tweakreg_hack-bincut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81450c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pdastro import pdastrostatsclass,pdastroclass,AnotB\n",
    "import re\n",
    "import numpy as np\n",
    "import re,os,sys,shutil\n",
    "### Get tweakreg_hack here:\n",
    "### https://github.com/jpierel14/tweakreg_hack\n",
    "### pip install .\n",
    "### or \n",
    "### python setup.py install\n",
    "### all credit for this awesome hack goes to Justin Pierel\n",
    "import tweakreg_hack\n",
    "from jwst import datamodels\n",
    "from astropy.table import Table\n",
    "import astropy.io.fits as fits\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "###\n",
    "### define cal_image, which then derives the name of the photometric catalog\n",
    "### Make sure cal_image has the correct distortion coeffs applied!!\n",
    "\n",
    "#cal_image = 'test/pid1069_testonly/jw01069001001_02101_00001_nrca1_cal.fits' # 0.7 pixels stdev F070W\n",
    "cal_image = 'nrc24_calfiles/jw01073003001_02101_00001_nrca1_cal.fits' # 0.08 pixels stdev F150W\n",
    "#cal_image = 'coron_offsets/NRCA3_FULL_WEDGE_RND/jw01070001001_04101_00001_nrca3_cal.fits' #  \n",
    "#cal_image = '/ifs/jwst/wit/nircam/commissioning/01069/dist_applied/v1/jw01069002002_04101_00002_nrcblong_cal.fits'\n",
    "#cal_image = '/ifs/jwst/wit/nircam/commissioning/01070/dist_applied/v1/jw01070001001_04101_00001_nrca2_cal.fits'\n",
    "cal_phot_filename = re.sub('\\.fits$','.phot.txt',cal_image)\n",
    "if cal_phot_filename == cal_image:\n",
    "    raise RuntimeError(f'Could not determine photometry filename for {cal_image}')\n",
    "\n",
    "\n",
    "#d2d is calculated when gaia is matched to the objects, and is the distance \n",
    "# between the objects and their matched gaia positions. Give a rough upper limit on \n",
    "# what a good match should fall into\n",
    "d2d_max = None\n",
    "dmag_max = 0.1\n",
    "Nbright=None\n",
    "showplots=2\n",
    "\n",
    "###################################\n",
    "# A couple of convenience functions\n",
    "\n",
    "# for a given catalog phot, calculate dx, dy, and make some rough cuts on dmag, d2d, and Nbright\n",
    "def calc_dxdy(phot,d2d_max=None,dmag_max=None,Nbright=None, ixs=None):\n",
    "    ixs = phot.getindices(ixs)\n",
    "    if d2d_max is not None:\n",
    "        ixs = phot.ix_inrange('d2d',None,3*d2d_max,indices=ixs)\n",
    "    if dmag_max is not None:\n",
    "        ixs = phot.ix_inrange('dmag',None,dmag_max,indices=ixs)\n",
    "    if Nbright is not None:\n",
    "        ixs_sort = phot.ix_sort_by_cols(['mag'],indices=ixs)\n",
    "        ixs = ixs_sort[:Nbright]\n",
    "        phot.write(columns=['mag'],indices=ixs)\n",
    "        \n",
    "    print(f'# of matched objects that pass initial cuts: {len(ixs)}')\n",
    "    phot.t.loc[ixs,'ddec'] = 3600.0*(phot.t.loc[ixs,'cat_dec'] - phot.t.loc[ixs,'dec'])\n",
    "    phot.t.loc[ixs,'dra'] = 3600.0*(phot.t.loc[ixs,'cat_ra'] - phot.t.loc[ixs,'ra'])*np.cos(np.deg2rad(phot.t.loc[ixs,'dec']))\n",
    "    phot.t.loc[ixs,'dx'] = phot.t.loc[ixs,'cat_x'] - phot.t.loc[ixs,'x']\n",
    "    phot.t.loc[ixs,'dy'] = phot.t.loc[ixs,'cat_y'] - phot.t.loc[ixs,'y']\n",
    "    return(ixs)\n",
    "\n",
    "# These are the info plots to check out dx, dy for good and bad detections\n",
    "def infoplots(phot,ixs_good,ixs_bad,dy_plotlim=(-4,4),dx_plotlim=(-4,4)):\n",
    "    sp=[]\n",
    "    xfigsize=15.0\n",
    "    yfigsize=10.0\n",
    "    plt.figure(figsize=(xfigsize,yfigsize))\n",
    "    sp.append(plt.subplot(231))\n",
    "    sp.append(plt.subplot(232))\n",
    "    sp.append(plt.subplot(233))\n",
    "    sp.append(plt.subplot(234))\n",
    "    sp.append(plt.subplot(235))\n",
    "\n",
    "    phot.t.loc[ixs_bad].plot.scatter('y','dx',ax=sp[0],ylim=dx_plotlim,color='red')\n",
    "    phot.t.loc[ixs_good].plot.scatter('y','dx',ax=sp[0],ylim=dx_plotlim,ylabel='dx in pixels')\n",
    "    phot.t.loc[ixs_bad].plot.scatter('x','dy',ax=sp[1],ylim=dy_plotlim,color='red')\n",
    "    phot.t.loc[ixs_good].plot.scatter('x','dy',ax=sp[1],ylim=dy_plotlim,ylabel='dy in pixels')\n",
    "\n",
    "    phot.t.loc[ixs_bad].plot.scatter('x','y',ax=sp[2],color='red')\n",
    "    phot.t.loc[ixs_good].plot.scatter('x','y',ax=sp[2])\n",
    "    \n",
    "    phot.t.loc[ixs_bad].plot.scatter('sharpness','mag',ax=sp[3],color='red')\n",
    "    phot.t.loc[ixs_good].plot.scatter('sharpness','mag',ax=sp[3])\n",
    "\n",
    "    phot.t.loc[ixs_bad].plot.scatter('sharpness','dmag',ax=sp[4],color='red')\n",
    "    phot.t.loc[ixs_good].plot.scatter('sharpness','dmag',ax=sp[4])\n",
    "\n",
    "    return(sp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f77b2afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v20200428.1) - Connection:\n",
      "\tHost: gea.esac.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n",
      "Created TAP+ (v20200428.1) - Connection:\n",
      "\tHost: gea.esac.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n",
      "\n",
      "### Doing photometry on nrc24_calfiles/jw01073003001_02101_00001_nrca1_cal.fits\n",
      "NO photometry catalog filename\n",
      "Filename: nrc24_calfiles/jw01073003001_02101_00001_nrca1_cal.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     351   ()      \n",
      "  1  SCI           1 ImageHDU       112   (2048, 2048)   float32   \n",
      "  2  ERR           1 ImageHDU        10   (2048, 2048)   float32   \n",
      "  3  DQ            1 ImageHDU        11   (2048, 2048)   int32 (rescales to uint32)   \n",
      "  4  AREA          1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  5  VAR_POISSON    1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  6  VAR_RNOISE    1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  7  VAR_FLAT      1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  8  ASDF          1 BinTableHDU     11   1R x 1C   [23852B]   \n",
      "None\n",
      "Instrument: NIRCAM, aperture:NRCA1_FULL\n",
      "Applying Pixel Area Map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Input data contains invalid values (NaNs or infs), which were automatically clipped. [astropy.stats.sigma_clipping]\n",
      "2022-05-30 22:33:48,672 - stpipe - WARNING - Input data contains invalid values (NaNs or infs), which were automatically clipped.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting units from MJy/sr to DN/s\n",
      "Finding stars --- Detector: NRCA1, Filter: F150W\n",
      "FWHM for the filter F150W: 1.77 px\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Input data contains invalid values (NaNs or infs), which were automatically clipped. [astropy.stats.sigma_clipping]\n",
      "2022-05-30 22:33:49,469 - stpipe - WARNING - Input data contains invalid values (NaNs or infs), which were automatically clipped.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background and rms using MMMBackground and MADStdBackgroundRMS: 0.13985030353069305 0.11468812676465207\n",
      "\n",
      "Number of sources found in the image: 25925\n",
      "-------------------------------------\n",
      "\n",
      "radii:[3.54]pixels radius_sky_in:7.08 radius_sky_out:10.620000000000001  radius_for_mag:3.54\n",
      "Performing aperture photometry for radius r = 3.54 px\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 22:34:12,776 - stpipe - WARNING - /Users/arest/nircam/jwst_distortions_tools/simple_jwst_phot.py:632: RuntimeWarning: invalid value encountered in log10\n",
      "  table_aper['mag'] = -2.5 * np.log10(table_aper[self.colname('aper_sum_bkgsub',rad)])\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Elapsed: 20.337520194000717\n",
      "6324 out of 25925 entries remain in photometry table\n",
      "Getting ./LMC_gaia_DR3.nrcposs and matching it\n",
      "80.42989590062733 -69.52537943567059 0.012625532391511846\n",
      "Matching reference catalog ./LMC_gaia_DR3.nrcposs\n",
      "image objects are in x_idl=[-31.87,31.78] and y_idl=[-31.98,31.63] range\n",
      "Keeping 332 out of 22544 catalog objects\n",
      "Keeping 332  after removing NaNs from ra/dec\n",
      "DONE!\n"
     ]
    }
   ],
   "source": [
    "# simple aperture photometry, and matching to Gaia\n",
    "# !!!! You only need to run this if the cal.phot.txt file does not exist yet!!!\n",
    "from simple_jwst_phot import jwst_photclass\n",
    "calphot=jwst_photclass()\n",
    "calphot.verbose = 2\n",
    "\n",
    "# This is the LMC catalog we are using. I need to add a routine that gets Gaia sources from full sky\n",
    "gaia_catname_for_testing='./LMC_gaia_DR3.nrcposs'\n",
    "# minimum S/N for sources in image\n",
    "SNR_min = 10.0\n",
    "\n",
    "if not os.path.isfile(cal_image):\n",
    "    raise RuntimeError(f'file {cal_image} does not exist!')\n",
    "\n",
    "# Run photometry.\n",
