Evaluate_with_error_interval_10samp_VAEmodel_GRU2x32_encoding16_DR5_ps_SDSS_QSO_balanced_lcsorg.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dq6DGgWETPyH",
        "outputId": "eacb654f-eedf-42df-da3b-46272f4a2801"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/a2/5ccf0a418eb22e0a2ae9edc1e7f5456d0a4b8b49524572897564b4030a9b/tensorflow_gpu-2.5.0-cp37-cp37m-manylinux2010_x86_64.whl (454.3MB)\n",
            "\u001b[K     |████████████████████████████████| 454.3MB 22kB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorboard~=2.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu) (2.5.0)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow-gpu) (3.0.4)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.5->tensorflow-gpu) (3.4.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow-gpu) (3.1.0)\n",
            "Installing collected packages: tensorflow-gpu\n",
            "Successfully installed tensorflow-gpu-2.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oowr-f5XTibb",
        "outputId": "ba6f7f73-1c16-4102-df34-f7c8c9fd51a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "print(tf.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtwCKpWDuSQZ",
        "outputId": "a514872e-136c-4cc5-8510-16708e67fff1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount(\"/content/drive\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZI7bbrRATieU"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "import sys\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import tensorflow.keras as keras\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bzzo0JutTiiW"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import Sequential\n",
        "import tensorflow.keras.backend as K\n",
        "from keras.layers import (\n",
        "    Input,\n",
        "    Dense,\n",
        "    Lambda,\n",
        "    TimeDistributed,\n",
        "    LSTM,\n",
        "    GRU,\n",
        "    Dropout,\n",
        "    merge,\n",
        "    Concatenate,\n",
        "    Flatten,\n",
        "    RepeatVector,\n",
        "    Bidirectional,\n",
        "    SimpleRNN,\n",
        ")\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.activations import relu, sigmoid\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCl0rn0DTik5",
        "outputId": "13216f05-3b7d-4d76-b71f-c415d148fb61"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  \"\"\"\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.callbacks import (\n",
        "    Callback,\n",
        "    TensorBoard,\n",
        "    EarlyStopping,\n",
        "    ModelCheckpoint,\n",
        "    CSVLogger,\n",
        "    ProgbarLogger,\n",
        ")\n",
        "import sys\n",
        "import csv\n",
        "from collections import Iterable, OrderedDict\n",
        "import datetime\n",
        "from sklearn.manifold import TSNE\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBp_n5uxTsMx",
        "outputId": "10a2d777-7715-4323-df7a-f17c7075a6b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount(\"/content/drive\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itzx4iVkT1bS"
      },
      "source": [
        "Reading AGN light curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U2JJklxD2MoM"
      },
      "outputs": [],
      "source": [
        "# reading list of lcs already saved\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_final_sample_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_largechi2_noblazar_sample_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomalous_noblazar_var_sample_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/Frederick_Ricci_targets_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomalous_NLS1_sample_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_csagncand_sample_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "# lcs= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomQSO_IFscore_lcs_gband_lcs_list.npy',allow_pickle=True)\n",
        "lcs = np.load(\n",
        "    \"/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_CSAGN_cand_recent_gband_lcs_list.npy\",\n",
        "    allow_pickle=True,\n",
        ")\n",
        "\n",
        "# reading list of oids already saved\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_final_sample_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_largechi2_noblazar_sample_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomalous_noblazar_var_sample_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/Frederick_Ricci_targets_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomalous_NLS1_sample_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_csagncand_sample_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "# oids_fids= np.load('/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_anomQSO_IFscore_lcs_gband_oid_list.npy',allow_pickle=True)\n",
        "oids_fids = np.load(\n",
        "    \"/content/drive/Shareddrives/Stargaze/datasets/ps_DR5_data/DR5_VAE_32_16_CSAGN_cand_recent_gband_oid_list.npy\",\n",
        "    allow_pickle=True,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCpyadZhYbV0",
        "outputId": "aa775147-88de-4219-ca84-0a723ae6fbb4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12\n"
          ]
        }
      ],
      "source": [
        "print(len(oids_fids))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PViLGo0WloJZ"
      },
      "outputs": [],
      "source": [
        "#lcs = lcs[120000:150000]\n",
        "#oids_fids = oids_fids[120000:150000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSigeLmNTxRC",
        "outputId": "d61fb05e-d85e-419e-bdd3-312e7a844d57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[451103200005537]\n",
            " [495111100005833]\n",
            " [566112100004425]\n",
            " [646113100002570]\n",
            " [648101400002016]\n",
            " [649113400004567]\n",
            " [674113200005713]\n",
            " [678115400001961]\n",
            " [693106300007466]\n",
            " [712112100003746]]\n"
          ]
        }
      ],
      "source": [
        "print(oids_fids[0:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hq_V_20PUKxc"
      },
      "outputs": [],
      "source": [
        "# padding the lcs\n",
        "lcs_raw = pad_sequences(lcs, value=np.nan, dtype=\"float\", padding=\"post\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoRZmyjmX4_p"
      },
      "source": [
        "**Preprocess for the input data:** \n",
        "\n",
        "MJD -> $\\Delta$MJD/365.0\n",
        "\n",
        "mag -> [mag - mean(mag)]/std(mag)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GSicY9NxUKuy"
      },
      "outputs": [],
      "source": [
        "def times_to_lags(T):\n",
        "    \"\"\"(N x n_step) matrix of times -> (N x n_step) matrix of lags.\n",
        "    First time is assumed to be zero.\n",
        "    \"\"\"\n",
        "    assert T.ndim == 2, \"T must be an (N x n_step) matrix\"\n",
        "    return np.c_[np.diff(T, axis=1) / 365.0, np.zeros(T.shape[0])]\n",
        "\n",
        "\n",
        "def preprocess(X_raw, m_max=np.inf):\n",
        "    X = X_raw.copy()\n",
        "    print(X.shape)\n",
        "    wrong_units = np.all(np.isnan(X[:, :, 1])) | (np.nanmax(X[:, :, 1], axis=1) > m_max)\n",
        "    X = X[~wrong_units, :, :]\n",
        "    X[:, :, 0] = times_to_lags(X[:, :, 0])\n",
        "    means = np.atleast_2d(np.nanmean(X[:, :, 1], axis=1)).T\n",
        "    X[:, :, 1] -= means\n",
        "    scales = np.atleast_2d(np.nanstd(X[:, :, 1], axis=1)).T\n",
        "    X[:, :, 1] /= scales\n",
        "    errors = X[:, :, 2] / scales\n",
        "    X = X[:, :, :2]\n",
        "    print(X.shape)\n",
        "    return X, means, scales, errors, wrong_units\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2tV8sG_UKsE",
        "outputId": "492485d4-ea27-40a9-ae63-8ad5f861b83f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(12, 553, 3)\n",
            "(12, 553, 2)\n"
          ]
        }
      ],
      "source": [
        "lcs_scaled, means, scales, errors, wrong_units = preprocess(lcs_raw)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-M-V0rlQwP9u",
        "outputId": "d42aea5e-7811-4fa9-d743-55f03a9cdbb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.0274376  -1.32299392]\n",
            " [ 0.01639758 -1.22907791]\n",
            " [ 0.00816169 -1.32114143]\n",
            " ...\n",
            " [        nan         nan]\n",
            " [        nan         nan]\n",
            " [ 0.                 nan]]\n"
          ]
        }
      ],
      "source": [
        "print(lcs_scaled[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EamNQcdpws09",
        "outputId": "67d60a15-8b92-44d2-f80d-0501c7d3a6ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[5.83044632e+04 1.77298660e+01 2.63450723e-02]\n",
            "  [5.83144779e+04 1.77579117e+01 2.67319083e-02]\n",
            "  [5.83204630e+04 1.77304192e+01 2.63526049e-02]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]\n",
            "\n",
            " [[5.82854420e+04 2.07285671e+01 1.67948842e-01]\n",
            "  [5.82914413e+04 2.08288326e+01 1.75302744e-01]\n",
            "  [5.82954452e+04 2.05564804e+01 1.55327201e-01]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]\n",
            "\n",
            " [[5.82051526e+04 1.83085155e+01 2.89182924e-02]\n",
            "  [5.82271864e+04 1.83583660e+01 2.98638903e-02]\n",
            "  [5.82351972e+04 1.83525734e+01 2.97518689e-02]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[5.82022764e+04 1.91161423e+01 6.09129816e-02]\n",
            "  [5.82052526e+04 1.90119324e+01 5.65505326e-02]\n",
            "  [5.82052715e+04 1.90686569e+01 5.88799305e-02]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]\n",
            "\n",
            " [[5.82023522e+04 1.86012840e+01 4.15020250e-02]\n",
            "  [5.82023580e+04 1.84944553e+01 3.85308228e-02]\n",
