1.044139e-03\n",
      "it 72000, train loss = 1.028569e-03\n",
      "it 73000, train loss = 1.014286e-03\n",
      "it 74000, train loss = 1.001138e-03\n",
      "it 75000, train loss = 9.890322e-04\n",
      "it 76000, train loss = 9.779027e-04\n",
      "it 77000, train loss = 9.676601e-04\n",
      "it 78000, train loss = 9.582359e-04\n",
      "it 79000, train loss = 9.495601e-04\n",
      "it 80000, train loss = 9.415943e-04\n",
      "it 81000, train loss = 9.342742e-04\n",
      "it 82000, train loss = 9.275275e-04\n",
      "it 83000, train loss = 9.213046e-04\n",
      "it 84000, train loss = 9.155536e-04\n",
      "it 85000, train loss = 9.116227e-04\n",
      "it 86000, train loss = 9.054558e-04\n",
      "it 87000, train loss = 9.009565e-04\n",
      "it 88000, train loss = 8.968129e-04\n",
      "it 89000, train loss = 8.929706e-04\n",
      "it 90000, train loss = 8.894081e-04\n",
      "it 91000, train loss = 8.861248e-04\n",
      "it 92000, train loss = 8.830661e-04\n",
      "it 93000, train loss = 8.802360e-04\n",
      "it 94000, train loss = 8.775902e-04\n",
      "it 95000, train loss = 8.751139e-04\n",
      "it 96000, train loss = 8.728473e-04\n",
      "it 97000, train loss = 8.706635e-04\n",
      "it 98000, train loss = 8.686475e-04\n",
      "it 99000, train loss = 8.667514e-04\n",
      "it 100000, train loss = 8.649652e-04\n"
     ]
    }
   ],
   "source": [
    "# train with uniaxial only\n",
    "params_icnn_UT = init_icnn(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_icnn_UT)\n",
    "mdlnumber = 2\n",
    "params_icnn_UT, train_loss, val_loss = train_jp(loss_P11_UT, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = ICNN_model(params_icnn_UT[0], params_icnn_UT[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/ICNN_params_UT.npy', 'wb') as f:\n",
    "    pickle.dump(params_icnn_UT, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 1.225846e+00\n",
      "it 2000, train loss = 9.301191e-01\n",
      "it 3000, train loss = 1.792584e-01\n",
      "it 4000, train loss = 6.556834e-02\n",
      "it 5000, train loss = 5.820786e-02\n",
      "it 6000, train loss = 4.905494e-02\n",
      "it 7000, train loss = 3.892144e-02\n",
      "it 8000, train loss = 2.889778e-02\n",
      "it 9000, train loss = 1.987820e-02\n",
      "it 10000, train loss = 1.256655e-02\n",
      "it 11000, train loss = 7.409242e-03\n",
      "it 12000, train loss = 4.329416e-03\n",
      "it 13000, train loss = 2.820422e-03\n",
      "it 14000, train loss = 2.257671e-03\n",
      "it 15000, train loss = 2.114239e-03\n",
      "it 16000, train loss = 2.061859e-03\n",
      "it 17000, train loss = 1.820031e-03\n",
      "it 18000, train loss = 8.898612e-04\n",
      "it 19000, train loss = 5.745568e-04\n",
      "it 20000, train loss = 5.015832e-04\n",
      "it 21000, train loss = 4.637416e-04\n",
      "it 22000, train loss = 4.252956e-04\n",
      "it 23000, train loss = 3.811273e-04\n",
      "it 24000, train loss = 3.285138e-04\n",
      "it 25000, train loss = 2.722813e-04\n",
      "it 26000, train loss = 2.390418e-04\n",
      "it 27000, train loss = 2.256600e-04\n",
      "it 28000, train loss = 2.194400e-04\n",
      "it 29000, train loss = 2.158786e-04\n",
      "it 30000, train loss = 2.136259e-04\n",
      "it 31000, train loss = 2.121217e-04\n",
      "it 32000, train loss = 2.110768e-04\n",
      "it 33000, train loss = 2.103311e-04\n",
      "it 34000, train loss = 2.097855e-04\n",
      "it 35000, train loss = 2.093792e-04\n",
      "it 36000, train loss = 2.090682e-04\n",
      "it 37000, train loss = 2.088249e-04\n",
      "it 38000, train loss = 2.086277e-04\n",
      "it 39000, train loss = 2.084641e-04\n",
      "it 40000, train loss = 2.083239e-04\n",
      "it 41000, train loss = 2.081994e-04\n",
      "it 42000, train loss = 2.080853e-04\n",
      "it 43000, train loss = 2.079777e-04\n",
      "it 44000, train loss = 2.078736e-04\n",
      "it 45000, train loss = 2.077720e-04\n",
      "it 46000, train loss = 2.076709e-04\n",
      "it 47000, train loss = 2.075677e-04\n",
      "it 48000, train loss = 2.074603e-04\n",
      "it 49000, train loss = 2.073480e-04\n",
      "it 50000, train loss = 2.072268e-04\n",
      "it 51000, train loss = 2.070927e-04\n",
      "it 52000, train loss = 2.069400e-04\n",
      "it 53000, train loss = 2.067578e-04\n",
      "it 54000, train loss = 2.065237e-04\n",
      "it 55000, train loss = 2.061945e-04\n",
      "it 56000, train loss = 2.056864e-04\n",
      "it 57000, train loss = 2.048521e-04\n",
      "it 58000, train loss = 2.034521e-04\n",
      "it 59000, train loss = 2.010862e-04\n",
      "it 60000, train loss = 1.970665e-04\n",
      "it 61000, train loss = 1.906598e-04\n",
      "it 62000, train loss = 1.822369e-04\n",
      "it 63000, train loss = 1.736853e-04\n",
      "it 64000, train loss = 1.660815e-04\n",
      "it 65000, train loss = 1.591394e-04\n",
      "it 66000, train loss = 1.525443e-04\n",
      "it 67000, train loss = 1.462726e-04\n",
      "it 68000, train loss = 1.404495e-04\n",
      "it 69000, train loss = 1.353214e-04\n",
      "it 70000, train loss = 1.311934e-04\n",
      "it 71000, train loss = 1.281747e-04\n",
      "it 72000, train loss = 1.261085e-04\n",
      "it 73000, train loss = 1.247510e-04\n",
      "it 74000, train loss = 1.238915e-04\n",
      "it 75000, train loss = 1.233512e-04\n",
      "it 76000, train loss = 1.229910e-04\n",
      "it 77000, train loss = 1.227390e-04\n",
      "it 78000, train loss = 1.225645e-04\n",
      "it 79000, train loss = 1.224393e-04\n",
      "it 80000, train loss = 1.223461e-04\n",
      "it 81000, train loss = 1.222728e-04\n",
      "it 82000, train loss = 1.222111e-04\n",
      "it 83000, train loss = 1.221574e-04\n",
      "it 84000, train loss = 1.221094e-04\n",
      "it 85000, train loss = 1.220652e-04\n",
      "it 86000, train loss = 1.220243e-04\n",
      "it 87000, train loss = 1.219861e-04\n",
      "it 88000, train loss = 1.219506e-04\n",
      "it 89000, train loss = 1.219176e-04\n",
      "it 90000, train loss = 1.218866e-04\n",
      "it 91000, train loss = 1.218573e-04\n",
      "it 92000, train loss = 1.218294e-04\n",
      "it 93000, train loss = 1.218030e-04\n",
      "it 94000, train loss = 1.217778e-04\n",
      "it 95000, train loss = 1.217539e-04\n",
      "it 96000, train loss = 1.217312e-04\n",
      "it 97000, train loss = 1.217096e-04\n",
      "it 98000, train loss = 1.216890e-04\n",
      "it 99000, train loss = 1.216693e-04\n",
      "it 100000, train loss = 1.216504e-04\n"
     ]
    }
   ],
   "source": [
    "# train with equibiaxial only\n",
    "params_icnn_ET = init_icnn(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_icnn_ET)\n",
    "mdlnumber = 2\n",
    "params_icnn_ET, train_loss, val_loss = train_jp(loss_P11_ET, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = ICNN_model(params_icnn_ET[0], params_icnn_ET[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/ICNN_params_ET.npy', 'wb') as f:\n",
    "    pickle.dump(params_icnn_ET, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 7.996592e-01\n",
      "it 2000, train loss = 6.100140e-01\n",
      "it 3000, train loss = 1.369548e-01\n",
      "it 4000, train loss = 4.573247e-02\n",
      "it 5000, train loss = 4.095813e-02\n",
      "it 6000, train loss = 3.499547e-02\n",
      "it 7000, train loss = 2.820212e-02\n",
      "it 8000, train loss = 2.085613e-02\n",
      "it 9000, train loss = 1.362650e-02\n",
      "it 10000, train loss = 7.739201e-03\n",
      "it 11000, train loss = 4.189850e-03\n",
      "it 12000, train loss = 2.527361e-03\n",
      "it 13000, train loss = 1.791708e-03\n",
      "it 14000, train loss = 1.463099e-03\n",
      "it 15000, train loss = 1.320192e-03\n",
      "it 16000, train loss = 1.263909e-03\n",
      "it 17000, train loss = 1.245847e-03\n",
      "it 18000, train loss = 1.241368e-03\n",
      "it 19000, train loss = 1.239289e-03\n",
      "it 20000, train loss = 1.235748e-03\n",
      "it 21000, train loss = 1.227911e-03\n",
      "it 22000, train loss = 1.209360e-03\n",
      "it 23000, train loss = 1.166483e-03\n",
      "it 24000, train loss = 1.085363e-03\n",
      "it 25000, train loss = 9.701966e-04\n",
      "it 26000, train loss = 8.380568e-04\n",
      "it 27000, train loss = 7.136393e-04\n",
      "it 28000, train loss = 6.213383e-04\n",
      "it 29000, train loss = 5.630971e-04\n",
      "it 30000, train loss = 5.302393e-04\n",
      "it 31000, train loss = 5.134419e-04\n",
      "it 32000, train loss = 5.051123e-04\n",
      "it 33000, train loss = 5.007227e-04\n",
      "it 34000, train loss = 4.977872e-04\n",
      "it 35000, train loss = 4.953119e-04\n",
      "it 36000, train loss = 4.931073e-04\n",
      "it 37000, train loss = 4.911835e-04\n",
      "it 38000, train loss = 4.895490e-04\n",
      "it 39000, train loss = 4.881772e-04\n",
      "it 40000, train loss = 4.870234e-04\n",
      "it 41000, train loss = 4.860414e-04\n",
      "it 42000, train loss = 4.851970e-04\n",
      "it 43000, train loss = 4.844680e-04\n",
      "it 44000, train loss = 4.838379e-04\n",
      "it 45000, train loss = 4.832971e-04\n",
      "it 46000, train loss = 4.828376e-04\n",
      "it 47000, train loss = 4.824502e-04\n",
      "it 48000, train loss = 4.821258e-04\n",
      "it 49000, train loss = 4.818550e-04\n",
      "it 50000, train loss = 4.816284e-04\n",
      "it 51000, train loss = 4.814371e-04\n",
      "it 52000, train loss = 4.812736e-04\n",
      "it 53000, train loss = 4.811312e-04\n",
      "it 54000, train loss = 4.810055e-04\n",
      "it 55000, train loss = 4.808930e-04\n",
      "it 56000, train loss = 4.807912e-04\n",
      "it 57000, train loss = 4.806986e-04\n",
      "it 58000, train loss = 4.806137e-04\n",
      "it 59000, train loss = 4.805357e-04\n",
      "it 60000, train loss = 4.804638e-04\n",
      "it 61000, train loss = 4.803971e-04\n",
      "it 62000, train loss = 4.803351e-04\n",
      "it 63000, train loss = 4.802775e-04\n",
      "it 64000, train loss = 4.802237e-04\n",
      "it 65000, train loss = 4.801734e-04\n",
      "it 66000, train loss = 4.801266e-04\n",
      "it 67000, train loss = 4.800827e-04\n",
      "it 68000, train loss = 4.800414e-04\n",
      "it 69000, train loss = 4.800027e-04\n",
      "it 70000, train loss = 4.799662e-04\n",
      "it 71000, train loss = 4.799318e-04\n",
      "it 72000, train loss = 4.798994e-04\n",
      "it 73000, train loss = 4.798687e-04\n",
      "it 74000, train loss = 4.798397e-04\n",
      "it 75000, train loss = 4.798122e-04\n",
      "it 76000, train loss = 4.797861e-04\n",
      "it 77000, train loss = 4.797614e-04\n",
      "it 78000, train loss = 4.797379e-04\n",
      "it 79000, train loss = 4.797155e-04\n",
      "it 80000, train loss = 4.796942e-04\n",
      "it 81000, train loss = 4.796739e-04\n",
      "it 82000, train loss = 4.796545e-04\n",
      "it 83000, train loss = 4.796359e-04\n",
      "it 84000, train loss = 4.796182e-04\n",
      "it 85000, train loss = 4.796012e-04\n",
      "it 86000, train loss = 4.795850e-04\n",
      "it 87000, train loss = 4.795694e-04\n",
      "it 88000, train loss = 4.795544e-04\n",
      "it 89000, train loss = 4.795401e-04\n",
      "it 90000, train loss = 4.795263e-04\n",
      "it 91000, train loss = 4.795134e-04\n",
      "it 92000, train loss = 4.795002e-04\n",
      "it 93000, train loss = 4.794879e-04\n",
      "it 94000, train loss = 4.794760e-04\n",
      "it 95000, train loss = 4.794645e-04\n",
      "it 96000, train loss = 4.794535e-04\n",
      "it 97000, train loss = 4.794428e-04\n",
      "it 98000, train loss = 4.794325e-04\n",
      "it 99000, train loss = 4.794225e-04\n",
      "it 100000, train loss = 4.794129e-04\n"
     ]
    }
   ],
   "source": [
    "# train with equibiaxial only\n",
    "params_icnn_PS = init_icnn(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_icnn_PS)\n",
    "mdlnumber = 2\n",