    "calphot.run_phot(cal_image,gaia_catname_for_testing,SNR_min=SNR_min,photfilename='auto')\n",
    "print('DONE!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e6c4bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nrc24_calfiles/jw01073003001_02101_00001_nrca1_cal.phot.txt\n",
      "outdir: nrc24_calfiles\n",
      "NRCA1\n",
      "filter:F150W\n",
      "pupil: CLEAR\n",
      "effective exptime: 85.894\n",
      "Nx=2048 Ny=2048\n",
      "# of matched objects that pass initial cuts: 5822\n",
      "dx median: 1.8179839999999672\n",
      "dy median: 4.287765999999998\n",
      "      aper_sum_3.5px  annulus_median_3.5px  aper_bkg_3.5px  \\\n",
      "0          57.188874              0.337081       13.270589   \n",
      "1          35.223304              0.193021        7.599092   \n",
      "2          51.869776              0.138721        5.461346   \n",
      "3          86.190122              0.129515        5.098915   \n",
      "4          36.401006              0.145514        5.728780   \n",
      "...              ...                   ...             ...   \n",
      "6319       83.418605              0.224751        8.848246   \n",
      "6320       33.111283              0.148323        5.839360   \n",
      "6321      266.966566              1.568980       61.769478   \n",
      "6322      266.411683              1.242580       48.919342   \n",
      "6323       44.481101              0.145730        5.737288   \n",
      "\n",
      "      aper_sum_bkgsub_3.5px  flux_err_3.5px       mag      dmag            x  \\\n",
      "0                 43.918285        3.067387 -4.106613  0.075850   134.866781   \n",
      "1                 27.624211        2.565801 -3.603225  0.100870   281.807014   \n",
      "2                 46.408430        2.823779 -4.166492  0.066079   685.759750   \n",
      "3                 81.091206        3.157422 -4.772434  0.042285   871.346349   \n",
      "4                 30.672226        2.644865 -3.716863  0.093646  1171.668574   \n",
      "...                     ...             ...       ...       ...          ...   \n",
      "6319              74.570359        3.187615 -4.681416  0.046423  1484.113781   \n",
      "6320              27.271924        2.520673 -3.589289  0.100376   942.906752   \n",
      "6321             205.197088       13.523416 -5.780428  0.071572   644.184829   \n",
      "6322             217.492341       12.247689 -5.843610  0.061156   653.337963   \n",
      "6323              38.743813        2.655029 -3.970506  0.074421   886.615411   \n",
      "\n",
      "                y  sharpness  ...        cat_y  cat_x_idl  cat_y_idl  cat_ID  \\\n",
      "0        7.957519   0.813238  ...    22.781693 -27.370539 -31.453409  6386.0   \n",
      "1        8.326770   0.782190  ...   -24.028293 -23.414721 -32.946639  6224.0   \n",
      "2        7.483427   0.774969  ...   107.785111 -10.693399 -28.804086  5301.0   \n",
      "3        7.969408   0.766235  ...   -18.616574  -2.493203 -32.811074  5006.0   \n",
      "4        8.554206   0.727982  ...    -3.880317   4.588717 -32.345756  4601.0   \n",
      "...           ...        ...  ...          ...        ...        ...     ...   \n",
      "6319  2035.107763   0.793120  ...  2017.508442  15.179802  31.048790  2593.0   \n",
      "6320  2035.532726   0.774196  ...  2034.518867  -0.652121  31.525122  3359.0   \n",
      "6321  2038.435362   0.656940  ...  2052.457818 -11.612344  32.069757  3978.0   \n",
      "6322  2038.046319   0.754555  ...  2052.457818 -11.612344  32.069757  3978.0   \n",
      "6323  2036.632488   0.894227  ...  2034.518867  -0.652121  31.525122  3359.0   \n",
      "\n",
      "      gaia_mag       d2d    ddec       dra          dx          dy  \n",
      "0       19.599  0.718103  0.3492  0.629504   17.229107   14.824174  \n",
      "1       18.315  1.029361     NaN       NaN         NaN         NaN  \n",
      "2       16.934  3.173139 -1.1700  2.949218   -2.506828  100.301684  \n",
      "3       19.217  2.464201  2.4624  0.021409   73.899284  -26.585982  \n",
      "4       19.140  0.394834  0.1188 -0.376592   -0.677451  -12.434523  \n",
      "...        ...       ...     ...       ...         ...         ...  \n",
      "6319    19.059  1.106673  1.0908 -0.171263   31.081484  -17.599321  \n",
      "6320    19.195  1.875329     NaN       NaN         NaN         NaN  \n",
      "6321    16.441  0.462758 -0.0072  0.463272    5.034300   14.022456  \n",
      "6322    16.441  0.465580 -0.2772  0.375151   -4.118834   14.411499  \n",
      "6323    19.195  3.616912  3.4092  1.208647  116.908588   -2.113621  \n",
      "\n",
      "[6324 rows x 29 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a few essential info from the fits header\n",
    "hdr = fits.getheader(cal_image)\n",
    "hdrsci = fits.getheader(cal_image,extname='SCI')\n",
    "#print(hdr)\n",
    "outdir = os.path.dirname(cal_phot_filename)\n",
    "print(f'{cal_phot_filename}\\noutdir: {outdir}')\n",
    "print(f'{hdr[\"DETECTOR\"]}\\nfilter:{hdr[\"FILTER\"]}\\npupil: {hdr[\"PUPIL\"]}\\neffective exptime: {hdr[\"EFFEXPTM\"]}')\n",
    "Nx=hdrsci['NAXIS1']\n",
    "Ny=hdrsci['NAXIS2']\n",
    "print(f'Nx={Nx} Ny={Ny}')\n",
    "\n",
    "# Load the photometry file\n",
    "phot_cal = pdastrostatsclass()\n",
    "phot_cal.load(cal_phot_filename)\n",
    "ixs_cal = calc_dxdy(phot_cal,d2d_max=d2d_max,dmag_max=dmag_max,Nbright=Nbright)\n",
    "\n",
    "# set up 2 panel figure\n",
    "sp=[]\n",
    "xfigsize=10.0\n",
    "yfigsize=5.0\n",
    "plt.figure(figsize=(xfigsize,yfigsize))\n",
    "sp.append(plt.subplot(121))\n",
    "sp.append(plt.subplot(122))\n",
    " \n",
    "# For both dx, dy:\n",
    "# get the median and plot them with y-axis limits median+-delta_pix_ylim\n",
    "delta_pix_ylim = 7\n",
    "\n",
    "dx_median = phot_cal.t.loc[ixs_cal,'dx'].median()\n",
    "dy_median = phot_cal.t.loc[ixs_cal,'dy'].median()\n",
    "\n",
    "print(f'dx median: {dx_median}\\ndy median: {dy_median}')\n",
    "\n",
    "# these are the general limits for the y-axis for the plots\n",
    "dy_plotlim = (dy_median-delta_pix_ylim,dy_median+delta_pix_ylim)\n",
    "dx_plotlim = (dx_median-delta_pix_ylim,dx_median+delta_pix_ylim)\n",
    "\n",
    "# plot the residuals\n",
    "phot_cal.t.loc[ixs_cal].plot.scatter('y','dx',ax=sp[0],ylim=dx_plotlim,color='red')\n",
    "phot_cal.t.loc[ixs_cal].plot.scatter('x','dy',ax=sp[1],ylim=dy_plotlim,color='red')\n",
    "print(phot_cal.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f1904e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define some functions\n",
    "\n",
    "def initplot(nrows=1, ncols=1, figsize4subplot=5, **kwargs):\n",
    "    sp=[]\n",
    "    xfigsize=figsize4subplot*ncols\n",
    "    yfigsize=figsize4subplot*nrows\n",
    "    plt.figure(figsize=(xfigsize,yfigsize))\n",
    "    counter=1\n",
    "    for row in range(nrows):\n",
    "        for col in range(ncols):\n",
    "            sp.append(plt.subplot(nrows, ncols, counter,**kwargs))\n",
    "            counter+=1\n",
    "\n",
    "    for i in range(len(sp)):\n",
    "        plt.setp(sp[i].get_xticklabels(),'fontsize',12)\n",
    "        plt.setp(sp[i].get_yticklabels(),'fontsize',12)\n",
    "        sp[i].set_xlabel(sp[i].get_xlabel(),fontsize=14)\n",
    "        sp[i].set_ylabel(sp[i].get_ylabel(),fontsize=14)\n",
    "        sp[i].set_title(sp[i].get_title(),fontsize=14)\n",
    "\n",
    "    return(sp)\n",
    "\n",
    "# plot the rotated dx or dy versus the original one\n",
    "def plot_rotated(phot,ixs,d_col,col,\n",
    "                 d_col_rot='__d_rot_tmp',\n",
    "                 histolim=(-28,-12),\n",
    "                 bins=None,\n",
    "                 bin_weights_flag=False,\n",
    "                 title=None):\n",
    "    sp=initplot(1,2)\n",
    "\n",
    "    phot.t.loc[ixs].plot.scatter(col,d_col,ax=sp[0],ylim=histolim,color='red',title=title)\n",
    "    phot.t.loc[ixs].plot.scatter(col,d_col_rot,ax=sp[0],ylim=histolim,color='blue',ylabel=f'{d_col} in pixels')\n",
    "    \n",
    "    if bins is not None:\n",
    "        if bin_weights_flag:\n",
    "            phot.t.loc[ixs,d_col_rot].plot.hist(ax=sp[1],bins=bins,weights=phot.t.loc[ixs,'__weights'],xlim=histolim,color='blue')\n",
    "        else:\n",
    "            phot.t.loc[ixs,d_col_rot].plot.hist(ax=sp[1],bins=bins,xlim=histolim,color='blue')\n",
    "\n",
    "    return(sp)\n",
    "\n",
    "# find the maximum value in yvals, its index, and the corresponding value in xvals\n",
    "# also indicate if there are multiple entries with the same maximum value (multiple_max=True)\n",
    "def find_info_for_maxval(xvals,yvals,use_firstindex_if_multiple=True):\n",
    "    # get the max value of the histogram, and its associated bin\n",
    "    #print(histo)\n",
    "    maxval = np.max(yvals)\n",
    "    ixs_max = np.where(yvals==maxval)\n",
    "    multiple_max = None\n",
