TutorialNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a851128-36e1-4589-975e-ce3afe97d208",
   "metadata": {},
   "outputs": [],
   "source": [
    "run(`julia dependencies.jl`)\n",
    "run(`python dependenciesPy.py`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33690fd7-f2e8-4ad2-887c-b5f83521e77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyCall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11aa4ad6-ca1d-4f8d-9052-170bc1715384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject <module 'numpy' from '/home/eddieberman/miniconda3/envs/myenv/lib/python3.11/site-packages/numpy/__init__.py'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyimport(\"astropy\")\n",
    "pyimport(\"matplotlib\")\n",
    "pyimport(\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1332ef0d-6e31-4194-91f8-5ee721b0b592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure `export JULIA_NUM_THREADS=4` was been ran in terminal\n",
    "\n",
    "using Base.Threads\n",
    "nthreads()\n",
    "\n",
    "#run(`julia shopt.jl [configdir] [outdir] [catalog]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d4333f0-3ab2-40b3-8b70-cf87759fcc0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/eddieberman/research/mcclearygroup/shopt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Process(`\u001b[4mpwd\u001b[24m`, ProcessExited(0))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reconstruct the PSF at an arbitrary (u,v)\n",
    "include(\"reader.jl\")\n",
    "u = rand(1)\n",
    "v = rand(1)\n",
    "polyMatrix, deg s_matrix, g1_matrix, g2_matrix = read_shopt(\"/path/summary.shopt\")\n",
    "p(u,v,polyMatrix, deg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d898c0db-7899-49d3-94a5-8679b6b68ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example Runthrough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "741517e5-a7a5-406e-8073-880b0c840f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------\n",
      "| Parsing Arguments |\n",
      "---------------------\n",
      "━ Config Directory: /home/eddieberman/research/mcclearygroup/shopt/configdir\n",
      "━ Output Directory: /home/eddieberman/research/mcclearygroup/shopt/outdir\n",
      "━ Catalog: /home/eddieberman/research/mcclearygroup/cweb_psf/sim_mosaics_working/mosaic_nircam_f115w_COSMOS-Web_i2d_starcat.fits\n",
      "━ Outdir found\n",
      "  0.039938 seconds (22.09 k allocations: 1.805 MiB, 92.49% compilation time)\n",
      "--------------------\n",
      "| Handling Imports |\n",
      "--------------------\n",
      " 16.323933 seconds (26.57 M allocations: 1.641 GiB, 5.24% gc time, 16.84% compilation time: 88% of which was recompilation)\n",
      "━ Start Time 2024-01-16T13:38:48.912\n",
      "---------------------\n",
      "| Reading .jl Files |\n",
      "---------------------\n",
      "############################################################################################################################################################\n",
      "# Julia is using 4 threads to run ShOpt.jl. To change this, on UNIX run export JULIA_NUM_THREADS=[Integer] and on Windows set JULIA_NUM_THREADS=[Integer]. #\n",
      "############################################################################################################################################################\n",
      "######################################################################\n",
      "# For example: export JULIA_NUM_THREADS=4 or set JULIA_NUM_THREADS=4 #\n",
      "######################################################################\n",
      "Key Config Choices:\n",
      "━ Mode: PCA\n",
      "━ α:0.01\n",
      "━ Chisq Stopping Gradient:1.0e-8\n",
      "━ Iterations: 3\n",
      "━ Summary Name: \n",
      "━ PCA Terms: 50\n",
      "━ Lanczos n: 3\n",
      "━ Polynomial Interpolation Stopping Gradient: 1.0e-8\n",
      "━ Max Epochs: 50\n",
      "━ Polynomial Degree: 1\n",
      "━ Stamp Size: 100\n",
      "━ Signal to Noise Ratio Cutoff: 0.33\n",
      "━ Save YAML: true\n",
      "━ Stopping Analytic Fit Gradient Star Vignets: 1.0e-6\n",
      "━ Stopping Analytic Fit Gradient Learned Vignets: 1.0e-6\n",
      "━ Analytic Fit Stamp Size: 30\n",
      "━ Stopping Pixel Fit Gradient: 1.0e-5\n",
      "━ Print Unicode Plots: true\n",
      "━ Python Plots: false\n",
      "━ Parameters Histogram: false\n",
      "━ Parameters Scatterplot: false\n",
      "━ Stream Plots: false\n",
      "━ s Lower Bound: 0.075\n",
      "━ s Upper Bound: 1\n",
      "━ Training Ratio: 0.9\n",
      "━ Sum Pixel Grid and Inputs to Unity: true\n",
      "━ Truncate Summary File: true\n",
      "━ Comments: ** This is where you can leave comments or notes to self on the run! **\n",
      "  3.121636 seconds (4.20 M allocations: 281.927 MiB, 4.09% gc time, 94.84% compilation time: 1% of which was recompilation)\n",
      "---------------------------\n",
      "| Processing Data for Fit |\n",
      "---------------------------\n",
      "                  signal to noise ratio           \n",
      "       ┌                                        ┐ \n",
      "        ┐                                ╷        \n",
      "   snr  ├────────────────────────────────┤        \n",
      "        ┘                                ╵        \n",
      "       └                                        ┘ \n",
      "        0               300000            600000  \n",
      "━ Cutting off Stars below the 0.33 Percentile of Signal to Noise Ratio: 375.01463317871094 \n",
      "━ outlier indices: [1, 16, 19, 25, 27, 28, 30, 31, 35, 36, 37, 40, 41, 42, 51, 54, 59, 64, 66, 68, 69, 71, 72, 76, 77, 82, 84, 86, 87, 89, 91, 93, 97, 101, 103, 104, 106, 108, 109, 113, 114, 116, 117, 120, 124, 125, 126, 128, 133, 135, 136, 138, 146, 150, 154, 160, 164, 165, 166, 170, 174, 175, 177, 181, 183, 184, 185, 188, 190, 192, 194, 196, 200, 203, 208, 211, 217, 218, 220, 222, 229, 231, 233, 236, 246, 256, 257, 260, 265, 267, 269, 270, 279, 281, 287, 289, 290, 292, 294, 296, 299, 300, 310, 316, 317, 322, 324, 325]\n",
      "━ Number of vignets: 326\n",
      "━ Removed 108 outliers on the basis of Signal to Noise Ratio\n",
      "━ Sampled all vignets to 100 x 100 from 101 x 101 via over/under sampling\n",
      "      Sampled Vignet 44              \n",
      "      ┌───────────────┐              \n",
      "   15 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  ┌──┐ 0.1    \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;221;48;5;229m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;208;48;5;214m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;167;48;5;166m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;125m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;17m▄▄\u001b[0m│        \n",
      "    1 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  └──┘ -0.0001\n",
      "      └───────────────┘              \n",
      "       1            15               \n",
      " 14.239377 seconds (630.28 M allocations: 9.832 GiB, 6.84% gc time, 19.90% compilation time: <1% of which was recompilation)\n",