    "params_icnn_PS, train_loss, val_loss = train_jp(loss_P11_PS, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n9+X0DpHcdXpnp8M6JhvOYYyDVJUVO9MZ0L6R06EYY0r5btVu6kXW4LTuzZ0OpVyqmqGqWe7fZwE1RaQpsBtoV2LXtu5txgSk2NhYwk+MpuHpt3Bk42+cmL40oDpxlMWKaGMctCstmwOH8xjQvqHToSAi7URknoisE5G1InJPGfuIiPzbPdt/tYgMdCJWY3wtt6CQH9bs5YxeLRxfWvhYRKSluCsNERmK63P9ALAU6CIinUQkArgC+M65SI05toY9RtD4rLvJ2ZlA1pzXOP20GKdDOi4bzmGMg1bsSgdgQLuAOBNdANyvqstFJApYJiJzVHVdiX3G4rpU3AXX+Mo3KWecpTHBbPaavWTmFHDhgDaOxiEiM4AYoKmIJAF/A2oCqOp/gUuA20SkAMgGrlBVBQpE5E7gRyAcmOoeK21MwFm+M51/L8umU+NITmsknHHXbKKjo50O67iOW0SLSOMKHKdIVQ9WPRxjqpcVOw9SJyKcri3qVfoYaWlpAOHHydXj5qiqJgPJ7t8zRWQ9rslJJYvo84EP3B/Si0WkoYi0cj/WmJDx4eIddGhSh5NObFqpx7vzsjzF+VqRvLzyOPe/BrxWzn2zgFnHjtQYZ23al8lN7y+lef1IPr91BM2ijtfRMXBU5Ez0HvfPsQamhAM2pdcYD63YmU6/tg2rtFJh69atwdUHdtkxdvMoR0WkIzAAWFLqrrJm/LfBXXwbEwoS92awdHs6j47rQVhY5cZktm7dmtatW+P6vvknxflqn52mWktKP8J1U3+nZngY028aRrMo/y9oVBUVKaLXq+qAY+0gIiu8FI8x1UZOfiFr92Qw4ZQTqnScHj16sHLlygRVHVzePp7kqIjUA74E7lXVjMrEZC2zTCArbqMVExNT5iXjDxfvIKJGGJcMalvp5+jRowcrVpSddiKSoKqD7bPTVGd7DmZz1dtLyMot4NMJ0bRvUsfpkDxWkSK6IoNSAn/gijEBZs3uQxQUaZU7c8TFxVG7du3j7VahHBWRmrgK6I9U9asydqnQjH9rmWUCVXEbrby8PCIiIpg7d+4fCums3AK+Xr6bc/u2plHdiCo9TwXYZ6eplvYeyuGqtxeTfjiP6TcPo2fr4Fxs7LhFtKrmAIhIE+AyIAdYCySoanbJfYwxFbdi50EA+rdrWKXj1KrlWgq1qjnqnuH/Lq6rT6+Us9t3wJ0i8gmuCYWHbDy0CSaxsbHk5eVRWFhIXl4esbGxfyiiv16xm8N5hVwb3aFKz1OclwcOHOCzzz6jVq1a9OrViz59+hzdxz47TXWUkuEqoPdn5fHB+KFV/gx0kifdOb4GfgZuAzbiWiFpq6p290lkxoS4FbvSade4tjfHgFU1R08CrgUSRGSle9sjuMdsujsBzALGAZuBI8CN3greGH+IiYkhIiLi6JnomJiYo/epKh/G7aB3m/r089IywxdeeCGjR4/mzTffpGvXrsVnqHt55eDGBJnUzFyufHsxezNy+OCmoQwM8jUSPCmio1T1aRG5SFVPFZGLgX6+CsyYULdi50GGdKxI85sKq1KOqupCjj2BGHdXjjuqGKcxjomOjmbu3Llljoleuj2dDfsyeeHiPl5b5CEzM5MnnniCr776ivnz5/Pll19yySWXpHvl4MYEkf1ZuVz19mL2HMzh/RuHMNi7n3+O8KQlQPFlp1wRqa2qXwJneCMIEfmLe3GHNSIyQ0RqeeO4xgSq5EPZJB/K8fYiKz7LUWNCSXR0NJMmTfrTpMIPF+8gqlYNzu3nvWW+i4d1REZGkp2dzcUXXwwQnANAjamktMN5XPPOEnalH2HqDUMYdkITp0PyCk/ORL/s7mv5KTBVRH4DGlY1ABFpA9wN9FTVbBH5DNfKSu9X9djGBKqV7vHQXl7u2yc5akx1kJqZyw9rkrlmeAfqRHhvHbIHHniAtLQ0Lr/8cm666SZGjBgBrtZ2xlQLKRk5XPPuEnYcOMK71w8h+sTQKKDBgzPRqvqlqqa5JxzNwjVD/yIvxVEDqC0iNYA6uPpSGxOyVuw6SESNMHq28t4JKR/nqDEh7bP4XeQXKtcMr9qEwtIuvvhiGjduzH333ce4cePYtWsXwBavPokxAWpX2hEufSuO3enZvHfjEEZ2qdziRYHquEW0iFwvIvtFJE1EPhCRKFWdrqp/LbUccKWo6m7gZWAnrgUbDqnqT1U9rjGBbPmOdPq0aUBEjcovslJs2rRpAP18laPGhLrCIuXjJTsZcWITTmxW+dVDS5o2bRpNmzalcePGXHfddWRmZnLttdfy4osvwv+GXhkTsjanZHHpf+NIP5zHhzcPY0QlV/8MZBX5BH8cGAN0B3YAf/dmACLSCNdSwp2A1kBdEbmmjP0miEi8iMSnpqZ6MwRj/CqvoIiE3YcY4KW2PpMnTwZXNw6f5KgxoW5eYgq7D2ZzrRfPQk+ePJk5c+aQmJhIhw4deOSRR7x2bGMC3do9h7j8rTgKior4dGK0t4cuBoyKFNEZqrpCVVNU9XFgqJdjGA1sU9VUVc0HvgJGlN5JVaeo6mBVHdysWTMvh2CM/yTuzSC3oMhrbyr169cHyPZhjhoT0qYv3kHzqEhG92zhtWPWr1+fAQMG0Lx5cyZPnszvv//utWMbE8iW7UjniimLiawRxmcTo+nhxWGLgaYiRXQr91ngU0SkGVDTyzHsBIaLSB33Yg+jgPVefg5jAsaKo5MKG3rleMnJyQBNfZijxoSsHQcOs2BTKlcObU/N8KoPryqWnJzMlClTWLBgAampqeTn53vt2MYEqoWb9nPtu0toUjeCz26N5gQvDY8KVBWZgvw3oA9wtfvfeiIyC1gFrFbVGVUJQFWXiMgXwHKgAFiBe7lgY0LRip3ptKgfSasG3unk+NRTTzFx4sTawGR8kKPGhLK3FmylZlgYVw5t79XjPvXUUyQkJPDRRx+RkJBAVlYW48aNo1+/fgDB3yDXmFI+WrKDv327ls7N6/HB+KE0jwr9bsUVWfb7DwWtiLTF9UHdF9fKZVX+gFbVv+Eq1o0JeSt2HWRAu0ZeW8xhwoQJTJw4cZeqngq+yVFjQtGeg9l8Hr+LSwe3o6WXvtQWmzBhwh9uJyUlkZCQwOrVqwG8sxyiMQGgoLCIO9/9hdlbc+nfvCbTb40mqlb1uCBake4co9yXiAFQ1SRV/UFVX1DVa30bnjGhZX9WLjsOHGFgh4ZeO+bcuXOhxBdiy1FjKuat+VtQhdtOPdHrx547dy4lJ8G3bduWsWPH8tBDDwFs8/oTGuOAjJx8Lnr1Z2ZvzSUz/jt+evwi1qyIdzosv6nIcI45QIqIFAFrgARgtfvftaqa68P4jAkpvlhkZcyYMQC9RGQPlqPGVEhKRg4zlu7iooFtaNe4jtePP2bMGJo3b05YWBi9e/emT58+9O3blz59+gB45zKUMQ7aceAw46fFszUlj/QfXydj5WzCw8OJjY3902qgoaoisyjuwrX4yb+BZ4BEYBDwD1zttIwxFbRiVzo1woTerb13Nfc///kPQB6Wo8ZU2JQFWyksUu44rbNPjv+f//yH1q1bc/fdd/PYY4/RvXt3li1bxv333w+uoVbGBK0lWw9wweuL2J+Vy6Mn1Sd/w3zCw8OJiIggJibG6fD85rhFtKq+DpwEKPAvIB+4R1VPU9WWvg3PmNCyYudBerSqT+0I7636e8cdd4CrcLYcNaYC9mfl8uGSHZzfrzUdmtT1yXPccccdLFq0CBHh3nvvpWbNmrz66qvMmzcPXJN+K0REpopIioisKef+q0VktYgkiMhvItKvxH3b3dtXikj1ucZufOrDxTu4+p0lNK4bwTe3n8T4c09h7ty5TJ48mblz51abs9BQwWW/VTVbVV8ATgM6A7+LyDCfRmZMiCksUlbtOui11nalqOWoMRXzzq/byC0o4nYfnYUuVrt2bR566CHmzZvH5s2bGTp0KEuWLPH0MO8DZx3j/m3AqaraB1eHntLdrU5T1f6qOtjTJzampLyCIiZ9lcBj36zh5C5N+er2k+jY1PUlNDo6mkmTJlWrAhoqMCZaRE7BtRJad6AH0BzIBJr4NjRjQsumlEwO5xV6vYhesGABuPpEv4LlqKlG4uLiiI2NJSYmpsIf3umH85get51z+ramc3Pf9bBdsGABiYmJJCYmsn79elJSUoiKiuLAgQMeHUdVF4hIx2Pc/1uJm4uBtpWL2JjypWTmcNuHy1m2I53bY07k/jO6ER5mQ/srMrEwFlgJfAL8W1W3+zAeY0LW8h0HARjQzrvLn7rHnzUD9mI5aqqJuLg4Ro0aRV5eHhERERW+jDx10TYO5xVyp4/PQsfExNC/f3+uuOIK7r77bjp27OjT53MbD/xQ4rYCP4mIAm+VbllbTEQmABMA2rf3br9sE/xWJx1kwgfLOJidx3+uHMC5/Vo7HVLAqMhwjtuARcDZwBIRWScin4rIYyJygU+jMyaE/LZlP82jIunQxLudAN58802ALCxHTTUSGxtLXl4ehYWF5OXlERsbe9zHHMrO5/1F2xnbuyXdWkb5NL4333yTk046iZkzZzJs2DB69uzJ5ZdfzjPPPAPQ0NvPJyKn4SqiHyqxeaSqDgTGAne4ryz/iapOUdXBqjq4WbNmZe1iqqmvVyRxyX/jCA8TvrxthBXQpVRksZW3St4utZDDxcA3PonMmBBSVKT8tuUAMV2beW2RlWITJ07k1ltvLW+xFctRE5JiYmKIiIg4eia6Ih0Bpv22nczcAu483bdnocGVlyWVWmzFq5ejRKQv8A4wVlWPjhdR1d3uf1NE5GtgKLDAm89tQlNBYREvzE7k7V+3MaxTY964eiBN6kU6HVbAqchwjj9Q1SQgiT9eMjLGHMO65AzSDucxsktTnz+X5aipDqKjo5k7d26Fx0Rn5uTz7sJtjO7RnF5ebDFZUW3btj264MrDDz/stcVWRKQ98BVwrapuLLG9LhCmqpnu388AnvbW85rQdfBIHnfNWMGvm/ZzXXQHHj+nJzXDK9SHotqpyMTC5e7LQVXax5jq7NdN+wEY2dn7RfTAgcdPPctRE4qio6MrPKHwvUXbOZSdz12nd/FxVC4DBw5k+fLlx9yngp+vM4AYXJOHk4C/ATUBVPW/wBO4JhG/4b7KVeDuxNEC+Nq9rQbwsarOrsprMqFv475Mbvkgnj0Hs3n+oj5cMdTGyB9LRc5E9xCR1ce4XwD/f603Jogs2ryfbi2iaF6/ltePvX79eoCex8hTy1FTre09lMObsVs4s1cL+rVr6JfnXL9+PX37lrumSk8RSaACeamqVx7n/puBm8vYvhXo9+dHGFO2H9fu5b5PV1I7ogafTBjOoA6NnQ4p4FWkiO5egX0KqxqIMaEqJ7+Q37ence3wDj45fmJiIh07dtwMnHuM3SxHTbX1/A/rKVTlsbN7+u05ExMTy72vRL5aXhrHFRUp//llM//8eSN92zbgrWsH0apBbafDCgoVmVh4zGWDReQh9yIPxpgyLN2eRl5Bkc/GQ3fo0AEgr7xctRw11dmyHWl8s3IPd57WmXaNvdsZ51jceVmePOAKy0vjtMO5Bdz/2Spmr93LRQPa8PeL+lCrpvdW1A11Hk8sFJHPSt4E+gP2RmBMORZu2k9EeBjDOvnn0pjlqDEuRUXKU9+vo0X9SG6LOdHRWC677LKSN0/ANQTD8tI4ZueBI0yYHs/GfZk8dnYPxo/s5PXuUaHO4yIayHCPwQJARN70YjzGhJxfN+1nYIeG1ImoTLpViuWoMcAXy5NYnXSIf17ej7qRfsu/MtWvX5933nkHABHZCix1NCBTrf22eT+3f7wcVZh201BO7mL9wSujMj1Lni11+1FvBGJMKNqflcu65Ax/v0FZjppqLzMnnxdnb2Bg+4Zc0L+N0+Hw6KN/SkPLS+N3qsr7i7Zx7dTfaVovkm/vOMkK6CqoTJ/obaVup3kvHGNCy6LNvmttVx7LUWPgtXmb2Z+Vy7vXDw6IS9SdOnX6w23LS+NvuQWF3DplHvN25jKoZU3ev3UEUbVqOh1WUKty92wReej4exlTPS3ctJ8GtWvSu41zHeYsR011s23/YaYu3Malg9r6raWdpywvjT+lZOZw7is/M29nLhlxn/LDIxeyZkW802EFPZtYaIyPqCoLN+9nxIlNCA/z35kwy1FT3T07cx0R4WE8eFY3p0M5yiYWGqesTjrIxOnLSD2Ux4HvXiJr/a+Eh4cTGxtb4cWKTNlsYqExPrIl9TDJh3K483T/DeVwsxw11db8jan8vD6Fh8d2p3mU9xc3qiybWGic8O3K3fz1i9U0rRfJ5JiGjP93POHh4URERBATE+N0eEGvMkW0TVoypgIWbkoF4OTOfp+0UakcFZGpwDlAiqr2LuP+GOBboHjM9Veq+nTlwzTGu7JyC3jsmwQ6NqnDjSd1dDqcP7CJhcafCouUF39M5K35WxnaqTFvXj2QJvUi6Th3LrGxscTExNhZaC847phoEbleRPaLSJqIfADsL3m/TY4wpmwLN++nfeM6tG/i2wUepk2bBtDPCzn6PnDWcfb5VVX7u3+sgDYB5dmZ60lKz+alS/sRWcPZBSOmTZtG06ZNady4Mddddx1Nm/7xipR9dhpfycjJ5+ZpS3lr/lauHtaeD8cPo0m9SACio6OZNGmSFdBeUpGJhY8DY3At/70D+LtPIzImBOQXFrF4a5rPViksafLkyQAbqWKOquoCwD7YTVCatyGFGb/vZMLJJzCko38WNjqWyZMnM2fOHBITE+nQoQOPPPKI0yGZamBrahYXvL6IXzft55kLevPshX2IqFHlHhKmHBUZzpGhqivcvz8uIkt8GZAxoWDlroNk5RZwchVb28XFxR330lv9+vUBslU1Bd/naLSIrAL2AA+o6lofPpcxFXLwSB4PfbGabi2i+MuYrk6HA7jycsCAAYCroB42bJjDEZlQN39jKnd+vJya4WF8ePMwhp/QxOmQQl5FiuhWIjIBSATWA9ZU0Jjj+HXTfsIERpxY+SI6Li6OUaNGkZeXR0REBHPnzi2zkE5OTgZoKiKn4NscXQ50UNUsERkHfAN0KWtH93vGBID27dv7KBwT7CryJbEiHv92LWmH85h6wxBq1XR2GEex5ORkpkyZQvfu3enRowf5+flOh2RClKry7sJt/H3Werq2iOLt6wbTrrFvhxEal4oU0X8D+gBXu/+tJyKzgFXAalWd4cP4jAlKCzel0qdtQxrUqXw9GxsbS15eHoWFheTl5ZXbjuipp55i4sSJtYHJ+DBHVTWjxO+zROQNEWmqqvvL2HcKMAVg8ODB6o3nN6Glol8Sj+f7VXv4ftUeHjijq6P92Et76qmnSEhI4KOPPiIhIYGsrCzGjRtHv379AJwfb2JCQk5+IY9+vYYvlycxtndLXr7U+SXuq5Pj/pd2fxgeJSJtcX1Q9wXGAVZEG1NCRk4+q5IOcdupJ1bpODExMURERBwtMsprRzRhwgQmTpy4S1VPBd/lqIi0BPapqorIUFxzKg5449im+qnol8RjScnI4fFv19C/XUNurWK+eduECRP+cDspKYmEhARWr14NEDjVvglaKRk5TJi+jJW7DnLv6C7cfXoXwvy4JoGp3LLfSUAS8IO3ghCRhsA7QG9AgZtUNc5bxzfGn+K2HKCwSKs8qTA6Opq5lWhHVNkcFZEZQAyuoSFJuK5C1XQf87/AJcBtIlIAZANXqKqdZTaVUtEvieVRVR76cjU5+YX847J+1AgP7MlTbdu2pW3btowdO5aHH3542/EfYUz5Vicd5JYP4snILuDNqwcytk8rp0OqlgLlnP+rwGxVvUREIgAbzGOC1sJN+6kTEc7A9o2qfKzo6Gi/tSJS1SuPc/9rwGt+CcaEvMp+SSz2ydJdzNuQypPn9uTEZvV8FKUxgafkAipf3jaCnq3rOx1SteV4ES0iDYBTgBsAVDUPyHMyJmMqq6hI+XHtXk7u0tTaChlzHJX9krhm9yGe+n4tJ3VuwnXRHb0fWACpwCJIgutE1DjgCHCDqi5333c98Jh712dUdZp/oja+UFSk/GPOBl6ft4WhHRvzxjUDaeru/2ycUeFPeRG5VESi3L8/JiJfichAL8TQCUgF3hORFSLyjojU9cJxjfG7+B3ppGTmMs6BS2s+zFFjAsaBrFwmTl9GozoR/OvyAQE/BvTzzz8nMzMTgGeeeYaLLroIPLva+j7HXgRpLK4uOV1wdcR5E0BEGuMakjUMGAr8TUSqfnnMOCIrt4AJ05fx+rwtXDGkHR/ePMwK6ADgyamyx1U1U0RGAqOBd3EnaxXVAAYCb6rqAOAw8HDpnURkgojEi0h8amqqF57WGO+blZBMRI0wRvVo4cTT+ypHjQkI+YVF3PHxcvZn5fLWtYNoFhX4RcTkyZOJiopi4cKF/Pzzz4wfPx6gwn0fK7AI0vnAB+qyGGgoIq2AM4E5qpqmqunAHI6/IqkJQLvSjnDxG78xb0MKT57bk+cusgVUAoUn/xcK3f+eDUxR1ZlAhBdiSAKSVLV4gYgvcBXVf6CqU1R1sKoObtasmRee1hjvKipSfliTTEzXZtRzpsWQr3LUmIDw7Mz1LN6axnMX9aFv24ZOh1Mh4eGuvtUzZ85kwoQJnH322eDZZ+/xtAF2lbid5N5W3vY/sZNUgWvx1gOc99pC9mbkMO3GodxwUidcI3hMIPAkkXeLyFvA5cAsEYn08PFlUtW9wC4R6ebeNApYV9XjGuNvy3amsy8jl7P7OjZL2ic5akwg+Dx+F+//tp3xIztx0cC2TodTYW3atGHixIl8+umnjBs3jtzcXKdD+hM7SRWYPl6yk2veWULjuhF8c8dJVe74ZLzPkw/Yy4AfgTNV9SCuZvEPeimOu4CPRGQ10B/4u5eOa4zfzFzt6FAO8G2OGuOYlbsO8ug3azipcxMmje3udDge+eyzzzjzzDP58ccfadiwIWlpaeA6K+wtu4F2JW63dW8rb7sJcAWFRTz53Voe+TqBkzo35es7TqJTU5sqFogqfM1ZVY8AX5W4nQwkeyMIVV0JDPbGsYxxQvFQjlOdG8rh0xw1xikpmTlMnB5P86hIXrtyYMD3gy6tTp06xZMJAWjVqhVARrkP8Nx3wJ0i8gmuSYSHVDVZRH4E/l5iMuEZwCQvPq/xgUNH8rlzxnJ+3bSfm0d2YtK4HoQH+OTZ6iwQunMYE/SWFw/l8EJXjri4OJ577jni4jxbb8hy1ISa7LxCbp2+jIzsAt6+bjCN6gbfEP+qdudwL4IUB3QTkSQRGS8it4rIre5dZgFbgc3A28DtAKqaBkwGlrp/nnZvMwFqS2oWF76xiMVbDzChf13CE77l9yWLnQ7LHIMnp8weV9XPS8z8fwnXzP9hPonMmCAy82hXjuZVOk5cXByjRo06uorb3LlzPemjazlqQkZeQRG3fbSMFbsO8sZVA+nRKjgXlJg8eTKXXnrp0e4cDz74IF9//bUn3TmOtwiSAneUc99UYKpnERsn/LoplTs+Wk6N8DAeia7HHZedVdnPAeNHgdCdw5igVlSk/JCwl1O7NiOqVs0qHSs2Npa8vDwKCwvJy8sjNjbWk4dbjpqQUFik/OXTlcRuSOXvF/YJ6iWN/dCdwwS5D+K2c8N7S2nVoDbf3nESexMWVeVzwPhRZbpzXIHN/DfmqBW70tmbkeOVoRwxMTFEREQQHh5OREQEMTExnjzcctQEPVXlka8SmJmQzKPjenDl0AqftA1Ixd05Pvnkk4DtzmGckV9YxGPfJPDEt2s5rVszvrx9BO0a16nq54DxI0+Gc1yGq1H7y6p60N3M3Wb+m2rv/1Z7ZygHuJZBnjt3LrGxscTExHh6Cc9y1PhMXFxcZf8uK0xVeWbmej6N38Xdp3fmllNO8Mnz+NNnn33G7NmzeeCBB2jYsCHJycng3e4cJggdOpLP7R8vY9HmA0w89QT+emb3oxMIq/g5YPzIkyI6G6gLXAk8DdQEDvogJmOCRvFQjlO6VH0oR7Ho6OjKvmlajhqfqOJY/Qp7de4m3l24jRtGdOQvY7p6/fhOqF27NocPH2bGjBk88cQT5OfnAxQ4HZdxztbULG6eFs+u9CO8dElfLh3c7k/7VOFzwPiRJ5d63wCG4/qABsgEXvd6RMYEkaNDOfq2dDoUsBw1PlLFsfoV8u7Cbfzr501cMqgtT5zTM2RWZbv99ttZvHgxM2bMACAqKgqgg6NBGccs2ryfC15fxMHsfD6+ZXiZBbQJHp6ciR6mqgNFZAWAqqaLiE1aMtXazNV7iQiv+gIrXrpUbjlqfKJ4jGbxmWhvj9F8e8FWnp21nrG9W/L8RX0IC6G+uEuWLGH58uUMGDAAgEaNGgGEzgs0FfbRkh088e1aTmxWl3evH0K7xhXudGgClCdFdL6IhAMKICLNgCKfRGVMECheYOWUrk2pX4WhHF68VG45anzCV2M0VZXnZyfy1vytnN23Ff+8rH/QLaZyPDVr1qSwsPDomfXU1FSHIzL+VlikPDtzPVMXbSOmWzP+c+UArw3/M87ypIj+N/A10FxEngUuAR7zSVTGBIEVuw6SfCiHv57VrUrHKetSeSWLFMtR4zPeHqNZUFjEpK8S+HxZEtcMb89T5/UOyZXZ7r77bi688EJSUlJ49NFH+eKLL8BWEq02MnPyueeTlfySmMKNJ3Xk0XE9Qu6LYnVWoSJaXF+hFwDLgFG4LkVdoKrrfRibMQFtVkKyV4ZyePFSueWoCQo5+YXcNWMFc9bt455RXbh3dJeQGQNdkqpyyimnMGjQIObOnYuq8s0339CzZ890p2Mzvrcr7Qg3T4tnc2oWz1zQm2uG21D4UFOhIlpVVURmqWofINHHMRkT8PILi/h+1R5O6dqsSkM5wKuXyi1HTcDLyMnn5mnxLN2exlPn9eL6ER2dDslnRIRx48aRkJBA9+7dnQ7H+NGyHelM+CCe/MIipt04lJFdmjodkvEBT4ZzLBeRIaq61GfRGBMk5qzbR0pmLlcO9c7Mai9dKrccNQFtX0YON7y3lE37MvnX5f05v38bp0PyuYEDB7J06VKGDBnidCjGT75ftYf7P19Fqwa1ePf6IXRuXs/pkIyPeNSdA7haRHYAh3FdLlZV7euTyIwJYNPjdtCmYW1iulV9gRUvshw1AWvZjnRu+3AZmTkFvHP94EDLHZ9ZsmQJH330ER06dKBu3bqoKkBPp+My3qeqvPbLZv4xZyNDOzbmv9cOonFda5AUyjwpos/0WRTGBJHNKZnEbT3AX8/qVqWJUD5YAc5y1ASkGb/v5Ilv19CqQW0+GD+U7i3rOx2S3/z4449/2taxY8fNDoRifCi3oJBJXybw1YrdXDigDc9f3IfIGuFOh2V8zJMi+nZVfajkBhF5AXionP2NCUkfLt5JRHgYl1WhSb6PVoCzHDUBJa+giCe/X8vHS3Zycpem/OfKATSsU73OzL3xxhu88MILpTc3cyIW4xvph/OYOH0Zv29P474xXbnr9M4hOVHW/JknfVbGlLFtrLcCMSYYHMkr4MtlSYzt05Km9SIrfRwfrQBnOWoCRkpmDle9vZiPl+xk4qkn8P6NQ6tdAQ0wZ86csjY38Hccxje2pmZx4RuLWJl0kFev6M/do0Kz04wp23HPRIvIbcDtwIkisrrEXfWBRb4KzJhA9O3KPWTmFnBtFVsVeXMFuDfffBPcYywtR00gWLYjjds/Wk5GdgH/uXIA5/Zr7XRIfvfmm2/yxhtvsGXLFvr2/d+0hIyMDIBsxwIzXvP7tjQmTI8nTIQZtwxjUIfGTodk/Kwiwzk+Bn4AngMeLrE9U1XTfBKVMQFIVZket4PuLaMY1KFRlY7lzRXgrrrqKm6//fbNwFosR42DCgqLeG3eZv7zy2ZaN6zFl7eNoGfr6jP+uaSrrrqKsWPHMmnSJJ5//vmj26OiomjSpMk2B0MzXvDtyt08+Plq2jauzfs3DKV9E1vCuzo6bhGtqoeAQyLyFZCmqpki8hgwUEQmq+oKn0dpTABYsesg65IzePbC3l65XOetFeAaNGgAkAdYjhrH7Eo7wj2frGD5zoNcOKANT53fq8o91INZgwYNaNCgARdddBGNGzcmKiqKZ555huXLlwPUdjo+UzmqyuvzNvPyTxsZ2qkxU64dVC2HKRkXT8ZEP+7+cB4JjAbeBf7rm7CMCTwfxu2gXmQNLqhCb9u4uDiee+454uLivBjZUZajxhFfr0hi7Ku/smlfFq9e0Z9/Xt6/WhfQJU2ePJmoqCgWLlzIzz//zPjx4wE8Gg8mImeJyAYR2SwiD5dx/z9FZKX7Z6OIHCxxX2GJ+76r8guqxn5d9BtnPfEhL/+0kfP7t2b6+Oo5zt/8jyfdOQrd/54NTFHVmSLyjA9iMibgpB3O4/9WJ3PF0HbUjfQkbf7HRx05SrIcNX6VkZPP49+s4duVexjSsRGvXNafdo3tsnZJ4eGuNmczZ85kwoQJnH322eDBCSwRCQdexzVxOAlYKiLfqeq64n1U9S8l9r8LGFDiENmq2r8qr8HA3AWLuPa/C4ho35fDS77g8vNusRZ2xqMz0btF5C3gcmCWiER6+Hhjgtbn8bvIKyzimipMKPRRR46SLEeN3/y8bh9nvLKA/1udzP1juvLJhGgroMvQpk0bJk6cyKeffsq4cePIzc319BBDgc2qulVV84BPgPOPsf+VwIxKhmvKkHwomwdnJ1OzTU/2z/oX6b9OZ/78+U6HZQKAJx+wlwE/Ameq6kGgMfCgL4IyJpAUFSkfLtnBsE6N6doiqtLHKe7IER4eXuWOHOWoUo6KyFQRSRGRNeXcLyLyb/cl5dUiMtArUZugsj8rlzs/Xs7NH8TToHZNvrxtBHeN6lKlhYdC2WeffcaZZ57Jjz/+SMOGDUlLSwPXGeWKagPsKnE7yb3tT0SkA9AJ+KXE5loiEi8ii0XkAs+iN4l7M7jw9d/IDqvNwe/+Ts66eb56/zZBqMLXpVX1CK6JS8W3k4FkXwRlTCCZvymVXWnZPHRW9yodx5sdOcrihRx9H3gN+KCc+8cCXdw/w4A33f+aakBV+XL5bp6ZuY4juYXcP6YrE089kYgadrHjWOrUqcNFF1109HarVq0AMnz0dFcAX6hqYYltHVR1t4icAPwiIgmquqXkg0RkAjABoH379j4KLfj8tnk/E6cvo25kDb6642QOnv0vn71/m+BUucGdxlQjH8btoFlUJGf0bFnlY3mrI4cvqOoCEel4jF3OBz5QVQUWi0hDEWnlLtZNCNuVdoRHvk7g1037GdyhEc9f3IfOzSt/VcZ4ZDdQcnnUtu5tZbkCuKPkBlXd7f53q4jE4hovvaXUPlOAKQCDBw9Wr0Qd5L5ZsZsHv1hFp6Z1ef/GobRuWBtaBe77t3GGFdHGHMPGfZn8siGFu07rbGfcyr+sbEV0iDqSV8B/52/lrflbqBEmPH1+L64Z1oEwG7rhT0uBLiLSCVfxfAVwVemdRKQ70AiIK7GtEXBEVXNFpClwEvCiX6IOUqrKm/O38OLsDQw/oTFvXTuYBrWt04wpmxXRxhzDKz9tpF5EDW4a2anSx4iLi6t2lwDt8rDv+fLvSlX5btUenv8hkeRDOZzbrzWTxnZ3nY0zfqWqBSJyJ675DuHAVFVdKyJPA/GqWty27grgE/eVomI9gLdEpAjXHKjnS3b1MH9UWKQ8+d1api/ewXn9WvPSpX2tA4c5poos+50JlHV5RwBVVa8sR+Vu4xMP7FbVc7xxTGOqIiHpELPX7uXe0V0q3QvUD23tiIqKAhggIqXHWXo1R/HgsrJdHvYtX/5dJSQd4qnv1xK/I51erevz6hUDGNrJljP2VFRUVJmLMrlr3AF/uuMYVHUWMKvUtidK3X6yjMf9BvTx5Lmqq5z8Qv7y6Up+WLOXiaecwENndbcrLua4KrJiob8Gvt0DrAeq5xqxJuC8MmcDDevUrNJZ6LLa2nm7iM7MzEREVqjqYK8e+M++A+4UkU9wTSg8ZOOhneGLv6vdB7N59eeNfL4siSZ1I3jh4j5cMqiddd2opMzMzHLvExFbRTSAZOTkM+GDeBZvTePxc3oyvgrv+aZ68Wg4h3t8VRegVvE2VV1Q1SBEpC2uBSKeBe6r6vGMqaplO9KYtyGVh87qXqWV14rb2hWfMfR1W6Sq5KiIzABigKYikgT8DajpPsZ/cZ0JGwdsBo4AN3ozdlNx3vy72nsoh9fnbeaTpTsRhJtHduKuUV1sxUEvSk9PZ9OmTeTk5BRvqudkPOZ/UjJyuP69pWzal8mrV/Tn/CqsSGuqnwoX0SJyM66zxW2BlcBwXBMYTvdCHP8C/gqUe9bbxlgaf/rHTxtpWi+C60dUfnEV8H1bu5KqmqOqeuVx7ldKzfw3zvDG31VKZg5vxm7hoyU7KSpSLhvSjjtO60wbG/fsVe+88w6vvvoqSUlJ9O/fn8WLFwO0djouA9v2H+a6qUs4kJXH1BuGcErXZk6HZIKMJ2ei7wGGAItV9TT3TOC/VzUAETkHSFHVZSISU95+NsbS+Mtvm/fz25YDPHFOT+pEVH3urR/b2vkkR01gquzf1YGsXN5asJUP4raTX6hcPLANd53exVYb9JFXX32VpUuXMnz4cObNm0diYiI9evQoPP4jjS8lJB3ihvd+R4EZtwynX7uGTodkgpAnFUKOquaICCISqaqJItLNCzGcBJwnIuNwXYKuLyIfquo1Xji2MR5RVV7+aQMt69fiqmFBd8XDVzlqQkDa4TymuIvnnPxCLujfhrtHdaFj07pOhxbSatWqRa1artFVubm5dO/eHUoMtzL+99uW/dwyLZ5GdSP44KahnNDMRteYyvGkiE4SkYbAN8AcEUkHdlQ1AFWdBEwCcJ+JfsAKaOOU2A2pLN95kGcv7E2tmp63NnK4nZ1PctQEt4NH8nj71628v2g7R/ILOa9fa+4e1YUTrXDwi7Zt23Lw4EEuuOACxowZQ6NGjQBynY6rupqzbh93fLycjk3qMH38MFrUt+8zpvI8Wfb7QvevT4rIPKAB8INPojLGAcVnods1rs2lg9od/wGl+KOd3bFYjpqSDh3J592FW5m6aDtZuQWc07cV94zqQpcWttKgP3399dcAPPnkk5x22mkcOnSI7777bstxHmZ84NuVu7nvs1X0btOA928YQqO6lWtdakwxTyYWRgIXAx1LPK4/8LS3glHVWCDWW8czxhM/rt3L2j0Z/OPSfpVandAf7eyOxR85agJfRk4+7y3czjsLt5KZU8DY3i25Z3QXure07qFOyM3N5csvv2T79u0UFBQUb27pZEzV0fTFO3ji2zUM69SYd64fQr1IW2vOVJ0nf0XfAoeAZdilKBNiCouUV+Zs5MRmdblgQOVaHPm7nV0ZLEersazcAqb9tp0pC7ZyKDufM3q24N7RXenZ2opnJ51//vk0aNCAQYMGERkZWby5yMmYqps3Yjfz4uwNjO7RnNeuGlipoXrGlMWTIrqtqp7ls0iMcdD0uO1s3JfF61cNrPTiEv5sZ1cOy9Fq6EheAR/E7eCt+VtIP5LPqO7NuXd0V/q0beB0aAZISkpi9uzZf9j2wAMP7HMonGpFVXlh9gb+O38L5/dvzcuX9qNmuOdXGY0pjydF9G8i0kdVE3wWjTEOSEo/wos/buCUrs0Y16dqV1n92M6uLJaj1UhOfiEfLt7Bf+dvYX9WHqd2bcZfxnSlv7XqCigjRowgISGBPn1s9W1fKzmxe/jw4Tz1/Tre/207Vw9rz+Tze9sy3sbrPCmiRwI3iMg2XJeKBdf6C319EpkxfqCqPPL1GgD+fmFvRIL6TdZytBrIyS/kk9938kbsFlIycxnZuSl/GdOFQR0aOx2aKcPChQt5//336dSpE5GRkbjWLKKn03GFmj9O7I7k6le+Zc72XMaP7MRjZ/cI9vd2E6A8KaLH+iwKYxzy1fLdLNiYylPn9aJto+MvNuFwC7vjsRwNYXkFRXwWv4vX520m+VAOQzs15j9XDmDYCU2cDs0cww8//LlBTseOHTc7EEpI+9/E7iLqnHITc7bnMvGUE3h4bHcroI3PeNLizvrNmpCSmpnL0/+3jsEdGnHt8OMv7+10C7vjsRwNTQWFRXy1fDf//mUTSenZDGzfkJcv7ceIE5tYcRAEOnQo870lz99xhDrXxO5I6sbcQt2+Yzi/Sy0roI3PHbeIFpGFqjpSRDKBksttF18qtqnfJig9+f1asvMKef7ivhUaK+d0C7vyjBw5EgDLUef44gpFYZHy/ao9vDp3E9v2H6Zv2wZMvqA3MV2bWWEQBEaOHMnChQuJior6w/8v93COAY4FFqKGDhvOpS99w/xdeVzUtRb/uPF0yxPjc8ctolV1pPtf69BvQsaPa/cyc3UyD57Zjc7NK7ZyWwC0sCvTwoULERHLUYd4+wpFUZEye+1e/jlnI5tSsujeMoop1w5iTM8WVhQEkYULFwKQmZn5p/tEZIW/4wllhUXKA5+vYv6uPP4yuiv3jO7idEimmrBu46baOZSdz+PfrKFHq/pMOOWECj8uAFrYmQDkrSsUqsoviSn846eNrEvO4MRmdXntqgGM693KugoYU46iIuXBL1bx9YrdPHBGV+483Qpo4z+erFg4GHgU6OB+nM38N0HpuVnrOXA4j6k3DPG4Z6jDLeyOyXLUGd64QrFo835e/mkDK3YepH3jOrxyWT/O79+m0j3LTeCIj4/n2WefZceOHRQUFFh3Di9SVZ74bg1fLd/NfWOsgDb+58mZ6I+AB4EEbLUlE6TmbUjhk6W7mHjqCfRuc+zFKAK8E0dZLEcdUJUrFMt2pPPyjxuI23qAVg1q8dxFfbhkUFtbECKEXH311bz00kv06dOHsDDX/1dPu3OIyFnAq0A48I6qPl/q/huAl4Dd7k2vqeo77vuuBx5zb39GVadV9rUEElXl+R8S+XDxTm499UTuOr2z0yGZasiTIjpVVb/zWSTG+NjW1CzunrGCHq3q85fRXY+5b6B34iiH5ahDPL1CsXbPIf7x00Z+SUyhab0InjinJ1cNa2/LEYegZs2acd5555XeXOHuHCISDrwOjAGSgKUi8p2qriu166eqemepxzYG/gYMxjXpeJn7sekevoyA859fNvPWgq1cF92Bh87qZvMFjCM8KaL/JiLvAHNxLeQAgKp+5fWojPGyjJx8bv4gnprhYbx93aDjFiuB2onjOCxHA9zW1CxembOR/1udTIPaNfnrWd24YURH6kTY9JRQ9dRTT3HzzTczatQoIiMjizc39OAQQ4HNqroVQEQ+Ac4HShfRZTkTmKOqae7HzgHOAmZ48PwB551ft/LKnI1cPLAtT57bywpo4xhP3rlvBLoDNfnfpWIF7APaBLTCIuWeGSvYeeAIH948rEKLqgRqJ47jsBwNUHsOZvPvuZv4fFkSkTXCuPO0ztxyygk0qF3T6dCMj7333nskJiaSn59/dDgHnhXRbYBdJW4nAcPK2O9iETkF2Aj8RVV3lfPYNh48d8D5eMlOnpm5nnF9WvLCxX1s0q1xlCdF9BBV7eazSIzxkZd/2sC8DalMvqA3wyu4uluQduKwHA0waYfzeH3eZqYv3gEK1w7vwB2ndaZZVOTxH2xCwtKlS9mwYcMftr3//vvbvfw03wMzVDVXRCYC04DTK/pgEZkATABo3769l0Pznm9X7ubRbxI4rVsz/nX5AGrY3AHjME+K6N9EpGcZ47CMCVjfrtzNm7FbuGpY+wqtSlhSIHfiKIflqBd4Y0LpkbwC3v11G28t2MqRvAIuHtiWe0Z3qdBVEBNaRowYwbp16+jZs9INOXYD7Urcbsv/JhACoKoHStx8B3ixxGNjSj02tvQTqOoUYArA4MGDtfT9gSBuywEe+HwVQzs25s1rBhFRwwpo4zxPiujhwEoR2YZrvKW1zzIBLSHpEH/9YjVDOzbmyXN7OR2OP1iOVlFVJ5TmFxbxydJdvPrzJvZn5XJGzxb89axudG5u6+BUV4sXL6Z///506tSJyMjIyrS4Wwp0EZFOuIriK4CrSu4gIq1UNdl98zxgvfv3H4G/i0gj9+0zgEmVfCmO2ZySycTp8XRsUpcp1w22CbgmYHhSRJ/lsyiM8bLUzFwmTI+nab1I3rhm4DHPWgRhK7vyWI5WUWUnlBYVKbPWJPPyjxvYfuAIQzs25q1rBzGoQ6PjPtaEttmzZ/9pmyct7lS1QETuxFUQhwNTVXWtiDwNxLs78twtIucBBUAacIP7sWkiMhlXIQ7wdPEkw2CRkpnD9VOXElEjnKk3DLF5BCagVLiIVtUdItIPONm96VdVXeWbsIypvNTMXK5+ZzHpR/L44tYRNK1X/vjTIG1lVybL0aqrzITSxVsP8PdZ61mddIjuLaOYesNgTuvW3DoGGAA6dOjAqlWr+PXXXwE4+eSTwYMWdwCqOguYVWrbEyV+n0Q5Z5hVdSow1bOoA8ORvALGvx9P2uE8Pp04nHaNbTiUCSwVHlQkIvfgWsyhufvnQxG5y1eBGVMZew/lcPmUOHalZTP1+iHHXVClrDOPwcpytOqKJ5ROnjz5uF+oNqdkcfO0eK6YspjUzFxevrQfM+8+mdO7t7AC2hz16quvcvXVV5OSkkJKSgrXXHMNuPLTHENhkXL3jBWs3XOI164aQN+2DZ0OyZg/8WQ4x3hgmKoeBhCRF4A44D++CMwYT+0+mM1Vby9mf2Yu024aytBOjY/7mCBtZVcey1EvON6E0v1Zubz68yY+/n0ntWuG8+CZ3Rg/spON0zRlevfdd1myZAl169YF4KGHHqJevXpNHQ4roKkqT32/lp/XpzD5/F6M6tHC6ZCMKZMnRbQAhSVuF7q3GeO4XWlHuPLtxRzKzmf6zcMY2L5iY1GDtJVdeSxHfSgnv5Cpi7bxxrwtZOcXctXQ9twzussxhwsZo6qEh//vC1bJ303Zc1LeXbiND+J2MOGUE7g2uqOzARpzDJ4U0e8BS0Tka1wfzOcTpOOsTGjZtv8wV729mCN5hXx883D6tD32EI7SgrCVXXksR31AVflp3T6enbmenWlHGN2jOQ+P7UHn5vWcDs0EgRtvvJFhw4Zx4YUXoqp8++23APudjisQlDUnRZt34e+z1jO2d0sePqu70yEac0yeTCx8RURigZG4VkG7UVVX+CowYypic0omV729hIIiZcYtw+nZur7TITnGctT7NuzN5On/W8uizQfo0rwe08cP5eQuzZwOywSR++67j5iYGBYuXIiI8N577zFw4MAUp+MKBKXnpMz8ZSGzNYtOTevy8qX9bDVCE/COW0SLyBPl3HWuiJyjqpO9HJMxFfLj2r088NkqImuG88mE4XRt4erFG0It6yrk6aefBmhVRq5ajlbSwSN5/HPORj5cspN6kTV46rxeXD2sva2QZirMnZd/8v333wO08mswAeoPc1Jq1WZpzb4cySpkxi3DqRvpyYVyY5xRkb/Sw6VuK1AXuBloAtgHtPGrgsIiXvppA2/N30rftg144+qBR1eCC6WWdRXlnrBUxP9y1XK0klSVL5Yl8fdZ6zmUnc81wzvwl9FdaVQ3wunQTJApnkhYTEQ4fPgw77zzDkBLR4IKMCXnpOxqOpRZW3J49Yr+dGlhixOZ4HDcIlpV/1H8u4hEAfcANwGfAP8o73HG+EJKZg53fbyCJdvSuGZ4ex4/pyeRNf43Uaeyi2UEs/vvv58HHnhgn6r+o6o5KiJnAa/iWtThHVV9vtT9NwAv8b9lh19T1Xeq+BJ8wtMrEptTMnn06zUs2ZbGoA6NeOaC3vRoVX2HB5mquf/++4/+npmZyauvvsrUqVO54oorePHFFxMcDC2gREdHk16vI299tJzrojtwfv82TodkTIVV6HqJiDQG7gOuBqYBA1U13ZeBGVPa79vSuOPj5WTm5PPKZf24aGDbP+0TYi3rPBEuIs9QhRwVkXDgdWAMkAQsFZHvVHVdqV0/VdU7vRG0r3hyRSInv5DXftnMWwu2ULtmOM9d1IfLB7ez8ZimytLS0njllVf46KOPuP7661m+fDmNGjXixRdfLHA6tkCxNTWLv36xmv7tGvLo2T2cDscYj1RkTPRLwEXAFKCPqmZ5MwARaQd8ALTAdRl6iqq+6s3nMMGtoLCIt3/dxss/baB94zpMHz+U7i3LPkMYYi3rKuTBBx8E6IHrzHNVcnQosFlVtwKIyCe4OnyULqIDXkWvSPy6KZXHvlnDjgNHuHBAGx49u4e1rDNe8eCDD/LVV18xYcIEEhISqFfPurmUdiSvgNs+XE7NcOH1qwf+4aqiMcFAVPXYO4gUAblAAa4i9+hdgKpqla53ikgroJWqLndfil4GXFDG2a+jBg8erPHx8VV5WhMkVuxM55Gv17A+OYOxvVvywiV9qV+rptNhBZSwsDDUlciHqUKOisglwFmqerP79rW4Fm+5s8Q+NwDPAanARuAvqrqrjGNNACYAtG/fftCOHTsq89Iq7Xhnog/nFvDsrPV8vGQnnZrW5ZkLenNSZ1v/wnhPWFgYkZGR1KhR4w8rWKoqWVlZRaoakBWjPz9f7/tsJV+v2M20G4dySlfremMCk4gsU9XBZd1XkTHRPp2OrqrJQLL790wRWQ+0IQjPfhnvOXQknxd/TOTj33fSIqoW/71mIPUztvH6P1+uNmeYK6qoqAgRWV5eknvZ98AMVc0VkYm4ho6cXnonVZ2C6+oVgwcPPvY3dR841hWJZTvSuO+zVexMO8ItJ3fi/jO62WqDxuuKiorKvU9EqmXryZLzFA5FdeSr5bu5e1QXK6BN0AqoHjIi0hEYACwp476SZ7b8G5jxG1Xl25V7eGbmOtIO53HTSZ34y5iuJCxfyqjRo6tV1w0H7Abalbjdlv9NIARAVQ+UuPkO8KIf4qqU0ovo5BUU8a+fN/Lf+Vto1aA2M24ZzvATmjgYoTHVR8mrQ5FRjehy9zR6tKrPXad3djo0YyotYIpoEakHfAncq6oZpe93+syW8b1lO9J56cdEFm9No1+7hrx/41B6t3GtPlgdu244YCnQRUQ64SqerwCuKrmDiLRyXz0COA9Y798Qy3a8Thwb9mZy76crWZ+cwWWD2/L4OT2JsmFBxvhNyffwOiOu4VBuER9e0pea1nvdBLGAKKJFpCauAvojVf3K6XiMfy3bkca/ft7Er5v207huBJMv6M1VQ9sTXqI7QjXuuuE3qlogIncCP+JqcTdVVdeKyNNAvKp+B9wtIufhmiORBtzgWMBuxxv//Hn8Lh79Zg1RkTWYcu0gzuhlLXqN8bfi93Bp1YO6fUZzbpdaR0+SGBOsHC+ixTXj4l1gvaq+4nQ8xn/it6fx6lxX8dykbgSPjOvONcM7UCfiz3+W1bHrhhNUdRYwq9S2J0r8PgmY5O+4jqW8qxQFhUU8O2s97y3azogTm/DvKwdY5w1jHBIdHc3MH3/mrln7iKoTxovXxTgdkjFV5ngRDZwEXAskiMhK97ZH3B/mJsQUFimxG1J4b9F2Fm7eT9N6ETw6rgdXD29fZvFcUukxrsZA2Vcp0g/nccfHy/ltywFuOqkTj4zrbkt2G+Ow+ekNyNJ03rt2mE3mNSHB8SJaVRfiasVlQlhqZi6fxe/i4yU72X0wm+ZRkRUuno05ltJXKRp27MV5ry9kX0YuL1/aj0sG/XlRHmOMf8VvT2Na3Haui+7AkI6NnQ7HGK+w6sX4jKqyZFsaHy7ewY9r95JfqLQJz+TeIa2444KTbUKJ8ZriqxSzEpK56Y3fqF+7Bp9NjKZ/u4ZOh2ZMtZeTX8hDX66mdYPa/PWs7k6HY4zXWBFtvEpVWZecwferkvl+1R52H8ymQe2ajOkYwfS/3UpSynZWREQwzFrUGS+b9tt2/vbdWga0b8hb1wyief1aTodkjFeIyFnAq7gm/L6jqs+Xuv8+4GZcE35TgZtUdYf7vkIgwb3rTlU9z2+Bu732y2a2pB5m2k1DqRdpZYcJHfbXbLxiS2oW36/aw/er9rAl9TA1woSRXZpy/xldGdenFf98+UWyU7ZbizrjEx8v2cnfvlvLmJ4teO2qAbZ8sAkZIhIOvA6MAZKApSLyXalVfVcAg1X1iIjchqt/++Xu+7JVtb8/Yy5pc0oW/52/hYsHtuVUW1TFhBgrok2lFBYpK3amMzcxhbnr97FxXxYiMLxTE8aPPIGzerekcd2Io/tbizrjDWX1g/48fhePfJ3A6d2bWwFtQtFQYLOqbgUQkU+A8ymxqq+qziux/2LgGr9GWErJPP1oWyQRNcJ4ZJwN4zChx4poU2EZOfks3LSfn9fvI3ZDKmmH86gRJgzt1JgrhrTn7L6taFHOJXRrUWeqqqx+0PtqteevX67m5C5NeePqgVZAm1DUBthV4nYSMOwY+48Hfihxu5aIxOMa6vG8qn5T+gHeXBG4ZJ7WbtWZJlf/gztP60wTay9pQpAV0aZcOfmFLNuRzm9b9rNo8wESdh+isEhpULsmp3VrxqgeLTilazMa1K7Yym/Wos5URel+0O/OWcEv2WkM79SEKdcOtpZZptoTkWuAwcCpJTZ3UNXdInIC8IuIJKjqlpKP8+aKwH9YmXDopURQyC0nn1CVQxoTsKyINkdl5xWyKukgS7elMXvFVhL351FIGOFhQr+2Dbg95kRGdm7KoA6NrOeu8buSQ4LqdR/BvOwODOrQiHeuH0ztCCugTcjaDbQrcbute9sfiMho4FHgVFXNLd6uqrvd/24VkVhgALCl9OO9pThPtXF7ancZzvnda9OgTsVOtBgTbKyIrsbSDucRvz2N+B3pLN2exprdh8gvdJ2EyE/dTs6OVRTuWcdX/32R008Z4XC0prorHhL04c/xzD5yAn3aNmTqDUOoa7P9TWhbCnQRkU64iucrgKtK7iAiA4C3gLNUNaXE9kbAEVXNFZGmuBY3e9GXwRbn6YMzd3AwTPjblaf48umMcZR9+lQTqkpSeja/b0sjfkcaS7enszklC4CI8DD6tm3A+JEnMLRTI+Z/OY1nX36EwsJCwsPDWbJovhXRJiB07zuI3385zAnNajLtpqFE1bIzXCa0qWqBiNwJ/Iirxd1UVV0rIk8D8ar6HfASUA/4XETgf63segBviUgREIZrTPS6Mp/Ii8JbdiOpII1HxnWzlnYmpNlfd4gqKlI27Mtk6fY0ft+WxtLtaezLcF3hi6pVg8EdGnHhgDYM7dSYPm0a/GE8ae3TT+alZ62ThgkMxTP9Tz31VD7cFsnBI3m8f+OQCo/FNybYqeosYFapbU+U+H10OY/7Dejj2+j+9Jy8/NMGmkVFcu3wjv58amP8zoroEJFXUETC7oP8vs01NCN+exoZOQUAtKxfi6GdmtBUD5K5dSUXnj6Mk0YMLfdY1knDBIK4uDg++OAD3nvvPQoKCojqM4oGZ97NX8/qRq/WDZwOzxhThkWbD/D7tjSePLenzVUwIc+K6CB1JK+A5TsO8vu2A/y+PY0VOw+SW1AEwInN6nJ231YM6diYIR0b07ZRbRYvXsyoUWeTl5fHG393tQc7VnFsnTSMk4rbZOXk5KCqhEc1IypmPC3DDzPxlBOdDs8YU4bis9CtG9TiymFVa5VnTDCwIjpIHMrOJ357Gku2uX7W7j5EQZESJtCrdQOuGd7BXTQ3KrMfZ+n2YLZioAlkxX+vqgoITc++FySMh09rSXiYOB2eMaaUuLg4ps1ZxsojnXjuoj7Ws91UC1ZEB6i0w3n8vu2Aq2jemsb6vRmoQo0waCqHOefEJlx4cl8Gtm9YoclVtmKgCSYl/17rDz6fWh36MaF/XS4YPdLp0IwxpbiuHI2m4eXPE15rL23zGwF2JtqEPiuiA8SBrFx+35bG4q0HWLw1jQ37MgGoVTOMQR0ace+ortTLTuauq85je3YWqyMiuHzuXKK6Vuxsso1zNsGk+O/1q7lxfHOkG6d2a86kywc7HZYxpgyxsbGEte1LRIsTSJv1Lxa2P52TT7KOTib0WRHtkPTDeSzZdoDfthxg8dYDbNznajdXJyKcQR0acV7/1gw/oTF92jQkooZrYZPnnvuCvOysSg/JsHHOJpgMGjKMZ5YWUF9zeO6ivrhbdxljAkxMTAz/Xp5D4eF0CrbEERPztNMhGeMXVkT7SWZOPr9vS+O3LQeI23Lg6PCMOhHhDO7YmAsGtGH4CU3o06YBNctZDdCGZJjq5M3YLazdk8GUawfRLOrP4/yNMYGhS5+B1OmcRq+aqUz6eY6drDHVhhXRPpJbUMiKnQdZtHk/CzfvZ3XSIQqLlHCK6N40gvtGd2VE5yb0bduw3KK5NBuSYaqDuLg45sybz6fZfTmzVwvO6NXS6ZCMMcfwzYrdFCq8fMfFdG0R5XQ4xviNFdFeoqps3JfFB3OWsmDDPlK0PrmFEB4m9GvbgPM6RzL1uYc4vHMtyeHCY3PnMqhDF4+fx4ZkmFBW3NquZo/TaTSmDyMaH3E6JGPMMagqXyxLol/bBlZAm2rHiugqOJSdz6LN+5m/IZX5G1PZm5EDQP6BQ+QnLeLxWy7l+nEjqF+rJs899xyHt610jWcOD7cWc8aUwdXaLp9GA88hb+8mdq/KhrNPcTosY0w51u7JIHFvJpPP7+V0KMb4nRXRHlBVPv9pIV/8toFDddux+WAhhUVKVK0anNylKVmb4/n0X38j7+A+wsPDyTqlDfUvOhWw8czGVERMTAz1ugyhZpN2HPrxVU6b+JTTIRljjuGLZUlEhIdxbr/WTodijN9ZEV2GuLi4o+OOBw8dxtJtacxZv4+ZK3aScqQIaEX+zg1cdnIvrj59AP3bNaRGeBhxcXl88UIG4eHhfyqUbTyzMccXHR3Nqbc8yabUI0x/7UnLE2MCWF5BEd+u3M2YXi1oWCfC6XCM8TsrokuJi4tj1JgzCWvVg38tTqdJn3SOFCgRNcJoyUHSf5rO4U1LIPsQzbtOZnDHUUcfe7xC2cYzG1O+uLg4vp4bx6qsbtw/phcnn+T5nAFjjH/ExcUxdc5y0o905JJBbZ0OxxhHVPsiuvis8ymnnoo078rTszbRZPxbhNeuT1FOFm3lAPdfO4aTuzRl1bKljHolFo4xJMMKZWM8VzyhsM4pN1K3dye6hqcAVkQbE4iK8zVq3INEtKxPzf2boVtzp8Myxu+qdREdFxfHGRdfQ0TPUbyxpx3hddOJDG9C/o4FpK+bT9GedTw150ei3S22bEiGMb4RGxtLflgEdXqezpH181nepAVnxpzkdFjGmDLExsZSUKMOtU4YRFb8N/y6IImRtkKhqYaqVRFdfNb55FNO5XDDE5g8cwdNrvs3WphP9palnN+/Df9+eCIrlzUmNrYhMTH/tCEZxvhBTEwMDWauIiyiFjmrZxPz2HtOh2SMKUdMTAxR/7cCCQsnb8OvxDx7i9MhGeOIalNEx8XFMfqci4joHsMbSa0Jj0qnca0GZP36ERkrZlOj4Ag3PTiX2hHhVigb42dDhg6jw5jriczLYMZn71n+GRPAhg8fTs9xN5F7JIMZX3xg+WqqrZAvouPi4vhp3gIWpETQ9IbXkZqR5GxfydgWWbzx6G0sPbMhsbG9bHiGMQ76ce0+DmQXMeXaU48OnzLGBKY1uzPYlVnIMxcMI3p4B6fDMcYxFVtv2sdE5CwR2SAim0XkYW8dd9Fvv3H23X/nneQ2bKnVlZwtS9j77u1kfPcsN44ZSI3wMKKjo5k0aZIV0MZw/FwUkUgR+dR9/xIR6eiN5526aBvtG9dhVI8W3jicMSGlKnkpIpPc2zeIyJneiOfzZbuIqGG9oY1xvIgWkXDgdWAs0BO4UkR6VuWYU6ZMYeQltzD+8y3UH3Ur+el7SPnwQS5qlcHf7ruVuXPnWtFsTCkVzMXxQLqqdgb+CbxQ1ef9cNavLNuRzqmtITxMqno4Y0JKVfLSvd8VQC/gLOAN9/EqbcHC3/h08RYGtahBg9o1q3IoY4Ke40U0MBTYrKpbVTUP+AQ4v7IHe/H1d3l8TjJJnS8gMzuPtO9fZP8nj0Dadq677jo762xM+SqSi+cD09