{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Demo_DAWGI_HBM",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gq9iKH4wz8k"
      },
      "source": [
        "#Introduction\n",
        "\n",
        "\n",
        "##In this practice we are going to use an algorithm based on a herarchic Bayesian model to infer the age-metallicity distribution of a sample of stars from the Pleidaes open cluster.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "az48QMihxAxR"
      },
      "source": [
        "#Gaia EDR3: The sample is selected using the following query:\n",
        "\n",
        "SELECT*\n",
        "FROM gaiaedr3.gaia_source AS gaia\n",
        "WHERE contains(POINT('ICRS', 56.75, 24.12),CIRCLE('ICRS',gaia.ra, gaia.dec, 5)) = 1\n",
        "AND sqrt(power(gaia.pmra - 20.5, 2) + power(gaia.pmdec + 45.5, 2)) < 6.0\n",
        "AND ruwe<=1.4\n",
        "AND phot_bp_rp_excess_factor < 1.25 + 0.052*power(bp_rp,2) - 0.0045*power(bp_rp,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jEfhiS37z46"
      },
      "source": [
        "##Code for 3D plots\n",
        "\n",
        "###This script plots the AMD in a 3D bar representation. The high of each bar refers to the 50th persentile of the posterior marginal distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8q6zEgqE78-Q"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "plt.style.use('classic')\n",
        "\n",
        "def list_ticks(x):\n",
        "    x_tk=[]\n",
        "    for i in x:\n",
        "        if i%1.==0.:\n",
        "            x_tk.append(str(int(i)))\n",
        "        else:\n",
        "            x_tk.append(str(i))\n",
        "            \n",
        "    return x_tk\n",
        "\n",
        "Zw = [0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.010, 0.014, 0.017, 0.020, 0.030, 0.040]\n",
        "N_zw = len(Zw)\n",
        "color_map = plt.cm.gist_rainbow(np.linspace(0., 1., N_zw))\n",
        "colors={ Zw[i] : color_map[i] for i in range(N_zw)}\n",
        "\n",
        "\n",
        "################################################################################\n",
        "################################################################################\n",
        "\n",
        "def sfh_plot_mode(name, Z0, age0, SFR0, typ, fig):\n",
        "\n",
        "    Z = np.unique(Z0)\n",
        "    Nz=len(Z)\n",
        "    idx_Z = range(1,Nz+1)\n",
        "\n",
        "    ages = np.unique(age0)\n",
        "    age_list = list_ticks(np.round(ages,1))\n",
        "\n",
        "    Nag=len(ages)\n",
        "    ages_aux=np.arange(1,Nag+1)\n",
        "    \n",
        "    niso = Nz*Nag\n",
        "\n",
        "    ### plot ###\n",
        "    ax = fig.add_subplot(131, projection='3d',autoscale_on=True)\n",
        "\n",
        "    nn=1\n",
        "    for zn in Z:\n",
        "        sfr = SFR0[np.where(Z0==zn)]\n",
        "        cs = [colors[zn]] * len(ages)\n",
        "        plt.bar(ages_aux-0.2, sfr, width=0.3, zs=nn, zdir='x', align='center', color=cs, alpha=0.8, linewidth=0)\n",
        "        nn = nn + 1\n",
        "\n",
        "    ax.view_init(30, -135)\n",
        "    sz=10\n",
        "    ax.set_xlabel('Z')\n",
        "    ax.set_ylabel('Age (Gyr)')\n",
        "    ax.set_zlabel('Stellar Fraction')\n",
        "\n",
        "\n",
        "    idx_Z = np.arange(1,4)\n",
        "    plt.xticks(idx_Z, ('0.014', '0.017', '0.020'))\n",
        "    plt.yticks(ages_aux, ('0.03','0.06','0.10','0.18','0.32','0.56','1.00'))\n",
        "\n",
        "\n",
        "    plt.xlim(min(idx_Z)-0.5,max(idx_Z)+0.5)\n",
        "    \n",
        "    \n",
        "    plt.ylim(min(ages_aux)-0.5,max(ages_aux)+0.5)\n",
        "\n",
        "    #ax.set_zlim(0., 1.)\n",
        "\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TjG4RHW8CJ8"
      },
      "source": [
        "##Code for stats\n",
        "\n",
        "###Script to calculate the percentiles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHg67rUc8E-K"
      },
      "source": [
        "from scipy.optimize import minimize\n",
        "from scipy import stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#############################################\n",
        "################# HISTOGRAM #################\n",
        "#############################################\n",
        "\n",
        "def a_stat(a):\n",
        "\n",
        "    Na=len(a)\n",
        "\n",
        "    perc = []\n",
        "    for i in range(Na):\n",
        "\n",
        "        p = np.percentile(a[i],[0,50,100])\n",
        "\n",
        "        perc.append([p[0], p[1], p[2]])\n",
        "\n",
        "    perc = np.array(perc)\n",
        "#    Nzeros=len(perc[0])\n",
        "#    perc[0] = np.zeros(Nzeros)\n",
        "#    perc[-1] = np.zeros(Nzeros)\n",
        "\n",
        "    return perc"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAlsySL58pia"
      },
      "source": [
        "##Code to visualise marginal distributions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLqvirXG8t0O"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy.interpolate\n",
        "from scipy.optimize import minimize\n",
        "from scipy import stats\n",
        "#import a_statistics_def_fun as st_def\n",
        "plt.style.use('classic')\n",
        "\n",
        "def a_stat(a):\n",
        "\n",
        "    Na=len(a)\n",
        "\n",
        "    perc = []\n",
        "    for i in range(Na):\n",
        "\n",
        "        p = np.percentile(a[i],[0,50,100])\n",
        "\n",
        "        perc.append([p[0], p[1], p[2]])\n",
        "\n",
        "    perc = np.array(perc)\n",
        "    #Nzeros=len(perc[0])\n",
        "    #perc[0] = np.zeros(Nzeros)\n",
        "    #perc[-1] = np.zeros(Nzeros)\n",
        "\n",
        "    return perc\n",
        "\n",
        "def list_ticks(x):\n",
        "    x_tk=[]\n",
        "    for i in x:\n",
        "        if i%1.==0.:\n",
