test_pkdgrav_sims.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e0d149-97f8-4685-82ef-b578f29dd0c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cd63335c-c593-45b3-85ca-5d9d2ef78d4e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/50 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/50 [00:12<10:16, 12.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 2/50 [00:24<09:50, 12.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:36<09:37, 12.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 4/50 [00:48<09:19, 12.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 5/50 [01:00<09:08, 12.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 6/50 [01:13<08:59, 12.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 7/50 [01:25<08:46, 12.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 8/50 [01:37<08:34, 12.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 9/50 [01:49<08:19, 12.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 10/50 [02:00<07:58, 11.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 11/50 [02:12<07:45, 11.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 12/50 [02:24<07:29, 11.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 13/50 [02:37<07:26, 12.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|██▊       | 14/50 [02:49<07:18, 12.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 15/50 [03:02<07:11, 12.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███▏      | 16/50 [03:14<07:01, 12.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|███▍      | 17/50 [03:26<06:46, 12.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▌      | 18/50 [03:39<06:32, 12.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 19/50 [03:51<06:19, 12.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 20/50 [04:03<06:03, 12.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 21/50 [04:15<05:50, 12.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████▍     | 22/50 [04:27<05:38, 12.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|████▌     | 23/50 [04:39<05:26, 12.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|████▊     | 24/50 [04:51<05:14, 12.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 25/50 [05:03<05:05, 12.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|█████▏    | 26/50 [05:16<04:55, 12.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████▍    | 27/50 [05:28<04:44, 12.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|█████▌    | 28/50 [05:41<04:32, 12.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 29/50 [05:53<04:21, 12.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 30/50 [06:05<04:04, 12.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 31/50 [06:17<03:50, 12.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|██████▍   | 32/50 [06:29<03:36, 12.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 33/50 [06:41<03:26, 12.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 34/50 [06:53<03:10, 11.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 35/50 [07:05<02:58, 11.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 36/50 [07:17<02:48, 12.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|███████▍  | 37/50 [07:29<02:36, 12.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|███████▌  | 38/50 [07:41<02:24, 12.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|███████▊  | 39/50 [07:53<02:12, 12.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 40/50 [08:05<02:00, 12.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 41/50 [08:17<01:48, 12.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████▍ | 42/50 [08:29<01:36, 12.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 43/50 [08:42<01:25, 12.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 44/50 [08:54<01:13, 12.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 45/50 [09:06<01:00, 12.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 46/50 [09:18<00:48, 12.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 47/50 [09:30<00:36, 12.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 48/50 [09:43<00:24, 12.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 49/50 [09:57<00:12, 12.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [10:09<00:00, 12.18s/it]\n"
