Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>ksat</th>\n",
       "      <th>kmin</th>\n",
       "      <th>kdry</th>\n",
       "      <th>mysat</th>\n",
       "      <th>rsat</th>\n",
       "      <th>kmin_fls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.616670e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.481409e+09</td>\n",
       "      <td>6.392145e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.678292e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.444695e+09</td>\n",
       "      <td>6.352564e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7.740902e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.404618e+09</td>\n",
       "      <td>6.309501e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.803862e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.360374e+09</td>\n",
       "      <td>6.262134e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7.866270e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.310806e+09</td>\n",
       "      <td>6.209285e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7.907728e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.273197e+09</td>\n",
       "      <td>6.169342e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.926768e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.254152e+09</td>\n",
       "      <td>6.149166e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7.983129e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.187509e+09</td>\n",
       "      <td>6.078836e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8.031227e+09</td>\n",
       "      <td>2.524804e+10</td>\n",
       "      <td>6.105485e+09</td>\n",
       "      <td>5.992855e+09</td>\n",
       "      <td>1772.61575</td>\n",
       "      <td>2.625225e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          ksat          kmin          kdry         mysat  \\\n",
       "0           0  7.616670e+09  2.524804e+10  6.481409e+09  6.392145e+09   \n",
       "1           1  7.678292e+09  2.524804e+10  6.444695e+09  6.352564e+09   \n",
       "2           2  7.740902e+09  2.524804e+10  6.404618e+09  6.309501e+09   \n",
       "3           3  7.803862e+09  2.524804e+10  6.360374e+09  6.262134e+09   \n",
       "4           4  7.866270e+09  2.524804e+10  6.310806e+09  6.209285e+09   \n",
       "5           5  7.907728e+09  2.524804e+10  6.273197e+09  6.169342e+09   \n",
       "6           6  7.926768e+09  2.524804e+10  6.254152e+09  6.149166e+09   \n",