      "---------------------------------------\n",
      "| Analytic Profile Fit for Model Star |\n",
      "---------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
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      "Star 136/218 Complete 62.4%┣█████████████        ┫ 136/218 [00:14<00:08, 10it/s]\n",
      "Star 137/218 Complete 62.8%┣█████████████▏       ┫ 137/218 [00:14<00:08, 10it/s]\n",
      "Star 138/218 Complete 63.3%┣█████████████▎       ┫ 138/218 [00:14<00:08, 10it/s]\n",
      "Star 139/218 Complete 63.8%┣█████████████▍       ┫ 139/218 [00:14<00:08, 10it/s]\n",
      "Star 140/218 Complete 64.2%┣█████████████▌       ┫ 140/218 [00:14<00:08, 10it/s]\n",
      "Star 141/218 Complete 64.7%┣█████████████▋       ┫ 141/218 [00:14<00:08, 10it/s]\n",
      "Star 143/218 Complete 65.6%┣█████████████▊       ┫ 143/218 [00:14<00:07, 10it/s]\n",
      "Star 144/218 Complete 66.1%┣█████████████▉       ┫ 144/218 [00:14<00:07, 10it/s]\n",
      "Star 145/218 Complete 66.5%┣██████████████       ┫ 145/218 [00:14<00:07, 10it/s]\n",
      "Star 146/218 Complete 67.0%┣██████████████       ┫ 146/218 [00:14<00:07, 10it/s]\n",
      "Star 147/218 Complete 67.4%┣██████████████▏      ┫ 147/218 [00:14<00:07, 10it/s]\n",
      "Star 148/218 Complete 67.9%┣██████████████▎      ┫ 148/218 [00:14<00:07, 10it/s]\n",
      "Star 150/218 Complete 68.8%┣██████████████▌      ┫ 150/218 [00:15<00:07, 10it/s]\n",
      "Star 151/218 Complete 69.3%┣██████████████▌      ┫ 151/218 [00:15<00:07, 10it/s]\n",
      "Star 153/218 Complete 70.2%┣██████████████▊      ┫ 153/218 [00:15<00:06, 10it/s]\n",
      "Star 154/218 Complete 70.6%┣██████████████▉      ┫ 154/218 [00:15<00:06, 10it/s]\n",
      "Star 155/218 Complete 71.1%┣███████████████      ┫ 155/218 [00:15<00:06, 10it/s]\n",
      "Star 157/218 Complete 72.0%┣███████████████▏     ┫ 157/218 [00:15<00:06, 10it/s]\n",
      "Star 158/218 Complete 72.5%┣███████████████▏     ┫ 158/218 [00:15<00:06, 10it/s]\n",
      "Star 159/218 Complete 72.9%┣███████████████▎     ┫ 159/218 [00:15<00:06, 10it/s]\n",
      "Star 160/218 Complete 73.4%┣███████████████▍     ┫ 160/218 [00:15<00:06, 10it/s]\n",
      "Star 161/218 Complete 73.9%┣███████████████▌     ┫ 161/218 [00:15<00:05, 10it/s]\n",
      "Star 162/218 Complete 74.3%┣███████████████▋     ┫ 162/218 [00:15<00:05, 10it/s]\n",
      "Star 164/218 Complete 75.2%┣███████████████▉     ┫ 164/218 [00:15<00:05, 11it/s]\n",
      "Star 166/218 Complete 76.1%┣████████████████     ┫ 166/218 [00:16<00:05, 11it/s]\n",
      "Star 168/218 Complete 77.1%┣████████████████▏    ┫ 168/218 [00:16<00:05, 11it/s]\n",
      "Star 169/218 Complete 77.5%┣████████████████▎    ┫ 169/218 [00:16<00:05, 11it/s]\n",
      "Star 171/218 Complete 78.4%┣████████████████▌    ┫ 171/218 [00:16<00:04, 11it/s]\n",
      "Star 172/218 Complete 78.9%┣████████████████▋    ┫ 172/218 [00:16<00:04, 11it/s]\n",
      "Star 173/218 Complete 79.4%┣████████████████▋    ┫ 173/218 [00:16<00:04, 11it/s]\n",
      "Star 174/218 Complete 79.8%┣████████████████▊    ┫ 174/218 [00:16<00:04, 11it/s]\n",
      "Star 175/218 Complete 80.3%┣████████████████▉    ┫ 175/218 [00:16<00:04, 11it/s]\n",
      "Star 176/218 Complete 80.7%┣█████████████████    ┫ 176/218 [00:16<00:04, 11it/s]\n",
      "Star 177/218 Complete 81.2%┣█████████████████    ┫ 177/218 [00:16<00:04, 11it/s]\n",
      "Star 178/218 Complete 81.7%┣█████████████████▏   ┫ 178/218 [00:16<00:04, 11it/s]\n",
      "Star 180/218 Complete 82.6%┣█████████████████▍   ┫ 180/218 [00:16<00:03, 11it/s]\n",
      "Star 181/218 Complete 83.0%┣█████████████████▍   ┫ 181/218 [00:17<00:03, 11it/s]\n",
      "Star 183/218 Complete 83.9%┣█████████████████▋   ┫ 183/218 [00:17<00:03, 11it/s]\n",
      "Star 184/218 Complete 84.4%┣█████████████████▊   ┫ 184/218 [00:17<00:03, 11it/s]\n",
      "Star 185/218 Complete 84.9%┣█████████████████▉   ┫ 185/218 [00:17<00:03, 11it/s]\n",
      "Star 186/218 Complete 85.3%┣██████████████████   ┫ 186/218 [00:17<00:03, 11it/s]\n",
      "Star 188/218 Complete 86.2%┣██████████████████   ┫ 188/218 [00:17<00:03, 11it/s]\n",
      "Star 189/218 Complete 86.7%┣██████████████████▏  ┫ 189/218 [00:17<00:03, 11it/s]\n",
      "Star 190/218 Complete 87.2%┣██████████████████▎  ┫ 190/218 [00:17<00:03, 11it/s]\n",
      "Star 191/218 Complete 87.6%┣██████████████████▍  ┫ 191/218 [00:17<00:02, 11it/s]\n",
      "Star 192/218 Complete 88.1%┣██████████████████▌  ┫ 192/218 [00:17<00:02, 11it/s]\n",
      "Star 194/218 Complete 89.0%┣██████████████████▊  ┫ 194/218 [00:17<00:02, 11it/s]\n",
      "Star 196/218 Complete 89.9%┣██████████████████▉  ┫ 196/218 [00:17<00:02, 11it/s]\n",
      "Star 197/218 Complete 90.4%┣███████████████████  ┫ 197/218 [00:18<00:02, 11it/s]\n",
      "Star 199/218 Complete 91.3%┣███████████████████▏ ┫ 199/218 [00:18<00:02, 11it/s]\n",
      "Star 200/218 Complete 91.7%┣███████████████████▎ ┫ 200/218 [00:18<00:02, 11it/s]\n",
      "Star 201/218 Complete 92.2%┣███████████████████▍ ┫ 201/218 [00:18<00:02, 11it/s]\n",
      "Star 203/218 Complete 93.1%┣███████████████████▌ ┫ 203/218 [00:18<00:01, 11it/s]\n",
      "Star 205/218 Complete 94.0%┣███████████████████▊ ┫ 205/218 [00:18<00:01, 11it/s]\n",
      "Star 206/218 Complete 94.5%┣███████████████████▉ ┫ 206/218 [00:18<00:01, 11it/s]\n",
      "Star 207/218 Complete 95.0%┣████████████████████ ┫ 207/218 [00:18<00:01, 11it/s]\n",
      "Star 208/218 Complete 95.4%┣████████████████████ ┫ 208/218 [00:18<00:01, 11it/s]\n",
      "Star 209/218 Complete 95.9%┣████████████████████▏┫ 209/218 [00:18<00:01, 11it/s]\n",
      "Star 211/218 Complete 96.8%┣████████████████████▎┫ 211/218 [00:18<00:01, 11it/s]\n",
      "Star 213/218 Complete 97.7%┣████████████████████▌┫ 213/218 [00:19<00:00, 11it/s]\n",
      "Star 214/218 Complete 98.2%┣████████████████████▋┫ 214/218 [00:19<00:00, 11it/s]\n",
      "Star 215/218 Complete 98.6%┣████████████████████▊┫ 215/218 [00:19<00:00, 11it/s]\n",
      "Star 216/218 Complete 99.1%┣████████████████████▉┫ 216/218 [00:19<00:00, 11it/s]\n",
      "Star 217/218 Complete 99.5%┣█████████████████████┫ 217/218 [00:19<00:00, 11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 18.991524 seconds (397.46 M allocations: 24.265 GiB, 20.06% gc time, 28.40% compilation time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Star 218/218 Complete 100.0%┣████████████████████┫ 218/218 [00:19<00:00, 11it/s]\n",
      "Star 218/218 Complete 100.0%┣████████████████████┫ 218/218 [00:19<00:00, 11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "━ Blacklisted Stars: Any[2, 34, 92, 109, 122, 149, 170]\n",
      "\n",
      "━ Blacklisted 7 stars on the basis of s < 0.075 or s > 1 (Failed Stars Assigned 0).\n",
      "\n",
      "━ NB: These blacklisted stars are being indexed after the initial removal on the basis of signal to noise, not based off of their original location in the star catalog.\n",
      "------------------\n",
      "| Pixel Grid Fit |\n",
      "------------------\n",