            "  [5.82023797e+04 1.85092716e+01 3.89264077e-02]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]\n",
            "\n",
            " [[5.82033936e+04 1.99617767e+01 1.21098943e-01]\n",
            "  [5.82034042e+04 1.97318878e+01 1.03340052e-01]\n",
            "  [5.82044149e+04 1.96667957e+01 9.86799896e-02]\n",
            "  ...\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]\n",
            "  [           nan            nan            nan]]]\n"
          ]
        }
      ],
      "source": [
        "print(lcs_raw)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_WNSpKRif36C"
      },
      "outputs": [],
      "source": [
        "main_input = Input(shape=(lcs_scaled.shape[1], 2), name=\"main_input\")  # (lag, mag)\n",
        "aux_input = Input(shape=(lcs_scaled.shape[1], 1), name=\"aux_input\")  # (lag)\n",
        "model_input = [main_input, aux_input]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ar51AzPlf7y5"
      },
      "outputs": [],
      "source": [
        "sample_weight = 1.0 / errors\n",
        "sample_weight[np.isnan(sample_weight)] = 0.0\n",
        "lcs_scaled[np.isnan(lcs_scaled)] = 0.0\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bL7utdlaYC8D"
      },
      "source": [
        "##Reading the VAE model \n",
        "\n",
        "- Use the following cells to read the model weights from the local folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDci4EvGxcjE"
      },
      "outputs": [],
      "source": [
        "def sampling(samp_args):\n",
        "    z_mean, z_log_sigma = samp_args\n",
        "\n",
        "    batch = K.shape(z_mean)[0]\n",
        "    dim = K.int_shape(z_mean)[1]\n",
        "    # by default, random_normal has mean = 0 and std = 1.0\n",
        "    epsilon = K.random_normal(shape=(batch, dim))\n",
        "    return z_mean + K.exp(0.5 * z_log_sigma) * epsilon\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivKczcQOYIWE",
        "outputId": "9776aa57-b777-456e-9430-7524de5bd5a9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        }
      ],
      "source": [
        "lr = 1e-4  # learning rate\n",
        "optimizer = Adam(lr=lr)\n",
        "output_size = 16\n",
        "gru_size = 32\n",
        "nepochs = 2000\n",
        "batchsize = 512\n",
        "dropout_val = 0.25\n",
        "\n",
        "\n",
        "# encoder\n",
        "encoder = Bidirectional(GRU(gru_size, name=\"encoder1\", return_sequences=True))(\n",
        "    main_input\n",
        ")\n",
        "encoder = Dropout(dropout_val, name=\"drop_encoder1\")(encoder)\n",
        "encoder = Bidirectional(GRU(gru_size, name=\"encoder2\", return_sequences=False))(encoder)\n",
        "encoder = Dropout(dropout_val, name=\"drop_encoder2\")(encoder)\n",
        "codings_mean = Dense(units=output_size, name=\"encoding_mean\", activation=\"linear\")(\n",
        "    encoder\n",
        ")\n",
        "codings_log_var = Dense(\n",
        "    units=output_size, name=\"encoding_log_var\", activation=\"linear\"\n",
        ")(encoder)\n",
        "codings = Lambda(sampling, output_shape=(output_size,))([codings_mean, codings_log_var])\n",
        "\n",
        "\n",
        "# decoder\n",
        "decoder = RepeatVector(lcs_scaled.shape[1], name=\"repeat\")(codings)\n",
        "decoder = merge.concatenate([aux_input, decoder])\n",
        "decoder = GRU(gru_size, name=\"decoder1\", return_sequences=True)(decoder)\n",
        "decoder = Dropout(dropout_val, name=\"drop_decoder1\")(decoder)\n",
        "decoder = GRU(gru_size, name=\"decoder2\", return_sequences=True)(decoder)\n",
        "decoder = TimeDistributed(Dense(1, activation=\"linear\"), name=\"time_dist\")(decoder)\n",
        "\n",
        "# VAE\n",
        "model = Model(model_input, decoder)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JV3ZMI3wYmih",
        "outputId": "cc88b426-c73a-4ec1-d727-ef0d6f43179b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        }
      ],
      "source": [
        "log_dir = \"/content/drive/MyDrive/CSAGNs/models_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/\"\n",
        "weights_path = os.path.join(log_dir, \"weights_lr1e3_4paper.h5\")\n",
        "\n",
        "lr = 1e-3  # learning rate\n",
        "optimizer = Adam(lr=lr)\n",
        "\n",
        "model.load_weights(weights_path)\n",
        "\n",
        "latent_loss = -0.5 * K.sum(\n",
        "    1 + codings_log_var - K.exp(codings_log_var) - K.square(codings_mean), axis=-1\n",
        ")\n",
        "model.add_loss(K.mean(latent_loss) / 200.0)\n",
        "model.compile(\n",
        "    optimizer=optimizer,\n",
        "    loss=\"mse\",\n",
        "    metrics=[tf.keras.metrics.MeanAbsoluteError()],\n",
        "    weighted_metrics=[tf.keras.metrics.MeanAbsoluteError()],\n",
        "    sample_weight_mode=\"temporal\",\n",
        ")  # ,run_eagerly=True)\n",
        "\n",
        "# model.compile(optimizer=optimizer, loss='mse',  sample_weight_mode='temporal')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jRTM8xIxy1U",
        "outputId": "0c3bf3fa-d813-43df-dca8-beb3ba921e79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "main_input (InputLayer)         [(None, 553, 2)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional (Bidirectional)   (None, 553, 64)      6912        main_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "drop_encoder1 (Dropout)         (None, 553, 64)      0           bidirectional[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_1 (Bidirectional) (None, 64)           18816       drop_encoder1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "drop_encoder2 (Dropout)         (None, 64)           0           bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "encoding_mean (Dense)           (None, 16)           1040        drop_encoder2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "encoding_log_var (Dense)        (None, 16)           1040        drop_encoder2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "lambda (Lambda)                 (None, 16)           0           encoding_mean[0][0]              \n",
            "                                                                 encoding_log_var[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "aux_input (InputLayer)          [(None, 553, 1)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "repeat (RepeatVector)           (None, 553, 16)      0           lambda[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 553, 17)      0           aux_input[0][0]                  \n",
            "                                                                 repeat[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "decoder1 (GRU)                  (None, 553, 32)      4896        concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "drop_decoder1 (Dropout)         (None, 553, 32)      0           decoder1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "decoder2 (GRU)                  (None, 553, 32)      6336        drop_decoder1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "time_dist (TimeDistributed)     (None, 553, 1)       33          decoder2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add (TFOpLambd (None, 16)           0           encoding_log_var[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.exp (TFOpLambda)        (None, 16)           0           encoding_log_var[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda)   (None, 16)           0           tf.__operators__.add[0][0]       \n",
            "                                                                 tf.math.exp[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.square (TFOpLambda)     (None, 16)           0           encoding_mean[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.subtract_1 (TFOpLambda) (None, 16)           0           tf.math.subtract[0][0]           \n",
            "                                                                 tf.math.square[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.reduce_sum (TFOpLambda) (None,)              0           tf.math.subtract_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.multiply (TFOpLambda)   (None,)              0           tf.math.reduce_sum[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.reduce_mean (TFOpLambda ()                   0           tf.math.multiply[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda)    ()                   0           tf.math.reduce_mean[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "add_loss (AddLoss)              ()                   0           tf.math.truediv[0][0]            \n",
            "==================================================================================================\n",
            "Total params: 39,073\n",
            "Trainable params: 39,073\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYTuVJBEZJci"
      },
      "source": [
        "Dividing the model into the encoding and the decoding parts, and getting encoded features and decoded light curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IQWgFXsdYmXc"
      },
      "outputs": [],
      "source": [
        "decode_model = Model(model.input, model.output)\n",
        "\n",
        "encode_mean = Model(model.input, model.get_layer(\"encoding_mean\").output)\n",
        "encode_log_var = Model(model.input, model.get_layer(\"encoding_log_var\").output)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OrZbUXs3DWpx"
      },
      "outputs": [],
      "source": [
        "encoding_mean = encode_mean.predict(\n",
        "    {\"main_input\": lcs_scaled, \"aux_input\": np.delete(lcs_scaled, 1, axis=2)}\n",
        ")\n",
        "encoding_log_var = encode_log_var.predict(\n",
        "    {\"main_input\": lcs_scaled, \"aux_input\": np.delete(lcs_scaled, 1, axis=2)}\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qxb5ZxY8M6Zk",
        "outputId": "02d65a31-1491-45b5-cafe-a3825779975d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".........."