        "            x_tk.append(str(int(i)))\n",
        "        else:\n",
        "            x_tk.append(str(i))\n",
        "            \n",
        "    return x_tk\n",
        "\n",
        "##################\n",
        "\n",
        "def marg_Z(ax, nag, n_z):\n",
        "    a_mar = []\n",
        "    x1, x2 = 0, nag\n",
        "    for i in range(n_z):\n",
        "        a_mar.append(np.sum(ax[x1:x2]))\n",
        "        x1+=nag\n",
        "        x2+=nag\n",
        "    return np.array(a_mar)\n",
        "\n",
        "\n",
        "def marg_AGE(ax, nag):\n",
        "    a_mar = []\n",
        "    x1, x2 = 0, nag\n",
        "    for i in range(nag):\n",
        "        a_mar.append(np.sum(ax[i::nag]))\n",
        "    return np.array(a_mar)\n",
        "\n",
        "\n",
        "################################################################################\n",
        "################################################################################\n",
        "################################################################################\n",
        "\n",
        "def marg_sfh_bar_age(name,sfh,a_sp,fig):\n",
        "\n",
        "    Z0, age0, mode = sfh[0], sfh[1], sfh[4]\n",
        "\n",
        "    Z = np.unique(Z0)\n",
        "    Nz=len(Z)\n",
        "    idx_Z = range(1,Nz+1)\n",
        "\n",
        "    age = np.unique(age0)\n",
        "    age_list = list_ticks(np.round(age,1))\n",
        "    Nag=len(age)\n",
        "    age_aux= np.arange(1,Nag+1)\n",
        "\n",
        "    SFR_mode_marg = marg_AGE(mode, Nag)\n",
        "    \n",
        "    ##\n",
        "    a_age = []\n",
        "    cont=0\n",
        "    for ai in a_sp:\n",
        "        a_aux = marg_AGE(ai, Nag)\n",
        "        a_age.append(a_aux)\n",
        "\n",
        "    a_age = np.array(a_age)\n",
        "\n",
        "    #perc = st_def.a_stat(a_age.T)\n",
        "    perc = a_stat(a_age.T)\n",
        "    ##\n",
        "\n",
        "    sfh_mgl=[]\n",
        "    for i in range(Nag):\n",
        "        sfh_mgl.append([age_aux[i], SFR_mode_marg[i], perc[i][0], perc[i][1], perc[i][2]])\n",
        "    sfh_mgl = np.array(sfh_mgl)\n",
        "    \n",
        "    sfh_mgl = sfh_mgl.T\n",
        "    \n",
        "\n",
        "    \n",
        "    ###########################################\n",
        "\n",
        "    ax = fig.add_subplot(132)\n",
        "\n",
        "    violin_parts = ax.violinplot(a_age, positions=age_aux, showmedians=True)\n",
        "    \n",
        "    for partname in ('cbars','cmins','cmaxes','cmedians'):\n",
        "        vp = violin_parts[partname]\n",
        "        vp.set_edgecolor('black')\n",
        "        vp.set_linewidth(1)\n",
        "\n",
        "    # Make the violin body blue with a red border:\n",
        "    for vp in violin_parts['bodies']:\n",
        "        vp.set_facecolor('y')\n",
        "        vp.set_edgecolor('black')\n",
        "        vp.set_linewidth(1)\n",
        "        vp.set_alpha(0.3)\n",
        "\n",
        "    \n",
        "    labels = age_list\n",
        "    ax.set_xticks(np.arange(1,len(labels) + 1))\n",
        "    ax.set_xticklabels(labels)\n",
        "    \n",
        "    ax.set_xlim(age_aux[-1]+0.5,age_aux[0]-0.5)\n",
        "    ax.set_ylim(0.,1.)\n",
        "\n",
        "    ax.set_xlabel('Age(Gyr)')\n",
        "    ax.set_ylabel('$a_{AGE}$', fontsize=15)\n",
        "    \n",
        "\n",
        "    \n",
        "\n",
        "################################################################################\n",
        "################################################################################\n",
        "################################################################################\n",
        "\n",
        "\n",
        "def marg_sfh_bar_Z(name,sfh,a_sp, niso,fig):\n",
        "\n",
        "    Z0, age0, mode = sfh[0], sfh[1], sfh[2]\n",
        "\n",
        "    Z = np.unique(Z0)\n",
        "    Z_list = list_ticks(Z)\n",
        "    Nz=len(Z)\n",
        "    idx_Z = range(1,Nz+1)\n",
        "\n",
        "    age = np.unique(age0)\n",
        "    Nag=len(age)\n",
        "    age_int = np.append(0.,age)\n",
        "    \n",
        "    SFR_mode_marg = marg_Z(mode, Nag, Nz)\n",
        "\n",
        "    ##\n",
        "    a_z = []\n",
        "    for ai in a_sp:\n",
        "        a_z.append(marg_Z(ai, Nag, Nz))\n",
        "    a_z = np.array(a_z)\n",
        "    perc = a_stat(a_z.T)\n",
        "\n",
        "    ##\n",
        "    sfh_mgl=[]\n",
        "    for i in range(Nz):\n",
        "        sfh_mgl.append([idx_Z[i], SFR_mode_marg[i], perc[i][0], perc[i][1], perc[i][2]])\n",
        "    sfh_mgl = np.array(sfh_mgl)\n",
        "\n",
        "    sfh_mgl = sfh_mgl.T\n",
        "    \n",
        "    ###########################################\n",
        "    ###########################################\n",
        "    \n",
        "    ax = fig.add_subplot(133)\n",
        "\n",
        "    violin_parts = ax.violinplot(a_z, positions=idx_Z, showmedians=True)\n",
        "\n",
        "    for partname in ('cbars','cmins','cmaxes','cmedians'):\n",
        "        vp = violin_parts[partname]\n",
        "        vp.set_edgecolor('black')\n",
        "        vp.set_linewidth(1)\n",
        "\n",
        "    # Make the violin body blue with a red border:\n",
        "    for vp in violin_parts['bodies']:\n",
        "        vp.set_facecolor('y')\n",
        "        vp.set_edgecolor('black')\n",
        "        vp.set_linewidth(1)\n",
        "        vp.set_alpha(0.3)\n",
        "    \n",
        "\n",
        "    labels = ['0.014', '0.017', '0.020']\n",
        "\n",
        "    tk=np.arange(1, len(labels) + 1)\n",
        "    ax.set_xticks(tk)\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.set_xlim(tk[0]-0.5,tk[-1]+0.5)\n",
        "\n",
        "    ax.set_ylim(0.,1.0)\n",
        "    \n",
        "    ax.set_xlabel('Z')\n",