      "━ PCA Mode...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.0%┣                                              ┫ 0/211 [00:00<00:00, -0s/it]\n",
      "Star 1/211 Complete 0.5%┣                      ┫ 1/211 [00:01<Inf:Inf, InfGs/it]\n",
      "Star 2/211 Complete 0.9%┣▎                          ┫ 2/211 [00:01<04:59, 1s/it]\n",
      "Star 3/211 Complete 1.4%┣▍                          ┫ 3/211 [00:02<02:39, 1it/s]\n",
      "Star 4/211 Complete 1.9%┣▌                          ┫ 4/211 [00:02<01:54, 2it/s]\n",
      "Star 5/211 Complete 2.4%┣▋                          ┫ 5/211 [00:02<01:31, 2it/s]\n",
      "Star 6/211 Complete 2.8%┣▊                          ┫ 6/211 [00:02<01:15, 3it/s]\n",
      "Star 7/211 Complete 3.3%┣█                          ┫ 7/211 [00:02<01:04, 3it/s]\n",
      "Star 8/211 Complete 3.8%┣█                          ┫ 8/211 [00:02<00:57, 4it/s]\n",
      "Star 9/211 Complete 4.3%┣█▏                         ┫ 9/211 [00:02<00:51, 4it/s]\n",
      "Star 10/211 Complete 4.7%┣█▏                       ┫ 10/211 [00:02<00:47, 4it/s]\n",
      "Star 12/211 Complete 5.7%┣█▍                       ┫ 12/211 [00:02<00:40, 5it/s]\n",
      "Star 13/211 Complete 6.2%┣█▌                       ┫ 13/211 [00:02<00:38, 5it/s]\n",
      "Star 14/211 Complete 6.6%┣█▋                       ┫ 14/211 [00:02<00:35, 6it/s]\n",
      "Star 15/211 Complete 7.1%┣█▊                       ┫ 15/211 [00:02<00:34, 6it/s]\n",
      "Star 16/211 Complete 7.6%┣██                       ┫ 16/211 [00:02<00:32, 6it/s]\n",
      "Star 17/211 Complete 8.1%┣██                       ┫ 17/211 [00:03<00:31, 6it/s]\n",
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      "Star 20/211 Complete 9.5%┣██▍                      ┫ 20/211 [00:03<00:27, 7it/s]\n",
      "Star 21/211 Complete 10.0%┣██▍                     ┫ 21/211 [00:03<00:27, 7it/s]\n",
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      "Star 28/211 Complete 13.3%┣███▏                    ┫ 28/211 [00:03<00:23, 8it/s]\n",
      "Star 29/211 Complete 13.7%┣███▎                    ┫ 29/211 [00:04<00:23, 8it/s]\n",
      "Star 30/211 Complete 14.2%┣███▍                    ┫ 30/211 [00:04<00:23, 8it/s]\n",
      "Star 31/211 Complete 14.7%┣███▌                    ┫ 31/211 [00:04<00:22, 8it/s]\n",
      "Star 32/211 Complete 15.2%┣███▋                    ┫ 32/211 [00:04<00:22, 8it/s]\n",
      "Star 33/211 Complete 15.6%┣███▊                    ┫ 33/211 [00:04<00:22, 8it/s]\n",
      "Star 34/211 Complete 16.1%┣███▉                    ┫ 34/211 [00:04<00:22, 8it/s]\n",
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      "Star 38/211 Complete 18.0%┣████▎                   ┫ 38/211 [00:04<00:20, 8it/s]\n",
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      "Star 40/211 Complete 19.0%┣████▌                   ┫ 40/211 [00:05<00:20, 9it/s]\n",
      "Star 41/211 Complete 19.4%┣████▋                   ┫ 41/211 [00:05<00:20, 9it/s]\n",
      "Star 42/211 Complete 19.9%┣████▊                   ┫ 42/211 [00:05<00:19, 9it/s]\n",
      "Star 43/211 Complete 20.4%┣█████                   ┫ 43/211 [00:05<00:19, 9it/s]\n",
      "Star 44/211 Complete 20.9%┣█████                   ┫ 44/211 [00:05<00:19, 9it/s]\n",
      "Star 45/211 Complete 21.3%┣█████▏                  ┫ 45/211 [00:05<00:18, 9it/s]\n",
      "Star 46/211 Complete 21.8%┣█████▎                  ┫ 46/211 [00:05<00:18, 9it/s]\n",
      "Star 48/211 Complete 22.7%┣█████▌                  ┫ 48/211 [00:05<00:18, 9it/s]\n",
      "Star 49/211 Complete 23.2%┣█████▋                  ┫ 49/211 [00:05<00:18, 9it/s]\n",
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      "Star 51/211 Complete 24.2%┣█████▉                  ┫ 51/211 [00:05<00:17, 9it/s]\n",
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      "Star 55/211 Complete 26.1%┣██████▎                 ┫ 55/211 [00:06<00:17, 9it/s]\n",
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      "Star 61/211 Complete 28.9%┣███████                 ┫ 61/211 [00:06<00:16, 9it/s]\n",
      "Star 62/211 Complete 29.4%┣███████                 ┫ 62/211 [00:06<00:16, 9it/s]\n",
      "Star 63/211 Complete 29.9%┣███████▏                ┫ 63/211 [00:07<00:16, 9it/s]\n",
      "Star 64/211 Complete 30.3%┣███████                ┫ 64/211 [00:07<00:15, 10it/s]\n",
      "Star 65/211 Complete 30.8%┣███████                ┫ 65/211 [00:07<00:15, 10it/s]\n",
      "Star 66/211 Complete 31.3%┣███████▏               ┫ 66/211 [00:07<00:15, 10it/s]\n",
      "Star 67/211 Complete 31.8%┣███████▎               ┫ 67/211 [00:07<00:15, 10it/s]\n",
      "Star 68/211 Complete 32.2%┣███████▍               ┫ 68/211 [00:07<00:15, 10it/s]\n",
      "Star 69/211 Complete 32.7%┣███████▌               ┫ 69/211 [00:07<00:15, 10it/s]\n",
      "Star 70/211 Complete 33.2%┣███████▋               ┫ 70/211 [00:07<00:15, 10it/s]\n",
      "Star 71/211 Complete 33.6%┣███████▊               ┫ 71/211 [00:07<00:15, 10it/s]\n",
      "Star 72/211 Complete 34.1%┣███████▉               ┫ 72/211 [00:07<00:14, 10it/s]\n",
      "Star 73/211 Complete 34.6%┣████████               ┫ 73/211 [00:07<00:14, 10it/s]\n",
      "Star 74/211 Complete 35.1%┣████████               ┫ 74/211 [00:08<00:14, 10it/s]\n",
      "Star 75/211 Complete 35.5%┣████████▏              ┫ 75/211 [00:08<00:14, 10it/s]\n",
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      "Star 77/211 Complete 36.5%┣████████▍              ┫ 77/211 [00:08<00:14, 10it/s]\n",
      "Star 78/211 Complete 37.0%┣████████▌              ┫ 78/211 [00:08<00:14, 10it/s]\n",
      "Star 79/211 Complete 37.4%┣████████▋              ┫ 79/211 [00:08<00:14, 10it/s]\n",
      "Star 80/211 Complete 37.9%┣████████▊              ┫ 80/211 [00:08<00:13, 10it/s]\n",
      "Star 81/211 Complete 38.4%┣████████▉              ┫ 81/211 [00:08<00:13, 10it/s]\n",
      "Star 82/211 Complete 38.9%┣█████████              ┫ 82/211 [00:08<00:13, 10it/s]\n",
      "Star 83/211 Complete 39.3%┣█████████              ┫ 83/211 [00:08<00:13, 10it/s]\n",
      "Star 84/211 Complete 39.8%┣█████████▏             ┫ 84/211 [00:08<00:13, 10it/s]\n",
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      "Star 92/211 Complete 43.6%┣██████████             ┫ 92/211 [00:09<00:12, 10it/s]\n",
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      "Star 97/211 Complete 46.0%┣██████████▋            ┫ 97/211 [00:10<00:11, 10it/s]\n",
      "Star 98/211 Complete 46.4%┣██████████▊            ┫ 98/211 [00:10<00:11, 10it/s]\n",
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      "Star 101/211 Complete 47.9%┣██████████           ┫ 101/211 [00:10<00:11, 10it/s]\n",
      "Star 102/211 Complete 48.3%┣██████████▏          ┫ 102/211 [00:10<00:11, 10it/s]\n",
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      "Star 110/211 Complete 52.1%┣███████████          ┫ 110/211 [00:11<00:10, 10it/s]\n",
      "Star 111/211 Complete 52.6%┣███████████          ┫ 111/211 [00:11<00:10, 10it/s]\n",
      "Star 112/211 Complete 53.1%┣███████████▏         ┫ 112/211 [00:11<00:10, 10it/s]\n",
      "Star 113/211 Complete 53.6%┣███████████▎         ┫ 113/211 [00:11<00:10, 10it/s]\n",