    "    if (len(ixs_max[0])==0):\n",
    "        raise RuntimeError('BUUUUGGGG!!!!')\n",
    "    elif (len(ixs_max[0])>1):\n",
    "        if not use_firstindex_if_multiple:\n",
    "            raise RuntimeError(f'More than one indices {ixs_max} for maxval={maxval}')\n",
    "        #print(f'\\nWARNING!! More than one indices {ixs_max} for maxval={maxval}!')\n",
    "        multiple_max=True\n",
    "        ix_best=ixs_max[0][0]\n",
    "    else:\n",
    "        multiple_max=False\n",
    "        ix_best=ixs_max[0][0]\n",
    "    # return ()\n",
    "    \n",
    "    # get the (rough) fwhm of the peak\n",
    "    ix_minus = ix_best-1 \n",
    "    if ix_minus<0: ix_minus=0\n",
    "    ix_plus = ix_best+1\n",
    "    if ix_plus>len(yvals)-1: ix_plus=len(yvals)-1\n",
    "    while (ix_minus>0):\n",
    "        if yvals[ix_minus]<=0.5*yvals[ix_best]:\n",
    "            break\n",
    "        ix_minus-=1\n",
    "    while (ix_plus<len(yvals)-1):\n",
    "        if yvals[ix_plus]<=0.5*yvals[ix_best]:\n",
    "            break\n",
    "        ix_plus+=1\n",
    "    fwhm = xvals[ix_plus]-xvals[ix_minus]\n",
    "    \n",
    "    return(xvals[ix_best],yvals[ix_best],ix_best,fwhm,multiple_max)\n",
    "\n",
    "# straight line. Sloppy: use global slope and intercept\n",
    "def f(val,slope,intercept):\n",
    "    return(val*slope+intercept)\n",
    "\n",
    "def rotate_d_and_find_binmax(phot,ixs,d_col,col,\n",
    "                             Naxis_px, # Nx or Ny, depending on col\n",
    "                             d_col_rot='__d_rot_tmp',\n",
    "                             binsize=0.5,\n",
    "                             bin_weights_flag=True,\n",
    "                             slope_min=-10.0/2048.0, # \n",
    "                             slope_max=10.0/2048.0, # \n",
    "                             slope_stepsize=1.0/2048,\n",
    "                             showplots=0):\n",
    "    rot_results = pdastroclass()\n",
    "\n",
    "    if bin_weights_flag:\n",
    "        phot.t.loc[ixs,'__weights']=10**(-0.4*phot.t.loc[ixs,'mag'])\n",
    "    else:\n",
    "        phot.t.loc[ixs,'__weights']=None\n",
    "        \n",
    "    slopes = np.arange(slope_min,slope_max,slope_stepsize)\n",
    "    for slope in slopes:\n",
    "        #slope = delta4slope_pix/Nx\n",
    "        intercept = -0.5*Naxis_px * slope\n",
    "\n",
    "        phot.t.loc[ixs,d_col_rot] = phot.t.loc[ixs,d_col] - f(phot.t.loc[ixs,col],slope,intercept)\n",
    "\n",
    "        # get the histogram\n",
    "        d_rotated = phot.t.loc[ixs,d_col_rot]\n",
    "        weights = phot.t.loc[ixs,'aper_sum_3.5px']\n",
    "        bins = np.arange(np.min(d_rotated),np.max(d_rotated),binsize)\n",
    "        if bin_weights_flag:\n",
    "            histo = np.histogram(d_rotated,bins=bins,weights=phot.t.loc[ixs,'__weights'])\n",
    "        else:\n",
    "            histo = np.histogram(d_rotated,bins=bins)\n",
    "        #sp = plt.subplot(111)\n",
    "        #print(histo[1])\n",
    "        #sp.plot(histo[1][1:], histo[0], 'ro')\n",
    "        # get the max value of the histogram, and its associated bin center. Note that the bincenter is \n",
    "        # the value in the bins (left edge of the bin) + the half of the binsize\n",
    "        (bincenter4maxval,maxval,index_maxval,fwhm,multiple_max) = find_info_for_maxval(histo[1]+0.5*binsize,histo[0])\n",
    "\n",
    "        # Save the results\n",
    "        rot_results.newrow({'slope':slope,\n",
    "                            'intercept':intercept,\n",
    "                            'maxval':maxval,\n",
    "                            'index':index_maxval,\n",
    "                            'd_bestguess':bincenter4maxval,\n",
    "                            'fwhm':fwhm,\n",
    "                            'multimax':multiple_max\n",
    "                            })\n",
    "        # plot it if wanted\n",
    "        if showplots>1:\n",
    "            plot_rotated(phot,ixs,\n",
    "                         d_col,col,\n",
    "                         d_col_rot=d_col_rot,\n",
    "                         bins=bins,\n",
    "                         bin_weights_flag=bin_weights_flag,\n",
    "                         histolim = (bincenter4maxval-8,bincenter4maxval+8),\n",
    "                         title=f'slope:{slope}')\n",
    "        #sys.exit(0)\n",
    "\n",
    "    # print the results        \n",
    "    rot_results.write()\n",
    "    \n",
    "    # find the best rotation\n",
    "    maxmaxval = np.max(rot_results.t['maxval'])\n",
    "    ixs_maxmax = np.where(rot_results.t['maxval']==maxmaxval)\n",
    "    if (len(ixs_maxmax[0])==0):\n",
    "        raise RuntimeError('BUUUUGGGG!!!!')\n",
    "    elif (len(ixs_maxmax[0])>1):\n",
    "        #print(f'\\nWARNING!! more than one bin with maxvalue={maxmaxval}!')\n",
    "        multiple_max=True\n",
    "        best_index=ixs_maxmax[0][0]\n",
    "    else:\n",
    "        multiple_max=False\n",
    "        best_index=ixs_maxmax[0][0]\n",
    "    print('####BEST:')\n",
    "    rot_results.write(indices=[best_index])\n",
    "    \n",
    "    sp = initplot(3,1)\n",
    "    rot_results.t.plot('slope','maxval',ax=sp[0],color='blue')\n",
    "    rot_results.t.plot.scatter('slope','d_bestguess',ax=sp[1],color='blue')\n",
    "    rot_results.t.plot.scatter('slope','fwhm',ax=sp[2],color='blue')\n",
    "    sp[0].axvline(rot_results.t.loc[best_index,'slope'],  color='red',linestyle='-', linewidth=2.0)\n",
    "    sp[1].axvline(rot_results.t.loc[best_index,'slope'],  color='red',linestyle='-', linewidth=2.0)\n",
    "    sp[2].axvline(rot_results.t.loc[best_index,'slope'],  color='red',linestyle='-', linewidth=2.0)\n",
    "    \n",
    "    \n",
    "    return(rot_results,best_index)\n",
    "\n",
    "def sigmacut_d_rot(phot,ixs,\n",
    "                   d_col,col,\n",
    "                   slope,intercept,d_rot_bestguess,\n",
    "                   rough_cut_px = 2.5, #This is the first rough cut:  get rid of everything d_rot_bestguess+-rough_cut_px\n",
    "                   d_col_rot='__d_rot_tmp',\n",
    "                   showplots=0,\n",
    "                   binsize=0.5,\n",
    "                   bin_weights_flag=True):\n",
    "\n",
    "    ### recover the slope and intercept of the best binning\n",
    "    phot.t.loc[ixs,d_col_rot] = phot.t.loc[ixs,d_col] - f(phot.t.loc[ixs,col],slope,intercept)\n",
    "    \n",
    "    # Now make the rough cut! only keep data for with dx_rotated within  d_rot_bestguess+-rough_cut_px\n",
    "    ixs_roughcut = phot.ix_inrange(d_col_rot,d_rot_bestguess-rough_cut_px,d_rot_bestguess+rough_cut_px,indices=ixs)\n",
    "    d_rotated = phot.t.loc[ixs,d_col_rot]\n",
    "    \n",
    "    if showplots:\n",
    "        bins = np.arange(np.min(d_rotated),np.max(d_rotated),binsize)\n",
    "        plot_rotated(phot,ixs_roughcut,\n",
    "                     d_col,col,\n",
    "                     d_col_rot=d_col_rot,\n",
    "                     bins=bins,\n",
    "                     bin_weights_flag=bin_weights_flag,\n",
    "                     histolim = (d_rot_bestguess-3*rough_cut_px,d_rot_bestguess+3*rough_cut_px),\n",
    "                     title=f'First rough cut: {d_rot_bestguess:.3f}+-{rough_cut_px:.3f} for slope={slope:.6f}')\n",
    "\n",
    "    print('\\n####################\\n### d_rotated cut')\n",
    "    #ixs_clean4average = phot_clear.ix_inrange(d_col,0,3,indices=ixs_clear_cut)\n",
    "    phot.calcaverage_sigmacutloop(d_col_rot,verbose=3,indices=ixs_roughcut,percentile_cut_firstiteration=35)\n",
    "    print(phot.statstring())\n",
    "    ixs_cut = phot.statparams['ix_good']\n",
    "\n",
    "    if showplots:\n",
    "        #infoplots(phot,ixs_cut,ixs_roughcut,dy_plotlim=dy_plotlim,dx_plotlim=dx_plotlim)\n",
    "        sp=initplot(1,2)\n",
    "\n",
    "        phot.t.loc[ixs_roughcut].plot.scatter(col,d_col_rot,ax=sp[0],color='red')\n",
    "        phot.t.loc[ixs_cut].plot.scatter(col,d_col_rot,ax=sp[0],ylabel='dx in pixels',title=f'3-sigma cut: {len(ixs_cut)} out of {len(ixs_roughcut)} left')\n",
    "        \n",
    "#        plot_rotated(phot,ixs_cut,\n",
    "#                     d_col,col,\n",
    "#                     d_col_rot=d_col_rot,\n",
    "#                     histolim = (d_rot_bestguess-rough_cut_px,d_rot_bestguess+rough_cut_px),\n",
    "#                     title=f'3-sigma cut: {len(ixs_cut)} out of {len(ixs_roughcut)} left')\n",
    "    return(ixs_cut,ixs_roughcut)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b461e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        slope     intercept        maxval  index  d_bestguess  fwhm  multimax\n",
      "-4.882812e-03  5.000000e+00  26699.912101   1884     3.036753   0.2     False\n",
