Group_5_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "343f879e-8672-4136-9263-ac0c55a4ee2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from astropy.timeseries import LombScargle\n",
    "from numpy.random import permutation\n",
    "from scipy.interpolate import interp1d\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "491a47a0-f563-45bb-ae2e-5c8a9ecc07f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ls(time,flux,osample=10):\n",
    "    '''Lomb Scargle periodogram\n",
    "\n",
    "    Returns periodogram in units of muHz and amplitude\n",
    "    '''\n",
    "    time = np.copy(time)\n",
    "    flux = np.copy(flux)\n",
    "    osample = float(osample)\n",
    "    time-=time[0]\n",
    "    c=np.median(np.diff(time))\n",
    "\n",
    "    #if in days, convert to seconds\n",
    "    if c < 1:\n",
    "        time*=86400.\n",
    "        c*=86400.\n",
    "\n",
    "    nyq=1./(2.*c)\n",
    "    df=(1./time[-1])\n",
    "\n",
    "    f = np.arange(df,nyq,df/osample)\n",
    "\n",
    "    ls=LombScargle(time,flux)\n",
    "    p =  ls.power(f,normalization='psd')\n",
    "    f = 1.e+6 * f # put f in micro Hz\n",
    "\n",
    "    return f,np.sqrt(p*4./time.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07d9201d-75e2-4732-bfca-ab3898dfe453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([5.88580000e+01, 1.17703000e+02, 1.76549000e+02, ...,\n",
       "        7.13939307e+06, 7.13945193e+06, 7.13951078e+06]),\n",
       " array([-0.025904,  0.008887,  0.01089 , ..., -0.013906,  0.000139,\n",
       "        -0.017334])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = np.loadtxt('kplr004552982.lc1')\n",
    "time = var[:,0]\n",
    "flux = var[:,1]\n",
    "[time,flux]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2256062d-dd13-45e8-9bd8-26f0d6d02f64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.014006677418365143\n"
     ]
    }
   ],
   "source": [
    "freq, FT_amp = ls(time,flux)\n",
    "df = np.median(np.diff(freq))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ee15c8b-e0c6-4f40-bf6b-06c99c22769f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAP (False Alarm Probability) <  0.1 for amplitude of  0.0006525860248972639\n"
     ]
    }
   ],
   "source": [
    "FT_max = []\n",
    "nmax = 10\n",
    "for i in range(nmax):\n",
    "    f0, FT0 = ls(time,permutation(flux))\n",
    "    FT_max.append(max(FT0))\n",
    "FAP = 1./nmax\n",
    "FT_thresh = max(FT_max)\n",
    "print('FAP (False Alarm Probability) < ',FAP,'for amplitude of ',FT_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84fcf972-1b4f-4299-9f3e-28e9181e78a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fractional Amplitude')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "ax = fig.subplots(1,1)\n",
    "ax.plot(freq,FT_amp)\n",
    "ax.plot(freq,FT_thresh + 0.*FT_amp,':')\n",
    "ax.set_xlabel('Frequency ($\\mu$Hz)')\n",
    "ax.set_ylabel('Fractional Amplitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3bec3eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def make_plot (filename) :\n",
    "    var = np.loadtxt (filename)\n",
    "    time = var[:,0]\n",
    "    flux = var[:, 1]\n",
    "    freq, FT_amp = ls(time, flux)\n",
    "    df = np.median(np.diff(freq) )\n",
    "    print(df)\n",
    "\n",
    "    f_grid = np.arange (0, 8000.015, 0.1)\n",
    "    print(f_grid)\n",
    "\n",
    "    f = interp1d(freq, FT_amp, fill_value=\"extrapolate\")\n",
    "    ft_grid = f(f_grid)\n",
    "    print(ft_grid)\n",
    "\n",
    "    fig = plt.figure(figsize= (10,4))\n",
    "    ax = fig.subplots (1,1)\n",
    "    ax.plot(freq, FT_amp)\n",
    "    ax.set_xlabel( 'Frequency ($\\mu$Hz) ')\n",
    "    ax.set_ylabel( 'Fractional Amplitude')\n",
    "\n",
    "    # save txt of the data\n",
    "    # FT_data = np. column_stack( (freq, FT_amp) )\n",
    "    FT_data = np. column_stack((f_grid, ft_grid))\n",
    "    np.savetxt(filename.replace('lc1', 'txt'), FT_data, fmt='%.6e')\n",
    "\n",
    "    # save pdf of the plot\n",
    "    plt.savefig (filename. replace('lcl', 'pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14679f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.014006677418365143\n",
      "[0.0000e+00 1.0000e-01 2.0000e-01 ... 7.9998e+03 7.9999e+03 8.0000e+03]\n",
      "[-1.16306189e-04 -8.23594676e-06  1.00479205e-04 ...  8.21343749e-05\n",