      "Star 114/211 Complete 54.0%┣███████████▍         ┫ 114/211 [00:11<00:09, 10it/s]\n",
      "Star 115/211 Complete 54.5%┣███████████▌         ┫ 115/211 [00:11<00:09, 10it/s]\n",
      "Star 116/211 Complete 55.0%┣███████████▌         ┫ 116/211 [00:11<00:09, 10it/s]\n",
      "Star 117/211 Complete 55.5%┣███████████▋         ┫ 117/211 [00:11<00:09, 10it/s]\n",
      "Star 118/211 Complete 55.9%┣███████████▊         ┫ 118/211 [00:11<00:09, 10it/s]\n",
      "Star 119/211 Complete 56.4%┣███████████▉         ┫ 119/211 [00:11<00:09, 10it/s]\n",
      "Star 120/211 Complete 56.9%┣████████████         ┫ 120/211 [00:11<00:09, 10it/s]\n",
      "Star 121/211 Complete 57.3%┣████████████         ┫ 121/211 [00:12<00:09, 10it/s]\n",
      "Star 122/211 Complete 57.8%┣████████████▏        ┫ 122/211 [00:12<00:09, 10it/s]\n",
      "Star 123/211 Complete 58.3%┣████████████▎        ┫ 123/211 [00:12<00:08, 10it/s]\n",
      "Star 124/211 Complete 58.8%┣████████████▍        ┫ 124/211 [00:12<00:08, 10it/s]\n",
      "Star 125/211 Complete 59.2%┣████████████▍        ┫ 125/211 [00:12<00:08, 10it/s]\n",
      "Star 126/211 Complete 59.7%┣████████████▌        ┫ 126/211 [00:12<00:08, 10it/s]\n",
      "Star 127/211 Complete 60.2%┣████████████▋        ┫ 127/211 [00:12<00:08, 10it/s]\n",
      "Star 128/211 Complete 60.7%┣████████████▊        ┫ 128/211 [00:12<00:08, 10it/s]\n",
      "Star 129/211 Complete 61.1%┣████████████▉        ┫ 129/211 [00:12<00:08, 10it/s]\n",
      "Star 130/211 Complete 61.6%┣█████████████        ┫ 130/211 [00:12<00:08, 10it/s]\n",
      "Star 131/211 Complete 62.1%┣█████████████        ┫ 131/211 [00:12<00:08, 10it/s]\n",
      "Star 132/211 Complete 62.6%┣█████████████▏       ┫ 132/211 [00:13<00:08, 10it/s]\n",
      "Star 133/211 Complete 63.0%┣█████████████▎       ┫ 133/211 [00:13<00:07, 10it/s]\n",
      "Star 134/211 Complete 63.5%┣█████████████▍       ┫ 134/211 [00:13<00:07, 10it/s]\n",
      "Star 135/211 Complete 64.0%┣█████████████▍       ┫ 135/211 [00:13<00:07, 10it/s]\n",
      "Star 136/211 Complete 64.5%┣█████████████▌       ┫ 136/211 [00:13<00:07, 10it/s]\n",
      "Star 137/211 Complete 64.9%┣█████████████▋       ┫ 137/211 [00:13<00:07, 10it/s]\n",
      "Star 138/211 Complete 65.4%┣█████████████▊       ┫ 138/211 [00:13<00:07, 11it/s]\n",
      "Star 139/211 Complete 65.9%┣█████████████▉       ┫ 139/211 [00:13<00:07, 11it/s]\n",
      "Star 140/211 Complete 66.4%┣██████████████       ┫ 140/211 [00:13<00:07, 11it/s]\n",
      "Star 141/211 Complete 66.8%┣██████████████       ┫ 141/211 [00:13<00:07, 11it/s]\n",
      "Star 142/211 Complete 67.3%┣██████████████▏      ┫ 142/211 [00:13<00:07, 11it/s]\n",
      "Star 143/211 Complete 67.8%┣██████████████▎      ┫ 143/211 [00:13<00:06, 11it/s]\n",
      "Star 144/211 Complete 68.2%┣██████████████▎      ┫ 144/211 [00:13<00:06, 11it/s]\n",
      "Star 145/211 Complete 68.7%┣██████████████▍      ┫ 145/211 [00:14<00:06, 11it/s]\n",
      "Star 146/211 Complete 69.2%┣██████████████▌      ┫ 146/211 [00:14<00:06, 11it/s]\n",
      "Star 147/211 Complete 69.7%┣██████████████▋      ┫ 147/211 [00:14<00:06, 11it/s]\n",
      "Star 148/211 Complete 70.1%┣██████████████▊      ┫ 148/211 [00:14<00:06, 11it/s]\n",
      "Star 149/211 Complete 70.6%┣██████████████▉      ┫ 149/211 [00:14<00:06, 11it/s]\n",
      "Star 150/211 Complete 71.1%┣███████████████      ┫ 150/211 [00:14<00:06, 11it/s]\n",
      "Star 151/211 Complete 71.6%┣███████████████      ┫ 151/211 [00:14<00:06, 11it/s]\n",
      "Star 152/211 Complete 72.0%┣███████████████▏     ┫ 152/211 [00:14<00:05, 11it/s]\n",
      "Star 153/211 Complete 72.5%┣███████████████▎     ┫ 153/211 [00:14<00:05, 11it/s]\n",
      "Star 154/211 Complete 73.0%┣███████████████▎     ┫ 154/211 [00:14<00:05, 11it/s]\n",
      "Star 155/211 Complete 73.5%┣███████████████▍     ┫ 155/211 [00:14<00:05, 11it/s]\n",
      "Star 156/211 Complete 73.9%┣███████████████▌     ┫ 156/211 [00:14<00:05, 11it/s]\n",
      "Star 157/211 Complete 74.4%┣███████████████▋     ┫ 157/211 [00:14<00:05, 11it/s]\n",
      "Star 158/211 Complete 74.9%┣███████████████▊     ┫ 158/211 [00:14<00:05, 11it/s]\n",
      "Star 159/211 Complete 75.4%┣███████████████▉     ┫ 159/211 [00:14<00:05, 11it/s]\n",
      "Star 160/211 Complete 75.8%┣████████████████     ┫ 160/211 [00:15<00:05, 11it/s]\n",
      "Star 161/211 Complete 76.3%┣████████████████     ┫ 161/211 [00:15<00:05, 11it/s]\n",
      "Star 162/211 Complete 76.8%┣████████████████▏    ┫ 162/211 [00:15<00:04, 11it/s]\n",
      "Star 163/211 Complete 77.3%┣████████████████▎    ┫ 163/211 [00:15<00:04, 11it/s]\n",
      "Star 164/211 Complete 77.7%┣████████████████▎    ┫ 164/211 [00:15<00:04, 11it/s]\n",
      "Star 165/211 Complete 78.2%┣████████████████▍    ┫ 165/211 [00:15<00:04, 11it/s]\n",
      "Star 166/211 Complete 78.7%┣████████████████▌    ┫ 166/211 [00:15<00:04, 11it/s]\n",
      "Star 167/211 Complete 79.1%┣████████████████▋    ┫ 167/211 [00:15<00:04, 11it/s]\n",
      "Star 168/211 Complete 79.6%┣████████████████▊    ┫ 168/211 [00:15<00:04, 11it/s]\n",
      "Star 169/211 Complete 80.1%┣████████████████▉    ┫ 169/211 [00:15<00:04, 11it/s]\n",
      "Star 170/211 Complete 80.6%┣█████████████████    ┫ 170/211 [00:15<00:04, 11it/s]\n",
      "Star 171/211 Complete 81.0%┣█████████████████    ┫ 171/211 [00:15<00:04, 11it/s]\n",
      "Star 172/211 Complete 81.5%┣█████████████████▏   ┫ 172/211 [00:15<00:03, 11it/s]\n",
      "Star 173/211 Complete 82.0%┣█████████████████▏   ┫ 173/211 [00:15<00:03, 11it/s]\n",
      "Star 174/211 Complete 82.5%┣█████████████████▎   ┫ 174/211 [00:15<00:03, 11it/s]\n",
      "Star 175/211 Complete 82.9%┣█████████████████▍   ┫ 175/211 [00:16<00:03, 11it/s]\n",
      "Star 176/211 Complete 83.4%┣█████████████████▌   ┫ 176/211 [00:16<00:03, 11it/s]\n",
      "Star 177/211 Complete 83.9%┣█████████████████▋   ┫ 177/211 [00:16<00:03, 11it/s]\n",
      "Star 178/211 Complete 84.4%┣█████████████████▊   ┫ 178/211 [00:16<00:03, 11it/s]\n",
      "Star 179/211 Complete 84.8%┣█████████████████▉   ┫ 179/211 [00:16<00:03, 11it/s]\n",
      "Star 180/211 Complete 85.3%┣██████████████████   ┫ 180/211 [00:16<00:03, 11it/s]\n",
      "Star 181/211 Complete 85.8%┣██████████████████   ┫ 181/211 [00:16<00:03, 11it/s]\n",
      "Star 182/211 Complete 86.3%┣██████████████████▏  ┫ 182/211 [00:16<00:03, 11it/s]\n",
      "Star 183/211 Complete 86.7%┣██████████████████▏  ┫ 183/211 [00:16<00:03, 11it/s]\n",
      "Star 184/211 Complete 87.2%┣██████████████████▎  ┫ 184/211 [00:16<00:02, 11it/s]\n",
      "Star 185/211 Complete 87.7%┣██████████████████▍  ┫ 185/211 [00:16<00:02, 11it/s]\n",
      "Star 186/211 Complete 88.2%┣██████████████████▌  ┫ 186/211 [00:16<00:02, 11it/s]\n",
      "Star 187/211 Complete 88.6%┣██████████████████▋  ┫ 187/211 [00:17<00:02, 11it/s]\n",
      "Star 188/211 Complete 89.1%┣██████████████████▊  ┫ 188/211 [00:17<00:02, 11it/s]\n",
      "Star 189/211 Complete 89.6%┣██████████████████▉  ┫ 189/211 [00:17<00:02, 11it/s]\n",