      "-4.557292e-03  4.666667e+00  25127.408556   1919     6.208646   0.2     False\n",
      "-4.231771e-03  4.333333e+00  29776.404488   1889     2.880540   0.2     False\n",
      "-3.906250e-03  4.000000e+00  27729.381836   1894     3.052433   0.3     False\n",
      "-3.580729e-03  3.666667e+00  30391.537987   1889     2.224326   0.2     False\n",
      "-3.255208e-03  3.333333e+00  33280.012435   1876     0.596220   0.2     False\n",
      "-2.929688e-03  3.000000e+00  38403.247998   1865    -0.831887   0.2     False\n",
      "-2.604167e-03  2.666667e+00  35821.526072   1892     1.540007   0.3     False\n",
      "-2.278646e-03  2.333333e+00  41603.148733   1919     3.911900   0.2     False\n",
      "-1.953125e-03  2.000000e+00  59535.183400   1908     2.483793   0.3     False\n",
      "-1.627604e-03  1.666667e+00  55325.930374   1911     2.455687   0.4     False\n",
      "-1.302083e-03  1.333333e+00  71812.516733   1913     2.327580   0.3     False\n",
      "-9.765625e-04  1.000000e+00  85245.351543   1915     2.199474   0.7     False\n",
      "-6.510417e-04  6.666667e-01 120946.353190   1917     2.071367   0.5     False\n",
      "-3.255208e-04  3.333333e-01 284290.136854   1919     1.943261   0.4     False\n",
      "-4.336809e-18  4.440892e-15 154509.952518   1922     1.915154   0.6     False\n",
      " 3.255208e-04 -3.333333e-01  80200.253147   1928     2.187047   1.0     False\n",
      " 6.510417e-04 -6.666667e-01  65584.986111   1925     1.558941   0.3     False\n",
      " 9.765625e-04 -1.000000e+00  57962.960521   1931     1.830834   0.2     False\n",
      " 1.302083e-03 -1.333333e+00  51775.380380   1930     1.402728   0.2     False\n",
      " 1.627604e-03 -1.666667e+00  46216.559226   1956     3.674621   0.3     False\n",
      " 1.953125e-03 -2.000000e+00  57424.415114   1963     4.046515   0.2     False\n",
      " 2.278646e-03 -2.333333e+00  55334.129335   1969     4.318408   0.2     False\n",
      " 2.604167e-03 -2.666667e+00  38459.845346   1976     4.690301   0.3     False\n",
      " 2.929687e-03 -3.000000e+00  49776.340998   1982     4.962195   0.2     False\n",
      " 3.255208e-03 -3.333333e+00  36721.511656   1988     5.234088   0.3     False\n",
      " 3.580729e-03 -3.666667e+00  31697.505403   1994     5.505982   0.2     False\n",
      " 3.906250e-03 -4.000000e+00  32637.388025   2001     5.877875   0.3     False\n",
      " 4.231771e-03 -4.333333e+00  31689.627693   2007     6.149768   0.2     False\n",
      " 4.557292e-03 -4.666667e+00  33404.212819   2013     6.421662   0.2     False\n",
      "####BEST:\n",
      "    slope  intercept        maxval  index  d_bestguess  fwhm  multimax\n",
      "-0.000326   0.333333 284290.136854   1919     1.943261   0.4     False\n",
      "\n",
      "####################\n",
      "### d_rotated cut\n",
      "Keeping 341 out of 341, skippin 0 because of null values in columns __d_rot_tmp\n",
      "median: 1.934583\n",
      "35.000000 percentile cut: max residual for cut: 0.045796\n",
      "median: 1.937118\n",
      "i:00 mean:1.937118(0.002478) stdev:0.026915(0.001745) X2norm:1.00 Nchanged:0 Ngood:119 Nclip:222\n",
      "\n",
      "mean: 1.937061\n",
      "i:01 mean:1.937061(0.003158) stdev:0.042715(0.002227) X2norm:1.00 Nchanged:65 Ngood:184 Nclip:157\n",
      "\n",
      "mean: 1.936393\n",
      "i:02 mean:1.936393(0.004084) stdev:0.064958(0.002882) X2norm:1.00 Nchanged:70 Ngood:254 Nclip:87\n",
      "\n",
      "mean: 1.939651\n",
      "i:03 mean:1.939651(0.004719) stdev:0.079392(0.003331) X2norm:1.00 Nchanged:30 Ngood:284 Nclip:57\n",
      "\n",
      "mean: 1.938831\n",
      "i:04 mean:1.938831(0.005190) stdev:0.088991(0.003664) X2norm:1.00 Nchanged:11 Ngood:295 Nclip:46\n",
      "\n",
      "mean: 1.936388\n",
      "i:05 mean:1.936388(0.005440) stdev:0.094061(0.003840) X2norm:1.00 Nchanged:5 Ngood:300 Nclip:41\n",
      "\n",
      "mean: 1.936388\n",
      "i:06 mean:1.936388(0.005440) stdev:0.094061(0.003840) X2norm:1.00 Nchanged:0 Ngood:300 Nclip:41\n",
      "i:06 mean:1.936388(0.005440) stdev:0.094061(0.003840) X2norm:1.00 Nchanged:0 Ngood:300 Nclip:41\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "showplots=1\n",
    "# showplots=0: no plots\n",
    "# showplots=1: important plots\n",
    "# showplots=2: all plots\n",
    "\n",
    "bin_weights_flag=True\n",
    "# If bin_weights_flag is set to True, then the dx/dy bins are weighted by\n",
    "# the flux of the detection.\n",
    "\n",
    "# Here we correct for rotation so that we can robustly remove outlier matches\n",
    "\n",
    "# A straight line with a given slope is subtracted from dx versus y, which is \n",
    "# effectively a correction for a rotation.\n",
    "\n",
    "# Then a histogram of the resulting dx_rotated is calculated with bin size = binsize_px in pixels.\n",
    "# The maximum value of the histogram, it's associated dx_rot position, a rough upper limit estimate\n",
    "# of the fwhm of the histogram peak are then saved for each slope and intercept in dx_rot_results (pdastro table).\n",
    "\n",
    "# best_index is the index for this table for which the largest maxval of the histograms, presumably for \n",
    "# the best rotation (or slope)\n",
    "binsize_px = 0.1\n",
    "\n",
    "# slope loops from slope_min to slope_max with slope_stepsize\n",
    "# slope_min=-10.0/2048.0 means that slopes with a range \n",
    "# of +-10 pixels of 2048 pixels are probed, i.e. dx does not change\n",
    "# by more than 10 pixels over the full detector width\n",
    "# A histogram of dx_rotated\n",
    "slope_Nsteps = 30\n",
    "slope_min=-10.0/2048.0\n",
    "slope_max=-slope_min\n",
    "slope_stepsize=(slope_max-slope_min)/slope_Nsteps\n",
    "\n",
    "(dx_rot_results,dx_best_index) = rotate_d_and_find_binmax(phot_cal,ixs_cal,'dx','y',\n",
    "                                                          Ny,\n",
    "                                                          binsize=binsize_px,\n",
    "                                                          bin_weights_flag=bin_weights_flag,\n",
    "                                                          slope_min=slope_min,\n",
    "                                                          slope_max=slope_max,\n",
    "                                                          slope_stepsize=slope_stepsize,\n",
    "                                                          showplots=showplots)\n",
    "# Using the best dx_rotated, we first remove all entries with dx_rotated outside of dx_bestguess+-Nfwhm*fwhm\n",
    "# Note that FWHM ~ 2.355 stdev, so Nfwhm*fwhm should be at least 3*stdev. This is the first ROUGH cut, with \n",
    "# which we just want to remove excessive amounts of outliers\n",
    "Nfwhm = 2.0\n",
    "\n",
    "(ixs_dx_cut,ixs_dx_roughcut) = sigmacut_d_rot(phot_cal,ixs_cal,'dx','y',\n",
    "                                              dx_rot_results.t.loc[dx_best_index,'slope'],\n",
    "                                              dx_rot_results.t.loc[dx_best_index,'intercept'],\n",
    "                                              dx_rot_results.t.loc[dx_best_index,'d_bestguess'],\n",
    "                                              rough_cut_px = Nfwhm*dx_rot_results.t.loc[dx_best_index,'fwhm'],\n",
    "                                              binsize=binsize_px,\n",
    "                                              bin_weights_flag=bin_weights_flag,\n",
    "                                              showplots=showplots\n",
    "                                              )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f549aed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        slope     intercept        maxval  index  d_bestguess  fwhm  multimax\n",
      "-4.882812e-03  5.000000e+00  29932.304305    799     2.057438   0.3     False\n",
      "-4.557292e-03  4.666667e+00  39286.414002    801     2.201965   0.2     False\n",
      "-4.231771e-03  4.333333e+00  31479.222556    803     2.346492   0.3     False\n",
      "-3.906250e-03  4.000000e+00  32048.664575    806     2.591020   0.3     False\n",
      "-3.580729e-03  3.666667e+00  43973.149354    808     2.735547   0.2     False\n",
      "-3.255208e-03  3.333333e+00  34286.124669    808     2.680074   0.2     False\n",
      "-2.929688e-03  3.000000e+00  34308.056644    813     3.124601   0.4     False\n",
      "-2.604167e-03  2.666667e+00  37385.592034    815     3.269128   0.2     False\n",
      "-2.278646e-03  2.333333e+00  35879.783389    853     7.013656   0.2     False\n",
      "-1.953125e-03  2.000000e+00  37613.724725    851     6.758183   0.2     False\n",
      "-1.627604e-03  1.666667e+00  49494.768193    821     3.702710   0.3     False\n",
      "-1.302083e-03  1.333333e+00  54967.286364    824     3.947237   0.3     False\n",
      "-9.765625e-04  1.000000e+00  54962.907307    826     4.091764   0.5     False\n",
