part2_gmm.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/irfanimaduddin/membership-hdbscan-gmm/blob/main/part2_gmm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmm0vOkQRpC9"
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      "source": [
        "# **Hands-On: Open Cluster Membership Determination using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Gaussian Mixture Model (GMM) - Part 2** \n",
        "\n",
        "Authors : Salma Zahra Fadiyah, Irfan Imaduddin,  M. Ikbal Arifyanto <br>\n",
        "Version : 1.0 <br>\n",
        "Date : 22 July 2021 <br>\n",
        "Contact : salmazahraa[at]gmail[dot]com <br>\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0sMT_Ds8oVB"
      },
      "source": [
        "## Welcome!\n",
        "\n",
        "<p align = \"justify\">  In this workshop, we will determine the cluster members of an open cluster using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Gaussian Mixture Model (GMM). This is the second part of two hands-on series. The first part has held yesterday (20/08/07). The open cluster data that will be used is NGC 752 from Gaia Early Data Release 3 (eDR3). </p> \n",
        "\n",
        "Hands-on part one: [day06_handson_hdbscan.ipynb](https://colab.research.google.com/drive/1dr1erdUtwVzeWnRlNTgfDK8GJu8YBXJ7?usp=sharing)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "neQNmi3wh9pg"
      },
      "source": [
        "## 1. Theoretical Background"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCx2gKYy61as"
      },
      "source": [
        "<p align = \"justify\"> Gaussian Mixture Model (GMM, McLachlan & Peel 2000) is a clustering method. In other words, GMM is a model to identify the existence of a sub-population within a population itself. This is an unsupervised algorithm, so we don't need to know physics of the cluster before using this method. Gaussian Mixture is a model that is comprised of several Gaussians, each identified by k ∈ {1,…, K}, where K is the number of clusters of the sample. Each gaussian has expected value ($\\mu$) that defines its center, covariance ($\\Sigma$) that defines its width dan mixing probability ($\\pi$) that defines its ratio with other gaussian function.\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "auCa_8N_h1l-"
      },
      "source": [
        "<figure>\n",
        "  <img src=\"https://angusturner.github.io/assets/images/mixture.png\" alt=\"Trulli\" width = \"500px\">\n",
        "  <figcaption align=\"left\"> Fig 1. Illustration of 1D Gaussian Mixture Model with 2 gaussian components (source: angusturner.github.io) </figcaption> \n",
        "</figure>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CS38TqSdiECp"
      },
      "source": [
        "<p align = \"justify\"> Before the calculation is started, the data in $M$-parameter space [$x^1, x^2, x^3, ..., x^M$] should be normalized. For $N$ data points, the normalized data point in $j$th dimension ($X^j_i$) is defined by Agarwal et al. (2021) as:\n",
        "\\begin{eqnarray}\n",
        "X^j_i = \\frac{x^j_i - \\mu^j}{\\sigma^j} (i=1, 2, ..., N; j=1, 2, ..., M) \\\\\n",
        "\\end{eqnarray}\n",
        "\n",
        "where $x^j_i$ is the original parameter, $\\mu^j$ is the median of the $x^j$ distribution and $\\sigma^j$ is standard deviation of the distribution. For $N$ data points $x = \\{x_1, x_2, x_3, ..., x_N\\}$ in an $M$-dimensional parameter space, the $K$-component GMM is defined as:\n",
        "</p>\n",
        "\n",
        "\\begin{eqnarray}\n",
        "P(x) = \\Sigma^K_{i=1}w_iG(x|\\mu_i,\\Sigma_i) \\\\\n",
        "\\end{eqnarray}\n",
        "where,\n",
        "\n",
        "\\begin{eqnarray}\n",
        "P(x) &=& \\Sigma^K_{i=1}w_i = 1 \\\\\n",
        "G(x|\\mu_i,\\Sigma_i) &=& \\frac{exp[-0.5(x-\\mu_i)^T\\Sigma^{-1}_{i}(x-\\mu_i)]}{(2\\pi)^{M/2}\\sqrt{|\\Sigma_{i}|}} \\\\\n",
        "\\end{eqnarray}\n",
        "\n",
        "<p align = \"justify\"> $w_i$ is defined as the mixture weight of the $i$th Gaussian component $G(x|\\mu_i,\\Sigma_i)$. $\\mu_i$ and $\\Sigma_i$ are the mean vector and the full covariance matrix of the $i$th Gaussian component, respectively. \n",
        "</p>\n"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "IZaA3TyWiVQK"
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      "source": [
        "## 2. Workshop Structure\n",
        "\n",
        "<p align = \"justify\"> The workshop is made up of two Jupyter Notebooks. The layout of the workshop is as follows:\n",
        "<ol type=\"1\">\n",
        "<li>Determine the center of the open cluster.</li>\n",
        "<li>Determine the membership probability.</li>\n",
        "</ol>\n",
        "</p>\n",
        "\n",
        "<p align = \"justify\"> Our membership assignment relies on the astrometric solution, and we only used the Gaia eDR3 photometry to manually confirm that the groups identified matched the expected aspect of a cluster in a colour-magnitude diagram.\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzcR7dqw0CTA"
      },
      "source": [
        "### Part 1: [Determine the Center of the Open Cluster](https://colab.research.google.com/drive/1dr1erdUtwVzeWnRlNTgfDK8GJu8YBXJ7?usp=sharing) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiopkDXn0X-I"
      },
      "source": [
        "### Part 2: Determine the Membership Probability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3N7UMkS64No"
      },
      "source": [
        "#### Setup\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_jUToKG8CR3"
      },
      "source": [
        "To determine the membership probability of each star around the open cluster NGC 752, we will use a module in python called [sklearn.mixture.GaussianMixture](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html). The algorithm is referred to the study conducted by [Agarwal et al. (2021)](https://academic.oup.com/mnras/article/502/2/2582/6133058). If this notebook is run on gColab, firstly we need to install some libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rxPVJdglaAs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7cd97cb-8c5a-4540-e6ea-4e4f9f1312b3"
      },
      "source": [
        "import sys\n",
        "IN_COLAB = 'google.colab' in sys.modules\n",
        "\n",
        "if IN_COLAB:\n",
        "    !pip install sklearn\n",
        "    !pip install astropy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.19.5)\n",
            "Requirement already satisfied: astropy in /usr/local/lib/python3.7/dist-packages (4.2.1)\n",
            "Requirement already satisfied: pyerfa in /usr/local/lib/python3.7/dist-packages (from astropy) (2.0.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from astropy) (1.19.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lG_IEYN6RpDC"
      },
      "source": [
        "Import the required packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGtGTo5RRpDD"
      },
      "source": [
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from astropy.coordinates import SkyCoord\n",
        "import astropy.units as u\n",
        "\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33xuiD96hcF3"
      },
      "source": [
        "Set some plotting configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WOKswikhdEu"
      },
      "source": [
        "SMALL_SIZE = 12\n",
        "MEDIUM_SIZE = 14\n",
        "BIGGER_SIZE = 25\n",
        "\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=10)            # legend fontsize\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VykOSFQ88woO"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#### Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mGwKeHpRpDF"
      },
      "source": [
        "Import data file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjMn8kGOjlB9"
      },
      "source": [
        "FILENAME = \"gaiaedr3_180_NGC752.csv\"\n",
        "datafile = pd.read_csv(FILENAME, delimiter=\",\") "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgbXN534jHne"
      },
      "source": [
        "Summary of data feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikScpvOTRpDG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91182cdc-a247-4961-d62e-355de0974048"
      },
      "source": [
        "datafile.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 216853 entries, 0 to 216852\n",