          ]
        }
      ],
      "source": [
        "decoding_10 = []\n",
        "\n",
        "for i in range(10):\n",
        "    decoding_aux = decode_model.predict(\n",
        "        {\"main_input\": lcs_scaled, \"aux_input\": np.delete(lcs_scaled, 1, axis=2)}\n",
        "    )\n",
        "    decoding_10.append(decoding_aux)\n",
        "\n",
        "    # if i%10 == 0:\n",
        "    print(\".\", end=\"\")\n",
        "\n",
        "decoding_10 = np.array(decoding_10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KQcsaNMCQoPf"
      },
      "outputs": [],
      "source": [
        "decoding_median = np.nanmedian(decoding_10, axis=0)\n",
        "decoding_mean = np.nanmean(decoding_10, axis=0)\n",
        "decoding_std = np.nanstd(decoding_10, axis=0)\n",
        "decoding_low = np.nanmin(decoding_10, axis=0)\n",
        "decoding_high = np.nanmax(decoding_10, axis=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQ2SUAaGVts_",
        "outputId": "c4d8b688-e4b4-49a7-91d1-e00db3e9e7c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12\n"
          ]
        }
      ],
      "source": [
        "print(len(decoding_mean))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtdEEXJ-xVky",
        "outputId": "5ce328a6-6532-47d1-ec3c-57b602c9d3a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[-1.3452377 ]\n",
            "  [-1.2819171 ]\n",
            "  [-1.1893532 ]\n",
            "  ...\n",
            "  [ 0.4037822 ]\n",
            "  [ 0.40285158]\n",
            "  [ 0.40189585]]\n",
            "\n",
            " [[ 0.95807916]\n",
            "  [ 1.0807153 ]\n",
            "  [ 1.0773938 ]\n",
            "  ...\n",
            "  [ 0.63667834]\n",
            "  [ 0.635362  ]\n",
            "  [ 0.6339913 ]]\n",
            "\n",
            " [[ 1.1794708 ]\n",
            "  [ 1.9041517 ]\n",
            "  [ 1.9443327 ]\n",
            "  ...\n",
            "  [ 0.64011186]\n",
            "  [ 0.6418788 ]\n",
            "  [ 0.64364433]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 0.99415624]\n",
            "  [ 1.2933311 ]\n",
            "  [ 1.3490678 ]\n",
            "  ...\n",
            "  [ 0.4375817 ]\n",
            "  [ 0.43816406]\n",
            "  [ 0.43882266]]\n",
            "\n",
            " [[ 0.70596933]\n",
            "  [ 0.5706465 ]\n",
            "  [ 0.5692497 ]\n",
            "  ...\n",
            "  [-1.538688  ]\n",
            "  [-1.5326986 ]\n",
            "  [-1.5262944 ]]\n",
            "\n",
            " [[-0.46400413]\n",
            "  [-0.4706745 ]\n",
            "  [-0.46229973]\n",
            "  ...\n",
            "  [ 0.66359365]\n",
            "  [ 0.6728534 ]\n",
            "  [ 0.67904305]]]\n"
          ]
        }
      ],
      "source": [
        "print(decoding_mean)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xfGaBXOZWJD"
      },
      "source": [
        "Computing reduced chi-square distribution "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBikZ5NEZVx8",
        "outputId": "238132cd-7b95-4d9e-f195-d467b6d53ccd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Caluculating reduced chi-square for each source\n",
            "."
          ]
        }
      ],
      "source": [
        "def calc_redchisq(x, x_pred, weight, x_pred_err):\n",
        "    mask = ~np.isnan(weight)\n",
        "    out = np.sum(\n",
        "        (((x[mask] - x_pred[mask])) ** 2) / (weight[mask] ** -2 + x_pred_err[mask] ** 2)\n",
        "    ) / len(weight[mask])\n",
        "    return out\n",
        "\n",
        "\n",
        "RedChiSq = []\n",
        "print(\"Caluculating reduced chi-square for each source\")\n",
        "for m in range(0, len(lcs_raw)):\n",
        "    if m % 100 == 0:\n",
        "        print(\".\", end=\"\")\n",
        "    RedChiSq.append(\n",
        "        calc_redchisq(\n",
        "            lcs_scaled[m][:, 1] * scales[m] + means[m],\n",
        "            np.squeeze(decoding_mean[m] * scales[m] + means[m]),\n",
        "            1 / lcs_raw[m][:, 2],\n",
        "            np.squeeze(decoding_std[m] * scales[m]),\n",
        "        )\n",
        "    )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cit_s8Jpzz_l",
        "outputId": "6ea8721b-2afd-4625-ece0-dd1053c502f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2.741926403227124, 2.690126018881052, 3.146674972394374, 1.1276919689786755, 1.701624086692311, 1.1414807549166295, 3.786827609950153, 18.390550368965272, 2.983600174339058, 1.891484241045782, 4.831880155000056, 1.319464847285751]\n"
          ]
        }
      ],
      "source": [
        "print(RedChiSq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "HAsQA65KZtBz",
        "outputId": "1738e843-4af7-4237-e522-b842cca89082"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 5))\n",
        "plt.hist(np.array(RedChiSq)[~np.isnan(RedChiSq)], bins=10 ** np.arange(-1, 3, 0.1))\n",
        "plt.xscale(\"log\")\n",
        "plt.xlabel(r\"$\\chi_{{\\rm red}}^2$\", fontsize=15)\n",
        "plt.ylabel(\"Number of sources\", fontsize=15)\n",
        "plt.title(\"full dataset\", fontsize=15)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_oOSVaEaAc6"
      },
      "source": [
        " Example of sources showing a large $\\chi_{\\rm red}^2$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YX6-zgJDZs2N"
      },
      "outputs": [],
      "source": [
        "len_list = []\n",
        "for i in range(0, len(lcs)):\n",
        "    len_list.append(len(lcs[i]))\n",
        "\n",
        "chi_mask = np.array(RedChiSq) >= 0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8rYzJltCaD3a",
        "outputId": "a0ca3f39-e366-49e9-ebef-82fd4007fbf2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.hist(np.array(len_list)[~chi_mask], bins=np.arange(50, 800, 5), alpha=0.5)\n",
        "plt.hist(np.array(len_list)[chi_mask], bins=np.arange(50, 800, 5), alpha=0.5)\n",
        "plt.xlabel(\"Data Length\", fontsize=15)\n",
        "plt.ylabel(\"Number of sources\", fontsize=15)\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.scatter(len_list, RedChiSq, alpha=1, s=0.25, color=\"black\")\n",
        "plt.xlabel(\"Data Length\", fontsize=15)\n",
        "plt.ylabel(r\"$\\chi_{{\\rm red}}^2$\", fontsize=15)\n",
        "plt.ylim(1e-1, 1e3)\n",
        "plt.yscale(\"log\")\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.scatter(\n",
        "    np.nanmean(lcs_raw[:, :, 1], axis=1), RedChiSq, alpha=1, s=0.25, color=\"black\"\n",
        ")\n",
        "plt.xlabel(\"Magnitude\", fontsize=15)\n",
        "plt.ylabel(r\"$\\chi_{{\\rm red}}^2$\", fontsize=15)\n",
        "plt.ylim(1e-1, 1e3)\n",
        "plt.xlim(16, 21)\n",
        "plt.yscale(\"log\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wxeVw82KaErp"
      },
      "outputs": [],
      "source": [
        "len_mask = (np.array(len_list) >= 50) & (np.array(len_list) < 1200)\n",
        "bad_fitting_mask = len_mask & chi_mask\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TGzFC67OeysD"
      },
      "outputs": [],
      "source": [
        "oids_all = np.array(oids_fids)[:, 0]\n",
        "# print(oids_all)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otWFVBpUNXgj",
        "outputId": "8ead2db8-e323-4be4-a546-f4726607bf96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12\n"
          ]
        }
      ],
      "source": [
        "encoding_val = encoding_mean[bad_fitting_mask]\n",
        "decoding_val_mean = decoding_mean[bad_fitting_mask]\n",
        "decoding_val_std = decoding_std[bad_fitting_mask]\n",
        "\n",
        "decoding_val_median = decoding_median[bad_fitting_mask]\n",
        "decoding_val_low = decoding_low[bad_fitting_mask]\n",
        "decoding_val_high = decoding_high[bad_fitting_mask]\n",
        "\n",
        "X_raw_val = lcs_raw[bad_fitting_mask]\n",
        "X_val = lcs_scaled[bad_fitting_mask]\n",
        "scales_val = scales[bad_fitting_mask]\n",
        "means_val = means[bad_fitting_mask]\n",
        "oids_val = oids_all[bad_fitting_mask]\n",
        "print(len(encoding_val))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZKaZ7GRBc2t",
        "outputId": "d50a9ff2-8cec-45f9-9dfa-4e8501ac734b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[451103200005537 495111100005833 566112100004425 646113100002570\n",
