TIFF_to_CSV.ipynb

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   "source": [
    "### 1. Set up environment"
   ]
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   "execution_count": 1,
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   "source": [
    "#optional; these are things I had to do on my laptop\n",
    "#%config IPCompleter.use_jedi = False #something I needed to do to get tab completion working\n",
    "#!rm /home/adam/cloud/gdrive/fire_project/local_data/fromGEE/*.csv #delete output files from previous run"
   ]
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   "cell_type": "code",
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   "source": [
    "import rasterio as rio\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "import re\n",
    "import multiprocessing as mp\n",
    "import time\n",
    "from multiprocess import Pool"
   ]
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   "cell_type": "markdown",
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   "source": [
    "**Set path to TIFF files that you want to convert.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/adam/cloud/gdrive/fire_project/local_data/fromGEE/'"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "['/home/adam/cloud/gdrive/fire_project/local_data/fromGEE/focalArea11.tif', '/home/adam/cloud/gdrive/fire_project/local_data/fromGEE/focalArea20_rr.tif']\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(path + \"*.tif\")\n",
    "#glob.glob()\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Define function to convert a tif to a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "#Define function to convert a tif to CSV\n",
    "def tifToCsv(file):\n",
    "    src= rio.open(file) #open raster\n",
    "    bandNames = src.descriptions #get tuple of band names\n",
    "    array = src.read() # convert raster to array\n",
    "    df = pd.DataFrame() #create empty df\n",
    "    outputName = re.search(pattern = '^(.+?).tif', #get output file name from input file\n",
    "                           string= os.path.basename(file)).group(1)\n",
    "    \n",
    "    #fill df with data by band\n",
    "    for i in range(len(bandNames)):\n",
    "        df[bandNames[i]] = array[i].ravel()\n",
    "    df = df.dropna(thresh=3)\n",
    "    df.index.name='pixelID'\n",
    "    df.to_csv(file +'.csv') #export as a CSV\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Map function over list of files in folder\n",
    "Nick, this is where I think your more advanced methods would work better..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<multiprocess.pool.Pool object at 0x7f24ab92fc50>\n"
     ]
    }
   ],
   "source": [
    "print(Pool(mp.cpu_count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1675913333892822\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "p = Pool(mp.cpu_count()) #initialize the pool function? \n",
    "list(p.map(tifToCsv, files)) #map function over list of files\n",
    "p.close()\n",
    "\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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