brent_predictor.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN71igwPAy2MDioDzo5fpta",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AjmalSarwary/BRENT-Model/blob/main/brent_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kDDYN6n7q5q",
        "outputId": "31d91381-08c7-4b1b-b826-b20590e05774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/my_packages')\n",
        "import api_alphavantage as av"
      ],
      "metadata": {
        "id": "l-VV33HX_7zw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UyqYP65d0og3"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import json\n",
        "import pandas as pd\n",
        "\n",
        "def alphavantage_get( symbol,dfrom, dto,interval,api_key):\n",
        "    \n",
        "    '''retrieves time series of prices for function in range dfrom to dto at interval with api_key'''\n",
        "    \n",
        "    interval = interval\n",
        "    dfrom=dfrom\n",
        "    dto=dto\n",
        "    api_key=api_key\n",
        "    \n",
        "    url = f'https://www.alphavantage.co/query?function={symbol}&interval={interval}&apikey={api_key}&outputsize=full'\n",
        "    \n",
        "    r = requests.get(url)\n",
        "    data = r.json()\n",
        "    \n",
        "    df = pd.DataFrame(data)\n",
        "    \n",
        "    df['date'] = df['data'].apply(lambda x: x['date'])\n",
        "    df['value'] = df['data'].apply(lambda x: x['value'])\n",
        "\n",
        "    df.drop('data', axis=1, inplace=True)\n",
        "    df['date'] = pd.to_datetime(df['date'])\n",
        "    df.set_index('date', inplace=True)\n",
        "\n",
        "    # filter data to specified time period\n",
        "    df = df[(df.index >= dfrom) & (df.index <= dto)]\n",
        "\n",
        "    #error message suggested that there is a string '.' ; ->locate and remove string\n",
        "    df = df[df['value'] != '.']\n",
        "    \n",
        "    df.value = df['value'].astype(float)\n",
        "    \n",
        "    return df\n",
        "    \n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def moving_avs(dataframe):\n",
        "    \n",
        "    df=dataframe\n",
        "    \n",
        "    # make sure type is correct\n",
        "    df['value'] = df.value.astype(float)\n",
        "    \n",
        "    # calculate weekly moving average\n",
        "    weekly_ma = df['value'].rolling('7D').mean()\n",
        "    df['weekly_ma'] = weekly_ma\n",
        "\n",
        "    # calculate quaterly moving average\n",
        "    yearly_ma = df['value'].rolling('365D').mean()\n",
        "    df['yearly_ma'] = yearly_ma\n",
        "    \n",
        "    return df"
      ],
      "metadata": {
        "id": "R03eX0NTB65e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot dataframe\n",
        "import plotly.express as px\n",
        "import plotly.graph_objs as go\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "def plotter(dataframe,symbol, moving=0,amount='', footnote=''):\n",
        "    \n",
        "    df=dataframe\n",
        "    \n",
        "    # plot the data\n",
        "    \n",
        "    \n",
        "    \n",
        "    if moving == 1:\n",
        "\n",
        "       # create a new DataFrame with the moving averages\n",
        "\n",
        "        df_ma = pd.concat([df['value'], df.weekly_ma, df.yearly_ma], axis=1)\n",
        "\n",
        "        # Create the plot\n",
        "        fig = go.Figure()\n",
        "        fig.add_trace(go.Scatter(x=df_ma.index, y=df_ma['weekly_ma'], name='Weekly Moving Average', opacity=0.3))\n",
        "        fig.add_trace(go.Scatter(x=df_ma.index, y=df_ma['yearly_ma'], name='Yearly Moving Average'))\n",
        "\n",
        "        # title and axis labels\n",
        "        fig.update_layout(title=symbol, xaxis_title='Date', yaxis_title=f'in {amount} (USD)', \n",
        "                          legend_title='Moving Average', plot_bgcolor='  #cce6ff',paper_bgcolor=' #e6f2ff')\n",
        "\n",
        "        # footnote\n",
        "        fig.add_annotation(xref='paper', yref='paper', x=-0.1, y=-0.17, text=footnote, showarrow=False,  font=dict(\n",
        "                family='Arial',\n",
        "                size=9,\n",
        "                \n",
        "            ))\n",
        "\n",
        "        \n",
        "        fig.show()\n",
        "        \n",
        "    else: \n",
        "\n",
        "        fig = go.Figure() \n",
        "        fig.add_trace(go.Scatter(x=dataframe.index, y=dataframe.value, name='Global GDP'))\n",
        "        fig.update_layout(title=symbol, xaxis_title='Date', yaxis_title=f'in {amount} (USD)', \n",
        "                          legend_title='symbol', plot_bgcolor='  #cce6ff',paper_bgcolor=' #e6f2ff')\n",
        "        fig.add_annotation(xref='paper', yref='paper', x=-0.1, y=-0.17, text=footnote, showarrow=False,  font=dict(\n",
        "                family='Arial',\n",
        "                size=9,\n",
        "                \n",
        "            ))\n",
        "        fig.show()\n"
      ],
      "metadata": {
        "id": "FBM5IVlG4rCI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#alphavantage_get entries\n",
        "#call alphavantage_get(symbol,dfrom,dto, intervel, api_key)\n",
        "\n",
        "#api key\n",
        "api_key = av\n",
        "\n",
        "#symbol\n",
        "symbols=['BRENT','ALL_COMMODITIES']\n",
        "\n",
        "# daily interval\n",
        "interval = 'daily' \n",
        "\n",
        "#time period\n",
        "start_date = '2005-01-01'\n",
        "end_date = '2015-12-31'\n"