     ]
    }
   ],
   "source": [
    "# # df0 = np.load('/global/cscratch1/sd/dominikz/DES_Y3_PKDGRAV_SIMS/fidu_run_1/kappa_maps/kappa_map_cosmo_Om=0.26_num=49_s8=0.84_bin0.npy')\n",
    "# from tqdm import tqdm\n",
    "# s1 = 30.*(1/60.)*(np.pi/180.)\n",
    "# s2 = 60*(1/60.)*(np.pi/180.)\n",
    "# s3 = 180*(1/60.)*(np.pi/180.)\n",
    "# nside = 256\n",
    "\n",
    "# yvals_all = []\n",
    "\n",
    "# df1_s1_all = []\n",
    "# df1_s2_all = []\n",
    "# df1_s3_all = []\n",
    "\n",
    "# df2_s1_all = []\n",
    "# df2_s2_all = []\n",
    "# df2_s3_all = []\n",
    "\n",
    "# df3_s1_all = []\n",
    "# df3_s2_all = []\n",
    "# df3_s3_all = []\n",
    "\n",
    "\n",
    "# for ji in tqdm(range(50)):\n",
    "#     df1 = np.load('/global/cscratch1/sd/dominikz/DES_Y3_PKDGRAV_SIMS/fidu_run_1/kappa_maps/kappa_map_cosmo_Om=0.26_num=' + str(ji) + '_s8=0.84_bin1.npy')\n",
    "#     df2 = np.load('/global/cscratch1/sd/dominikz/DES_Y3_PKDGRAV_SIMS/fidu_run_1/kappa_maps/kappa_map_cosmo_Om=0.26_num=' + str(ji) + '_s8=0.84_bin2.npy')\n",
    "#     df3 = np.load('/global/cscratch1/sd/dominikz/DES_Y3_PKDGRAV_SIMS/fidu_run_1/kappa_maps/kappa_map_cosmo_Om=0.26_num=' + str(ji) + '_s8=0.84_bin3.npy')\n",
    "\n",
    "\n",
    "#     df1_s1_all.append(hp.smoothing(hp.ud_grade(df1,nside), sigma=s1))\n",
    "#     df1_s2_all.append(hp.smoothing(hp.ud_grade(df1,nside), sigma=s2))\n",
    "#     df1_s3_all.append(hp.smoothing(hp.ud_grade(df1,nside), sigma=s3))\n",
    "\n",
    "#     df2_s1_all.append(hp.smoothing(hp.ud_grade(df2,nside), sigma=s1))\n",
    "#     df2_s2_all.append(hp.smoothing(hp.ud_grade(df2,nside), sigma=s2))\n",
    "#     df2_s3_all.append(hp.smoothing(hp.ud_grade(df2,nside), sigma=s3))\n",
    "\n",
    "#     df3_s1_all.append(hp.smoothing(hp.ud_grade(df3,nside), sigma=s1))\n",
    "#     df3_s2_all.append(hp.smoothing(hp.ud_grade(df3,nside), sigma=s2))\n",
    "#     df3_s3_all.append(hp.smoothing(hp.ud_grade(df3,nside), sigma=s3))\n",
    "    \n",
    "#     yvals = np.array([np.mean(df1_s1*df2_s2*df3_s3), np.mean(df1_s1*df2_s3*df3_s2), \n",
    "#                   np.mean(df1_s2*df2_s1*df3_s3), np.mean(df1_s2*df2_s3*df3_s1),\n",
    "#                   np.mean(df1_s3*df2_s2*df3_s1), np.mean(df1_s3*df2_s1*df3_s2)])\n",
    "    \n",
    "#     yvals_all.append(yvals)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e37c975-91af-41b9-baf9-3601a4ff5b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # saved = {'df1_s1':df1_s1_all, ''}\n",
    "# saved = pk.load(open('/global/cfs/cdirs/des/shivamp/cosmosis2p0/cosmosis-standard-library/gen_moments/kappa_1_2_3_smoothed.pk','rb'))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "636e7a24-901a-484f-a3c9-bab438a246a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saved = {'df1_s1':df1_s1_all, ''}\n",
    "saved = pk.load(open('/global/cfs/cdirs/des/shivamp/cosmosis2p0/cosmosis-standard-library/gen_moments/kappa_1_2_3_smoothed.pk','rb'))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93f02cf3-65f5-463d-876b-3bad66b26b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['df1_s1', 'df1_s2', 'df1_s3', 'df2_s1', 'df2_s2', 'df2_s3', 'df3_s1', 'df3_s2', 'df3_s3', 's1', 's2', 's3'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saved.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab18183a-b9b2-424b-a396-aaa3f4b7b41d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_s1_all = saved['df1_s1']\n",