      "Star 190/211 Complete 90.0%┣███████████████████  ┫ 190/211 [00:17<00:02, 11it/s]\n",
      "Star 191/211 Complete 90.5%┣███████████████████  ┫ 191/211 [00:17<00:02, 11it/s]\n",
      "Star 192/211 Complete 91.0%┣███████████████████  ┫ 192/211 [00:17<00:02, 11it/s]\n",
      "Star 193/211 Complete 91.5%┣███████████████████▏ ┫ 193/211 [00:17<00:02, 11it/s]\n",
      "Star 194/211 Complete 91.9%┣███████████████████▎ ┫ 194/211 [00:17<00:02, 11it/s]\n",
      "Star 195/211 Complete 92.4%┣███████████████████▍ ┫ 195/211 [00:17<00:01, 11it/s]\n",
      "Star 196/211 Complete 92.9%┣███████████████████▌ ┫ 196/211 [00:17<00:01, 11it/s]\n",
      "Star 197/211 Complete 93.4%┣███████████████████▋ ┫ 197/211 [00:17<00:01, 11it/s]\n",
      "Star 198/211 Complete 93.8%┣███████████████████▊ ┫ 198/211 [00:17<00:01, 11it/s]\n",
      "Star 199/211 Complete 94.3%┣███████████████████▉ ┫ 199/211 [00:17<00:01, 11it/s]\n",
      "Star 200/211 Complete 94.8%┣████████████████████ ┫ 200/211 [00:17<00:01, 11it/s]\n",
      "Star 201/211 Complete 95.3%┣████████████████████ ┫ 201/211 [00:18<00:01, 11it/s]\n",
      "Star 202/211 Complete 95.7%┣████████████████████ ┫ 202/211 [00:18<00:01, 11it/s]\n",
      "Star 203/211 Complete 96.2%┣████████████████████▏┫ 203/211 [00:18<00:01, 11it/s]\n",
      "Star 204/211 Complete 96.7%┣████████████████████▎┫ 204/211 [00:18<00:01, 11it/s]\n",
      "Star 205/211 Complete 97.2%┣████████████████████▍┫ 205/211 [00:18<00:01, 11it/s]\n",
      "Star 206/211 Complete 97.6%┣████████████████████▌┫ 206/211 [00:18<00:00, 11it/s]\n",
      "Star 207/211 Complete 98.1%┣████████████████████▋┫ 207/211 [00:18<00:00, 11it/s]\n",
      "Star 208/211 Complete 98.6%┣████████████████████▊┫ 208/211 [00:18<00:00, 11it/s]\n",
      "Star 209/211 Complete 99.1%┣████████████████████▉┫ 209/211 [00:18<00:00, 11it/s]\n",
      "Star 210/211 Complete 99.5%┣█████████████████████┫ 210/211 [00:18<00:00, 11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 18.443221 seconds (604.04 M allocations: 9.839 GiB, 6.38% gc time, 6.82% compilation time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Star 211/211 Complete 100.0%┣████████████████████┫ 211/211 [00:18<00:00, 11it/s]\n",
      "Star 211/211 Complete 100.0%┣████████████████████┫ 211/211 [00:18<00:00, 11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "━ failed stars:Any[]\n",
      "-----------------------------------------\n",
      "| Analytic Profile Fit for Learned Star |\n",
      "-----------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.0%┣                                              ┫ 0/211 [00:00<00:00, -0s/it]\n",
      "Star 1/211 Complete 0.5%┣                      ┫ 1/211 [00:01<Inf:Inf, InfGs/it]\n",
      "Star 3/211 Complete 1.4%┣▍                          ┫ 3/211 [00:01<01:07, 3it/s]\n",
      "Star 5/211 Complete 2.4%┣▋                          ┫ 5/211 [00:01<00:36, 6it/s]\n",
      "Star 7/211 Complete 3.3%┣█                          ┫ 7/211 [00:01<00:27, 8it/s]\n",
      "Star 9/211 Complete 4.3%┣█                         ┫ 9/211 [00:01<00:21, 10it/s]\n",
      "Star 11/211 Complete 5.2%┣█▎                      ┫ 11/211 [00:01<00:18, 11it/s]\n",
      "Star 12/211 Complete 5.7%┣█▍                      ┫ 12/211 [00:01<00:18, 11it/s]\n",
      "Star 14/211 Complete 6.6%┣█▋                      ┫ 14/211 [00:01<00:16, 13it/s]\n",
      "Star 16/211 Complete 7.6%┣█▉                      ┫ 16/211 [00:01<00:14, 14it/s]\n",
      "Star 18/211 Complete 8.5%┣██                      ┫ 18/211 [00:01<00:13, 15it/s]\n",
      "Star 20/211 Complete 9.5%┣██▎                     ┫ 20/211 [00:01<00:13, 15it/s]\n",
      "Star 22/211 Complete 10.4%┣██▍                    ┫ 22/211 [00:01<00:12, 16it/s]\n",
      "Star 24/211 Complete 11.4%┣██▋                    ┫ 24/211 [00:01<00:12, 16it/s]\n",
      "Star 26/211 Complete 12.3%┣██▉                    ┫ 26/211 [00:02<00:11, 17it/s]\n",
      "Star 28/211 Complete 13.3%┣███                    ┫ 28/211 [00:02<00:11, 17it/s]\n",
      "Star 30/211 Complete 14.2%┣███▎                   ┫ 30/211 [00:02<00:10, 17it/s]\n",
      "Star 32/211 Complete 15.2%┣███▌                   ┫ 32/211 [00:02<00:10, 18it/s]\n",
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      "Star 36/211 Complete 17.1%┣████                   ┫ 36/211 [00:02<00:09, 18it/s]\n",
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      "Star 40/211 Complete 19.0%┣████▍                  ┫ 40/211 [00:02<00:09, 19it/s]\n",
      "Star 42/211 Complete 19.9%┣████▋                  ┫ 42/211 [00:02<00:09, 19it/s]\n",
      "Star 45/211 Complete 21.3%┣█████                  ┫ 45/211 [00:02<00:08, 20it/s]\n",
      "Star 47/211 Complete 22.3%┣█████▏                 ┫ 47/211 [00:02<00:08, 20it/s]\n",
      "Star 49/211 Complete 23.2%┣█████▍                 ┫ 49/211 [00:02<00:08, 21it/s]\n",
      "Star 51/211 Complete 24.2%┣█████▋                 ┫ 51/211 [00:02<00:08, 21it/s]\n",
      "Star 53/211 Complete 25.1%┣█████▊                 ┫ 53/211 [00:02<00:07, 21it/s]\n",
      "Star 55/211 Complete 26.1%┣██████                 ┫ 55/211 [00:03<00:07, 21it/s]\n",
      "Star 57/211 Complete 27.0%┣██████▏                ┫ 57/211 [00:03<00:07, 21it/s]\n",
      "Star 59/211 Complete 28.0%┣██████▍                ┫ 59/211 [00:03<00:07, 21it/s]\n",
      "Star 61/211 Complete 28.9%┣██████▋                ┫ 61/211 [00:03<00:07, 21it/s]\n",
      "Star 63/211 Complete 29.9%┣██████▉                ┫ 63/211 [00:03<00:07, 22it/s]\n",
      "Star 64/211 Complete 30.3%┣███████                ┫ 64/211 [00:03<00:07, 21it/s]\n",
      "Star 66/211 Complete 31.3%┣███████▏               ┫ 66/211 [00:03<00:07, 21it/s]\n",
      "Star 69/211 Complete 32.7%┣███████▌               ┫ 69/211 [00:03<00:07, 22it/s]\n",
      "Star 71/211 Complete 33.6%┣███████▊               ┫ 71/211 [00:03<00:06, 22it/s]\n",
      "Star 73/211 Complete 34.6%┣████████               ┫ 73/211 [00:03<00:06, 22it/s]\n",
      "Star 75/211 Complete 35.5%┣████████▏              ┫ 75/211 [00:03<00:06, 22it/s]\n",
      "Star 77/211 Complete 36.5%┣████████▍              ┫ 77/211 [00:03<00:06, 22it/s]\n",
      "Star 79/211 Complete 37.4%┣████████▋              ┫ 79/211 [00:04<00:06, 22it/s]\n",
      "Star 81/211 Complete 38.4%┣████████▉              ┫ 81/211 [00:04<00:06, 22it/s]\n",
      "Star 83/211 Complete 39.3%┣█████████              ┫ 83/211 [00:04<00:06, 22it/s]\n",
      "Star 86/211 Complete 40.8%┣█████████▍             ┫ 86/211 [00:04<00:06, 23it/s]\n",
      "Star 88/211 Complete 41.7%┣█████████▋             ┫ 88/211 [00:04<00:05, 23it/s]\n",
      "Star 90/211 Complete 42.7%┣█████████▉             ┫ 90/211 [00:04<00:05, 23it/s]\n",
      "Star 92/211 Complete 43.6%┣██████████             ┫ 92/211 [00:04<00:05, 23it/s]\n",
      "Star 94/211 Complete 44.5%┣██████████▎            ┫ 94/211 [00:04<00:05, 23it/s]\n",
      "Star 96/211 Complete 45.5%┣██████████▌            ┫ 96/211 [00:04<00:05, 23it/s]\n",
      "Star 97/211 Complete 46.0%┣██████████▋            ┫ 97/211 [00:04<00:05, 23it/s]\n",
      "Star 99/211 Complete 46.9%┣██████████▉            ┫ 99/211 [00:04<00:05, 23it/s]\n",