      "  1.52433471e-04  7.46928622e-05]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Format 'lc1' is not supported (supported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff, webp)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/carolina/Documents/fri mentor/group 5 work/Group_5_notes (1).ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/carolina/Documents/fri%20mentor/group%205%20work/Group_5_notes%20%281%29.ipynb#X21sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m make_plot(\u001b[39m'\u001b[39;49m\u001b[39mkplr004552982.lc1\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "\u001b[1;32m/Users/carolina/Documents/fri mentor/group 5 work/Group_5_notes (1).ipynb Cell 8\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/carolina/Documents/fri%20mentor/group%205%20work/Group_5_notes%20%281%29.ipynb#X21sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m np\u001b[39m.\u001b[39msavetxt(filename\u001b[39m.\u001b[39mreplace(\u001b[39m'\u001b[39m\u001b[39mlc1\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mtxt\u001b[39m\u001b[39m'\u001b[39m), FT_data, fmt\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m%.6e\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/carolina/Documents/fri%20mentor/group%205%20work/Group_5_notes%20%281%29.ipynb#X21sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m \u001b[39m# save pdf of the plot\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/carolina/Documents/fri%20mentor/group%205%20work/Group_5_notes%20%281%29.ipynb#X21sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m plt\u001b[39m.\u001b[39;49msavefig (filename\u001b[39m.\u001b[39;49m replace(\u001b[39m'\u001b[39;49m\u001b[39mlcl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mpdf\u001b[39;49m\u001b[39m'\u001b[39;49m))\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:1023\u001b[0m, in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[39m.\u001b[39msavefig)\n\u001b[1;32m   1021\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msavefig\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1022\u001b[0m     fig \u001b[39m=\u001b[39m gcf()\n\u001b[0;32m-> 1023\u001b[0m     res \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39;49msavefig(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1024\u001b[0m     fig\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mdraw_idle()  \u001b[39m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[1;32m   1025\u001b[0m     \u001b[39mreturn\u001b[39;00m res\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/figure.py:3378\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3374\u001b[0m     \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes:\n\u001b[1;32m   3375\u001b[0m         stack\u001b[39m.\u001b[39menter_context(\n\u001b[1;32m   3376\u001b[0m             ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39m_cm_set(facecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m-> 3378\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcanvas\u001b[39m.\u001b[39;49mprint_figure(fname, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py:2313\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2310\u001b[0m     dpi \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, \u001b[39m'\u001b[39m\u001b[39m_original_dpi\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mdpi)\n\u001b[1;32m   2312\u001b[0m \u001b[39m# Remove the figure manager, if any, to avoid resizing the GUI widget.\u001b[39;00m\n\u001b[0;32m-> 2313\u001b[0m \u001b[39mwith\u001b[39;00m cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m, manager\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m), \\\n\u001b[1;32m   2314\u001b[0m      \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_switch_canvas_and_return_print_method(\u001b[39mformat\u001b[39m, backend) \\\n\u001b[1;32m   2315\u001b[0m          \u001b[39mas\u001b[39;00m print_method, \\\n\u001b[1;32m   2316\u001b[0m      cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, dpi\u001b[39m=\u001b[39mdpi), \\\n\u001b[1;32m   2317\u001b[0m      cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mcanvas, _device_pixel_ratio\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m), \\\n\u001b[1;32m   2318\u001b[0m      cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mcanvas, _is_saving\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m), \\\n\u001b[1;32m   2319\u001b[0m      ExitStack() \u001b[39mas\u001b[39;00m stack:\n\u001b[1;32m   2321\u001b[0m     \u001b[39mfor\u001b[39;00m prop \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mfacecolor\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[1;32m   2322\u001b[0m         color \u001b[39m=\u001b[39m \u001b[39mlocals\u001b[39m()[prop]\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/contextlib.py:119\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkwds, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunc\n\u001b[1;32m    118\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 119\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgen)\n\u001b[1;32m    120\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m:\n\u001b[1;32m    121\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mgenerator didn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt yield\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py:2220\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method\u001b[0;34m(self, fmt, backend)\u001b[0m\n\u001b[1;32m   