notebooks/Dfsu - Read.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dfsu - Read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:884)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  U velocity <u velocity component> (meter per sec)\n",
       "  2:  V velocity <v velocity component> (meter per sec)\n",
       "  3:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"../tests/testdata/HD2D.dfsu\"\n",
    "ds = mikeio.read(filename)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:884)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to read specific variables\n",
    "ds = mikeio.read(filename, items=[\"Surface elevation\",\"Current speed\"]) \n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surface elevation</th>\n",
       "      <th>Current speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7956.000000</td>\n",
       "      <td>7956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.034152</td>\n",
       "      <td>0.073658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.534890</td>\n",
       "      <td>0.119606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.758378</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.579662</td>\n",
       "      <td>0.029917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.101636</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.462013</td>\n",
       "      <td>0.070259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.172707</td>\n",
       "      <td>1.714988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Surface elevation  Current speed\n",
       "count        7956.000000    7956.000000\n",
       "mean            0.034152       0.073658\n",
       "std             0.534890       0.119606\n",
       "min            -0.758378       0.000082\n",
       "25%            -0.579662       0.029917\n",
       "50%             0.101636       0.046848\n",
       "75%             0.462013       0.070259\n",
       "max             1.172707       1.714988"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter in space to the element at our POI, (discrete values, no interpolation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Dimensions: (time:9)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(x=606200, y=6905480)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolate in space to the location of our POI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Dimensions: (time:9)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.interp(x=606200, y=6905480)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.interp(x=606200, y=6905480).Surface_elevation.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surface elevation</th>\n",
       "      <th>Current speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-08-06 07:00:00</th>\n",
       "      <td>0.459460</td>\n",
       "      <td>0.009572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-06 09:30:00</th>\n",
       "      <td>0.806965</td>\n",
       "      <td>0.011064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-06 12:00:00</th>\n",
       "      <td>0.100285</td>\n",
       "      <td>0.017169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-06 14:30:00</th>\n",
       "      <td>-0.727009</td>\n",
