docs/Python_bindings_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2af898bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import btwxt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "019c236d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>LWT (K)</th>\n",
       "      <th>30</th>\n",
       "      <th>35</th>\n",
       "      <th>40</th>\n",
       "      <th>45</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OAT db (C)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-20</th>\n",
       "      <td>0.389105</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-15</th>\n",
       "      <td>0.354610</td>\n",
       "      <td>0.389105</td>\n",
       "      <td>0.429185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-7</th>\n",
       "      <td>0.297619</td>\n",
       "      <td>0.330033</td>\n",
       "      <td>0.369004</td>\n",
       "      <td>0.414938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.265957</td>\n",
       "      <td>0.297619</td>\n",
       "      <td>0.332226</td>\n",
       "      <td>0.375940</td>\n",
       "      <td>0.421941</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.241546</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.303951</td>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.450450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.214592</td>\n",
       "      <td>0.239234</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.306748</td>\n",
       "      <td>0.348432</td>\n",
       "      <td>0.403226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.189753</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.240964</td>\n",
       "      <td>0.272480</td>\n",
       "      <td>0.310559</td>\n",
       "      <td>0.359712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.175439</td>\n",
       "      <td>0.198020</td>\n",
       "      <td>0.223714</td>\n",
       "      <td>0.253807</td>\n",
       "      <td>0.289017</td>\n",
       "      <td>0.346021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.155521</td>\n",
       "      <td>0.175131</td>\n",
       "      <td>0.198413</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.308642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "LWT (K)           30        35        40        45        50        55\n",
       "OAT db (C)                                                            \n",
       "-20         0.389105  0.425532  0.469484  1.000000  1.000000  1.000000\n",
       "-15         0.354610  0.389105  0.429185  1.000000  1.000000  1.000000\n",
       "-7          0.297619  0.330033  0.369004  0.414938  1.000000  1.000000\n",
       "-2          0.265957  0.297619  0.332226  0.375940  0.421941  1.000000\n",
       " 2          0.241546  0.270270  0.303951  0.342466  0.390625  0.450450\n",
       " 7          0.214592  0.239234  0.270270  0.306748  0.348432  0.403226\n",
       " 12         0.189753  0.212766  0.240964  0.272480  0.310559  0.359712\n",
       " 15         0.175439  0.198020  0.223714  0.253807  0.289017  0.346021\n",
       " 20         0.155521  0.175131  0.198413  0.225734  0.256410  0.308642"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EIR F(LWT, T_OA_DB)\n",
    "x_lwts = np.array([30, 35, 40, 45, 50, 55])\n",
    "x_oat_dbs = np.array([-20, -15, -7, -2, 2, 7, 12, 15, 20])\n",
    "ys = np.array([0.38910506, 0.35460993, 0.29761905, 0.26595745, 0.24154589, 0.21459227, 0.18975332,\n",
    "        0.1754386,  0.155521,   0.42553191, 0.38910506, 0.330033,   0.29761905, 0.27027027,\n",
    "        0.23923445, 0.21276596, 0.1980198,  0.17513135, 0.46948357, 0.42918455, 0.36900369,\n",
    "        0.33222591, 0.30395137, 0.27027027, 0.24096386, 0.22371365, 0.1984127,  1.,\n",
    "        1.,         0.41493776, 0.37593985, 0.34246575, 0.30674847, 0.27247956, 0.25380711,\n",
    "        0.22573363, 1.,         1.,         1.,         0.42194093, 0.390625,   0.34843206,\n",
    "        0.31055901, 0.28901734, 0.25641026, 1.,         1.,         1.,         1.,\n",
    "        0.45045045, 0.40322581, 0.35971223, 0.34602076, 0.30864198])\n",
    "\n",
    "df_ori = pd.DataFrame(ys.reshape(x_lwts.size, x_oat_dbs.size), index=x_lwts, columns=x_oat_dbs).T\n",
    "df_ori.index.name = 'OAT db (C)'\n",
    "df_ori.index = df_ori.index.astype(int)\n",
    "df_ori.columns.name = 'LWT (K)'\n",
    "df_ori.columns = df_ori.columns.astype(int)\n",
    "df_ori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "396b29fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_lwt = btwxt.GridAxis(\n",
    "    x_lwts.tolist(), # Values\n",
    "    btwxt.InterpolationMethod_cubic, # interpolation method\n",
    "    btwxt.ExtrapolationMethod_linear, # extrapolation_method\n",