       "7           7  7.983129e+09  2.524804e+10  6.187509e+09  6.078836e+09   \n",
       "8           8  8.031227e+09  2.524804e+10  6.105485e+09  5.992855e+09   \n",
       "\n",
       "         rsat      kmin_fls  \n",
       "0  1772.61575  2.625225e+10  \n",
       "1  1772.61575  2.625225e+10  \n",
       "2  1772.61575  2.625225e+10  \n",
       "3  1772.61575  2.625225e+10  \n",
       "4  1772.61575  2.625225e+10  \n",
       "5  1772.61575  2.625225e+10  \n",
       "6  1772.61575  2.625225e+10  \n",
       "7  1772.61575  2.625225e+10  \n",
       "8  1772.61575  2.625225e+10  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = pd.read_table('output.csv',delimiter =',')\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>fluid_pressure</th>\n",
       "      <th>brine_saturation</th>\n",
       "      <th>gas_saturation</th>\n",
       "      <th>porosity</th>\n",
       "      <th>sand_fraction</th>\n",
       "      <th>depth</th>\n",
       "      <th>rock_pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>20000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>25000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>30000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>35000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>40000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>43400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>45000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3000</td>\n",
       "      <td>55000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fluid_pressure  brine_saturation  gas_saturation  porosity  sand_fraction  \\\n",
       "0        20000000                 0               0      0.25            0.8   \n",
       "1        25000000                 0               0      0.25            0.8   \n",
       "2        30000000                 0               0      0.25            0.8   \n",
       "3        35000000                 0               0      0.25            0.8   \n",
       "4        40000000                 0               0      0.25            0.8   \n",
       "5        43400000                 0               0      0.25            0.8   \n",
       "6        45000000                 0               0      0.25            0.8   \n",
       "7        50000000                 0               0      0.25            0.8   \n",
       "8        55000000                 0               0      0.25            0.8   \n",