            "Data columns (total 23 columns):\n",
            " #   Column                     Non-Null Count   Dtype  \n",
            "---  ------                     --------------   -----  \n",
            " 0   source_id                  216853 non-null  int64  \n",
            " 1   ra                         216853 non-null  float64\n",
            " 2   ra_error                   216853 non-null  float64\n",
            " 3   dec                        216853 non-null  float64\n",
            " 4   dec_error                  216853 non-null  float64\n",
            " 5   parallax                   190235 non-null  float64\n",
            " 6   parallax_error             190235 non-null  float64\n",
            " 7   pm                         190235 non-null  float64\n",
            " 8   pmra                       190235 non-null  float64\n",
            " 9   pmra_error                 190235 non-null  float64\n",
            " 10  pmdec                      190235 non-null  float64\n",
            " 11  pmdec_error                190235 non-null  float64\n",
            " 12  phot_g_mean_flux           216674 non-null  float64\n",
            " 13  phot_g_mean_flux_error     216674 non-null  float64\n",
            " 14  phot_g_mean_mag            216674 non-null  float64\n",
            " 15  phot_bp_mean_flux          213607 non-null  float64\n",
            " 16  phot_bp_mean_flux_error    213607 non-null  float64\n",
            " 17  phot_bp_mean_mag           213607 non-null  float64\n",
            " 18  phot_rp_mean_flux          214155 non-null  float64\n",
            " 19  phot_rp_mean_flux_error    214154 non-null  float64\n",
            " 20  phot_rp_mean_mag           214154 non-null  float64\n",
            " 21  dr2_radial_velocity        2880 non-null    float64\n",
            " 22  dr2_radial_velocity_error  2880 non-null    float64\n",
            "dtypes: float64(22), int64(1)\n",
            "memory usage: 38.1 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEBatTp5jKnG"
      },
      "source": [
        "Handling the missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INQfsbevjK2k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "4e8414a4-16f9-4360-de94-3c2a0d479642"
      },
      "source": [
        "#datafile.fillna(method ='ffill', inplace = True)\n",
        "datafile.dropna(subset=['pmra', 'pmdec', 'parallax']).reset_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.445631</td>\n",
              "      <td>345.169571</td>\n",
              "      <td>1.384767</td>\n",
              "      <td>19.342285</td>\n",
              "      <td>150.154130</td>\n",
              "      <td>10.209387</td>\n",
              "      <td>19.897200</td>\n",
              "      <td>268.414468</td>\n",
              "      <td>10.885222</td>\n",
              "      <td>18.675880</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190230</th>\n",
              "      <td>216847</td>\n",
              "      <td>330550235108462080</td>\n",
              "      <td>30.595489</td>\n",
              "      <td>0.156786</td>\n",
              "      <td>36.556725</td>\n",
              "      <td>0.170469</td>\n",
              "      <td>0.272698</td>\n",
              "      <td>0.192468</td>\n",
              "      <td>1.748905</td>\n",
              "      <td>-1.125570</td>\n",
              "      <td>0.188344</td>\n",
              "      <td>-1.338567</td>\n",
              "      <td>0.228102</td>\n",
              "      <td>772.311473</td>\n",
              "      <td>1.726663</td>\n",
              "      <td>18.467886</td>\n",
              "      <td>390.546087</td>\n",
              "      <td>10.283116</td>\n",
              "      <td>18.859362</td>\n",
              "      <td>501.212800</td>\n",
              "      <td>11.304056</td>\n",
              "      <td>17.997840</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190231</th>\n",
              "      <td>216848</td>\n",
              "      <td>330550269467517056</td>\n",
              "      <td>30.607259</td>\n",
              "      <td>0.278281</td>\n",
              "      <td>36.580146</td>\n",
              "      <td>0.305836</td>\n",
              "      <td>0.091755</td>\n",
              "      <td>0.341891</td>\n",
              "      <td>0.820371</td>\n",
              "      <td>-0.805831</td>\n",
              "      <td>0.346888</td>\n",
              "      <td>-0.153765</td>\n",
              "      <td>0.408711</td>\n",
              "      <td>400.149974</td>\n",
              "      <td>1.401725</td>\n",
              "      <td>19.181810</td>\n",
              "      <td>214.881317</td>\n",
              "      <td>12.052973</td>\n",
              "      <td>19.508045</td>\n",
              "      <td>299.466066</td>\n",
              "      <td>13.435237</td>\n",
              "      <td>18.557026</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190232</th>\n",
              "      <td>216849</td>\n",
              "      <td>332190397219924608</td>\n",
              "      <td>32.748415</td>\n",
              "      <td>0.313848</td>\n",
              "      <td>37.945583</td>\n",
              "      <td>0.389015</td>\n",
              "      <td>2.488413</td>\n",
              "      <td>0.472230</td>\n",
              "      <td>27.014395</td>\n",
              "      <td>21.591297</td>\n",
              "      <td>0.483271</td>\n",
              "      <td>-16.235561</td>\n",
              "      <td>0.760983</td>\n",
              "      <td>309.450124</td>\n",
              "      <td>1.235525</td>\n",
              "      <td>19.460890</td>\n",
              "      <td>52.419576</td>\n",
              "      <td>9.426556</td>\n",
              "      <td>21.039808</td>\n",
              "      <td>402.458628</td>\n",
              "      <td>7.139021</td>\n",
              "      <td>18.236092</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190233</th>\n",
              "      <td>216850</td>\n",
              "      <td>332190401513966976</td>\n",
              "      <td>32.742828</td>\n",
              "      <td>0.229686</td>\n",
              "      <td>37.942214</td>\n",
              "      <td>0.234722</td>\n",
              "      <td>0.065527</td>\n",
              "      <td>0.278723</td>\n",
              "      <td>4.396603</td>\n",
              "      <td>-2.823685</td>\n",
              "      <td>0.302902</td>\n",
              "      <td>-3.370002</td>\n",
              "      <td>0.336413</td>\n",
              "      <td>501.864642</td>\n",
              "      <td>1.605236</td>\n",
              "      <td>18.935900</td>\n",
              "      <td>270.290868</td>\n",
              "      <td>7.842782</td>\n",
              "      <td>19.258965</td>\n",
              "      <td>388.430648</td>\n",
              "      <td>7.128442</td>\n",
              "      <td>18.274612</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190234</th>\n",
              "      <td>216852</td>\n",
              "      <td>332190401516680832</td>\n",
              "      <td>32.757551</td>\n",
              "      <td>0.051672</td>\n",
              "      <td>37.941607</td>\n",
              "      <td>0.047395</td>\n",
              "      <td>2.258156</td>\n",
              "      <td>0.059850</td>\n",
              "      <td>3.516985</td>\n",
              "      <td>0.501813</td>\n",
              "      <td>0.064346</td>\n",
              "      <td>-3.481001</td>\n",
              "      <td>0.064144</td>\n",
              "      <td>4921.767596</td>\n",
              "      <td>3.639319</td>\n",
              "      <td>16.457064</td>\n",
              "      <td>1531.996350</td>\n",
              "      <td>11.114927</td>\n",
              "      <td>17.375399</td>\n",
              "      <td>4957.800000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>190235 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         index  ...  dr2_radial_velocity_error\n",
              "0            0  ...                        NaN\n",
              "1            1  ...                        NaN\n",
              "2            2  ...                        NaN\n",
              "3            3  ...                        NaN\n",
              "4            4  ...                        NaN\n",
              "...        ...  ...                        ...\n",
              "190230  216847  ...                        NaN\n",
              "190231  216848  ...                        NaN\n",
              "190232  216849  ...                        NaN\n",
              "190233  216850  ...                        NaN\n",
              "190234  216852  ...                        NaN\n",
              "\n",
              "[190235 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsW5NbmHRpDI"
      },
      "source": [
        "To eliminate sources with high uncertainty while still retaining a fraction of sources down to G ∼ 21 mag, we need to select the errors in their G-mag must be less than 0.005. Calculate error of G ($|\\sigma_G|$), G$_{BP}$ ($|\\sigma_{BP}|$), and G$_{RP}$ ($|\\sigma_{RP}|$).\n",
        "\n",
        "\\begin{eqnarray}\n",
        "|\\sigma_G| = -\\frac{2.5}{ln \\ 10} \\frac{\\sigma_{F_G}}{F_G} \\\\\n",
        "|\\sigma_{BP}| = -\\frac{2.5}{ln \\ 10} \\frac{\\sigma_{F_{BP}}}{F_{BP}} \\\\\n",
        "|\\sigma_{RP}| = -\\frac{2.5}{ln \\ 10} \\frac{\\sigma_{F_{RP}}}{F_{RP}}\n",
        "\\end{eqnarray}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mb0C05UnRpDI"
      },
      "source": [
        "datafile['e_Gmag'] = abs(-2.5*datafile['phot_g_mean_flux_error']/math.log(10)/datafile['phot_g_mean_flux'])\n",
        "datafile['e_BPmag'] = abs(-2.5*datafile['phot_bp_mean_flux_error']/math.log(10)/datafile['phot_bp_mean_flux'])\n",
        "datafile['e_RPmag'] = abs(-2.5*datafile['phot_rp_mean_flux_error']/math.log(10)/datafile['phot_rp_mean_flux'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9SdFCmuj4oV"
      },
      "source": [
        "To make things easier, add the bp_rp and parallax_over_error features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMM1tLSHj5hX"
      },
      "source": [
        "datafile['bp_rp'] = datafile['phot_bp_mean_mag'] - datafile['phot_rp_mean_mag']\n",
        "datafile['parallax_over_error'] = datafile['parallax'] / datafile['parallax_error']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2PcDWf_RpDK"
      },
      "source": [
        "Select data with positive parallax value ($\\omega>0$), parallax_over_error ($\\omega/\\sigma_{\\omega}$) $>5$, and error of G magnitude ($\\sigma_G$) $< 0.005$.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzM_blN7RpDL"
      },
      "source": [
        "data = datafile[(datafile['parallax'] > 0.) & (datafile['e_Gmag'] < 0.005)].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mR-xm4X1RpDL"
      },
      "source": [
        "Select some parameters to be calculated as all source"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2c419kcPRpDM"
      },
      "source": [
        "allsource = data[[\n",
        "    'ra', \n",
        "    'ra_error', \n",
        "    'dec', \n",
        "    'dec_error', \n",
        "    'parallax',\n",
        "    'parallax_error', \n",
        "    'pmra', \n",
        "    'pmra_error', \n",
        "    'pmdec',\n",
        "    'pmdec_error', \n",
        "    'phot_g_mean_mag',\n",
        "    'bp_rp'\n",
        "]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJMP4FBuw2bE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "b63b4fb2-c096-4855-b88b-aa2fde30d264"
      },
      "source": [
        "allsource.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ra</th>\n",
              "      <th>ra_error</th>\n",
              "      <th>dec</th>\n",
              "      <th>dec_error</th>\n",
              "      <th>parallax</th>\n",
              "      <th>parallax_error</th>\n",
              "      <th>pmra</th>\n",
              "      <th>pmra_error</th>\n",
              "      <th>pmdec</th>\n",
              "      <th>pmdec_error</th>\n",
              "      <th>phot_g_mean_mag</th>\n",
              "      <th>bp_rp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.955921</td>\n",
              "      <td>0.122567</td>\n",
              "      <td>34.808910</td>\n",
              "      <td>0.114832</td>\n",
              "      <td>0.812759</td>\n",
              "      <td>0.143033</td>\n",
              "      <td>-0.407360</td>\n",
              "      <td>0.160723</td>\n",
              "      <td>-3.001847</td>\n",
              "      <td>0.153433</td>\n",
              "      <td>17.733475</td>\n",
              "      <td>1.306890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.930927</td>\n",
              "      <td>0.486134</td>\n",
              "      <td>34.811400</td>\n",
              "      <td>0.406819</td>\n",
              "      <td>0.023434</td>\n",
              "      <td>0.637204</td>\n",
              "      <td>13.967538</td>\n",
              "      <td>0.645873</td>\n",
              "      <td>-1.435553</td>\n",
              "      <td>0.599406</td>\n",
              "      <td>19.633541</td>\n",
              "      <td>2.095585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.916471</td>\n",
              "      <td>0.052384</td>\n",
              "      <td>34.821446</td>\n",
              "      <td>0.047474</td>\n",
              "      <td>0.374306</td>\n",
              "      <td>0.061127</td>\n",
              "      <td>-5.389088</td>\n",
              "      <td>0.068402</td>\n",
              "      <td>-0.792524</td>\n",
              "      <td>0.064079</td>\n",
              "      <td>16.174849</td>\n",
              "      <td>0.841805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.909332</td>\n",
              "      <td>0.069762</td>\n",
              "      <td>34.836200</td>\n",
              "      <td>0.066513</td>\n",
              "      <td>0.637443</td>\n",
              "      <td>0.084065</td>\n",
              "      <td>2.246712</td>\n",
              "      <td>0.090168</td>\n",
              "      <td>0.811313</td>\n",
              "      <td>0.090482</td>\n",
              "      <td>16.844282</td>\n",
              "      <td>1.054754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28.902070</td>\n",
              "      <td>0.093140</td>\n",
              "      <td>34.841362</td>\n",
              "      <td>0.084261</td>\n",
              "      <td>0.590544</td>\n",
              "      <td>0.114065</td>\n",
              "      <td>-1.856676</td>\n",
              "      <td>0.124143</td>\n",
              "      <td>-5.786173</td>\n",
              "      <td>0.119260</td>\n",
              "      <td>17.390295</td>\n",
              "      <td>1.174249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ra  ra_error        dec  ...  pmdec_error  phot_g_mean_mag     bp_rp\n",
              "0  28.955921  0.122567  34.808910  ...     0.153433        17.733475  1.306890\n",
              "1  28.930927  0.486134  34.811400  ...     0.599406        19.633541  2.095585\n",
              "2  28.916471  0.052384  34.821446  ...     0.064079        16.174849  0.841805\n",
              "3  28.909332  0.069762  34.836200  ...     0.090482        16.844282  1.054754\n",
              "4  28.902070  0.093140  34.841362  ...     0.119260        17.390295  1.174249\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xNjmHyZRpDN"
      },
      "source": [
        "---\n",
        "\n",
        "#### Sample Sources Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q705qoz170VR"
      },
      "source": [
        "<p align=\"justify\"> To select the sample source, we select range of proper motions and parallax of the all source that the mean of the enclosed values close to the mean of proper motions ($\\bar{\\mu}_{\\alpha*}, \\bar{\\mu}_{\\delta}$) and the mean of parallax ($\\bar{\\omega}$) values from the previous hands-on (refer to Part 1). According to Agarwal et al (2021), the range of proper motions has a width between 3 and 5 mas/yr. For range of parallax, the minimum value is 0.4 mas for distant cluster such as NGC 2243 (~4400 pc) and the maximum value is 2.5 mas for nearby cluster such as NGC 6405 (~450 pc).\n",
        "</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-I4CT3CjRpDP"
      },
      "source": [
        "HDBSCAN_MEAN_PMRA     = 9.789\n",
        "HDBSCAN_MEAN_PMDEC    = -11.833\n",
