notebooks for sherpa/sherpa#1246

bg.reg

annulus(3147.6844,4519.83,73.123398,146.2468)
-circle(3036.4982,4611.592,18.016347)
-circle(3199.2587,4600.8753,11.875275)
-box(3148.8565,4415.5092,228.06552,49.230003,0)

gistfile1.txt

The same notebook run with the main branch (`old`) and #1246 (`new`)
showing the issue that is fixed.

Note that re-creating the original filter from

    notice2d('src.reg')
    notice2d('bg.reg')
    orig = get_filter()

with

    notice2d()
    notice2d(orig)

fails with main (it filters out everything) but works with #1246

Also of note is that it appears that the same data has been selected
in both cases (as the `calc_stat_info` returns the same number of
points and the same statistic value), it's just that the filter
specification is wrong.

src.reg

circle(3145.9471,4520.7031,15.800277)

test-new.ipynb

test-old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c187b4fd-4e4b-4cb5-94d6-fb3d6580bc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.ui import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "200063bb-bb74-48bf-8bfc-10dae0148d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_image('acisf08478_000N001_r0043_regevt3_srcimg.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f06f5702-2fc5-4474-963c-9342d986ca66",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_coord('physical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a8add70-129b-4ecb-93e9-8259a8f942dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
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       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
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       "    border-top: 1px solid var(--sherpa-border-color);\n",
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       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
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       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2021-09-10T09:20:22.266396</dc:date>\n",
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       "    <dc:creator>\n",
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       "</details><details><summary>Coordinates: physical (3)</summary><div class=\"datavals\"><div class=\"dataname\">Center pixel (logical)</div><div class=\"dataval\">[0.5 0.5]</div><div class=\"dataname\">Center pixel (physical)</div><div class=\"dataval\">[2986.840088 4362.660156]</div><div class=\"dataname\">Pixel size</div><div class=\"dataval\">[1. 1.]</div></div></details><details><summary>Coordinates: world (6)</summary><div class=\"datavals\"><div class=\"dataname\">Center pixel (physical)</div><div class=\"dataval\">[4096.5 4096.5]</div><div class=\"dataname\">Center pixel (world)</div><div class=\"dataval\">[149.885332   2.607949]</div><div class=\"dataname\">Pixel size</div><div class=\"dataval\">[-0.000137  0.000137]</div><div class=\"dataname\">Rotation</div><div class=\"dataval\">0.0</div><div class=\"dataname\">Epoch</div><div class=\"dataval\">2000.0</div><div class=\"dataname\">Equinox</div><div class=\"dataval\">2000.0</div></div></details><details><summary>Metadata (6)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Object</div><div class=\"dataval\">CSC</div><div class=\"dataname\">Exposure time</div><div class=\"dataval\">17990.077192133</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2006-11-24T10:18:51</div><div class=\"dataname\">Program that created the image</div><div class=\"dataval\">destreak - CAT3.0.2</div></div></details></div>"
      ],
      "text/plain": [
       "<DataIMG data set instance 'acisf08478_000N001_r0043_regevt3_srcimg.fits'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39cdae12-0f22-4463-b00b-dc368a779502",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_stat('cash')\n",
    "set_source(const2d.bgnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ececb828-eb92-4c6f-a3cc-2b30a7798945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = cash\n",
      "Fit statistic value   = 197816\n",
      "Data points           = 98908\n",
      "Degrees of freedom    = 98907\n"
     ]
    }
   ],
   "source": [
    "calc_stat_info()  # what to see the number of points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf72ee57-11ed-4a12-959f-e756668e5d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "circle(3145.9471,4520.7031,15.800277)\n"
     ]
    }
   ],
   "source": [
    "!cat src.reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df8ea378-dda6-4528-8b91-109451b88822",
   "metadata": {},
   "outputs": [],
   "source": [
    "notice2d('src.reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e544d28-9409-481c-a1a4-6bf529e959d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Circle(3145.95,4520.7,15.8003)'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_filter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a81b3643-52f4-48f2-ac8d-74e83df4fb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = cash\n",
      "Fit statistic value   = 1578\n",
      "Data points           = 789\n",
      "Degrees of freedom    = 788\n"
     ]
    }
   ],
   "source": [
    "calc_stat_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "718667d9-d9f3-46ba-8a68-888b441d549b",
   "metadata": {},
   "outputs": [
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       "</details><details><summary>Coordinates: physical (3)</summary><div class=\"datavals\"><div class=\"dataname\">Center pixel (logical)</div><div class=\"dataval\">[0.5 0.5]</div><div class=\"dataname\">Center pixel (physical)</div><div class=\"dataval\">[2986.840088 4362.660156]</div><div class=\"dataname\">Pixel size</div><div class=\"dataval\">[1. 1.]</div></div></details><details><summary>Coordinates: world (6)</summary><div class=\"datavals\"><div class=\"dataname\">Center pixel (physical)</div><div class=\"dataval\">[4096.5 4096.5]</div><div class=\"dataname\">Center pixel (world)</div><div class=\"dataval\">[149.885332   2.607949]</div><div class=\"dataname\">Pixel size</div><div class=\"dataval\">[-0.000137  0.000137]</div><div class=\"dataname\">Rotation</div><div class=\"dataval\">0.0</div><div class=\"dataname\">Epoch</div><div class=\"dataval\">2000.0</div><div class=\"dataname\">Equinox</div><div class=\"dataval\">2000.0</div></div></details><details><summary>Metadata (6)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Object</div><div class=\"dataval\">CSC</div><div class=\"dataname\">Exposure time</div><div class=\"dataval\">17990.077192133</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2006-11-24T10:18:51</div><div class=\"dataname\">Program that created the image</div><div class=\"dataval\">destreak - CAT3.0.2</div></div></details></div>"
      ],
      "text/plain": [
       "<DataIMG data set instance 'acisf08478_000N001_r0043_regevt3_srcimg.fits'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8af98298-6cb7-4624-8150-03c08ccc389a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annulus(3147.6844,4519.83,73.123398,146.2468)\n",
      "-circle(3036.4982,4611.592,18.016347)\n",
      "-circle(3199.2587,4600.8753,11.875275)\n",
      "-box(3148.8565,4415.5092,228.06552,49.230003,0)\n"
     ]
    }
   ],
   "source": [
    "!cat bg.reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48c85f84-f803-4f01-8c04-a05cd2b0a9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "notice2d('bg.reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6385fc07-7c77-40c2-bf2a-edebbdc2d2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Circle(3145.95,4520.7,15.8003)&Annulus(3147.68,4519.83,73.1234,146.247)&!Circle(3036.5,4611.59,18.0163)&!Circle(3199.26,4600.88,11.8753)&!Box(3148.86,4415.51,228.066,49.23)'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig = get_filter()\n",
    "\n",
    "orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "382e8e7b-249f-40bd-96bd-bed5633e81ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = cash\n",