y/fwGMEpFKV75xcXE8MOX/KMo9wit3XkJcXFxlD2VMqKpKXp4PfKKquaq6DdjsPl6lxMXFcf7tj5GrNfjh9ScsX021FwhFdBtgV4nbSe5tfyAiE0QkXkTiU1NTyz3Yj7NmUrN5R9J+eZc979xKj3o5TJ482c4+G3N8FcnFo/uoagFwCGhS+kAVzdd582Ihsh5ZCXPIzTpEbGxs1V6BMaGnKnnp1c/X2NhYtFZ98g/sImvLMstXU+0FQhFdIao6RVUHq+rgZs2albvf5eefxe7/jidz6ddQWMD48ePt7LMxflbRfD3ttBgyvn+OjPnvl7uAkTHGtyqarzExMeSt+Yl9791FRM0alq+m2guE7hy7gXYlbrd1b6uUCRMmAPDll19y8cUXH71tjDmuiuRi8T5JIlIDaAAcqOwT2gJGxhxXVfLSq5+vlq/G/FEgFNFLgS4i0glXcl8BXFWVA06YMMGKZ2M8V5Fc/A64HogDLgF+UVWtypNaX3ZjjqnSeSki3wEfi8grQGugC/B7VYKxfDXmfxwvolW1QETuBH4EwoGpqrrW4bCMqXbKy0UReRqIV9XvgHeB6SKyGUjD9YFujPGRquSle7/PgHVAAXCHqhY68kKMCUGOF9EAqjoLmOV0HMZUd2Xloqo+UeL3HOBSf8dlTHVWlbxU1WeBZ30aoDHVVNBMLDTGGGOMMSZQWBFtjDHGGGOMh6yINsYYY4wxxkNWRBtjjDHGGOMhqWJ3KkeISCqww+k43JoC+50OwgdC8XWF4msC1+uqq6rlr5LgoArma6j9v7HXE9icfj0dLF/9zmL2j1CMudx8DcoiOpCISLyqDnY6Dm8LxdcViq8JQuN1hcJrKMleT2ALtdfjb8H4389i9o/qFrMN5zDGGGOMMcZDVkQbY4wxxhjjISuiq26K0wH4SCi+rlB8TRAarysUXkNJ9noCW6i9Hn8Lxv9+FrN/VKuYbUy0McYYY4wxHrIz0cYYY4wxxnjIimhjjDHGGGM8ZEV0JYlIOxGZJyLrRGStiNzjdEzeIiLhIrJCRP7P6Vi8RUQaisgXIpIoIutFJNrpmKpKRP7i/ttbIyIzRKSW0zF5SkSmikiKiKxxOpaqCrX3BBGpJSK/i8gq9+t5yumYvCEU39/8JRjzNRjzMlhzL9hyS0S2i0iCiKwUkfjKHMOK6MorAO5X1Z7AcOAOEenpcEzecg+w3ukgvOxVYLaqdgf6EeSvT0TaAHcDg1W1NxAOXOFsVJXyPnCW00F4Sai9J+QCp6tqP6A/cJaIDHc2JK8Ixfc3f3mf4MvXYMzLYM29YMyt01S1v/WJ9jNVTVbV5e7fM3H94bRxNqqqE5G2wNnAO07H4i0i0gA4BXgXQFXzVPWgo0F5Rw2gtojUAOoAexyOx2OqugBIczoObwi19wR1yXLfrOn+CeqZ6KH4/uZPwZivwZiXwZh71TW3rIj2AhHpCAwAljgcijf8C/grUORwHN7UCUgF3nNfanpHROo6HVRVqOpu4GVgJ5AMHFLVn5yNyhQLlfcE9+XZlUAKMEdVg/r1EJrvb6aCgikvgzD3/kXw5ZYCP4nIMhGZUJkDWBFdRSJSD/gSuFdVM5yOpypE5BwgRVWXOR2Ll9UABgJvquoA4DDwsLMhVY2INALOx/UFoTVQV0SucTYqA6H1nqCqharaH2gLDBWR3g6HVGkh/P5mKiDY8jKYci+Ic2ukqg4ExuIa5nOKpwewIroKRKQmrqT8SFW/cjoeLzgJOE9EtgOfAKeLyIfOhuQVSUBSiW/yX+AqqoPZaGCbqqaqaj7wFTDC4ZiqvRB8TwDAPfxpHsE3HrakUH1/M8cRzHkZJLkXlLnlvqKLqqYAXwNDPT2GFdGVJCKCa4ztelV9xel4vEFVJ6lqW1XtiGuS2i+qGvRnN1V1L7BLRLq5N40C1jkYkjfsBIaLSB333+Iogm9CR0gJtfcEEWkmIg3dv9cGxgCJjgZVBaH6/maOLRjzMthyLxhzS0TqikhU8e/AGYDHXWesiK68k4BrcX3jWun+Ged0UKZcdwEfichqXLOd/+5sOFXjPqv+BbAcSMCVy0G33KqIzADigG4ikiQi452OqQpC7T2hFTDPnTNLcY3LDIrWVcY3gjRfgzEvLfd8rwWwUERWAb8DM1V1tqcHsWW/jTHGGGOM8ZCdiTbGGGOMMcZDVkQbY4wxxhjjISuijTHGGGOM8ZAV0cYYY4wxxnjIimhjjDHGGGM8ZEW0McYYY4wxHrIiOkSIyKMislZEVrv7YA4TkYYicnsljnXcx4lIRxGpUGNyEekjIjtE5LZSj88WkZUVPEZt9+vKE5GmFXmMMYHK8tWY4GH5aspjRXQIEJFo4BxgoKr2xbUk9C6gIVBmsopLef//y31cZahqAq5VjK4rddcWVe1fwWNku/fd4624jHGC5asxwcPy1RyLFdGhoRWwX1VzAVR1v6ruAZ4HTnR/w3zJ/e10g4h8gGt5y3Yico2I/O7e5y0RCS/9OAARuc79LXyViEx3P2+4iLzt/ob+k3t50vKkAL189R/AmCBi+WpM8LB8NeVTVfsJ8h+gHrAS2Ai8AZzq3t4RWFNiv45AETDcfbsH8D1Q0337DVzfZks/rpf72E3dtxu79ykA+ru3fQZcc4wYPwdygQ5lxebBa91eHIf92E8w/li+2o/9BM+P5av9HOunBiboqWqWiAwCTgZOAz4VkYeB2DJ236Gqi92/jwIGAUtFBKA2rm+0C0o95nTgc1Xd736+NBGpD2xT1ZXufZbhStw/EZGxQF1gJq43jB3l7Pcz0LKMux5V1W/Leowxwcby1ZjgYflqjsWK6BChqoW4kjpWRBKA6yk7yQ+X+F2Aaao6qeQOItKxgk+bW+L3QlxvEn8gIrWAF4DzgBuB3sCscl7D6Ao+rzFBzfLVmOBh+WrKY2OiQ4CIdBORLiU29cf1bTQTiDrGQ+cCl4hIc/dxGotIhzIe9wtwqYg0Kd7Pg/AeAz5Q1e1AAq4kN6basnw1JnhYvppjsSI6NNQDponIOhFZDfQEnlTVA8AiEVlTPIGhJFVdhysJf3I/bg7QqvTjVHUt8CwwX0RWAa9UJCgR6QaMAf7l3mRJbozlqzHBxPLVlEvcg8mN8Sv3Ja3/U1WPkl5EtgODi8ePGWN8z/LVmOBh+eo/dibaOKUQaCAeNoMHauKaAW2M8R/LV2OCh+Wrn9iZaGOMMcYYYzxkZ6KNMcYYY4zxkBXRxhhjjDHGeMiKaGOMMcYYYzxkRbQxxhhjjDEesiLaGGOMMcYYD1kRbYwxxhhjjIesiDbGGGOMMcZDVkQbY4wxxhjjof8HFQeFaPjv0iIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = ICNN_model(params_icnn_PS[0], params_icnn_PS[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/ICNN_params_PS.npy', 'wb') as f:\n",
    "    pickle.dump(params_icnn_PS, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train CANN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 1.570347e+00\n",
      "it 2000, train loss = 7.656242e-01\n",
      "it 3000, train loss = 2.673336e-01\n",
      "it 4000, train loss = 9.246830e-02\n",
      "it 5000, train loss = 5.695183e-02\n",
      "it 6000, train loss = 4.293643e-02\n",
      "it 7000, train loss = 3.360374e-02\n",
      "it 8000, train loss = 2.776273e-02\n",
      "it 9000, train loss = 2.366846e-02\n",
      "it 10000, train loss = 2.043378e-02\n",
      "it 11000, train loss = 1.874515e-02\n",
      "it 12000, train loss = 1.762262e-02\n",
      "it 13000, train loss = 1.649886e-02\n",
      "it 14000, train loss = 1.535975e-02\n",
      "it 15000, train loss = 1.423437e-02\n",
      "it 16000, train loss = 1.323824e-02\n",
      "it 17000, train loss = 1.235044e-02\n",
      "it 18000, train loss = 1.140514e-02\n",
      "it 19000, train loss = 1.035771e-02\n",
      "it 20000, train loss = 9.577693e-03\n",
      "it 21000, train loss = 9.117638e-03\n",
      "it 22000, train loss = 8.865151e-03\n",
      "it 23000, train loss = 8.716361e-03\n",
      "it 24000, train loss = 8.611133e-03\n",
      "it 25000, train loss = 8.555976e-03\n",
      "it 26000, train loss = 8.556629e-03\n",
      "it 27000, train loss = 8.485276e-03\n",
      "it 28000, train loss = 8.450486e-03\n",
      "it 29000, train loss = 8.412874e-03\n",
      "it 30000, train loss = 8.431793e-03\n",
      "it 31000, train loss = 8.385640e-03\n",
      "it 32000, train loss = 8.409872e-03\n",
      "it 33000, train loss = 8.371969e-03\n",
      "it 34000, train loss = 8.398760e-03\n",
      "it 35000, train loss = 8.408933e-03\n",
      "it 36000, train loss = 8.359859e-03\n",
      "it 37000, train loss = 8.410105e-03\n",
      "it 38000, train loss = 8.408257e-03\n",
      "it 39000, train loss = 8.352789e-03\n",
      "it 40000, train loss = 8.363948e-03\n",
      "it 41000, train loss = 8.420357e-03\n",
      "it 42000, train loss = 8.470739e-03\n",
      "it 43000, train loss = 8.386843e-03\n",
      "it 44000, train loss = 8.349966e-03\n",
      "it 45000, train loss = 8.350368e-03\n",
      "it 46000, train loss = 8.363842e-03\n",
      "it 47000, train loss = 8.392580e-03\n",
      "it 48000, train loss = 8.432203e-03\n",
      "it 49000, train loss = 8.437925e-03\n",
      "it 50000, train loss = 8.398979e-03\n"
     ]
    }
   ],
   "source": [
    "key, subkey = random.split(key)\n",
    "params_cann_all = init_cann(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_cann_all)\n",
    "mdlnumber = 1\n",
    "params_cann_all, train_loss, val_loss = train_jp(loss_P11_all, mdlnumber, F11_data, opt_state, key, nIter = 50000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = CANN_model(params_cann_all[0], params_cann_all[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/CANN_params_all.npy', 'wb') as f:\n",
    "    pickle.dump(params_cann_all, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 7.780610e+03\n",
      "it 2000, train loss = 2.525536e+03\n",
      "it 3000, train loss = 1.139521e+03\n",
      "it 4000, train loss = 5.836761e+02\n",
      "it 5000, train loss = 3.166544e+02\n",
      "it 6000, train loss = 1.762213e+02\n",
      "it 7000, train loss = 9.888569e+01\n",
      "it 8000, train loss = 5.542338e+01\n",
      "it 9000, train loss = 3.091856e+01\n",
      "it 10000, train loss = 1.724529e+01\n",
      "it 11000, train loss = 9.797609e+00\n",
      "it 12000, train loss = 5.905288e+00\n",
      "it 13000, train loss = 4.002718e+00\n",
      "it 14000, train loss = 3.168628e+00\n",
      "it 15000, train loss = 2.815634e+00\n",
      "it 16000, train loss = 2.198102e+00\n",
      "it 17000, train loss = 1.084940e+00\n",
      "it 18000, train loss = 4.153802e-01\n",
      "it 19000, train loss = 1.406707e-01\n",
      "it 20000, train loss = 4.799692e-02\n",
      "it 21000, train loss = 2.086422e-02\n",
      "it 22000, train loss = 1.337045e-02\n",
      "it 23000, train loss = 1.025137e-02\n",
      "it 24000, train loss = 8.976384e-03\n",
      "it 25000, train loss = 7.578095e-03\n",
      "it 26000, train loss = 5.174725e-03\n",
      "it 27000, train loss = 3.916854e-03\n",
      "it 28000, train loss = 3.348173e-03\n",
      "it 29000, train loss = 3.070318e-03\n",
      "it 30000, train loss = 2.919658e-03\n",
      "it 31000, train loss = 2.832439e-03\n",
      "it 32000, train loss = 2.779845e-03\n",
      "it 33000, train loss = 2.746937e-03\n",
      "it 34000, train loss = 2.724878e-03\n",
      "it 35000, train loss = 2.707473e-03\n",
      "it 36000, train loss = 2.689845e-03\n",
      "it 37000, train loss = 2.669682e-03\n",
      "it 38000, train loss = 2.646624e-03\n",
      "it 39000, train loss = 2.622045e-03\n",
      "it 40000, train loss = 2.598534e-03\n",
      "it 41000, train loss = 2.576222e-03\n",
      "it 42000, train loss = 2.554144e-03\n",
      "it 43000, train loss = 2.530793e-03\n",
      "it 44000, train loss = 2.505821e-03\n",
      "it 45000, train loss = 2.479136e-03\n",
      "it 46000, train loss = 2.450705e-03\n",
      "it 47000, train loss = 2.420528e-03\n",
      "it 48000, train loss = 2.388632e-03\n",
      "it 49000, train loss = 2.355089e-03\n",
      "it 50000, train loss = 2.319916e-03\n",
      "it 51000, train loss = 2.283275e-03\n",
      "it 52000, train loss = 2.245277e-03\n",
      "it 53000, train loss = 2.206085e-03\n",
      "it 54000, train loss = 2.165897e-03\n",
      "it 55000, train loss = 2.124949e-03\n",
      "it 56000, train loss = 2.084027e-03\n",
      "it 57000, train loss = 2.041928e-03\n",
      "it 58000, train loss = 2.000541e-03\n",
      "it 59000, train loss = 1.959765e-03\n",
      "it 60000, train loss = 1.920059e-03\n",
      "it 61000, train loss = 1.881926e-03\n",
      "it 62000, train loss = 1.845920e-03\n",
      "it 63000, train loss = 1.812616e-03\n",
      "it 64000, train loss = 1.782605e-03\n",
      "it 65000, train loss = 1.756476e-03\n",
      "it 66000, train loss = 1.734766e-03\n",
      "it 67000, train loss = 1.717904e-03\n",
      "it 68000, train loss = 1.706046e-03\n",
      "it 69000, train loss = 1.698928e-03\n",
      "it 70000, train loss = 1.695646e-03\n",
      "it 71000, train loss = 1.694671e-03\n",
      "it 72000, train loss = 1.694508e-03\n",
      "it 73000, train loss = 1.694480e-03\n",
      "it 74000, train loss = 1.694462e-03\n",
      "it 75000, train loss = 1.694446e-03\n",
      "it 76000, train loss = 1.694430e-03\n",
      "it 77000, train loss = 1.694416e-03\n",
      "it 78000, train loss = 1.695065e-03\n",
      "it 79000, train loss = 1.694390e-03\n",
      "it 80000, train loss = 1.694378e-03\n",
      "it 81000, train loss = 1.694367e-03\n",
      "it 82000, train loss = 1.694357e-03\n",
      "it 83000, train loss = 1.694347e-03\n",
      "it 84000, train loss = 1.694337e-03\n",
      "it 85000, train loss = 1.694329e-03\n",
      "it 86000, train loss = 1.694321e-03\n",
      "it 87000, train loss = 1.694313e-03\n",
      "it 88000, train loss = 1.694306e-03\n",
      "it 89000, train loss = 1.694299e-03\n",
      "it 90000, train loss = 1.694292e-03\n",
      "it 91000, train loss = 1.694286e-03\n",
      "it 92000, train loss = 1.694280e-03\n",
      "it 93000, train loss = 1.694280e-03\n",
      "it 94000, train loss = 1.694270e-03\n",
      "it 95000, train loss = 1.694265e-03\n",
      "it 96000, train loss = 1.694260e-03\n",
      "it 97000, train loss = 1.694256e-03\n",
      "it 98000, train loss = 1.694251e-03\n",
      "it 99000, train loss = 1.694247e-03\n",
      "it 100000, train loss = 1.694244e-03\n"
     ]
    }
   ],
   "source": [
    "key, subkey = random.split(key)\n",
    "params_cann_UT = init_cann(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_cann_UT)\n",
    "mdlnumber = 1\n",
    "params_cann_UT, train_loss, val_loss = train_jp(loss_P11_UT, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = CANN_model(params_cann_UT[0], params_cann_UT[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/CANN_params_UT.npy', 'wb') as f:\n",
    "    pickle.dump(params_cann_UT, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 6.401516e-01\n",
      "it 2000, train loss = 3.186481e-01\n",
      "it 3000, train loss = 2.226334e-01\n",
      "it 4000, train loss = 1.359946e-01\n",
      "it 5000, train loss = 6.612172e-02\n",
      "it 6000, train loss = 2.642648e-02\n",
      "it 7000, train loss = 7.560056e-03\n",
      "it 8000, train loss = 1.948449e-03\n",
      "it 9000, train loss = 1.172463e-03\n",
      "it 10000, train loss = 9.711189e-04\n",
      "it 11000, train loss = 7.583407e-04\n",
      "it 12000, train loss = 5.841491e-04\n",
      "it 13000, train loss = 4.953667e-04\n",
      "it 14000, train loss = 4.612630e-04\n",
      "it 15000, train loss = 4.340705e-04\n",
      "it 16000, train loss = 3.968463e-04\n",
      "it 17000, train loss = 3.478654e-04\n",
      "it 18000, train loss = 3.039952e-04\n",
      "it 19000, train loss = 2.739491e-04\n",
      "it 20000, train loss = 2.535901e-04\n",
