Frequentist_Regression_With_TFP.ipynb

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    "colab": {
      "name": "Frequentist Regression With TFP",
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/Frequentist_Regression_With_TFP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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        "colab_type": "code",
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          "height": 170
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      "source": [
        "#@title Imports and Global Variables (run this cell first)  { display-mode: \"form\" }\n",
        "\"\"\"\n",
        "The book uses a custom matplotlibrc file, which provides the unique styles for\n",
        "matplotlib plots. If executing this book, and you wish to use the book's\n",
        "styling, provided are two options:\n",
        "    1. Overwrite your own matplotlibrc file with the rc-file provided in the\n",
        "       book's styles/ dir. See http://matplotlib.org/users/customizing.html\n",
        "    2. Also in the styles is  bmh_matplotlibrc.json file. This can be used to\n",
        "       update the styles in only this notebook. Try running the following code:\n",
        "\n",
        "        import json\n",
        "        s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n",
        "        matplotlib.rcParams.update(s)\n",
        "\"\"\"\n",
        "!pip3 install -q wget\n",
        "from __future__ import absolute_import, division, print_function\n",
        "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n",
        "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n",
        "import warnings\n",
        "warnings.filterwarnings(warning_status)\n",
        "with warnings.catch_warnings():\n",
        "    warnings.filterwarnings(warning_status, category=DeprecationWarning)\n",
        "    warnings.filterwarnings(warning_status, category=UserWarning)\n",
        "\n",
        "import numpy as np\n",
        "import os\n",
        "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n",
        "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n",
        "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n",
        "import matplotlib.axes as axes;\n",
        "from matplotlib.patches import Ellipse\n",
        "%matplotlib inline\n",
        "import seaborn as sns; sns.set_context('notebook')\n",
        "from IPython.core.pylabtools import figsize\n",
        "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n",
        "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n",
        "%config InlineBackend.figure_format = notebook_screen_res\n",
        "\n",
        "import tensorflow as tf\n",
        "tfe = tf.contrib.eager\n",
        "\n",
        "# Eager Execution\n",
        "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n",
        "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n",
        "use_tf_eager = False #@param {type:\"boolean\"}\n",
        "\n",
        "# Use try/except so we can easily re-execute the whole notebook.\n",
        "if use_tf_eager:\n",
        "    try:\n",
        "        tf.enable_eager_execution()\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "import tensorflow_probability as tfp\n",
        "tfd = tfp.distributions\n",
        "tfb = tfp.bijectors\n",
        "\n",
        "  \n",
        "def evaluate(tensors):\n",
        "    \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n",
        "    Args:\n",
        "    tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n",
        "        `namedtuple` or combinations thereof.\n",
        "\n",
        "    Returns:\n",
        "        ndarrays: Object with same structure as `tensors` except with `Tensor` or\n",
        "          `EagerTensor`s replaced by Numpy `ndarray`s.\n",
        "    \"\"\"\n",
        "    if tf.executing_eagerly():\n",
        "        return tf.contrib.framework.nest.pack_sequence_as(\n",
        "            tensors,\n",
        "            [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n",
        "             for t in tf.contrib.framework.nest.flatten(tensors)])\n",
        "    return sess.run(tensors)\n",
        "\n",
        "class _TFColor(object):\n",
        "    \"\"\"Enum of colors used in TF docs.\"\"\"\n",
        "    red = '#F15854'\n",
        "    blue = '#5DA5DA'\n",
        "    orange = '#FAA43A'\n",
        "    green = '#60BD68'\n",
        "    pink = '#F17CB0'\n",
        "    brown = '#B2912F'\n",
        "    purple = '#B276B2'\n",
        "    yellow = '#DECF3F'\n",
        "    gray = '#4D4D4D'\n",
        "    def __getitem__(self, i):\n",