            " 648101400002016 649113400004567 674113200005713 678115400001961\n",
            " 693106300007466 712112100003746 719114100002677 795106100002111]\n"
          ]
        }
      ],
      "source": [
        "print(oids_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "QhCnJVY4PoBq",
        "outputId": "81929540-b59f-4d71-854f-3523aa4325b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "451103200005537\n",
            "495111100005833\n",
            "566112100004425\n",
            "646113100002570\n",
            "648101400002016\n",
            "649113400004567\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1728x576 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fignum = 6  # 6,3\n",
        "i = 0\n",
        "k = 0\n",
        "fig = plt.figure(\n",
        "    figsize=(24, 8)\n",
        ")  # plt.figure(figsize=(11,4))#plt.figure(figsize=(25,9))\n",
        "\n",
        "# ids =  [3,4,9,0,1,2]#[0,1,2,3,4,5]#np.random.randint(0,10000, size=6)\n",
        "# ids2 =  [5,6,7,8,10,11]#[6,7,8,9,10,11]#np.random.randint(0,10000, size=6)\n",
        "\n",
        "# plotting raw-lightcurves and the decoded lightcurves\n",
        "# for num in [3,4,9]:\n",
        "for num in range(i, i + fignum):\n",
        "    ax = fig.add_subplot(2, fignum, k + 1)\n",
        "    # print(X_raw_val[num][:,0])\n",
        "    # print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\n",
        "    print(oids_val[num])\n",
        "    # print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\n",
        "    ax.set_title(oids_val[num], fontsize=20)\n",
        "    ax.errorbar(\n",
        "        X_raw_val[num][:, 0],\n",
        "        X_raw_val[num][:, 1],\n",
        "        yerr=X_raw_val[num][:, 2],\n",
        "        fmt=\"o\",\n",
        "        color=\"black\",\n",
        "        ms=3,\n",
        "    )\n",
        "    ax.errorbar(\n",
        "        365.0 * np.cumsum(X_val[num][:, 0][X_val[num][:, 0] > 0])\n",
        "        + X_raw_val[num][0, 0],\n",
        "        decoding_val_mean[num][X_val[num][:, 0] > 0] * scales_val[num] + means_val[num],\n",
        "        yerr=(decoding_val_std[num][X_val[num][:, 0] > 0] * scales_val[num]).flatten(),\n",
        "        fmt=\".\",\n",
        "        color=\"red\",\n",
        "        ms=3,\n",
        "    )\n",
        "    ax.fill_between(\n",
        "        365.0 * np.cumsum(X_val[num][:, 0][X_val[num][:, 0] > 0])\n",
        "        + X_raw_val[num][0, 0],\n",
        "        (\n",
        "            decoding_val_low[num][X_val[num][:, 0] > 0] * scales_val[num]\n",
        "            + means_val[num]\n",
        "        ).flatten(),\n",
        "        (\n",
        "            decoding_val_high[num][X_val[num][:, 0] > 0] * scales_val[num]\n",
        "            + means_val[num]\n",
        "        ).flatten(),\n",
        "        alpha=0.2,\n",
        "        facecolor=\"#D42F4B\",\n",
        "    )\n",
        "    ax.set_ylim(plt.ylim()[::-1])\n",
        "    ax.set_xlabel(\"MJD\", fontsize=20)\n",
        "    ax.set_ylabel(\"magnitude\", fontsize=20)\n",
        "    ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n",
        "\n",
        "    k += 1\n",
        "\n",
        "i = fignum\n",
        "\n",
        "# for num in ids2:\n",
        "for num in range(i, i + fignum):\n",
        "    ax = fig.add_subplot(2, fignum, k + 1)\n",
        "    # print(X_raw_val[num][:,0])\n",
        "    # print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\n",
        "    # print(oids_val[num])\n",
        "    # print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\n",
        "    ax.set_title(oids_val[num], fontsize=20)\n",
        "    ax.errorbar(\n",
        "        X_raw_val[num][:, 0],\n",
        "        X_raw_val[num][:, 1],\n",
        "        yerr=X_raw_val[num][:, 2],\n",
        "        fmt=\"o\",\n",
        "        color=\"black\",\n",
        "        ms=3,\n",
        "    )\n",
        "    ax.errorbar(\n",
        "        365.0 * np.cumsum(X_val[num][:, 0][X_val[num][:, 0] > 0])\n",
        "        + X_raw_val[num][0, 0],\n",
        "        decoding_val_mean[num][X_val[num][:, 0] > 0] * scales_val[num] + means_val[num],\n",
        "        yerr=(decoding_val_std[num][X_val[num][:, 0] > 0] * scales_val[num]).flatten(),\n",
        "        fmt=\".\",\n",
        "        color=\"red\",\n",
        "        ms=3,\n",
        "    )\n",
        "    ax.fill_between(\n",
        "        365.0 * np.cumsum(X_val[num][:, 0][X_val[num][:, 0] > 0])\n",
        "        + X_raw_val[num][0, 0],\n",
        "        (\n",
        "            decoding_val_low[num][X_val[num][:, 0] > 0] * scales_val[num]\n",
        "            + means_val[num]\n",
        "        ).flatten(),\n",
        "        (\n",
        "            decoding_val_high[num][X_val[num][:, 0] > 0] * scales_val[num]\n",
        "            + means_val[num]\n",
        "        ).flatten(),\n",
        "        alpha=0.2,\n",
        "        facecolor=\"#D42F4B\",\n",
        "    )\n",
        "    ax.set_ylim(plt.ylim()[::-1])\n",
        "    ax.set_xlabel(\"MJD\", fontsize=20)\n",
        "    ax.set_ylabel(\"magnitude\", fontsize=20)\n",
        "    ax.tick_params(axis=\"both\", which=\"major\", labelsize=15)\n",
        "\n",
        "    k += 1\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\n",
        "    \"/content/drive/MyDrive/CSAGNs/plots_4paper/recent_sdss_spec_sample_modeling.pdf\"\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "wIJ5lpDx_2N4",
        "outputId": "78513ba3-a26b-4dda-d0a2-0ccc41865c73"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'oid_ZTF = [\"ZTF19abvgxrq\",\"ZTF19aaiqmgl\",\"ZTF18aaabltn\",\"ZTF18aaabltn\",\"ZTF19aatubsj\",\"ZTF18aaidlyq\",\\n           \"ZTF18aahiqfi\",\"ZTF18aajupnt\",\"ZTF18aasszwr\",\"ZTF18aasuray\",\"ZTF18abjjkeo\"]\\n\\nfignum = 6\\ni = 0\\nk=0\\nfig = plt.figure(figsize=(25, 9))\\n\\n\\n#plotting raw-lightcurves and the decoded lightcurves \\nfor num in range(i,i+fignum):\\n    ax= fig.add_subplot(2, fignum, k+1)\\n    #print(X_raw_val[num][:,0])\\n    #print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\\n    #print(oids_val[num])\\n    #print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\\n    ax.set_title(oid_ZTF[num],fontsize=20)\\n    ax.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=3)\\n    ax.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    ax.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    ax.set_ylim(plt.ylim()[::-1])\\n    #ax.set_xlabel(\\'MJD\\',fontsize=20)\\n    if k==0: ax.set_ylabel(\\'magnitude\\',fontsize=18)\\n    ax.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    \\n    k += 1\\n\\n\\n#fig = plt.figure(figsize=(18, 4))\\n\\ni = fignum\\n#fignum = 5\\n\\n#k=0\\nfor num in range(i,i+fignum):\\n  try:\\n    ax= fig.add_subplot(2, fignum, k+1)\\n    print(oids_val[num])\\n    ax.set_title(oid_ZTF[num],fontsize=20)\\n    ax.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=3)\\n    ax.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n            decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    ax.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    ax.set_ylim(plt.ylim()[::-1])\\n    ax.set_xlabel(\\'MJD\\',fontsize=18)\\n    if k==6: ax.set_ylabel(\\'magnitude\\',fontsize=18)\\n    ax.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n  except: \\n    print(\"not plot\")\\n    ax= fig.add_subplot(2, fignum, k+1)\\n    fig.delaxes(ax)\\n\\nplt.tight_layout()\\n\\nplt.savefig(\\'/content/drive/MyDrive/CSAGNs/plots_4paper/Frederick_sample_modeling.pdf\\')\\n'"
            ]
          },
          "execution_count": 39,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''oid_ZTF = [\"ZTF19abvgxrq\",\"ZTF19aaiqmgl\",\"ZTF18aaabltn\",\"ZTF18aaabltn\",\"ZTF19aatubsj\",\"ZTF18aaidlyq\",\n",
        "           \"ZTF18aahiqfi\",\"ZTF18aajupnt\",\"ZTF18aasszwr\",\"ZTF18aasuray\",\"ZTF18abjjkeo\"]\n",
        "\n",
        "fignum = 6\n",
        "i = 0\n",
        "k=0\n",
        "fig = plt.figure(figsize=(25, 9))\n",
        "\n",
        "\n",
        "#plotting raw-lightcurves and the decoded lightcurves \n",
        "for num in range(i,i+fignum):\n",