      "-6.510417e-04  6.666667e-01  81552.760756    829     4.336292   0.4     False\n",
      "-3.255208e-04  3.333333e-01 110837.869642    832     4.580819   0.3     False\n",
      "-4.336809e-18  4.440892e-15 135100.332423    834     4.725346   0.7     False\n",
      " 3.255208e-04 -3.333333e-01 301973.574127    836     4.869873   0.3     False\n",
      " 6.510417e-04 -6.666667e-01 126911.161410    838     5.014400   0.4     False\n",
      " 9.765625e-04 -1.000000e+00  82987.854953    841     5.258928   0.6     False\n",
      " 1.302083e-03 -1.333333e+00  58267.251911    845     5.603455   0.9     False\n",
      " 1.627604e-03 -1.666667e+00  54848.890280    845     5.547982   0.6     False\n",
      " 1.953125e-03 -2.000000e+00  60922.268185    847     5.692509   0.2     False\n",
      " 2.278646e-03 -2.333333e+00  61632.499498    859     6.837036   0.2     False\n",
      " 2.604167e-03 -2.666667e+00  58282.573311    863     7.181564   0.2     False\n",
      " 2.929687e-03 -3.000000e+00  56104.997399    867     7.526091   0.2     False\n",
      " 3.255208e-03 -3.333333e+00  44830.446774    870     7.770618   0.2     False\n",
      " 3.580729e-03 -3.666667e+00  44830.446774    874     8.115145   0.2     False\n",
      " 3.906250e-03 -4.000000e+00  35681.060201    878     8.459672   0.3     False\n",
      " 4.231771e-03 -4.333333e+00  35293.654837    882     8.804200   0.3     False\n",
      " 4.557292e-03 -4.666667e+00  30890.058293    886     9.148727   0.3     False\n",
      "####BEST:\n",
      "   slope  intercept        maxval  index  d_bestguess  fwhm  multimax\n",
      "0.000326  -0.333333 301973.574127    836     4.869873   0.3     False\n",
      "\n",
      "####################\n",
      "### d_rotated cut\n",
      "Keeping 281 out of 281, skippin 0 because of null values in columns __d_rot_tmp\n",
      "median: 4.912063\n",
      "35.000000 percentile cut: max residual for cut: 0.037897\n",
      "median: 4.908202\n",
      "i:00 mean:4.908202(0.002174) stdev:0.021407(0.001529) X2norm:0.99 Nchanged:0 Ngood:98 Nclip:183\n",
      "\n",
      "mean: 4.907470\n",
      "i:01 mean:4.907470(0.002861) stdev:0.035959(0.002017) X2norm:1.00 Nchanged:61 Ngood:159 Nclip:122\n",
      "\n",
      "mean: 4.914961\n",
      "i:02 mean:4.914961(0.003569) stdev:0.053297(0.002518) X2norm:1.00 Nchanged:65 Ngood:224 Nclip:57\n",
      "\n",
      "mean: 4.916882\n",
      "i:03 mean:4.916882(0.004312) stdev:0.069804(0.003044) X2norm:1.00 Nchanged:39 Ngood:263 Nclip:18\n",
      "\n",
      "mean: 4.916779\n",
      "i:04 mean:4.916779(0.004667) stdev:0.076963(0.003294) X2norm:1.00 Nchanged:10 Ngood:273 Nclip:8\n",
      "\n",
      "mean: 4.916759\n",
      "i:05 mean:4.916759(0.004863) stdev:0.080784(0.003432) X2norm:1.00 Nchanged:4 Ngood:277 Nclip:4\n",
      "\n",
      "mean: 4.916759\n",
      "i:06 mean:4.916759(0.004863) stdev:0.080784(0.003432) X2norm:1.00 Nchanged:0 Ngood:277 Nclip:4\n",
      "i:06 mean:4.916759(0.004863) stdev:0.080784(0.003432) X2norm:1.00 Nchanged:0 Ngood:277 Nclip:4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the indices after the dx cut, we now also do a dy cut\n",
    "slope = -dx_rot_results.t.loc[dx_best_index,'slope']\n",
    "(dy_rot_results,dy_best_index) = rotate_d_and_find_binmax(phot_cal,ixs_dx_cut,'dy','x',\n",
    "                                                          Nx,\n",
    "                                                          binsize=binsize_px,\n",
    "                                                          bin_weights_flag=bin_weights_flag,\n",
    "                                                          slope_min=slope_min,\n",
    "                                                          slope_max=slope_max,\n",
    "                                                          slope_stepsize=slope_stepsize,\n",
    "                                                          showplots=0)\n",
    "(ixs_dy_cut,ixs_dy_roughcut) = sigmacut_d_rot(phot_cal,ixs_dx_cut,'dy','x',\n",
    "                                              dy_rot_results.t.loc[dy_best_index,'slope'],\n",
    "                                              dy_rot_results.t.loc[dy_best_index,'intercept'],\n",
    "                                              dy_rot_results.t.loc[dy_best_index,'d_bestguess'],\n",
    "                                              rough_cut_px = Nfwhm*dy_rot_results.t.loc[dy_best_index,'fwhm'],\n",
    "                                              binsize=binsize_px,\n",
    "                                              bin_weights_flag=bin_weights_flag,\n",
    "                                              showplots=showplots\n",
    "                                              )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e46b7308",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c05d46e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good objects left: 277\n",
      "median dmag of good object: 0.003802\n",
      "median gaia_mag of good object: 18.951\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show how good the selection of good data point is\n",
    "\n",
    "# good data points\n",
    "ixs_cal_good = ixs_dy_cut\n",
    "\n",
    "# get the bad data points\n",
    "ixs_cal_bad = AnotB(phot_cal.getindices(),ixs_cal_good)\n",
    "\n",
    "infoplots(phot_cal,ixs_cal_good,ixs_cal_bad,dy_plotlim=dy_plotlim,dx_plotlim=dx_plotlim)\n",
    "\n",
    "#phot_cal.write(indices=ixs_cal_good)\n",
    "\n",
    "print(f'good objects left: {len(ixs_cal_good)}')\n",
    "\n",
    "# some extra info:\n",
    "dmag_median = phot_cal.t.loc[ixs_cal_good,'dmag'].median()\n",
    "gaia_mag_median = phot_cal.t.loc[ixs_cal_good,'gaia_mag'].median()\n",
    "print(f'median dmag of good object: {dmag_median}')\n",
    "print(f'median gaia_mag of good object: {gaia_mag_median}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d84f0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-31 10:06:46,552 - stpipe.TweakRegStep - INFO - TweakRegStep instance created.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nrc24_calfiles/jw01073003001_02101_00001_nrca1_tweakregstep.fits\n",
      "Setting output directory for tweakregstep.fits file to nrc24_calfiles\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-31 10:06:47,843 - stpipe.TweakRegStep - INFO - Step TweakRegStep running with args ([<ImageModel(2048, 2048) from jw01073003001_02101_00001_nrca1_cal.fits>],).\n",
      "2022-05-31 10:06:47,847 - stpipe.TweakRegStep - INFO - Step TweakRegStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'nrc24_calfiles', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_catalogs': True, 'catalog_format': 'ecsv', 'kernel_fwhm': 2.5, 'snr_threshold': 50, 'brightest': 1000, 'peakmax': None, 'enforce_user_order': False, 'expand_refcat': False, 'minobj': 50, 'searchrad': 0.5, 'use2dhist': True, 'separation': 9, 'tolerance': 0.7, 'xoffset': 0, 'yoffset': 0, 'fitgeometry': 'rshift', 'nclip': 3, 'sigma': 3.0, 'align_to_gaia': False, 'gaia_catalog': 'GAIADR2', 'min_gaia': 30, 'save_gaia_catalog': True}\n",
      "2022-05-31 10:06:47,865 - stpipe.TweakRegStep - INFO - NUMBER OF IMAGES: 1\n",
      "2022-05-31 10:06:47,869 - stpipe.TweakRegStep - INFO - Detected 277 sources in jw01073003001_02101_00001_nrca1_cal.fits.\n",
      "2022-05-31 10:06:47,899 - stpipe.TweakRegStep - INFO - Wrote source catalog: jw01073003001_02101_00001_nrca1_cal_cat.ecsv\n",
      "2022-05-31 10:06:47,933 - stpipe.TweakRegStep - INFO - \n",
      "2022-05-31 10:06:47,934 - stpipe.TweakRegStep - INFO - Number of image groups to be aligned: 1.\n",
      "2022-05-31 10:06:47,934 - stpipe.TweakRegStep - INFO - Image groups:\n",
      "2022-05-31 10:06:47,963 - stpipe.TweakRegStep - INFO - * Images in GROUP 'jw01073003001_02101_00001_nrca1_cal':\n",
      "2022-05-31 10:06:47,964 - stpipe.TweakRegStep - INFO -      jw01073003001_02101_00001_nrca1_cal\n",
      "2022-05-31 10:06:47,965 - stpipe.TweakRegStep - INFO - \n",
      "2022-05-31 10:06:47,967 - stpipe.TweakRegStep - INFO -  \n",
      "2022-05-31 10:06:47,968 - stpipe.TweakRegStep - INFO - ***** tweakwcs.imalign.align_wcs() started on 2022-05-31 10:06:47.967715\n",
      "2022-05-31 10:06:47,968 - stpipe.TweakRegStep - INFO -       Version 0.7.4\n",
      "2022-05-31 10:06:47,969 - stpipe.TweakRegStep - INFO -  \n",
      "2022-05-31 10:06:48,033 - stpipe.TweakRegStep - INFO - Aligning image catalog 'GROUP ID: jw01073003001_02101_00001_nrca1_cal' to the reference catalog.\n",
      "2022-05-31 10:06:48,224 - stpipe.TweakRegStep - INFO - No matching of sources from 'GROUP ID: jw01073003001_02101_00001_nrca1_cal' catalog with sources from the reference 'Unnamed' catalog was requested.\n",
      "2022-05-31 10:06:48,224 - stpipe.TweakRegStep - INFO - Catalogs are assumed matched with 1-to-1 correspondence.\n",
      "2022-05-31 10:06:48,226 - stpipe.TweakRegStep - INFO - Found 277 matches for 'GROUP ID: jw01073003001_02101_00001_nrca1_cal'...\n",