        "        return [\n",
        "            self.red,\n",
        "            self.orange,\n",
        "            self.green,\n",
        "            self.blue,\n",
        "            self.pink,\n",
        "            self.brown,\n",
        "            self.purple,\n",
        "            self.yellow,\n",
        "            self.gray,\n",
        "        ][i % 9]\n",
        "TFColor = _TFColor()\n",
        "\n",
        "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n",
        "    \"\"\"\n",
        "    Allowing the notebook to make use of GPUs if they're available.\n",
        "    \n",
        "    XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n",
        "    algebra that optimizes TensorFlow computations.\n",
        "    \"\"\"\n",
        "    config = tf.ConfigProto()\n",
        "    config.log_device_placement = True\n",
        "    if enable_gpu_ram_resizing:\n",
        "        # `allow_growth=True` makes it possible to connect multiple colabs to your\n",
        "        # GPU. Otherwise the colab malloc's all GPU ram.\n",
        "        config.gpu_options.allow_growth = True\n",
        "    if enable_xla:\n",
        "        # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n",
        "        config.graph_options.optimizer_options.global_jit_level = (\n",
        "            tf.OptimizerOptions.ON_1)\n",
        "    return config\n",
        "\n",
        "\n",
        "def reset_sess(config=None):\n",
        "    \"\"\"\n",
        "    Convenience function to create the TF graph & session or reset them.\n",
        "    \"\"\"\n",
        "    if config is None:\n",
        "        config = session_options()\n",
        "    global sess\n",
        "    tf.reset_default_graph()\n",
        "    try:\n",
        "        sess.close()\n",
        "    except:\n",
        "        pass\n",
        "    sess = tf.InteractiveSession(config=config)\n",
        "\n",
        "reset_sess()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEz94a0p2piT",
        "colab_type": "text"
      },
      "source": [
        "# Frequentist or Standard Linear Regression: The Market Model\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "An asset's Market Model (MM) is a standard time series regression of the the realized excess returns of the asset against the realized excess returns of the market. Excess returns are defined as returns in excess of some benchmark such as the risk-free rate. Model is formulated as:\n",
        "\n",
        "\n",
        "**(R - F) = 𝛂 + 𝛃 (M - F) + 𝛔**\n",
        "\n",
        "Y = (R - F) is the outcome variable,  X = (M - F) is the predictor variable, R is the realized return of a stock, F is the return on a risk-free asset (US treasury bill) , M is the realized return of a market portfolio (S&P 500), 𝛼 (alpha) is the expected stock-specific return, 𝛽 (beta) is the level of systematic risk exposure to the market, 𝛔 (sigma) is the unexpected stock-specific return\n",
        "\n",
        "\n",
        "\n",
        "Note that an asset's MM is different from its Capital Asset Pricing Model (CAPM). Unlike the CAPM, an asset's MM has both an idiosyncratic risk term 𝛼 and an error term 𝛔 in its formulation. However, the systematic risk term beta of the asset is the same as the one calculated using the CAPM. The expected value of 𝛔 is zero. That is the reason it does not appear in the CAPM which formulates expected returns.\n",
        "\n",
        "According to the CAPM which subscribes to the efficient market hypothesis, the intercept, or 𝛼, of an asset's MM has an expected value of zero. The asset's realized positive/(negative) 𝛼 shows that the market participants under priced/(overpriced) the security during the holding period.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nr5YXRO8AcUO",
        "colab_type": "text"
      },
      "source": [
        "# Import your data\n",
        "\n",
        "We use the following free data sources to get the daily closing prices of various assets:\n",
        "\n",
        "Risk free rate: https://fred.stlouisfed.org/series/TB4WK\n",
        "\n",
        "Stock and market data: https://finance.yahoo.com"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emzfHCFA2gpb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "810b0b55-c183-449f-cf2d-68c4f03a68de"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "import pandas_datareader.data as pdr\n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn')\n",
        "from datetime import datetime\n",
        "\n",
        "\n",
        "\n",
        "start = datetime(2018, 9, 13)\n",
        "end = datetime(2019, 9, 13)\n",
        "\n",