        "HDBSCAN_MEAN_PARALLAX = 2.263"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9VySXa3x6qn"
      },
      "source": [
        "PMRA_RANGE     = 3.\n",
        "PMDEC_RANGE    = 3.\n",
        "PARALLAX_RANGE = 2.25"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbC28wh72Z6g"
      },
      "source": [
        "samplesource = allsource[\n",
        "    (allsource['pmra'] >= HDBSCAN_MEAN_PMRA-(PMRA_RANGE/2.)) & (allsource['pmra'] <= HDBSCAN_MEAN_PMRA+(PMRA_RANGE/2.)) & \n",
        "    (allsource['pmdec'] >= HDBSCAN_MEAN_PMDEC-(PMDEC_RANGE/2.)) & (allsource['pmdec'] <= HDBSCAN_MEAN_PMDEC+(PMDEC_RANGE/2.)) &\n",
        "    (allsource['parallax'] >= HDBSCAN_MEAN_PARALLAX-(PARALLAX_RANGE/2.)) & (allsource['parallax'] <= HDBSCAN_MEAN_PARALLAX+(PARALLAX_RANGE/2.))\n",
        "].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrcQ6-fJ2sjB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01d14ebf-0b70-4fd9-d0e9-31f9443e6df0"
      },
      "source": [
        "samplesource['pmra'].mean(), samplesource['pmdec'].mean(), samplesource['parallax'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9.805761162365425, -11.749510338273351, 2.0738418696900887)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8tW8T-WRpDU"
      },
      "source": [
        "---\n",
        "\n",
        "####Visualization I "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0AuiXdW3lUx"
      },
      "source": [
        "Vector Point Diagram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCaVh1aFRpDU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "1753ccc4-6f67-41de-bbd6-396ac17b8e39"
      },
      "source": [
        "fig = plt.figure(figsize=(6, 6))\n",
        "plt.plot(allsource['pmra'], allsource['pmdec'], '.', color='gray', markersize=2., label=\"All Sources\")\n",
        "plt.plot(samplesource['pmra'], samplesource['pmdec'], '.', color='blue', markersize=2., label=\"Sample Sources\")\n",
        "\n",
        "plt.xlabel(r\"$\\mu_{\\alpha*}$ (mas/yr)\")\n",
        "plt.ylabel(r\"$\\mu_{\\delta}$ (mas/yr)\")\n",
        "\n",
        "plt.xticks()\n",
        "plt.yticks()\n",
        "\n",
        "plt.xlim(0,20)\n",
        "plt.ylim(-20,0)\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cr4fIKLKRpDW"
      },
      "source": [
        "Parallax Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VP0xKjKORpDW"
      },
      "source": [
        "bins_all = np.arange(allsource['parallax'].min(), allsource['parallax'].max(), .01)\n",
        "bins_sam = np.arange(samplesource['parallax'].min(), samplesource['parallax'].max(), .01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diOL292uRpDW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "b0993d34-7dd5-46e7-9f11-8dd70ddb5ceb"
      },
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "allsource['parallax'].hist(bins=bins_all, color='gray', label=\"All Sources\")\n",
        "samplesource['parallax'].hist(bins=bins_sam, color='b', label=\"Sample Sources\")\n",
        "\n",
        "plt.xlabel(r\"$\\omega$ (mas)\")\n",
        "plt.ylabel(\"Number of Sources\")\n",
        "\n",
        "plt.xlim([0, 5])\n",
        "\n",
        "plt.xticks()\n",
        "plt.yticks()\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Idt_SYY8RpDX"
      },
      "source": [
        "Color Magnitude Diagram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2DQFc_WRpDX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "b6cc7626-51ef-48ff-bc3d-9f46b6095ef3"
      },
      "source": [
        "plt.figure(figsize=(6, 8))\n",
        "plt.plot(allsource['bp_rp'], allsource['phot_g_mean_mag'], '.', color='gray', markersize=2., label=\"All Sources\")\n",
        "plt.plot(samplesource['bp_rp'], samplesource['phot_g_mean_mag'], '.', color='b', markersize=2., label=\"Sample Sources\")\n",
        "\n",
        "plt.xlabel(r\"$G_{BP}-G_{RP}$\")\n",
        "plt.ylabel(r\"$G$ (mag)\")\n",
        "\n",
        "plt.xlim([0., 3.])\n",
        "\n",
        "plt.gca().invert_yaxis()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xQndg53RpDX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce9b3b12-29ef-401c-9285-4f5afaf2eac4"
      },
      "source": [
        "print('All Sources = %d \\nSample Sources = %d' %(len(allsource), len(samplesource)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All Sources = 119718 \n",
            "Sample Sources = 512\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZt6BvlKRpDY"
      },
      "source": [
        "---\n",
        "\n",
        "####Let's Start the Game!!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsUgBgqS7wr6"
      },
      "source": [
        "Normalize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWnmIBBzYKpR"
      },
      "source": [
        "df = samplesource[[\"pmra\", \"pmdec\", \"parallax\"]]\n",
        "df = df.to_numpy().astype(\"float32\", copy = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGQcgX4AX3OD"
      },
      "source": [
        "stscaler_df = StandardScaler().fit(df)\n",
        "df_ = stscaler_df.transform(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmy8IGFoZEVL"
      },
      "source": [
        "norm_pmra = df_[:,0]\n",
        "norm_pmde = df_[:,1]\n",
        "norm_para = df_[:,2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzNOVOVSRpDa"
      },
      "source": [
        "Select some parameters to be calculated"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i1h1tQaRpDa"
      },
      "source": [
        "sample_data_dict = {\n",
        "    'norm_pmra' : norm_pmra,\n",
        "    'norm_pmde' : norm_pmde,\n",
        "    'norm_para' : norm_para,\n",
        "}\n",
        "\n",
        "sample_data = pd.DataFrame(sample_data_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZedBMkrRpDa"
      },
      "source": [
        "Train Gaussian Mixture Model (GMM) with whole data, consider it models two gaussian components (field and cluster)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YhWasJoRpDa"
      },
      "source": [
        "gmm = GaussianMixture(n_components=2, max_iter=1000, covariance_type='full', random_state=None).fit(sample_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgGkBoqRRpDb"
      },
      "source": [
        "Calculate means, covariances and weights of trained/fitted models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueWHgfHTRpDb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7fb7a59-004a-4ddd-ed4d-4f88c0488bcb"
      },
      "source": [
        "gmm.means_, gmm.covariances_, gmm.weights_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 0.02647034, -0.05696691,  0.47934096],\n",
              "        [-0.08784734,  0.18905651, -1.59079226]]),\n",
              " array([[[ 0.71579776,  0.06872797,  0.05649506],\n",
              "         [ 0.06872797,  0.70201271, -0.00519505],\n",
              "         [ 0.05649506, -0.00519505,  0.23980225]],\n",
              " \n",
              "        [[ 1.9331458 , -0.15852605,  0.16801871],\n",
              "         [-0.15852605,  1.94242454, -0.1055576 ],\n",
              "         [ 0.16801871, -0.1055576 ,  0.22972609]]]),\n",
              " array([0.76844922, 0.23155078]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGd-KEjYRpDb"
      },
      "source": [
        "Calculate the probabilities of the whole data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvmPQFxIRpDc"
      },
      "source": [
        "pred_data = gmm.predict_proba(sample_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDjhY5cY-vhL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47e93cc7-780f-4bbf-e8af-cb94cdc2501d"
      },
      "source": [
        "pred_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.24172644e-01, 5.75827356e-01],\n",
              "       [1.00850836e-03, 9.98991492e-01],\n",
              "       [9.97275566e-01, 2.72443353e-03],\n",
              "       ...,\n",
              "       [2.79986015e-05, 9.99972001e-01],\n",
              "       [9.97348539e-01, 2.65146133e-03],\n",
              "       [4.03072639e-04, 9.99596927e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0btsQ01RpDc"
      },
      "source": [
        "Check the calculated probabilities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlJ_CdZVRpDc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "697be56d-7dd9-41be-83b5-7063d7b5dadc"