        "    ax= fig.add_subplot(2, fignum, k+1)\n",
        "    #print(X_raw_val[num][:,0])\n",
        "    #print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\n",
        "    #print(oids_val[num])\n",
        "    #print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\n",
        "    ax.set_title(oid_ZTF[num],fontsize=20)\n",
        "    ax.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=3)\n",
        "    ax.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    ax.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    ax.set_ylim(plt.ylim()[::-1])\n",
        "    #ax.set_xlabel('MJD',fontsize=20)\n",
        "    if k==0: ax.set_ylabel('magnitude',fontsize=18)\n",
        "    ax.tick_params(axis='both', which='major', labelsize=14)\n",
        "    \n",
        "    k += 1\n",
        "\n",
        "\n",
        "#fig = plt.figure(figsize=(18, 4))\n",
        "\n",
        "i = fignum\n",
        "#fignum = 5\n",
        "\n",
        "#k=0\n",
        "for num in range(i,i+fignum):\n",
        "  try:\n",
        "    ax= fig.add_subplot(2, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    ax.set_title(oid_ZTF[num],fontsize=20)\n",
        "    ax.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=3)\n",
        "    ax.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "            decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    ax.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    ax.set_ylim(plt.ylim()[::-1])\n",
        "    ax.set_xlabel('MJD',fontsize=18)\n",
        "    if k==6: ax.set_ylabel('magnitude',fontsize=18)\n",
        "    ax.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "  except: \n",
        "    print(\"not plot\")\n",
        "    ax= fig.add_subplot(2, fignum, k+1)\n",
        "    fig.delaxes(ax)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('/content/drive/MyDrive/CSAGNs/plots_4paper/Frederick_sample_modeling.pdf')\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "HmDKdFHzaEg7",
        "outputId": "e08d441d-e535-4e18-a9c6-4c6da9cdca34"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'i = 49*20 #init num of the plotting data\\nk=0 #initialize the counter\\nfignum = 7\\n#plt.figure(figsize=(20, 17))\\nplt.figure(figsize=(20, 20))\\n\\n#plotting raw-lightcurves and the decoded lightcurves \\nfor num in range(i,i+fignum):\\n    plt.subplot(7, fignum, k+1)\\n    #print(X_raw_val[num][:,0])\\n    #print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\\n    print(oids_val[num])\\n    #print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\\n    #print(len(X_raw_val[num][:,0][X_raw_val[num][:, 0]>0]))\\n    #print(len(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0]))\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    if k==0 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n#k=0\\nfor num in range(i+fignum,i+2*fignum):\\n    plt.subplot(7, fignum, k+1)\\n    print(oids_val[num])\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    if k==0+fignum*1 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n\\nfor num in range(i+2*fignum,i+3*fignum):\\n    plt.subplot(7, fignum, k+1)\\n    print(oids_val[num])\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    if k==0+fignum*2 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n\\nfor num in range(i+3*fignum,i+4*fignum):\\n    plt.subplot(7, fignum, k+1)\\n    print(oids_val[num])\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    if k==0+fignum*3 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n\\nfor num in range(i+4*fignum,i+5*fignum):\\n    plt.subplot(7, fignum, k+1)\\n    print(oids_val[num])\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    if k==0+fignum*4 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    k += 1\\n\\nfor num in range(i+5*fignum,i+6*fignum):\\n    plt.subplot(7, fignum, k+1)\\n    print(oids_val[num])\\n    plt.title(oids_val[num],fontsize=16)\\n    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n    plt.ylim(plt.ylim()[::-1])\\n    #plt.xlabel(\\'MJD\\',fontsize=16)\\n    plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n    if k==0+fignum*5 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n    k += 1\\n\\n\\nfor num in range(i+6*fignum,i+7*fignum):\\n    try:\\n      plt.subplot(7, fignum, k+1)\\n      print(oids_val[num])\\n      plt.title(oids_val[num],fontsize=16)\\n      plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt=\\'o\\', color=\\'black\\', ms=5)\\n      plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \\n              decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt=\\'.\\', color=\\'red\\', ms=3)\\n      plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor=\\'#D42F4B\\')\\n\\n      plt.xlabel(\\'MJD\\',fontsize=16)\\n      if k==0+fignum*6 : plt.ylabel(\\'magnitude\\',fontsize=16)\\n      plt.ylim(plt.ylim()[::-1])\\n      plt.tick_params(axis=\\'both\\', which=\\'major\\', labelsize=14)\\n\\n      k += 1\\n\\n\\n\\n    except: \\n      print(\"not plot\")\\n      ax= fig.add_subplot(2, fignum, k+1)\\n      fig.delaxes(ax)\\n\\n\\n#plt.tight_layout()\\n#plt.savefig(\\'/content/drive/MyDrive/CSAGNs/plots_4paper/CSAGNcand_classQ_sample_modeling.pdf\\')\\n'"
            ]
          },
          "execution_count": 40,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "'''i = 49*20 #init num of the plotting data\n",
        "k=0 #initialize the counter\n",
        "fignum = 7\n",
        "#plt.figure(figsize=(20, 17))\n",
        "plt.figure(figsize=(20, 20))\n",
        "\n",
        "#plotting raw-lightcurves and the decoded lightcurves \n",
        "for num in range(i,i+fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    #print(X_raw_val[num][:,0])\n",
        "    #print(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0]))\n",
        "    print(oids_val[num])\n",
        "    #print((decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten())\n",
        "    #print(len(X_raw_val[num][:,0][X_raw_val[num][:, 0]>0]))\n",
        "    #print(len(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0]))\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    if k==0 : plt.ylabel('magnitude',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "#k=0\n",
        "for num in range(i+fignum,i+2*fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    if k==0+fignum*1 : plt.ylabel('magnitude',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "\n",
        "for num in range(i+2*fignum,i+3*fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    if k==0+fignum*2 : plt.ylabel('magnitude',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "\n",
        "for num in range(i+3*fignum,i+4*fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    if k==0+fignum*3 : plt.ylabel('magnitude',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "\n",
        "for num in range(i+4*fignum,i+5*fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    if k==0+fignum*4 : plt.ylabel('magnitude',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    k += 1\n",
        "\n",
        "for num in range(i+5*fignum,i+6*fignum):\n",
        "    plt.subplot(7, fignum, k+1)\n",
        "    print(oids_val[num])\n",
        "    plt.title(oids_val[num],fontsize=16)\n",
        "    plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "    plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "             decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "    plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "    plt.ylim(plt.ylim()[::-1])\n",
        "    #plt.xlabel('MJD',fontsize=16)\n",
        "    plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "    if k==0+fignum*5 : plt.ylabel('magnitude',fontsize=16)\n",
        "    k += 1\n",
        "\n",
        "\n",
        "for num in range(i+6*fignum,i+7*fignum):\n",
        "    try:\n",
        "      plt.subplot(7, fignum, k+1)\n",
        "      print(oids_val[num])\n",
        "      plt.title(oids_val[num],fontsize=16)\n",
        "      plt.errorbar(X_raw_val[num][:,0], X_raw_val[num][:,1], yerr=X_raw_val[num][:, 2], fmt='o', color='black', ms=5)\n",
        "      plt.errorbar(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], \n",
        "              decoding_val_mean[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num], yerr=(decoding_val_std[num][X_val[num][:, 0]>0]*scales_val[num]).flatten(), fmt='.', color='red', ms=3)\n",
