DLS.ipynb

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   "source": [
    "# DLS"
   ]
  },
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   "source": [
    "# Import libraries\n",
    "import glob\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import micasense.dls as dls\n",
    "import micasense.imageset as imageset\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input Directory\n",
    "inDir = r'C:\\Users\\crist\\OneDrive - Pontificia Universidad Javeriana Cali\\MDPI'           \n",
    "os.chdir(inDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Files\n",
    "rawFiles = glob.glob('data\\**\\**\\**\\**SET')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
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     "text": [
      "Processing dataset:2017B-1\n",
      "Processing dataset:2017B-2\n",
      "Processing dataset:2017B-3\n",
      "Processing dataset:2017B-4\n",
      "Processing dataset:2018B-1\n",
      "Processing dataset:2019A-1\n",
      "Processing dataset:2019A-2\n",
      "Processing dataset:2019A-3\n",
      "Processing dataset:2019A-4\n",
      "Processing dataset:2019B-1\n",
      "Processing dataset:2019B-2\n",
      "Processing dataset:2019B-3\n"
     ]
    }
   ],
   "source": [
    "#Do the job!\n",
    "DLS_stats = pd.DataFrame()\n",
    "\n",
    "for rawFile in rawFiles:\n",
    "    dataset = rawFile.split(\"\\\\\")[3]\n",
    "    season = rawFile.split(\"\\\\\")[1]\n",
    "    print('Processing dataset:' + dataset)\n",
    "    \n",
    "    #Import Capture data into Pandas DataFrame\n",
    "    imgset = imageset.ImageSet.from_directory(rawFile)\n",
    "    data, columns = imgset.as_nested_lists()\n",
    "    dlsData = pd.DataFrame.from_records(data, index='timestamp', columns=columns)\n",
    "    \n",
    "    #Separating ground and flight images\n",
    "    cutoff_altitude = 997\n",
    "    dlsData = dlsData[dlsData.altitude>cutoff_altitude]\n",
    "    \n",
    "    #location\n",
    "    location = dlsData[['latitude', 'longitude', 'altitude']]\n",
    "    tuples = [tuple(x) for x in location.values]\n",
    "    dlsData['location']=tuples\n",
    "    \n",
    "    #pose\n",
    "    dls_pose = dlsData[['dls-yaw', 'dls-pitch', 'dls-roll']]\n",
    "    tuples = [tuple(x) for x in dls_pose.values]\n",
    "    dlsData['dls_pose']=tuples\n",
    "    \n",
    "    #Computing Solar orientation\n",
    "    compute_sun_angle = dlsData[['location', 'dls_pose']]\n",
    "    compute_sun_angle['utc_time'] = dlsData.index\n",
    "    dls_orientation_vector = np.array([0,0,-1])\n",
    "    \n",
    "    dlsData['solar_zenith']=[np.degrees(dls.compute_sun_angle(x[0],\n",
    "                          (0,0,0),\n",
    "                          x[2],\n",
    "                          dls_orientation_vector)[2]) for x in compute_sun_angle.values]\n",
    "    \n",
    "    dlsData['solar_azimuth']=[np.degrees(dls.compute_sun_angle(x[0],\n",
    "                      (0,0,0),\n",
    "                      x[2],\n",
    "                      dls_orientation_vector)[4]) for x in compute_sun_angle.values]\n",
    "    \n",
    "    #Computing Irradiance\n",
    "    irr_col = [col for col in dlsData if col.startswith((\"irr\"))]  \n",
    "    dlsData['irr-avg'] = dlsData[irr_col].mean(axis = 1, skipna = True) \n",
    "\n",
    "    #Results\n",
    "    columns = ['irr-avg', 'solar_zenith', 'solar_azimuth']\n",
    "    local_stats = dlsData[columns].mean()\n",
    "    local_stats['SEASON'] = season\n",
    "    local_stats['DATASET'] = dataset\n",
    "    DLS_stats = DLS_stats.append(local_stats, ignore_index=True)\n",
    "\n",
    "DLS_stats = DLS_stats.dropna() \n",
    "\n",
    "#Save the results\n",
    "DLS_stats.to_csv('DLS.csv', index = True)"
   ]
  }
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