      },
      "source": [
        "plt.hist(pred_data[:,0], bins=[0., .1, .2, .3, .4, .5, .6, .7, .8, .9, 1.])\n",
        "plt.xlim([0., 1.])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD9CAYAAAC85wBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVM0lEQVR4nO3df5Bd5X3f8fcHREWNJBBIBmLXUiGAHNGKFDm08VDbpY1xGCeu9UfB2IWQIBuPpk6Z2KUZYVTAwTGZyYw9tmMx/DAEiONWeExImRYXcHDSTtZp5CBDyDBYNraEF1CElh/CkG//uM9WN8tKe6/23r0LvF8zZ3TveZ5zznMe7d7PnvOce06qCkmSDhl1AyRJ84OBIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQv6qZzkJOCvgP9aVR9s8z4AXAMsA/4ncFFVPd3KjgauB34BeBL4z1V120zbWbZsWa1cubKfpknS6963v/3tJ6tq+cEu31cgAJ8H/nzyTZLVwJeAc4C/ADYDXwDO7ar/InAscBpwV5KtVbXtQBtZuXIlY2NjfTZNkl7fkmyfzfI9nzJKci7wt8A3umafD9xZVd+sqgngcuD9SRYnOQJYB1xeVRNV9QDwdeBDs2mwJGk4egqEJEuAK4FLpxStBrZOvqmqR+kcEZzcppeq6pGu+lvbMtNtY32SsSRj4+Pjve+BJGkgej1CuAq4vqoenzJ/EbB7yrzdwOJW9sx+yl6hqjZX1dqqWrt8+UGfApMkHaQZxxCSnAb8a+BnpymeAJZMmbcE2AP83QHKJEnzTC+Dyu8EVgLfTwKdv/wPTfIzwN3AmsmKSU4AFgKP0AmEBUlOqqq/aVXWAAccUJYkjUYvgbAZ+IOu979BJyAuAd4I/FmSM+lcZXQlsKWq9gAk2QJcmeTX6Fxl9MvAzw+s9ZKkgZkxEKrqOeC5yfdJJoAXqmocGE/yEeBW4BjgHuBXuhb/KHAD8GPgKeCSmS45lSSNRr/fQ6CqNk15fxsw7ZfN2hfU3ndQLZMkzSlvXSFJAg7iCEGS9PetvOyuUTdhIDxCkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBPQYCEl+P8mOJM8keaQ9I5kkK5NUkomu6fKu5RYmuaEttzPJpcPaEUnS7PT6gJxrgF+tqr1JVgH3Jfm/dJ6TDHBUVb00zXKbgJOAFcBxwL1JvltVd8+y3ZKkAevpCKGqtlXV3sm3bTqxh0UvAK6qql1V9RBwHXDhwTRUkjRcPY8hJPlCkueAh4EdwB93FW9P8niSG5Msa/WXAscDW7vqbQVW72f965OMJRkbHx/vdz8kSbPUcyBU1UeBxcCZwBZgL/Ak8DY6p4ROb+W3tkUWtX93d61md6sz3fo3V9Xaqlq7fPnyfvZBkjQAfV1lVFUvV9UDwJuBS6pqoqrGquqlqnoC2AD8QpLFwERbbEnXKpYAewbRcEnSYB3sZacLmH4MoSbXW1W76JxaWtNVvgbYdpDblCQN0YyBkOSNSc5NsijJoUneDZwHfCPJGUlOSXJIkmOAzwL3VdXkaaKbgY1Jlrarky4GbhrSvkiSZqGXI4QCLgEeB3YBvwP8elV9HTgBuJvOaaAH6YwrnNe17BXAo8B24H7gWi85laT5acbvIVTVOPCO/ZTdDtx+gGX3Ahe1SZI0j3nrCkkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEtBjICT5/SQ7kjyT5JEkv9ZVdlaSh5M8l+TeJCu6yhYmuaEttzPJpcPYCUnS7PV6hHANsLKqlgC/BFyd5PQky4AtwOXA0cAY8JWu5TYBJwErgHcBn0hy9oDaLkkaoBmfqQxQVdu637bpROB0YFtVfRUgySbgySSrquph4ALgwqraBexKch1wIXD3wPZAkjQQPY8hJPlCkueAh4EdwB8Dq4Gtk3Wq6lngUWB1kqXA8d3l7fXq/ax/fZKxJGPj4+N974gkaXZ6DoSq+iiwGDiTzmmivcAiYPeUqrtbvUVd76eWTbf+zVW1tqrWLl++vNdmSZIGpK+rjKrq5ap6AHgzcAkwASyZUm0JsKeVMaV8skySNM8c7GWnC+iMIWwD1kzOTHLE5Pw2brCju7y97h6PkCTNEzMGQpI3Jjk3yaIkhyZ5N3Ae8A3gDuDUJOuSHA58EvhOG1AGuBnYmGRpklXAxcBNQ9kTSdKs9HKEUHRODz0O7AJ+B/j1qvp6VY0D64BPtbIzgHO7lr2CziDzduB+4Nqq8gojSZqHZrzstH3ov+MA5fcAq/ZTthe4qE2SpHnMW1dIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJKA3p6pvDDJ9Um2J9mT5C+TvKeVrUxSSSa6psunLHtDkmeS7Exy6TB3RpJ08GZ8hGar8wM6j9H8PvCLwB8m+SdddY6qqpemWXYTcBKwAjgOuDfJd32usiTNPzMeIVTVs1W1qaq+V1V/V1V/BDwGnN7D+i8ArqqqXVX1EHAdcOGsWixJGoq+xxCSHAucDGzrmr09yeNJbkyyrNVbChwPbO2qtxVYvZ/1rk8ylmRsfHy832ZJkmapr0BIchhwK/DlqnoYeBJ4G51TQqcDi1s5wKL27+6uVexudV6hqjZX1dqqWrt8+fJ+miVJGoBexhAASHIIcAvwIrABoKomgLFW5YkkG4AdSRYDE23+EuCFrtd7BtBuSdKA9XSEkCTA9cCxwLqq+sl+qtbkeqtqF7ADWNNVvoa/f6pJkjRP9HrK6IvAW4H3VtXzkzOTnJHklCSHJDkG+CxwX1VNnia6GdiYZGmSVcDFwE2Da74kaVB6+R7CCuDDwGnAzq7vG5wPnADcTec00IPAXuC8rsWvAB4FtgP3A9d6yakkzU8zjiFU1XYgB6hy+wGW3Qtc1CZJ0jzmrSskSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqenliWkLk1yfZHuSPUn+Msl7usrPSvJwkueS3NuesNa97A1JnkmyM8mlw9oRSdLs9HKEsAD4AfAO4EhgI/CHSVYmWQZsAS4HjgbGgK90LbsJOAlYAbwL+ESSswfWeknSwPTyCM1n6XywT/qjJI8BpwPHANuq6qsASTYBTyZZVVUPAxcAF1bVLmBXkuuAC+k8h1mSNI/0PYaQ5FjgZGAbsBrYOlnWwuNRYHWSpcDx3eXt9erZNFiSNBx9BUKSw4BbgS+3I4BFwO4p1XYDi1sZU8ony6Zb9/okY0nGxsfH+2mWJGkAeg6EJIcAtwAvAhva7AlgyZSqS4A9rYwp5ZNlr1BVm6tqbVWtXb58ea/NkiQNSE+BkCTA9cCxwLqq+kkr2gas6ap3BHAinXGFXcCO7vL2etsA2i1JGrBejxC+CLwVeG9VPd81/w7g1CTrkhwOfBL4TjudBHAzsDHJ0iSrgIuBmwbTdEnSIPXyPYQVwIeB04CdSSbadH5VjQPrgE8Bu4AzgHO7Fr+CziDzduB+4Nqq8gojSZqHernsdDuQA5TfA6zaT9le4KI2SZLmMW9dIUkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAnoMhCQbkowl2Zvkpq75K5NU12M1J5Jc3lW+MMkNSZ5JsjPJpUPYB0nSAMz4CM3mR8DVwLuBfzhN+VFV9dI08zcBJwErgOOAe5N81+cqS9L809MRQlVtqaqvAU/1uf4LgKuqaldVPQRcB1zY5zokSXNgUGMI25M8nuTGJMsAkiwFjge2dtXbCqyebgVJ1rfTUmPj4+MDapYkqVezDYQngbfROSV0OrAYuLWVLWr/7u6qv7vVeYWq2lxVa6tq7fLly2fZLElSv3odQ5hWVU0AY+3tE0k2ADuSLAYm2vwlwAtdr/fMZpuSpOEY9GWnNbneqtoF7ADWdJWvAbYNeJuSpAHo9bLTBUkOBw4FDk1yeJt3RpJTkhyS5Bjgs8B9VTV5muhmYGOSpUlWARcDNw1hPyRJs9TrEcJG4HngMuCD7fVG4ATgbjqngR4E9gLndS13BfAosB24H7jWS04laX7qaQyhqjbR+U7BdG4/wHJ7gYvaJEmax7x1hSQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJGCWz1Qelr/64W5WXnbXSNvwvU+fM9LtS9Jc8whBkgT0/kzlDUnGkuxNctOUsrOSPJzkuST3JlnRVbYwyQ1JnkmyM8mlA26/JGlAej1C+BFwNXBD98wky4AtwOXA0cAY8JWuKpuAk4AVwLuATyQ5e3ZNliQNQ0+BUFVbquprwFNTit4PbKuqr1bVC3QCYE2SVa38AuCqqtpVVQ8B1wEXDqTlkqSBmu0Ywmpg6+SbqnoWeBRYnWQpcHx3eXu9eroVJVnfTkuNvfzc7lk2S5LUr9kGwiJg6qf3bmBxK2NK+WTZK1TV5qpaW1VrD33DkbNsliSpX7MNhAlgyZR5S4A9rYwp5ZNlkqR5ZraBsA1YM/kmyRHAiXTGFXYBO7rL2+tts9ymJGkIer3sdEGSw4FDgUOTHJ5kAXAHcGqSda38k8B3qurhtujNwMYkS9tA88XATQPfC0nSrPV6hLAReB64DPhge72xqsaBdcCngF3AGcC5XctdQWeQeTtwP3BtVd09mKZLkgapp1tXVNUmOpeUTld2D7BqP2V7gYvaJEmax7x1hSQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCRhQICS5L8kLSSba9NddZR9Isj3Js0m+luToQWxTkjRYgzxC2FBVi9p0CkCS1cCXgA8BxwLPAV8Y4DYlSQPS0zOVZ+F84M6q+iZAksuBh5Isrqo9Q962JKkPgzxCuCbJk0m+leSdbd5qYOtkhap6FHgROHnqwknWJxlLMvbyc7sH2CxJUi8GFQj/CTgBeBOwGbgzyYnAImDqp/tuYPHUFVTV5qpaW1VrD33DkQNqliSpVwMJhKr6P1W1p6r2VtWXgW8BvwhMAEumVF8CeLpIkuaZYV12WkCAbcCayZlJTgAWAo8MabuSpIM060HlJEcBZwD3Ay8B/w74l8DHgMOAP0tyJvAXwJXAFgeUJWn+GcRVRocBVwOrgJeBh4H3VdUjAEk+AtwKHAPcA/zKALYpSRqwWQdCVY0DbztA+W3AbbPdjiRpuLx1hSQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCZiDQEhydJI7kjybZHuSDwx7m5Kk/g3imcoz+TzwInAscBpwV5KtVbVtDrZ90FZedteomzBvfO/T54y6CdJ++bs6OEMNhCRHAOuAU6tqAnggydeBDwGXDXPbeu3xF18arlTV8Fae/Czwrap6Q9e83wDeUVXvnVJ3PbC+vT0VeHBoDXt1WQY8OepGzBP2xT72xT72xT6nVNXig1142KeMFgHPTJm3G3hFg6tqM7AZIMlYVa0dctteFeyLfeyLfeyLfeyLfZKMzWb5YQ8qTwBLpsxbAuwZ8nYlSX0adiA8AixIclLXvDXAvB5QlqTXo6EGQlU9C2wBrkxyRJK3A78M3DLDopuH2a5XGftiH/tiH/tiH/tin1n1xVAHlaHzPQTgBuDfAE8Bl1XVbUPdqCSpb0MPBEnSq4O3rpAkAQaCJKkZSSD0en+jdPx2kqfa9NtJMtftHaY++uLjSR5MsifJY0k+PtdtHbZ+73uV5B8keSjJ43PVxrnST18k+WdJvplkIskTST42l20dtj5+RxYm+b3WB08nuTPJm+a6vcOUZEOSsSR7k9w0Q93/mGRnkmeS3JBk4UzrH9URQvf9jc4Hvphk9TT11gPvo3Op6j8F3gt8eK4aOUd67YsA/x5YCpwNbEhy7py1cm702heTPg6Mz0XDRqCnvkiyDLgb+BJwDPDTwP+Yw3bOhV5/Lj4G/As6nxU/BewCPjdXjZwjPwKupnOhzn4leTed2wOdBawATgD+y4xrr6o5nYAj6Pznntw17xbg09PU/VNgfdf7XwX+91y3eT70xTTLfhb43Kj3YVR9Afxj4CHgPcDjo27/qPoC+C3gllG3eZ70xReBz3S9Pwf461Hvw5D65WrgpgOU3wb8Vtf7s4CdM613FEcIJwMvVdUjXfO2AtMl/upWNlO9V6t++uL/a6fNzuS19QW/fvvic8BvAs8Pu2Ej0E9f/HPg6SR/muTH7TTJW+aklXOjn764Hnh7kp9K8gY6RxP/fQ7aOB9N99l5bJJjDrTQKAKh5/sbtbq7p9Rb9BoaR+inL7ptovN/d+MQ2jQqPfdFkn8LHFpVd8xFw0agn5+LNwMX0Dld8hbgMeD2obZubvXTF38D/AD4YVvmrcCVQ23d/DXdZyfM8NkyikDo5/5GU+suASaqHQO9BvR9r6ckG+iMJZxTVXuH2La51lNftFuqfwb4D3PUrlHo5+fieeCOqvrzqnqBznnin09y5JDbOFf66YvPAwvpjKUcQecuCa/XI4TpPjthhvvIjSIQ+rm/0bZWNlO9V6u+7vWU5CLaQFFVvdaurOm1L04CVgJ/kmQnnV/649vVFCvnoJ1zoZ+fi+8A3X8gvVb+WJrUT1+cRue8+tPtj6XPAT/XBt5fb6b77Hyiqp464FIjGhD5AzqHtUcAb6dzOLN6mnofoTNw+CY6Vw1sAz4y6gGdEfXF+cBO4K2jbvMo+4LOLduP65reT+fKi+PonEYa+X7M8c/Fv6JzNc1pwGHA7wJ/Mur2j6gvbgT+G3Bk64vfBH446vYPuC8WAIcD19AZXD8cWDBNvbPb58XPAEcB/4teLlYZ0U4dDXwNeBb4PvCBNv9MOqeEJuuFzumBp9v0GdrtNl4rUx998RjwEzqHgpPT7426/aPoiynLvJPX2FVG/fYFcAmd8+a7gDuBfzTq9o+iL+icKroV+DHwt8ADwM+Nuv0D7otNdI4Cu6dNdMaPJoC3dNW9FHiCznjKjcDCmdbvvYwkSYC3rpAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJAD+Hw9UM97VS5qnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cnba8Bs8_vzu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "1e341269-eeac-4df8-a609-fa385bb24189"
      },
      "source": [
        "plt.hist(pred_data[:,1], bins=[0., .1, .2, .3, .4, .5, .6, .7, .8, .9, 1.])\n",
        "plt.xlim([0., 1.])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE5jf1f_RpDd"
      },
      "source": [
        "---\n",
        "\n",
        "####The Probabilities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuQnmHA_RpDd"
      },
      "source": [
        "Input the calculated probabilities to sample source dataframe. Run the below codes until the Visualization II (Result). Because of the gaussian mixture can't determine which one is the cluster or the field stars, you could change the column. If the result of the `pred_data[:,0]` shows the field stars in the Vector Point Diagram (VPD), you should change it to `pred_data[:,1]`.   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwO2vLTsRpDd"
      },
      "source": [