      "Star 101/211 Complete 47.9%┣██████████           ┫ 101/211 [00:04<00:05, 23it/s]\n",
      "Star 102/211 Complete 48.3%┣██████████▏          ┫ 102/211 [00:04<00:05, 23it/s]\n",
      "Star 103/211 Complete 48.8%┣██████████▎          ┫ 103/211 [00:04<00:05, 23it/s]\n",
      "Star 105/211 Complete 49.8%┣██████████▌          ┫ 105/211 [00:05<00:05, 23it/s]\n",
      "Star 107/211 Complete 50.7%┣██████████▋          ┫ 107/211 [00:05<00:05, 23it/s]\n",
      "Star 109/211 Complete 51.7%┣██████████▉          ┫ 109/211 [00:05<00:04, 23it/s]\n",
      "Star 111/211 Complete 52.6%┣███████████          ┫ 111/211 [00:05<00:04, 23it/s]\n",
      "Star 113/211 Complete 53.6%┣███████████▎         ┫ 113/211 [00:05<00:04, 23it/s]\n",
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      "Star 118/211 Complete 55.9%┣███████████▊         ┫ 118/211 [00:05<00:04, 23it/s]\n",
      "Star 120/211 Complete 56.9%┣████████████         ┫ 120/211 [00:05<00:04, 23it/s]\n",
      "Star 122/211 Complete 57.8%┣████████████▏        ┫ 122/211 [00:05<00:04, 23it/s]\n",
      "Star 124/211 Complete 58.8%┣████████████▍        ┫ 124/211 [00:05<00:04, 23it/s]\n",
      "Star 125/211 Complete 59.2%┣████████████▍        ┫ 125/211 [00:05<00:04, 23it/s]\n",
      "Star 127/211 Complete 60.2%┣████████████▋        ┫ 127/211 [00:05<00:04, 23it/s]\n",
      "Star 129/211 Complete 61.1%┣████████████▉        ┫ 129/211 [00:05<00:04, 23it/s]\n",
      "Star 132/211 Complete 62.6%┣█████████████▏       ┫ 132/211 [00:06<00:03, 24it/s]\n",
      "Star 133/211 Complete 63.0%┣█████████████▎       ┫ 133/211 [00:06<00:03, 23it/s]\n",
      "Star 134/211 Complete 63.5%┣█████████████▍       ┫ 134/211 [00:06<00:03, 23it/s]\n",
      "Star 136/211 Complete 64.5%┣█████████████▌       ┫ 136/211 [00:06<00:03, 23it/s]\n",
      "Star 137/211 Complete 64.9%┣█████████████▋       ┫ 137/211 [00:06<00:03, 23it/s]\n",
      "Star 139/211 Complete 65.9%┣█████████████▉       ┫ 139/211 [00:06<00:03, 23it/s]\n",
      "Star 141/211 Complete 66.8%┣██████████████       ┫ 141/211 [00:06<00:03, 23it/s]\n",
      "Star 143/211 Complete 67.8%┣██████████████▎      ┫ 143/211 [00:06<00:03, 23it/s]\n",
      "Star 145/211 Complete 68.7%┣██████████████▍      ┫ 145/211 [00:06<00:03, 24it/s]\n",
      "Star 147/211 Complete 69.7%┣██████████████▋      ┫ 147/211 [00:06<00:03, 24it/s]\n",
      "Star 149/211 Complete 70.6%┣██████████████▉      ┫ 149/211 [00:06<00:03, 24it/s]\n",
      "Star 151/211 Complete 71.6%┣███████████████      ┫ 151/211 [00:06<00:03, 24it/s]\n",
      "Star 153/211 Complete 72.5%┣███████████████▎     ┫ 153/211 [00:06<00:02, 24it/s]\n",
      "Star 155/211 Complete 73.5%┣███████████████▍     ┫ 155/211 [00:07<00:02, 24it/s]\n",
      "Star 156/211 Complete 73.9%┣███████████████▌     ┫ 156/211 [00:07<00:02, 24it/s]\n",
      "Star 158/211 Complete 74.9%┣███████████████▊     ┫ 158/211 [00:07<00:02, 23it/s]\n",
      "Star 160/211 Complete 75.8%┣████████████████     ┫ 160/211 [00:07<00:02, 24it/s]\n",
      "Star 162/211 Complete 76.8%┣████████████████▏    ┫ 162/211 [00:07<00:02, 24it/s]\n",
      "Star 164/211 Complete 77.7%┣████████████████▎    ┫ 164/211 [00:07<00:02, 24it/s]\n",
      "Star 166/211 Complete 78.7%┣████████████████▌    ┫ 166/211 [00:07<00:02, 24it/s]\n",
      "Star 167/211 Complete 79.1%┣████████████████▋    ┫ 167/211 [00:07<00:02, 24it/s]\n",
      "Star 168/211 Complete 79.6%┣████████████████▊    ┫ 168/211 [00:07<00:02, 24it/s]\n",
      "Star 170/211 Complete 80.6%┣█████████████████    ┫ 170/211 [00:07<00:02, 24it/s]\n",
      "Star 172/211 Complete 81.5%┣█████████████████▏   ┫ 172/211 [00:07<00:02, 24it/s]\n",
      "Star 174/211 Complete 82.5%┣█████████████████▎   ┫ 174/211 [00:07<00:02, 24it/s]\n",
      "Star 176/211 Complete 83.4%┣█████████████████▌   ┫ 176/211 [00:07<00:01, 24it/s]\n",
      "Star 178/211 Complete 84.4%┣█████████████████▊   ┫ 178/211 [00:07<00:01, 24it/s]\n",
      "Star 180/211 Complete 85.3%┣██████████████████   ┫ 180/211 [00:08<00:01, 24it/s]\n",
      "Star 182/211 Complete 86.3%┣██████████████████▏  ┫ 182/211 [00:08<00:01, 24it/s]\n",
      "Star 184/211 Complete 87.2%┣██████████████████▎  ┫ 184/211 [00:08<00:01, 24it/s]\n",
      "Star 186/211 Complete 88.2%┣██████████████████▌  ┫ 186/211 [00:08<00:01, 24it/s]\n",
      "Star 188/211 Complete 89.1%┣██████████████████▊  ┫ 188/211 [00:08<00:01, 24it/s]\n",
      "Star 190/211 Complete 90.0%┣███████████████████  ┫ 190/211 [00:08<00:01, 24it/s]\n",
      "Star 192/211 Complete 91.0%┣███████████████████  ┫ 192/211 [00:08<00:01, 24it/s]\n",
      "Star 194/211 Complete 91.9%┣███████████████████▎ ┫ 194/211 [00:08<00:01, 24it/s]\n",
      "Star 196/211 Complete 92.9%┣███████████████████▌ ┫ 196/211 [00:08<00:01, 24it/s]\n",
      "Star 198/211 Complete 93.8%┣███████████████████▊ ┫ 198/211 [00:08<00:01, 24it/s]\n",
      "Star 200/211 Complete 94.8%┣████████████████████ ┫ 200/211 [00:08<00:00, 24it/s]\n",
      "Star 202/211 Complete 95.7%┣████████████████████ ┫ 202/211 [00:08<00:00, 24it/s]\n",
      "Star 204/211 Complete 96.7%┣████████████████████▎┫ 204/211 [00:08<00:00, 24it/s]\n",
      "Star 206/211 Complete 97.6%┣████████████████████▌┫ 206/211 [00:08<00:00, 24it/s]\n",
      "Star 208/211 Complete 98.6%┣████████████████████▊┫ 208/211 [00:09<00:00, 24it/s]\n",
      "Star 210/211 Complete 99.5%┣█████████████████████┫ 210/211 [00:09<00:00, 24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  8.626450 seconds (384.65 M allocations: 23.443 GiB, 20.93% gc time, 5.98% compilation time)\n",
      "━ failed stars: Any[38, 44, 64, 81, 84, 131]\n",
      "\n",
      "━ Rejected 6 more stars for failing or having either s < 0.075 or s > 1 when fitting an analytic profile to an autoencoded image.\n",
      "\n",
      "━ NB: These failed stars are being indexed after both the screening of signal to noise and the blacklisting of s values.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Star 211/211 Complete 100.0%┣████████████████████┫ 211/211 [00:09<00:00, 24it/s]\n",
      "Star 211/211 Complete 100.0%┣████████████████████┫ 211/211 [00:09<00:00, 24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------\n",
      "| Transforming (x,y) -> (u,v) | Interpolation [s, g1, g2] Across the Field of View |\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "s(u,v) = \n",
      "0.043137549533344005 u³ \n",
      "+ -32.248064031757636 v³ \n",
      "+ -0.01296835995283181 u²v \n",
      "+ 1.080461360644579 v²u \n",
      "+ -12.904437347018339 u² \n",
      "+ 69.35993280610599 v² \n",
      "+ -1.850465204390673 uv \n",
      "+ 967.8824784315469 u \n",
      "+ 15.885473303453791 v \n",
      "+ 19.729170670202777\n",
      "\n",
      "\n",
      "g1(u,v) = \n",
      "0.017051541319426374 u³ \n",
      "+ -66.32404131869116 v³ \n",
      "+ -0.022265064924691772 u²v \n",
      "+ 3.0789736810564574 v²u \n",
      "+ -5.097149337968067 u² \n",
      "+ 3.7501978115920376 v² \n",
      "+ -3.9635712943478176 uv \n",
      "+ 386.51013929943144 u \n",
