{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58\n",
      "['\\n/////  ', '\\n// Band Arithmetic  ', '\\n/////  ', '\\n// Calculate NDVI using Sentinel 2  ', '\\n// Import and filter imagery by location and date.  ', '\\n// Display the image as a false color composite.  ', '\\n// Extract the near infrared and red bands.  ', '\\n// Calculate the numerator and the denominator using subtraction and addition respectively.  ', '\\n// Now calculate NDVI.  ', '\\n// Add the layer to our map with a palette.  ', '\\n// Now use the built-in normalizedDifference function to achieve the same outcome.  ', '\\n// Use normalizedDifference to calculate NDWI  ', '\\n// Create an NDVI image using Sentinel 2.  ', '\\n// And map it.  ', '\\n// Implement a threshold.  ', '\\n// Map the threshold.  ', '\\n// Implement .where.  ', '\\n// Create a starting image with all values = 1.  ', '\\n// Make all NDVI values less than -0.1 equal 0.  ', '\\n// Make all NDVI values greater than 0.5 equal 2.  ', '\\n// Map our layer that has been divided into three classes.  ', '\\n// Implement masking.  ', \"\\n// View the seaVeg layer's current mask.  \", '\\n// Create a binary mask of non-forest.  ', '\\n// Update the seaVeg mask with the non-forest mask.  ', '\\n// Map the updated Veg layer  ', '\\n// Map the updated mask  ', '\\n// Implement remapping.  ', '\\n// Remap the values from the seaWhere layer.  ', '\\n// Create an Earth Engine Point object over Milan.  ', '\\n// Filter the Landsat 8 collection and select the least cloudy image.  ', '\\n// Center the map on that image.  ', '\\n// Add Landsat image to the map.  ', '\\n// Combine training feature collections.  ', '\\n// Define prediction bands.  ', '\\n// Sample training points.  ', '\\n//////////////// CART Classifier ///////////////////  ', '\\n// Train a CART Classifier.  ', '\\n// Classify the Landsat image.  ', '\\n// Define classification image visualization parameters.  ', '\\n// Add the classified image to the map.  ', '\\n/////////////// Random Forest Classifier /////////////////////  ', '\\n// Train RF classifier.  ', '\\n// Classify Landsat image.  ', '\\n// Add classified image to the map.  ', '\\n//////////////// Unsupervised classification ////////////////  ', '\\n// Make the training dataset.  ', '\\n// Instantiate the clusterer and train it.  ', '\\n// Cluster the input using the trained clusterer.  ', '\\n// Display the clusters with random colors.  ', '\\n// Import the reference dataset.  ', '\\n// Define the prediction bands.  ', '\\n// Split the dataset into training and testing sets.  ', '\\n// Train the Random Forest Classifier with the trainingSet.  ', \"\\n// Now, to test the classification (verify model's accuracy),  \", '\\n// we classify the testingSet and get a confusion matrix.  ', '\\n// Print the results.  ', '\\n// Hyperparameter tuning.  ']\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def captions(f):\n",
    "    regex=r'(!\\[\\].+\\))\\n(\\nFig\\..*)'\n",
    "\n",
    "    figures=re.findall(regex, f)\n",
    "    print(len(figures))\n",
    "    sub=[]\n",
    "    for fig in figures:\n",
    "        formatted=fig[0].replace('[]','[{}]'.format(fig[1].replace('\\n','')))\n",
    "        f=f.replace(fig[0],formatted)\n",
    "        f=f.replace(fig[1],\"\")\n",
    "    return f\n",
    "\n",
    "def codeblocks(f):\n",
    "    \n",
    "    regex=r'(\\n//.*)'\n",
    "    code=re.findall(regex, f)\n",
    "    print(len(code))\n",
    "    print(code)\n",
    "    return f\n",
    "\n",
    "for i in [2]:#,4,5,6]:\n",
    "    f = open(\"F{}.qmd\".format(i), \"r\").read().replace('\\xa0', ' ')\n",
    "    #f = captions(f)\n",
    "    f = codeblocks(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'urllib2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6q/jt4x0r8n1rs0kbrrqrbj61fr0000gn/T/ipykernel_60302/1639298892.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0murllib2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'F2.qmd'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'text/javascript'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\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     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"It has js.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'urllib2' is not defined"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "if urllib2.open('F2.qmd').read().find('text/javascript') == 0:\n",
    "    print(\"It has js.\")\n"
   ]
  }
 ],
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