      "2022-05-31 10:06:48,227 - stpipe.TweakRegStep - INFO - Performing 'rshift' fit\n",
      "2022-05-31 10:06:48,230 - stpipe.TweakRegStep - INFO - Computed 'rshift' fit for GROUP ID: jw01073003001_02101_00001_nrca1_cal:\n",
      "2022-05-31 10:06:48,230 - stpipe.TweakRegStep - INFO - XSH: 0.16555  YSH: 0.00273138    ROT: 0.0166886    SCALE: 1\n",
      "2022-05-31 10:06:48,231 - stpipe.TweakRegStep - INFO - \n",
      "2022-05-31 10:06:48,231 - stpipe.TweakRegStep - INFO - FIT RMSE: 0.00367359   FIT MAE: 0.00320188\n",
      "2022-05-31 10:06:48,232 - stpipe.TweakRegStep - INFO - Final solution based on 277 objects.\n",
      "2022-05-31 10:06:48,282 - stpipe.TweakRegStep - INFO -  \n",
      "2022-05-31 10:06:48,282 - stpipe.TweakRegStep - INFO - ***** tweakwcs.imalign.align_wcs() ended on 2022-05-31 10:06:48.282051\n",
      "2022-05-31 10:06:48,283 - stpipe.TweakRegStep - INFO - ***** tweakwcs.imalign.align_wcs() TOTAL RUN TIME: 0:00:00.314336\n",
      "2022-05-31 10:06:48,283 - stpipe.TweakRegStep - INFO -  \n",
      "2022-05-31 10:06:49,833 - stpipe.TweakRegStep - INFO - Saved model in nrc24_calfiles/jw01073003001_02101_00001_nrca1_tweakregstep.fits\n",
      "2022-05-31 10:06:49,833 - stpipe.TweakRegStep - INFO - Step TweakRegStep done\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "### Pass the good matches after the dx and dy cuts to tweakreg and align it!\n",
    "\n",
    "tweakreg = tweakreg_hack.TweakRegStep()\n",
    "clobber = True\n",
    "cal_image_dat = datamodels.open(cal_image)\n",
    "\n",
    "tweakreg_image = f'{outdir}/'+re.sub('_[a-zA-Z0-9]+\\.fits$','_tweakregstep.fits',os.path.basename(cal_image))\n",
    "print(f'{tweakreg_image}')\n",
    "if os.path.basename(tweakreg_image)==cal_image: raise RuntimeError(f'Could not determine tweakreg_image from {cal_image}')\n",
    "print(f'Setting output directory for tweakregstep.fits file to {outdir}')\n",
    "tweakreg.output_dir = outdir\n",
    "if not os.path.isdir(outdir):\n",
    "    makepath(outdir)\n",
    "\n",
    "if os.path.isfile(tweakreg_image):\n",
    "    if clobber:\n",
    "        os.remove(tweakreg_image)\n",
    "    else:\n",
    "        print('Not clobbering, so leaving.')\n",
    "        sys.exit(1)\n",
    "    \n",
    "tweakreg.align_to_gaia = False\n",
    "tweakreg.save_gaia_catalog = True\n",
    "tweakreg.save_results = True\n",
    "tweakreg.save_catalogs = True\n",
    "tweakreg.snr_threshold = 50\n",
    "tweakreg.separation = 9\n",
    "tweakreg.searchrad = 0.5\n",
    "tweakreg.minobj = 50\n",
    "tweakreg.min_gaia = 30\n",
    "#tweakreg.faintest_gaia_mag = 19\n",
    "#tweakreg.brightest_gaia_mag = 15\n",
    "# if you want to use x/yoffset, you need to set tweakreg.use2dhist=False\n",
    "tweakreg.xoffset = 0\n",
    "tweakreg.yoffset = 0\n",
    "tweakreg.brightest = 1000\n",
    "tweakreg.fitgeometry = 'rshift'\n",
    "\n",
    "tweakreg.already_matched = True\n",
    "# phot_cal.t.loc[ixs_cal_good] is the table with the good matches!\n",
    "t =  Table.from_pandas(phot_cal.t.loc[ixs_cal_good])\n",
    "tweakreg.refcat = t\n",
    "tweakreg.ref_racol = 'cat_ra'\n",
    "tweakreg.ref_deccol = 'cat_dec'\n",
    "\n",
    "### Provide your own source catalog, to be used in place of the default daofinder stuff. If you actually have a list\n",
    "### of images, it's okay to provide a source catalog for each. \n",
    "cal_image_dat.source_catalog = t\n",
    "cal_data = [cal_image_dat]\n",
    "tweakreg.source_xcol = 'x'\n",
    "tweakreg.source_ycol = 'y'\n",
    "\n",
    "tweakreg.run(cal_data)\n",
    "print('DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56f062b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "### Doing photometry on nrc24_calfiles/jw01073003001_02101_00001_nrca1_tweakregstep.fits\n",
      "Filename: nrc24_calfiles/jw01073003001_02101_00001_nrca1_tweakregstep.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     353   ()      \n",
      "  1  SCI           1 ImageHDU       112   (2048, 2048)   float32   \n",
      "  2  ERR           1 ImageHDU        10   (2048, 2048)   float32   \n",
      "  3  DQ            1 ImageHDU        11   (2048, 2048)   int32 (rescales to uint32)   \n",
      "  4  AREA          1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  5  VAR_POISSON    1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  6  VAR_RNOISE    1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  7  VAR_FLAT      1 ImageHDU         9   (2048, 2048)   float32   \n",
      "  8  ASDF          1 BinTableHDU     11   1R x 1C   [117548762B]   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-25 17:11:35,615 - stpipe - WARNING - /Users/arest/anaconda/envs/jwst/lib/python3.8/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.\n",
      "  warnings.warn('Input data contains invalid values (NaNs or '\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Instrument: NIRCAM, aperture:NRCA1_FULL\n",
      "Applying Pixel Area Map\n",
      "Converting units from MJy/sr to DN/s\n",
      "Finding stars --- Detector: NRCA1, Filter: F150W\n",
      "FWHM for the filter F150W: 1.77 px\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-25 17:11:36,434 - stpipe - WARNING - /Users/arest/anaconda/envs/jwst/lib/python3.8/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.\n",
      "  warnings.warn('Input data contains invalid values (NaNs or '\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background and rms using MMMBackground and MADStdBackgroundRMS: 0.13985030353069305 0.11468812676465207\n",
      "\n",
      "Number of sources found in the image: 25925\n",
      "-------------------------------------\n",
      "\n",
      "radii:[3.54]pixels radius_sky_in:7.08 radius_sky_out:10.620000000000001  radius_for_mag:3.54\n",
      "Performing aperture photometry for radius r = 3.54 px\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-25 17:11:59,949 - stpipe - WARNING - /Users/arest/nircam/jwst_distortions_tools/simple_jwst_phot.py:467: RuntimeWarning: invalid value encountered in log10\n",
      "  table_aper['mag'] = -2.5 * np.log10(table_aper[self.colname('aper_sum_bkgsub',rad)])\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Elapsed: 20.448614271999986\n",
      "6324 out of 25925 entries remain in photometry table\n",
      "Matching Gaia catalog ./LMC_gaia_DR3.nrcposs\n",
      "image objects are in x_idl=[-31.80,31.85] and y_idl=[-31.83,31.78] range\n",
      "Keeping 330 out of 22544 catalog objects\n",
      "Keeping 330  after removing NaNs from ra/dec\n",
      "Saving nrc24_calfiles/jw01073003001_02101_00001_nrca1_tweakregstep.phot.txt\n",
      "DONE!\n"
     ]
    }
   ],
   "source": [
    "# Do simple aperture photometry on the tweakreg fits file which should be aligned now!\n",
    "from simple_jwst_phot import jwst_photclass\n",
    "tweakregphot=jwst_photclass()\n",
    "tweakregphot.verbose = 2\n",
    "\n",
    "# This is the LMC catalog we are using. I need to add a routine that gets Gaia sources from full sky\n",
    "gaia_catname_for_testing='./LMC_gaia_DR3.nrcposs'\n",
    "\n",
    "# Minimum SNR of sources in image for the Gaia matching\n",
    "SNR_min = 10.0\n",
    "if not os.path.isfile(tweakreg_image):\n",
    "    raise RuntimeError(f'BUG {tweakreg_image}')\n",
    "# Run the photometry\n",
    "tweakregphot.run_phot(tweakreg_image,gaia_catname_for_testing,SNR_min=SNR_min,photfilename='auto')\n",
    "print('DONE!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1ffaeaa",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tweakreg_image' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### show how good the alignment is: \u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# load the photometric catalog from tweakreg image.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# make a rough cut of 0+-cut_delta_pix\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# cut_delta_pix=2.5 should be big enough to not cut out any good measurements \u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# After the rough cut, in the next cell a real 3-sigma clipping is done\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m tweakreg_phot_filename \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfits$\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphot.txt\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[43mtweakreg_image\u001b[49m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tweakreg_phot_filename\u001b[38;5;241m==\u001b[39mtweakreg_image:\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBUG!! \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtweakreg_image\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tweakreg_image' is not defined"
     ]
    }
   ],
   "source": [
    "### show how good the alignment is: \n",
    "# load the photometric catalog from tweakreg image.\n",
