PioPetro/piopetro_session4_decline_curve_analysis.ipynb

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      "name": "piopetro-session4-decline-curve-analysis.ipynb",
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      "authorship_tag": "ABX9TyP2wZc6eioq1vzdeq4bDvsR",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/python-bootcamp-for-geoengineers/blob/master/PioPetro/piopetro_session4_decline_curve_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSyICYwiymzs",
        "outputId": "91b1b319-4051-438e-b728-fb4137a77f90"
      },
      "source": [
        "# Get DCA utility\r\n",
        "!wget https://raw.githubusercontent.com/yohanesnuwara/pyreservoir/master/dca/dca.py"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-02 02:59:51--  https://raw.githubusercontent.com/yohanesnuwara/pyreservoir/master/dca/dca.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7857 (7.7K) [text/plain]\n",
            "Saving to: ‘dca.py’\n",
            "\n",
            "\rdca.py                0%[                    ]       0  --.-KB/s               \rdca.py              100%[===================>]   7.67K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-03-02 02:59:51 (85.2 MB/s) - ‘dca.py’ saved [7857/7857]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uj9pkNRbc09L"
      },
      "source": [
        "# Import libraries\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import pandas as pd\r\n",
        "from scipy.optimize import curve_fit\r\n",
        "\r\n",
        "from dca import remove_outlier, arps_fit"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwuTSmAdpg8M"
      },
      "source": [
        "# Production data link (same as session 2)\r\n",
        "path = \"http://bit.ly/piopetro-data1\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "kJtZKPcNpuyR",
        "outputId": "d8c29d3e-5361-450a-8a52-a8a21a1a9c9e"
      },
      "source": [
        "# Read data\r\n",
        "df = pd.read_csv(path)\r\n",
        "\r\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATEPRD</th>\n",
              "      <th>NPD_WELL_BORE_NAME</th>\n",
              "      <th>ON_STREAM_HRS</th>\n",
              "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
              "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
              "      <th>AVG_DP_TUBING</th>\n",
              "      <th>AVG_ANNULUS_PRESS</th>\n",
              "      <th>AVG_CHOKE_SIZE_P</th>\n",
              "      <th>AVG_WHP_P</th>\n",
              "      <th>AVG_WHT_P</th>\n",
              "      <th>DP_CHOKE_SIZE</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "      <th>BORE_GAS_VOL</th>\n",
              "      <th>BORE_WAT_VOL</th>\n",
              "      <th>BORE_WI_VOL</th>\n",
              "      <th>FLOW_KIND</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07-Apr-14</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>08-Apr-14</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>09-Apr-14</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.97901</td>\n",
              "      <td>0.000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10-Apr-14</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.54576</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11-Apr-14</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>310.376</td>\n",
              "      <td>96.876</td>\n",
              "      <td>277.278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.21599</td>\n",
              "      <td>33.098</td>\n",
              "      <td>10.48</td>\n",
              "      <td>33.072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "     DATEPRD NPD_WELL_BORE_NAME  ...  BORE_WI_VOL   FLOW_KIND\n",
              "0  07-Apr-14         15/9-F-1 C  ...          NaN  production\n",
              "1  08-Apr-14         15/9-F-1 C  ...          NaN  production\n",
              "2  09-Apr-14         15/9-F-1 C  ...          NaN  production\n",
              "3  10-Apr-14         15/9-F-1 C  ...          NaN  production\n",
              "4  11-Apr-14         15/9-F-1 C  ...          NaN  production\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 305
        },
        "id": "NmzalrE5p8-G",
        "outputId": "2e235951-cc8c-4812-f16e-986dedd3b87c"
      },
      "source": [
        "# Convert date column to pandas datetime format\r\n",
        "df[\"DATEPRD\"] = pd.to_datetime(df[\"DATEPRD\"], format=\"%d-%b-%y\")\r\n",
        "\r\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATEPRD</th>\n",
              "      <th>NPD_WELL_BORE_NAME</th>\n",
              "      <th>ON_STREAM_HRS</th>\n",
              "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
              "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
              "      <th>AVG_DP_TUBING</th>\n",
              "      <th>AVG_ANNULUS_PRESS</th>\n",
              "      <th>AVG_CHOKE_SIZE_P</th>\n",
              "      <th>AVG_WHP_P</th>\n",
              "      <th>AVG_WHT_P</th>\n",
              "      <th>DP_CHOKE_SIZE</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "      <th>BORE_GAS_VOL</th>\n",
              "      <th>BORE_WAT_VOL</th>\n",
              "      <th>BORE_WI_VOL</th>\n",
              "      <th>FLOW_KIND</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-04-07</td>\n",
              "      <td>15/9-F-1 C</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-04-08</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>production</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-04-09</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-04-10</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-04-11</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>0.0</td>\n",
              "      <td>310.376</td>\n",
              "      <td>96.876</td>\n",
              "      <td>277.278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.21599</td>\n",
              "      <td>33.098</td>\n",
              "      <td>10.48</td>\n",
              "      <td>33.072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     DATEPRD NPD_WELL_BORE_NAME  ...  BORE_WI_VOL   FLOW_KIND\n",