      "+ 6.848806109557644 v \n",
      "+ 8.269250117403185\n",
      "\n",
      "\n",
      "g2(u,v) = \n",
      "0.00035091316547648376 u³ \n",
      "+ -44.892932584455444 v³ \n",
      "+ -0.042819878020977385 u²v \n",
      "+ 2.116070005770653 v²u \n",
      "+ -0.030485437741170077 u² \n",
      "+ 0.6065679904213188 v² \n",
      "+ 1.412484561759082 uv \n",
      "+ 0.6105935387455848 u \n",
      "+ 0.733405715659466 v \n",
      "+ 0.587853158724203\n",
      "\n",
      "---------------------------------------------------------------------------------------\n",
      "| Transforming (x,y) -> (u,v) | Interpolation Pixel by Pixel Across the Field of View |\n",
      "---------------------------------------------------------------------------------------\n",
      "184 training stars\n",
      "━ Iteration: 1\n",
      "165 training stars\n",
      "━ Iteration: 2\n",
      "148 training stars\n",
      "━ Iteration: 3\n",
      "  9.624700 seconds (454.17 M allocations: 14.172 GiB, 13.85% gc time, 102.61% compilation time)\n",
      "------------\n",
      "| Plotting |\n",
      "------------\n",
      "             Heatmap of star 77                     \n",
      "      ┌──────────────────────────────┐              \n",
      "   75 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  ┌──┐ 0.0003 \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;228;48;5;229m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;221;48;5;221m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;214;48;5;214m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;208;48;5;208m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;166;48;5;202m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;167;48;5;167m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;131;48;5;131m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;125m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;89m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;17m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;16m▄▄\u001b[0m│        \n",
      "    1 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  └──┘ -0.0001\n",
      "      └──────────────────────────────┘              \n",
      "       1                           75               \n",
      "        Heatmap of Pixel Grid Fit 77                \n",
      "      ┌──────────────────────────────┐              \n",
      "   75 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  ┌──┐ 0.0003 \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;228;48;5;229m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;221;48;5;221m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;214;48;5;214m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;208;48;5;208m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;166;48;5;202m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;167;48;5;167m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;131;48;5;131m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;125m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;89m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;17m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;16m▄▄\u001b[0m│        \n",
      "    1 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  └──┘ -9.0e-5\n",
      "      └──────────────────────────────┘              \n",
      "       1                           75               \n",
      "            Heatmap of Residuals                    \n",
      "      ┌──────────────────────────────┐              \n",
      "   75 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  ┌──┐ 0.0006 \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;228;48;5;229m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;221;48;5;221m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;214;48;5;214m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;208;48;5;208m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;166;48;5;202m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;167;48;5;167m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;131;48;5;131m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;125m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;89;48;5;89m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;53;48;5;53m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;17m▄▄\u001b[0m│        \n",
      "      │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  │\u001b[38;5;16;48;5;16m▄▄\u001b[0m│        \n",
      "    1 │▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄│  └──┘ -0.0005\n",
      "      └──────────────────────────────┘              \n",
      "       1                           75               \n",
      "  1.779692 seconds (5.16 M allocations: 344.683 MiB, 4.07% gc time, 99.20% compilation time)\n",
      "                        Boxplot of df.shopt            \n",
      "            ┌                                        ┐ \n",
      "                                    ╷  ┌┐   ╷          \n",
      "    s model                         ├──┤├───┤          \n",
      "                                    ╵  └┘   ╵          \n",
      "                                  ╷    ┌┐   ╷          \n",
      "     s data                       ├────┤├───┤          \n",
      "                                  ╵    └┘   ╵          \n",
      "                          ╷    ┌─┬─┐    ╷              \n",
      "   g1 model               ├────┤ │ ├────┤              \n",
      "                          ╵    └─┴─┘    ╵              \n",
      "                          ╷    ┌─┬─┐     ╷             \n",
      "    g1 data               ├────┤ │ ├─────┤             \n",
      "                          ╵    └─┴─┘     ╵             \n",
      "                     ╷   ┌┬─┐    ╷                     \n",
      "   g2 model          ├───┤│ ├────┤                     \n",
      "                     ╵   └┴─┘    ╵                     \n",
      "                     ╷   ┌─┬─┐   ╷                     \n",
      "    g2 data          ├───┤ │ ├───┤                     \n",
      "                     ╵   └─┴─┘   ╵                     \n",
      "            └                                        ┘ \n",
      "             -1                 0                   1  \n",
      "--------------------------------\n",
      "| Saving Data to summary.shopt |\n",
      "--------------------------------\n",
      "Creating /home/eddieberman/research/mcclearygroup/shopt/outdir/2024_01_16_13_40_14 directory...\n",
      "------------\n",
      "| Done! =] |\n",
      "------------\n",
      "━ Total Time: 85.984 seconds\n",
      "For more on ShOpt.jl, see https://github.com/EdwardBerman/shopt\n",
      "/home/eddieberman/research/mcclearygroup/cweb_psf/sim_mosaics_working/mosaic_nircam_f115w_COSMOS-Web_i2d_starcat.fits\n",