        "riskfree_rate = pdr.DataReader('TB4WK', 'fred', start, end)\n",
        "market = pdr.get_data_yahoo('SPY', start, end)\n",
        "stock = pdr.get_data_yahoo('AAPL', start, end)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hwNh44e3v_w",
        "colab_type": "text"
      },
      "source": [
        "# Explore your data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Au_-qqDL3c4C",
        "colab_type": "code",
        "outputId": "f2a34872-35bd-492b-f469-d4c92e54425b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "riskfree_rate = pdr.DataReader('TB4WK', 'fred', start, end)\n",
        "riskfree_scaling = 28 #Need to scale the risk free rate by its maturity to get the daily risk free rate\n",
        "riskfree_rate = riskfree_rate['TB4WK'].dropna()/riskfree_scaling\n",
        "riskfree_rate.plot(), plt.title('Daily Risk Free Rate'), plt.show()\n",
        "\n",
        "\n",
        "market_return = market['Adj Close'].pct_change(1)*100\n",
        "market_return = market_return.dropna()\n",
        "plt.hist(market_return), plt.title('Distribution of Market (SPY) Daily Returns'), plt.xlabel('Daily Percentage Returns'), plt.ylabel('Frequency'), plt.show()\n",
        "print(\"Descriptive Stastics of the Market's daily percentage returns:\\n{}\".format(market_return.describe()))\n",
        "\n",
        "stock_return = stock['Adj Close'].pct_change(1)*100\n",
        "stock_return = stock_return.dropna()\n",
        "plt.hist(stock_return), plt.title('Distribution of Stock (AAPL) Daily Returns'), plt.xlabel('Daily Percentage Returns'), plt.ylabel('Frequency'), plt.show()\n",
        "print(\"Descriptive stastics of Apple stock's percentage returns:\\n{}\".format(stock_return.describe()))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+8vX1rfH6iIgISdLu3burbQEdFxcnSeZKlMpql+Li4ho/q6SkRCUllwbV9e/f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PvJ0pkscXFx+ueff9SoUSM5OTlZbDOZTPL29tb69et1+vRpubu7pxu/b98+SVL9+vXT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\nu+/q6aef1okTJ/TDDz/o//yf/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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 556,
              "height": 369
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fe++9TJgwIXfccUdeffXVtG7dOgcccEBOP/30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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 548,
              "height": 358
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Descriptive Stastics of the Market's daily percentage returns:\n",
            "count    250.000000\n",
            "mean       0.027516\n",
            "std        1.054556\n",
            "min       -3.240233\n",
            "25%       -0.385208\n",
            "50%        0.050687\n",
            "75%        0.623282\n",
            "max        5.052480\n",
            "Name: Adj Close, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lly7p9OnTatiwYU67CgBA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\nMEPazQP+OSQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 548,
              "height": 358
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Descriptive stastics of Apple stock's percentage returns:\n",
            "count    250.000000\n",
            "mean       0.020877\n",
            "std        2.026331\n",
            "min       -9.960742\n",
            "25%       -0.910270\n",
            "50%        0.115801\n",
            "75%        1.115335\n",
            "max        7.042150\n",
            "Name: Adj Close, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdArmpIB46fX",
        "colab_type": "text"
      },
      "source": [
        "# Build your linear regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4ZCQGnG4l5J",
        "colab_type": "code",
        "outputId": "531a54e0-392e-4942-db45-e81eb62b40bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "source": [
        "#AAPL's Market Model\n",
        "y = stock_return - riskfree_rate.mean()\n",
        "x = market_return - riskfree_rate.mean()\n",
        "plt.scatter(x,y)\n",
        "x = sm.add_constant(x)\n",
        "market_model = sm.OLS(y, x).fit()\n",
        "plt.plot(x, x*market_model.params[1]+market_model.params[0])\n",
        "plt.title('Market Model of AAPL'), plt.xlabel('SPY Daily Excess Returns'), plt.ylabel('AAPL Daily Excess Returns'), plt.show();"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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48Alny7Hl6NNfNmjnuR8Na0c1H6ohTfuVm2VJ1qwcvbrqkGLiUmQv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\nUFCtigaq2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oqKWL9+PQ4ODhbLhqwd383QqVOnazaerq+v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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 562,