       "      <td>0.014452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-06 17:00:00</th>\n",
       "      <td>-0.579541</td>\n",
       "      <td>0.007349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Surface elevation  Current speed\n",
       "1985-08-06 07:00:00           0.459460       0.009572\n",
       "1985-08-06 09:30:00           0.806965       0.011064\n",
       "1985-08-06 12:00:00           0.100285       0.017169\n",
       "1985-08-06 14:30:00          -0.727009       0.014452\n",
       "1985-08-06 17:00:00          -0.579541       0.007349"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.sel(x=606200, y=6905480).to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPmLp0qXExsbSvXt3nn/+edq3b09kZCT33HMPPXv25L777qNXr1512mdoaCizZ89m/Pjx9OzZk379+pV/uVfntttu46OPPqryYvbrr79Ojx49SEhIoFGjRtx0000A9O/fn2nTptGjRw86duzInXfeWW3dNQ1D/s477/DYY4/Rv3//amftU8oRDWKY8ZwzBdw9fQ1nC0qYP6U/XdoGOyG6uisuLWPwn74hsmVj5k3pX/sGHkSHnb46K1as4NVXX+Xzzz+3OpQrov/f7s9thxkXkRtFZLeI7BORadWUGSIim0Rku4isdEa9dXHyXBH3v7WOE/lFvPtgSr0nCQB/Xx8eHhjDugMn+OGQDhaolPIsDicKEfEF3gBuAuKA8SISV6lMc+CfwO3GmO7AGEfrrYv8whImzt5ARt55Zk5IJiGyeX1UW6VxfSJpFuSnw3ooAIYMGeJxrQnVcDmjRZEC7DPGpBtjioC5QOX5Fe8FFhljDgEYY1x+sr6guJRH3k1l2+HTvHFvbwbEtnZ1lTVqEujH/f078NX2o2TknrM0FqXcxe6jZ7nx9VUs2X7U6lBUDZyRKMKBzAqvs+zLKuoCtBCRFSKSJiIPVLczEZksIqkiknr8+PGrCqiktIwn5vzA2vQ8Xh3Tk+Fxba9qP842YUA0/r4+zPzWu1oV7nydSzmPs/+f9+ac5d6Z37Pr6Fl+/8UOikrKat9IWcIZiaKqm7grv6P8gCTgFmAk8D8i0qWqnRljZhhjko0xyaGhdZ806KKyMsOzC7ewdEcOL4zqzp293KfvQpvgIO7qHc6CtCxy8wutDscpgoKCyMvL02Th5Ywx5OXllfcqd9S+Y/mMn7kOHx/hd7fFkXniAvNSM2vfUFnCGb3NsoCKkzdEAEeqKJNrjDkHnBORVUAC4NQhPo0xvPD5Dhb9cJhfDO/CA/2jnbl7p5g0MIa5GzJ5b00GPx/R1epwHBYREUFWVhZX2/pTniMoKKi8V7kjDuSe496Z3wOGOY/0Iza0KV9uzebvy/YyJinCZSMlqKvnjESxAegsIh2Bw8A4bNckKvoE+IeI+AEBQF/gNSfUfYnXlu5h9poMHhnYkcdv6OTs3TtFbGhTRsS15d21B5kyONblPcNdzd/fn44dHZu/QzUcB/POMX7G95SUGeZO7kenNra7EJ8Z2Y173lzLe2szGsa4aB7G4VNPxpgS4HFgMbATmG+M2S4iU0Vkqr3MTuArYAuwHphljNnmaN0Vzfo2nb8t38fY5Ej+383XuPWwBpMHxXL6QjHztamtGpDME+cZP+N7CktK+WBS30tuVU/p2JLBXUL554r9OtqyG3JKPwpjzJfGmC7GmFhjzEv2ZdONMdMrlHnFGBNnjOlhjHndGfVeNH9DJi9+sZOb49vxf6Pj3TpJACR1aEGf6BbM+vYAJaV6AU95v6yT5xk343vOFZXy/qS+XBPW7LIyvxzRlVPni5n1rc7h4m48fgiP/27NZtqiLQzs3JrXxibi68Q5JVxp8qBYDp+6wBdbs60ORSmXOnLqAuNnfs/ZgmI+mNSX7u1DqiwXHxHCzfHtmPVtOifOFdVzlKomHp0oVu05zpNzf6BXVAvevD+JQD/PuQg2tFsbYkOb8ObKdL1jSHmt7NO2JHHqfDH/frgvPcKrThIX/Xx4Fy4Ul/KvFfvqKUJVFx6bKNIOnmDKv9Po1CaYtyf2oXGAZ10U9vERpgyKZUf2Gb7bl2d1OEo5Xc6ZAu6duY68/CLeeyilTiMjdGoTzOjeEby79iDZpy+4PkhVJx6ZKHZmn+HBdzbQLiSI9x5KIaSRv9UhXZVRvdrTJjiQN1fttzoUpZzq2NkCxs/8nmNnCnj3oT70impR522fGtoZYwx/X66tCnfhcYniQO457n9rPU0C/fj3wymEBgdaHdJVC/Tz5cFrO/Lt3ly2HT5tdThKOUVufiH3zlzH0dMFzH4ohaQOLa9o+8iWjRmfEsX8DZkczNPhbtyBRyWK7NMX+MmsdZQZw78f7ktEi8ZWh+Swe/tG0TTQz+uG9VANU15+IffNXEfWyfO8PbEPfaKvLElc9Pj1nfDzFV7/eq+TI1RXw2MSRV5+IT+ZtY4zF4p576EUOrVpanVIThHSyJ/xKZF8viWbzBPnrQ5Hqat28lwR981aR0beOd6e0Id+Ma2uel9tmgUxcUBHPt50mN1Hq545UNUfj0gUZwqKmfDOerJOXmDWhORa75zwNA9d1xEB3lqt948rz3TqvC1JpOeeY9aEZAZ0cny05qmDY2ga4Mefl+x2QoTKEW6fKAqKS5n0biq7ss8y/SdJ9HXgV4q7CgtpxKjEcOZtyOSk3j+uPMzpC8Xc/9Z69h3LZ8b9SQzsfOWDeValeeMAJg+KYcmOHDZlnnLKPtXVcetEYYBHP9jIhowTvDY2keu7tbE6JJeZPCiGC8WlvP/9QatDUarOzhQU88Bb69h19AzT7+/NkK7O/Yw+eF1HWjUJ4NXF2qqwklsniqwT51m+6xgv3RHPbQntrQ7Hpbq2C+b6rqG8uzaDguJSq8NRqlZnC4qZ8PZ6th85wz/vS+KGbs6f96VpoB+PXt+J1ftyWbMv1+n7V3Xj1oni1IVinruxG/f2jbI6lHoxZXAsuflFfLgxy+pQlKpRfmEJE9/ZwNas0/zj3t4unRzsvr5RhIUE8cqS3TqKgUXcOlGEBgfy0yENZ8jhvh1bkhARwsxV6ZSW6QdCuafzRSU89M4GNmWe4m/je3Fjj3YurS/I35cnh3bmh0OnWL7L5bMoqyq4daJo18w5s2l5ChFhyuBYMvLOs3SHziGs3M+FolIemr2B1IMneH1sIjfHh9VLvXcnRRDdqjGvLN5Nmf6IqndunSgaopHd29GhVWP+pYMFKjdTUFzKpPc2sP6A7eaS+rxu6O/rw9PDu7Dr6Fk+1xGX650mCjfj6yNMGhjD5sxTrD9wwupwlAJsSeKR91JZsz+PV+5OYFRieL3HcFvP9nRrF8xfluymWOdxqVeaKNzQmKQIWjUJYMYqHdZDWa+wpJSp76fx7d5cXr6rJ3clOT5v9tXw8RF+OaIrGXnn+TBNb/ioT05JFCJyo4jsFpF9IjKthnJ9RKRURO52Rr3eKsjflwf6R7Ns1zH25OjwBco6RSVlPPr+RlbsPs4fRsdzT3KkpfEMvaYNvaKa89dle/U28nrkcKIQEV/gDeAmIA4YLyJx1ZR7Gdvc2qoWD/TvQCN/X21VKMsUlZTx2H82smzXMV68owfjU6y/TV1EeGZkV7JPF/DBukNWh9NgOKNFkQLsM8akG2OKgLnAqCrKPQF8COj9bXXQokkAY/tE8smmwxw9XWB1OKqBKS4t48k5P7B0Rw7/e3t3ftKvg9UhlRsQ25prO7Xin9/s41xhidXhNAjOSBThQGaF11n2ZeVEJBy4E5he285EZLKIpIpI6vHjx50Qnud6+LqOlJYZ3vlOBwtU9aektIyfzd3EV9uP8ttb45gwINrqkC7zyxFdyTtXpJ+NeuKMRCFVLKt8X+frwHPGmFpPKhpjZhhjko0xyaGhzhlczFNFtmzMLT3b88G6Q5wpKLY6HNUAlJSW8fT8zXyxNZvf3HIND13X0eqQqtQrqgXD49ry5qp0Tp3XgTRdzRmJIguoeIUrAjhSqUwyMFdEMoC7gX+KyB1OqNvrTRkUQ35hCXP0fKxysdIywzMLt/DZ5iNMu6kbkwbGWB1SjX4xogv5hSW8qdfxXM4ZiWID0FlEOopIADAO+LRiAWNMR2NMtDEmGlgIPGqM+dgJdXu9HuEhXNupFW9/d4DCEr3LQ7lGWZnh2YVb+OiHwzwzsitTB7v/0Dnd2jVjVEJ73vnuAMfO6nU8V3I4URhjSoDHsd3NtBOYb4zZLiJTRWSqo/tXMGVQLDlnCvlkU+WGmlKOKysz/GrRVj7cmMXTw7rw2PWdrA6pzn42rAslpYY3lu+zOhSv5pR+FMaYL40xXYwxscaYl+zLphtjLrt4bYyZaIxZ6Ix6G4qBnVtzTVgzZq5K13FulFOVlRl+/fE25qVm8uQNnXhqWGerQ7oi0a2bcE+fSP6z/pBOJexC2jPbA4gIUwbFsPdYPt/s1ruLlXMYY/jtp9uYs/4Qjw6J5enhXawO6ao8eUNnRIS/LttrdSheSxOFh7ilZxjhzRvx5kq9cKccZ4zhfz/bwfvfH2LKoBieGdkVkapuYHR/7UKCeKBfBxZtzGLfMR3JwBU0UXgIf18fHr6uI+szTrDx0Emrw1EezBjD7z/fyew1GTx8XUem3dTNY5PERT8dEksjf19eW6qtClfQROFBxvaJJKSRPzO0VaGukjGGP/x3F29/d4CJA6L5zS3XeHySAGjVNJCHB8bwxdZsth0+bXU4XkcThQdpEujH/f06sHjHUdKP51sdjvIwxhj+tHg3M1alc3+/DvzutjivSBIXTRrYkeaN/Xl1yW6rQ/E6mig8zIQB0fj7+jBrtQ5doK7Ma0v38K8V+7m3bxT/e3t3r0oSAM2C/Pnp4FhW7D6uc7k4mSYKDxMaHMhdvSNYmJbF8bOFVoejPMRfv97L35bvY2xyJC+O6oGPj3cliYse6B9Nm+BAXlm8S2eIdCJNFB7okYEdKS4t4721GVaHojzAP5bv5bWv93B3UgR/GB3vtUkCoFGAL08M7cyGjJOs3NOwBxV1Jk0UHigmtCkj4try3tqDOsyyqtG/Vuzn1SV7uLNXOC/f1dOrk8RFY5MjiWzZiFcW79YOqk6iicJDTRkcy+kLxczbkFl7YdUgzfo2nZe/2sXtCe15dUwCvg0gSQAE+Pnws6Fd2H7kDF9tP2p1OF5BE4WH6h3VgpTolry1+oBONK8uk5dfyMtf7WJ4XFv+ck/DSRIX3dErnE5tmvLnJbsp1VaFwzRReLDJg2I4fOoCX27NtjoU5WY+3nSE4lLDMyO74ufb8D7mvj7CL0d0Yf/xc3z0w2Grw/F4De8d5EVu6NaGTm2aMn1lut7hocoZY1iQmklCRAhd2gZbHY5lRnZvR3x4CK8t3aND9DtIE4UH8/ERJg+KYWf2GVbvy7U6HOUmth0+w66jZxmTHFl7YS8mIjwzsiuHT13Qa3kO0kTh4UYltqdNcKAOFqjKLUjLJNDPh9sS2lsdiuUGdm5N344t+duyfZwv0jsEr5YmCg8X6OfLQ9d1ZPW+XB3jRlFQXMrHPxzmxh7tCGnkb3U4lrvYqsjNL+TdNQetDsdjOSVRiMiNIrJbRPaJyLQq1t8nIlvsjzUikuCMepXNvX2jaBroxwydO7jBW7ojhzMFJYxJatinnSpKjm7J9V1Dmb5yP6cvFFsdjkdyOFGIiC/wBnATEAeMF5G4SsUOAIONMT2B3wMzHK1X/ahZkD/39o3ii63ZOstXAzc/NZPw5o0YENvK6lDcyi9GdOX0hWJmfas/pq6GM1oUKcA+Y0y6MaYImAuMqljAGLPGGHNxEoXvgQgn1KsqePDaaHwE3tLBAhusI6cusHpfLnclRTSIHthXokd4CLf0DOOt1QfIzdcx0q6UMxJFOFDxloIs+7LqPAz81wn1qgrCQhoxKjGceRsyOXmuyOpwlAUWbczCGBiTpL/DqvLz4V0oKC7lXyv2Wx2Kx3FGoqjqp0uVN/WLyPXYEsVz1e5MZLKIpIpI6vHjOqjXlZg8KIYLxaX8+3u9aNfQGGNYkJZFv5iWRLZsbHU4bik2tCl3J0Xw7+8PcuTUBavD8SjOSBRZQMUrZxHAkcqFRKQnMAsYZYzJq25nxpgZxphkY0xyaGioE8JrOLq0DeaGbm14d00GBcXawaghWX/gBAfzznNPA+87UZsnh3YGA39frlOmXglnJIoNQGcR6SgiAcA44NOKBUQkClgE3G+M2eOEOlU1pgyKIe9cEQvTsqwORdWjBWlZNA3046YeYVaH4tYiWjTm3r5RzE/N4kDuOavD8RgOJwpjTAnwOLAY2AnMN8ZsF5GpIjLVXuy3QCvgnyKySURSHa1XVS2lY0sSIpsz89t0HQytgcgvLOGLLdnclhBGowBfq8Nxe49d34kAXx9eW6q/WevKKf0ojDFfGmO6GGNijTEv2ZdNN8ZMtz+fZIxpYYxJtD+SnVGvupyIMHVQDAfzzrNYh1huEL7cks2F4lLu1r4TdRIaHMiD10bz6eYj7Mw+Y3U4HkF7ZnuhEd3bEd2qMW+u3K+DBTYA81MziQltQu+o5laH4jGmDIolOMiPPy/RVkVdaKLwQr4+wqSBMWzOOs06nWTeq6Ufzyf14EnuSY5ERPtO1FVIY3+mDo7l6505bDx0svYNGjhNFF7q7qQIWjUJ4M2Ves+4N1uYloWvjzC6V01dl1RVJg6IpnXTAF5dvNvqUNyeJgovFeTvy4QB0Xyz+zi7j561OhzlAqVlhg83ZjGkSyhtmgVZHY7HaRLox2PXd2LN/jy+02H6a6SJwovd368Djfx9dbBAL7Vq73FyzhQyJll7Yl+te/tG0T4kiD8t3q3X82qgicKLtWgSwNg+kXy6+TDZp7UnqrdZkJpJyyYB3NCtrdWheKxAP19+NqwLmzNPsXRHjtXhuC1NFF7u4es6Umbgne8yrA5FOdHJc0V8veMYdySGE+CnH2NHjO4dTkzrJvx5yR7te1QNfYd5uciWjbklPoz/rDtEfqHO8OUtPtl0mKLSMj3t5AR+vj48PbwLu3PO8tnmy0YfUmiiaBAmDIgmv7CEz/VD4DXmp2bRI7wZ14Q1szoUr3BLfBjXhDXjta/3UFxaZnU4bkcTRQPQO6o5Xdo2Za5OMO8Vth85zY7sMzoAoBP5+AjPjOzCwbzzLEjVcdIq00TRAIgIY/tEsSnzFLuO6pAFnm5BahYBvj7cntDe6lC8yvVd25DUoQV/W7ZXR1+uRBNFAzG6VzgBvj7MXa+tCk9WWFLKx5sOM6J7W5o3DrA6HK8iIjwzsitHzxTwvs7pcglNFA1EiyYBjOzRjo9+OKy/ljzYsp3HOHW+mDF62skl+sW0YmDn1rzxzT7OFhRbHY7b0ETRgIzvE8npC8U6qqwHm5+aSVhIENd1am11KF7rmZFdOXm+mLdXZ1gditvQRNGA9ItpRVTLxsxZf8jqUNRVOHq6gFV7jnNX7wh8fXQAQFfpGdGcG7u3Y+a36Tr/vJ0migbEx0cY2yeS79NP6OxeHmjRD1mUGduAj8q1fj6iC+eKSpiug2oCmiganDFJtl+j8/RWWY9ijGFBahYp0S2Jbt3E6nC8Xpe2wdyZGM67azPIOVNgdTiW83PGTkTkRuCvgC8wyxjzx0rrxb7+ZuA8MNEYs9EZdXs9Y6CsBEqLoawYykp/fF5aXGFdiX1ZyaXrLq43pSA+tBEfno5MZ9+GLZR0TMTP1w9EQHx+fPj4Xvr6kvWV19nXX7ZNHR/l2+mplJqkHTzJgdxzPDok1upQPFNZWdWfi0s+N5c+f+6as+Ru2cJXHx9mQt9w23qk6vd/5c9QxUf5NtVtW83rKuuqS33O53CiEBFf4A1gOJAFbBCRT40xOyoUuwnobH/0Bf5l/1uzCydh0xzA2L4wL/lbVsWyqsrVUr58HVXsq6xu+6r4RX7JG664hi/5ar7QK667uNzJHr/4ZK7Td+2Yi4nINwB8/e2PAPDxq2KZ/6Wvff3tywLA92L5Om7rU6H8xW19/GuII+DyxFY+8qip+rWDZZas2UGngBPcElkMJw9WWeby1/bnptT2ni2z/zWlti/Oi88rrzNlNawvq6Z8betrqK+s7Mf3enWfoUu+2Kv5TF3yvOTSz5G58t7WbYH3/IF0+6MBE0eH1hWR/sDzxpiR9te/AjDG/KFCmTeBFcaYOfbXu4Ehxpjsmvad3N7XpE5u6lB8jrNnbypk8crLfPxtXzAXv4B8/OxfKv4V/l5c53tpufLyNe2j8rqK+6iwzsevUrmL6/xsX8D2JFdSUsKk2euJad2I395yzaUfcFPpA38xYVb1ZVC+vqrlZZW+ICo/Si/fvyn78UugtBhKi378srj4umJr6rJlRbYvh9Ii+7YlP643ekuw26iqdXrJe7iKz9BlnwW/Kp5X8fmqdn+VPzdVlz9RYJj6ny3069yGn4+Is8Vf1Wej/EdlVe91c3nZy7aradtqXldTnwx+Ns0Yk+zM/zJnnHoKByqe8M7i8tZCVWXCgRoTBW3i4MlPqNOXdcXmXfkyailf2zrvPB3iB8SnNOaNb/YxKbgH7Zs3sjok1ys/9VBUTSKqsLzKchWSTmnRpb/ay98nUul15fVVlalqm0uXpR48xbzUTB4d0omOrZv8uL7G/Vx8XfnUoG+lU34+Na+rdr1UUf7ia59q9ndxO8/5XLUEeg1oy9+/TefWGzvSpW2w1SHVwbNO36MzEkVV/+uVmyl1KWMrKDIZmAwQFRUFLWMci05V6Z7kSP6+fB8LUrN4alhnq8NxPR8f8AkEv0CrI7lif9qwluMtehI9bLBHfcl6i6mDY/lg3SH+smQP0+9PsjocSzjjykcWULGbaARQeZjSupQBwBgzwxiTbIxJDg0NdUJ4qiqRLRszsHNr5qdm6hj8buxg3jnWHzjB3UkRiCYJS7RoEsCkgR35avtRNmeesjocSzgjUWwAOotIRxEJAMYBn1Yq8ynwgNj0A07Xdn1Cud7YPpEcPnWB1TpfsNtamJaFj8BdvbXvhJUevq4jLZsE8OqS3VaHYgmHE4UxpgTbjTSLgZ3AfGPMdhGZKiJT7cW+xHbfwD5gJvCoo/Uqxw2Pa0vLJgHM1Z7abqm0zLAwLYtBXUJpFxJkdTgNWnCQP48OieXbvbmsaYA/rJxy060x5ktjTBdjTKwx5iX7sunGmOn258YY85h9fbwxJtUZ9SrHBPr5MrpXOEt35JCbX2h1OKqS7/blkn26gDFJOgCgO/hJvw60Dwni5cW7cfRuUU+jPbMbuHEpkZSUGT5M08la3M381EyaN/ZnWFwbq0NRQJC/L08N68zmzFMs2ZFjdTj1ShNFA9epTTDJHVowb0Nmg/uV5M5Ony9myY4c7kgMJ9DP1+pwlN1dvSOICW3Cq4t3N6ibQDRRKMalRJGea7u7RrmHTzcfpqikTAcAdDN+vj78YnhX9h7L5+MfDlsdTr3RRKG4Ob4dwYF+OlCgG5mfmsU1Yc3oER5idSiqkpt6tKNHeDNe+3oPRSVXPjSIJ9JEoWgc4MeoXu35Yms2p8/rrF5W25l9hq2HT3NPsrYm3JGPj/DMyG5knbzQYOZ20UShABjXJ4rCkjI+2dxwmtPuakFqFv6+wqjEcKtDUdUY1Lk1/WJa8vfl+zhX6PyBO92NJgoFQI/wEHqEN2POer2obaWikjI+3nS4vI+Lck8iwrM3diM3v5B3vjtgdTgup4lClRvbJ6r8tIeyxvJdxzhxrkj7TniA3lEtGHZNW95clc6p8949ZaomClVuVGJ7gvx9mLNeL2pbZUFqJm2CAxnYubXVoag6eGZkV/ILS/iXl0+ZqolClWsW5M8t8e35dNPhBnHe1d0cO1vAij3HuSspAj9f/Wh6gq7tgrkjMZzZ33n3lKn6blSXGJ8SybmiUr7YomM21rePNh6mtMwwRvtOeJSnh3WhtMzwt2V7rQ7FZTRRqEskdWhBpzZNmbuhYdz25y6MMcxPzSSpQwtiQq2e1VFdiahWjRmfEsW8DZkczDtndTguoYlCXUJEGNcnko2HTrEn56zV4TQYP2SeYv/xc9p3wkM9cUMn/H19+MvSPVaH4hKaKNRl7uwVjr+vMFcvatebBamZNPL35Zae7a0ORV2FNs2CePDaaD7ZdIQdR85YHY7TaaJQl2nVNJAR3dux6IcsCopLrQ7H610oKuWzzdncHB9G00BnzE6srDBlUCzNgvy8cnIjTRSqSuP6RHLKPoKpcq2vtmeTX1jCGD3t5NFCGvszdUgsy3cdIzXDuwbY1EShqnRtbGsiWjTS2e/qwfwNWUS1bEzfji2tDkU56MEBHQkNDuRPX3nX5EYOJQoRaSkiS0Vkr/1viyrKRIrINyKyU0S2i8hTjtSp6oePjzA2OZI1+/O89k4Od5B54jxr0/MYkxSBiFgdjnJQowBfnryhE+szTrBiz3Grw3EaR1sU04BlxpjOwDL768pKgF8YY64B+gGPiUicg/WqejAmORIfQYcfd6GFaVmIwF3ad8JrjO0TRWTLRrzy1W7KvGRyI0cTxSjgXfvzd4E7KhcwxmQbYzban58FdgI6LKYHaBcSxPVd27AgLYuS0oYx7n59KiszLEzL4rpOrWnfvJHV4SgnCfCzTW60I/sMX2z1jo6rjiaKtsaYbLAlBKDGyX1FJBroBayrocxkEUkVkdTjx72n6eapxqVEcfxsIct3HbM6FK+zNj2Pw6cuMCZZBwD0NrcntKdbu2D+vGQ3xV7wI6vWRCEiX4vItioeo66kIhFpCnwI/MwYU+2NxsaYGcaYZGNMcmho6JVUoVzg+q6htAkO1NNPLjA/NZNmQX6MiGtrdSjKyXx8hF+O6EpG3nkWpGZZHY7Dak0UxphhxpgeVTw+AXJEJAzA/rfKn50i4o8tSXxgjFnkzANQruXn68OY5Ai+2X2M7NMXrA7Ha5y+UMxX244yKjGcIH9fq8NRLjD0mjb0jmrOX5ft8fj+SI6eevoUmGB/PgH4pHIBsd3K8Raw0xjzFwfrUxa4JzmSMgMLveCXkbv4fMsRCkvKtO+EF7s4uVHOmULeW5thdTgOcTRR/BEYLiJ7geH214hIexH50l7mWuB+4AYR2WR/3OxgvaoedWjVhGs7tWJeaqbX3MVhtfmpWXRtG0x8eIjVoSgX6hfTikFdQvnniv2cKfDc+egdShTGmDxjzFBjTGf73xP25UeMMTfbn682xogxpqcxJtH++LLmPSt3M7ZPFFknL/Dd/lyrQ/F4e3LOsjnzFGOSte9EQ/DsyK6cOl/MrFXpVody1bRntqqTkd3b0ryxvw4U6AQLUjPx8xHu7KV3iTcEPcJDuCU+jFmrD5CbX2h1OFdFE4Wqk0A/X0b3imDJjqPkeeib3R0Ul5bx0Q+HGXpNG1o1DbQ6HFVPfj6iC4UlZfxj+T6rQ7kqmihUnY1LiaS41LBo42GrQ/FYK3YfJze/iDFJ2neiIYkNbcqYpAj+s+4QWSfPWx3OFdNEoeqsS9tgekc1Z+6GQ1414Fl9mp+aSeumgQzpqn2EGpqnhnUGgde/9rwpUzVRqCsyLiWK/cfPkXrwpNWheJzc/EK+2XWMu3qH4+erH72GJiykEQ/068CijVns9bDZI/Xdqq7IrT1tk+voRe0r9/EPhykpM9p3ogF79PpONA7w489LPGvKVE0U6oo0DvDj9sT2fLH1CKcveO594fXNGMP81EwSI5vTqU2w1eEoi7RsEsCkgR35avtRNmeesjqcOtNEoa7YuD6RFBSX8enmI1aH4jG2ZJ1mT04+9+gAgA3epIExtGwSwJ8W77I6lDrTRKGuWHx4CHFhzXT2uyswPzWTIH8fbk0IszoUZbGmgX48dn0nvtuXx3f7PKMDqyYKdcVEhHEpkWw/coZth09bHY7bKygu5dPNR7ipRxjNgvytDke5gfv6RtE+JIg/LfaMKVM1UairMioxnEA/H+Zoq6JWi7cf5WxBCWN0FjtlF+Tvy8+GdWFz5ikWb8+xOpxaaaJQVyWkkT+3xIfx6aYjnC8qsToct7YgNYuIFo3oF9PK6lCUGxndO5zY0Cb8ecluSt18sE1NFOqqjUuJ4mxhCV9s8Y7pHl0h6+R5vtufy91JEfj46ACA6kd+vj78YkRX9h7L56Mf3Hu0A00U6qr1iW5BTGgTnf2uBh+mHcYYuKu3nnZSl7upRzviw0N4bekeCkvcd3IjTRTqqokI4/pEknrwpMf1NK0PZWWGhRszubZTKyJbNrY6HOWGRIRnRnbl8KkL/Ged+17v00ShHDK6dwT+vqKtiiqsO3CCzBMXdABAVaOBnVvTP6YV/1i+j3OF7nm9TxOFckjrpoEMj2vLhxuz3LrpbIUFqZkEB/oxsns7q0NRbkxEeObGruSdK+Lt1QesDqdKDiUKEWkpIktFZK/9b4sayvqKyA8i8rkjdSr3M7ZPFCfPF7N0h/vf5ldfzhYU8+W2bG5LbE+jAF+rw1FurndUC4bHtWXGqnROniuyOpzLONqimAYsM8Z0BpbZX1fnKWCng/UpNzSwU2vCmzfSgQIr+GJLNgXFZdp3QtXZL0d0Jb+ohOkr91sdymUcTRSjgHftz98F7qiqkIhEALcAsxysT7khHx/hnuRIVu/LJfOE503K4grzUzPp1KYpiZHNrQ5FeYiu7YK5MzGc2WsyOHq6wOpwLuFoomhrjMkGsP9tU02514FngbLadigik0UkVURSjx8/7mB4qr6MSY7AR9CL2sC+Y/lsPHSKe5IjENG+E6runh7ehTJj+Nty95rcqNZEISJfi8i2Kh6j6lKBiNwKHDPGpNWlvDFmhjEm2RiTHBqqs4B5ivbNGzG4SygL0jIpKa3194BXW5CWia+PcEevcKtDUR4msmVjxqdEMW9DJgdyz1kdTrlaE4UxZpgxpkcVj0+AHBEJA7D/PVbFLq4FbheRDGAucIOIvO/EY1BuYlxKFDlnClmxu+G2BEtKy1i08TDXd21Dm+Agq8NRHujxGzoR4OvDX5a6z+RGjp56+hSYYH8+AfikcgFjzK+MMRHGmGhgHLDcGPMTB+tVbuiGbm1o3TSQuQ349NOqvcc5frZQZ7FTV61NcBAPXRfNZ5uPsP2Ie4zO7Gii+CMwXET2AsPtrxGR9iLypaPBKc/i7+vDmOQIvtl9jJwz7nUxrr7M35BFqyYB3NCtust1StVu8qBYQhr58+ri3VaHAjiYKIwxecaYocaYzva/J+zLjxhjbq6i/ApjzK2O1Knc29jkSErLDAvTsqwOpd7l5ReybFcOd/YKx99X+7KqqxfSyJ+pg2P5ZvdxNmScsDoc7ZmtnCu6dRP6x7Ri7oZDlLn50MnO9vGmIxSXGsbodKfKCSYOiKZNcCB/+mqX5ZMbaaJQTjcuJZLMExdYm55ndSj1xhjDgtRMekaE0LVdsNXhKC/QKMCXJ4Z2ZkPGSctvENFEoZxuZPd2hDTyb1Cz320/coZdR89qa0I51djkSKJaNuZPi3db2kLXRKGcLsjflzt7hbNkew4n3HDcGleYn5pJgJ8Pt/dsb3UoyosE+PnwixFd2Jl9hs+2HLEsDk0UyiXGpURSVFrGoo3ef1G7oLiUTzYd4cbu7Qhp7G91OMrL3NazPd3aBfOXpXsotqgzqyYK5RLd2jUjMbI58zZkWn4hztW+3pnD6QvF2ndCuYSPj21yo4N555mfak0fJU0UymXG9Ylk77F8Nh46aXUoLjU/NYv2IUEMiG1tdSjKS93QrQ1JHVrwt2V7KSiu/3lfNFEol7ktoT1NAny9evjxI6cu8O3e49ydFIGvjw4AqFxDRHh2ZFdyzhTy7pqMeq9fE4VymSaBftyW0J7Pt2RztqDY6nBcYtHGLIyBu3W6U+VifWNaMbhLKP9csZ/TF+r386SJQrnUuJQoLhSX8ulm6+7YcBVjbD3Q+8W0JKpVY6vDUQ3AMyO7cvpCMTNXpddrvZoolEslRITQrV2wV55+2pBxkoy884zR1oSqJz3CQ7i1Zxhvf3eA42cL661eTRTKpUSEcX0i2Xr4NNsOu8dImM4yPzWTpoF+3BTfzupQVAPyixFdKSwp441v9tVbnZoolMvd0SucAD8fr5r9Lr+whC+3ZnNrzzAaB/hZHY5qQDq2bsI9yRF8sO5gvU09rIlCuVzzxgHc3KMdH286zIWi+r+1zxW+3JLN+aJS7TuhLPHk0M6ICK9/XT9TpmqiUPVibJ8ozhbYfoV7gwVpmcSENqF3VAurQ1ENUFhIIyb078BHP2SxJ+esy+vTRKHqRb+YlkS3auwVp5/Sj+ezIeMkY5IiEdG+E8oaPx3SicYBfvUyuZFDiUJEWorIUhHZa/9b5c8rEWkuIgtFZJeI7BSR/o7UqzyPiDC2TxTrM06w71i+1eFctbIyw3trD+IjMLp3uNXhqAasZZMAHhkYw5IdOfzg4tEPHG1RTAOWGWM6A8vsr6vyV+ArY0w3IAHY6WC9ygPdlRSOn49YNl6NIwqKS5m7/hDDX1vJ7DUZ3BwfRttmQVaHpRq4hwd2pFWTAF5xcavC0UQxCnjX/vxd4I7KBUSkGTAIeAvAGFNkjDnlYL3KA7UJDmLoNW34MC2LohJrRsG8UifPFfGP5Xu57uVvmLZoK4F+vvx1XCKvjU20OjSlaBrox2PXd2LN/jxW7811WT2O3tfX1hiTDWCMyRaRqmaUjwGOA++ISAKQBjxljDnnYN3KA41LiWLx9hy+3pnDzfFhVodTrUN553lrdTrzU7O4UFzK4C6hTBkUQ//YVnpdQrmV+/pF8dbqA7yyeBfXdrrWJXXUmihE5Gugqh5Fv76COnoDTxhj1onIX7GdovqfauqbDEwGiIqKqmMVylMM6hxK+5Ag5qw/5JaJYlPmKWauSue/27Lx9RFGJYbzyMAYnd5Uua1AP1+eGtaZZxduYfH2oy6po9ZEYYwZVt06EckRkTB7ayIMOFZFsSwgyxizzv56IdVfy8AYMwOYAZCcnOzdExk0QL4+wpjkSP62fC+ZJ84T2dL6MZLKygzLdh1j5qp01mecIDjIjymDY5k4IFqvQyiPMLpXOG+u3M+rS/a4ZP+OXqP4FJhgfz4B+KRyAWPMUSBTRLraFw0FdjhYr/JgFzupLbD4onZBcSlz1h9i2GsreeS9VA6fusD/3BrH2l8N5bkbu2mSUB7Dz9eHX47o6rI7Ch29RvFHYL6IPAwcAsYAiEh7YJYx5mZ7uSeAD0QkAEgHHnSwXuXBIlo0ZlDnUOanZvHUsC71Po/DyXNF/Pv7g7y3NoPc/CJ6hDfjb+N7cXOPdvj5atci5Zlu7NGOnhEhHHTBvh1KFMaYPGwthMrLjwA3V3i9CUh2pC7lXcb1ieSnH2xk5Z5j3NCtbb3UeTDvHG+tPsD81EwKisu4vmsojwyKoX+MXqBWns82uVE3PnPBvnU0M2WJode0pXXTAOauz3R5ovjh0ElmrErnq+1H8fMR7kgM55FBMXRpqxeolXe5rrNrpuPVRKEsEeDnw129I5i1+gDHzhTQxsnXA8rKDF/vzGHmt+lsyDhJsyA/fmq/QO3supTydpoolGXG9onkzVXpLNyYxaNDOjllnwXFpSzaeJhZ36aTnnuO8OaN+O2tcYztE0mTQH27K3U19JOjLBMT2pSUji2ZtyGTqYNi8XHgovaJc0X8e63tAnXeuSLiw0P4+/he3KQXqJVymCYKZanxKZE8PW8z3x/IY0DslZ9fzci1XaBekGa7QH1DtzY8MjCGfjEt9QK1Uk6iiUJZ6qYeYfzuk+3MXZ95RYli46GTzFiZzuIdR/H38eHOXuFMGtiRznqBWimn00ShLBXk78udvcKZsz6Tk+eKaNEkoNqyZWWGpTtzmLkqndSDJwlp5M+jQ2KZMCCaNsF6gVopV9FEoSw3tk8U7649yMebDvPgtR0vW19QXMqHG7OY9e0BDuSeI6JFI56/LY4xyXqBWqn6oJ8yZbm49s1IiAhh7vpMJg6ILr+2kJdfaO9BfZAT54roGRHCP+7txY3d9QK1UvVJE4VyC2P7RPH/PtrKpsxTNG8cwFur01mQmkVhSRnDrrFdoE7pqBeolbKCJgrlFm5PbM+LX+zgp+9vJOdsAf4+PozubbtA3amNXqBWykqaKJRbaBrox9g+kSzaeJjHhnTigQEd9AK1Um5CjHHfKR+Sk5NNamqq1WGoenLxvainl5S6eiKSZoxx6iCs2qJQbkMThFLuSW8dUUopVSNNFEoppWqkiUIppVSNHEoUItJSRJaKyF773xbVlHtaRLaLyDYRmSMiejuLUkp5CEdbFNOAZcaYzsAy++tLiEg48CSQbIzpAfgC4xysVymlVD1xNFGMAt61P38XuKOacn5AIxHxAxoDRxysVymlVD1xNFG0NcZkA9j/tqlcwBhzGHgVOARkA6eNMUscrFcppVQ9qbUfhYh8DbSrYtWv61KB/brFKKAjcApYICI/Mca8X035ycBk+8tCEdlWl3pcKAQ4bXEMzqDH4X685Vi85ThaA7lWB+EEXZ29w1oThTFmWHXrRCRHRMKMMdkiEgYcq6LYMOCAMea4fZtFwACgykRhjJkBzLCXTXV2D8MrJSIzjDGTay/p3vQ43I+3HIsXHYfl3zfOICJOH87C0VNPnwIT7M8nAJ9UUeYQ0E9EGout6+1QYKeD9danz6wOwEn0ONyPtxyLtxyHqoZDYz2JSCtgPhCFLSGMMcacEJH2wCxjzM32cv8LjAVKgB+AScaYwjrs3ysyvFLK/XnL940rjsOtBwUUkcn2U1FKKeVS3vJ944rjcOtEoZRSynoNaggPEblRRHaLyD4RmWZfliAia0Vkq4h8JiLN6rqtfXmdeqc7+TjeFpFjFe8IE5FXRGSXiGwRkY9EpLmHHsfzInJYRDbZHzd76HEkisj39mNIFZEUDziOSBH5RkR22kdSeMq+fIz9dZmIVHtKw52ORTmZMaZBPLD1CN8PxAABwGYgDtgADLaXeQj4fV23ta/7EzDN/nwa8HI9HMsgoDewrcKyEYCf/fnLVcXhIcfxPPDLq/m/dLPjWALcZH9+M7DCA44jDOhtfx4M7LF/Rq7BdsvlCmwjLLj9/4k+nPtoSC2KFGCfMSbdGFMEzMXWv6MrsMpeZilw1xVsC3Xvne40xphVwIlKy5YYY0rsL78HIqrY1O2Po4484TgMcLF1GkLVoxG423FkG2M22p+fxXZ3YrgxZqcxZnctm7vVsVRz9qCuY9O5VcuommP5vf3swSYRWWK/gchlx9KQEkU4kFnhdZZ92TbgdvuyMUAkgIi0F5Eva9kW6tA73QIPAf8Fjz2Ox+0fgrcvvoE98Dh+BrwiIpnYRib4FXjOcYhINNALWFdDGbc8FhHxBd4AbsLWIhovInHUbWy66ralLtvX47G8YozpaYxJBD4HfuvKY2lIiaKq6dMMti/Vx0QkDVtzuwjAGHPE2G/vrWFbtyMiv8Z2G/IH4JHH8S8gFkjENuTLn8Ejj+OnwNPGmEjgaeAt8IzjEJGmwIfAz4wxZ6or58bHUl3rpi4tG7dqGVUXT6X/lyZU/W/ttGNpSIkiC3trwS4COGKM2WWMGWGMSQLmYDvPWqdt7c9zxNYrHam+d3q9EJEJwK3AfcaYqt44bn8cxpgcY0ypMaYMmIntzV6Z2x8Htg6oi+zPF+AhxyEi/tiSxAfGmEW1la/AnY6lutZNlS0bd20Z1RaPiLxkb7Heh71F4apjaUiJYgPQWUQ6ikgAtqHOPxWRi28WH+A3wPS6bmtfV5fe6S4nIjcCzwG3G2POV1PME44jrMLLO7GdGqzM7Y8D25fkYPvzG4C9VZRxq+MQEcHW8tlpjPnLFW7uTsdyRa0bN24ZQQ3xGGN+bW+xfgA8bl/mmmOx+mp6fT6w3X2yB1ur4df2ZU/Zl+0B/siPfUvaA1/WtK19eSts5/j22v+2rIfjmIPttEwxtl8JDwP7sP162GR/TPfQ4/g3sBXYgu0LJsxDj+M6IA3b3T/rgCQPOI7rsH2RbKnwProZW8LOAgqBHGCxOx8L0P9ijPbXv7I/dld4P4UBu+u6rf15rdvX17FUKtOBCnfcueJYtMOdUsqriG3emz3YxpU7jK21cy8wEcgzxvzRfgdQS2PMs3XZ1hizXUReqW37ejyWImPMXnuZJ7Dd4n+3q45FE4VSyuuIraPm69j6d7xtjHlJ6j423WXb2pdXub1Fx/Ihtlv7y4CDwFRjzGFXHYsmCqWUUjVqSBezlVJKXQVNFEoppWqkiUIp5RWqGepinvw4wGSGiGyqYfteImJEZGS9Be0hap0KVSml3F2F4SqGY7uVd4OIfGqMGVuhzJ+peW7v8cBq+9/FLgzX42iLQinlDWoaruJiZ8J7sPV5uYx9/d3YbqEdISJB9uXRcunw8b8Ukeftz/vYxyRbK7Zh/qvqHOoVNFEopbxBTcNVAAwEci72PajCtcABY8x+bMOpVzkPSiXvYLsttT9QesURexBNFEopb1DbcBXjqaY1UWH9XPvzufbX1Vdmmxgs2Bizxr7oP3UL0zPpNQqllDeodlBCew/l0UDSxZUi8g62YdSPALdhm4fmdvvoywK0EpFgbCMxV/xBHXRxF645DPekLQqllDeoaVDCYcAuY0zWxcLGmAeNMYn2HszDgM3GmEhjTLQxpgO2EXTvwDa2VRsRaSUigdhGZ8YYcxI4KyL97LscVw/HaBlNFEopj2dsszs+ju1upZ3AfGPMdvvqcdR+2umjSss+xDYuUjHwAraBHT8HdlUo8zAwQ0TWYmth1HRHlUfTITyUUuoqiEhTY0y+/fk0bKOxPmVxWC6h1yiUUurq3CIiv8L2PXoQ2621XklbFEoppWqk1yiUUkrVSBOFUkqpGmmiUEopVSNNFEoppWqkiUIppVSNNFEopZSq0f8H0S00HItlyLoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other ways to subset data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that we interested in these 3 points only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1 = (606200, 6905480)\n",