        "    ax.set_ylabel('$a_Z$', fontsize=15)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4mWkt9NxYnO"
      },
      "source": [
        "#Selection effects\n",
        "\n",
        "\n",
        "##We should infer the AMD of the Pleiades sample for stars brighter than G=20 mag and G=15, S20 y S15.\n",
        "\n",
        "\n",
        "###1) Does the solution changes between S20 and S15? What are the differences?\n",
        "\n",
        "\n",
        "###2) There is a degeneration problem for the inference with S20. Is it solved with S15? If so, why is the problem solved?\n",
        "\n",
        "#Model dependencies: AMD-IMF\n",
        "\n",
        "\n",
        "###3) Does the solution changes between Salpeter and Kroupa? Is there a correlations between ages and the IMF?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLebqY0jxCiA"
      },
      "source": [
        ""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tdb5oriV7wDZ"
      },
      "source": [
        "##SFH Sampling for limiting magnitudes of 20 mag in each Gaia band\n",
        "\n",
        "###Main body of the algorithm. This consist of three sections: 1) Statistical model, 2) posterior sampling and 3) statatistical plots."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPvPbDq9gVqP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4306e77b-53a6-4b4c-9d57-20bd801273dc"
      },
      "source": [
        "from scipy import genfromtxt, special\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os, sys\n",
        "import pystan\n",
        "import pickle\n",
        "##\n",
        "#import SFH_3D_plot_no_scaled as sf3p_ns\n",
        "#import Marginal_SFH_Bar_NoScale as mgl_b_ns\n",
        "#path = os.getcwd()\n",
        "\n",
        "try:\n",
        "    dt, list_iso, it_title = sys.argv[1], sys.argv[2], sys.argv[3]\n",
        "    name = dt[:dt.find('.txt')]\n",
        "except:\n",
        "    url = 'https://raw.githubusercontent.com/sundarjhu/DAWGI_Lectures_2021/main/Demo_DAWGI_HBM/Pleiades/'\n",
        "    name = url + 'Pleiades_ruwe_cexcess_SelDist.txt'\n",
        "    list_iso = url + 'List_Iso21_PARSEC_GaiaEDR3.txt'\n",
        "    it_title = 'Pleiades'\n",
        "\n",
        "print('file: ', name)\n",
        "\n",
        "\n",
        "######################## Integration rutine ########################\n",
        "def trapz(yt,xt):\n",
        "    del_x = xt[1:]-xt[:len(xt)-1]\n",
        "    y2 = 0.5*(yt[1:]+yt[:len(yt)-1])\n",
        "    return sum(y2*del_x)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################### PDF DEFINITION #######################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "######################## Likelihood ########################\n",
        "def Normal_MGk(gk_dat,gk_err,Iso_sig):                  ## Like apparente magnitude\n",
        "    sig2 = gk_err*gk_err+Iso_sig*Iso_sig\n",
        "    return  lambda gk_iso : np.exp( -0.5*(gk_dat-gk_iso)**2 / sig2 ) / np.sqrt(2.*np.pi*sig2)\n",
        "\n",
        "\n",
        "def Phi_MGk(gkj2, sig_gkj2, gklim, sig_i2):             ## Limit magnitude function.\n",
        "    b = sig_i2*sig_i2+sig_gkj2*sig_gkj2\n",
        "    b1 = sig_i2*sig_i2/b\n",
        "    b2 = sig_gkj2*sig_gkj2/b\n",
        "    b3 = sig_i2*sig_gkj2/np.sqrt(b)\n",
        "    return  lambda gk_i2 : special.ndtr( ( gklim - b1*gkj2 - b2*gk_i2 ) / b3 )\n",
        "\n",
        "\n",
        "\n",
        "########################## Priors #############################\n",
        "def IMF_Krp(m,ml=0.1,mint=0.5,mu=100.,a1=1.3,a2=2.3):   ##    IMF Kroupa\n",
        "\n",
        "    h2 = (mu**(1.-a2)-mint**(1.-a2))/(1.-a2)\n",
        "    h1 = (mint**(1.-a1)-ml**(1.-a1))/(1.-a1)\n",
        "\n",
        "    c1 = 1./(h1+h2*mint**(a2-a1))\n",
        "    c2 = c1*mint**(a2-a1)\n",
        "\n",
        "    c = np.ones(len(m))\n",
        "    c[np.where(m < mint)] = c1\n",
        "    c[np.where(m >= mint)] = c2\n",
        "\n",
        "    a = np.ones(len(m))\n",
        "    a[np.where(m < mint)] = -a1\n",
        "    a[np.where(m >= mint)] = -a2\n",
        "    \n",
        "    imf = c*m**a\n",
        "    \n",
        "    return imf\n",
        "\n",
        "def IMF_Salp(m, xs=1.35, ml0=0.1, mu0=100.):            ## IMF Salpeter\n",
        "    cte=-xs/(mu0**(-xs)-ml0**(-xs))\n",
        "    return cte*m**(-1.-xs) \n",
        "\n",
        "\n",
        "\n",
        "############################# Posterior #############################\n",
        "def P_ij(dat, Ndat, gk1_lim, gk2_lim, gk3_lim, Iso, Niso, sig_i):\n",
        "    p_ij = []\n",
        "    for j in range(Ndat):\n",
        "\n",
        "        P_gk1 = Normal_MGk(dat[2][j],dat[3][j],sig_i)\n",
        "        P_gk2 = Normal_MGk(dat[4][j],dat[5][j],sig_i)\n",
        "        P_gk3 = Normal_MGk(dat[6][j],dat[7][j],sig_i)\n",
        "\n",
        "        Phi_gk1 = Phi_MGk(dat[2][j], dat[3][j], gk1_lim, sig_i)\n",
        "        Phi_gk2 = Phi_MGk(dat[4][j], dat[5][j], gk2_lim, sig_i)\n",
        "        Phi_gk3 = Phi_MGk(dat[6][j], dat[7][j], gk3_lim, sig_i)\n",
        "\n",
        "        pi=[]\n",
        "        for i in range(Niso):\n",
        "            Intg = IMF_Krp(Iso[i][0])*P_gk1(Iso[i][1])*P_gk2(Iso[i][2])*P_gk3(Iso[i][3])*Phi_gk1(Iso[i][1])*Phi_gk2(Iso[i][2])*Phi_gk3(Iso[i][3])\n",
        "            p = trapz(Intg,Iso[i][0])\n",
        "            pi.append(p)\n",
        "\n",
        "        p_ij.append(pi)\n",
        "\n",
        "        if j%200==0:\n",
        "            print(100.*(float(j)/float(Ndat)),'%')\n",
        "\n",
        "    p_ij = np.array(p_ij)\n",
        "    \n",
        "    return p_ij\n",
        "\n",
        "\n",
        "\n",
        "################### Normalization constant #####################\n",
        "def phi(gkk_lim,sig_i4):                                                ## Limit magnitude function.\n",