              "height": 358
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtcWEW9xkEzF",
        "colab_type": "text"
      },
      "source": [
        "# Analyze your results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFEm39LfkLKF",
        "colab_type": "code",
        "outputId": "3343ff88-6126-44a0-80dc-72c4975f1e13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "print(\"According to AAPL's Market Model, the security had a realized Alpha of {0}% and Beta of {1}%\".format(round(market_model.params[0],2), round(market_model.params[1],2)))\n",
        "print(\"The Market Model of AAPL is summarized below:\\n{}\".format(market_model.summary()));"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "According to AAPL's Market Model, the security had a realized Alpha of 0.02% and Beta of 1.52%\n",
            "The Market Model of AAPL is summarized below:\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Adj Close   R-squared:                       0.625\n",
            "Model:                            OLS   Adj. R-squared:                  0.624\n",
            "Method:                 Least Squares   F-statistic:                     413.4\n",
            "Date:                Mon, 16 Sep 2019   Prob (F-statistic):           9.61e-55\n",
            "Time:                        18:46:58   Log-Likelihood:                -408.18\n",
            "No. Observations:                 250   AIC:                             820.4\n",
            "Df Residuals:                     248   BIC:                             827.4\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          0.0210      0.079      0.267      0.790      -0.134       0.176\n",
            "Adj Close      1.5191      0.075     20.331      0.000       1.372       1.666\n",
            "==============================================================================\n",
            "Omnibus:                       42.593   Durbin-Watson:                   1.827\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              395.399\n",
            "Skew:                          -0.183   Prob(JB):                     1.38e-86\n",
            "Kurtosis:                       9.150   Cond. No.                         1.08\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Fz3Yo8I6x4R",
        "colab_type": "text"
      },
      "source": [
        "# Build your linear regression model using TensorFlow Probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvMgVBW47V6f",
        "colab_type": "code",
        "outputId": "9145d661-b0cd-42bb-a3ec-378f0cf05b9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "stock_return_vals_ = stock_return.values\n",
        "stock_return_vals =  tf.convert_to_tensor(stock_return_vals_, dtype=tf.float32)\n",
        "market_return_vals_ = market_return.values\n",
        "market_return_vals = tf.convert_to_tensor(market_return_vals_, dtype=tf.float32)\n",
        "\n",
        "# Specify model.\n",
        "model = tfp.glm.Normal()\n",
        "\n",
        "# Fit model given data.\n",
        "coeffs, linear_response, is_converged, num_iter = tfp.glm.fit(\n",
        "    model_matrix=market_return_vals[:, tf.newaxis],\n",
        "    response=stock_return_vals,\n",
        "    model=model)\n",
        "\n",
        "mean_linear_response = tf.reduce_mean(linear_response)\n",
        "\n",
        "# Execute graph\n",
        "[   \n",
        "    coeffs_,\n",
        "    mean_linear_response_,\n",
        "    linear_response_\n",
        "] = evaluate (\n",
        "[\n",
        "    coeffs,\n",
        "    mean_linear_response,\n",
        "    linear_response,\n",
        "])\n",
        "print(\"beta=\",coeffs_)\n",
        "print(\"daily average return=\", mean_linear_response_)\n",
        "plt.scatter(market_return,stock_return) \n",
        "plt.plot(market_return_vals_,linear_response_,color='red')\n",
        "plt.xlabel('SPY Daily Excess Returns'), plt.ylabel('AAPL Daily Excess Returns'), plt.show();"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/glm/fisher_scoring.py:352: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "beta= [1.5185828]\n",
            "daily average return= 0.041785184\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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TpwyNXLgWbfi9hmjR8l4MFoFMB8M/Nog1/CIFrMPnDdEslm4+rf6suRs9Qa00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\nE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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 562,
              "height": 344
            }
          }
        }
      ]
    }
  ]
}