      "it 21000, train loss = 2.418809e-04\n",
      "it 22000, train loss = 2.354823e-04\n",
      "it 23000, train loss = 2.301998e-04\n",
      "it 24000, train loss = 2.246207e-04\n",
      "it 25000, train loss = 2.183242e-04\n",
      "it 26000, train loss = 2.115080e-04\n",
      "it 27000, train loss = 2.056883e-04\n",
      "it 28000, train loss = 2.027615e-04\n",
      "it 29000, train loss = 2.016283e-04\n",
      "it 30000, train loss = 2.010885e-04\n",
      "it 31000, train loss = 2.007957e-04\n",
      "it 32000, train loss = 2.006320e-04\n",
      "it 33000, train loss = 2.005415e-04\n",
      "it 34000, train loss = 2.004931e-04\n",
      "it 35000, train loss = 2.004685e-04\n",
      "it 36000, train loss = 2.004568e-04\n",
      "it 37000, train loss = 2.004519e-04\n",
      "it 38000, train loss = 2.004504e-04\n",
      "it 39000, train loss = 2.004502e-04\n",
      "it 40000, train loss = 2.004501e-04\n",
      "it 41000, train loss = 2.004501e-04\n",
      "it 42000, train loss = 2.004501e-04\n",
      "it 43000, train loss = 2.004501e-04\n",
      "it 44000, train loss = 2.004501e-04\n",
      "it 45000, train loss = 2.004506e-04\n",
      "it 46000, train loss = 2.004502e-04\n",
      "it 47000, train loss = 2.004501e-04\n",
      "it 48000, train loss = 2.004501e-04\n",
      "it 49000, train loss = 2.004501e-04\n",
      "it 50000, train loss = 2.004501e-04\n",
      "it 51000, train loss = 2.004501e-04\n",
      "it 52000, train loss = 2.004613e-04\n",
      "it 53000, train loss = 2.004502e-04\n",
      "it 54000, train loss = 2.004501e-04\n",
      "it 55000, train loss = 2.004501e-04\n",
      "it 56000, train loss = 2.004501e-04\n",
      "it 57000, train loss = 2.004501e-04\n",
      "it 58000, train loss = 2.004501e-04\n",
      "it 59000, train loss = 2.004501e-04\n",
      "it 60000, train loss = 2.004501e-04\n",
      "it 61000, train loss = 2.004501e-04\n",
      "it 62000, train loss = 2.004501e-04\n",
      "it 63000, train loss = 2.004504e-04\n",
      "it 64000, train loss = 2.004502e-04\n",
      "it 65000, train loss = 2.004501e-04\n",
      "it 66000, train loss = 2.004501e-04\n",
      "it 67000, train loss = 2.004501e-04\n",
      "it 68000, train loss = 2.004501e-04\n",
      "it 69000, train loss = 2.004501e-04\n",
      "it 70000, train loss = 2.004501e-04\n",
      "it 71000, train loss = 2.004501e-04\n",
      "it 72000, train loss = 2.004501e-04\n",
      "it 73000, train loss = 2.004501e-04\n",
      "it 74000, train loss = 2.004501e-04\n",
      "it 75000, train loss = 2.004501e-04\n",
      "it 76000, train loss = 2.004512e-04\n",
      "it 77000, train loss = 2.004501e-04\n",
      "it 78000, train loss = 2.004501e-04\n",
      "it 79000, train loss = 2.004501e-04\n",
      "it 80000, train loss = 2.004501e-04\n",
      "it 81000, train loss = 2.004501e-04\n",
      "it 82000, train loss = 2.004501e-04\n",
      "it 83000, train loss = 2.004501e-04\n",
      "it 84000, train loss = 2.004501e-04\n",
      "it 85000, train loss = 2.004501e-04\n",
      "it 86000, train loss = 2.004501e-04\n",
      "it 87000, train loss = 2.004501e-04\n",
      "it 88000, train loss = 2.004501e-04\n",
      "it 89000, train loss = 2.004501e-04\n",
      "it 90000, train loss = 2.004501e-04\n",
      "it 91000, train loss = 2.004501e-04\n",
      "it 92000, train loss = 2.004501e-04\n",
      "it 93000, train loss = 2.004501e-04\n",
      "it 94000, train loss = 2.004501e-04\n",
      "it 95000, train loss = 2.004501e-04\n",
      "it 96000, train loss = 2.004501e-04\n",
      "it 97000, train loss = 2.004501e-04\n",
      "it 98000, train loss = 2.004501e-04\n",
      "it 99000, train loss = 2.004501e-04\n",
      "it 100000, train loss = 2.004504e-04\n"
     ]
    }
   ],
   "source": [
    "key, subkey = random.split(key)\n",
    "params_cann_ET = init_cann(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_cann_ET)\n",
    "mdlnumber = 1\n",
    "params_cann_ET, train_loss, val_loss = train_jp(loss_P11_ET, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = CANN_model(params_cann_ET[0], params_cann_ET[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the result with pickle\n",
    "with open('savednet/CANN_params_ET.npy', 'wb') as f:\n",
    "    pickle.dump(params_cann_ET, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 5.416528e-01\n",
      "it 2000, train loss = 1.053952e-01\n",
      "it 3000, train loss = 4.447409e-02\n",
      "it 4000, train loss = 2.947100e-02\n",
      "it 5000, train loss = 1.824671e-02\n",
      "it 6000, train loss = 1.012564e-02\n",
      "it 7000, train loss = 4.990278e-03\n",
      "it 8000, train loss = 2.483594e-03\n",
      "it 9000, train loss = 1.533324e-03\n",
      "it 10000, train loss = 1.232976e-03\n",
      "it 11000, train loss = 1.150479e-03\n",
      "it 12000, train loss = 1.119902e-03\n",
      "it 13000, train loss = 1.071679e-03\n",
      "it 14000, train loss = 9.574634e-04\n",
      "it 15000, train loss = 8.224870e-04\n",
      "it 16000, train loss = 7.292809e-04\n",
      "it 17000, train loss = 6.598997e-04\n",
      "it 18000, train loss = 6.057676e-04\n",
      "it 19000, train loss = 5.686771e-04\n",
      "it 20000, train loss = 5.465408e-04\n",
      "it 21000, train loss = 5.349803e-04\n",
      "it 22000, train loss = 5.300285e-04\n",
      "it 23000, train loss = 5.285436e-04\n",
      "it 24000, train loss = 5.283000e-04\n",
      "it 25000, train loss = 5.282751e-04\n",
      "it 26000, train loss = 5.282683e-04\n",
      "it 27000, train loss = 5.282655e-04\n",
      "it 28000, train loss = 5.282644e-04\n",
      "it 29000, train loss = 5.282639e-04\n",
      "it 30000, train loss = 5.282638e-04\n",
      "it 31000, train loss = 5.282637e-04\n",
      "it 32000, train loss = 5.282637e-04\n",
      "it 33000, train loss = 5.282637e-04\n",
      "it 34000, train loss = 5.282636e-04\n",
      "it 35000, train loss = 5.282636e-04\n",
      "it 36000, train loss = 5.282636e-04\n",
      "it 37000, train loss = 5.282636e-04\n",
      "it 38000, train loss = 5.282636e-04\n",
      "it 39000, train loss = 5.282636e-04\n",
      "it 40000, train loss = 5.282636e-04\n",
      "it 41000, train loss = 5.282636e-04\n",
      "it 42000, train loss = 5.282636e-04\n",
      "it 43000, train loss = 5.282636e-04\n",
      "it 44000, train loss = 5.282636e-04\n",
      "it 45000, train loss = 5.282636e-04\n",
      "it 46000, train loss = 5.282636e-04\n",
      "it 47000, train loss = 5.282636e-04\n",
      "it 48000, train loss = 5.282636e-04\n",
      "it 49000, train loss = 5.282636e-04\n",
      "it 50000, train loss = 5.282636e-04\n",
      "it 51000, train loss = 5.282636e-04\n",
      "it 52000, train loss = 5.282636e-04\n",
      "it 53000, train loss = 5.282636e-04\n",
      "it 54000, train loss = 5.282636e-04\n",
      "it 55000, train loss = 5.282636e-04\n",
      "it 56000, train loss = 5.282637e-04\n",
      "it 57000, train loss = 5.282638e-04\n",
      "it 58000, train loss = 5.282636e-04\n",
      "it 59000, train loss = 5.282636e-04\n",
      "it 60000, train loss = 5.282636e-04\n",
      "it 61000, train loss = 5.282636e-04\n",
      "it 62000, train loss = 5.282636e-04\n",
      "it 63000, train loss = 5.282636e-04\n",
      "it 64000, train loss = 5.282636e-04\n",
      "it 65000, train loss = 5.282636e-04\n",
      "it 66000, train loss = 5.282636e-04\n",
      "it 67000, train loss = 5.282636e-04\n",
      "it 68000, train loss = 5.282636e-04\n",
      "it 69000, train loss = 5.282636e-04\n",
      "it 70000, train loss = 5.282636e-04\n",
      "it 71000, train loss = 5.282636e-04\n",
      "it 72000, train loss = 5.282636e-04\n",
      "it 73000, train loss = 5.282636e-04\n",
      "it 74000, train loss = 5.282637e-04\n",
      "it 75000, train loss = 5.282636e-04\n",
      "it 76000, train loss = 5.282636e-04\n",
      "it 77000, train loss = 5.282636e-04\n",
      "it 78000, train loss = 5.282636e-04\n",
      "it 79000, train loss = 5.282636e-04\n",
      "it 80000, train loss = 5.282637e-04\n",
      "it 81000, train loss = 5.282637e-04\n",
      "it 82000, train loss = 5.282637e-04\n",
      "it 83000, train loss = 5.282636e-04\n",
      "it 84000, train loss = 5.282636e-04\n",
      "it 85000, train loss = 5.282636e-04\n",
      "it 86000, train loss = 5.282636e-04\n",
      "it 87000, train loss = 5.282636e-04\n",
      "it 88000, train loss = 5.282636e-04\n",
      "it 89000, train loss = 5.282636e-04\n",
      "it 90000, train loss = 5.282636e-04\n",
      "it 91000, train loss = 5.282638e-04\n",
      "it 92000, train loss = 5.282636e-04\n",
      "it 93000, train loss = 5.282636e-04\n",
      "it 94000, train loss = 5.282636e-04\n",
      "it 95000, train loss = 5.282636e-04\n",
      "it 96000, train loss = 5.282636e-04\n",
      "it 97000, train loss = 5.282639e-04\n",
      "it 98000, train loss = 5.282636e-04\n",
      "it 99000, train loss = 5.282636e-04\n",
      "it 100000, train loss = 5.282636e-04\n"
     ]
    }
   ],
   "source": [
    "key, subkey = random.split(key)\n",
    "params_cann_PS = init_cann(key)\n",
    "opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params_cann_PS)\n",
    "mdlnumber = 1\n",
    "params_cann_PS, train_loss, val_loss = train_jp(loss_P11_PS, mdlnumber, F11_data, opt_state, key, nIter = 100000) #Original 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 6.0)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FbOk4VS2o9vsnIvK0iLQBdgFdq921i+c6Y8KSw+Fg4q330Wz8byj/cSN3TEinaVJ4f6ZH7FdsE5uKyytIbBRnHbiMMT4rK68M2zXLxyIiHcSzmFNERuL+/M4DlgJ9RaSniCQCVwKzQxepMUf3/BcraTb+N5Tt3kjuWw+xZMHXoQ7pmMI7lTemhhKnK+LXKxtjQqM0jOssi8gsIANoIyI7gT8DCQCq+l/gUuA2EakASoArVVWBChG5A/gciAemq+q6ELwEY47pg1W7+KqkO+V71pP3zhQSxBURe3aOmSyLSCsvnqdSVQ80PBxjjq7Y6aJxhJ5GDQYbr8bULRAb/PLzj1rAIt4zJo855lT1qmPc/h/cpeVqu+0T4JOjR2pMaL2/che/e3MVI3u24hfnnMyi4/8UMXt2vJlZ3u35Odp573ggfFqtmKhV4nRF/Oa+ALPxakwdSiv8X2e5U6dOdOrUCfck788cDyzHxpyJcW8v38kf3l7NqJ6teeHGNBonNuL0U0aHOiyveZMsb1DVYUe7g4is9FM8xhxVsbMiKjb3BZCNV2NqUe6qxFWppPh5ZnnAgAGsXFn7kBKRLFVNszFnYtmLC7bw8IfrGdOnDc9dnxaRE17eZB3ezI+H/xy6iQrFET6z7HA4Al0uzsarMbUoLXdX0vH3Mgwv29XbmDMxR1WZOvcH/vnlD4wb2J5/XTUsrMvDHc0xk2VVLQUQkdbA5UApsA7IUtWS6vcxJtBKyl20apIY6jDqJRgtrm28GlO7Ek+y7O/SccnJyQDk5eXx5ptvkpyczMCBAxk8ePDh+9iYM7GmslJ55KP1zFi4lUtHdOHxiweHbcMRb/gS+XtAW+CvwJPAQRHZGJCojKlDcQRXwwhyi2sbr8ZUU1ZeCUByo8B8YF900UXk5ubypz/9ibvvvpvmzZsDDDzW44yJNhWuSn7/9mpmLNzKzSf35G+XDInoRBl8S5ZTVfURYK+qngZcBbwZmLCMqV2J00VKQmSuWQ5yi2sbr8ZUE6hlGFUKCwt58MEHad++PV9//TWzZs0C2B+QgxkTpkrLXdz26greXbGL3511HA+cO4C4KOiL4EuyXHUaqUxEUlT1HeDsAMRkTJ3cG/wic2a5qsX1lClTArIEowYbr8ZUU1o1sxygZLlqOUZSUhIlJSVccsklAM0CcjBjwlBBaTk3vbiUOev38tB5x3Pn2L54+uhEPF+m6P7XUy/yDWC6iCwEWgQkKmPqEMnLMCCoLa5tvBpTTWlF1cxyYE4H//73vyc/P58rrriCm2++mdGjR4O7ZJwxUW9vQSk3TF/Cppwi/u+KE7hoWJdQh+RXXifLnpkpgH+IyHXAYOBiXw4mIluBQsAFVKhqmi+PN7HNVamUVVRGdDWMYPHHeDUmmgR6GYZnJpnf/e53vPzyy2RlZQFkB+RgxoSRTTmF3DB9KQeKnUy/8UROPa5tqEPyu2N+xRaRG0Rkn4jki8hMEUlV1ZdV9Q+qur4exzxdVYdaomx8VbWbPZJnlgMtAOPVmKhweBmGn0tXvfTSS7Rp04ZWrVpx/fXXU1hYyHXXXcff/vY3+Gk5lDFRafm2fC79r4OyChdv3JoelYkyeLdm+QHgLKA/sA337npjgq7YWQFAijUlORobr8bU4qeZZf8uw5gyZQpz5sxh48aNdO/enT/96U9+fX5jwtWc9Xu5+rnFtEhJ4N3bTmZQ5+ahDilgvMk6ClS1qvvQAyKyuAHHU+ALEVHgWVWd1oDnMjGmxOmZWQ7QadQo4c/xakzUCNQyjGbNmjFsmLtp5pQpUxg1apRfn9+YcPTa4u3c/34Wgzs3Z/qNJ9K6aVKoQwoob5LljiIyGdgIbAASGnC8Maq6S0TaAXNEZKOqflN1o+c4kwG6devWgMOYaHSozJZheMGf49WYqFFa4V6GkeTnmeU9e/Ywbdo0+vfvz4ABAygvL/fr8xsTTlSV//3iO56al01Gv7Y8fc1wGsfA2V5vXuGfcW8Ousbzb1MR+QRYDaxR1VneHkxVd3n+zRGR94CRwDfVbp8GTANIS0tTb5/XxIaS8qplGJYsH4Xfxqsx0aQsQDPLDz/8MFlZWbz66qtkZWVRVFTExIkTOeGEEwBa+fVgxoRQWYWLP7y9hg9W7eaqkd2YcsHAiG824i1v2l0fsVRCRLrg/hAeAkwEvPrwFZEmQJyqFnp+Pxt4xOeITcwq9izDaJIUWd9iHQ4HmZmZZGRkBLxsnL/GqzHR5vAyDD9v8Js8efIRl3fu3ElWVhZr1qwBiN5FnCamHCh2Mvnl5SzZks/IpD2c16FlzCTK4EWyLCJjcc9I5QKo6k5gJ/Cpj8dqD7znKVDdCHhNVT/z8TlMDKtKllMiaM2yw+Fg7NixOJ1OEhMTA96MxI/j1ZioUlpeSZxAQrx/myTMnTuXIUOG0LatuwpAly5d6NKlCxMmTOCee+7Z4teDGRMCO/KLufHFJWzLO8TBz6by7tqv+OjJwH+ehRNvpujmADkiUgmsBbKANZ5/16lqmTcHUtXNwAn1DdSYwxv8ImgZRmZmJk6nE5fLhdPpJDMzM9BvLn4Zr8ZEm9JyF8kJ8X7vKHbWWWfRrl074uLiGDRoEIMHD2bIkCEMHjwYIDral5mYtXrHASa9tJRylzIhJZtn1n4VzM+zsOFNsvwrYBLwJrAQ6AeMAG4EBgAdAhWcMdUVH06WI2cZRkZGBomJiYdnljMyMgJ9SBuvxtRif3E5zVP8v9/13//+Ny+88AKXX345o0eP5rvvvmP58uXMmDED3MufjIlIn6/7kV+/vpI2TZN4ffJIcrNTeeGxoH6ehY1jLjhR1aeAk3GXffsnUA78WlVPV1X74DVB81Od5ciZWU5PT2fu3LlMmTIlKKesbLwaU7ucwlLaN0v2+/PefvvtLFiwABHhN7/5DQkJCUydOpV58+aBe2OtV0RkuojkiMjaOm6/RkTWiEiWiCwUkROq3bbVc/0qEVnW8FdlYpmqMu2bbH7xynL6d2jGe788mT7tmgb98yyceLU6W1VLVPUJ4HSgD7BERKyYpAmqSFyGAe6E+d577w3aG4s/x6uIxIvIShH5yK9BGhNkewtKad8sMLVgU1JS+OMf/8i8efPYtGkTI0eOZPFin0uczwDGH+X2LcBpqjoYmIKnclQ11h3XNFi5q5L73l/LXz/ZyIRBHXh98km0Tf1p3AT78yxceLPB71Tc3cD64z