    "df1_s2_all = saved['df1_s2']\n",
    "df1_s3_all = saved['df1_s3']\n",
    "\n",
    "df2_s1_all = saved['df2_s1']\n",
    "df2_s2_all = saved['df2_s2']\n",
    "df2_s3_all = saved['df2_s3']\n",
    "\n",
    "df3_s1_all = saved['df3_s1']\n",
    "df3_s2_all = saved['df3_s2']\n",
    "df3_s3_all = saved['df3_s3']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2d553d7-4aed-4d53-8aed-8cf8932b320a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle \\\\kappa^{(1)}\\\\kappa^{(2)}\\\\kappa^{(3)} \\\\rangle$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# xlabels = ['123', '132', '213', '231', '321', '312']\n",
    "# # yvals = np.array([np.mean(df1_s1*df2_s2*df3_s3), np.mean(df1_s1*df2_s3*df3_s2), \n",
    "# #                   np.mean(df1_s2*df2_s1*df3_s3), np.mean(df1_s2*df2_s3*df3_s1),\n",
    "# #                   np.mean(df1_s3*df2_s2*df3_s1), np.mean(df1_s3*df2_s1*df3_s2)])\n",
    "\n",
    "pl.figure()\n",
    "for ji in range(50):\n",
    "    df1_s1  = df1_s1_all[ji]\n",
    "    df1_s2  = df1_s2_all[ji]\n",
    "    df1_s3  = df1_s3_all[ji]\n",
    "    \n",
    "    df2_s1  = df2_s1_all[ji]\n",
    "    df2_s2  = df2_s2_all[ji]\n",
    "    df2_s3  = df2_s3_all[ji] \n",
    "    \n",
    "    df3_s1  = df3_s1_all[ji]\n",
    "    df3_s2  = df3_s2_all[ji]\n",
    "    df3_s3  = df3_s3_all[ji]     \n",
    "    \n",
    "    yvals = np.array([np.mean(df1_s1*df2_s2*df3_s3), np.mean(df1_s1*df2_s3*df3_s2), \n",
    "                  np.mean(df1_s2*df2_s1*df3_s3), np.mean(df1_s2*df2_s3*df3_s1),\n",
    "                  np.mean(df1_s3*df2_s2*df3_s1), np.mean(df1_s3*df2_s1*df3_s2)])\n",
    "    \n",
    "    pl.plot(yvals, color='k', alpha=0.2)\n",
    "pl.xticks([0,1,2,3,4,5], xlabels)\n",
    "pl.xlabel('Smoothing scale combinations')\n",
    "pl.ylabel(r'$\\langle \\kappa^{(1)}\\kappa^{(2)}\\kappa^{(3)} \\rangle$')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31d0e6fd-fd6c-438a-b834-e5d0956963d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle \\\\kappa^{(1)}\\\\kappa^{(1)}\\\\kappa^{(1)} \\\\rangle$')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabels = ['123', '132', '213', '231', '321', '312']\n",
    "# yvals = np.array([np.mean(df1_s1*df2_s2*df3_s3), np.mean(df1_s1*df2_s3*df3_s2), \n",
    "#                   np.mean(df1_s2*df2_s1*df3_s3), np.mean(df1_s2*df2_s3*df3_s1),\n",
    "#                   np.mean(df1_s3*df2_s2*df3_s1), np.mean(df1_s3*df2_s1*df3_s2)])\n",
    "\n",
    "pl.figure()\n",
    "for ji in range(10):\n",
    "    df1_s1  = df1_s1_all[ji]\n",
    "    df1_s2  = df1_s2_all[ji]\n",
    "    df1_s3  = df1_s3_all[ji]\n",
    "    \n",
    "    df2_s1  = df2_s1_all[ji]\n",
    "    df2_s2  = df2_s2_all[ji]\n",
    "    df2_s3  = df2_s3_all[ji] \n",
    "    \n",
    "    df3_s1  = df3_s1_all[ji]\n",
    "    df3_s2  = df3_s2_all[ji]\n",
    "    df3_s3  = df3_s3_all[ji]    \n",
    "    \n",
    "    yvals = np.array([np.mean(df1_s1*df1_s2*df1_s3), np.mean(df1_s1*df1_s3*df1_s2), \n",
    "                  np.mean(df2_s2*df2_s1*df2_s3), np.mean(df2_s2*df2_s3*df2_s1),\n",
    "                  np.mean(df3_s3*df3_s2*df3_s1), np.mean(df3_s3*df3_s1*df3_s2)])\n",
    "    pl.plot(yvals)\n",
    "pl.xticks([0,1,2,3,4,5], xlabels)\n",
    "pl.xlabel('Smoothing scale combinations')\n",
    "pl.ylabel(r'$\\langle \\kappa^{(1)}\\kappa^{(1)}\\kappa^{(1)} \\rangle$')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e2f75b98-9900-414c-bd3f-463c95a71496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure()\n",
    "_ = pl.hist(df1, range=(-0.05, 0.06), bins=100, density=True)\n",
    "_ = pl.hist(df2, range=(-0.05, 0.06), bins=100, density=True)\n",
    "_ = pl.hist(df3, range=(-0.05, 0.06), bins=100, density=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "813eb065-a3b4-4eb2-aded-4fd1a170b45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using provided beam window function\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    }