        "      plt.fill_between(365.0*np.cumsum(X_val[num][:, 0][X_val[num][:, 0]>0])+X_raw_val[num][0,0], (decoding_val_low[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(), (decoding_val_high[num][X_val[num][:, 0]>0]*scales_val[num]+means_val[num]).flatten(),alpha=0.2,  facecolor='#D42F4B')\n",
        "\n",
        "      plt.xlabel('MJD',fontsize=16)\n",
        "      if k==0+fignum*6 : plt.ylabel('magnitude',fontsize=16)\n",
        "      plt.ylim(plt.ylim()[::-1])\n",
        "      plt.tick_params(axis='both', which='major', labelsize=14)\n",
        "\n",
        "      k += 1\n",
        "\n",
        "\n",
        "\n",
        "    except: \n",
        "      print(\"not plot\")\n",
        "      ax= fig.add_subplot(2, fignum, k+1)\n",
        "      fig.delaxes(ax)\n",
        "\n",
        "\n",
        "#plt.tight_layout()\n",
        "#plt.savefig('/content/drive/MyDrive/CSAGNs/plots_4paper/CSAGNcand_classQ_sample_modeling.pdf')\n",
        "'''\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8G3eaWTMV2qW",
        "outputId": "3f3bd734-2120-48a1-94d3-245fa1413822"
      },
      "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>e1</th>\n",
              "      <th>e2</th>\n",
              "      <th>e3</th>\n",
              "      <th>e4</th>\n",
              "      <th>e5</th>\n",
              "      <th>e6</th>\n",
              "      <th>e7</th>\n",
              "      <th>e8</th>\n",
              "      <th>e9</th>\n",
              "      <th>e10</th>\n",
              "      <th>e11</th>\n",
              "      <th>e12</th>\n",
              "      <th>e13</th>\n",
              "      <th>e14</th>\n",
              "      <th>e15</th>\n",
              "      <th>e16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.003238</td>\n",
              "      <td>-0.005945</td>\n",
              "      <td>0.203569</td>\n",
              "      <td>-2.184880</td>\n",
              "      <td>0.182565</td>\n",
              "      <td>-0.013606</td>\n",
              "      <td>0.033957</td>\n",
              "      <td>-0.000652</td>\n",
              "      <td>0.023302</td>\n",
              "      <td>-0.008659</td>\n",
              "      <td>0.018037</td>\n",
              "      <td>0.001849</td>\n",
              "      <td>0.032394</td>\n",
              "      <td>-0.497517</td>\n",
              "      <td>1.249906</td>\n",
              "      <td>-0.003231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.014179</td>\n",
              "      <td>-0.024504</td>\n",
              "      <td>1.759378</td>\n",
              "      <td>2.591482</td>\n",
              "      <td>-0.637879</td>\n",
              "      <td>-0.012524</td>\n",
              "      <td>-0.021837</td>\n",
              "      <td>-0.013343</td>\n",
              "      <td>-0.009206</td>\n",
              "      <td>0.004661</td>\n",
              "      <td>-0.016846</td>\n",
              "      <td>-0.014839</td>\n",
              "      <td>-0.004596</td>\n",
              "      <td>-0.462419</td>\n",
              "      <td>1.283101</td>\n",
              "      <td>0.022255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.002479</td>\n",
              "      <td>0.015126</td>\n",
              "      <td>-1.061464</td>\n",
              "      <td>2.895471</td>\n",
              "      <td>0.093969</td>\n",
              "      <td>0.032638</td>\n",
              "      <td>-0.039563</td>\n",
              "      <td>0.003740</td>\n",
              "      <td>0.006295</td>\n",
              "      <td>-0.022272</td>\n",
              "      <td>-0.010591</td>\n",
              "      <td>-0.018227</td>\n",
              "      <td>-0.030199</td>\n",
              "      <td>1.344322</td>\n",
              "      <td>0.375402</td>\n",
              "      <td>0.029542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.008021</td>\n",
              "      <td>0.037203</td>\n",
              "      <td>-1.457215</td>\n",
              "      <td>1.022100</td>\n",
              "      <td>0.669599</td>\n",
              "      <td>-0.003150</td>\n",
              "      <td>0.017671</td>\n",
              "      <td>-0.020746</td>\n",
              "      <td>0.020022</td>\n",
              "      <td>-0.037045</td>\n",
              "      <td>0.042614</td>\n",
              "      <td>-0.047913</td>\n",
              "      <td>0.018157</td>\n",
              "      <td>-2.161340</td>\n",
              "      <td>-1.603281</td>\n",
              "      <td>-0.024957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.008218</td>\n",
              "      <td>-0.019403</td>\n",
              "      <td>0.780225</td>\n",
              "      <td>-1.027650</td>\n",
              "      <td>0.157358</td>\n",
              "      <td>0.030963</td>\n",
              "      <td>-0.027564</td>\n",
              "      <td>0.026140</td>\n",
              "      <td>-0.011220</td>\n",
              "      <td>0.030438</td>\n",
              "      <td>-0.048784</td>\n",
              "      <td>0.051884</td>\n",
              "      <td>-0.025386</td>\n",
              "      <td>3.148975</td>\n",
              "      <td>0.291604</td>\n",
              "      <td>0.016504</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         e1        e2        e3  ...       e14       e15       e16\n",
              "0 -0.003238 -0.005945  0.203569  ... -0.497517  1.249906 -0.003231\n",
              "1 -0.014179 -0.024504  1.759378  ... -0.462419  1.283101  0.022255\n",
              "2 -0.002479  0.015126 -1.061464  ...  1.344322  0.375402  0.029542\n",
              "3  0.008021  0.037203 -1.457215  ... -2.161340 -1.603281 -0.024957\n",
              "4 -0.008218 -0.019403  0.780225  ...  3.148975  0.291604  0.016504\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_enc_feats = pd.DataFrame(\n",
        "    data=encoding_mean,\n",
        "    columns=[\n",
        "        \"e1\",\n",
        "        \"e2\",\n",
        "        \"e3\",\n",
        "        \"e4\",\n",
        "        \"e5\",\n",
        "        \"e6\",\n",
        "        \"e7\",\n",
        "        \"e8\",\n",
        "        \"e9\",\n",
        "        \"e10\",\n",
        "        \"e11\",\n",
        "        \"e12\",\n",
        "        \"e13\",\n",
        "        \"e14\",\n",
        "        \"e15\",\n",
        "        \"e16\",\n",
        "    ],\n",
        ")\n",
        "df_enc_feats.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "7LhtDgwjc94I",
        "outputId": "edf77686-2581-42a0-dd25-dcaa0ec3ec48"
      },
      "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>logsig_e1</th>\n",
              "      <th>logsig_e2</th>\n",
              "      <th>logsig_e3</th>\n",
              "      <th>logsig_e4</th>\n",
              "      <th>logsig_e5</th>\n",
              "      <th>logsig_e6</th>\n",
              "      <th>logsig_e7</th>\n",
              "      <th>logsig_e8</th>\n",
              "      <th>logsig_e9</th>\n",
              "      <th>logsig_e10</th>\n",
              "      <th>logsig_e11</th>\n",
              "      <th>logsig_e12</th>\n",
              "      <th>logsig_e13</th>\n",
              "      <th>logsig_e14</th>\n",
              "      <th>logsig_e15</th>\n",
              "      <th>logsig_e16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.009750</td>\n",
              "      <td>-0.001927</td>\n",
              "      <td>-1.982220</td>\n",
              "      <td>-2.803640</td>\n",
              "      <td>-1.220382</td>\n",
              "      <td>0.011671</td>\n",
              "      <td>-0.007001</td>\n",
              "      <td>-0.004826</td>\n",
              "      <td>-0.017337</td>\n",
              "      <td>0.005236</td>\n",
              "      <td>-0.008808</td>\n",
              "      <td>-0.012787</td>\n",
              "      <td>-0.003548</td>\n",
              "      <td>-2.868975</td>\n",
              "      <td>-1.656902</td>\n",
              "      <td>-0.006962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000517</td>\n",
              "      <td>-0.041285</td>\n",