        "    return lambda gkk_i4: special.ndtr((gkk_lim-gkk_i4)/sig_i4)\n",
        "\n",
        "def C_ij(Ndat_c, gk1_lim, gk2_lim, gk3_lim, Iso_c, Niso_c, sig_i):      ## Normalization Constant ##\n",
        "\n",
        "    phi_gk1c = phi(gk1_lim,sig_i)\n",
        "    phi_gk2c = phi(gk2_lim,sig_i)\n",
        "    phi_gk3c = phi(gk3_lim,sig_i)\n",
        "    \n",
        "    w=np.array([])\n",
        "    for i in range(Niso_c):\n",
        "        intg_c = IMF_Krp(Iso_c[i][0])*phi_gk1c(Iso_c[i][1])*phi_gk2c(Iso_c[i][2])*phi_gk2c(Iso_c[i][3])\n",
        "        p_c = trapz(intg_c,Iso_c[i][0])\n",
        "        w = np.append(w,p_c)\n",
        "    \n",
        "    return np.array([w for k in range(Ndat_c)])\n",
        "\n",
        "##########################################################################################################################\n",
        "##################################################### ISOCRHONES #########################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "gk1_lim0 = 20.    ##  27.5  ##   29.\n",
        "gk2_lim0 = 20.    ##  28.   ##   28.4\n",
        "gk3_lim0 = 20.\n",
        "\n",
        "isofilelist = np.genfromtxt(list_iso,dtype='str')\n",
        "\n",
        "isos0=[]\n",
        "nn=0\n",
        "path = url\n",
        "for k in isofilelist:\n",
        "    nn+=1\n",
        "    isos0.append(np.loadtxt(path+k, unpack=True))\n",
        "\n",
        "\n",
        "N_iso = len(isos0)\n",
        "print( 'N_iso = ', N_iso )\n",
        "print( 'N_col = ', len(isos0[0]) )\n",
        "\n",
        "\n",
        "dismod = 5.667\n",
        "for j in range(N_iso):\n",
        "    isos0[j][1]=isos0[j][1]+dismod\n",
        "    isos0[j][2]=isos0[j][2]+dismod\n",
        "    isos0[j][3]=isos0[j][3]+dismod\n",
        "\n",
        "isos = []\n",
        "for j in range(N_iso):\n",
        "    f_lim=np.where(isos0[j][1]<=gk1_lim0)\n",
        "    i_aux = isos0[j].T[f_lim]\n",
        "    isos.append(i_aux.T)\n",
        "\n",
        "##########################################################################################################################\n",
        "#####################################################    DATA    #########################################################\n",
        "##########################################################################################################################\n",
        "#    0         1        2      3      4      5       6      7\n",
        "#   Plx    Plx_error    G    Gerr    GB    GBerr    GR    GRerr\n",
        "#dat0 = np.loadtxt(dt, unpack = True)\n",
        "dat0 = np.loadtxt(name, unpack = True)\n",
        "\n",
        "dat0[2] = dat0[2]-0.11        ## Extinction correction\n",
        "dat0[4] = dat0[4]-0.14\n",
        "dat0[6] = dat0[6]-0.083\n",
        "\n",
        "gk_filter = np.where(dat0[2]<=gk1_lim0)\n",
        "dat = dat0.T[gk_filter]\n",
        "dat = dat.T\n",
        "\n",
        "N_dat = len(dat[0])\n",
        "print( 'N_dat = ', N_dat )\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################### Pij CALCULATION ######################################################\n",
        "##########################################################################################################################\n",
        "print( ' ' )\n",
        "\n",
        "sig_i0 = 0.05\n",
        "\n",
        "print( 'Calculating Cij ...' )\n",
        "cc = C_ij(N_dat, gk1_lim0, gk2_lim0, gk3_lim0, isos, N_iso, sig_i0)\n",
        "print( 'C_ij = ', len(cc), len(cc[0]) )\n",
        "print( 'Cij end.' )\n",
        "\n",
        "print( ' ' )\n",
        "\n",
        "print( 'Calculating Pij ...' )\n",
        "start = time.time()\n",
        "pp=P_ij(dat, N_dat, gk1_lim0, gk2_lim0, gk3_lim0, isos, N_iso, sig_i0)\n",
        "end = time.time()\n",
        "print( (end - start)/60., 'mins' )\n",
        "print( 'P_ij = ', len(pp), len(pp[0]) )\n",
        "print( 'Pij end.' )\n",
        "\n",
        "print( ' ' )\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################# POSTERIOR SAMPLING #####################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "############ Stan code ############\n",
        "code = \"\"\"\n",
        "\n",
        "functions{\n",
        "    real P(int N1, int N2, vector v, matrix M) {\n",
        "       vector[N1] Mj;\n",
        "        vector[N1] ln_Mj;\n",
        "\n",
        "        Mj= M*v;\n",
        "        for (j in 1:N1){\n",
        "            if (Mj[j]<=0.)\n",
        "                Mj[j] = 1.;\n",
        "        }\n",
        "        ln_Mj = log(Mj);\n",
        "        return sum(ln_Mj);\n",
        "    }\n",
        "}\n",
        "\n",
        "data {\n",
        "    int<lower=0> Nj; // number of data\n",
        "    int<lower=0> Ni; // number of isochrones\n",
        "    matrix[Nj,Ni] Pij; // Probability matrix\n",
        "    matrix[Nj,Ni] Cij; // Normalization matrix\n",
        "}\n",
        "\n",
        "parameters {\n",
        "    simplex[Ni] a;\n",
        "}\n",
        "\n",
        "model {\n",
        "    target += dirichlet_lpdf(a | rep_vector(1., Ni));\n",
        "    target += P(Nj,Ni,a,Pij);\n",
        "    target += -1.*P(Nj,Ni,a,Cij);\n",
        "}\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "dats = {'Nj' : N_dat,\n",
        "        'Ni' : N_iso,\n",
        "        'Pij': pp,\n",
        "        'Cij': cc  }\n",
        "\n",
        "\n",
        "\n",
        "############ Running pystan ############\n",
        "if not os.path.isfile('model.pkl'):\n",
        "    print( 'model.pkl does not exist' )\n",
        "    sm = pystan.StanModel(model_code=code)\n",
        "\n",
        "    print( 'Star sampling' )\n",
        "    start = time.time()\n",
        "    fit = sm.sampling(data=dats, iter=1000, chains=50, n_jobs=-1)\n",
        "    end = time.time()\n",
        "    print( (end - start), 's' )\n",
        "    \n",
        "    sp = fit.extract(permuted=True)\n",
        "\n",
        "    with open('model.pkl', 'wb') as f:\n",
        "        pickle.dump(sm, f)\n",
        "\n",
        "else:\n",
        "    print( 'model.pkl do exist' )\n",
        "    sm = pickle.load(open('model.pkl', 'rb'))\n",
        "\n",
        "    print( 'Star sampling' )\n",
        "    start = time.time()\n",
        "    fit = sm.sampling(data=dats, iter=1000, chains=50, n_jobs=-1)\n",
        "    end = time.time()\n",
        "    print( (end - start), 's' )\n",
        "    \n",
        "    sp = fit.extract(permuted=True)\n",
        "\n",
        "\n",
        "\n",
        "######### Saving the MCMC sample #########\n",
        "a_sp = sp['a']\n",
        "\n",
        "N_iso = len(a_sp[0])\n",
        "print( 'a_col =', N_iso )\n",
        "\n",
        "print( 'a_row =', len(a_sp) )\n",
        "\n",
        "#np.savetxt(\"ai_sampling_\"+name+\"_Iso21_sig_i0.05.txt\", a_sp, fmt=\"%.6f\", delimiter=\"  \")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################ PLOTS AND STATISTICS ####################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "################# Star formation history (SFH) #################\n",
        "\n",
        "\n",
        "a_perc = np.array([ np.percentile(ai,[10,50,90]) for ai in a_sp.T])       ##  10th, 50th, 90th percentiles\n",
        "\n",
        "\n",
        "sfh=[]\n",
        "#f = open(list_iso, 'r')\n",
        "#for n,fi in zip(range(N_iso),f):\n",
        "for n,fi in zip(range(N_iso), isofilelist):\n",
        "    Zi   = float('0.'+fi[fi.find('Z.')+2:fi.find('_AGE')])\n",
        "    AGEi = float(fi[fi.find('_AGE')+4:fi.find('Gyr')])\n",
        "    sfh.append([Zi,AGEi,a_perc[n][0],a_perc[n][1],a_perc[n][2]])\n",
        "\n",
        "sfh=np.array(sfh)\n",
        "\n",
        "#hd='       Z       age        p10        p50       p90'\n",
        "#np.savetxt(\"SFH_\"+name+\"_sigIso0.05.txt\", sfh, fmt=\"%.6f\", header=hd, delimiter=\"  \")      ##  Save SFH\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#################### SFH plot ####################\n",
        "fig = plt.figure(1,figsize=(14,4))\n",
        "\n",
        "#sf3p_ns.sfh_plot_mode(name,sfh.T[0],sfh.T[1],sfh.T[3],'median',fig)    ###  Age-Metalicity relation\n",
        "sfh_plot_mode(name,sfh.T[0],sfh.T[1],sfh.T[3],'median',fig)    ###  Age-Metalicity relation\n",
        "plt.title(it_title, loc='left', fontsize='large')\n",
        "#mgl_b_ns.marg_sfh_bar_age(name,sfh.T,a_sp,fig)                         ###  SFH\n",
        "#mgl_b_ns.marg_sfh_bar_Z(name,sfh.T,a_sp,N_iso,fig)                     ###  Metal distribution\n",
        "marg_sfh_bar_age(name,sfh.T,a_sp,fig)                         ###  SFH\n",
        "marg_sfh_bar_Z(name,sfh.T,a_sp,N_iso,fig)                     ###  Metal distribution\n",
        "\n",
        "plt.subplots_adjust(left=0.03, bottom=0.12, right=0.98, top=0.94, wspace=0.24, hspace=0.20)\n",
        "#plt.savefig('SFH_'+name+'_'+str(sig_i0)+'.png')\n",
        "#plt.close(1)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "###########  Color-magnitude diagram  ############\n",
        "\n",
        "#### Isocrone ####\n",
        "idn_max = np.argmax(sfh[:,3])   ## Maximum p50 - iscocrone contribution\n",
        "\n",
        "Z_max = sfh[:,0][idn_max]\n",
        "AGE_max = sfh[:,1][idn_max]\n",
        "\n",
        "lgn=\"Z=\"+str(Z_max)+\",  AGE=\"+str(AGE_max)+\" Gyr\"   ## Isocrone legend\n",
        "\n",
        "Iso_max = isos[idn_max]\n",
        "col_iso, mag_iso = Iso_max[2]-Iso_max[3], Iso_max[1]   ## Colour and magnitudes\n",
        "\n",
        "\n",
        "###### Data ######\n",
        "col_dat = dat0[4]-dat0[6]\n",
        "mag_dat = dat0[2]\n",
        "\n",
        "\n",
        "##################\n",
        "\n",
        "plt.figure(2,figsize=(5.6,5.6))\n",
        "\n",
        "plt.plot(col_dat, mag_dat, 'ko', markersize=4, markeredgewidth=0., alpha=0.4, label = it_title)   ## Data CMD\n",
        "plt.plot(col_iso, mag_iso,'ro', markersize=2, markeredgewidth=0., alpha=0.4, label = lgn)         ## Iso CMD\n",
        "\n",
        "sz=15\n",
        "plt.xlabel(r'$G_{BP}-G_{RP}$')\n",
        "plt.ylabel(r'$G$')\n",
        "\n",
        "min_x ,min_y = min(np.min(col_dat),np.min(col_iso)), min(np.min(mag_dat),np.min(mag_iso))\n",
        "max_x ,max_y = max(np.max(col_dat),np.max(col_iso)), max(np.max(mag_dat),np.max(mag_iso))\n",
        "\n",
        "plt.xlim(min_x+0.2,max_x-0.2)\n",
        "plt.ylim(max_y+0.5,min_y-0.5)\n",
        "plt.grid(linestyle='--', alpha=0.5)\n",
        "plt.title(it_title)\n",
        "plt.legend(frameon=False, loc=0)\n",
        "#plt.savefig('CMD_'+it_title+'.png')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "file:  https://raw.githubusercontent.com/sundarjhu/DAWGI_Lectures_2021/main/Demo_DAWGI_HBM/Pleiades/Pleiades_ruwe_cexcess_SelDist.txt\n",
            "N_iso =  21\n",
            "N_col =  4\n",
            "N_dat =  1234\n",
            " \n",
            "Calculating Cij ...\n",
            "C_ij =  1234 21\n",
            "Cij end.\n",
            " \n",
            "Calculating Pij ...\n",
            "0.0 %\n",
            "16.207455429497568 %\n",
            "32.414910858995135 %\n",
            "48.62236628849271 %\n",
            "64.82982171799027 %\n",
            "81.03727714748784 %\n",
            "97.24473257698541 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_5aa9e300ef531ddf608f7d7838acb099 NOW.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.4811198115348816 mins\n",
            "P_ij =  1234 21\n",
            "Pij end.\n",
            " \n",
            "model.pkl does not exist\n",
            "Star sampling\n",
            "319.56897377967834 s\n",
            "a_col = 21\n",
            "a_row = 25000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1120x320 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 448x448 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-WFg_ez3gbe"