   ],
   "source": [
    "# import scipy as sp\n",
    "# s1 = 8.2*(1/60.)*(np.pi/180.)\n",
    "# # s2 = 20*(1/60.)*(np.pi/180.)\n",
    "# # # s3 = 20*(1/60.)*(np.pi/180.)\n",
    "\n",
    "# def top_hat(b, radius):\n",
    "#     return np.where(abs(b)<=radius, 1, 0)\n",
    "\n",
    "# nside = hp.npix2nside(len(df2))\n",
    "# b = np.linspace(0,0.5,1000)\n",
    "# bw = top_hat(b, s1) #top_hat function of radius 45°\n",
    "# bw = bw/sp.integrate.simps(bw, b)\n",
    "# beam = hp.sphtfunc.beam2bl(bw, b, nside*3)\n",
    "\n",
    "\n",
    "# #Smooth map\n",
    "# tst_map_smoothed = hp.smoothing(df2, beam_window=beam)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e99956ba-3718-4ae6-b6c1-5d9524b0a4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pl.figure()\n",
    "# # pl.plot(beam)\n",
    "# # pl.plot(b,bw)\n",
    "# # pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "52de5848-a2c9-481b-9966-63f5b93d897b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # s1\n",
    "# # b\n",
    "# # bw\n",
    "# pl.figure()\n",
    "# # _ = pl.hist(df1, range=(-0.05, 0.06), bins=100, density=True)\n",
    "# _ = pl.hist(df2, range=(-0.05, 0.06), bins=100, density=True)\n",
    "# _ = pl.hist(df2_s1, range=(-0.05, 0.06), bins=100, density=True)\n",
    "# _ = pl.hist(tst_map_smoothed, range=(-0.05, 0.06), bins=100, density=True)\n",
    "# # _ = pl.hist(tst_map_smoothed, range=(-0.05, 0.06), bins=100, density=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b1f5ab1a-49cf-4917-a30c-0ce84c6bf657",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (1/1e-7)*(8.2**2)*(8.998738694815995e-10)\n",
    "# (1/1e-7)*(54**2)*(3.19e-11)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5563ccbe-8a2c-4e5d-bf23-a028bf597471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.mean((tst_map_smoothed)**3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8b059c89-da4e-4f75-b107-9a1e9b2ae8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.mean((df2 - np.mean(df2))**3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "745b9881-9403-404c-9618-2422ec3f07e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hp.mollview((np.abs(hp.ud_grade(df2, 32))))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5cf1fe45-af3e-4dc4-8490-19536524be23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hp.mollview((np.abs(hp.ud_grade(tst_map_smoothed,32))))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6dcc189b-1b3f-42fa-9d2f-9c393978f690",
   "metadata": {},
   "outputs": [],
   "source": [
    "# s4 = 120*(1/60.)*(np.pi/180.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "39354230-edc8-42ff-9fae-e73bd22ee3d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = 30.*(1/60.)*(np.pi/180.)\n",
    "s2 = 60*(1/60.)*(np.pi/180.)\n",
    "s3 = 180*(1/60.)*(np.pi/180.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5802e906-0288-4abb-983f-017938b8a708",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 30.000000 arcmin (0.008727 rad) \n",
      "-> fwhm is 70.644601 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 60.000000 arcmin (0.017453 rad) \n",
      "-> fwhm is 141.289203 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n",
      "Sigma is 180.000000 arcmin (0.052360 rad) \n",
      "-> fwhm is 423.867608 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    }
   ],
   "source": [
    "nside = 256\n",
    "df1_s1 = hp.smoothing(hp.ud_grade(df1,nside), sigma=s1)\n",
    "df1_s2 = hp.smoothing(hp.ud_grade(df1,nside), sigma=s2)\n",
    "df1_s3 = hp.smoothing(hp.ud_grade(df1,nside), sigma=s3)\n",
    "\n",
    "df2_s1 = hp.smoothing(hp.ud_grade(df2,nside), sigma=s1)\n",
    "df2_s2 = hp.smoothing(hp.ud_grade(df2,nside), sigma=s2)\n",