              "0 2014-04-07         15/9-F-1 C  ...          NaN  production\n",
              "1 2014-04-08         15/9-F-1 C  ...          NaN  production\n",
              "2 2014-04-09         15/9-F-1 C  ...          NaN  production\n",
              "3 2014-04-10         15/9-F-1 C  ...          NaN  production\n",
              "4 2014-04-11         15/9-F-1 C  ...          NaN  production\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "tbu7Yj2PrO8w",
        "outputId": "7aa36d25-abf0-453b-9c62-b78ffba7ae30"
      },
      "source": [
        "# Select well 15/9-F-14 (Don't forget to reset index)\r\n",
        "df = df[df[\"NPD_WELL_BORE_NAME\"]==\"15/9-F-14\"].reset_index()\r\n",
        "\r\n",
        "# Dataframe only has 2 columns of interest: time and rate\r\n",
        "df = df[[\"DATEPRD\", \"BORE_OIL_VOL\"]]\r\n",
        "\r\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "        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>DATEPRD</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-02-12</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-02-13</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-02-14</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-02-15</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-02-16</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     DATEPRD  BORE_OIL_VOL\n",
              "0 2008-02-12           0.0\n",
              "1 2008-02-13           0.0\n",
              "2 2008-02-14           0.0\n",
              "3 2008-02-15           0.0\n",
              "4 2008-02-16           0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "NvZtac4Ytu5N",
        "outputId": "e782d17a-6324-42f8-eee7-3de65b3ed647"
      },
      "source": [
        "# Plot oil production rate\r\n",
        "plt.figure(figsize=(10,5))\r\n",
        "plt.step(df[\"DATEPRD\"], df[\"BORE_OIL_VOL\"])\r\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "vP6jek98xmKz",
        "outputId": "adbbbdce-43e0-457f-d8cf-b7c17c89dace"
      },
      "source": [
        "# Removing outliers (window=150, num_stdev=50)\r\n",
        "df2 = remove_outlier(df, \"BORE_OIL_VOL\", 50, 50, trim=True)\r\n",
        "\r\n",
        "df2.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>DATEPRD</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "      <th>BORE_OIL_VOL_rol_Av</th>\n",
              "      <th>BORE_OIL_VOL_rol_Std</th>\n",
              "      <th>BORE_OIL_VOL_is_Outlier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-11-01</td>\n",
              "      <td>4645.0</td>\n",
              "      <td>4016.40</td>\n",
              "      <td>593.445732</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-11-02</td>\n",
              "      <td>3732.0</td>\n",
              "      <td>4009.00</td>\n",
              "      <td>593.123727</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-11-03</td>\n",
              "      <td>3476.0</td>\n",
              "      <td>4001.48</td>\n",
              "      <td>592.919798</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-11-04</td>\n",
              "      <td>3461.0</td>\n",
              "      <td>3953.06</td>\n",
              "      <td>670.047802</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-11-05</td>\n",
              "      <td>3458.0</td>\n",
              "      <td>3930.94</td>\n",
              "      <td>679.680219</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     DATEPRD  BORE_OIL_VOL  ...  BORE_OIL_VOL_rol_Std  BORE_OIL_VOL_is_Outlier\n",
              "0 2008-11-01        4645.0  ...            593.445732                    False\n",
              "1 2008-11-02        3732.0  ...            593.123727                    False\n",
              "2 2008-11-03        3476.0  ...            592.919798                    False\n",
              "3 2008-11-04        3461.0  ...            670.047802                    False\n",
              "4 2008-11-05        3458.0  ...            679.680219                    False\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "77wz8q9V-vVu",
        "outputId": "80eaa1d4-4280-4489-f5e9-0da052b42941"
      },
      "source": [
        "# Plot outlier-removed oil production rate, compare w/ original\r\n",
        "plt.figure(figsize=(10,5))\r\n",
        "\r\n",
        "plt.step(df[\"DATEPRD\"], df[\"BORE_OIL_VOL\"], color=\"red\", label=\"Original data\")\r\n",
        "plt.step(df2[\"DATEPRD\"], df2[\"BORE_OIL_VOL_rol_Av\"], color=\"blue\", label=\"Outlier removed\")\r\n",
        "plt.xlabel(\"Year\"); plt.ylabel(\"Rate [STB/D]\")\r\n",
        "\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNt7rwTKw20j"
      },
      "source": [
        "# Define time and production rate (from outlier-removed dataframe)\r\n",
        "t = df2[\"DATEPRD\"]\r\n",
        "q = df2[\"BORE_OIL_VOL_rol_Av\"]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "vb3wV1b_yy0w",
        "outputId": "b444998c-2877-4f28-e42a-10c3736f013b"
      },
      "source": [
        "# Decline curve analysis\r\n",
        "arps_fit(t, q, plot=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial production rate (qi)  : 3835.95682 VOL/D\n",
            "Initial decline rate (di)     : 0.00080 VOL/D\n",
            "Decline coefficient (b)       : -0.28729\n",
            "RMSE of regression            : 0.00753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3835.956818560814,\n",
              " 0.0008015095854182389,\n",
              " -0.2872863626589032,\n",
              " 0.007526803769319763)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKI5TnYjdCDw"
      },
      "source": [
        "# x = np.array([-0.5, -0.25, -1, 0, 1, 0.25, 0.5])\r\n",
        "\r\n",
        "# def f(x, a, b, c):\r\n",
        "#   return (a*x**2) + (b*x) + c\r\n",
        "\r\n",
        "# y = f(x, 0.5, 0.8, 10)\r\n",
        "# noise = np.random.random(7) * 0.1\r\n",
        "# y = np.round((y+noise), 2)\r\n",
        "\r\n",
        "# [a,b,c], pcov = curve_fit(f, x, y)\r\n",
        "# print(a, b, c)\r\n",
        "\r\n",
        "# plt.scatter(x,y)\r\n",
        "# plt.show()"
      ],
      "execution_count": 13,
      "outputs": []
    }
  ]
}