              "      <td>-2.221286</td>\n",
              "      <td>-2.926428</td>\n",
              "      <td>-1.336514</td>\n",
              "      <td>-0.008277</td>\n",
              "      <td>0.001206</td>\n",
              "      <td>0.004362</td>\n",
              "      <td>-0.020893</td>\n",
              "      <td>0.000684</td>\n",
              "      <td>-0.005988</td>\n",
              "      <td>-0.006581</td>\n",
              "      <td>-0.015820</td>\n",
              "      <td>-3.371349</td>\n",
              "      <td>-1.877479</td>\n",
              "      <td>-0.010791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.006289</td>\n",
              "      <td>-0.036124</td>\n",
              "      <td>-2.070144</td>\n",
              "      <td>-2.535569</td>\n",
              "      <td>-1.099438</td>\n",
              "      <td>-0.012016</td>\n",
              "      <td>0.004499</td>\n",
              "      <td>-0.001076</td>\n",
              "      <td>-0.018655</td>\n",
              "      <td>0.002451</td>\n",
              "      <td>-0.011246</td>\n",
              "      <td>0.000394</td>\n",
              "      <td>-0.014110</td>\n",
              "      <td>-3.179582</td>\n",
              "      <td>-1.751832</td>\n",
              "      <td>-0.017672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.018940</td>\n",
              "      <td>-0.040134</td>\n",
              "      <td>-2.724202</td>\n",
              "      <td>-4.039315</td>\n",
              "      <td>-1.797777</td>\n",
              "      <td>-0.010147</td>\n",
              "      <td>-0.030347</td>\n",
              "      <td>-0.012361</td>\n",
              "      <td>-0.009419</td>\n",
              "      <td>-0.034386</td>\n",
              "      <td>-0.012681</td>\n",
              "      <td>-0.007657</td>\n",
              "      <td>-0.017454</td>\n",
              "      <td>-3.701325</td>\n",
              "      <td>-2.283098</td>\n",
              "      <td>-0.038544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.002227</td>\n",
              "      <td>-0.018310</td>\n",
              "      <td>-2.396154</td>\n",
              "      <td>-4.473517</td>\n",
              "      <td>-1.663883</td>\n",
              "      <td>-0.015179</td>\n",
              "      <td>-0.006202</td>\n",
              "      <td>-0.016414</td>\n",
              "      <td>-0.048740</td>\n",
              "      <td>-0.002328</td>\n",
              "      <td>-0.028130</td>\n",
              "      <td>-0.041824</td>\n",
              "      <td>-0.012484</td>\n",
              "      <td>-3.227398</td>\n",
              "      <td>-2.163723</td>\n",
              "      <td>0.003172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   logsig_e1  logsig_e2  logsig_e3  ...  logsig_e14  logsig_e15  logsig_e16\n",
              "0  -0.009750  -0.001927  -1.982220  ...   -2.868975   -1.656902   -0.006962\n",
              "1  -0.000517  -0.041285  -2.221286  ...   -3.371349   -1.877479   -0.010791\n",
              "2  -0.006289  -0.036124  -2.070144  ...   -3.179582   -1.751832   -0.017672\n",
              "3  -0.018940  -0.040134  -2.724202  ...   -3.701325   -2.283098   -0.038544\n",
              "4  -0.002227  -0.018310  -2.396154  ...   -3.227398   -2.163723    0.003172\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_enc_feats_std = pd.DataFrame(\n",
        "    data=encoding_log_var,\n",
        "    columns=[\n",
        "        \"logsig_e1\",\n",
        "        \"logsig_e2\",\n",
        "        \"logsig_e3\",\n",
        "        \"logsig_e4\",\n",
        "        \"logsig_e5\",\n",
        "        \"logsig_e6\",\n",
        "        \"logsig_e7\",\n",
        "        \"logsig_e8\",\n",
        "        \"logsig_e9\",\n",
        "        \"logsig_e10\",\n",
        "        \"logsig_e11\",\n",
        "        \"logsig_e12\",\n",
        "        \"logsig_e13\",\n",
        "        \"logsig_e14\",\n",
        "        \"logsig_e15\",\n",
        "        \"logsig_e16\",\n",
        "    ],\n",
        ")\n",
        "df_enc_feats_std.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqP3f0sbf5nN",
        "outputId": "bce5bd8a-3f5a-44a9-99e8-0170279fdcfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 2.7419264   2.69012602  3.14667497  1.12769197  1.70162409  1.14148075\n",
            "  3.78682761 18.39055037  2.98360017  1.89148424  4.83188016  1.31946485]\n",
            "               oid  chi2_gband\n",
            "0  451103200005537    2.741926\n",
            "1  495111100005833    2.690126\n",
            "2  566112100004425    3.146675\n",
            "3  646113100002570    1.127692\n",
            "4  648101400002016    1.701624\n"
          ]
        }
      ],
      "source": [
        "chi_all = np.array(RedChiSq)\n",
        "print(chi_all)\n",
        "\n",
        "dataset = pd.DataFrame({\"oid\": oids_all, \"chi2_gband\": chi_all})\n",
        "\n",
        "print(dataset.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DsHM2tQWkC8",
        "outputId": "67b434ed-6722-4963-da9f-79a19ccbd971"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                oid  chi2_gband        e1  ...  logsig_e14  logsig_e15  logsig_e16\n",
            "0   451103200005537    2.741926 -0.003238  ...   -2.868975   -1.656902   -0.006962\n",
            "1   495111100005833    2.690126 -0.014179  ...   -3.371349   -1.877479   -0.010791\n",
            "2   566112100004425    3.146675 -0.002479  ...   -3.179582   -1.751832   -0.017672\n",
            "3   646113100002570    1.127692  0.008021  ...   -3.701325   -2.283098   -0.038544\n",
            "4   648101400002016    1.701624 -0.008218  ...   -3.227398   -2.163723    0.003172\n",
            "5   649113400004567    1.141481 -0.008975  ...   -3.201330   -2.126309    0.004454\n",
            "6   674113200005713    3.786828 -0.012524  ...   -3.019183   -1.808102    0.003686\n",
            "7   678115400001961   18.390550 -0.012555  ...   -5.650127   -3.928250   -0.042645\n",
            "8   693106300007466    2.983600 -0.002605  ...   -2.946004   -1.884771    0.001177\n",
            "9   712112100003746    1.891484  0.002763  ...   -3.411378   -2.372839   -0.004222\n",
            "10  719114100002677    4.831880  0.000938  ...   -5.009892   -3.241592   -0.035837\n",
            "11  795106100002111    1.319465 -0.006787  ...   -4.450218   -2.879760   -0.050876\n",
            "\n",
            "[12 rows x 34 columns]\n"
          ]
        }
      ],
      "source": [
        "df_col_merged = pd.concat([dataset, df_enc_feats, df_enc_feats_std], axis=1)\n",
        "print(df_col_merged)\n",
        "\n",
        "# df_col_merged.to_csv('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_Frederick_Ricci_sample_with_prederr10_encoding_features_chi2_gband.csv')\n",
        "# df_col_merged.to_parquet('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_final_sample_with_prederr10_encoding_features_chi2_gband_1st.parquet')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86tZ1yFskpp8"
      },
      "outputs": [],
      "source": [
        "#X_reduced_train =TSNE(n_components=2, perplexity=100, random_state=32, n_iter=500).fit_transform(encoding_mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2DGOtxhC1CnU"
      },
      "outputs": [],
      "source": [
        "#print(X_reduced_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QsvQ_ABG1GG6"
      },
      "outputs": [],
      "source": [
        "#plt.scatter(X_reduced_train[:, 0], X_reduced_train[:, 1],c=np.log10(np.array(RedChiSq)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "F6ia2_OZEhSy",