      "Fit statistic value   = 80898\n",
      "Data points           = 40449\n",
      "Degrees of freedom    = 40448\n"
     ]
    }
   ],
   "source": [
    "calc_stat_info()  # Is the number of points >> 789"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6165efcb-ed80-402a-81a3-c9ce4fc36ca1",
   "metadata": {},
   "outputs": [
    {
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\" y=\"-34\"/>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\">\n",
       "      <defs>\n",
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      ],
      "text/plain": [
       "<DataIMG data set instance 'acisf08478_000N001_r0043_regevt3_srcimg.fits'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "104f866a-4950-4869-a3bc-e576ac1b83da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Statistic             = cash\n",
      "Fit statistic value   = 197816\n",
      "Data points           = 98908\n",
      "Degrees of freedom    = 98907\n"
     ]
    }
   ],
   "source": [
    "notice2d()\n",
    "\n",
    "calc_stat_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "19dc54b1-d116-4379-9a98-d10771287893",
   "metadata": {},
   "outputs": [],
   "source": [
    "notice2d(orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "751ce12e-8780-40f6-89da-de010dbf2635",
   "metadata": {},
   "outputs": [
    {
     "ename": "FitErr",
     "evalue": "no noticed bins found in data set",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFitErr\u001b[0m                                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-a2454582f7c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcalc_stat_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mcalc_stat_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/ui/utils.py\u001b[0m in \u001b[0;36mcalc_stat_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   7828\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7829\u001b[0m         \"\"\"\n\u001b[0;32m-> 7830\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_stat_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7831\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstatinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstatinfo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/astro/ui/utils.py\u001b[0m in \u001b[0;36m_get_stat_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m  10250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10251\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_fit_obj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10252\u001b[0;31m         \u001b[0mstatinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_stat_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10253\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10254\u001b[0m             \u001b[0mstatinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Dataset %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36mcalc_stat_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1171\u001b[0m         \u001b[0;31m# TODO: This logic would be better in the stat class than here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m         \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0mstatval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_model_to_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/fit.py\u001b[0m in \u001b[0;36m_calc_stat\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1102\u001b[0m         \u001b[0;31m# TODO: is there anything missing here that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0;31m#       self._iterfit.get_extra_args calculates?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/stats/__init__.py\u001b[0m in \u001b[0;36mcalc_stat\u001b[0;34m(self, data, model)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 331\u001b[0;31m         \u001b[0mfitdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodeldata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_fit_model_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         return self._calc(fitdata[0], modeldata, None,\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/stats/__init__.py\u001b[0m in \u001b[0;36m_get_fit_model_data\u001b[0;34m(self, data, model)\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_fit_model_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m         \u001b[0mfitdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstaterrfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_staterror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mmodeldata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_model_to_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/stats/__init__.py\u001b[0m in \u001b[0;36m_validate_inputs\u001b[0;34m(self, data, model)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_background_subtraction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/stats/__init__.py\u001b[0m in \u001b[0;36m_validate_inputs\u001b[0;34m(self, data, model)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bundle_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_has_bins\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_sizes_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/stats/__init__.py\u001b[0m in \u001b[0;36m_check_has_bins\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    136\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFitErr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nobins'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFitErr\u001b[0m: no noticed bins found in data set"
     ]
    }
   ],
   "source": [
    "calc_stat_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df8189b0-4f85-49fa-959a-728c716e95fc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "zero-size array to reduction operation minimum which has no identity",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/astro/data.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3620\u001b[0m         \"\"\"Return a HTML (string) representation of the data\n\u001b[1;32m   3621\u001b[0m         \"\"\"\n\u001b[0;32m-> 3622\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mhtml_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3624\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/astro/data.py\u001b[0m in \u001b[0;36mhtml_img\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m     \u001b[0msvg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msvg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhtml_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'{} Plot'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/sherpa/astro/data.py\u001b[0m in \u001b[0;36mimg_plot\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m    722\u001b[0m         \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m         \u001b[0mx0min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    725\u001b[0m         \u001b[0mx1min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mamin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2856\u001b[0m     \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2857\u001b[0m     \"\"\"\n\u001b[0;32m-> 2858\u001b[0;31m     return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n\u001b[0m\u001b[1;32m   2859\u001b[0m                           keepdims=keepdims, initial=initial, where=where)\n\u001b[1;32m   2860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ciao414b1/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<DataIMG data set instance 'acisf08478_000N001_r0043_regevt3_srcimg.fits'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "416cf85d-8b20-4bc6-aa51-4024afc855a5",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

Unfortunately the get_data() calls, which generate matplotlib image plots, are not shown here, but they show the expected behavior of the filters.