Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:/Users/willi/Desktop/Hubble Data/candidates/1/hst_06773_03_wfpc2_f450w_pc_sci.fits'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"C:/Users/willi/Desktop/Hubble Data/candidates/1/hst_06773_03_wfpc2_f450w_pc_sci.fits\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(1000, 1000)\n"
     ]
    }
   ],
   "source": [
    "image_data = fits.getdata(\"C:/Users/willi/Desktop/Hubble Data/candidates/1/hst_06773_03_wfpc2_f450w_pc_sci.fits\")\n",
    "print(type(image_data))\n",
    "print(image_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1b81b002460>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_data, cmap='gray')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: -3.9064257\n",
      "Max: 340.00992\n",
      "Mean: 0.01367251\n",
      "Stdev: 1.050172\n"
     ]
    }
   ],
   "source": [
    "print('Min:', np.min(image_data))\n",
    "print('Max:', np.max(image_data))\n",
    "print('Mean:', np.mean(image_data))\n",
    "print('Stdev:', np.std(image_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(image_data.flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NBINS = 1000\n",
    "histogram = plt.hist(image_data.flatten(), NBINS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-dd050853ba18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'gray'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLogNorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# I chose the tick marks based on the histogram above\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcbar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5.e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.e4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2.e4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'5,000'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'10,000'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'20,000'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.imshow(image_data, cmap='gray', norm=LogNorm())\n",
    "\n",
    "# I chose the tick marks based on the histogram above\n",
    "cbar = plt.colorbar(ticks=[5.e3,1.e4,2.e4])\n",
    "cbar.ax.set_yticklabels(['5,000','10,000','20,000'])"
   ]
  }
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