    "# make a rough cut of 0+-cut_delta_pix\n",
    "# cut_delta_pix=2.5 should be big enough to not cut out any good measurements \n",
    "# After the rough cut, in the next cell a real 3-sigma clipping is done\n",
    "tweakreg_phot_filename = re.sub('fits$','phot.txt',tweakreg_image)\n",
    "if tweakreg_phot_filename==tweakreg_image:\n",
    "    raise RuntimeError(f'BUG!! {tweakreg_image}')\n",
    "\n",
    "# Load the file into pdastrostatsclass, which has robust 3-sigma cutting\n",
    "phot_tweakreg = pdastrostatsclass()\n",
    "phot_tweakreg.load(tweakreg_phot_filename)\n",
    "\n",
    "# calculate dx and dy, and make some rough cuts\n",
    "ixs_tweakreg = calc_dxdy(phot_tweakreg,d2d_max,dmag_max)\n",
    "\n",
    "# Rough cut:\n",
    "print('### Rough cut:')\n",
    "cut_delta_pix = 2.5\n",
    "\n",
    "dx_median = phot_tweakreg.t.loc[ixs_tweakreg,'dx'].median()\n",
    "dy_median = phot_tweakreg.t.loc[ixs_tweakreg,'dy'].median()\n",
    "print(f'dx median: {dx_median}')\n",
    "print(f'dy median: {dy_median}')\n",
    "dy_plotlim = (dy_median-3*cut_delta_pix,dy_median+3*cut_delta_pix)\n",
    "dx_plotlim = (dx_median-3*cut_delta_pix,dx_median+3*cut_delta_pix)\n",
    "\n",
    "dxlim = (dx_median-cut_delta_pix,dx_median+cut_delta_pix)\n",
    "dylim = (dy_median-cut_delta_pix,dy_median+cut_delta_pix)\n",
    "print(f'Cutting dx with {dxlim[0]:.3f} to {dxlim[1]:.3f} and dy with {dylim[0]:.3f} to {dylim[1]:.3f}')\n",
    "ixs_tweakreg_roughcut = phot_tweakreg.ix_inrange('dx',dxlim[0],dxlim[1])\n",
    "ixs_tweakreg_roughcut = phot_tweakreg.ix_inrange('dy',dylim[0],dylim[1],indices=ixs_tweakreg_roughcut)\n",
    "ixs_tweakreg_bad = AnotB(phot_tweakreg.getindices(),ixs_tweakreg_roughcut)\n",
    "\n",
    "infoplots(phot_tweakreg,ixs_tweakreg_roughcut,ixs_tweakreg_bad,dy_plotlim=dy_plotlim,dx_plotlim=dx_plotlim)\n",
    "\n",
    "print(f'objects left: {len(ixs_tweakreg_roughcut)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b217c297",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### 3-sigma cut:\n",
      "\n",
      "####################\n",
      "### dx\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'phot_tweakreg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m####################\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m### dx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#ixs_clean4average = phot_clear.ix_inrange('dx',0,3,indices=ixs_clear_cut)\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mphot_tweakreg\u001b[49m\u001b[38;5;241m.\u001b[39mcalcaverage_sigmacutloop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdx\u001b[39m\u001b[38;5;124m'\u001b[39m,verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,indices\u001b[38;5;241m=\u001b[39mixs_tweakreg_roughcut,percentile_cut_firstiteration\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m70\u001b[39m)\n\u001b[1;32m      7\u001b[0m dx_statparams \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(phot_tweakreg\u001b[38;5;241m.\u001b[39mstatparams)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# this are the good indices after the dx 3-sigma cut. They are getting fed into the dy 3-sigma cut!\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'phot_tweakreg' is not defined"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "# 3-sigma cut:\n",
    "print('### 3-sigma cut:')\n",
    "print('\\n####################\\n### dx')\n",
    "#ixs_clean4average = phot_clear.ix_inrange('dx',0,3,indices=ixs_clear_cut)\n",
    "phot_tweakreg.calcaverage_sigmacutloop('dx',verbose=3,indices=ixs_tweakreg_roughcut,percentile_cut_firstiteration=70)\n",
    "dx_statparams = copy.deepcopy(phot_tweakreg.statparams)\n",
    "# this are the good indices after the dx 3-sigma cut. They are getting fed into the dy 3-sigma cut!\n",
    "ixs_dx_tweakreg_cut = dx_statparams['ix_good']\n",
    "print(phot_tweakreg.statstring())\n",
    "\n",
    "print('\\n####################\\n### dy')\n",
    "phot_tweakreg.calcaverage_sigmacutloop('dy',verbose=3,indices=ixs_dx_tweakreg_cut,percentile_cut_firstiteration=70)\n",
    "dy_statparams = copy.deepcopy(phot_tweakreg.statparams)\n",
    "# this are the final good indices after both dx and dy 3-sigma cut\n",
    "ixs_tweakreg_cut = dy_statparams['ix_good']\n",
    "print(phot_tweakreg.statstring())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9c68baf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'phot_tweakreg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m sp\u001b[38;5;241m.\u001b[39mappend(plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m223\u001b[39m))\n\u001b[1;32m     10\u001b[0m sp\u001b[38;5;241m.\u001b[39mappend(plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m224\u001b[39m))\n\u001b[0;32m---> 11\u001b[0m ixs_tweakreg_cut \u001b[38;5;241m=\u001b[39m \u001b[43mphot_tweakreg\u001b[49m\u001b[38;5;241m.\u001b[39mstatparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mix_good\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m re\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m5$|LONG$\u001b[39m\u001b[38;5;124m'\u001b[39m,hdr[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdetector\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     pixelscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.06\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'phot_tweakreg' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAJDCAYAAAASKTJQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAhW0lEQVR4nO3dYYilZ3k+8Os221SaRi3NCpJNTOS/qS5aMB3SFKGmmJYkH5IPbSUBsZbgom2koBRSLKnET7bUgpBWFyqpgsboB1lwJaU2EhBjsyEaTUJkjdZslCZqzBfRGHr/P5xjGSfP7rwz+85Mduf3g4Fz3vMw535yZi+uvHPOvNXdAQAAftmLdnoAAAB4IVKUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgYN2iXFUfraonq+obJ3i8qupDVXWsqh6sqkvnHxOAqeQ2wDymnFG+PclVJ3n86iT7l18Hk/zLqY8FwCm4PXIb4JStW5S7+54kPzrJkuuSfKwX7k3ysqp6xVwDArAxchtgHnO8R/n8JI+vun98eQyAFya5DTDBnu18sqo6mMWv+XLOOef8zqtf/ertfHqAWdx///0/6O69Oz3HVpPZwJlis7k9R1F+IskFq+7vWx57nu4+lORQkqysrPTRo0dneHqA7VVV/73TM5yiSbkts4EzxWZze463XhxO8tblp6gvT/JMd39/hu8LwNaQ2wATrHtGuao+meSKJOdV1fEkf5fkV5Kkuz+c5EiSa5IcS/KTJH++VcMCsD65DTCPdYtyd9+wzuOd5C9nmwiAUyK3AebhynwAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADAwqShX1VVV9WhVHauqmwePX1hVd1fVA1X1YFVdM/+oAEwhswHmsW5RrqqzktyW5OokB5LcUFUH1iz72yR3dvfrk1yf5J/nHhSA9clsgPlMOaN8WZJj3f1Ydz+b5I4k161Z00lesrz90iTfm29EADZAZgPMZM+ENecneXzV/eNJfnfNmvcl+feqeleSc5JcOct0AGyUzAaYyVwf5rshye3dvS/JNUk+XlXP+95VdbCqjlbV0aeeemqmpwZgg2Q2wARTivITSS5YdX/f8thqNya5M0m6+8tJXpzkvLXfqLsPdfdKd6/s3bt3cxMDcDIyG2AmU4ryfUn2V9XFVXV2Fh/8OLxmzXeTvClJquo1WYSu0w8A209mA8xk3aLc3c8luSnJXUkeyeKT0g9V1a1Vde1y2XuSvL2qvpbkk0ne1t29VUMDMCazAeYz5cN86e4jSY6sOXbLqtsPJ3nDvKMBsBkyG2AerswHAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAgKIMAAADijIAAAwoygAAMKAoAwDAwKSiXFVXVdWjVXWsqm4+wZo3V9XDVfVQVX1i3jEBmEpmA8xjz3oLquqsJLcl+cMkx5PcV1WHu/vhVWv2J/mbJG/o7qer6uVbNTAAJyazAeYz5YzyZUmOdfdj3f1skjuSXLdmzduT3NbdTydJdz8575gATCSzAWYypSifn+TxVfePL4+tdkmSS6rqS1V1b1VdNdeAAGyIzAaYybpvvdjA99mf5Iok+5LcU1Wv6+4fr15UVQeTHEySCy+8cKanBmCDZDbABFPOKD+R5IJV9/ctj612PMnh7v55d387yTezCOFf0t2Hunulu1f27t272ZkBODGZDTCTKUX5viT7q+riqjo7yfVJDq9Z89kszkykqs7L4td6j803JgATyWyAmaxblLv7uSQ3JbkrySNJ7uzuh6rq1qq6drnsriQ/rKqHk9yd5K+7+4dbNTQAYzIbYD7V3TvyxCsrK3306NEdeW6AU1FV93f3yk7PsZ1kNnA622xuuzIfAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA5OKclVdVVWPVtWxqrr5JOv+uKq6qlbmGxGAjZDZAPNYtyhX1VlJbktydZIDSW6oqgODdecm+askX5l7SACmkdkA85lyRvmyJMe6+7HufjbJHUmuG6x7f5IPJPnpjPMBsDEyG2AmU4ry+UkeX3X/+PLY/6mqS5Nc0N2fm3E2ADZOZgPM5JQ/zFdVL0rywSTvmbD2YFUdraqjTz311Kk+NQAbJLMBpptSlJ9IcsGq+/uWx37h3CSvTfLFqvpOksuTHB59OKS7D3X3Snev7N27d/NTA3AiMhtgJlOK8n1J9lfVxVV1dpLrkxz+xYPd/Ux3n9fdF3X3RUnuTXJtdx/dkokBOBmZDTCTdYtydz+X5KYkdyV5JMmd3f1QVd1aVddu9YAATCezAeazZ8qi7j6S5MiaY7ecYO0Vpz4WAJslswHm4cp8AAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwoCgDAMCAogwAAAOKMgAADCjKAAAwMKkoV9VVVfVoVR2rqpsHj7+7qh6uqger6gtV9cr5RwVgCpkNMI91i3JVnZXktiRXJzmQ5IaqOrBm2QNJVrr7t5N8Jsnfzz0oAOuT2QDzmXJG+bIkx7r7se5+NskdSa5bvaC77+7unyzv3ptk37xjAjCRzAaYyZSifH6Sx1fdP748diI3Jvn8qQwFwKbJbICZ7Jnzm1XVW5KsJHnjCR4/mORgklx44YVzPjUAGySzAU5uyhnlJ5JcsOr+vuWxX1JVVyZ5b5Jru/tno2/U3Ye6e6W7V/bu3buZeQE4OZkNMJMpRfm+JPur6uKqOjvJ9UkOr15QVa9P8pEsAvfJ+ccEYCKZDTCTdYtydz+X5KYkdyV5JMmd3f1QVd1aVdcul/1Dkl9P8umq+mpVHT7BtwNgC8lsgPlMeo9ydx9JcmTNsVtW3b5y5rkA2CSZDTAPV+YDAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgYFJRrqqrqurRqjpWVTcPHv/VqvrU8vGvVNVFs08KwCQyG2Ae6xblqjoryW1Jrk5yIMkNVXVgzbIbkzzd3f8vyT8l+cDcgwKwPpkNMJ8pZ5QvS3Ksux/r7meT3JHkujVrrkvyb8vbn0nypqqq+cYEYCKZDTCTKUX5/CSPr7p/fHlsuKa7n0vyTJLfnGNAADZEZgPMZM92PllVHUxycHn3Z1X1je18/heA85L8YKeH2Gb2vDvstj3/1k4PsB1k9q77uU7sebfYjXveVG5PKcpPJLlg1f19y2OjNcerak+Slyb54dpv1N2HkhxKkqo62t0rmxn6dGXPu4M9n/mq6uhOz3ASMnsm9rw72PPusNncnvLWi/uS7K+qi6vq7CTXJzm8Zs3hJH+2vP0nSf6zu3szAwFwSmQ2wEzWPaPc3c9V1U1J7kpyVpKPdvdDVXVrkqPdfTjJvyb5eFUdS/KjLIIZgG0mswHmM+k9yt19JMmRNcduWXX7p0n+dIPPfWiD688E9rw72POZ7wW9X5k9G3veHex5d9jUnstv2wAA4PlcwhoAAAa2vCjvxkupTtjzu6vq4ap6sKq+UFWv3Ik557Tenlet++Oq6qo6rT9tO2W/VfXm5ev