      },
      "source": [
        "##Run the code again, this time for limiting magnitudes of 15 mag in each Gaia band."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kePjTLFBATT0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a649104f-4909-4792-a136-7579b275f5c3"
      },
      "source": [
        "from scipy import genfromtxt, special\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os, sys\n",
        "import pystan\n",
        "import pickle\n",
        "##\n",
        "#import SFH_3D_plot_no_scaled as sf3p_ns\n",
        "#import Marginal_SFH_Bar_NoScale as mgl_b_ns\n",
        "#path = os.getcwd()\n",
        "\n",
        "try:\n",
        "    dt, list_iso, it_title = sys.argv[1], sys.argv[2], sys.argv[3]\n",
        "    name = dt[:dt.find('.txt')]\n",
        "except:\n",
        "    url = 'https://raw.githubusercontent.com/sundarjhu/DAWGI_Lectures_2021/main/Demo_DAWGI_HBM/Pleiades/'\n",
        "    name = url + 'Pleiades_ruwe_cexcess_SelDist.txt'\n",
        "    list_iso = url + 'List_Iso21_PARSEC_GaiaEDR3.txt'\n",
        "    it_title = 'Pleiades'\n",
        "\n",
        "print('file: ', name)\n",
        "\n",
        "\n",
        "######################## Integration rutine ########################\n",
        "def trapz(yt,xt):\n",
        "    del_x = xt[1:]-xt[:len(xt)-1]\n",
        "    y2 = 0.5*(yt[1:]+yt[:len(yt)-1])\n",
        "    return sum(y2*del_x)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################### PDF DEFINITION #######################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "######################## Likelihood ########################\n",
        "def Normal_MGk(gk_dat,gk_err,Iso_sig):                  ## Like apparente magnitude\n",
        "    sig2 = gk_err*gk_err+Iso_sig*Iso_sig\n",
        "    return  lambda gk_iso : np.exp( -0.5*(gk_dat-gk_iso)**2 / sig2 ) / np.sqrt(2.*np.pi*sig2)\n",
        "\n",
        "\n",
        "def Phi_MGk(gkj2, sig_gkj2, gklim, sig_i2):             ## Limit magnitude function.\n",
        "    b = sig_i2*sig_i2+sig_gkj2*sig_gkj2\n",
        "    b1 = sig_i2*sig_i2/b\n",
        "    b2 = sig_gkj2*sig_gkj2/b\n",
        "    b3 = sig_i2*sig_gkj2/np.sqrt(b)\n",
        "    return  lambda gk_i2 : special.ndtr( ( gklim - b1*gkj2 - b2*gk_i2 ) / b3 )\n",
        "\n",
        "\n",
        "\n",
        "########################## Priors #############################\n",
        "def IMF_Krp(m,ml=0.1,mint=0.5,mu=100.,a1=1.3,a2=2.3):   ##    IMF Kroupa\n",
        "\n",
        "    h2 = (mu**(1.-a2)-mint**(1.-a2))/(1.-a2)\n",
        "    h1 = (mint**(1.-a1)-ml**(1.-a1))/(1.-a1)\n",
        "\n",
        "    c1 = 1./(h1+h2*mint**(a2-a1))\n",
        "    c2 = c1*mint**(a2-a1)\n",
        "\n",
        "    c = np.ones(len(m))\n",
        "    c[np.where(m < mint)] = c1\n",
        "    c[np.where(m >= mint)] = c2\n",
        "\n",
        "    a = np.ones(len(m))\n",
        "    a[np.where(m < mint)] = -a1\n",
        "    a[np.where(m >= mint)] = -a2\n",
        "    \n",
        "    imf = c*m**a\n",
        "    \n",
        "    return imf\n",
        "\n",
        "def IMF_Salp(m, xs=1.35, ml0=0.1, mu0=100.):            ## IMF Salpeter\n",
        "    cte=-xs/(mu0**(-xs)-ml0**(-xs))\n",
        "    return cte*m**(-1.-xs) \n",
        "\n",
        "\n",
        "\n",
        "############################# Posterior #############################\n",
        "def P_ij(dat, Ndat, gk1_lim, gk2_lim, gk3_lim, Iso, Niso, sig_i):\n",
        "    p_ij = []\n",
        "    for j in range(Ndat):\n",
        "\n",
        "        P_gk1 = Normal_MGk(dat[2][j],dat[3][j],sig_i)\n",
        "        P_gk2 = Normal_MGk(dat[4][j],dat[5][j],sig_i)\n",
        "        P_gk3 = Normal_MGk(dat[6][j],dat[7][j],sig_i)\n",
        "\n",
        "        Phi_gk1 = Phi_MGk(dat[2][j], dat[3][j], gk1_lim, sig_i)\n",
        "        Phi_gk2 = Phi_MGk(dat[4][j], dat[5][j], gk2_lim, sig_i)\n",
        "        Phi_gk3 = Phi_MGk(dat[6][j], dat[7][j], gk3_lim, sig_i)\n",
        "\n",
        "        pi=[]\n",
        "        for i in range(Niso):\n",
        "            Intg = IMF_Krp(Iso[i][0])*P_gk1(Iso[i][1])*P_gk2(Iso[i][2])*P_gk3(Iso[i][3])*Phi_gk1(Iso[i][1])*Phi_gk2(Iso[i][2])*Phi_gk3(Iso[i][3])\n",
        "            p = trapz(Intg,Iso[i][0])\n",
        "            pi.append(p)\n",
        "\n",
        "        p_ij.append(pi)\n",
        "\n",
        "        if j%200==0:\n",
        "            print(100.*(float(j)/float(Ndat)),'%')\n",
        "\n",
        "    p_ij = np.array(p_ij)\n",
        "    \n",
        "    return p_ij\n",
        "\n",
        "\n",
        "\n",
        "################### Normalization constant #####################\n",
        "def phi(gkk_lim,sig_i4):                                                ## Limit magnitude function.\n",
        "    return lambda gkk_i4: special.ndtr((gkk_lim-gkk_i4)/sig_i4)\n",
        "\n",
        "def C_ij(Ndat_c, gk1_lim, gk2_lim, gk3_lim, Iso_c, Niso_c, sig_i):      ## Normalization Constant ##\n",
        "\n",
        "    phi_gk1c = phi(gk1_lim,sig_i)\n",
        "    phi_gk2c = phi(gk2_lim,sig_i)\n",
        "    phi_gk3c = phi(gk3_lim,sig_i)\n",
        "    \n",
        "    w=np.array([])\n",
        "    for i in range(Niso_c):\n",
        "        intg_c = IMF_Krp(Iso_c[i][0])*phi_gk1c(Iso_c[i][1])*phi_gk2c(Iso_c[i][2])*phi_gk2c(Iso_c[i][3])\n",
        "        p_c = trapz(intg_c,Iso_c[i][0])\n",
        "        w = np.append(w,p_c)\n",
        "    \n",
        "    return np.array([w for k in range(Ndat_c)])\n",
        "\n",
        "##########################################################################################################################\n",
        "##################################################### ISOCRHONES #########################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "gk1_lim0 = 15.\n",
        "gk2_lim0 = 15.\n",