       "\n",
       "   depth  rock_pressure  \n",
       "0   3000       20000000  \n",
       "1   3000       25000000  \n",
       "2   3000       30000000  \n",
       "3   3000       35000000  \n",
       "4   3000       40000000  \n",
       "5   3000       43400000  \n",
       "6   3000       45000000  \n",
       "7   3000       50000000  \n",
       "8   3000       55000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input = pd.read_table('test.csv',delimiter =',')\n",
    "\n",
    "input\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd97a4b6590>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(68e6 - input['fluid_pressure'],output['kdry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    48.0\n",
       "1    43.0\n",
       "2    38.0\n",
       "3    33.0\n",
       "4    28.0\n",
       "5    24.6\n",
       "6    23.0\n",
       "7    18.0\n",
       "8    13.0\n",
       "Name: fluid_pressure, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Peff = 68e6 - input['fluid_pressure']\n",
    "Peff = Peff*10e-7\n",
    "Peff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14.208446\n",
       "1    14.205394\n",
       "2    14.196352\n",
       "3    14.169607\n",
       "4    14.090890\n",
       "5    13.962836\n",
       "6    13.862533\n",
       "7    13.226921\n",
       "8    11.643833\n",
       "Name: fluid_pressure, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdry = 14.21/(1+3.72*np.exp(-Peff/4.6))\n",
    "kdry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd979cb2890>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Peff,output['kdry']/np.max(output['kdry']))\n",
    "plt.plot(Peff,kdry*1e9/np.max(kdry*1e9))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd979d31910>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Peff,kdry*1e9/np.max(kdry*1e9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd979c17cd0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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23D68O031Fn4SQyzWTmkPhUKempoadAyRSm3IyuOu11axaPNeerdpzD0jetJfYxoJiJktc/dQRev0l7Eix6n8mGbKyF6MGaQxjcQuFb1IlI6Maab8ay3ZB8Jjmtsu0phGYp+KXiQKG7Ly+M2rq1i8ZS992jZm+vgQ/do1CTqWSFRU9CLHkJdfxMPzNzLr4600rKMxjcQnFb1IBTSmkUSiohcpZ/3O8Nk0GtNIolDRi0Tk5Rfx0PyNPBMZ09w7sjc/GNROYxqJeyp6SXruzmsrtjPl9bXsPlDAmEEp3H5RN07RmEYShIpektr6nXn85rVVLImMaZ4eH6KvxjSSYFT0kpQ0ppFkoqKXpKIxjSQjFb0kjXU7c7nrtdUs2bKXvhrTSBJR0UvCy80v4qF5G5n97/CYZuqo3vwg1I5qGtNIklDRS8Jyd15dkcm9r69j94ECxg5O4bbvakwjyUdFLwlp3c5c7np1NUu2hsc0M64L0aetxjSSnFT0klDyi0p4cN4GZny4hUZ1anDfqN5cpTGNJDkVvSSMTz7fx+SX09icfZCxg9tx+0XdNaYRQUUvCSC/qIQ/z9vAUx9splXjujx345mc16V50LFEYoaKXuLa8shR/Kbsg4wdnMKvL+5Owzo1g44lElNU9BKX8otKeGj+Rqa/v4nTGtVhzg2D+VbXFkHHEolJKnqJO2nbcpj8chobdx1gzKB2/PqSM2iko3iRo1LRS9woKA4fxT/53iZaNqrD7BsGM0RH8SKVUtFLXCh7FP+DUDv+81IdxYtES0UvMa2guIRH3tnIE+9tpkWD2sy6fhAXdDs16FgicUVFLzHr04z9TH45jfVZeVw5sC3/dWkPGtfVUbzI8aoWzUZmNtzM1ptZupndUcH69mb2jpmtNLN3zaxtZPkFZraizEe+mV1e1U9CEkthcSl/ens9lz/+ETmHC5k1YRB/vLKvSl7ka6r0iN7MqgPTgAuBDGCpmc119zVlNnsAmOPus81sGDAVGOfuC4F+kc/TFEgH3q7i5yAJZFVm+Ch+3c48Rg9oy12X9qBxPRW8yDcRzehmMJDu7psBzOwFYARQtuh7AL+M3F4IvFrB57kCeMPdD339uJKoCotLeWxhOo8vTKdp/VrMnBBiWPeWQccSSQjRFH0bYFuZ+xnAmeW2SQNGAQ8DI4GGZtbM3feU2WYM8GBFX8DMJgITAVJSUqJLLglj9fb9TH55JWt35DJqQBvuvrSnjuJFqlBVvRg7GXjMzCYA7wOZQMmRlWbWCugNvFXRg919OjAdIBQKeRVlkhhXWFzKtIXpTFuYzin1a/H0+BDf6aGjeJGqFk3RZwLtytxvG1n2BXffTviIHjNrAIx295wym1wF/MPdi75ZXEkUa7bnMvnlNNbsyGVk/zbc/f0eNKmnK02KnAjRFP1SoIuZdSRc8GOAq8tuYGbNgb3uXgrcCcws9znGRpZLkisqKeXxhZt4dMFGTqlfi6fGh7hQR/EiJ1SlRe/uxWY2ifDYpTow091Xm9k9QKq7zwWGAlPNzAmPbm458ngz60D4N4L3qjy9xJW1O8JH8au353J5v9b892U9dRQvchKYe2yNxEOhkKempgYdQ6pQUUkpT7y7iUcWbKRx3ZpMGdmbi3qeFnQskYRiZsvcPVTROv1lrJxQ63fmcevLK1iVmctlfVvz28t66l2fRE4yFb2cEKWlzsyPtnD/m+tpVLcGT1w7gOG9WgUdSyQpqeilyu3Ky+fWl9L4YONuLurZkqmj+tBUR/EigVHRS5VasC6L215eycHCYu4d2Zuxg9thZkHHEklqKnqpEvlFJdz3xjqe+XgrZ7RqxKNj+9H51IZBxxIRVPRSBTZk5fHT55ezbmceN5zbkduHd6NOzepBxxKRCBW9fG3uznOLP+f3/1xDwzo19KYgIjFKRS9fy96Dhdz+ykrmr81iSNcWPHBlX1o0rB10LBGpgIpejttH6bv5xYsryDlUxG8u7cH153SgWjW94CoSq1T0ErXC4lIenLeBJ9/fRKfm9Zl1/SB6tm4cdCwRqYSKXqKyZfdBfvbCclZm7Gfs4BR+c+kZ1Kulfz4i8UDfqVKpN1ft4NaX0qhRvZr+wlUkDqno5ahKS52H5m/gkQXp9GvXhMevGUDrJnWDjiUix0lFLxXKyy/iFy+uYP7aXVwVasvvLu9F7Ro6N14kHqno5Ss2ZR9g4pxUPttziHtG9GTcWe11GQOROKaily9ZsC6Lnz2/gpo1qvHcD8/krE7Ngo4kIt+Qil6A8F+5TluYzp/mbaBHq0ZMHx+ijebxIglBRS8cLCjmtlfSeP3TnYzo15r7RvWhbi3N40UShYo+yX2+5xATn01lQ1Ye/3nxGfzw/I6ax4skGBV9EvtgYzaT/rYcgNk3DOb8Li0CTiQiJ4KKPgm5O09/sIWpb6yly6kNeWp8iJRm9YKOJSIniIo+yeQXlXDH/6zk1RXb+V6v03jgyr7Ur61/BiKJTN/hSSQz5zA3PZvK6u253HZRN24eerrm8SJJQEWfJBZt3sMtf/2EwuJSZlwXYlj3lkFHEpGTREWf4NydZxd9xj3/u4aUZvV4anyI01s0CDqWiJxE1aLZyMyGm9l6M0s3szsqWN/ezN4xs5Vm9q6ZtS2zLsXM3jaztWa2xsw6VF18OZaC4hJ+9T8rueu11Qzt1oJXbzlXJS+ShCo9ojez6sA04EIgA1hqZnPdfU2ZzR4A5rj7bDMbBkwFxkXWzQGmuPs8M2sAlFbpM5AKZeXmc9Ozy1ixLYefDuvMz7/TVe8CJZKkohndDAbS3X0zgJm9AIwAyhZ9D+CXkdsLgVcj2/YAarj7PAB3P1BFueUYln22jx8/t4yDBcW6fryIRDW6aQNsK3M/I7KsrDRgVOT2SKChmTUDugI5ZvZ3M1tuZn+M/IYgJ8gLSz5nzPR/U69Wdf5x87kqeRGJbkYfhcnAEDNbDgwBMoESwr8xnB9ZPwjoBEwo/2Azm2hmqWaWmp2dXUWRkkthcSm/eXUVd/z9U87q1Iy5t5xHt9MaBh1LRGJANKObTKBdmfttI8u+4O7biRzRR+bwo909x8wygBVlxj6vAmcBM8o9fjowHSAUCvnXeyrJa/eBAm5+7hOWbN3LTUM6cftF3amuebyIRERT9EuBLmbWkXDBjwGuLruBmTUH9rp7KXAnMLPMY5uYWQt3zwaGAalVFV7g04z9THw2lX2HCnlkbH8u69s66EgiEmMqHd24ezEwCXgLWAu85O6rzeweM7ssstlQYL2ZbQBaAlMijy0hPLZ5x8w+BQx4qsqfRZL6x/IMrnjiY6qZ8cqPz1HJi0iFzD22JiWhUMhTU3XQfyzFJaXc98Y6nv5wC2d1asq0qwfQrEHtoGOJSIDMbJm7hypap7+MjTP7DhYy6flP+Ch9D9ef24FfX3wGNatX1WvqIpKIVPRxZO2OXCY+m0pWbgF/vKIPV4baVf4gEUl6Kvo48a+VO5j8chqN69bkpZvOpl+7JkFHEpE4oaKPcSWlzoPz1jNt4SYGtj+Fv1w7gFMb1gk6lojEERV9DNt/uIifv7CcheuzGTs4hd9e1pNaNTSPF5Hjo6KPUem78vjRnGVk7DvElJG9uObM9kFHEpE4paKPQfPWZPGLF1dQp2Z1/vajsxjUoWnQkUQkjqnoY0hpqfPognT+PH8Dfdo25slxA2nVuG7QsUQkzqnoY8SBgmJ++eIK3l6TxegBbZkyshd1aupCnyLyzanoY8DW3Qf50ZxUNu8+yF2X9uD6czvoTbtFpMqo6AP27vpd/PT55VSvZjx7w2DO6dw86EgikmBU9AFxd558fzP3v7mObqc1Yvq4gbRrWi/oWCKSgFT0ASgpde78+0peSs3g0j6t+OMVfalbS/N4ETkxVPQnWUmpc9srafz9k0x++u0u/OI7XTSPF5ETSkV/EpWUOpNfTuMfyzO59cKu/OTbXYKOJCJJQEV/khSXlHLry2m8tmI7t13UjVsu6Bx0JBFJEir6k6C4pJRfvpTG3LTt3D68GzcPVcmLyMmjoj/BiktK+fmLK/jnyh3c8b3u/HjI6UFHEpEko6I/gYpKSvn5Cyv416c7+PXF3Zn4LZW8iJx8KvoTpKiklJ8+v5w3Vu3kvy45gx+e3ynoSCKSpFT0J0BhcSk/ef4T3lqdxW8u7cGN53UMOpKIJDEVfRUrLC5l0t8+4e01Wdz9/R5cf65KXkSCpaKvQoXFpdz810+YvzaL317Wk+vO6RB0JBERFX1VKSgu4Za/fsL8tbu4Z0RPxp/dIehIIiKAir5KFBSX8B/PfcKCdbv4/eW9uPYsve2fiMSOqN5p2syGm9l6M0s3szsqWN/ezN4xs5Vm9q6ZtS2zrsTMVkQ+5lZl+FiQX1TCj59dxoJ1u7h3ZG+VvIjEnEqP6M2sOjANuBDIAJaa2Vx3X1NmsweAOe4+28yGAVOBcZF1h929XxXnjgn5RSXc9Owy3tuQzX2jejNmcErQkUREviKaI/rBQLq7b3b3QuAFYES5bXoACyK3F1awPuHkF5XwozmpvL8xm/tH91HJi0jMiqbo2wDbytzPiCwrKw0YFbk9EmhoZs0i9+uYWaqZLTKzy79R2hhxpOQ/TN/NH0b34apB7YKOJCJyVFHN6KMwGRhiZsuBIUAmUBJZ197dQ8DVwENm9pXrAJjZxMgPg9Ts7OwqinRiHC4s4YezwyX/xyv6clVIJS8isS2aos8EyrZZ28iyL7j7dncf5e79gf+MLMuJ/Dcz8t/NwLtA//JfwN2nu3vI3UMtWrT4Os/jpDhUWMyNs5fy0abd/OnKvlwxsG3lDxIRCVg0Rb8U6GJmHc2sFjAG+NLZM2bW3MyOfK47gZmR5aeYWe0j2wDnAmVfxI0bhwqLueGZpSzavIcHr+rLqAEqeRGJD5UWvbsXA5OAt4C1wEvuvtrM7jGzyyKbDQXWm9kGoCUwJbL8DCDVzNIIv0h7X7mzdeLCwYJiJsxaypIte/nzD/oxsr9KXkTih7l70Bm+JBQKeWpqatAxvnCwoJjrZy0l9bO9PDSmP5f1bR10JBGRrzCzZZHXQ79Cfxl7DAcKirl+1hI++TyHh8f05/sqeRGJQyr6o8jLL2LCrKWs2JbDI2P6c0mfVkFHEhH5WlT0FcjLL+K6mUtYmbGfx8b253u9VfIiEr9U9OXkRkr+04z9PHZ1f4b3UsmLSHxT0Zex/3AR42cuYXXmfqZdM4CLep4WdCQRkW9MRR+x/3AR42csZs2OXB6/ZgDfVcmLSIJQ0QP7DxUxbuZi1u3I4y/XDOQ7PVoGHUlEpMokfdHnHCrk2hmL2bDzAE+MG8Cw7ip5EUksSV30+w4Wcs3Ti0nPPsCT4wdyQbdTg