    "    (-float(np.inf), float(np.inf)), # extrapolation_limits,\n",
    "    \"LWT\", # name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "583632af",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_oat_db = btwxt.GridAxis(\n",
    "    x_oat_dbs.tolist(), # Values\n",
    "    btwxt.InterpolationMethod_cubic, # interpolation method\n",
    "    btwxt.ExtrapolationMethod_linear, # extrapolation_method\n",
    "    (-float(np.inf), float(np.inf)), # extrapolation_limits\n",
    "    \"OAT_db\", # name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04db6489",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_point_y = btwxt.GridPointDataSet(ys, \"EIRFT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9863a82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "interpolator = btwxt.RegularGridInterpolator([x_lwt, x_oat_db], [grid_point_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ba87dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "RATED_LWT_C = 35.0\n",
    "RATED_OAT_DB_C = 7.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b134805b",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_normalization = [RATED_LWT_C, RATED_OAT_DB_C]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65d57361",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23923445"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalizationDivisor = 1.0 # This is the Table:Lookup global normalization divisor\n",
    "normalizationDivisor = interpolator.normalize_grid_point_data_set_at_target(\n",
    "    0, target_normalization, normalizationDivisor\n",
    ") * normalizationDivisor\n",
    "normalizationDivisor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0748286",
   "metadata": {},
   "outputs": [],
   "source": [
    "interp_lwts = np.arange(25.0, 60.1, 1.0)\n",
    "interp_oat_dbs = np.arange(-25.0, 25.1, 1.0)\n",
    "\n",
    "ut, vt = np.meshgrid(interp_lwts, interp_oat_dbs, indexing='ij')\n",
    "test_points = np.array([ut.ravel(), vt.ravel()]).T\n",
    "\n",
    "vals = np.apply_along_axis(\n",
    "    func1d=lambda x: interpolator.get_value_at_target([x[0], x[1]], 0), \n",
    "    axis=1,\n",
    "    arr=test_points\n",
    ")\n",
    "\n",
    "df_interp = pd.DataFrame(vals.reshape(interp_lwts.size, interp_oat_dbs.size), index=interp_lwts, columns=interp_oat_dbs).T\n",
    "df_interp.index.name = 'OAT db (C)'\n",
    "df_interp.index = df_interp.index.astype(int)\n",
    "df_interp.columns.name = 'LWT (K)'\n",
    "df_interp.columns = df_interp.columns.astype(int)\n",
    "df_interp = df_interp * normalizationDivisor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6bd6959",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>LWT (K)</th>\n",
       "      <th>30</th>\n",
       "      <th>35</th>\n",
       "      <th>40</th>\n",
       "      <th>45</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OAT db (C)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-20</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-15</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-7</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "LWT (K)               30            35            40            45   50   55\n",
       "OAT db (C)                                                                  \n",
       "-20         0.000000e+00  5.551115e-17  0.000000e+00  0.000000e+00  0.0  0.0\n",
       "-15         0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.0\n",
       "-7          0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.0\n",
       "-2          0.000000e+00  0.000000e+00  5.551115e-17  0.000000e+00  0.0  0.0\n",
       " 2          2.775558e-17  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.0\n",
       " 7          0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.0\n",
       " 12         0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.0\n",
       " 15         0.000000e+00  0.000000e+00  0.000000e+00  5.551115e-17  0.0  0.0\n",
       " 20         0.000000e+00  0.000000e+00  2.775558e-17  0.000000e+00  0.0  0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_interp.reindex_like(df_ori) - df_ori"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ad77ff",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de8e17f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6cc333f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LWT (K)', ylabel='OAT db (C)'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9), nrows=2)\n",
    "sns.heatmap(df_interp, ax=ax[0], vmin=0, vmax=1.5, cmap='RdYlGn_r')\n",
    "sns.heatmap(df_ori, ax=ax[1], vmin=0, vmax=1.5, cmap='RdYlGn_r', annot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}