2218\u001b[0m     canvas \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mswitch_backends(canvas_class)\n\u001b[1;32m   2219\u001b[0m \u001b[39mif\u001b[39;00m canvas \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 2220\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   2221\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mFormat \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m is not supported (supported formats: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   2222\u001b[0m             fmt, \u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(\u001b[39msorted\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_supported_filetypes()))))\n\u001b[1;32m   2223\u001b[0m meth \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(canvas, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mprint_\u001b[39m\u001b[39m{\u001b[39;00mfmt\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   2224\u001b[0m mod \u001b[39m=\u001b[39m (meth\u001b[39m.\u001b[39mfunc\u001b[39m.\u001b[39m\u001b[39m__module__\u001b[39m\n\u001b[1;32m   2225\u001b[0m        \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(meth, \u001b[39m\"\u001b[39m\u001b[39mfunc\u001b[39m\u001b[39m\"\u001b[39m)  \u001b[39m# partialmethod, e.g. backend_wx.\u001b[39;00m\n\u001b[1;32m   2226\u001b[0m        \u001b[39melse\u001b[39;00m meth\u001b[39m.\u001b[39m\u001b[39m__module__\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: Format 'lc1' is not supported (supported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff, webp)"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_plot('kplr004552982.lc1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "539be1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "files = glob.glob ('*. lc1')\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec3c27bc-9235-405f-adc0-1c80320d7db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This notebook will output two files:\n",
    "- source_ids+coords.csv, which has three columns (Gaia DR3 Source ID, RA, Dec)\n",
    "- UMAP_FILELABEL.csv, which has the X and Y coordinates for the t-SNE plot\n",
    "\"\"\"\n",
    "#!pip install umap\n",
    "\n",
    "import numpy as np\n",
    "#import h5py\n",
    "import matplotlib.pyplot as p\n",
    "from astropy.io import fits\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.interpolate import UnivariateSpline, InterpolatedUnivariateSpline\n",
    "from scipy.stats import sigmaclip\n",
    "import pandas as pd\n",
    "#from startup import *\n",
    "from astropy.table import Table\n",
    "import umap.umap_ as umap\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import join_skycoord\n",
    "from gaiaxpy.calibrator.calibrator import _generate_xp_matrices_and_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3fe351c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'log(cm.s**-2)' did not parse as fits unit: 'log' is not a recognized function If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'dex' did not parse as fits unit: At col 0, Unit 'dex' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'electron.s**-1' did not parse as fits unit: At col 0, Unit 'electron' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "# D = Table.read('/home/mk43259/Gaia_WDs/Gentile_WD21_BPRPspec.fits')\n",
    "D = Table.read('Gentile_WD21_BPRPspec.fits')\n",
    "D = D[np.where((D['Pwd'] > 0.9)&(D['phot_g_mean_mag'] <21))[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02e6956f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize the bp and rp coefficients by the mean g flux\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "spectra = []\n",
    "for i in range(len(D)):\n",
    "    spectra.append(np.hstack([D['bp_coefficients'][i]/D['phot_g_mean_flux'][i],\\\n",
    "                           D['rp_coefficients'][i]/D['phot_g_mean_flux'][i]]))\n",
    "spectra = np.array(spectra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59af5170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97530, 110)\n"
     ]
    }
   ],
   "source": [
    "spectra = np.array(spectra)  # normalized by the mean g flux\n",
    "print(np.shape(spectra))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3a4c68c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(97530, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reducer = umap.UMAP(n_neighbors=50, min_dist = 0.05)\n",
    "embedding = reducer.fit_transform(spectra)\n",
    "embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd8cf246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'coeff normed by G flux')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.close('all')\n",
    "p.figure()\n",
    "ax1=p.subplot(2,1,1)\n",
    "p.plot(embedding[:,0],embedding[:,1],'.',ms=1)\n",
    "ax1.set_title('coeff normed by G flux')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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