Handler_spotfinder.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "7d4d2650",
   "metadata": {},
   "source": [
    "kernel: cosmosislocal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e7d61474",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import os\n",
    "from spotfinder import spotfinder\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import math\n",
    "# import sys \n",
    "# sys.path.append(\"/data/common/software/products/\")\n",
    "import sbigCam as sbc\n",
    "import time\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "import matplotlib.colors as mcolors\n",
    "plt.rcParams.update({\"figure.dpi\":130})\n",
    "# import circle_fit as cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca30583",
   "metadata": {},
   "source": [
    "## Handlers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d6c51914",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_spot(fitsname, fitspath,  \n",
    "              expected_spot_count=1, \n",
    "              regionsname='regions.reg', \n",
    "              verbose=False):\n",
    "    \"\"\"\n",
    "    spotfinder handler\n",
    "    input:\n",
    "        fitsname:\n",
    "        fitspath (str): relative or full path to the folder\n",
    "        regionsname (str):\n",
    "        verbose (bool):\n",
    "    output: \n",
    "        centroids (dict): raw output from spotfinder\n",
    "\n",
    "    \"\"\"\n",
    "    assert isinstance(fitsname, str)\n",
    "\n",
    "    _ifn = f\"{fitspath}/{fitsname}\"\n",
    "\n",
    "    if expected_spot_count != 1:\n",
    "        raise NotImplementedError(\"This mode wasn't tested here\")\n",
    "    try: \n",
    "        sf=spotfinder.SpotFinder(_ifn, expected_spot_count)\n",
    "        centroids = sf.get_centroids(print_summary = verbose, \n",
    "                                     region_file=regionsname)\n",
    "        if verbose: print(centroids)\n",
    "    \n",
    "    except: #ignore photo if an error is raised\n",
    "        print(\"Warning: spot not found \")\n",
    "        inval_number = np.nan\n",
    "        return {  'peaks': [inval_number], \n",
    "                      'x': [inval_number], \n",
    "                      'y': [inval_number], \n",
    "                   'fwhm': [inval_number], \n",
    "                 'energy': [inval_number]} \n",
    "    return centroids\n",
    "\n",
    "\n",
    "def collect_xy(files, picspath):\n",
    "    # treating for single file\n",
    "    if isinstance(files, str):\n",
    "        files = [files]\n",
    "    x2, y2 = [],[]\n",
    "    for fullname in files:\n",
    "        iname = fullname.split('/')[-1]\n",
    "        _c = find_spot(iname, picspath)\n",
    "        #todo, flatten x, y,\n",
    "        x2.append(_c['x'][0])\n",
    "        y2.append(_c['y'][0])\n",
    "    return x2, y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5fe5a2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_img(plt):\n",
    "\n",
    "    plt.colorbar(label ='Photo ID')\n",
    "    plt.title('Detected spots')\n",
    "    plt.xlabel('X coordinate (pix)')\n",
    "    plt.ylabel('Y coordinate (pix)')\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    plt.axis('equal')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2263edd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 83928.72879623307 brightness appears out of expected range\n",
      "here\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'peaks': [83928.72879623307],\n",
       " 'x': [1099.5330746797667],\n",
       " 'y': [877.6762076818566],\n",
       " 'fwhm': [3.9569108299716342],\n",
       " 'energy': [5.067498221096617]}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_spot(testfiles[0], 'sbigpics/20221212-172028/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4a304bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['20221212-172028.fits',\n",
       "  '20221212-172036.fits',\n",
       "  '20221212-172044.fits',\n",
       "  '20221212-172052.fits'],\n",
       " '20221212-172036.fits')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfiles=!ls sbigpics/29Nov202210_38_06/*fits\n",
    "testfiles=!ls sbigpics/20221212-172028/\n",
    "testfits = testfiles[1]\n",
    "testfiles, testfits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e55cfe4",
   "metadata": {},
   "source": [
    "### unit test:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7117122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 83928.72879623307 brightness appears out of expected range\n",
      "here\n"
     ]
    }
   ],
   "source": [
    "path_val = \"sbigpics/20221212-172028/\"\n",
    "ifn_val = \"20221212-172028.fits\"\n",
    "spotfinder_val = {'peaks': [83928.72879623307], \n",
    "                      'x': [1099.5330746797667], \n",
    "                      'y': [877.6762076818566], \n",
    "                   'fwhm': [3.9569108299716342], \n",
    "                 'energy': [5.067498221096617]}\n",
    "\n",
    "res = find_spot(ifn_val, path_val, expected_spot_count=1) \n",
    "\n",
    "assert res== spotfinder_val, \"Error in results\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3961526e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'peaks': [83928.72879623307],\n",
       " 'x': [1099.5330746797667],\n",
       " 'y': [877.6762076818566],\n",
       " 'fwhm': [3.9569108299716342],\n",
       " 'energy': [5.067498221096617]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758b9aac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b7b8f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb895c0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e847c832",
   "metadata": {},
   "source": [