6N2w4oBFoHNjRjjlRc7iIhXkiIoR24vgrAeP017nrNzfwSoDEhsregjJN6+f9j65tvvmHjxo1s3LiRDRs2kJOTQ2pqKnl5eT49j6p+IyI9jnL7wmoXFwFd6hexMbUrKC3n9ldX8O0P+7gtozd3n92PuDhbdg/erVnOBFYBrwP/UtWtAYzHmDqVOF0RVQkjRDLx03j1lJ07B/gL8Ds/xGZMSJSWuzhYUh6QZRgZGRkMHTqUK6+8kjvvvJMePXr4/Ri1mMSRFW6sO65pkB35xUx6aSmbcw/xt0uGcPmJXUMdUljxJlm+DRiE+0PzLhHJw72zPgtYq6rvBy48Y35S7KyIqM19IeLP8fpP4A9Aal13sK6bJhLkFLiLwAQiWX7mmWdYu3YtH3/8MX//+99p3bo1gwcPrqqG0cLfxxOR03Eny2OqXX3U7rjVHmvj1fzMqh0HuOWlZZRVuHjp5pGc3KdNqEMKO940JXm2+uUaTQ4uAd4PSGTG1FDsdEXceuVg89d4FZFzgRxVXS4iGUc5nnXdNGHvx4JSgICsWb711luPuFyjKUlLfx5LRIYAzwMTVPXwOo9jdcetdj8br+YIH63ZzV1vrqZdsyRenzyKPu3qnBuJaT5P01mTAxMqJU5XRFXCCAcNGK8nA+eLyEQgGWgmIq+o6rX+jtGYQNt7OFn2/8xyTYFqSiIi3YB3getU9ftq11t3XOMzVeXfX23iH3O+58QeLfnvtSNo3TQwG2CjwTF3SonICn/cx5iGspnlY/PXeFXVe1W1i6r2AK4EvrJE2USqw8lyqv+T5eHDhx/zPl6Oy1mAA+gnIjtFZJKI/EJEfuG5y4O4N+o+XaNEXHtgvoisBpYAH1t3XHM0peUufvPGKv4x53suHt6ZV24ZZYnyMXgzszxARNYc5XYBmvspHmPqVFzuCkhTgUBwOBxkZmaSkZER7BI7Nl6NqSGnsIykRnE0S/H/nocNGzYwZEidvUeOF5EsvBhzqnrVMW6/BbilluutO67x2r6iMibPXMaK7Qe4e1w/fpnR2+9dLaORN+8c/b24j6uhgRhzLCXOCjoG4TRqQzkcDsaOHXu4y1GQi7f7fbyqaibuKhvGRCR3jeXkgCQFGzdurPO2Hj16bALOwz4jTRj4fm8hN89Yyr6iMp6+ZjgTB3cMdUgRw5sNftuOdruI/NHTAMGYgIqUZRiZmZk4nU5cLhdOp5PMzMxgNiSx8WpMDYFsSNK9e/ej3ewErrQxZ0Jt3sYcfjVrJSmJ8bwxOZ0TurYIdUgRxedzUiLyZvWLwFDA3ghMwEXKBr+MjAwSExMPzyxnZGSELBYbr8a4S8cN6BScvjqXX3559Yu9cC+dsDFnQkJVmb5gK3/5eD0DOjbj+RvS6Ng8JdRhRZz6LOAq8KydAkBEnvFjPMbUKVJmltPT05k7d26o1izXZOPVxLy9BaVk9GsXlGM1a9aM559/HgAR2QwsDcqBjamh3FXJgx+sZdaSHYwf2IF/XHGC9Sqop/r8V/tLjcv3+SMQY46mslIpKXeREiEDPT09PdRJchUbryamFZVVcMjpokPz4Oz2v+++nw0xG3Mm6A4UO7ntlRU4Nudx++m9uessa13dEPWps7ylxuV8/4VjTO1KK9z7YyJhZjmc2Hg1se7Hg8GrsQzQs2fPIy7bmDPBtjm3iEkvLWPX/hL+cfkJXDy8S6hDinjHrLN8LCLyR38EYszRHCqzZNkfbLyaWJPjqbHcLgA1lr1hY84E07c/5HLhUwvIKyhmQvL3dCzbEeqQooJt8DMRocTpTpZTEixZ9oWNVxPr9hYGrtV1bWyDnwmVlx1beejD9XRqGsfKqbexPm8Xzz0W9PKlUck2+JmIUFxeAUCTpMhYsxxGbLyamLa3oAyAdkFahmEb/EywVbgqeeSj9cx0bOPMAe3ouusrHHm7QlK+NFrZBj8TEYqrZpbDeBlGCLv2HY2NVxPT9haU0jSpEU2D9EXbNviZYDpYXM7tr61g/qZ93HpaL/4wrj9LFlfwRJiUL40Wx1yzLCI3iMg+EckXkZnAvuq32+YFEwxVyzAah+kyjKqufQ888ABjx47F4XCEJA4br8YcKaegjHYBXILx0ksv0aZNG1q1asX1119PmzZtjrjdxpwJlM25RVz09AIWb8njyUuHcO+EAcTHyeHypVOmTLElGH7izQa/B4CzcLfR3Qb8NaARGVOLqpnlcK0RWVvXvhCx8WpMNXsLSmkfwM19U6ZMYc6cOWzcuJHu3bvzpz/9KWDHMqbK/B/2ceFTCzhQUs5r/3MSl6V1PeL29PR07r33XkuU/cSbzKNAVVd6fn9ARBY35IAiEg8sA3ap6rkNeS4TO4qd7jXL4boMI4y69vl1vBoT6fYWljKiW8uAPX+zZs0YNmwY4E6cR40aFbBjGQM/beTr07Ypz9+QRtdWjUMdUtTzJlnuKCKTgY3ABiChgcf8ted5gtN71ESFw8swwjRZDqOuff4er8ZELFVlb0FZQGss79mzh2nTptG/f38GDBhAeXl5wI5lYlu5q5JHPlzPy4u2MbZ/O6ZeNSxoa/FjnTf/lf8MDAau8fzbVEQ+AVYDa1R1lrcHE5EuwDm4Nx39zvdwTawqDvNkGcKma5/fxqsxke5gSTnOisqAVsJ4+OGHycrK4tVXXyUrK4uioiImTpzICSecANAqYAc2MeVgcTm/fG05CzblceupvfjD+P7EW0e+oDlmsqyq06pf9iS8g4EhwETAlw/ffwJ/AFJ9eIwxlJSHfzWMcODn8WpMRPvR05CkQwCT5cmTJx9xeefOnWRlZbFmzRqA5gE7sIkZ2blF3PLSMnbuL+bJS4f8bH2yCbz6tLveCewEPvXlcSJyLpCjqstFJKOO+0wGJgN069bN19BMFCt2VhAfJyTGN7jpZEyp73g1JhpU1VgOVkMSgC5dutClSxcmTJjAPffcs+XYjzCmbvN/2McvX11Oo/g4Xvufkzixh52sCIVgZh4nA+eLyFbgdeAMEXml+h1UdZqqpqlqWtu2bYMYmgl3xU4XjRPiEbHTTsYY7+wtqOreF5pW18Y0xEzHVm54cQkdm6fwwe0nW6IcQkFLllX1XlXtoqo9gCuBr1T12mAd30S2EqfLlmAYY3yS40mW26YGb2a5vkRkuojkiMjaOm4XEfmXiGwSkTUiMrzabTeIyA+enxuCF7UJhHJXJfe/n8WDH6zj9H5teeeXo63iRYh5nSyLyGUikur5/X4Rebf6YDUmkIqdrrDe3BdubLwa416G0aJxAslBaGb01ltvUVhYCMCjjz7KxRdfDOBLhjMDGH+U2ycAfT0/k4FnAESkFe6NvaOAkcCfRSRwtfJMQB0odnLji0t4ZdF2bj2tF89el2YVL8KALzPLD6hqoYiMAc4EXsAzWH2lqplWY9n4otjpIiVMG5KEKb+NV2MiVaAbklQ3ZcoUUlNTmT9/Pl9++SWTJk0C8Hrzjap+Axyt298FwEx1WwS0EJGOwDhgjqrmq+p+YA5HT7pNmMrOLeKipxeyZEv+ER35TOj5kiy7PP+eA0xT1Y+BRP+HZMzPlZRXhNXMssPh4LHHHgtZW2sv2Hg1MW9vYWBbXVcXH+9+f/r444+ZPHky55xzDvh3qWNnYEe1yzs919V1vYkg3/6Qy0VPLaCgpJxZtXTkM6Hly1TdLhF5Fncr3SdEJIngbhA0MazY6QqbU1EOh4OxY8ce7tY3d+7ccKivXJONVxPzcgpK6duuTVCO1blzZ2699VbmzJnDH//4R8rKyoJyXF9YxanwNNOxlYetI19Y8+XD83Lgc2Ccqh7AXWz97kAEZUxNJU4XKUFYd+iNzMxMnE4nLpcLp9NJZmZmqEOqjY1XE9MqK5WcwrKglY178803GTduHJ9//jktWrQgPz8f3LO8/rILqD7d2MVzXV3X/4xVnAov5a5KHnh/rW3kiwBeT9WpajHwbrXLe4A9gQjKmJrCaYNfRkYGiYmJh2eWMzIyQh3Sz9h4NbFu36EyXJUatLJxjRs3rtrUB0DHjh0BCvx4iNnAHSLyOu7NfAdVdY+IfA78tdqmvrOBe/14XBMAR3TkO60XfxhnHfnCmdfJsohcBnzm2TR0PzAceFRVVwQsOmM8wmmDX3p6OnPnziUzM5OMjIxwXIJh49XEvBxPQ5J2Qdrg99ZbbzF+/HhSU1N59NFHWbFiBfhQDUNEZgEZQBsR2Ym7wkUCgKr+F/gEdxfOTUAxcJPntnwRmQIs9TzVI6p6tI2CJsQ2ezry7bCOfBHDl+zjAVV9q9ru+idx764fFZDIjPGocFVysMRJi8YJoQ7lsPT09LBMkqux8WpiWlVDkg7Ng1cN47LLLjtcDePuu+/mvffe86UaxlXHuF2B2+u4bTow3beITSgs2LSP215xd+T7U3pTNs15BUd5eE66mJ/4kiz/bHe9iDwagJiMOcKO/SWUu5RebZqEOpRIYuPVxLRgt7oOQjUME+FeWbSNP89eR++2Tbh9cDxXnX92uG8UNx6+DOSq3fVXAp/Y7noTLNk5RQD0btc0xJFElAaNVxFJFpElIrJaRNaJyMMBi9SYANhbUIoItGkanGS5qhrG66+/zsSJE8OyGoYJjQpXJQ/NXsf976/l1L5teOe20axf+m0kbBQ3HvWphnG27a43wZSd60mW21iy7IOGjtcy4AxVPQEYCowXkZP8HaQxgZJTWErrJkkkxAdnTqeqGsYXX3wRqGoYJgIVlJYz6aVlzFi4lUljevL8DSeSmpxweKN4fHx82G4UNz/x5V2kBGgCVK2rSgAO+DsgY2rKzi2iTdMkmofRmuUI0KDx6ukSVlTtsQmA+jNAYwJpb0HwysYBpKSkcOjQIWbNmgVAeXk5QEXQAjBhZ3teMRc/vZAFm/bx2MWDeeDc4w9XvKjaKD5lyhRbghEBfFmz/DRQCZwBPAIUAu8AJwYgLmMOy849RJ92tl7ZRw0eryISDywH+gBPqeriWu5jTQ5MWNpbUBq0snEAv/zlL4mLi+Orr77iwQcfJDU1FaB70AIwYWXJlnxufXkZlQozJ41kdO+fN8eJgI3ixsOXmeVRqno7UArg6UFv7XNNQKkqm3KK6N02uEswIqCd9bE0eLyqqktVh+JucjBSRAbVch9rcmDCUrBnlhcvXsxTTz1FcrI7QW/ZsiWAFc6NQe8s38k1zy+iZeNE3r/95FoTZRNZfJlZLvfMNCmAiLTFPXNlTMDkHXJysKQ8qMlyhLSzPha/jVdVPSAi84DxwFr/hWhMYJS7Ksk7VBa0GssACQkJuFwuRNz5cW5ubtCObcJDZaXy9znf8dS8bEb3bs0z14yw5YNRwpeZ5X8B7wHtROQvwHzgrwGJyhiPUFTCiJB21sfSoPEqIm1FpIXn9xTgLGBjAOI0xu9yC8tQJajLMO68804uuugicnJyuO+++xgzZgxY18yYUeJ0cftrK3hqXjZXjezKSzePtEQ5ing1syzur8rf4F6/OBb3qaULVXVDAGMzhuzcQwD0bhu8NcuR0M76aPw0XjsCL3lmp+OAN1X1I78Ha0wAVDUkCdYyDFXl1FNPZcSIEcydOxdV5f333+f444/fH5QATEjlFJRyy8xlZO06yP3nDGDSmJ6HzzCY6OBVsqyqKiKfqOpgbHbJBFF2bhEpCfF0ap4StGNGQjvro/HHeFXVNcAw/0ZmTHD81JAkODPLIsLEiRPJysqif//+QTmmCQ/rdh/klpeWcbCknOeuS+PM49uHOiQTAL6sWV4hIieq6tJj39UY/9iUU0Svtk2Iiwvut/Qo2KVs49XErJzCqpnl4C3DGD58OEuXLuXEE61AVKyYu2Evv5q1kuYpCbz1i3QGdmoe6pBMgPiSLI8CrhGRbcAh3Kd2VVWHBCQyY3DPLA/v1jLUYUQiG68mZu0tKCU+TmjdJHgFmxYvXsyrr75K9+7dadKkCaoKcHzQAjBBo6q8uGArj368noGdmvPCDWm0C+IXMxN8viTL4wIWhTG1KHG62HWghMtGdA11KJHIxquJWXsLymiXmhTUM1Kff/75z67r0aPHpqAFYIKiwlXJIx+tZ6ZjG+MGtuf/rhhK40RfUikTiXyphvFLVd1W/Qf4ZaACM2bLvkOoQm9rSFIfNl5NzNpbUBr0mb6nn36a7t27H/EDWPHxKFJYWs4tM5cx07GNyaf24plrRliiHCN8SZbPquW6Cf4KxJiasnPdZeP6BLFsXBSx8WpiVk5BGe1Tg9eQBGDOnDm1XW2LWKPErgMlXPZfB9/+sI+/XjSYP00cEPS9NCZ0jvmVSERuwz0j1VtE1lS7qRmwIFCBGbMppwgR6NHaZpa9ZePVGNhbWMrInq2CcqxnnnmGp59+muzsbIYM+WlLQEFBAUBJUIIwAZW18yA3v7SUUqeLGTedyCl97YRBrPHm/MFrwKfAY8A91a4vVNV8bw8kIsm4a78meY77tqr+2YdYTYzJzi2ia8vGJCfEhzqUSOKX8WpMpCotd3GguDxoNZavvvpqJkyYwL333svjjz9++PrU1FRat269JShBmID5cr274kWrJom8+stRHNc+NdQhmRA4ZrKsqgeBgyLyLpCvqoUicj8wXESmqOpKL49VBpyhqkUikgDMF5FPVXVR/cM30Sw791DAm5E4HI6IradcGz+OV2MiUo6nxnKw1iw3b96c5s2bc/HFF9OqVStSU1N59NFHWbFiBUDwCsQbv5uxYAuPfLSeQZ2b8/wNaUFtn27Ciy9rlh/wfPCOAc4EXgD+6+2D1a3IczHB86M+HN/EkMpKZXNuUUDXKzscDsaOHcsDDzzA2LFjcTgcATtWCDRovBoTqfaGoMYywJQpU0hNTWX+/Pl8+eWXTJo0CaC7t48XkfEi8p2IbBKRe2q5/f9EZJXn53sROVDtNle122b75QXFsAULF3LegzN46MP1jB3Qntcnn2SJcozzJVl2ef49B5imqh8DPhWxFJF4EVkF5ABzVHWxL483sWPXgRLKKirp3TZwyXJmZiZOpxOXy4XT6SQzMzNgxwqBBo9XYyJRsFtdV4mPdy8X+/jjj5k8eTLnnHMOePkZ62kr/xTuTbjHA1eJyBE1mlX1t6o6VFWHAv8G3q12c0nVbap6foNfTAyb9+1CLv2/z8lytqV45Ufc2MdpFS+MT8nyLhF5FrgC+EREknx8PKrq8gz0LsBIERlU/XYRmSwiy0RkWW5uri9PbaLMJk8ljN4BnFnOyMggMTGR+Ph4EhMTycjICNixQqDB49WYSFTV6rpDkGeWO3fuzK233sobb7zBxIkTKSsr8+XhI4FNqrpZVZ3A68AFR7n/VcCsBoRrapFbWMYfPttFYq808r98lvy5z/HN11+HOiwTBnz58Lwc+BwYp6oHgFbA3fU5qOfx84DxNa6fpqppqprWtq3tNo1l2TmeZDmAM8vp6enMnTuXKVOmMHfu3KhYs1yN38arMZEkp6CUxEZxNE9JCOpx33zzTcaNG8fnn39OixYtyM/PB9jp5cM7AzuqXd7pue5nRKQ70BP4qtrVyZ6JpkUicqHv0ZvNuUVc8sxCDtKYgx8/SfGqT6JxEsXUk9fnFlS1mGqnfVR1D7DH28eLSFugXFUPiEgK7jqwT/gQq4kh2bmHaNk4gVYBblebnp4ebUky0PDxakyk2ltQSvtmSYgErgZubRuDGzduzMUXX3z4Ph07dgQoCMDhr8RdTcpV7bruqrpLRHoBX4lIlqpm13ygiEwGJgN069YtAKFFpuXb9nPLS0uJE+HNX5xMyblPRNXGb9NwwVyI0xF4ybM2Kw54U1U/CuLxTQTJDvDmPmNMdNqeXxzQJRhVG4OdTieJiYn+Oiu1C+ha7XIXz3W1uRK4vfoVqrrL8+9mEckEhgE/S5ZVdRowDSAtLc022AOfr/uRO2etpGPzZGbcNJIebZpA1+icRDH1F7Q1jKq6RlWHqeoQVR2kqo8E69gm8mzOLQroEgxjTPQpdlaQtesgw7u3DNgxArQxeCnQV0R6ikgi7oT4Z1UtRKQ/0BJwVLuupWdPAiLSBjgZWO+PoKLdTMdWfvHKcgZ0bMY7t412J8rG1MK2eJqwc6DYyb4ipyXLxhifLNu6n3KXMrp3m4Ado2pjcNXMsj/WtKpqhYjcgXufQTwwXVXXicgjwDJVrUqcrwReV9Xqs8IDgGdFpBL3BNjjqmrJ8lFUVipPfL6RZ7/ezJkD2vPvq4aRkmjNr0zdvGl3XUjt9ZAFd/nkZn6PysS07MOVMOxbvq9svJpYtjA7j0Zxwok9AjezXLUxuGpN69lnn13r+mhPPjvM2+dV1U+AT2pc92CNyw/V8riFwGBvjxPrnBWV/OHt1by/ajfXndSdh84fSHxc4Na3m+jgTQc/6+1ogio75xAQ2EoY0crGq4lljs15DO3aIuB1catvDC4sLKzzfiJiHTPDyKGyCm57dQXffJ/L3eP68cuM3gHdCGqih0/vKCLSEugLHN49oarf+DsoE9uyc4tIbBRHl5aN/fac0dbW2hs2Xk0sKSgtJ2vnAe44vU/IYti/fz8//PADpaWlVVfZN/4wkVdUxs0zlpK16yBPXDKYK060aiDGe14nyyJyC/Br3Lt0VwEn4d5kcEZAIjMxKzu3iF5tmvjt1FiAdq+HNRuvJtYs3ZJPpUJ6ANcrH83zzz/P1KlT2blzJ0OHDmXRokUAnUISjDnCjvxirp++hN0HSph2XRpnHt8+1CGZCONLNYxfAycC21T1dNxrsQ4EIigT2zbl+LcSRpS3ta6LjVcTUxZm55HYKI5h3VrU+zkcDgePPfYYDofj2HeuYerUqSxdupTu3bszb948Vq5cCT+1nTchsmFPAZc8s5D8Q05e+59RliibevFlGUapqpaKCCKSpKobRaRfwCIzMamswsX2/GLOP8F/EzKB2L0eAWy8mpiyMDuPtO4tSU6oX1WDhp6BSk5OJjnZveKprKyM/v37Q7UlUCb4Fm/O45aXltE0uRFv/yKdvu1tS4epH1+S5Z0i0gJ4H5gjIvuBbYEIysSubXnFVCr09mNDkpq716N9CYaHjVcTM/YfcrJhTwG/P/u4ej9HbWegfHmv6NKlCwcOHODCCy/krLPOomXLlgBl9Q7INMi8jTn84pXldGmZwsuTRtGpRUqoQzIRzJd21xd5fn1IROYBzYFPAxKViVnZOZ6ycX6uhBGtba3rYuPVxJJFm/MASO/dut7P0dAzUO+99x4ADz30EKeffjoHDx5k9uzZP+uiZwLvw9W7+e0bq+jfMZWXbhpJ66ZJoQ7JRDhfNvglAZcAPao9bihgnfiM31TVWO7V1mosN4SNVxNLFmbn0TgxniFdWtT7ORp6BqqsrIx33nmHrVu3UlFRUXV1h3oHZOrltcXbue/9LE7s0Yrnb0ijWXJCqEMyUcCXZRgfAAeB5dipJRMgm3KK6NwiJeB1UmOAjVcTMxyb8xjZsxUJ8b7sWf+5hpyBuuCCC2jevDkjRowgKenwTGZlgwIyPnn262we+3Qjp/dryzPXjqj3+nVjavIlI+miquMDFokxQHbuIZtV9g8bryYm5BSUsimniMtGdAlpHDt37uSzzz474rrf//73e0MUTkxRVf73i+94al425w7pyD8uH0pio4Z9cTKmOl/+mhaKiLXUNAGjqmTn+rdsXAxr0HgVka4iMk9E1ovIOhH5tT+DM8ZfHJ71yqNDVF+5yujRo8nKygppDLGieok/VeXhD9fz1LxsrhrZjalXDrNE2fidLzPLY4AbRWQL7tO6AqiqDglIZCbm/JBTRLHTRd/2liz7QUPHawVwl6quEJFUYLmIzFHV9QGK15h6cWTn0Sy5Ecd3ahbSOObPn8+MGTPo2bMnSUlJqCrA8SENKgodWeIviav/8QFfbi3jljE9ue+cAda+2gSEL8nyhIBFYQww07GVxEZxjBtoe2L8oEHjVVX3AHs8vxeKyAagM2DJsgkrC7PzGNWrtd86ftbXp5/+vNhMjx49NoUglKj2U4m/Shpn3MKXW8u4LaM3fxjXzxJlEzC+lI6zGq0mYA4UO3ln+S4uHNqJNl6W+XE4HLFWO9lr/hyvItIDdwfAxf56TmP8Yef+YrbnF3PTyT1qvT2Y7xHdu3ev7WpnQA8agzIyMkhMSqbJ6ZNpMmgsFx2XbImyCbhjJssiMl9Vx4hIIaDVb8J9Wje0575MVJi1ZAcl5S5uOrmnV/dvaLetaOXv8SoiTYF3gN+oakEtt08GJgN069at/oEbUw+O7LrXKwfrPWLMmDHMnz+f1NTUIxI2zzKMYX4/YIw7ceQoLn7iPebvdHJp/xT+98YzQh2SiQHHXAWvqmM8/6aqarNqP6mWKBt/KHdVMtOxldG9WzOgo3d/UrV12zL+Ha8ikoA7UX5VVd+t43jTVDVNVdPatm3b8BdgjA8c2Xm0bpLIcbXscwjWe8T8+fMBKCwspKCg4PBPYWEhwMqAHDRGVbgq+e2bq5m/08nd4/pZomyCxraMmpD7dO2P7DlYyqQx3s0qw0/dtuLj4+vVbcscnbinyF4ANqjqP0IdjzE1qSqOzXmc1Lt1rafg7T0iulS4Kvn1G6v4cPVu7p3Qn9tP7xPqkEwM8aWDXxpwH9Dd8zirhmH8Yvr8LfRo3ZjT+7Xz+jEN7bYV7fwwXk8GrgOyRGSV57o/qeon/o7VmPrYmlfMnoOljK6jxXWw3yOWLVvGX/7yF7Zt20ZFRYVVw/Cjykrlj+9k8fGaPfxpYn8mn9o71CGZGONLNYxXgbuBLKwrkfGTFdv3s2rHAR4+fyBxPu5mb0i3rRjQoPGqqvNxJ9jGhKWF2fuAo9dXDuZ7xDXXXMOTTz7J4MGDiYtzn7S1ahgNp6r8efY63lmxk9+eeZwlyiYkfEmWc1V1dsAiMTHphflbSE1uxKUh7r4VhWy8mqjmyM6jQ7NkerRuHOpQAGjbti3nn39+zat9qoYhIuOBqUA88LyqPl7j9huBJ4Fdnqv+o6rPe267Abjfc/2jqvqSTy8gDKkqj3+6kZcXbePWU3tx51hbemFCw5dk+c8i8jwwF3eTAwDq2vhjzLHsOlDCZ2t/ZNKYnjRJ8uVP0XjBxquJWgWl5Xz7wz7G9m8XNiXDHn74YW655RbGjh1LUtLh8pctvH28iMQDTwFnATuBpSIyu5ZGQG+o6h01HtsK+DOQhrsKznLPY/fX79WEh3/N3cSz32zmupO6c8+E/mHz/9rEHl8ylJuA/kACP53WVcCrD18R6QrMBNp7HjdNVaf6cHwTZWY6tqKqXJ9ea31S0zANGq/GhJOa9ZKfnpdNQWk5N/uwKTjQXnzxRTZu3Eh5efnhZRj4kCwDI4FNqroZQEReBy7Au0ZA44A5qprveewcYDwwy4fjh5XnvtnM/335PZeO6MLD5w+0RNmElC/J8omq2q8Bx7L2ueawQ2UVzFq8nQmDOtKlZXicRo0yDR2vxoSFmvWSX//wC6bPL+SiYZ0Z1Ll5qMM7bOnSpXz33XdHXDdjxoytPjxFZ2BHtcs7gVG13O8SETkV+B74raruqOOxnWs+MFLqor+yaBt/+WQD5wzpyBOXDPF5P4sx/uZL6biFIlLvnb2qukdVV3h+LwSq2ueaGPTuip0UlFZw85geR1zvcDh47LHHcDgcoQksejRovBoTLmrWS56auY24OLh7XHh9Fxw9ejTr1wd87udDoIenqs0cwKd1yZFQF/3zdT/ywAdrOaN/O/7v8qEhb2NuDPg2s3wSsEpEtuBeA1nv0nF1tc+NlG+9pmEqK5UXF2zlhC7NGd6t5eHrrSufX/ltvBoTSlX1kp1OJ427DSS7vAV3ntGLjs1TQh3aERYtWsTQoUPp2bMnSUlJ9SkdtwvoWu1yF37ayAeAquZVu/g88Ldqj82o8dhMH44dFlZs38+ds1YypEsLnrp6OImNrBWECQ++JMvj/XHAo7XPVdVpwDSAtLQ0reXhJgrMWLiVzfsOMfXKoUesQ6ut45Yly/Xml/FqTKhV1UueNy+TefHDOVARz62nhV/5sM8+++xn1/lYOm4p0FdEeuJOfq8Erq5+BxHpqKp7PBfPx32GFuBz4K8iUjX7cDZwrw/HDrlteYe45aVltG+WzAs3pJGSGB/qkIw5zOtkWVW3icgJwCmeq75V1dW+HMyb9rkmun2wahePfLSeMwe059whnY64rfoMknXcahh/jFdjwkV6ejp5Tboz7bWV/O2S48Oyek737t1ZvXo13377LQCnnHIK+FA6TlUrROQO3IlvPDBdVdeJyCPAMk8pyDtF5Hzce4DygRs9j80XkSm4E26AR6o2+0WC/ENObnxxKZWqzLjpRNo0TTr2g4wJIl86+P0a+B9+2k3/iohMU9V/e/l4a58b4zK/y+GuN1czsmcr/nP1sJ+tRbOufP7T0PFqTDgpLXfxxGcb6d8hlUvCtCb71KlTee6557j44osBuPbaawG8b0sKeDpkflLjuger/X4vdcwYq+p0YLpvUYdeabmL/5m5jF0HSnjtllH0ats01CEZ8zO+fD2fBIxS1UMAIvIE4AC8/fC19rkxbPm2/dz2ygqOa5/K8zekkZxQ+yk268rnNw0dr8aEjZcWbmVHfgmvTBoVthu+XnjhBRYvXkyTJk0A+OMf/0jTpk3rbi9oqKxU7npzNcu37eepq4eT1qNVqEMypla+JMsCuKpdduFDO1xrnxu7vt9byM0zltK+WRIv3TySZskJoQ4pFjRovBoTLvKKyvjPV5s4o387xvQN39xTVYmP/2kSoPrv5ue1sgEe/2wjH2ft4b6JAzhnSMcQR2hM3XxJll8EFovIe7g/dC8gAk/5mODakV/MdS8sJqlRHC9PGkXbVFuLFiQ2Xk1UmDr3B4rLXfxpYv9Qh3JUN910E6NGjeKiiy5CVfnggw8A9oU6rnBQW6WjH5O7Mu2bzVyf3p1bTgmf5jLG1MaXDX7/EJFMYAzuTmA3qerKQAVmIl9OQSnXT19CidPFm79Ip2sraz4SLDZeTbiobUbRWx+u3s2ri7dz9chu9GmXGqAI/eN3v/sdGRkZzJ8/HxHhxRdfZPjw4Tmhjisc1Kx09M7cRXxUdoCRPVvx4LnHW3c+E/aOmSyLyIN13HSeiJyrqlP8HJOJAp9m7eG+99dS7KzglUmj6N+hWahDigk2Xk04aUjt9DeWbueed7M4sXsr/jghfGeVH3nkkVqv//DDDwFsbQFHVjpKatqc+dqf1OQE/nPVMBrFWy1lE/68mVk+VOOyAk2AW4DWgH34msMOlpTz0Ox1vLdyF4M7N+cfl59A3/bhPSMUZWy8mrBR39rp0+dv4ZGP1nPqcW159toRYV1zt2pDXxUR4dChQzz//PMAHUISVJipXit7VZMRLN9bwWu3DKNds+RQh2aMV46ZLKvq36t+F5FU4NfAzcDrwN/repyJPd98n8sf3l5DTmEpI5J+5DfDW1qiHGQ2Xk048bV2uqry1LxN/O8X3zNuYHv+ddUwkhqFb6IMcNdddx3+vbCwkKlTpzJ9+nSuvPJK/va3v2WFMLSwkp6eztry9iz5ZAP3TRzAqF6tQx2SMV7z6vyHiLQSkUeBNbgT7OGq+kdVtfVYhmJnBfe/n8X105cQX+lk36w/8sFfbmXcWWficDhCHV7MsfFqwkXVjOKUKVOOuQRDVXnis+/43y++5+JhnXnq6uFhnyhXyc/P5/7772fIkCFUVFSwYsUKnnjiCXA3DzHA4s15PP7ZRsYP7GAb+kzE8WbN8pPAxbjbUA9W1aKAR2UiQrmrkjeW7uBfc38gt6iMW8b0RNfMZvHOjdayOkRsvJpw403t9ApXJQ9/uJ6XF23jmlHdmHLBIOLCtJ5yTXfffTfvvvsukydPJisri6ZNralGTTkFpdwxayXdWjXmycuG2IY+E3FEVY9+B5FKoAz3N+TqdxZAVTUgO7fS0tJ02bJlgXhq00CVlcqHa3bzjznfsy2vmBN7tOSeCf0Z0b1Vgzb0mKMTkeWqmnaM+4RkvIKNWVM/izfn8efZ69j4YyG3ntqLeyb0j6hkKi4ujqSkJBo1anRE3KpKUVFRpaqG5fR4sMZrhauSq59bTNaug7x/+8n062BL80x4OtpnrDdrlm2rqgHcb/7zvsvhyc+/Z8OeAgZ0bMaLN55IRr+2hz8krGV1aNl4NQ3VkFJvvvjxYCl//WQDs1fvpnOLFJ6+ZjgTBnWIqEQZoLKyss7bRCQmyzVW/xtaUdqWJVvz+cflJ1iibCKWL01JTIyqrFS+3LCX/36dzYrtB+jeujFTrxzKeUM61Xqq1FpWGxOZgnFmqKzCxQvzt/CfrzZRUanceUYfbsvoE9YVL4z3qv8NpbTvQYcbpjJhUAcuHt4l1KEZU2+WLJs6lVW4+GDlbp79Jpvs3EN0aZnCoxcO4ooTu5JgtTGNiTr1LfXmjdJyFx+v2cN/5m1iy75DnHV8ex4453i6tbZmRdHk8N9QpdL0jF8QpxU8fMHAUIdlTINYsmx+pqC0nNcWb2f6/C3kFJYxsFMz/nXVMCYO6mAF5I2JYr6WevPG7gMlvLp4G7OW7CD/kJO+7Zry0s0jOe24tg0P2ISdqr+hxIFnk9R5ADcPbUK7VKunbCKbJcvmsK37DvGSYytvLdtJUVkFY/q04e+Xn8CYPm0ibh2hMcZ3/tpzoKos2pzPTMdWvli/F1Vl7ID23JDeg5P7tLb3kyiWnp7OrNlfcNeX+xnYLoE/XH5aqEMypsEsWY5xqsqCTXm8uGALX32XQ6M44ZzBHZk0pheDuzQPdXjGmCBryJ6D734s5MPVu/lozW625hXTonECt5zSk2tHdadrK1tuEQtUlbe2JpCclMjTN59iX4xMVLBkOUYVOyt4b+UuZizYyg85RbRpmsivzujLtaO6WQtSY4zXsnOL+Gj1Hj5as5sfcoqIExjduw2/PL0P55/QieQE27gXS95ctoP5m/bx6IWD6NwiJdThGOMXlizHmM25Rby8aBtvL99JYWkFPZrHk5Gyg5syhnDamONCHZ4xJsy5KpVVOw7w1ca9zN2Qw8YfCxGBE7u3YsoFAxk/qCNtU5NCHaYJgb0FpTz68QZG9WzF1SO7hTocY/zGkuUY4KpUvtqYw0zHVr79YR8J8cL4QR0ZnlrI7ZePZ77TyZtPWAMR8xMRmQ6cC+So6qBQx2NCq7C0nG9/2MfcDTlkfpdD3iEn8XFCWveW3H/OAM4d0okOze2MVEOIyHhgKhAPPK+qj9e4/XfALbgbDuUCN6vqNs9tLiDLc9ftqnp+0AL3UFXue28t5a5KnrhkSMR0YDTGG5YsR7F9RWW8sXQHry3ezq4DJXRolsxdZx3HFSO70i41mcceeyxgZaJMxJsB/AeYGeI4TAi4KpWsXQf59vtcvv1hHyu276eiUmmeksDp/dpyxoD2nNa3Lc0bJ4Q61KggIvHAU8BZwE5gqYjMVtX11e62EkhT1WIRuQ34G3CF57YSVR0azJhr+mztj3y5YS/3TRxAjzZNQhmKMX5nyXKUUVWWbdvPy45tfLp2D+UuZXTv1jxw7gDOHND+iNJvgSgTZaKDqn4jIj1CHYcJnu15xSzI3sf8H/Yxf9M+DpaUIwKDOjVn8qm9yOjXjuHdWlj5yMAYCWxS1c0AIvI6cAFwOFlW1XnV7r8IuDaoEdaiqlPfKaeexpOL3GUBbx7TM9RhGeN3lixHiaIy94a9Vxzb+G5vIanJjbj2pO5cM6o7fdo1rfUx1pramPAXqPbTOQWlLMzOY2H2PhZm57FzfwkAHZolc/bx7TnluLac3Ls1rZva+uMg6AzsqHZ5JzDqKPefBHxa7XKyiCzDvUTjcVV9v7YHichkYDJAt24NW1NcvVNfsyFn0ezsO/jvtcOJt+UXJgpZshzh1u8u4JXF2/hg5S4OOV0M6tyMJy4ZzHkndKJx4rH/91pratMQ/vzwNT/nz/bTeUVlLN6SjyM7D8fmPDblFAHQLLkR6b1bM/nUXozu3ZrebZtaua8wJiLXAmlA9QLG3VV1l4j0Ar4SkSxVza75WFWdBkwDSEtL04bEcbhTnwqNR11Gm7hixg3s0JCnNCZsWbIcgUrLXXy6dg+vLNrO8m37SWoUx7lDOnHtSd0o2bmBr798ldUVNlNsAs+fH77m5xrSfjr/kJMlW/JwZOexaHM+3+0tBKBxYjwjurfk0hFdOLl3G47v1MxmA0NvF9C12uUunuuOICJnAvcBp6lqWdX1qrrL8+9mEckEhgE/S5b9qWoZX8KAM2jUvD2TRtqXLBO9gpYs2+76htuWd4jXFm/nzWU72F9cTq82Tbj/nAFcOqILLRon4nA4OPPMM/0yC2WMCT1f9hXkFpaxeEseizfns3hLHt/vdc8cpyTEk9ajJecP7cRJvVozpEtzEmzdcbhZCvQVkZ64k+Qrgaur30FEhgHPAuNVNafa9S2BYlUtE5E2wMm4N/8FVHp6Op9+8SW3fZxDl5aN+MUFpwb6kMaETDBnlmdgu+t9VuGq5KuNObyyeDvffJ9LfJxw1oD2XJfendG9j2wb25BZKGOqE5FZQAbQRkR2An9W1RdCG1XsOdq+gh8PlrqT4y35LN6cR3buIcA9c5zWoxUXDO3MSb1aMbhzCxIbWXIczlS1QkTuAD7HXTpuuqquE5FHgGWqOht4EmgKvOV5368qETcAeFZEKoE43GuW19d6ID/7QTtQrPt5+JI0m1U2US1oybLtrvdNTmEpbyzZwawl29l9sJT2zZL4zZl9ufLEbnXWM7XqFsZfVPWqUMdg3NLT0znppJPYub+Et5btYMmWfBZvyWd7fjEAqUmNSOvRksvSujKqZysGdbaZ40ikqp8An9S47sFqv59Zx+MWAoMDG93PHSqr4JnMbE7u05r03q2DfXhjgiqs1izH+mYhVWXJlnxeXrSNz9b+SEWlckrfNjx43kDOHNDumCWbrLqFMdFBVcnOLWLxlnyWeH72HCwFoEXjBEb2aMUNo3swqmcrBnS0Nccm+GYs3EreISd3nd0v1KEYE3BhlSzH6mahmmXfmiU34obRPbhmVDd6ta297FtdrLqFMYETqDJurkplw56Cw4nx0q355B1yAtA2NYlRPVsxqmcrRvZsTd92Ta07mgmpgyXlPPt1NmP7t2N4t5ahDseYgAurZDnWbMopZKZjG++u2EVRWQWDOjfjb5cMoV3pDhzzP2Bv6wx6tbXE15hw4M8ybs6KSrJ2HfQkx3ks27qfwrIKALq0TCGjXztPctyK7q0b23pQEzYcDgdPfraBgrL2/O7s40IdjjFBYclykFW4KvlyQw4zHVtZmJ1HYnwc5w7pyHXp3RnatQWLFi1i7DiraGFMuGnIBtrSchcrtx9wJ8db81ix7QAl5S4A+rRrynlDOzGqZytO7NGKTi1SAvkyjKk3h8PBmedeROsb/oNz20IKtrWCTvb5ZKJfMEvHxfTu+ryiMl5fuoNXF21j98FSOjVP5u5x/bjyxK5HdMiyihbGhCdfNtAeKqtg+bb9h0u5rd55gHKXIgIDOjTjihPdm/FO7NmKNtYhz0SIzMxMkgaPRxolcuDbV8lMa2qfTyYmBLMaRkzurl+3+yAvLtjK7NW7cVZUcnKf1vz5/IGM7V/7hj2raGFMcPi6/vhoG2gLS8tZtnU/izzJcdaug7gqlfg4YXDn5tw8piejerZiRPdWNE9JCOTLMiZgTjn1NJ7e3ZXSzcuIK8qxzycTM2wZRgC4l1rsZfqCrSzZkk9KQjyXp3XhhvQe9G2fetTHWkULYwKvvuuPqzbQFpaWM29jDos257Focx5Zuw5SqZAQL5zQpQW/OK0Xo3q2ZkT3ljRJsrdZEx2crXsT32Q/Fwxpz0132RJBEzvsXdyPDpaU8/qS7cx0bGPXgRK6tEzhvokDuPzErj7NJllFC2MCy9flTofKKli6Nd/TOvqn5DgxPo6h3Vpwxxl9OalnK4Z1a0lKYnwQX4kxwfPWsp20apLIf+69zRrdmJhiybIf7Mgv5oX5W3hz2Q6KnS6Ob9OIgY23csMpwzh5dK9Qh2eMqeFYy51Ky12s2L6fRdl5LMzOY9WOA1RUqjs57upJjnu1Yni3liQnWHJsol/+ISdfbtjLdSf1sETZxBxLlhtgxfb9PP/tZj5b+yPxccJ5J3TixGaFTLp4HE6nk1cet2oWxoSjmsudRo06iXW7DzL/h33M37SPJVvyKauoJE5gSJcWTD61F6N7t2FEd5s5NrFp9qpdlLuUy9K6hDoUY4LOkmUfVVYqczbs5dmvs1mx/QDNkhtx62m9uSG9Bx2aJ/PYY49ZNQtjgqi+jUJ6DRzGjoQuvPbDPu748svDTUCOa9+Ua0Z15+Q+rRnZsxWpybYhz5i3lu9kYKdmDOjYLNShGBN0lix7qdxVyexVu/nv19n8kFNEu8ZxnJy8i1vGDuL0U/ofvp9VszAmeHzZqFfhqmTljgPM25hD5ne5rN9TALg75J12XFtO7tOGMX3b0L5ZcjBfgjFhb/3uAtbtLuCh844PdSjGhIQly8dQ4nTxxtLtPPftFnYdKKF/h1TuGNGEB288lxVlpbz3tyM/oK2ahTHBc6yNeoWl5Xy1MYcv1u/l2+9zKSitID5OGNG9JX8c35+Mfm3p3yHVOuQZUweHw8FfP/2OeGnLBUM7hzocY0LCkuU6zPt2Ic98uZ6N2okCp5LWvSVTLhzI6f3a8fjjj+MsK63zA9qqWRjjf7Utt6jtTE5eURlz1u/l83U/smBTHk5XJW2aJjFuYAdO79+Ok/u0sVrHxnjB4XAw9qxxtL75v5TvXsTGNa3ss83EJEuWaygtd/Ho618zc+mPxDXuSNm2lfz1mlO46dzRh+9jSy2MCa66lltUncn5fN43NOo5kn+ugaWzv6RSoUvLFK5P7874QR0Y1q0l8XE2e2yMLzIzM4nrMpj4xs3Zv+YLMjMbW7JsYpIlyx5lFS5eX7KDp+ZtIqewDGfOFvZ/+wquvT/w45ApcO6ph+9rSy2MCa7alluMHHUSC7P38fa2ZD4rHkLZ6mJ6txXuOL0P4wZ14PiOzWx5hTENkJGRwb9WluEqyqdy1zoyMv4Z6pCMCYmYTpYdDgdfzctEeqXz0RYXuw+WMrJHK24dksiv/vUXXEeZObalFsYET/WzOcmtO7O33UjGPPEVew6W0iy5EZendeXSEV0Y0qW5JcjG+EmfQcNJ6Z3P4IQc7vlyjn3mmZgVs8myw+Fg/HW30+SUG0ksOESflvG8PGkkY/q0QUQ43maOjQmZmuuT09PTmfHe5zw3fxvZFS2ZvamU045ry/3nHM/YAe2sMYgxAfDBql1UKvz9jkvp0y411OEYEzIxmSzvOlDC/Z9spuWlU6goyCHvwyeZfM04Tuk7/vB9bObYmNCouT752bc/59t9KXyxvoCUhDbcdHI3bjmlJx2bp4Q6VGP8SkTGA1OBeOB5VX28xu1JwExgBJAHXKGqWz233QtMAlzAnar6eUNiUVXeWraToV1bWKJsYl5MJMtVs1TpY05jZWkb/vt1NqotKFr0BgcXvU1CHJx++mOhDtMYw0/rkxt16EuzU67jgW8KaJ5Swp1j+3Lj6B60apIY6hCN8TsRiQeeAs4CdgJLRWS2qq6vdrdJwH5V7SMiVwJPAFeIyPHAlcBAoBPwpYgcp6qu+sbz6qfz+W5vIZNOaFzfpzAmakR9slw1SyXt+9FqR0fim+3n3CEduXfiALaf24rMzAG21MKYEHI4HMycOROA66+/npPGnEarM2+l8dCJuIryuWZgCvdefipNk6L+7crEtpHAJlXdDCAirwMXANWT5QuAhzy/vw38R9yL9C8AXlfVMmCLiGzyPJ+jPoE4HA5+9+83STn+dB6/7QbO7PGhfUaamBb1nz5fzvualNHXkJp2IeX5u7ioySb+dfU5AHS2pRbGhJTD4SAjIwOn091q+pUvFnP8jX+h8dCJDErcx11X9OX0U0Yf41mMiQqdgR3VLu8ERtV1H1WtEJGDQGvP9YtqPPZnHUREZDIwGaBbt251BpKZmQmJKRz6bgFlRQd+1kvAmFgTdcly9Y1BLXoM5AuGkpo2mKJVn1C88DWu+vyTUIdojPHIzMykvLwcSUiixak30CztfEpKSnh98mmc1Kt1qMMzJqqo6jRgGkBaWprWdb+MjAymTBmL01luvQSMIcqS5Z82BpXTfNTFtDztelo0TuIPJ6VwsHkXMv78iX07NiZMOBwOtm/fTuPug2l+9q9IaNmRQys/5sX7r7VE2cSiXUDXape7eK6r7T47RaQR0Bz3Rj9vHus16yVgzJGiKlnOzMykIj6ZNpc9QHL3IXSJO8g7v7mE1k2T4MLTQh2eMcbj8F6CTgNpc+nDJLlKGFq0kN8+dJ19MJtYtRToKyI9cSe6VwJX17jPbOAG3GuRLwW+UlUVkdnAayLyD9wb/PoCSxoSjFWEMuYnUZMsOxwOvt+RQ7urHiM+tS0FXz7NX/91nztRNsaElczMTGjbm9YX3EN53g5u6lPCQ3/6S6jDMiZkPGuQ7wA+x106brqqrhORR4BlqjobeAF42bOBLx93Qo3nfm/i3gxYAdzekEoYxpgjRUWy7HA4OPviq2l+4QPENUnlpLLl/Obf99u3YmPCkMPhIGvnAdpc/ACugn0c/OAvjPvovVCHZUzIqeonwCc1rnuw2u+lwGV1PPYvgH3jNCYA4kIdgD+8O9dBi0seQRKSyX3zAYZ2bmqJsjFhyOFwcPal1/Nt/AlUlhVzVsJGvvzoPRuvxhhjwlZQk2URGS8i34nIJhG5xx/PuWbnAT519gcg9437kP3bbeeuMX4QiPE6+6sFtLjwAbTSRe6b99OvaztLlI0xxoS1oC3D8LI7kdccDgf/fPUjljU5kVZNknj0wp5s6DzZdu4a4wf+Hq8AH3+1gPcPdEMSKsl9/T7ii/Pti60xxpiwF8yZ5cPdiVTVCVR1J/KZw+Hg7Bt+gyNpOKX5P7LuX5No3ySee++91xJlY/zDb+MV4PPMBdw6K4tDrjjy353CTRedzdy5c228GmOMCXuiWmddcv8eSORSYLyq3uK5fB0wSlXvqHafw92FgH7Ad3U8XQd+3p1oF/CjX4M+UhtgXwCfP1Si8XVF42sC6KeqqcE4kDfj1XN9fcasArsJ7HgNlmj6W4um1wLh8Xq6q2rbEMdQKxHJBbYd427h8N/QVxZzcERazN7EW+d4DatqGNW7C4UbEVmmqmmhjsPfovF1ReNrAvfrCnUMNfk6ZqPt/000vZ5oei0Qfa/H37xJ4iPxv6HFHByRFnND4w3mMgy/dhgyxgSUjVdjjDGG4CbLh7sTiUgi7mLqs4N4fGOM92y8GmOMMQRxGUZd3YmCdXw/CMvlIX4Qja8rGl8TBPF1BXC8Rtv/m2h6PdH0WiD6Xk8oROJ/Q4s5OCIt5gbFG7QNfsYYY4wxxkSaqOjgZ4wxxhhjTCBYsmyMMcYYY0wdLFk+BhHpKiLzRGS9iKwTkV+HOiZ/EZF4EVkpIh+FOhZ/EZEWIvK2iGwUkQ0iEvFdL0Tkt56/vbUiMktEkkMdk69EZLqI5IjI2lDH0lDR9p4gIskiskREVntez8OhjskfovH9LVgibbxG4piM5HEXaWNLRLaKSJaIrKpvCVZLlo+tArhLVY8HTgJuF5HjQxyTv/wa2BDqIPxsKvCZqvYHTiDCX5+IdAbuBNJUdRDuzXZXhjaqepkBjA91EH4Sbe8JZcAZqnoCMBQYLyInhTYkv4jG97dgmUFkjddIHJORPO4icWydrqpD61tr2ZLlY1DVPaq6wvN7Ie4/kJrdAyOOiHQBzgGeD3Us/iIizYFTgRcAVNWpqgdCGpR/NAJSRKQR0Bh397uIoqrfAPmhjsMfou09Qd2KPBcTPD8RvfM7Gt/fginSxmskjslIHXexOrYsWfaBiPQAhgGLQxyKP/wT+ANQGeI4/KknkAu86DlF9LyINAl1UA2hqruA/wW2A3uAg6r6RWijMlWi5T3Bc1p1FZADzFHViH49ROf7m/FCJI3JCB13/yTyxpYCX4jIchGZXJ8nsGTZSyLSFHgH+I2qFoQ6noYQkXOBHFVdHupY/KwRMBx4RlWHAYeAe0IbUsOISEvgAtxfBDoBTUTk2tBGZSC63hNU1aWqQ3F3ahwpIoNCHFK9RfH7mzmGSBuTkTbuInhsjVHV4cAE3Et0TvX1CSxZ9oKIJOAegK+q6ruhjscPTgbOF5GtwOvAGSLySmhD8oudwM5q387fxp08R7IzgS2qmquq5cC7wOgQxxTzovA9AQDPsqV5RNZ61Zqi9f3NHEUkj8kIGncRObY8Z2hR1RzgPWCkr89hyfIxiIjgXgO7QVX/Eep4/EFV71XVLqraA/dmsa9UNeJnK1X1R2CHiPTzXDUWWB/CkPxhO3CSiDT2/C2OJfI2VkSVaHtPEJG2ItLC83sKcBawMaRBNUC0vr+ZukXimIzEcReJY0tEmohIatXvwNmAz1VeLFk+tpOB63B/g1rl+ZkY6qBMnX4FvCoia3DvMP5raMNpGM8s+dvACiAL95iNtDajiMgswAH0E5GdIjIp1DE1QLS9J3QE5nnGzFLcaycjoiSUCYwIHK+ROCZt3AVHe2C+iKwGlgAfq+pnvj6Jtbs2xhhjjDGmDjazbIwxxhhjTB0sWTbGGGOMMaYOliwbY4wxxhhTB0uWjTHGGGOMqYMly8YYY4wxxtTBkmVjjDHGGGPqYMlyBBGR+0RknYis8dSRHCUiLUTkl/V4rmM+TkR6iIhXxbtFZLCIbBOR22o8vkREVnn5HCme1+UUkTbePMaYcGZj1pjIYePV1MWS5QghIunAucBwVR2Cuw3yDqAFUOuAFLe6/h/X+bj6UNUs3B19rq9xU7aqDvXyOUo8993tr7iMCRUbs8ZEDhuv5mgsWY4cHYF9qloGoKr7VHU38DjQ2/Nt8UnPN83vRGQm7paOXUXkWhFZ4rnPsyISX/NxACJyvecb9WoRedlz3HgRec7zbfsLT1vOuuQAAwP1H8CYCGNj1pjIYePV1E1V7ScCfoCmwCrge+Bp4DTP9T2AtdXu1wOoBE7yXB4AfAgkeC4/jfubac3HDfQ8dxvP5Vae+1QAQz3XvQlce5QY3wLKgO61xebDa91aFYf92E+k/tiYtR/7iZwfG6/2c7SfRpiIoKpFIjICOAU4HXhDRO4BMmu5+zZVXeT5fSwwAlgqIgApuL+dflPjMWcAb6nqPs/x8kWkGbBFVVd57rMc9+D8GRGZADQBPsb9prCtjvt9CXSo5ab7VPWD2h5jTCSyMWtM5LDxao7GkuUIoqou3AM3U0SygBuofSAfqva7AC+p6r3V7yAiPbw8bFm131243wiOICLJwBPA+cBNwCDgkzpew5leHteYiGdj1pjIYePV1MXWLEcIEeknIn2rXTUU9zfLQiD1KA+dC1wqIu08z9NKRLrX8rivgMtEpHXV/XwI735gpqpuBbJwD2RjYpqNWWMih41XczSWLEeOpsBLIrJeRNYAxwMPqWoesEBE1lZtIqhOVdfjHmhfeB43B+hY83Gqug74C/C1iKwG/uFNUCLSDzgL+KfnKhvIxrjZmDUmcth4NXUSz2JvY/zOcxrqI1X1aWCLyFYgrWptlzEmOGzMGhM5bLwGj80sm0ByAc3Fx4LpQALu3cbGmOCyMWtM5LDxGiQ2s2yMMcYYY0wdbGbZGGOMMcaYOliybIwxxhhjTB0sWTbGGGOMMaYOliwbY4wxxhhTB0uWjTHGGGOMqYMly8YYY4wxxtTBkmVjjDHGGGPqYMmyMcYYY4wxdfh/ESW+sT+DY/8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lamUT_vec = np.linspace(1,UTdata['F11'].iloc[-1],30)\n",
    "lamET_vec = np.linspace(1,ETdata['F11'].iloc[-1],30)\n",
    "lamPS_vec = np.linspace(1,PSdata['F11'].iloc[-1],30)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "model = CANN_model(params_cann_PS[0], params_cann_PS[1], normalization)\n",
    "P11_NN_UT_p = P11_UT(lamUT_vec, model, normalization)\n",
    "P11_NN_ET_p = P11_ET(lamET_vec, model, normalization)\n",
    "P11_NN_PS_p = P11_PS(lamPS_vec, model, normalization) \n",
    "\n",
    "fig, ax = plotstresses([UTdata['F11'], ETdata['F11'], PSdata['F11']],\n",
    "                       [UTdata['P11'], ETdata['P11'], PSdata['P11']],\n",
    "                       [lamUT_vec, lamET_vec, lamPS_vec],\n",
    "                       [P11_NN_UT_p, P11_NN_ET_p, P11_NN_PS_p])\n",
    "\n",
    "ax[0].set(ylim=[0,7])\n",
    "ax[1].set(ylim=[0,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('savednet/CANN_params_PS.npy', 'wb') as f:\n",
    "    pickle.dump(params_cann_PS, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT ALL RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "I1_factor = 30\n",
    "Psi1_factor = 0.3\n",
    "I2_factor = 250\n",
    "Psi2_factor = 0.001\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "UTdata = pd.read_csv('Data/UT20.csv')\n",
    "ETdata = pd.read_csv('Data/ET20.csv')\n",
    "PSdata = pd.read_csv('Data/PS20.csv')\n",
    "\n",
    "# ALL\n",
    "with open('savednet/NODE_params_all.npy', 'rb') as f:\n",
    "    params_node_all = pickle.load(f)\n",
    "with open('savednet/ICNN_params_all.npy', 'rb') as f:\n",