44kIlLlkrbo90ZKflP2AaaPG8hQlbyIJKikLPq9Bwu5+qlFbNl9kKfHh/hW19i9YqaIyDeVdEW/50AB1zy9OFzy14U4v4tKXkQSW1IV/e4DBVzz1GI+23uQmRMGcW7n5kFHEhE54ZKm6LPzCrj6qUVs23eImdcN4hyVvIgkiaQo+l15+Vz91GIy9x1m1oTBnH16s8ofJCKSIBK+6Hfl5jP2qUVsz8ln1vWDOKuTSl5EkktCF/2u3HzGPLWInfvzeeb6QZypkheRJJSwRZ+Vm8/Y6YvIys1n9g2DGdShadCRREQCkZBFv3N/eFyzK1LyIZW8iCSxhCv6HfsPM3b6InYfKGTOjYMZ2F4lLyLJLaGKPjMnXPL7DoZLfkDKKUFHEhEJXMIU/Y79hxkz/d/kHCxizo2D6a+SFxEBEqjoG9WpSddTG/KTb3ehX7smQccREYkZCVP09WvXYMaEQUHHEBGJOdWCDiAiIieWil5EJMFFVfRmNtzM1ptZupndUcH69mb2jpmtNLN3zaxtufWNzCzDzB6rquAiIhKdSovezKoD04DvAT2AsWbWo9xmDwBz3L0PcA8wtdz63wHvf/O4IiJyvKI5oh8MpLv7ZncvBF4ARpTbpgewIHJ7Ydn1ZjYQaAm8/c3jiojI8Yqm6NsA28rcz4gsKysNGBW5PRJoaGbNzKwa8Cdg8rG+gJlNNLNUM0vNzs6OLrmIiESlql6MnQwMMbPlwBAgEygBbgZed/eMYz3Y3ae7e/VNyOYAAASESURBVMjdQy1a6K39RESqUjTn0WcC7crcbxtZ9gV3307kiN7MGgCj3T3HzM4Gzjezm4EGQC0zO+DuX3lBV0RETgxz92NvYFYD2AB8m3DBLwWudvfVZbZpDux191IzmwKUuPtd5T7PBCDk7pMq+XrZwGdf47lUpebA7oAzHI94yhtPWSG+8irriRMPedu7e4UjkUqP6N292MwmAW8B1YGZ7r7azO4BUt19LjAUmGpmTvjsmlu+btKjBT2ZzCzV3UNB54hWPOWNp6wQX3mV9cSJt7zlRXUJBHd/HXi93LK7ytx+BXilks/xDPDMcScUEZFvRH8ZKyKS4FT0FZsedIDjFE954ykrxFdeZT1x4i3vl1T6YqyIiMQ3HdGLiCQ4Fb2ISIJL+qI3s5lmtsvMVpVZ9t9mlmlmKyIfFweZ8Qgza2dmC81sjZmtNrOfRZY3NbN5ZrYx8t/A30fxGFljdd/WMbMlZpYWyfvbyPKOZrY4cuXWF82sVgxnfcbMtpTZt/2CzlqWmVU3s+Vm9s/I/Zjbt0dUkDWm921lkr7oCZ/yObyC5X92936Rj9crWB+EYuBWd+8BnAXcErmS6B3AO+7eBXgncj9oR8sKsblvC4Bh7t4X6AcMN7OzgD8QztsZ2AfcGGDGI46WFeC2Mvt2RXARK/QzYG2Z+7G4b48onxVie98eU9IXvbu/D+wNOkc03H2Hu38SuZ1H+B9iG8JXC50d2Ww2cHkwCf/fMbLGJA87ELlbM/LhwDD+/29EYmXfHi1rzIq8R8UlwNOR+0YM7lv4atZEkPRFfwyTIm+kMjMWRiHlmVkHoD+wGGjp7jsiq3YSvix0zCiXFWJ030Z+XV8B7ALmAZuAHHcvjmxS0ZVbA1E+q7sf2bdTIvv2z2ZWO8CI5T0E3A6URu43I0b3LV/NekSs7ttKqegr9hfgdMK/Fu8gfKnlmBG5cNz/AD9399yy6zx8vmzMHN1VkDVm9627l7h7P8IX7hsMdA840lGVz2pmvYA7CWceBDQFfhVgxC+Y2aXALndfFnSWyhwja0zu22ip6Cvg7lmRb6RS4CnC3/QxwcxqEi7Ov7r73yOLs8ysVWR9K8JHeYGrKGss79sj3D2H8BvonA00iVzYDyq4cmvQymQdHhmXubsXALOInX17LnCZmW0l/MZFw4CHic19+5WsZvZcDO/bqKjoK3CkNCNGAquOtu3JFJlrzgDWuvuDZVbNBa6L3L4OeO1kZyvvaFljeN+2MLMmkdt1gQsJv66wELgislms7NuKsq4r88PeCM+7Y2Lfuvud7t7W3TsAY4AF7n4NMbhvj5L12ljdt9GK6qJmiczMnid89c3mZpYB3A0MjZw+5cBW4KbAAn7ZucA44NPIfBbg18B9wEtmdiPhSzxfFVC+so6WdWyM7ttWwGwLv0dyNeAld/+nma0BXjCz3wPLCf/wCtrRsi4wsxaAASuAHwcZMgq/Ivb27dH8Nc727ZfoEggiIglOoxsRkQSnohcRSXAqehGRBKeiFxFJcCp6EZEEp6IXEUlwKnoRkQT3f3l+BGxOQpXNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Peff,output['kdry']/np.max(output['kdry']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}