    "pt2 = (606300, 6905410)\n",
    "pt3 = (606400, 6905520)\n",
    "pts_x = [pt1[0], pt2[0], pt3[0]]\n",
    "pts_y = [pt1[1], pt2[1], pt3[1]]\n",
    "elem_ids = ds.geometry.find_index(pts_x, pts_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these element ids either when we select the data from the complete dataset using the method isel() as shown above or already when we read the data from file (particular useful for files larger than memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:3)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  U velocity <u velocity component> (meter per sec)\n",
       "  2:  V velocity <v velocity component> (meter per sec)\n",
       "  3:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pts = mikeio.read(filename, elements=elem_ids)\n",
    "ds_pts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a new dfsu file\n",
    "\n",
    "* Subset of items\n",
    "* Renamed variables\n",
    "\n",
    "First inspect the source file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:884)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  Surface elevation <Surface Elevation> (meter)\n",
       "  1:  U velocity <u velocity component> (meter per sec)\n",
       "  2:  V velocity <v velocity component> (meter per sec)\n",
       "  3:  Current speed <Current Speed> (meter per sec)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.read(\"../tests/testdata/HD2D.dfsu\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:884)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  eastward_sea_water_velocity <u velocity component> (meter per sec)\n",
       "  1:  northward_sea_water_velocity <v velocity component> (meter per sec)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfilename2 = \"HD2D_selected.dfsu\"\n",
    "\n",
    "newds = ds[[\"U velocity\", \"V velocity\"]].rename({'U velocity': 'eastward_sea_water_velocity',\n",
    "                                                 'V velocity': 'northward_sea_water_velocity'})\n",
    "newds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "newds.to_dfs(outfilename2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the newly created file to verify the contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Geometry: Dfsu2D\n",
       "Dimensions: (time:9, element:884)\n",
       "Time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)\n",
       "Items:\n",
       "  0:  eastward_sea_water_velocity <u velocity component> (meter per sec)\n",
       "  1:  northward_sea_water_velocity <v velocity component> (meter per sec)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newds2 = mikeio.read(outfilename2)\n",
    "newds2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write mesh from dfsu file\n",
    "Don't you have the original mesh? No problem - you can re-create it from the dfsu file... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "outmesh = 'mesh_from_HD2D.mesh'\n",
    "ds.geometry.to_mesh(outmesh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove(outfilename2)\n",
    "os.remove(outmesh)"
   ]
  }
 ],
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