Jupiter Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7952dbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine.data import economics\n",
    "from plotnine import ggplot, aes, geom_histogram, scale_y_log10\n",
    "from matplotlib.pyplot import imshow\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e56fff94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3666a110",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = '/Users/egomez/Downloads/lcogtdata-20211007-1/tfn0m410-kb98-20211002-0093-e91.fits.fz'\n",
    "hdul = fits.open(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "229936ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "fldata = hdul[1].data.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51e4a1f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7fbe46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08888407\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8790550379210)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.median(fldata))\n",
    "data = fldata[fldata>100]\n",
    "df = DataFrame(data={'vals':data})\n",
    "(\n",
    "    ggplot(df, aes(x='vals'))\n",
    "    + geom_histogram(bins=1000)\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbc5a319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.21025"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = np.histogram(data, bins=1000)\n",
    "hist[1][np.max(hist[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af6060ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "mu, std = norm.fit(hist[0])\n",
    "x = np.linspace(hist[1],100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad35db22",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "If using all scalar values, you must pass an index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/1k/28975nv93jn02m6_hvhs2qg00000gp/T/ipykernel_5062/2528467805.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m (\n\u001b[1;32m      4\u001b[0m     \u001b[0mggplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m+\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/imaging/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    612\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m             \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/imaging/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m    462\u001b[0m         \u001b[0;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m     return arrays_to_mgr(\n\u001b[0m\u001b[1;32m    465\u001b[0m         \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m     )\n",
      "\u001b[0;32m~/anaconda3/envs/imaging/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/imaging/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"If using all scalar values, you must pass an index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhave_series\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
     ]
    }
   ],
   "source": [
    "q = norm.pdf(x, mu, std)\n",
    "df1 = DataFrame(data={'x':x,'y':q})\n",
    "(\n",
    "    ggplot(df1)\n",
    "    + aes(x=\"x\", y=\"y\")\n",
    "    + geom_line()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "116ffe24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9286e663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7feb6d4557c0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = Image.fromarray(data).convert('L')\n",
    "pdata = hdul[1].data\n",
    "pdata[pdata<100]=0.\n",
    "pdata = pdata -100.\n",
    "imshow(pdata)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}