    "df2_s3 = hp.smoothing(hp.ud_grade(df2,nside), sigma=s3)\n",
    "\n",
    "df3_s1 = hp.smoothing(hp.ud_grade(df3,nside), sigma=s1)\n",
    "df3_s2 = hp.smoothing(hp.ud_grade(df3,nside), sigma=s2)\n",
    "df3_s3 = hp.smoothing(hp.ud_grade(df3,nside), sigma=s3)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "23719995-00c8-4a02-b100-59e89db72b8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 150.000000 arcmin (0.043633 rad) \n",
      "-> fwhm is 353.223007 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    }
   ],
   "source": [
    "# df3_s3 = hp.smoothing(df3, sigma=s3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4bb85d82-3139-430a-8581-25b3b9b9f5ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle \\\\kappa^3 \\\\rangle$')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabels = ['123', '132', '213', '231', '321', '312']\n",
    "yvals = np.array([np.mean(df1_s1*df2_s2*df3_s3), np.mean(df1_s1*df2_s3*df3_s2), \n",
    "                  np.mean(df1_s2*df2_s1*df3_s3), np.mean(df1_s2*df2_s3*df3_s1),\n",
    "                  np.mean(df1_s3*df2_s2*df3_s1), np.mean(df1_s3*df2_s1*df3_s2)])\n",
    "\n",
    "pl.figure()\n",
    "pl.plot(yvals)\n",
    "pl.xticks([0,1,2,3,4,5], xlabels)\n",
    "pl.xlabel('Smoothing scale combinations')\n",
    "pl.ylabel(r'$\\langle \\kappa^3 \\rangle$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8d12454b-6aae-435a-b25d-b1313f2f3874",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1_s4 = hp.smoothing(df1, sigma=s4)\n",
    "# df2_s4 = hp.smoothing(df2, sigma=s4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55a37979-395d-4643-bdad-fa9e5b10582b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma is 120.000000 arcmin (0.034907 rad) \n",
      "-> fwhm is 282.578405 arcmin\n",
      "Sigma is 0.000000 arcmin (0.000000 rad) \n",
      "-> fwhm is 0.000000 arcmin\n"
     ]
    }
   ],
   "source": [
    "# df3_s4 = hp.smoothing(df3, sigma=s4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d5bbdc0-ff0b-42dc-9514-9d3a58adfd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2419352908430291e-08"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df1_s1*df2_s2*df3_s2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea048828-71eb-48be-9230-464e940d2996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2426344939381886e-08"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2_s1*df1_s2*df3_s2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2381692c-0399-4bb2-9d33-de329694a818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2419352908430291e-08"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df1_s1*df3_s2*df2_s2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c13cc8d4-6ce2-4e38-a3ff-16393d9be98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.811591350284648e-06, 5.811591347036536e-06)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df1_s1*df2_s2), np.mean(df1_s2*df2_s1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94384748-31f2-41c7-b28b-e6742123d16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.3690391983377407e-06, 1.369039198250633e-06)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df1_s1*df3_s4), np.mean(df1_s4*df3_s1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e5a8db-894e-4db6-8a5b-524a8d23bbe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfm = pk.load(open('/global/cfs/cdirs/des/shivamp/cosmosis2p0/cosmosis-standard-library/gen_moments/namaster_stuff.pk','rb'))  \n",
    "dfm = dfm['mask']\n",
    "\n",
    "rot1 = hp.Rotator(rot=(0,105,0))\n",
    "dfm_rot1 = rot1.rotate_map_pixel(dfm)\n",
    "\n",
    "rot2 = hp.Rotator(rot=(0,-105,0))\n",
    "dfm_rot2 = rot2.rotate_map_pixel(dfm)\n",
    "\n",
    "\n",
    "hp.mollview(dfm_rot1+dfm+dfm_rot2)\n",
    "\n"
   ]
  }
 ],
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