        "samplesource['prob'] = pred_data[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHzaIi-U7s_f"
      },
      "source": [
        "Determine the probability member classes. According to Agarwal et al. (2021), there are three main classes: `member_high` is high probability members ($P(x) \\ge 0.6$); `member_moder` is moderate probability members ($0.2 \\lt P(x) \\lt 0.6$); and `member_low` is low probability members ($P(x) \\le 0.2$). There is also one additional class: `member_ultra` is ultra-high probability members ($P(x) \\ge 0.8$).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ngT63n8RpDe"
      },
      "source": [
        "member_ultra = samplesource[samplesource['prob'] >= .8].reset_index(drop=True)\n",
        "member_high  = samplesource[samplesource['prob'] >= .6].reset_index(drop=True)\n",
        "member_moder = samplesource[(samplesource['prob'] > .2) & (samplesource['prob'] < .6)].reset_index(drop=True)\n",
        "member_low   = samplesource[samplesource['prob'] <= .2].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n38D3f2aRpDg"
      },
      "source": [
        "Stars with a high probability values are automatically considered as members of the cluster. Stars with medium probability values ​​can be considered as the cluster members if their parallax values lie in the parallax value range of ultra-high probability cluster members. We can calculate it using the code below and consider it as `member_incl`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-ZyaLB_RpDg"
      },
      "source": [
        "member_incl = member_moder[(member_moder['parallax'] >= member_ultra['parallax'].min()) & \n",
        "                           (member_moder['parallax'] <= member_ultra['parallax'].max())].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByqaLkgpRpDg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ec6c469-ab4d-4ea1-e866-5df67a344b6c"
      },
      "source": [
        "print('Sample Sources = %d \\nHigh probability member sources (p >= 0.6) = %d \\nModerate probability member sources (0.2 <= p < 0.6) = %d \\nIncluded moderate probability member sources = %d' %(len(samplesource),\n",
        "                                                                                                                                          len(samplesource[samplesource['prob'] >= .6]),\n",
        "                                                                                                                                          len(samplesource[(samplesource['prob'] >= .2) & (samplesource['prob'] < .6)]),\n",
        "                                                                                                                                          len(member_incl)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample Sources = 512 \n",
            "High probability member sources (p >= 0.6) = 392 \n",
            "Moderate probability member sources (0.2 <= p < 0.6) = 7 \n",
            "Included moderate probability member sources = 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Jl7SkeVAhK5"
      },
      "source": [
        "Combine `member_high` and `member_incl` to get all members. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxcIZOCfRpDh"
      },
      "source": [
        "member_all = pd.concat([member_high, member_incl]).sort_values(by=['prob'], ascending=False).reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LtaAOqGRpDh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "306500c7-cc5a-4e7b-be5d-88887b610798"
      },
      "source": [
        "len(member_all)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "392"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dXHvCYbBMCC"
      },
      "source": [
        "Calculate some important parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14C4Xy-zBP6r"
      },
      "source": [
        "mean_para_val = np.mean(member_all['parallax'] + 0.013)\n",
        "mean_para_std = np.std(member_all['parallax'] + 0.013)\n",
        "\n",
        "member_dist = 1000./(member_all['parallax'] + 0.013)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF-bv030RpDh"
      },
      "source": [
        "mean_pmra_val = np.mean(member_all['pmra'])\n",
        "mean_pmra_std = np.std(member_all['pmra'])\n",
        "mean_pmde_val = np.mean(member_all['pmdec'])\n",
        "mean_pmde_std = np.std(member_all['pmdec'])\n",
        "mean_dist_val = np.mean(member_dist )\n",
        "mean_dist_std = np.std(member_dist )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJwY4B06RpDh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dca73079-eb41-4e1d-cabd-e167cab328f5"
      },
      "source": [
        "mean_pmra_val, mean_pmra_std, mean_pmde_val, mean_pmde_std, mean_para_val, mean_para_std, mean_dist_val, mean_dist_std"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9.823116215598855,\n",
              " 0.5055702001747391,\n",
              " -11.780912034884059,\n",
              " 0.48093878769976467,\n",
              " 2.2866083044054504,\n",
              " 0.19705662647295802,\n",
              " 440.30199574483095,\n",
              " 35.08529087809963)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMeMZDJoRpDi"
      },
      "source": [
        "---\n",
        "\n",
        "####Visualization II (Result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vnxQXic87mQY"
      },
      "source": [
        "Probability Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwtopNWnRpDj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "c010349a-d995-4d05-ec60-437cb14d5c86"
      },
      "source": [
        "bins_samp = np.arange(samplesource['prob'].min(), samplesource['prob'].max(), .1)\n",
        "bins_high = np.arange(samplesource['prob'][samplesource['prob'] >= .6].min(), samplesource['prob'][samplesource['prob'] >= .6].max(), .1)\n",
        "bins_mode = np.arange(samplesource['prob'][(samplesource['prob'] >= .2) & (samplesource['prob'] < .6) & \n",
        "                                       (samplesource['parallax'] >= member_ultra['parallax'].min()) & \n",
        "                                       (samplesource['parallax'] <= member_ultra['parallax'].max())].min(), \n",
        "                      samplesource['prob'][(samplesource['prob'] >= .2) & (samplesource['prob'] < .6) & \n",
        "                                       (samplesource['parallax'] >= member_ultra['parallax'].min()) & \n",
        "                                       (samplesource['parallax'] <= member_ultra['parallax'].max())].max(), .1)\n",
        "\n",
        "bins = np.linspace(0., 1., 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-f285f72a91c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m                       samplesource['prob'][(samplesource['prob'] >= .2) & (samplesource['prob'] < .6) & \n\u001b[1;32m      7\u001b[0m                                        \u001b[0;34m(\u001b[0m\u001b[0msamplesource\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'parallax'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mmember_ultra\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'parallax'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m                                        (samplesource['parallax'] <= member_ultra['parallax'].max())].max(), .1)\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: arange: cannot compute length"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UusBp7rRpDj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "a7244ae6-cd24-48e9-8b78-9582ae86ca6c"
      },
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "plt.hist(samplesource['prob'], bins=[0., .1, .2, .3, .4, .5, .6, .7, .8, .9, 1.], color='darkgray', rwidth=.975, label=\"Sample Sources\")\n",
        "plt.hist(member_high['prob'], bins=[.6, .7, .8, .9, 1.], color='tab:orange', rwidth=.975, label=\"High Probability Members\")\n",
        "plt.hist(member_incl['prob'], bins=[.2, .3, .4, .5, .6], color='tab:green', rwidth=.975, label=\"Moderate Probability Members\")\n",
        "\n",
        "plt.xlabel(\"Probability\")\n",
        "plt.ylabel(\"Number of Sources\")\n",
        "\n",
        "plt.xlim([0., 1.])\n",
        "\n",
        "plt.xticks()\n",
        "plt.yticks()\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nejiNvk4RpDj"
      },
      "source": [
