TestLABL.ipynb

TestLABL_NEW.ipynb

{
 "cells": [
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import logging\nlogging.root.level = logging.DEBUG\n\nimport numpy as np\nimport pandas as pd\n\n%matplotlib inline\nfrom matplotlib.pyplot import *\n\n#from LABasedLine import LABasedLine\n\nfrom sensors import sensors",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "labl = LABasedLine(sensors['LANDSAT_7'])",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "labl.run('/Users/robin/HOTBAR/FMaskTest/LE72020252002135EDC00/LE72020252002135EDC00_MTL.txt', 'Continental')",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "DEBUG:root:gdalwarp -overwrite -of GTiff -cutline wrs2_descending.shp -csql \"SELECT * FROM wrs2_descending WHERE path=202 and row=25\" -crop_to_cutline GLOBCOVER_L4_200901_200912_V2.3.tif /Users/robin/HOTBAR/FMaskTest/LE72020252002135EDC00/GlobCover_subset.tif\nINFO:root:LUT already exists, no need to re-create\nINFO:root:Loading LUT from /Users/robin/HOTBAR/LUTDirectory/LE72020252002135EDC00_Continental.lut\nINFO:root:Got BOAReflectance LUT from cache\nINFO:root:Removing invalid points in LABL: previously 2169, now 2169\n"
    },
    {
     "execution_count": 9,
     "output_type": "execute_result",
     "data": {
      "text/plain": "(26.688868478666944, -0.39483754220407674)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "(rel_m, rel_c,\n                          hots, aots,\n                          b1_withaots, b3_withaots,\n                          b1_withaots_boap, b3_withaots_boap,\n                          x_on_line, y_on_line,\n                          sample,\n                          cl_m, cl_c) = labl.temp_data",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "scatter(hots, aots, marker='x')",
   "execution_count": 11,
   "outputs": [
    {
     "execution_count": 11,
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1039643d0>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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GRvcEnawQ8JLsRE+BkP0EJRACURkppSqUUq8opf6klLoyTptypdSLSqk/KKXagnguIUFi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\nhBBCAFAgEEIIiUKBQAghBADw/wHtpDdPq2fmZQAAAABJRU5ErkJggg==\n",
      "text/plain": "<matplotlib.figure.Figure at 0x10f61f510>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "from RegressionLines import RegressionLines",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "rl = RegressionLines(sensors['LANDSAT_7'], '/Users/robin/HOTBAR/FMaskTest/LE72020252002135EDC00/LE72020252002135EDC00_MTL.txt')",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "DEBUG:root:gdalwarp -overwrite -of GTiff -cutline wrs2_descending.shp -csql \"SELECT * FROM wrs2_descending WHERE path=202 and row=25\" -crop_to_cutline GLOBCOVER_L4_200901_200912_V2.3.tif /Users/robin/HOTBAR/FMaskTest/LE72020252002135EDC00/GlobCover_subset.tif\n"
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "rl.run_LABL('Continental')",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "INFO:root:LUT not found in LUT Directory - creating and saving\nDEBUG:root:2002-05-15 10:47:41.126467+00:00\nDEBUG:root:lat = 51.288410, lon = -3.463190\nDEBUG:root:[ 388.77596808]\nINFO:root:Successfully created LUT\n"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Moving to line m = 1.598849, c = -0.012865\n{'scale': 1, 'red_band': (-140, 0.615, 0.7025), 'metadata_fname': '/Users/robin/HOTBAR/FMaskTest/LE72020252002135EDC00/LE72020252002135EDC00_MTL.txt', 'self': <get_radiance_with_aot.TOARadiance instance at 0x103ee41b8>, 'blue_band': (-138, 0.435, 0.52), 'lut_directory': '/Users/robin/HOTBAR/LUTDirectory/LANDSAT_7'}\nCreating BOA LUT"
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "DEBUG:root:Timestamp: 2002-05-15 10:47:41.126467+00:00\nDEBUG:root:Latitude 51.288410, Longitude -3.463190\nDEBUG:root:Ozone: 388.775968\nINFO:root:Getting bands for Landsat 7\n"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n0\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n10\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n20\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n30\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n40\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n50\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n60\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n70\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n80\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n90\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n100\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n110\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n120\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n130\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n140\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n150\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n160\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n170\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n180\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n190\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n200\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n210\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n220\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n230\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n240\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n250\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n260\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n270\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n280\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone\n290\n(-138, 0.435, 0.52)\n(-139, 0.5, 0.6225)\n(-140, 0.615, 0.7025)\n(-141, 0.74, 0.9125)\n(-142, 1.51, 1.7875)\nNone\n(-143, 2.015, 2.3775)\nNone"
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "DEBUG:root:LUT creation finished\nINFO:root:Removing invalid points in LABL: previously 4168, now 4168\n"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nSensor(la_filename='LAL7_v10_RemoveBrookhaven.csv', blue_band_Py6S=(-138, 0.435, 0.52), red_band_Py6S=(-140, 0.615, 0.7025), blue_band_number=1, red_band_number=3, name='LANDSAT_7')\nCalculating distance using m = 1.598849, c = -0.012865\n"
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "(hots, aots,\n                          b1_withaots, b3_withaots,\n                          b1_withaots_boap, b3_withaots_boap,\n                          x_on_line, y_on_line) = rl.temp_data",
   "execution_count": 16,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "scatter(hots, aots, marker='x')",
   "execution_count": 17,
   "outputs": [
    {
     "execution_count": 17,
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x10f84df10>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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MpWefLS6jc+cCTz4JFBW5r3/nOxJnYPfV1EhevtypYnbttU7cwXPPifpo4UI5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\nG2TGbA/y+fniCZRo5tEZM9z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      "text/plain": "<matplotlib.figure.Figure at 0x10ec8f950>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
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