    "# Test arc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "48294f20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73429.16174515795 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 72855.3913041894 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 71519.86683703448 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 70578.28658909236 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 69691.22480740737 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 75850.8315917966 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 72183.8412698017 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 71034.15951596115 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 72828.61478465634 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73100.77177041538 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73203.80330138796 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 71316.5880953716 brightness appears out of expected range\n",
      "here\n"
     ]
    }
   ],
   "source": [
    "root = 'sbigpics/testarc/'\n",
    "files = !ls '{root}'\n",
    "\n",
    "files\n",
    "x,y = collect_xy(files, root)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c31d59f2",
   "metadata": {},
   "source": [
    "#plot the detected spots, color order indicates order of photos\n",
    "\n",
    "cmap = plt.cm.get_cmap(\"RdBu_r\", len(x)+1)\n",
    "plt.scatter(x[:], y[:], c=np.arange(len(x)), cmap=cmap, edgecolor='k', linewidth=0.4, alpha=0.7)\n",
    "# plt.plot(xx[8], yy[8], 'bx')\n",
    "# plt.plot(xx[9], yy[9], 'gx')\n",
    "\n",
    "\n",
    "plt.colorbar(label ='Photo ID')\n",
    "plt.title('Detected spots')\n",
    "plt.xlabel('X coordinate (pix)')\n",
    "plt.ylabel('Y coordinate (pix)')\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "39b83221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780x520 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cmap = plt.cm.get_cmap(\"RdBu_r\", len(x)+1)\n",
    "plt.scatter(x[:], y[:], c=np.arange(len(x)), \n",
    "            cmap=cmap, \n",
    "            edgecolor='k', linewidth=0.4, alpha=0.7)\n",
    "format_img(plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ce5b5e5",
   "metadata": {},
   "source": [
    "# saving "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7be27947",
   "metadata": {},
   "outputs": [],
   "source": [
    "xflatten = np.array([i[0] for i in x])\n",
    "yflatten = np.array([i[0] for i in y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e376be32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1811.4864033493266 939.4166267012608\n",
      "1806.9010046933593 845.9371324405779\n",
      "1856.748417532216 759.8414715894679\n",
      "1945.9918660041722 716.3147531185626\n",
      "2045.0413266017797 730.1676941892483\n",
      "2119.024318426958 796.8152554497041\n",
      "2049.7428760049875 732.4642570840178\n",
      "1951.285063075784 715.536909568173\n",
      "1860.5300254772062 756.4589058692003\n",
      "1808.2046965337809 840.829341987301\n",
      "1811.477919537307 939.403362561772\n",
      "1973.5244281532694 876.953654371503\n"
     ]
    }
   ],
   "source": [
    "session_lbl='test_savespot'\n",
    "file = open(f\"{session_lbl}.csv\", 'w')\n",
    "for ix, iy in zip(xflatten,yflatten):\n",
    "    print(ix, iy)\n",
    "    file.write(f\"{ix} {iy}\\n\")\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3569a4de",
   "metadata": {},
   "source": [
    "1. session_label:\n",
    "    timestamp,\n",
    "    posids: tspeed, pspeed, spinup, spindown\n",
    "    home_theta, home_phi, arc_center\n",
    "    \n",
    "2. get: current_mountposition\n",
    "\n",
    "    3.a collect: pos info into posid\n",
    "            posid x,y, ... \n",
    "    3.b join asked move\n",
    "            dir, speed, motor, angle, movetime, xcenter, ycenter\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "19e916dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 78575.0264808495 brightness appears out of expected range\n",
      "here\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'peaks': [78575.0264808495],\n",
       " 'x': [1972.7341757826825],\n",
       " 'y': [877.1064515692935],\n",
       " 'fwhm': [3.5166109917819997],\n",
       " 'energy': [4.216339388145526]}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff = fits.getdata(\"sbigpics/curreposition/20230201-114451.fits\")\n",
    "find_spot(\"20230201-114451.fits\", \"sbigpics/curreposition/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "53ede050",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 78915.76573291933 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73284.29316206879 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73325.29841595024 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 74367.51998432691 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73764.0599124732 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73224.46143955286 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 73079.98753695947 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 71707.06160358677 brightness appears out of expected range\n",
      "here\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "Edge hot spot\n",
      "peak = 71332.84069096512 brightness appears out of expected range\n",
      "here\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780x520 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "files = !ls sbigpics/curreposition/\n",
    "plt.plot([int(i.replace('.fits','').split('-')[-1]) for i in files])\n",
    "print(len(files))\n",
    "x, y = collect_xy(files[13:], 'sbigpics/curreposition/'          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "856d8321",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22ae91ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7c06a5cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1066.0, 704.0)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780x520 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xc, yc = (1973, 885)\n",
    "r = 171+10\n",
    "\n",
    "\n",
    "plt.scatter(x,y,c = range(len(x)), cmap='viridis_r', alpha=0.4); plt.colorbar()\n",
    "plt.xlim(xc-r,xc+r)\n",
    "plt.ylim(yc+r, yc-r)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b54b51a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}