8UFV9YrtnnNuEn+sLq+ruqnpg+bN9zU7MOaeq+mhVPXmiP4tWCx9a/jd5sKou3e4Z5yazZfaadWdEZidyezfk9pZkdndv2VcWHyT5VpJXJTk7ydeSHFiz5i+SfHh5+/okn9rKmbb6a+Ke/yDJry1vv3M37Hm57twk9yS5N8nKTs+9xa/x/iQPJPmN5f2X7/Tc27DnQ0neubx9IMl3dnruGfb9+0kuTfKNEzx+TZLPJ6kklyf5yk7PvA2vs8zeBXterjsjMnsDr7PcPs1zeysye6vPKO/GS6muu+fuvru7f7K8e28Wf+f0dDbldU6S9yf5QJKfbudwW2DKft+e5LbufjpJuvvJbZ5xblP23Elesrz90iTf28b5tkR335PFX4U4keuSfKwX7k3ysqp6xfZMtyVktsxe7UzJ7ERu74rc3orM3uqivBsvpTplz6vdmMX/3ZzO1t3z8tcbF3T357ZzsC0y5TW+JMklVfWlqrq3qq7atum2xpQ9vy/JW6rqeBZ/ceFd2zPajtrov/cXOpkts5OccZmdyO1EbiebyOxtvYQ1v6yq3pJkJckbd3qWrVRVL0rywSRv2+FRttOeLH6Nd0UWZ5/uqarXdfePd3KoLXZDktu7+x+r6vey+Du9r+3u/93pwWAOMvuMJ7fl9vNs9RnljVxKNXWSS6meRqbsOVV1ZZL3Jrm2u3+2TbNtlfX2fG6S1yb5YlV9J4v3BR0+jT8cMuU1Pp7kcHf/vLu/neSbWQTw6WrKnm9McmeSdPeXk7w4yXnbMt3OmfTv/TQis2V2cuZldiK3E7mdbCKzt7oo78ZLqa6756p6fZKPZBG4p/t7oJJ19tzdz3T3ed19UXdflMV7/K7t7k1dd/0FYMrP9WezOCuRqjovi1/pPbaNM85typ6/m+RNSVJVr8kicJ/a1im33+Ekb11+kvryJM909/d3eqhTILNl9pmY2YncltsLG8/sbfgE4jVZ/F/Zt5K8d3ns1iz+0SWLF+XTSY4l+a8kr9rqmV4Ae/6PJP+T5KvLr8M7PfNW73nN2i/m9P8E9XqvcWXxq8uHk3w9yfU7PfM27PlAki9l8cnqryb5o52eeYY9fzLJ95P8PIuzTTcmeUeSd6x6nW9b/jf5+un+cz3xdZbZMvu0/JLbZ35ub0VmuzIfAAAMuDIfAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCwblGuqo9W1ZNV9Y0TPF5V9aGqOlZVD1bVpfOPCcBUchtgHlPOKN+e5KqTPH51kv3Lr4NJ/uXUxwLgFNweuQ1wytYtyt19T5IfnWTJdUk+1gv3JnlZVb1irgEB2Bi5DTCPOd6jfH6Sx1fdP748BsALk9wGmGDPdj5ZVR3M4td8Oeecc37n1a9+9XY+PcAs7r///h90996dnmOryWzgTLHZ3J6jKD+R5IJV9/ctjz1Pdx9KcihJVlZW+ujRozM8PcD2qqr/3ukZTtGk3JbZwJlis7k9x1svDid56/JT1Jcneaa7vz/D9wVga8htgAnWPaNcVZ9MckWS86rqeJK/S/IrSdLdH05yJMk1SY4l+UmSP9+qYQFYn9wGmMe6Rbm7b1jn8U7yl7NNBMApkdsA83BlPgAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYmFeWquqqqHq2qY1V18+DxC6vq7qp6oKoerKpr5h8VgClkNsA81i3KVXVWktuSXJ3kQJIbqurAmmV/m+TO7n59kuuT/PPcgwKwPpkNMJ8pZ5QvS3Ksux/r7meT3JHkujVrOslLlrdfmuR7840IwAbIbICZ7Jmw5vwkj6+6fzzJ765Z874k/15V70pyTpIrZ5kOgI2S2QAzmevDfDckub279yW5JsnHq+p537uqDlbV0ao6+tRTT8301ABskMwGmGBKUX4iyQWr7u9bHlvtxiR3Jkl3fznJi5Oct/Ybdfeh7l7p7pW9e/dubmIATkZmA8xkSlG+L8n+qrq4qs7O4oMfh9es+W6SNyVJVb0mi9B1+gFg+8lsgJmsW5S7+7kkNyW5K8kjWXxS+qGqurWqrl0ue0+St1fV15J8Msnburu3amgAxmQ2wHymfJgv3X0kyZE1x25ZdfvhJG+YdzQANkNmA8zDlfkAAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBAUQYAgAFFGQAABhRlAAAYUJQBAGBgUlGuqquq6tGqOlZVN59gzZur6uGqeqiqPjHvmABMJbMB5rFnvQVVdVaS25L8YZLjSe6rqsPd/fCqNfuT/E2SN3T301X18q0aGIATk9kA85lyRvmyJMe6+7HufjbJHUmuW7Pm7Ulu6+6nk6S7n5x3TAAmktkAM5lSlM9P8viq+8eXx1a7JMklVfWlqrq3qq6aa0AANkRmA8xk3bdebOD77E9yRZJ9Se6pqtd1949XL6qqg0kOJsmFF14401MDsEEyG2CCKWeUn0hywar7+5bHVjue5HB3/7y7v53km1mE8C/p7kPdvdLdK3v37t3szACcmMwGmMmUonxfkv1VdXFVnZ3k+iSH16z5bBZnJlJV52Xxa73H5hsTgIlkNsBM1i3K3f1ckpuS3JXkkSR3dvdDVXVrVV27XHZXkh9W1cNJ7k7y1939w60aGoAxmQ0wn+ruHXnilZWVPnr06I48N8CpqKr7u3tlp+fYTjIbOJ1tNrddmQ8AAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAICBSUW5qq6qqker6lhV3XySdX9cVV1VK/ONCMBGyGyAeaxblKvqrCS3Jbk6yYEkN1TVgcG6c5P8VZKvzD0kANPIbID5TDmjfFmSY939WHc/m+SOJNcN1r0/yQeS/HTG+QDYGJkNMJMpRfn8JI+vun98eez/VNWlSS7o7s/NOBsAGyezAWZyyh/mq6oXJflgkvdMWHuwqo5W1dGnnnrqVJ8agA2S2QDTTSnKTyS5YNX9fctjv3Buktcm+WJVfSfJ5UkOjz4c0t2Hunulu1f27t27+akBOBGZDTCTKUX5viT7q+riqjo7yfVJDv/iwe5+prvP6+6LuvuiJPcmuba7j27JxACcjMwGmMm6Rbm7n0tyU5K7kjyS5M7ufqiqbq2qa7d6QACmk9kA89kzZVF3H0lyZM2xW06w9opTHwuAzZLZAPNwZT4AABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGFGUAABhQlAEAYEBRBgCAAUUZAAAGJhXlqrqqqh6tqmNVdfPg8XdX1cNV9WBVfaGqXjn/qABMIbMB5rFuUa6qs5LcluTqJAeS3FBVB9YseyDJSnf/dpLPJPn7uQcFYH0yG2A+U84oX5bkWHc/1t3PJrkjyXWrF3T33d39k+Xde5Psm3dMACaS2QAzmVKUz0/y+Kr7x5fHTuTGJJ8/laEA2DSZDTCTPXN+s6p6S5KVJG88weMHkxxMkgsvvHDOpwZgg2Q2wMlNOaP8RJILVt3ftzz2S6rqyiTvTXJtd/9s9I26+1B3r3T3yt69ezczLwAnJ7MBZjKlKN+XZH9VXVxVZye5Psnh1Quq6vVJPpJF4D45/5gATCSzAWayblHu7ueS3JTkriSPJLmzux+qqlur6trlsn9I8utJPl1VX62qwyf4dgBsIZkNMJ9J71Hu7iNJjqw5dsuq21fOPBcAmySzAebhynwAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADCgKAMAwICiDAAAA4oyAAAMKMoAADAwqShX1VVV9WhVHauqmweP/2pVfWr5+Feq6qLZJwVgEpkNMI91i3JVnZXktiRXJzmQ5IaqOrBm2Y1Jnu7u/5fkn5J8YO5BAVifzAaYz5QzypclOdbdj3X3s0nuSHLdmjXXJfm35e3PJHlTVdV8YwIwkcwGmMmUonx+ksdX3T++PDZc093PJXkmyW/OMSAAGyKzAWayZzufrKoOJjm4vPuzqvrGdj7/C8B5SX6w00NsM3veHXbbnn9rpwfYDjJ71/1cJ/a8W+zGPW8qt6cU5SeSXLDq/r7lsdGa41W1J8lLk/xw7Tfq7kNJDiVJVR3t7pXNDH26sufdwZ7PfFV1dKdnOAmZPRN73h3seXfYbG5PeevFfUn2V9XFVXV2kuuTHF6z5nCSP1ve/pMk/9ndvZmBADglMhtgJuueUe7u56rqpiR3JTkryUe7+6GqujXJ0e4+nORfk3y8qo4l+VEWwQzANpPZAPOZ9B7l7j6S5MiaY7esuv3TJH+6wec+tMH1ZwJ73h3s+cz3gt6vzJ6NPe8O9rw7bGrP5bdtAADwfC5hDQAAA1telHfjpVQn7PndVfVwVT1YVV+oqlfuxJxzWm/Pq9b9cVV1VZ3Wn7adst+qevPydX6oqj6x3TPObcLP9YVVdXdVPbD82b5mJ+acU1V9tKqePNGfRauFDy3/mzxYVZdu94xzk9kye826MyKzE7m9G3J7SzK7u7fsK4sPknwryauSnJ3ka0kOrFnzF0k+vLx9fZJPbeVMW/01cc9/kOTXlrffuRv2vFx3bpJ7ktybZGWn597i13h/kgeS/Mby/st3eu5t2POhJO9c3j6Q5Ds7PfcM+/79JJcm+cYJHr8myeeTVJLLk3xlp2fehtdZZu+CPS/XnRGZvYHXWW6f5rm9FZm91WeUd+OlVNfdc3ff3d0/Wd69N4u/c3o6m/I6J8n7k3wgyU+3c7gtMGW/b09yW3c/nSTd/eQ2zzi3KXvuJC9Z3n5pku9t43xborvvyeKvQpzIdUk+1gv3JnlZVb1ie6bbEjJbZq92pmR2Ird3RW5vRWZvdVHejZdSnbLn1W7M4v9uTmfr7nn5640Luvtz2znYFpnyGl+S5JKq+lJV3VtVV23bdFtjyp7fl+QtVXU8i7+48K7tGW1HbfTf+wudzJbZSc64zE7kdiK3k01k9rZewppfVlVvSbKS5I07PctWqqoXJflgkrft8CjbaU8Wv8a7IouzT/dU1eu6+8c7OdQWuyHJ7d39j1X1e1n8nd7Xdvf/7vRgMAeZfcaT23L7ebb6jPJGLqWaOsmlVE8jU/acqroyyXuTXNvdP9um2bbKens+N8lrk3yxqr6TxfuCDp/GHw6Z8hofT3K4u3/e3d9O8s0sAvh0NWXPNya5M0m6+8tJXpzkvG2ZbudM+vd+GpHZMjs58zI7kduJ3E42kdlbXZR346VU191zVb0+yUeyCNzT/T1QyTp77u5nuvu87r6ouy/K4j1+13b3pq67/gIw5ef6s1mclUhVnZfFr/Qe28YZ5zZlz99N8qYkqarXZBG4T23rlNvvcJK3Lj9JfXmSZ7r7+zs91CmQ2TL7TMzsRG7L7YWNZ/Y2fALxmiz+r+xbSd67PHZrFv/oksWL8ukkx5L8V5JXbfVML4A9/0eS/0ny1eXX4Z2eeav3vGbtF3P6f4J6vde4svjV5cNJvp7k+p2eeRv2fCDJl7L4ZPVXk/zRTs88w54/meT7SX6exdmmG5O8I8k7Vr3Oty3/m3z9dP+5nvg6y2yZfVp+ye0zP7e3IrNdmQ8AAAZcmQ8AAAYUZQAAGFCUAQBgQFEGAIABRRkAAAYUZQAAGFCUAQBgQFEGAICB/w+YCfk/FcDTpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the good measurments after the 3-sigma cut\n",
    "saveplot=False\n",
    "sp=[]\n",
    "xfigsize=12.0\n",
    "yfigsize=10.0\n",
    "plt.figure(figsize=(xfigsize,yfigsize))\n",
    "sp.append(plt.subplot(221))\n",
    "sp.append(plt.subplot(222))\n",
    "sp.append(plt.subplot(223))\n",
    "sp.append(plt.subplot(224))\n",
    "ixs_tweakreg_cut = phot_tweakreg.statparams['ix_good']\n",
    "\n",
    "if re.search('5$|LONG$',hdr[\"detector\"]) is not None:\n",
    "    pixelscale=0.06\n",
    "else:\n",
    "    pixelscale=0.03\n",
    "\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_roughcut].plot.scatter('y','dx',ax=sp[0],color='red')\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_cut].plot.scatter('y','dx',ax=sp[0],ylabel='dx in pixels',title = f'{hdr[\"detector\"]} {hdr[\"filter\"]}, stdev = {dx_statparams[\"stdev\"]:.3f} px = {dx_statparams[\"stdev\"]*pixelscale*1000:.2f} mas')\n",
    "sp[0].axhline(0.0,  color='black',linestyle='--', linewidth=1.3)\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_roughcut].plot.scatter('x','dx',ax=sp[1],color='red')\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_cut].plot.scatter('x','dx',ax=sp[1],ylabel='dx in pixels',title = f'{hdr[\"detector\"]} {hdr[\"filter\"]}, stdev = {dx_statparams[\"stdev\"]:.3f} px = {dx_statparams[\"stdev\"]*pixelscale*1000:.2f} mas')\n",
    "sp[1].axhline(0.0,  color='black',linestyle='--', linewidth=1.3)\n",
    "\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_roughcut].plot.scatter('x','dy',ax=sp[2],color='red')\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_cut].plot.scatter('x','dy',ax=sp[2],ylabel='dy in pixels',title = f'{hdr[\"detector\"]} {hdr[\"filter\"]}, stdev = {dy_statparams[\"stdev\"]:.3f} px = {dy_statparams[\"stdev\"]*pixelscale*1000:.2f} mas')\n",
    "sp[2].axhline(0.0,  color='black',linestyle='--', linewidth=1.3)\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_roughcut].plot.scatter('y','dy',ax=sp[3],color='red')\n",
    "phot_tweakreg.t.loc[ixs_tweakreg_cut].plot.scatter('y','dy',ax=sp[3],ylabel='dy in pixels',title =  f'{hdr[\"detector\"]} {hdr[\"filter\"]}, stdev = {dy_statparams[\"stdev\"]:.3f} px = {dy_statparams[\"stdev\"]*pixelscale*1000:.2f} mas')\n",
    "sp[3].axhline(0.0,  color='black',linestyle='--', linewidth=1.3)\n",
    "for i in range(4):\n",
    "    plt.setp(sp[i].get_xticklabels(),'fontsize',12)\n",
    "    plt.setp(sp[i].get_yticklabels(),'fontsize',12)\n",
    "    sp[i].set_xlabel(sp[i].get_xlabel(),fontsize=14)\n",
    "    sp[i].set_ylabel(sp[i].get_ylabel(),fontsize=14)\n",
    "    sp[i].set_title(sp[i].get_title(),fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "if saveplot:\n",
    "    tweakreg_plotfilename = re.sub('\\.fits$','',tweakreg_image)\n",
    "    tweakreg_plotfilename+='.png'\n",
    "    print(f'Saving {tweakreg_plotfilename}')\n",
    "    plt.savefig(f'{tweakreg_plotfilename}')\n",
    "    #outdir='.'\n",
    "    #plt.savefig(f'{outdir}/{hdr[\"detector\"]}.{hdr[\"filter\"]}.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9e99a1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7014d8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}