pCTI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading EO results is based on Jim's code here\n",
    "# https://github.com/lsst-camera-dh/eotest/blob/0dc45fd0e2c11ced3da4714e18c699ef5c1df78f/python/lsst/eotest/sensor/EOTestPlots.py#L285\n",
    "\n",
    "from astropy.io import fits\n",
    "import pylab\n",
    "import re\n",
    "import glob\n",
    "import numpy\n",
    "import traceback\n",
    "from astropy.stats import mad_std\n",
    "from lsst.eotest.sensor.EOTestPlots import OverscanTestPlots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/ipykernel_launcher.py:26: RuntimeWarning: invalid value encountered in true_divide\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 55, in <module>\n",
      "    res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
      "  File \"<ipython-input-3-99346b56b88d>\", line 28, in getpctilimit\n",
      "    pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
      "  File \"<__array_function__ internals>\", line 6, in argmin\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 1269, in argmin\n",
      "    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n",
      "  File \"/gpfs/slac/lsst/fs3/g/software/stack/v21.0.0/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/numpy/core/fromnumeric.py\", line 58, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: attempt to get argmin of an empty sequence\n"
     ]
    }
   ],
   "source": [
    "ctilimit=3e-6\n",
    "\n",
    "figsize=(5,5)\n",
    "\n",
    "def getpctilimit(\n",
    "    slot=\"R31_S02\",\n",
    "    runnum=13127\n",
    "    ):\n",
    "    \n",
    "    overscan_file=glob.glob(f\"/gpfs/slac/lsst/fs3/g/data/jobHarness/jh_stage/LCA-10134_Cryostat/LCA-10134_Cryostat-0001/{runnum}/overscan_BOT/v0/*/{slot}_{runnum}_overscan_results.fits\")[0]\n",
    "    plots=OverscanTestPlots(slot,overscan_file,title_addendum=None)\n",
    "\n",
    "    datasec = plots.header['DATASEC']\n",
    "    xmin,xmax,ymin,ymax=re.search(r\"(\\d+):(\\d+),(\\d+):(\\d+)\",datasec).groups()\n",
    "    num_transfers = int(ymax)\n",
    "\n",
    "    xarr = []\n",
    "    for amp in plots.all_amps:\n",
    "        if amp > 8: marker='s'\n",
    "        else: marker = '^'    \n",
    "        data = plots.overscan_data[amp]['ROW_MEAN']\n",
    "        signal = plots.overscan_data[amp]['FLATFIELD_SIGNAL']\n",
    "        lastpixel = data[:, 0]\n",
    "        overscan1 = data[:, 1]\n",
    "        overscan2 = data[:, 2]\n",
    "        cti = (overscan1+overscan2)/(num_transfers*lastpixel)\n",
    "        indices = (signal<175000.) &  (signal>5e4)\n",
    "        pctilimit = signal[indices][numpy.argmin(numpy.fabs(cti[indices]-ctilimit))]\n",
    "        xarr.append(pctilimit)\n",
    "\n",
    "    return list(plots.all_amps), xarr\n",
    "\n",
    "\n",
    "raft_types = {}\n",
    "raft_types['e2v'] = 'R11 R12 R13 R14 R21 R22 R23 R24 R30 R31 R32 R33 R34'.split()\n",
    "\n",
    "arr=[]\n",
    "\n",
    "targets = [\n",
    "                (13039, 5),\n",
    "                (13038, 0),\n",
    "#                (13126, 1),\n",
    "                (13136, 1),\n",
    "#                (13125, 2),\n",
    "                (13135, 2),\n",
    "                (13127, 3),\n",
    "                (13128, 4)\n",
    "                ]\n",
    "\n",
    "for arun, order in targets:\n",
    "    res = []\n",
    "    for araft in raft_types['e2v']:        \n",
    "        for sensor in '00 01 02 10 11 12 20 21 22'.split():\n",
    "            try:\n",
    "                res.append(getpctilimit(f\"{araft}_S{sensor}\",arun)[1])\n",
    "            except:\n",
    "                res.append( [numpy.nan]*16 )\n",
    "                traceback.print_exc()                \n",
    "\n",
    "    arr.append(numpy.array(res))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[140096.3214375 , 127745.51582813, 132788.88490625,\n",
       "        135749.96674219, 137737.72528125, 139757.58519531],\n",
       "       [148984.82583594, 137679.40816406, 141989.72990625,\n",
       "        144980.61484375, 146884.811875  , 148677.93267969]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.nanpercentile(arr,[100-68.27,68.27],axis=(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, bx = pylab.subplots(1, 1, figsize=figsize, facecolor=\"white\",dpi=150)\n",
    "#pylab.errorbar([ order[1] for order in targets ], numpy.nanmean(arr,axis=(1,2)), yerr=numpy.nanstd(arr,axis=(1,2)), fmt=\"o\", label=\"mean, std\")\n",
    "med=numpy.nanmedian(arr,axis=(1,2))\n",
    "pylab.errorbar([ order[1] for order in targets ], med,\n",
    "               yerr=numpy.abs(numpy.nanpercentile(arr,[100-68.27,68.27],axis=(1,2))-med),\n",
    "               fmt=\"ko\")\n",
    "#pylab.legend()\n",
    "bx.set_xticklabels([\"0\",\"v23 (13038)\",\"p1 (13126)\",\"p2 (13135)\",\"p3 (13127)\",\"p4 (13128)\",\"v25 (13039)\"],rotation=45)\n",
    "#pylab.ylim(90000,180000)\n",
    "pylab.ylabel(f\"Light level where pCTI goes above {ctilimit} [ADU]\")\n",
    "pylab.tight_layout()\n",
    "pylab.savefig(f\"fullwelloptimization.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Light level where pCTI goes above 3e-06')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, bx = pylab.subplots(1, 1, figsize=figsize, facecolor=\"white\",dpi=150)\n",
    "for a,target in zip(numpy.array(arr),targets):\n",
    "    try:\n",
    "        pylab.hist(a.flatten(),range=(100000,170000),bins=(70),histtype=\"step\",label=target[0])\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "pylab.legend()\n",
    "pylab.xlabel(f\"Light level where pCTI goes above {ctilimit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}