        "gk3_lim0 = 15.\n",
        "\n",
        "isofilelist = np.genfromtxt(list_iso,dtype='str')\n",
        "\n",
        "isos0=[]\n",
        "nn=0\n",
        "path = url\n",
        "for k in isofilelist:\n",
        "    nn+=1\n",
        "    isos0.append(np.loadtxt(path+k, unpack=True))\n",
        "\n",
        "\n",
        "N_iso = len(isos0)\n",
        "print( 'N_iso = ', N_iso )\n",
        "print( 'N_col = ', len(isos0[0]) )\n",
        "\n",
        "\n",
        "dismod = 5.667\n",
        "for j in range(N_iso):\n",
        "    isos0[j][1]=isos0[j][1]+dismod\n",
        "    isos0[j][2]=isos0[j][2]+dismod\n",
        "    isos0[j][3]=isos0[j][3]+dismod\n",
        "\n",
        "isos = []\n",
        "for j in range(N_iso):\n",
        "    f_lim=np.where(isos0[j][1]<=gk1_lim0)\n",
        "    i_aux = isos0[j].T[f_lim]\n",
        "    isos.append(i_aux.T)\n",
        "\n",
        "##########################################################################################################################\n",
        "#####################################################    DATA    #########################################################\n",
        "##########################################################################################################################\n",
        "#    0         1        2      3      4      5       6      7\n",
        "#   Plx    Plx_error    G    Gerr    GB    GBerr    GR    GRerr\n",
        "#dat0 = np.loadtxt(dt, unpack = True)\n",
        "dat0 = np.loadtxt(name, unpack = True)\n",
        "\n",
        "dat0[2] = dat0[2]-0.11        ## Extinction correction\n",
        "dat0[4] = dat0[4]-0.14\n",
        "dat0[6] = dat0[6]-0.083\n",
        "\n",
        "gk_filter = np.where(dat0[2]<=gk1_lim0)\n",
        "dat = dat0.T[gk_filter]\n",
        "dat = dat.T\n",
        "\n",
        "N_dat = len(dat[0])\n",
        "print( 'N_dat = ', N_dat )\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################### Pij CALCULATION ######################################################\n",
        "##########################################################################################################################\n",
        "print( ' ' )\n",
        "\n",
        "sig_i0 = 0.05\n",
        "\n",
        "print( 'Calculating Cij ...' )\n",
        "cc = C_ij(N_dat, gk1_lim0, gk2_lim0, gk3_lim0, isos, N_iso, sig_i0)\n",
        "print( 'C_ij = ', len(cc), len(cc[0]) )\n",
        "print( 'Cij end.' )\n",
        "\n",
        "print( ' ' )\n",
        "\n",
        "print( 'Calculating Pij ...' )\n",
        "start = time.time()\n",
        "pp=P_ij(dat, N_dat, gk1_lim0, gk2_lim0, gk3_lim0, isos, N_iso, sig_i0)\n",
        "end = time.time()\n",
        "print( (end - start)/60., 'mins' )\n",
        "print( 'P_ij = ', len(pp), len(pp[0]) )\n",
        "print( 'Pij end.' )\n",
        "\n",
        "print( ' ' )\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################# POSTERIOR SAMPLING #####################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "############ Stan code ############\n",
        "code = \"\"\"\n",
        "\n",
        "functions{\n",
        "    real P(int N1, int N2, vector v, matrix M) {\n",
        "       vector[N1] Mj;\n",
        "        vector[N1] ln_Mj;\n",
        "\n",
        "        Mj= M*v;\n",
        "        for (j in 1:N1){\n",
        "            if (Mj[j]<=0.)\n",
        "                Mj[j] = 1.;\n",
        "        }\n",
        "        ln_Mj = log(Mj);\n",
        "        return sum(ln_Mj);\n",
        "    }\n",
        "}\n",
        "\n",
        "data {\n",
        "    int<lower=0> Nj; // number of data\n",
        "    int<lower=0> Ni; // number of isochrones\n",
        "    matrix[Nj,Ni] Pij; // Probability matrix\n",
        "    matrix[Nj,Ni] Cij; // Normalization matrix\n",
        "}\n",
        "\n",
        "parameters {\n",
        "    simplex[Ni] a;\n",
        "}\n",
        "\n",
        "model {\n",
        "    target += dirichlet_lpdf(a | rep_vector(1., Ni));\n",
        "    target += P(Nj,Ni,a,Pij);\n",
        "    target += -1.*P(Nj,Ni,a,Cij);\n",
        "}\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "dats = {'Nj' : N_dat,\n",
        "        'Ni' : N_iso,\n",
        "        'Pij': pp,\n",
        "        'Cij': cc  }\n",
        "\n",
        "\n",
        "\n",
        "############ Running pystan ############\n",
        "if not os.path.isfile('model.pkl'):\n",
        "    print( 'model.pkl does not exist' )\n",
        "    sm = pystan.StanModel(model_code=code)\n",
        "\n",
        "    print( 'Star sampling' )\n",
        "    start = time.time()\n",
        "    fit = sm.sampling(data=dats, iter=1000, chains=50, n_jobs=-1)\n",
        "    end = time.time()\n",
        "    print( (end - start), 's' )\n",
        "    \n",
        "    sp = fit.extract(permuted=True)\n",
        "\n",
        "    with open('model.pkl', 'wb') as f:\n",
        "        pickle.dump(sm, f)\n",
        "\n",
        "else:\n",
        "    print( 'model.pkl do exist' )\n",
        "    sm = pickle.load(open('model.pkl', 'rb'))\n",
        "\n",
        "    print( 'Star sampling' )\n",
        "    start = time.time()\n",
        "    fit = sm.sampling(data=dats, iter=1000, chains=50, n_jobs=-1)\n",
        "    end = time.time()\n",
        "    print( (end - start), 's' )\n",
        "    \n",
        "    sp = fit.extract(permuted=True)\n",
        "\n",
        "\n",
        "\n",
        "######### Saving the MCMC sample #########\n",
        "a_sp = sp['a']\n",
        "\n",
        "N_iso = len(a_sp[0])\n",
        "print( 'a_col =', N_iso )\n",
        "\n",
        "print( 'a_row =', len(a_sp) )\n",
        "\n",
        "#np.savetxt(\"ai_sampling_\"+name+\"_Iso21_sig_i0.05.txt\", a_sp, fmt=\"%.6f\", delimiter=\"  \")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##########################################################################################################################\n",