        "Vector Point Diagram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGDfWaCORpDj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "6f00a863-314f-454b-9bbd-297ab85b5386"
      },
      "source": [
        "fig = plt.figure(figsize=(6, 6))\n",
        "plt.plot(samplesource['pmra'], samplesource['pmdec'], 'o', mec='silver', mfc='darkgray', markersize=5., label=\"Sample Sources\")\n",
        "plt.plot(member_high['pmra'], member_high['pmdec'], 'o', mfc='tab:orange', mec='None', markersize=5., label=r\"High probability ($p >= 0.6$)\")\n",
        "plt.plot(member_incl['pmra'], member_incl['pmdec'], 'o', mfc='tab:green', mec='None', markersize=5., label=r\"Moderate probability ($0.2 <= p <= 0.6$)\")\n",
        "\n",
        "plt.xlabel(r\"$\\mu_{\\alpha*}$ (mas/yr)\")\n",
        "plt.ylabel(r\"$\\mu_{\\delta}$ (mas/yr)\")\n",
        "\n",
        "plt.xticks()\n",
        "plt.yticks()\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYDS72Y4RpDk"
      },
      "source": [
        "Parallax and proper motions (pmra & pmdec) distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGVbzoZbRpDk"
      },
      "source": [
        "bins_samp = np.arange(samplesource['parallax'].min(), samplesource['parallax'].max(), .05)\n",
        "bins_high = np.arange(member_high['parallax'].min(), member_high['parallax'].max(), .05)\n",
        "#bins_mode = np.arange(member_incl['parallax'].min(), member_incl['parallax'].max(), .05)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZCYBg8kRpDl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "3d419c98-acdb-49ea-8ac8-bfe9192511f3"
      },
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "samplesource['parallax'].hist(bins=bins_samp, color='silver', rwidth=.85, label=\"Sample Sources\")\n",
        "member_high['parallax'].hist(bins=bins_high, color='tab:orange', rwidth=.85, label=r\"High probability ($p >= 0.6$)\")\n",
        "#member_incl['parallax'].hist(bins=bins_mode, color='tab:green', rwidth=.85, label=r\"Moderate probability ($0.2 <= p <= 0.6$)\")\n",
        "\n",
        "plt.xlabel(r\"$\\omega$ (mas)\")\n",
        "plt.ylabel(\"Number of Sources\")\n",
        "\n",
        "plt.xticks()\n",
        "plt.yticks()\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lyS/e6aefzosvvsi8efM46aSTquxz4IEHUlZWVjH51OLFi/noo4/YuXMn27dv55ZbbmHy5MlV9hs3bhwDBgxg9erVPProo8yZM4fzzjsvYbIAeO2111i0aFGVT3myABg0aBArV65k9erVlJaWMmvWrIppdeN9+9vfpkOHDqxYsQKAl19+mZ49ewJBkmnTpk3CZLG3otLCWELQTwGAmR0AHE7Qr7HZzNYDfYGXYpv0je0jEk1FW2veJl4G58MYNGgQp556Kn369KFdu3b07t2bli3DP7oLiSdV2rBhwx7bDBgwgAsuuIAjjzwSgIsuuoj+/ftXdI4nmpRp5MiR3H///fTo0YNu3bpVzNud7Jw1ad26NUOGDCE/P5+TTz6Z2267jdzcXIYPH06rVq2SPgV14okn8vrrr3P88cezePFizjjjDAYPHszu3buZPHlyxRzn8X74wx8ybdo0GjVK3a/VRo0ace+993LSSSdRVlbG+PHj6dWrV8X6U045hYceeohDDjmEe+65h7Fjx1JaWsphhx3GI488AgQtou9973upCcjdM/YhSFBNgV8Bv4t9bwS0JbjNdGas7FZgbtx+U4A5wIFAd2A9MLKm8w0cONCz3SuvvFLXIURWKutm6dKltTvAxwsTf9Jk27Zt7u6+fft2HzhwoC9YsGCP9V988UXSfVevXu29evVKW2zpVlZW5n379vX33nsv6TYLFizwcePGubv7scce68uXL69YV13dRNHpp5/uK1asSLo+0bULzPcEv1MzfUvql8AO4GpgXOz7L939s1iyuBnYDAwGRsftdx2wCviQIHHc5u4vZjBukQZlwoQJ9OvXjwEDBnDmmWcyYMCAug4pI5YuXUqXLl0YMWIEXbt2TbrdgAEDGD58OGVlZaxatarabaOstLSU0047Len0tnsro7ek3L2I4JHZROtmE7QeEq3bBYyPfUSklmbOnLnP+9bnSZV69uzJBx98EGrb8eODXzfr1q1LZ0hplZuby3nnnZey40Wl01tERCJOCUNEREJRwhBJEU/RcBAimbK316wShkgKNG3alE2bNilpSL3h7mzatCnpeyKJROU9DJF6rX379qxbt47PPqs8YEFIWzYkLt+6bN+DqoWdO3fu1S+SbNKQ6qZp06a0b98+9PZKGCIp0LhxYzp37rzvByg6Kkn5Xr4AmCLFxcWhh4vINtlcN6FuSZlZTzPrFrd8gpn93sz+28zCDxgvIiL1Vtg+jIeB/gBm1gF4mmDeiksJ5rcQEZEGLmzC6A4sjH0/C3jL3U8BzgXGpCMwERGJlrAJIwcojX0fAZQP5L4KaJfqoEREJHrCJox3gYlmNpQgYZSP4/QdYGM6AhMRkWgJmzD+C7gYKAYec/d/xcpPBd5OQ1wiIhIxoR6rdfdXzawt0MLdN8etegD4MsluIiLSgIR+09vdy4AcMxtsZk1iZWvcPckbRyIi0pCEfQ+juZk9DmwA3iDou8DM7jezovSFJyIiURG2hXErcAgwgGDSo3LPAaenOigREYmesEODnAqc7u6LzCx+dLVlwGGpD0tERKImbAvjQGBTgvLmQFnqwhERkagKmzDmEbQyypW3Mi4h6NMQEZEGLuwtqf8H/NXMesX2uSL2/Ujg2HQFJyIi0RGqheHubwDHALkEw4GMAD4Bjnb3hdXtKyIiDUPo+TBib3efn8ZYREQkwsK+h3G2mY1KUD7KzM5KfVgiIhI1YTu9i4CdCcq3x9aJiEgDFzZhHAasSFD+PnoPQ0QkK4RNGJuBrgnKjwC2pS4cERGJqrAJ42ng/8zsiPKC2BzfdwBPpSMwERGJlr2ZD2MrsNTM1prZWmAJ8AVwZSoCMbNOZvYXM9tsZp+a2b1m1ii2rp+ZLTCzL2N/9kvFOUVEJLywCeMrYBhwMnB37DMSGOLuX6Qoll8TjIZ7MNAvdr5JZpZL0ML5PcEQJdOBp2PlIiKSITW+h2FmOQSti77u/hLwUppi6Qzc6+47gU/N7EWgF1AYi/NOd3fgbjP7OXAc30wVKyIiaVZjCyM2cdKHBG95p9OdwGgza2Zm3yFozZQnjXdiyaLcO7FyERHJENvz93CSjczOB8YA49x9Y1oCMetBcNupL5BDcOvpQuCXQC93Hx237QxgpbsXJTjOBGACQLt27QbOmjUrHeHWGyUlJeTl5dV1GJEUqbpZvyhx+cF1010XqbqJmGyom+HDhy9w94LK5WGHBvk5wS2jj81sHcELexXcvU9tgjOz/QhaE1MJxqzKAx4mmLhpPdCi0i4tSPI4r7tPjR2HgoICLywsrE1o9V5xcTHZXgfJRKpuiqoMpBAYszWzccREqm4iJpvrJmzC+FNao4CDgEMJ+jB2AbvM7BHgJuAK4D/NzOJuS/UB7ktzTCIiEidUwnD369MZhLtvNLPVwEQzu52ghXE+QV9FMcEkTT8zs/uBi2O7/T2dMYmIyJ7CPlabCWcQPKr7GcGQI7uBye5eCpwGnAdsAcYDp8XKRUQkQ0K1MMxsG9/MsleFu1fuY9hr7r6I4BHaROv+CQys7TlERGTfhe3DuKzScmOgP3AmcHNKIxIRkUgK24cxPVG5mS0kmH3vnlQGJSIi0VPbPoxXgB+kIhAREYm22iaM0UBaXuQTEZFoCdvp/S/27PQ2oB3B+xMT0xCXiIhEzL6+uPc1weOvxe6+PLUhiYhIFEXixT0REYm+sC0MAMzsOKAnwe2pJe5enI6gREQkesL2YXwHeJLg5blPYsWHmNl84HR3/yTpziIi0iCEfUrqboLxnLq4ewd37wB0jZXdna7gREQkOsLekjoBKHT31eUF7v6Bmf0MeDktkYmISKTszXsYicaSqnn2JRERaRDCJoyXgXvMrEN5gZkdSjCtqloYIiJZIGzC+BlwAPCBmX1oZh8Cq2JlP0tXcCIiEh1h38NYa2YDgOOB7rHiZe4+O22RiYhIpIR+DyM2PepLsY+IiGSZam9JmVlfMxteqWysmX1gZhvM7H4zy01viCIiEgU19WHcBHy3fMHMegKPACuBx4CxwH+lLToREYmMmhLGAOBvccujgaXufpK7Xw78B3BOuoITEZHoqClhtOaboUAAjgWejVsuBg5NcUwiIhJBNSWMz4DvAJhZDsFYUm/Frc8lGOpcREQauJoSRjFwnZkdBvxnrOyVuPU9gTWpD0tERKKmpsdqrwFmA+8TDDT4M3ffHrf+XPSmt4hIVqg2Ybj7GjPrDvQCPkswjPl1wLp0BSciItFR44t77v4VsDjJuoTlIiLS8OzNaLUiIpLFlDBERCQUJQwREQklacIws4fNrHns+7FmFnqgwtows9FmtszMtpvZKjMbGisfYWbLzexLM3vFzDpmIh4REQlU18IYRzDfBQTvXhyU7mDM7ATgVuBCoDnBm+UfmFkb4M8Ej/keBMwH/pDueERE5BvVtRrWAD81s78BBhxtZpsTbejur6YonuuBG9x9bmz5YwAzmwAscffHY8tFwEYz6+7uy1N0bhERqYYF01wkWGE2CniIYDwpJ0gaibi759Q6kGDokR3AtcBFQFPgKeBKYAqQ6+4T47Z/F7jO3Z+odJwJwASAdu3aDZw1a1ZtQ6vXSkpKyMvLq+swIilSdbN+UeLyg/tlNo6YSNVNxGRD3QwfPnyBuxdULk/awnD3p4GnzawV8DnBy3sb0hci7YDGwFnAUGA38DTwSyCPYFyreFsJblvtwd2nAlMBCgoKvLCwMH0R1wPFxcVkex0kE6m6KRqVuHzM1szGEROpuomYbK6bMC/ubYlNorQy9hJfuuyI/XmPu68HMLM7CBLGq0CLStu3ALalMR4REYkTdk7vOWbWxMzOIxhw0IGlwEx335WKQNx9s5mtix27ojj25xLg/PJCMzsAODxWLiIiGRAqYcRm2nsBaAn8K1Z8MVBkZiPdfVmK4nmEoKP9RYJbUpOB54AngdvM7EzgeYJ+jnfU4S31yZw5cxKWDxs2LMORiOybsC/u3QUsAg5196HuPpRg4qTFwJ0pjOdGYB7wHrAM+Cdws7t/BpwJ3AxsBgYTzP4nIiIZEvZlvCHAIHf/orzA3b8ws18Ac5PvtnfcfTcwKfapvG420D1V5xIRkb0TNmHsBFolKG8ZWyciaaJbWRIVYW9JPQs8aGZDzCwn9vku8ADwTPrCExGRqAjbwrgcmA68RjDzHgTJ5hngP9IQl0iDM+yVU5OsqP5di33dTyTVwj5WuwUYZWZdgB6x4mXu/n7aIhMRkUjZqxFoYwlCSUJEJAtpPgwREQlFCUNEREJRwhARkVBqTBhm1sjMJpnZIZkISEREoqnGhBEbofY2gqHHRUQkS4W9JTUXGJDOQEREJNrCPlb7IPC/ZtYRWABsj1/p7gtTHZiIiERL2IQxM/bnHQnWOVDrKVpFRCTawiaMzmmNQkREIi/s0CAfpjsQERGJttDvYZjZyWb2nJktNbMOsbKLzGxE+sITEZGoCJUwzGws8EdgJcHtqfJHbHOAq9ITmoiIREnYFsZVwMXuPhn4Kq58LtAv5VGJiEjkhE0YXYE3E5SXAC1SF46IiERV2ITxCXBEgvJjgVWpC0dERKIqbMKYCtxtZkNiyx3M7Hzgf4DfpCUyERGJlLCP1f6PmbUEXgKaAq8Au4Db3f2+NMYnIiIREXrGPXf/hZndDPQkaJksdfeStEUmIiKRsldTtBIMA7Iz9r0sxbGIiEiEhX0Po4mZ3Ql8DiwG3gE+N7O7zKxpOgMUEZFoCNvC+A1wInAR3zxeezTwK6A5MD71oYmISJSETRhnA2e4+0txZR+Y2QbgCZQwREQavLCP1W4HPk5Q/jGwI3XhgJl1NbOdZvb7uLIfmdmHZrbdzJ4ys4NSeU4REalZ2IRxD3Cdme1fXhD7fk1sXSrdB8yLO08v4AHgXKAd8CXw6xSfU0REapD0lpSZPVOpqBD42MzeiS33ju1/QKqCMbPRwBbgDaBLrHgs8Ky7vxrb5hpgmZk1d/dtqTq3iIhUz9w98QqzR8IexN0vrHUgZi2A+cBxBJ3rXdx9nJk9Dbzh7rfGbVsCDHP3BQmOMwGYANCuXbuBs2bNqm1o9VpJSQl5eXl1HUYkZbxu1i9KXH5wv31flya6bpLLhroZPnz4AncvqFyetIWRiiSwl24Efuvu68wsvjwP2Fpp260ET2dV4e5TCYYyoaCgwAsLC1MfaT1SXFxMttdBMhmvm6JRicvHbN33dWmi6ya5bK6bvX1xLy3MrB9wPNA/wepEI+K2AHQ7SkQkg0IlDDM7ECgChgPfolJnubt/q5ZxFAKdgI9irYs8IMfMegIvAn3jYjkMaAK8V8tziojIXgjbwngU6AVMB/5NMERIKk0F4jsbfk6QQCYSJKg3zWwosBC4AfizOrxFRDIrbMIoJOhkXpiOINz9S4LHZYGKTu2d7v4Z8JmZ/QSYAbQGZgOZ7l8REcl6YRPGKsK/s1Fr7l5UaXkmMDNT5xcRkarCJoHLgV+ZWV8zy0lnQCIiEk1hWxjvA/sT9CFQ6bFX3F1JRESkgQubMB4DWgI/Iz2d3iIiEnFhE0YBcKS7v5vOYEREJLrC9mEsperLcyIikkXCJoxfAneY2fFm1s7MDor/pDNAERGJhrC3pP4S+/Nv7Nl/YbFldXqLiDRwYRPG8LRGISIikRcqYbj7nHQHIiIi0RZ28MEB1a1P15AhIiISHWFvSc0n6KuIf2Mvvi9DfRgiIg1c2ITRudJyY4K5K34B/HdKIxIRkUgK24fxYYLi981sK3Ad8EJKoxIRkcip7Qi0q4H0TSwsIiKREbbTu/LLeQYcTDAL34oUxyQiIhEUtg9jI1UHHDRgLXBOSiMSEZFI2tcX974GPgPed/evUhuSiIhEkV7cExGRUKpNGGEHFnT3z1MTjoiIRFVNLYxEfReVeYjjiIhIPVfTL/rqBh0cSTDXt/owRESyQLUJI1HfhZn1B24DhgIPADemJzQREYmS0C/umVlnM5sJvA1sAnq6+8/c/bO0RSciIpFRY8Iws9ZmdhewHPg2cIy7n+Puq9IenYiIREa1CcPMfgGsAoYBo9z9OHefl5HIREQkUmrq9L4R2AGsAyaZ2aREG7n7qakOTKReKmqZpHxrZuMQSYOaEsaj1PxYrbNJelcAAAo9SURBVIiIZIGanpK6IENxYGZNgF8DxwMHEdwK+293fyG2fgRwH3Ao8BZwQZJh10VEJA1qO7x5KjUiGMxwGNAS+CXwRzPrZGZtgD8D1xAkk/nAH+oqUBGRbBSZN7TdfTvBcOnlnjOz1cBAoDWwxN0fBzCzImCjmXV39+WZjlUkMtRnIhlk7tHsojCzdsCHBBM0TQRy3X1i3Pp3gevc/YlK+00AJgC0a9du4KxZszIXdASVlJSQl5dX12FE0j7XzfpFicsP7hetdbWg6ya5bKib4cOHL3D3gsrlkWlhxDOzxsAMYLq7LzezPILh1ONtBZpX3tfdpwJTAQoKCrywsDDN0UZbcXEx2V4Hyexz3RSNSlw+Zmu01tWCrpvksrluotSHAYCZ7Qf8DigFLosVlwAtKm3aAtiWwdBE6o05czQjgaRepBKGmRnwW6AdcKa7746tWgL0jdvuAODwWLmIiGRApBIG8BugB/ADd98RV/4kkG9mZ5pZU+Ba4B11eIuIZE5kEoaZdQQuIejk/tTMSmKfsbEBDs8EbgY2A4OB0XUXrYhI9olMp3fsJTyrZv1soHvmIpKspsdVRaqITAtDRESiTQlDRERCUcIQEZFQItOHUZ8ke8Z92LBhGY5EamX9osQvvqmfQiQhtTBERCQUtTBSTK0PEWmo1MIQEZFQ1MJIQi0FEZE9KWGINGR6AVFSSLekREQkFCUMEREJRQlDRERCUR9GBlXXka5O9jTRPXyRlFELQ0REQlELowHY19aJWjVZTq0v2UtqYYiISChqYTRwJSUlCVsS6WpFqNWS5dRqadDUwhARkVDUwqgnovQ/9yjFIiKZoxaGiIiEohaGZEy6WiZq8YhkhhKG1HvDXjk1yQp1tIqkkm5JiYhIKGphSPQleVRzzvBndNspG+hR3chQC0NEREJRC0MSynhHsv4XWX/s699Vdfvp779eUAtDRERCUQtDRKpavwiKRlUtj9r/+NUyyah608Iws4PM7Ekz225mH5rZj+o6JhGRbFKfWhj3AaVAO6Af8LyZLXb3JXUblohEUk1P12W6TyUdfT8ZVi9aGGZ2AHAmcI27l7j768AzwLl1G5mISPYwd6/rGGpkZv2Bf7h7s7iynwPD3P0HlbadAEyILXYDVmQs0GhqA2ys6yAiSnWTnOomuWyom47u3rZyYX25JZUHfFGpbCvQvPKG7j4VmJqJoOoDM5vv7gV1HUcUqW6SU90kl811Uy9uSQElQItKZS2AbXUQi4hIVqovCeM9oJGZdY0r6wuow1tEJEPqRcJw9+3An4EbzOwAMxsCjAJ+V7eR1Qu6PZec6iY51U1yWVs39aLTG4L3MICHgROATcDV7j6zbqMSEcke9SZhiIhI3aoXt6RERKTuKWGIiEgoShj1nJldZmbzzWyXmU2rZrsLzKzMzEriPoWZizSzzKyJmf02Nu7YNjNbZGYnV7P9ZDP71My+MLOHzaxJJuPNpL2pm2y7bgDM7Pdmtj52LbxnZhdVs23WXDeghNEQfALcRPBAQE3edPe8uE9xekOrU42AtcAwoCXwS+CPZtap8oZmdhJwNTAC6AgcBlyfqUDrQOi6icmm6wbgV0And28BnArcZGYDK2+UhdeNEkZ95+5/dvenCJ4ckxh33+7uRe6+xt2/dvfngNVAlX/4wPnAb919ibtvBm4ELshguBm1l3WTdWLXwa7yxdjn8ASbZtV1A0oY2aa/mW2MNbOvMbP6MjRMrZlZO+AIEr/s2QtYHLe8GGhnZq0zEVtdq6FuIAuvGzP7tZl9CSwH1gN/SbBZ1l03ShjZ41UgH/gWwci/Y4Ar6zSiDDGzxsAMYLq7L0+wSR7B2GTlyr9XGausoQlRN1l53bj7JIK//6EELw3vSrBZ1l03ShhZwt0/cPfVsVsQ/wJuAM6q67jSzcz2IxgRoBS4LMlmlccqK//eoMcqC1M32XrdALh7WWwqhfbAxASbZN11o4SRvRywug4inczMgN8STLp1prvvTrLpEoKxycr1Bf7t7g22X2gv6qayBn/dJNCIxH0YWXfdKGHUc2bWyMyaAjlAjpk1TXSP2cxOjt2rxsy6A9cAT2c22oz7DdAD+IG776hmu0eBH5tZTzNrRfDU0LQMxFeXQtVNtl03ZvYtMxttZnlmlhN7EmoM8HKCzbPvunF3ferxByjimyc5yj9FwKEETeZDY9vdDvwb2A58QHBroXFdx5/GeukYq4udsXoo/4ytXDex7a+I1c8XwCNAk7r+GaJQN1l43bQF5gBbYtfCv4CLY+uy+rpxd40lJSIi4eiWlIiIhKKEISIioShhiIhIKEoYIiISihKGiIiEooQhIiKhKGGIiEgoShgiIhKKEoZIipnZgWb2bzNLNP5QJuN43Mz+sy5jkIZFb3qLpJiZ3Qa0cfcL6ziO3gTDXHR29601bS9SE7UwRFLIzJoBFxGMBFunPBiO/ANgXF3HIg2DEoZIJWb2f2Y2PzZfROV1883szmp2P4VgYL9/xO1TbGa/MbP/NbPPzewzM7vczJqY2X1mtsXMPjKzcyuda6SZvWZmm2P7/dXMesStP9bM5ppZiZltNbO3zSy/UjzPEIy2KlJrShgiccysG/BT4Ep3/zrBJsuA/tUcYiiwwKve6x1LMLHOYGAKcCfwFPAeUABMBx4ys4Pj9jkgtt2RQCHBjG7PmllubAj7p4HXCeZhGBzbtqzSed8GjjSz/auJWSQU9WGIxDGzB4EB7j4wyfq7gJPd/Ygk658Ctrr7+XFlxQTDXh8dWzZgA/Cmu58aK2tMMIT4j9z9T0mOfQDBMNrDgKXAJqDQ3edU8/P0IZhruou7r6ruZxepiVoYIjGxW1BnAX+KK/s/M/tp3GbNCX6xJ7M/wTwTlb1T/iXW+thAMNdCedluYDPB3Nnl5z7czGaa2Soz+4Jg3oX9COZj+Jxgsp6/mtnzZnaFmR2a4LzlkyOphSG1poQh8o3OQCvifpEDPwS+jFvuS/C/+2Q2AgcmKK88BaonKYv/N/kcwYQ+lxDccuoPfAXkAsSewhoMvAqcCqyIzRAX76DYn59VE7NIKEoYIt8o/0VfAmBmhcAhQGlsuSvQD3iymmP8E+hZ20DMrDXQHbjF3We7+zKC1s0e0++6+2J3v9XdC4Fi4PxKh8oHPnb3f9c2JhElDJFvfAR8DfzIzPoBdwPPAt+P9QU8QnBrqbqE8VegR+wXfm1sJmitXGxmXcxsGHA/QQsDM+tsZlPM7Bgz62hmw4E+VG39DI3FJFJrShgiMe6+Afhv4Gzgb8ADwM8JbgXNJehkPsXdKz+JFH+MfxE8mTS6lrF8DZxDkATeBe4DrgF2xTb5EjgCeJzgSavpwAzg1vJjmFlT4HTgwdrEIlJOT0mJpJiZjQTuAnpWl1wyEMelwCh3P7GuYpCGRS0MkRRz9xcJWgTt6ziU3QTvlIikhFoYIiISiloYIiISihKGiIiEooQhIiKhKGGIiEgoShgiIhKKEoaIiITy/wGVvli1DOPFOQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rW1uii4-RpDl"
      },
      "source": [
        "Spatial distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5R6ycc1RpDm"
      },
      "source": [
        "coor_all  = SkyCoord(ra=samplesource.ra, dec=samplesource.dec, frame='icrs', unit=(u.deg, u.deg))\n",
        "coor_high = SkyCoord(ra=member_high.ra, dec=member_high.dec, frame='icrs', unit=(u.deg, u.deg))\n",
        "coor_mid  = SkyCoord(ra=member_incl.ra, dec=member_incl.dec, frame='icrs', unit=(u.deg, u.deg))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2jJEZDcRpDm"
      },
      "source": [
        "ra_all  = coor_all.ra.wrap_at(180 * u.deg).degree\n",
        "de_all  = coor_all.dec.degree\n",
        "\n",
        "ra_high = coor_high.ra.wrap_at(180 * u.deg).degree\n",
        "de_high = coor_high.dec.degree\n",
        "\n",
        "ra_mid  = coor_mid.ra.wrap_at(180 * u.deg).degree\n",
        "de_mid  = coor_mid.dec.degree"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zIp8t50RpDm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "5421849c-8e2f-46e1-d730-080a05391ccb"
      },
      "source": [
        "fig = plt.figure(figsize=(6, 6))\n",
        "ax = plt.subplot()\n",
        "plt.plot(ra_all, de_all, 'o', mec='silver', mfc='darkgray', markersize=2., label=\"Sample sources\")\n",
        "plt.plot(ra_high, de_high, 'o', mfc='tab:orange', markersize=2., label=r\"High probability ($p >= 0.6$)\")\n",
        "plt.plot(ra_mid, de_mid, 'o', mfc='tab:green', markersize=2., label=r\"Moderate probability ($0.2 <= p <= 0.6$)\")\n",
        "\n",
        "plt.xlabel(r'$\\alpha$ (deg)')\n",
        "plt.ylabel(r'$\\delta$ (deg)')\n",
        "\n",
        "plt.legend()\n",
        "#ax.set_xticklabels([358.25, 358.5, 358.75, 359.0, 359.25, 359.5, 359.75, 0.00, 0.25], fontsize=18.)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFnvCnsfRpDm"
      },
      "source": [
        "Color Magnitude Diagram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP7TkK3WRpDn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "baf00ae2-77cc-4f35-a9e2-50fdb7d96f09"
      },
      "source": [
        "plt.figure(figsize=(6, 8))\n",
        "plt.plot(samplesource['bp_rp'], samplesource['phot_g_mean_mag'], 'o', mec='silver', mfc='darkgray', markersize=2., label=\"Sample Sources\")\n",
        "plt.plot(member_high['bp_rp'], member_high['phot_g_mean_mag'], 'o', color='tab:orange', markersize=2., label=r\"High probability ($p >= 0.6$)\")\n",
        "plt.plot(member_incl['bp_rp'], member_incl['phot_g_mean_mag'], 'o', color='tab:green', markersize=2., label=r\"Moderate probability ($0.2 <= p <= 0.6$)\")\n",
        "\n",
        "plt.xlabel(r\"$G_{BP}-G_{RP}$\")\n",
        "plt.ylabel(r\"$G$ (mag)\")\n",
        "\n",
        "plt.xlim([0., 3.])\n",
        "\n",
        "plt.gca().invert_yaxis()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8w2fVZERpDn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "84154a31-c2bd-4977-eb13-8fd978acb165"
      },
      "source": [
        "plt.figure(figsize=(6, 8))\n",
        "plt.plot(member_all['bp_rp'], member_all['phot_g_mean_mag'], 'o', color='tab:blue', markersize=2., label=r\"All members\")\n",
        "\n",
        "plt.xlabel(r\"$G_{BP}-G_{RP}$\")\n",
        "plt.ylabel(r\"$G$ (mag)\")\n",
        "\n",
        "plt.xlim([0., 3.])\n",
        "\n",
        "plt.gca().invert_yaxis()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDGgnNv2dvrn"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "####Tasks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGC0ygfaeWoR"
      },
      "source": [
        "You can also try with other data! <br>\n",
        "* M 67 = [gaiaedr3_150_M67.csv](https://drive.google.com/file/d/1eX41_G_W1eP3-9Qxi0OzM-BAIQOAQjbU/view?usp=sharing) <br>\n",
        "* NGC 7789 = [gaiaedr3_45_NGC7789.csv](https://drive.google.com/file/d/12TAgIMdPgFUTtpL2jely0Y7umQCRhcEy/view?usp=sharing) \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7wav6hS517X"
      },
      "source": [
        "## 6. References"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tv1hTDh53-F"
      },
      "source": [
        "1. Agarwal, Manan, et al. \"ML-MOC: Machine Learning (kNN and GMM) based Membership determination for Open Clusters.\" Monthly Notices of the Royal Astronomical Society 502.2 (2021): 2582-2599.\n",
        "2. Gaia Collaboration. \"VizieR Online Data Catalog: Gaia DR2 (Gaia Collaboration, 2018).\" VizieR Online Data Catalog (2018): I-345.\n",
        "3. McLachlan, G. and Peel., D. \"Finite mixture models.\" (2000).\n",
        "4. Pedregosa, Fabian, et al. \"Scikit-learn: Machine learning in Python.\" the Journal of machine Learning research 12 (2011): 2825-2830.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}