    "    params_icnn_all = pickle.load(f)\n",
    "with open('savednet/CANN_params_all.npy', 'rb') as f:\n",
    "    params_cann_all = pickle.load(f)\n",
    "\n",
    "# UT\n",
    "with open('savednet/NODE_params_UT.npy', 'rb') as f:\n",
    "    params_node_UT = pickle.load(f)\n",
    "with open('savednet/ICNN_params_UT.npy', 'rb') as f:\n",
    "    params_icnn_UT = pickle.load(f)\n",
    "with open('savednet/CANN_params_UT.npy', 'rb') as f:\n",
    "    params_cann_UT = pickle.load(f)\n",
    "\n",
    "# ET\n",
    "with open('savednet/NODE_params_ET.npy', 'rb') as f:\n",
    "    params_node_ET = pickle.load(f)\n",
    "with open('savednet/ICNN_params_ET.npy', 'rb') as f:\n",
    "    params_icnn_ET = pickle.load(f)\n",
    "with open('savednet/CANN_params_ET.npy', 'rb') as f:\n",
    "    params_cann_ET = pickle.load(f)\n",
    "\n",
    "# PS\n",
    "with open('savednet/NODE_params_PS.npy', 'rb') as f:\n",
    "    params_node_PS = pickle.load(f)\n",
    "with open('savednet/ICNN_params_PS.npy', 'rb') as f:\n",
    "    params_icnn_PS = pickle.load(f)\n",
    "with open('savednet/CANN_params_PS.npy', 'rb') as f:\n",
    "    params_cann_PS = pickle.load(f)\n",
    "\n",
    "params_UT = [params_cann_UT, params_icnn_UT, params_node_UT]\n",
    "params_ET = [params_cann_ET, params_icnn_ET, params_node_ET]\n",
    "params_PS = [params_cann_PS, params_icnn_PS, params_node_PS]\n",
    "params_all = [params_cann_all, params_icnn_all, params_node_all]\n",
    "params = [params_UT, params_ET, params_PS, params_all]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(3,4,figsize=(12,8))\n",
    "\n",
    "lamUT_exp = np.array(UTdata['F11'])\n",
    "lamET_exp = np.array(ETdata['F11'])\n",
    "lamPS_exp = np.array(PSdata['F11'])\n",
    "\n",
    "lamUT_prd = np.linspace(1,UTdata['F11'].iloc[-1],50)\n",
    "lamET_prd = np.linspace(1,ETdata['F11'].iloc[-1],50)\n",
    "lamPS_prd = np.linspace(1,PSdata['F11'].iloc[-1],50)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "panels_list = [['a', 'e', 'i'], ['b', 'f', 'j'], ['c', 'g', 'k'], ['d', 'h', 'l']]\n",
    "\n",
    "for axi, paramsi, panels in zip(axs.T, params, panels_list): #loop over columns\n",
    "    params_cann, params_icnn, params_node = paramsi\n",
    "\n",
    "    cann_model = CANN_model(params_cann[0], params_cann[1], normalization)\n",
    "    icnn_model = ICNN_model(params_icnn[0], params_icnn[1], normalization)\n",
    "    node_model = NODE_model(params_node[0], params_node[1])\n",
    "\n",
    "    models = [cann_model, icnn_model, node_model]\n",
    "    modelnames = ['CANN', 'ICNN', 'NODE']\n",
    "    r2lists = [[],[],[]]\n",
    "    for model, modelname in zip(models, modelnames):\n",
    "        P11funs     = [P11_UT,          P11_ET,         P11_PS]\n",
    "        lambdas_exp = [lamUT_exp,       lamET_exp,      lamPS_exp]\n",
    "        lambdas_prd = [lamUT_prd,       lamET_prd,      lamPS_prd]\n",
    "        ylims       = [[-0.1,7],        [-0.1,3],       [-0.1,3]]\n",
    "        P11_gt_list = [UTdata['P11'],   ETdata['P11'],  PSdata['P11']]\n",
    "\n",
    "        for axii, P11fun, lam_exp, lam_prd, ylim, P11_gt, panel in zip(axi, P11funs, lambdas_exp, lambdas_prd, ylims, P11_gt_list, panels): #loop over rows\n",
    "            # plot gt values\n",
    "            axii.plot(lam_exp, P11_gt, 'k.', label='Exp.')\n",
    "\n",
    "            # plot pr values\n",
    "            P11 = P11fun(lam_prd, model, normalization)\n",
    "            axii.plot(lam_prd, P11, label=modelname)\n",
    "            axii.set(ylim=ylim, xlabel = r'Stretch $\\lambda [-]$', ylabel = r'Nominal stress $P_{11} [MPa]$')\n",
    "\n",
    "            # save for TikZ\n",
    "            data = np.array([lam_exp, P11_gt]).T\n",
    "            onp.savetxt('TikZ/fig_rubber_data/data_'+panel+'.csv', data, delimiter = ' ')\n",
    "            P11 = np.clip(P11, a_min=-100, a_max = 100)\n",
    "            data = np.array([lam_prd, P11]).T\n",
    "            onp.savetxt('TikZ/fig_rubber_data/'+modelname+'_'+panel+'.csv', data, delimiter = ' ')\n",
    "axs[0,0].legend()\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Temporary: plot 5 different CANN models\n",
    "fig,axs = plt.subplots(3,4,figsize=(12,9))\n",
    "\n",
    "lamUT_exp = np.array(UTdata['F11'])\n",
    "lamET_exp = np.array(ETdata['F11'])\n",
    "lamPS_exp = np.array(PSdata['F11'])\n",
    "\n",
    "lamUT_prd = np.linspace(1,UTdata['F11'].iloc[-1],50)\n",
    "lamET_prd = np.linspace(1,ETdata['F11'].iloc[-1],50)\n",
    "lamPS_prd = np.linspace(1,PSdata['F11'].iloc[-1],50)\n",
    "\n",
    "normalization = [I1_factor,I2_factor,Psi1_factor,Psi2_factor ]\n",
    "loads = ['UT', 'ET', 'PS', 'ALL']\n",
    "for axi, load in zip(axs.T, loads): #loop over columns\n",
    "\n",
    "    P11funs     = [P11_UT,          P11_ET,         P11_PS]\n",
    "    lambdas_exp = [lamUT_exp,       lamET_exp,      lamPS_exp]\n",
    "    lambdas_prd = [lamUT_prd,       lamET_prd,      lamPS_prd]\n",
    "    ylims       = [[-0.1,7],        [-0.1,3],       [-0.1,3]]\n",
    "    P11_gt_list = [UTdata['P11'],   ETdata['P11'],  PSdata['P11']]\n",
    "\n",
    "    for axii, P11fun, lam_exp, lam_prd, ylim, P11_gt, panel in zip(axi, P11funs, lambdas_exp, lambdas_prd, ylims, P11_gt_list, panels): #loop over rows\n",
    "        for i in range(5):\n",
    "            with open('savednet/CANN_params_'+load+'_'+str(i+1)+'.npy', 'rb') as f:\n",
    "                params_cann = pickle.load(f)\n",
    "            model = CANN_model(params_cann[0], params_cann[1], normalization)\n",
    "            # plot gt values\n",
    "            axii.plot(lam_exp, P11_gt, 'k.')\n",
    "\n",
    "            # plot pr values\n",
    "            P11 = P11fun(lam_prd, model, normalization)\n",
    "            axii.plot(lam_prd, P11, label='CANN')\n",
    "            axii.set(ylim=ylim, xlabel = r'Stretch $\\lambda [-]$', ylabel = r'Nominal stress $P_{11} [MPa]$')\n",
    "\n",
    "axs[0,0].legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 50 runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "# Next: find the standard deviation of R2 from 50 runs for each case.\n",
    "# Takes more than 3 hours to finish\n",
    "lamUT_vec = np.array(UTdata['F11'])\n",
    "lamET_vec = np.array(ETdata['F11'])\n",
    "lamPS_vec = np.array(PSdata['F11'])\n",
    "\n",
    "cann_r2 = []\n",
    "icnn_r2 = []\n",
    "node_r2 = []\n",
    "for i in range(50):\n",
    "    print(i)\n",
    "    cann_r2i = []\n",
    "    icnn_r2i = []\n",
    "    node_r2i = []\n",
    "    r2lists     = [cann_r2i,    icnn_r2i,   node_r2i    ]\n",
    "    mdlnumbers  = [1,           2,          3           ]\n",
    "    init_fns    = [init_cann,   init_icnn,  init_node   ]\n",
    "    MODELS      = [CANN_model,  ICNN_model, NODE_model  ]\n",
    "\n",
    "    for mdlnumber, init_fn, MODEL, r2list in zip(mdlnumbers, init_fns, MODELS, r2lists):\n",
    "        for loss_fn in ([loss_P11_UT, loss_P11_ET, loss_P11_PS, loss_P11_all]):\n",
    "            key, subkey = random.split(key)\n",
    "            params = init_fn(key)\n",
    "            opt_init, opt_update, get_params = optimizers.adam(2.e-4) #Original: 1.e-4\n",
    "            opt_state = opt_init(params)\n",
    "            params, train_loss, val_loss = train_jp(loss_fn, mdlnumber, F11_data, opt_state, key, nIter = 100000, print_freq=200000)\n",
    "\n",
    "            #make predictions\n",
    "            if mdlnumber == 3:\n",
    "                model = MODEL(params[0], params[1])\n",
    "            else:\n",
    "                model = MODEL(params[0], params[1], normalization)\n",
    "\n",
    "            lambdas     = [lamUT_vec,       lamET_vec,      lamPS_vec       ]\n",
    "            P11_gt_list = [UTdata['P11'],   ETdata['P11'],  PSdata['P11']   ]\n",
    "            P11funs     = [P11_UT,          P11_ET,         P11_PS          ]\n",
    "            for lam, P11_gt, P11fun in zip(lambdas, P11_gt_list, P11funs):\n",
    "                P11 = P11fun(lam, model, normalization)\n",
    "                r2 = r2_score(P11_gt, P11)\n",
    "                r2list.append(r2)\n",
    "    cann_r2.append(cann_r2i)\n",
    "    icnn_r2.append(icnn_r2i)\n",
    "    node_r2.append(node_r2i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "CANN_r2 = np.array(cann_r2)\n",
    "ICNN_r2 = np.array(icnn_r2)\n",
    "NODE_r2 = np.array(node_r2)\n",
    "with open('savednet/CANN_r2_alldata.npy', 'wb') as f:\n",
    "    pickle.dump(CANN_r2, f)\n",
    "with open('savednet/ICNN_r2_alldata.npy', 'wb') as f:\n",
    "    pickle.dump(ICNN_r2, f)\n",
    "with open('savednet/NODE_r2_alldata.npy', 'wb') as f:\n",
    "    pickle.dump(NODE_r2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min and max in (b,f,j)\n",
      "CANN:  0.8142262814791459 0.9996617153237324\n",
      "ICNN:  0.673093226895022 0.9999022934305071\n",
      "NODE:  0.6561846716565575 0.9998441793960866\n",
      "Means in (d,h,l)\n",
      "CANN, ICNN, NODE:  0.97115023284107 0.9969548626085942 0.9969138593204406\n"
     ]
    }
   ],
   "source": [
    "with open('savednet/CANN_r2_alldata.npy', 'rb') as f:\n",
    "    CANN_r2 = pickle.load(f)\n",
    "with open('savednet/ICNN_r2_alldata.npy', 'rb') as f:\n",
    "    ICNN_r2 = pickle.load(f)\n",
    "with open('savednet/NODE_r2_alldata.npy', 'rb') as f:\n",
    "    NODE_r2 = pickle.load(f)\n",
    "\n",
    "CANN_r2 = CANN_r2.reshape([50,4,3])\n",
    "ICNN_r2 = ICNN_r2.reshape([50,4,3])\n",
    "NODE_r2 = NODE_r2.reshape([50,4,3])\n",
    "\n",
    "print('min and max in (b,f,j)')\n",
    "print('CANN: ', np.min(CANN_r2[:,1,:]), np.max(CANN_r2[:,1,:]))\n",
    "print('ICNN: ', np.min(ICNN_r2[:,1,:]), np.max(ICNN_r2[:,1,:]))\n",
    "print('NODE: ', np.min(NODE_r2[:,1,:]), np.max(NODE_r2[:,1,:]))\n",
    "\n",
    "print('Means in (d,h,l)')\n",
    "print('CANN, ICNN, NODE: ', np.mean(CANN_r2[:,3,:]), np.mean(ICNN_r2[:,3,:]), np.mean(NODE_r2[:,3,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "CANN_r2 = np.clip(CANN_r2, a_min=0.0, a_max=1.0)\n",
    "ICNN_r2 = np.clip(ICNN_r2, a_min=0.0, a_max=1.0)\n",
    "NODE_r2 = np.clip(NODE_r2, a_min=0.0, a_max=1.0)\n",
    "\n",
    "CANN_r2_mean = np.mean(CANN_r2,axis=0)\n",
    "CANN_r2_stdv = np.std(CANN_r2,axis=0)\n",
    "\n",
    "ICNN_r2_mean = np.mean(ICNN_r2,axis=0)\n",
    "ICNN_r2_stdv = np.std(ICNN_r2,axis=0)\n",
    "\n",
    "NODE_r2_mean = np.mean(NODE_r2,axis=0)\n",
    "NODE_r2_stdv = np.std(NODE_r2,axis=0)\n",
    "\n",
    "# Save for TikZ\n",
    "xs       = [0.0,          1.0,          2.0]\n",
    "r2_means = [CANN_r2_mean, ICNN_r2_mean, NODE_r2_mean]\n",
    "r2_stdvs = [CANN_r2_stdv, ICNN_r2_stdv, NODE_r2_stdv]\n",
    "names = ['CANN', 'ICNN', 'NODE']\n",
    "for name, x, r2_mean, r2_stdv in zip(names, xs, r2_means, r2_stdvs):\n",
    "    columns = ['x', 'a', 'e', 'i', 'b', 'f', 'j', 'c', 'g', 'k', 'd', 'h', 'l']\n",
    "    data = np.expand_dims(np.hstack([x,r2_mean]),axis=0)\n",
    "    df = pd.DataFrame(data, columns=columns)\n",
    "    df.to_csv('TikZ/fig_rubberR2_data/'+name+'_mean.csv', index=False, sep = ' ')\n",
    "\n",
    "    data1 = np.expand_dims(np.hstack([x,r2_mean-r2_stdv]),axis=0)\n",
    "    data2 = np.expand_dims(np.hstack([x,r2_mean+r2_stdv]),axis=0)\n",
    "    data = np.vstack([data1,data2])\n",
    "    df = pd.DataFrame(data, columns=columns)\n",
    "    df.to_csv('TikZ/fig_rubberR2_data/'+name+'_stdv.csv', index=False, sep = ' ')\n",
    "\n",
    "CANN_r2_mean = CANN_r2_mean.reshape([4,3]).T\n",
    "CANN_r2_stdv = CANN_r2_stdv.reshape([4,3]).T\n",
    "ICNN_r2_mean = ICNN_r2_mean.reshape([4,3]).T\n",
    "ICNN_r2_stdv = ICNN_r2_stdv.reshape([4,3]).T\n",
    "NODE_r2_mean = NODE_r2_mean.reshape([4,3]).T\n",
    "NODE_r2_stdv = NODE_r2_stdv.reshape([4,3]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,4, figsize=(20,12))\n",
    "\n",
    "r2_means = [CANN_r2_mean, ICNN_r2_mean, NODE_r2_mean]\n",
    "r2_stdvs = [CANN_r2_stdv, ICNN_r2_stdv, NODE_r2_stdv]\n",
    "for x, model_r2_mean, model_r2_stdv in zip(xs, r2_means, r2_stdvs):\n",
    "    for ax, row_r2_mean, row_r2_stdv in zip(axes, model_r2_mean, model_r2_stdv):\n",
    "        for axi, r2_mean, r2_stdv in zip(ax, row_r2_mean, row_r2_stdv):\n",
    "            axi.bar(x, r2_mean)\n",
    "            axi.errorbar(x, r2_mean, r2_stdv, color='black')\n",
    "            axi.set(xlim=[-0.5,2.5], ylim=[0,1])\n",
    "fig.savefig('Figures/fig_rubber_R2.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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