        "################################################ PLOTS AND STATISTICS ####################################################\n",
        "##########################################################################################################################\n",
        "\n",
        "\n",
        "################# Star formation history (SFH) #################\n",
        "\n",
        "\n",
        "a_perc = np.array([ np.percentile(ai,[10,50,90]) for ai in a_sp.T])       ##  10th, 50th, 90th percentiles\n",
        "\n",
        "\n",
        "sfh=[]\n",
        "#f = open(list_iso, 'r')\n",
        "#for n,fi in zip(range(N_iso),f):\n",
        "for n,fi in zip(range(N_iso), isofilelist):\n",
        "    Zi   = float('0.'+fi[fi.find('Z.')+2:fi.find('_AGE')])\n",
        "    AGEi = float(fi[fi.find('_AGE')+4:fi.find('Gyr')])\n",
        "    sfh.append([Zi,AGEi,a_perc[n][0],a_perc[n][1],a_perc[n][2]])\n",
        "\n",
        "sfh=np.array(sfh)\n",
        "\n",
        "#hd='       Z       age        p10        p50       p90'\n",
        "#np.savetxt(\"SFH_\"+name+\"_sigIso0.05.txt\", sfh, fmt=\"%.6f\", header=hd, delimiter=\"  \")      ##  Save SFH\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#################### SFH plot ####################\n",
        "fig = plt.figure(1,figsize=(14,4))\n",
        "\n",
        "#sf3p_ns.sfh_plot_mode(name,sfh.T[0],sfh.T[1],sfh.T[3],'median',fig)    ###  Age-Metalicity relation\n",
        "sfh_plot_mode(name,sfh.T[0],sfh.T[1],sfh.T[3],'median',fig)    ###  Age-Metalicity relation\n",
        "plt.title(it_title, loc='left', fontsize='large')\n",
        "#mgl_b_ns.marg_sfh_bar_age(name,sfh.T,a_sp,fig)                         ###  SFH\n",
        "#mgl_b_ns.marg_sfh_bar_Z(name,sfh.T,a_sp,N_iso,fig)                     ###  Metal distribution\n",
        "marg_sfh_bar_age(name,sfh.T,a_sp,fig)                         ###  SFH\n",
        "marg_sfh_bar_Z(name,sfh.T,a_sp,N_iso,fig)                     ###  Metal distribution\n",
        "\n",
        "plt.subplots_adjust(left=0.03, bottom=0.12, right=0.98, top=0.94, wspace=0.24, hspace=0.20)\n",
        "#plt.savefig('SFH_'+name+'_'+str(sig_i0)+'.png')\n",
        "#plt.close(1)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "###########  Color-magnitude diagram  ############\n",
        "\n",
        "#### Isocrone ####\n",
        "idn_max = np.argmax(sfh[:,3])   ## Maximum p50 - iscocrone contribution\n",
        "\n",
        "Z_max = sfh[:,0][idn_max]\n",
        "AGE_max = sfh[:,1][idn_max]\n",
        "\n",
        "lgn=\"Z=\"+str(Z_max)+\",  AGE=\"+str(AGE_max)+\" Gyr\"   ## Isocrone legend\n",
        "\n",
        "Iso_max = isos[idn_max]\n",
        "col_iso, mag_iso = Iso_max[2]-Iso_max[3], Iso_max[1]   ## Colour and magnitudes\n",
        "\n",
        "\n",
        "###### Data ######\n",
        "col_dat = dat0[4]-dat0[6]\n",
        "mag_dat = dat0[2]\n",
        "\n",
        "\n",
        "##################\n",
        "\n",
        "plt.figure(2,figsize=(5.6,5.6))\n",
        "\n",
        "plt.plot(col_dat, mag_dat, 'ko', markersize=4, markeredgewidth=0., alpha=0.4, label = it_title)   ## Data CMD\n",
        "plt.plot(col_iso, mag_iso,'ro', markersize=2, markeredgewidth=0., alpha=0.4, label = lgn)         ## Iso CMD\n",
        "\n",
        "sz=15\n",
        "plt.xlabel(r'$G_{BP}-G_{RP}$')\n",
        "plt.ylabel(r'$G$')\n",
        "\n",
        "min_x ,min_y = min(np.min(col_dat),np.min(col_iso)), min(np.min(mag_dat),np.min(mag_iso))\n",
        "max_x ,max_y = max(np.max(col_dat),np.max(col_iso)), max(np.max(mag_dat),np.max(mag_iso))\n",
        "\n",
        "plt.xlim(min_x+0.2,max_x-0.2)\n",
        "plt.ylim(max_y+0.5,min_y-0.5)\n",
        "plt.grid(linestyle='--', alpha=0.5)\n",
        "plt.title(it_title)\n",
        "plt.legend(frameon=False, loc=0)\n",
        "#plt.savefig('CMD_'+it_title+'.png')\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "file:  https://raw.githubusercontent.com/sundarjhu/DAWGI_Lectures_2021/main/Demo_DAWGI_HBM/Pleiades/Pleiades_ruwe_cexcess_SelDist.txt\n",
            "N_iso =  21\n",
            "N_col =  4\n",
            "N_dat =  403\n",
            " \n",
            "Calculating Cij ...\n",
            "C_ij =  403 21\n",
            "Cij end.\n",
            " \n",
            "Calculating Pij ...\n",
            "0.0 %\n",
            "49.62779156327544 %\n",
            "99.25558312655087 %\n",
            "0.12113152345021566 mins\n",
            "P_ij =  403 21\n",
            "Pij end.\n",
            " \n",
            "model.pkl do exist\n",
            "Star sampling\n",
            "75.46145701408386 s\n",
            "a_col = 21\n",
            "a_row = 25000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1120x320 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 448x448 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOiZDT0iHe4R"
      },
      "source": [
        "Copyright 2021 Jairo Andrés Alzate Trujillo\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy of \n",
        "this software and associated documentation files (the \"Software\"), to deal in \n",
        "the Software without restriction, including without limitation the rights to \n",
        "use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of \n",
        "the Software, and to permit persons to whom the Software is furnished to do so, \n",
        "subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all \n",
        "copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR \n",
        "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS \n",
        "FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR \n",
        "COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER \n",
        "IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN \n",
        "CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
      ]
    }
  ]
}