      "0.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Process(`\u001b[4mjulia\u001b[24m \u001b[4mshopt.jl\u001b[24m \u001b[4m/home/eddieberman/research/mcclearygroup/shopt/configdir\u001b[24m \u001b[4m/home/eddieberman/research/mcclearygroup/shopt/outdir\u001b[24m \u001b[4m/home/eddieberman/research/mcclearygroup/cweb_psf/sim_mosaics_working/mosaic_nircam_f115w_COSMOS-Web_i2d_starcat.fits\u001b[24m`, ProcessExited(0))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run(`julia shopt.jl /home/eddieberman/research/mcclearygroup/shopt/configdir /home/eddieberman/research/mcclearygroup/shopt/outdir /home/eddieberman/research/mcclearygroup/cweb_psf/sim_mosaics_working/mosaic_nircam_f115w_COSMOS-Web_i2d_starcat.fits`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b31d965-ce21-41bf-9d7a-92645d69a33f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100×100 Matrix{Float64}:\n",
       "  8.65678e-5   0.000116492   0.000139557  …   6.07275e-5    3.07603e-5\n",
       "  6.77583e-5   7.20957e-5    0.000124505      3.13321e-5   -8.4727e-6\n",
       "  6.21761e-5   8.31482e-5    0.000121078     -4.08754e-7   -4.15087e-6\n",
       "  5.36265e-5   0.000104021   0.000124383      5.28367e-6    1.30488e-6\n",
       "  2.14105e-5   8.59398e-5    0.000121509      1.00713e-5   -6.24967e-6\n",
       "  6.95793e-6   4.7339e-5     7.68129e-5   …   1.06142e-5    1.64964e-5\n",
       "  5.65962e-5   4.91804e-5    5.39404e-5      -4.50743e-5    1.76276e-5\n",
       "  7.1852e-5    5.75281e-5    6.57147e-5      -7.40092e-5   -1.86524e-5\n",
       "  8.71023e-5   9.24353e-5    6.48496e-5      -4.39792e-5   -1.76808e-5\n",
       "  7.60148e-5   6.27935e-5    2.27562e-5       3.07003e-5    1.89953e-5\n",
       "  1.52057e-5   1.54738e-5    4.98748e-6   …   9.26582e-5    3.66934e-5\n",
       "  3.80829e-5   4.05183e-5    2.07883e-5       5.95364e-5    1.99111e-5\n",
       "  4.85802e-5   3.65386e-5    2.43723e-6      -9.45511e-6    1.27176e-5\n",
       "  ⋮                                       ⋱                \n",
       " -1.54966e-5  -2.25591e-5    1.14994e-5       7.30291e-5    8.96356e-5\n",
       "  8.33811e-6  -2.37635e-5   -2.17613e-5       2.76597e-5    5.04212e-5\n",
       "  9.81562e-6  -8.16674e-6   -2.085e-5     …   1.78986e-5    1.57756e-6\n",
       " -1.85739e-5  -2.95931e-5   -3.61554e-5       3.19662e-5    1.92057e-5\n",
       "  1.13505e-5  -2.75021e-5   -8.48169e-5       0.000107697   0.000120346\n",
       "  3.45716e-5   1.00719e-6   -5.05417e-5       0.000124517   0.000131568\n",
       "  3.61876e-6   3.37529e-6    2.92839e-5       7.01332e-5    5.80126e-5\n",
       "  4.61037e-6   1.3778e-5     5.73453e-5   …   5.17035e-5    6.12e-5\n",
       "  4.14246e-5   2.46223e-5    2.632e-5         2.81588e-6    4.15466e-5\n",
       "  3.44414e-5   1.96172e-5    2.9129e-6        2.5397e-6     2.52892e-5\n",
       "  1.68117e-5   1.17759e-5    3.63936e-6       1.58699e-5    3.12153e-5\n",
       "  9.41824e-6  -1.56429e-5   -7.3113e-6        1.89985e-5    5.17581e-5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run(`ls /home/eddieberman/research/mcclearygroup/shopt/outdir`)\n",
    "include(\"reader.jl\")\n",
    "polyMatrix, deg, s_matrix, g1_matrix, g2_matrix = read_shopt(\"/home/eddieberman/research/mcclearygroup/shopt/outdir/summary.shopt\")\n",
    "PSF = p(150,2.3,polyMatrix, deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b44e7c1-4b90-47f6-bdec-134e52c39c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100×100 Matrix{Float64}:\n",
       "  2.70837e-5   3.35442e-5   3.79408e-5  …   2.0609e-5    1.16476e-5\n",
       "  2.24684e-5   2.3577e-5    3.51226e-5      1.18371e-5  -3.53204e-6\n",
       "  2.09987e-5   2.62803e-5   3.44546e-5     -1.77158e-7  -1.76629e-6\n",
       "  1.86466e-5   3.09676e-5   3.50991e-5      2.2361e-6    5.63035e-7\n",
       "  8.42563e-6   2.69372e-5   3.45392e-5      4.1674e-6   -2.63276e-6\n",
       "  2.9213e-6    1.68318e-5   2.47514e-5  …   4.38107e-6   6.63124e-6\n",
       "  1.94781e-5   1.73712e-5   1.87353e-5     -1.6159e-5    7.05091e-6\n",
       "  2.35155e-5   1.97358e-5   2.19361e-5     -2.40572e-5  -7.42764e-6\n",
       "  2.72079e-5   2.84285e-5   2.17088e-5     -1.583e-5    -7.07057e-6\n",
       "  2.45549e-5   2.11637e-5   8.90434e-6      1.16277e-5   7.55296e-6\n",
       "  6.14741e-6   6.24833e-6   2.11375e-6  …   2.84787e-5   1.35747e-5\n",
       "  1.4014e-5    1.47733e-5   8.20249e-6      2.0286e-5    7.88593e-6\n",
       "  1.71961e-5   1.35255e-5   1.04578e-6     -3.9236e-6    5.19918e-6\n",
       "  ⋮                                     ⋱               \n",
       " -6.25691e-6  -8.83454e-6   4.72727e-6      2.38119e-5   2.7792e-5\n",
       "  3.47813e-6  -9.25927e-6  -8.55091e-6      1.06054e-5   1.77309e-5\n",
       "  4.06641e-6  -3.40937e-6  -8.22468e-6  …   7.15085e-6   6.79778e-7\n",
       " -7.3989e-6   -1.12582e-5  -1.34035e-5      1.20463e-5   7.62971e-6\n",
       "  4.66923e-6  -1.05517e-5  -2.66742e-5      3.1743e-5    3.43106e-5\n",
       "  1.28953e-5   4.35228e-7  -1.77657e-5      3.5125e-5    3.64679e-5\n",
       "  1.54384e-6   1.44168e-6   1.11544e-5      2.30789e-5   1.98692e-5\n",
       "  1.95747e-6   5.60584e-6   1.96854e-5  …   1.80996e-5   2.07365e-5\n",
       "  1.50525e-5   9.55957e-6   1.01472e-5      1.20602e-6   1.50899e-5\n",
       "  1.28533e-5   7.77936e-6   1.24698e-6      1.08921e-6   9.79137e-6\n",
       "  6.74862e-6   4.83481e-6   1.55247e-6      6.39705e-6   1.17984e-5\n",
       "  3.90897e-6  -6.31191e-6  -3.06455e-6      7.55415e-6   1.81152e-5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "function symlog(x, linthresh)\n",
    "    sign_x = sign(x)\n",
    "    abs_x = abs(x)\n",
    "    scaled = linthresh * log10(abs_x / linthresh + 1)\n",
    "    return sign_x * scaled\n",
    "end\n",
    "\n",
    "log_data = symlog.(PSF, 0.0001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "855ab322-e4bc-49da-994c-cc883e3d0df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You Can Make This Nicer with Log Scaling and Image Processing Techniques\n",
    "heatmap(log_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "577d0a3f-1cde-4c2d-bc99-72be3e0c8ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12374006958721123"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Insert you're own radial function to this function and it will evaluate it at s(u,v), g1(u,v), g2(u,v)\n",
    "#analytic_profile = analytic_profile(u,v, s_matrix, g1_matrix, g2_matrix, radial_function)\n",
    "#Example\n",
    "u = 150\n",
    "v = 2.3\n",
    "\n",
    "s = s_matrix[1]*u^3 + s_matrix[2]*v^3 + s_matrix[3]*u^2*v + s_matrix[4]*v^2*u + s_matrix[5]*u^2 + s_matrix[6]*v^2 + s_matrix[7]*u*v + s_matrix[8]*u + s_matrix[9]*v + s_matrix[10]\n",
    "g1 = g1_matrix[1]*u^3 + g1_matrix[2]*v^3 + g1_matrix[3]*u^2*v + g1_matrix[4]*v^2*u + g1_matrix[5]*u^2 + g1_matrix[6]*v^2 + g1_matrix[7]*u*v + g1_matrix[8]*u + g1_matrix[9]*v + g1_matrix[10]\n",
    "g2 = g2_matrix[1]*u^3 + g2_matrix[2]*v^3 + g2_matrix[3]*u^2*v + g2_matrix[4]*v^2*u + g2_matrix[5]*u^2 + g2_matrix[6]*v^2 + g2_matrix[7]*u*v + g2_matrix[8]*u + g2_matrix[9]*v + g2_matrix[10]\n",
    "\n",
    "\n"
   ]
  }
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