      ],
      "metadata": {
        "id": "FZauzajf0xh6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get all_commodidities from alphavantage\n",
        "comod = alphavantage_get(symbols[1], start_date, end_date, interval, api_key)\n",
        "comod = moving_avs(comod)\n",
        "amount=''\n",
        "footnote='Source: International Monetary Fund (IMF Terms of Use), Global Price Index of All Commodities, retrieved from FRED, Federal Reserve Bank of St. Louiss; data accessed through AlphaVantage API.'\n",
        "plotter(comod,'GLOBAL COMMODITIES',1,amount,footnote)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "YxTxPgmR4u35",
        "outputId": "cbd6e1b1-57c8-46e8-8567-277dd3ff90b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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COMMODITIES\"},\"xaxis\":{\"title\":{\"text\":\"Date\"}},\"yaxis\":{\"title\":{\"text\":\"in  (USD)\"}},\"legend\":{\"title\":{\"text\":\"Moving Average\"}},\"plot_bgcolor\":\"  #cce6ff\",\"paper_bgcolor\":\" #e6f2ff\",\"annotations\":[{\"font\":{\"family\":\"Arial\",\"size\":9},\"showarrow\":false,\"text\":\"Source: International Monetary Fund (IMF Terms of Use), Global Price Index of All Commodities, retrieved from FRED, Federal Reserve Bank of St. Louiss; data accessed through AlphaVantage API.\",\"x\":-0.1,\"xref\":\"paper\",\"y\":-0.17,\"yref\":\"paper\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
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    {
      "cell_type": "code",
      "source": [
        "#get brent data from alphavantage and plot\n",
        "brent = alphavantage_get(symbols[0], start_date, end_date, interval, api_key)\n",
        "brent = moving_avs(brent)\n",
        "amount=''\n",
        "footnote='Source: U.S. Energy Information Administration, Crude Oil Prices: Brent - Europe, retrieved from FRED, Federal Reserve Bank of St. Louis; Alpha Vantage API, accessed on 2023-04-05.'\n",
        "plotter(brent,'BRENT-Europe',1,amount, footnote)"
      ],
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          "height": 542
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        "id": "5A_CCSw8Cdcm",
        "outputId": "4cae729e-db94-4f73-afc5-1319be41f987"
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      "execution_count": null,
      "outputs": [
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 (USD)\"}},\"legend\":{\"title\":{\"text\":\"Moving Average\"}},\"plot_bgcolor\":\"  #cce6ff\",\"paper_bgcolor\":\" #e6f2ff\",\"annotations\":[{\"font\":{\"family\":\"Arial\",\"size\":9},\"showarrow\":false,\"text\":\"Source: U.S. Energy Information Administration, Crude Oil Prices: Brent - Europe, retrieved from FRED, Federal Reserve Bank of St. Louis; Alpha Vantage API, accessed on 2023-04-05.\",\"x\":-0.1,\"xref\":\"paper\",\"y\":-0.17,\"yref\":\"paper\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('fa913c92-e3da-405a-b315-6cb54337e1e0');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/drive/MyDrive/datasets"
      ],
      "metadata": {
        "id": "u_oq0FhOD94P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import os\n",
        "\n",
        "# Create directory if it doesn't exist\n",
        "if not os.path.exists('/content/drive/MyDrive/datasets'):\n",
        "    os.mkdir('/content/drive/MyDrive/datasets')\n",
        "\n",
        "# Upload file\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Get filename and content of uploaded file\n",
        "filename, content = next(iter(uploaded.items()))\n",
        "\n",
        "# Write content to file\n",
        "with open('/content/drive/MyDrive/datasets/' + filename, 'wb') as f:\n",
        "    f.write(content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "d-MEoPH6Ci10",
        "outputId": "ec294482-0d0b-4d0d-c14a-0a72e36579d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9902664b-4b53-4a57-be84-6e3a8f849fd1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9902664b-4b53-4a57-be84-6e3a8f849fd1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving gdp_wb_2005_2015.csv to gdp_wb_2005_2015 (6).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "ggdp = pd.read_csv('/content/drive/MyDrive/datasets/gdp_wb_2005_2015.csv')\n",
        "\n",
        "#Transform and format global gdp\n",
        "\n",
        "ggdp['date']=ggdp['Year']\n",
        "ggdp=ggdp.drop(columns=['Year'])\n",
        "ggdp.set_index('date', inplace=True)\n",
        "\n",
        "ggdp['value']=np.divide(ggdp['\\tGDP (current US$)'],1e11)\n",
        "ggdp = ggdp.drop(columns=['\\tGDP (current US$)'])\n",
        "\n",
        "ggdp.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "pYglyq59GWv3",
        "outputId": "0bfc6332-87d0-4ba4-a0e0-c358f6583e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          value\n",
              "date           \n",
              "2005  48.136604"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5118391-c4dd-4d92-b011-8ee7230ddf31\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>48.136604</td>\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "amount='Trillion'\n",
        "footnote='Source: World Bank'\n",
        "plotter(ggdp, 'Global GDP',0,amount, footnote )"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "1YWlvI4pHyIz",
        "outputId": "deb904ec-0a08-4e28-e116-f6cff80c2f1e"
      },
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
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        "# Neuer Abschnitt"
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      "metadata": {
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    }
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}