        "outputId": "f3206d9a-9d66-4330-d2e3-9ddf42c79301"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'import statsmodels.nonparametric.api as smnp\\nfrom statsmodels.nonparametric.kernel_density import KDEMultivariate\\nimport seaborn as sns\\nfrom matplotlib import gridspec as grs\\nfrom scipy import stats\\n\\ndef kde_contour_dat(x, y, extent = \\'Auto\\', bw_type = \"silverman\", grid_bins = 200, BW=False):\\n    \"\"\"Determine normalized KDE PDF to draw contours\"\"\"\\n    \\n    if isinstance(x, pd.Series):\\n        x = x.values\\n    if isinstance(y, pd.Series):\\n        y = y.values\\n    \\n    if extent == \\'Auto\\':\\n        extent = (x.min(), x.max(), y.min(), y.max())\\n\\n    if bw_type == \"silverman\":\\n        bw = np.array([smnp.bandwidths.bw_silverman(x), smnp.bandwidths.bw_silverman(y)])\\n    elif bw_type == \"scott\":\\n        bw = np.array([smnp.bandwidths.bw_scott(x), smnp.bandwidths.bw_scott(y)])\\n    if BW:\\n        bw = BW\\n\\n    kde = KDEMultivariate([x,y], var_type=\\'cc\\', bw = bw)\\n\\n    xi, yi = np.mgrid[extent[0]:extent[1]:grid_bins*1j,extent[2]:extent[3]:grid_bins*1j]\\n\\n    kde_prob = kde.pdf(np.vstack([xi.flatten(), yi.flatten()]))\\n\\n    zi = (kde_prob-kde_prob.min())/(kde_prob.max() - kde_prob.min())\\n    zi = zi.reshape(xi.shape)\\n\\n    return xi, yi, zi'"
            ]
          },
          "execution_count": 48,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''import statsmodels.nonparametric.api as smnp\n",
        "from statsmodels.nonparametric.kernel_density import KDEMultivariate\n",
        "import seaborn as sns\n",
        "from matplotlib import gridspec as grs\n",
        "from scipy import stats\n",
        "\n",
        "def kde_contour_dat(x, y, extent = 'Auto', bw_type = \"silverman\", grid_bins = 200, BW=False):\n",
        "    \"\"\"Determine normalized KDE PDF to draw contours\"\"\"\n",
        "    \n",
        "    if isinstance(x, pd.Series):\n",
        "        x = x.values\n",
        "    if isinstance(y, pd.Series):\n",
        "        y = y.values\n",
        "    \n",
        "    if extent == 'Auto':\n",
        "        extent = (x.min(), x.max(), y.min(), y.max())\n",
        "\n",
        "    if bw_type == \"silverman\":\n",
        "        bw = np.array([smnp.bandwidths.bw_silverman(x), smnp.bandwidths.bw_silverman(y)])\n",
        "    elif bw_type == \"scott\":\n",
        "        bw = np.array([smnp.bandwidths.bw_scott(x), smnp.bandwidths.bw_scott(y)])\n",
        "    if BW:\n",
        "        bw = BW\n",
        "\n",
        "    kde = KDEMultivariate([x,y], var_type='cc', bw = bw)\n",
        "\n",
        "    xi, yi = np.mgrid[extent[0]:extent[1]:grid_bins*1j,extent[2]:extent[3]:grid_bins*1j]\n",
        "\n",
        "    kde_prob = kde.pdf(np.vstack([xi.flatten(), yi.flatten()]))\n",
        "\n",
        "    zi = (kde_prob-kde_prob.min())/(kde_prob.max() - kde_prob.min())\n",
        "    zi = zi.reshape(xi.shape)\n",
        "\n",
        "    return xi, yi, zi'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sEKLqTpbEjSI"
      },
      "outputs": [],
      "source": [
        "#rx, ry, rz = kde_contour_dat(X_reduced_train[:, 0], X_reduced_train[:, 1], grid_bins=100, BW=[2,2], extent=[-50, 50, -50, 50])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "-t95b8qfEzJr",
        "outputId": "99d269a8-8c11-4221-ce52-8a7b041ed68f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'X, Y = X_reduced_train[:, 0], X_reduced_train[:, 1]\\n\\nxlims = [-10, 10]\\nylims = [-10, 10]\\n\\norigin = \\'lower\\'\\nlevels = np.arange(0.1, 1.1, 0.1)\\ncmap = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\\n\\ngs = grs.GridSpec(2, 2, width_ratios=(4, 1), height_ratios=(1, 4))\\nplt.figure(figsize=(6, 6))\\nax = [plt.subplot(gs[1, 0]), plt.subplot(gs[1, 1]), plt.subplot(gs[0, 0]), plt.subplot(gs[0,1])]\\n\\nax[0].grid(alpha=0.5, lw=1, c=\\'grey\\', linestyle=\\':\\') \\nax[0].tick_params(which=\"both\", top=True, right=True)\\nax[0].minorticks_on()\\nax[0].set_axisbelow(True)\\nax[0].contourf(rx, ry, rz, levels = levels,\\n             origin = origin,\\n             cmap = cmap, alpha = 0.8)\\nax[0].contour(rx, ry, rz, levels = levels,\\n                    linewidths=(0.5,), origin = origin,\\n                    colors = (\"w\",), alpha = 0.5, zorder = 11)\\nax[0].scatter(X_reduced_train[:, 0], X_reduced_train[:, 1],s=1)\\nax[0].set_xlim(xlims); ax[0].set_ylim(ylims)\\nax[0].tick_params(labelsize = 15)\\nax[0].set_xlabel(\\'Dim1\\', fontsize=15)\\nax[0].set_ylabel(\\'Dim2\\', fontsize=15)\\n\\nn = (np.linspace(ylims[0]-5, ylims[1]+5,200))\\nkde_X = stats.gaussian_kde(X)\\nkde_Y = stats.gaussian_kde(Y)\\nax[1].fill_betweenx(n, kde_Y(n), alpha=0.75, color=cmap(0.25), lw=2)\\nax[1].set_ylim(ylims)\\nax[1].set_xlim(0, 1.1*np.max(kde_Y(n)))\\nax[1].set_xlabel(\\'PDF\\', fontsize=15)\\nax[1].set_xticklabels( () )\\nax[1].set_yticklabels( () )\\nax[1].set_xticks([])\\nax[1].minorticks_on()\\n\\nn = (np.linspace(xlims[0]-5, xlims[1]+5,200))\\nax[2].fill(n, kde_X(n), alpha=0.75, color=cmap(0.25), lw=2)\\nax[2].set_xlim(xlims)\\nax[2].set_ylim(0, 1.1*np.max(kde_X(n)))\\nax[2].set_ylabel(\\'PDF\\', fontsize=15)\\nax[2].set_xticklabels( () )\\nax[2].set_yticklabels( () )\\nax[2].set_yticks([])\\nax[2].minorticks_on()\\n\\nax[3].tick_params(labelbottom=\"off\",bottom=\"off\", labeltop=\\'off\\', top=\\'off\\')\\nax[3].tick_params(labelleft=\"off\",left=\"off\", labelright=\\'off\\', right=\\'off\\')\\nplt.box(\"off\")\\nplt.tight_layout()\\n\\nplt.savefig(\\'/content/drive/MyDrive/CSAGNs/plots_ps_length/VAE_psDR4_SDSS_QSO_balanced_GRU2x40-encoding20_gband_lcrand/milliquas_lcrand200_ZTF_alerts_tSNE_encoded_features.png\\')\\nplt.show()'"
            ]
          },
          "execution_count": 50,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''X, Y = X_reduced_train[:, 0], X_reduced_train[:, 1]\n",
        "\n",
        "xlims = [-10, 10]\n",
        "ylims = [-10, 10]\n",
        "\n",
        "origin = 'lower'\n",
        "levels = np.arange(0.1, 1.1, 0.1)\n",
        "cmap = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n",
        "\n",
        "gs = grs.GridSpec(2, 2, width_ratios=(4, 1), height_ratios=(1, 4))\n",
        "plt.figure(figsize=(6, 6))\n",
        "ax = [plt.subplot(gs[1, 0]), plt.subplot(gs[1, 1]), plt.subplot(gs[0, 0]), plt.subplot(gs[0,1])]\n",
        "\n",
        "ax[0].grid(alpha=0.5, lw=1, c='grey', linestyle=':') \n",
        "ax[0].tick_params(which=\"both\", top=True, right=True)\n",
        "ax[0].minorticks_on()\n",
        "ax[0].set_axisbelow(True)\n",
        "ax[0].contourf(rx, ry, rz, levels = levels,\n",
        "             origin = origin,\n",
        "             cmap = cmap, alpha = 0.8)\n",
        "ax[0].contour(rx, ry, rz, levels = levels,\n",
        "                    linewidths=(0.5,), origin = origin,\n",
        "                    colors = (\"w\",), alpha = 0.5, zorder = 11)\n",
        "ax[0].scatter(X_reduced_train[:, 0], X_reduced_train[:, 1],s=1)\n",
        "ax[0].set_xlim(xlims); ax[0].set_ylim(ylims)\n",
        "ax[0].tick_params(labelsize = 15)\n",
        "ax[0].set_xlabel('Dim1', fontsize=15)\n",
        "ax[0].set_ylabel('Dim2', fontsize=15)\n",
        "\n",
        "n = (np.linspace(ylims[0]-5, ylims[1]+5,200))\n",
        "kde_X = stats.gaussian_kde(X)\n",
        "kde_Y = stats.gaussian_kde(Y)\n",
        "ax[1].fill_betweenx(n, kde_Y(n), alpha=0.75, color=cmap(0.25), lw=2)\n",
        "ax[1].set_ylim(ylims)\n",
        "ax[1].set_xlim(0, 1.1*np.max(kde_Y(n)))\n",
        "ax[1].set_xlabel('PDF', fontsize=15)\n",
        "ax[1].set_xticklabels( () )\n",
        "ax[1].set_yticklabels( () )\n",
        "ax[1].set_xticks([])\n",
        "ax[1].minorticks_on()\n",
        "\n",
        "n = (np.linspace(xlims[0]-5, xlims[1]+5,200))\n",
        "ax[2].fill(n, kde_X(n), alpha=0.75, color=cmap(0.25), lw=2)\n",
        "ax[2].set_xlim(xlims)\n",
        "ax[2].set_ylim(0, 1.1*np.max(kde_X(n)))\n",
        "ax[2].set_ylabel('PDF', fontsize=15)\n",
        "ax[2].set_xticklabels( () )\n",
        "ax[2].set_yticklabels( () )\n",
        "ax[2].set_yticks([])\n",
        "ax[2].minorticks_on()\n",
        "\n",
        "ax[3].tick_params(labelbottom=\"off\",bottom=\"off\", labeltop='off', top='off')\n",
        "ax[3].tick_params(labelleft=\"off\",left=\"off\", labelright='off', right='off')\n",
        "plt.box(\"off\")\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('/content/drive/MyDrive/CSAGNs/plots_ps_length/VAE_psDR4_SDSS_QSO_balanced_GRU2x40-encoding20_gband_lcrand/milliquas_lcrand200_ZTF_alerts_tSNE_encoded_features.png')\n",
        "plt.show()'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QRwFlmOhqyHi"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "Evaluate-with-error-interval-10samp-VAEmodel-GRU2x32-encoding16-DR5-ps-SDSS-QSO-balanced-lcsorg.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}