finance_solar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Load data from the JSON file\n",
    "with open('7ShabazzWay.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box_data = data['boundingBox']\n",
    "main_box = [\n",
    "    main_box_data['sw']['latitude'],\n",
    "    main_box_data['sw']['longitude'],\n",
    "    main_box_data['ne']['latitude'],\n",
    "    main_box_data['ne']['longitude']\n",
    "]\n",
    "\n",
    "# Extract solar panel data\n",
    "solarPotential_data = data['solarPotential']\n",
    "solar_panels_data = solarPotential_data.get('solarPanels', [])\n",
    "\n",
    "# Print the number of solar panels\n",
    "#print(f\"Number of solar panels: {len(solar_panels_data)}\")\n",
    "\n",
    "# Extract roof segment statistics\n",
    "roofSegmentStats_data = solarPotential_data.get('roofSegmentStats', [])\n",
    "\n",
    "bounding_box_data = data['boundingBox']\n",
    "\n",
    "# Extract heights of each sample and store in a list\n",
    "sample_heights = []\n",
    "for i, segment in enumerate(roofSegmentStats_data):\n",
    "    height = segment.get('planeHeightAtCenterMeters', None)\n",
    "    if height is not None:\n",
    "        sample_heights.append(height)\n",
    "\n",
    "# Extract sample boxes\n",
    "sample_boxes = []\n",
    "for sample in roofSegmentStats_data:\n",
    "    box = sample.get('boundingBox', {})\n",
    "    sw = box.get('sw', {})\n",
    "    ne = box.get('ne', {})\n",
    "    sample_box = [\n",
    "        sw.get('latitude', None),\n",
    "        sw.get('longitude', None),\n",
    "        ne.get('latitude', None),\n",
    "        ne.get('longitude', None)\n",
    "    ]\n",
    "    sample_boxes.append(sample_box)\n",
    "\n",
    "# Dimensions of a single solar panel in inches\n",
    "panel_width = 100\n",
    "panel_height = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('50Q.json', 'r') as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: name, Value Type: <class 'str'>\n",
      "Key: center, Value Type: <class 'dict'>\n",
      "Key: imageryDate, Value Type: <class 'dict'>\n",
      "Key: postalCode, Value Type: <class 'str'>\n",
      "Key: administrativeArea, Value Type: <class 'str'>\n",
      "Key: statisticalArea, Value Type: <class 'str'>\n",
      "Key: regionCode, Value Type: <class 'str'>\n",
      "Key: solarPotential, Value Type: <class 'dict'>\n",
      "Key: boundingBox, Value Type: <class 'dict'>\n",
      "Key: imageryQuality, Value Type: <class 'str'>\n",
      "Key: imageryProcessedDate, Value Type: <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in data.items():\n",
    "    print(f\"Key: {key}, Value Type: {type(value)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sw': 'dict', 'ne': 'dict'}\n"
     ]
    }
   ],
   "source": [
    "bounding_box_data = data['boundingBox']\n",
    "bounding_box_structure = {key: type(value).__name__ for key, value in bounding_box_data.items()}\n",
    "print(bounding_box_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SW: (42.329780299999996, -71.08060549999999), NE: (42.3299027, -71.0804015)\n"
     ]
    }
   ],
   "source": [
    "sw_data = bounding_box_data['sw']\n",
    "ne_data = bounding_box_data['ne']\n",
    "sw_structure = {key: type(value).__name__ for key, value in sw_data.items()}\n",
    "ne_structure = {key: type(value).__name__ for key, value in ne_data.items()}\n",
    "\n",
    "sw_data = bounding_box_data['sw']\n",
    "ne_data = bounding_box_data['ne']\n",
    "sw_lat, sw_long = sw_data['latitude'], sw_data['longitude']\n",
    "ne_lat, ne_long = ne_data['latitude'], ne_data['longitude']\n",
    "print(f\"SW: ({sw_lat}, {sw_long}), NE: ({ne_lat}, {ne_long})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "import folium\n",
    "import math\n",
    "\n",
    "# Function to rotate a point\n",
    "def rotate_point(x, y, angle_degrees, cx, cy):\n",
    "    angle_radians = math.radians(angle_degrees)\n",
    "    dx = x - cx\n",
    "    dy = y - cy\n",
    "    rotated_dx = dx * math.cos(angle_radians) - dy * math.sin(angle_radians)\n",
    "    rotated_dy = dx * math.sin(angle_radians) + dy * math.cos(angle_radians)\n",
    "    return cx + rotated_dx, cy + rotated_dy\n",
    "\n",
    "# Calculate the midpoint of the bounding box\n",
    "mid_lat, mid_long = (sw_lat + ne_lat) / 2, (sw_long + ne_long) / 2\n",
    "\n",
    "# Calculate scaling factor based on latitude\n",
    "scale_factor = math.cos(math.radians(mid_lat))\n",
    "\n",
    "# Adjust the deltas based on the scaling factor\n",
    "scaled_delta_lat = (ne_lat - sw_lat) / scale_factor\n",
    "scaled_delta_long = ne_long - sw_long\n",
    "\n",
    "# Check if rotation is needed\n",
    "rotation_needed = abs(scaled_delta_lat - scaled_delta_long) > 0.01  # Set a suitable threshold\n",
    "\n",
    "# Initialize points to original coordinates\n",
    "rotated_sw = (sw_lat, sw_long)\n",
    "rotated_ne = (ne_lat, ne_long)\n",
    "rotated_se = (ne_lat, sw_long)\n",
    "rotated_nw = (sw_lat, ne_long)\n",
    "\n",
    "if rotation_needed:\n",
    "    # Calculate the angle of rotation based on the adjusted deltas\n",
    "    angle_degrees = math.degrees(math.atan2(scaled_delta_lat, scaled_delta_long))\n",
    "\n",
    "    # Rotate each corner of the bounding box\n",
    "    rotated_sw = rotate_point(sw_lat / scale_factor, sw_long, angle_degrees, mid_lat / scale_factor, mid_long)\n",
    "    rotated_ne = rotate_point(ne_lat / scale_factor, ne_long, angle_degrees, mid_lat / scale_factor, mid_long)\n",
    "    rotated_se = rotate_point(ne_lat / scale_factor, sw_long, angle_degrees, mid_lat / scale_factor, mid_long)\n",
    "    rotated_nw = rotate_point(sw_lat / scale_factor, ne_long, angle_degrees, mid_lat / scale_factor, mid_long)\n",
    "\n",
    "    # Rescale the rotated coordinates back to the original scale\n",
    "    rotated_sw = (rotated_sw[0] * scale_factor, rotated_sw[1])\n",
    "    rotated_ne = (rotated_ne[0] * scale_factor, rotated_ne[1])\n",
    "    rotated_se = (rotated_se[0] * scale_factor, rotated_se[1])\n",
    "    rotated_nw = (rotated_nw[0] * scale_factor, rotated_nw[1])\n",
    "\n",
    "# Initialize the folium map centered around the bounding box\n",
    "zoom_level = 6  # Adjust as needed\n",
    "m = folium.Map(location=[mid_lat, mid_long], zoom_start=zoom_level)\n",
    "\n",
    "# Draw the rotated bounding box as a polygon\n",
    "folium.Polygon([rotated_sw, rotated_se, rotated_ne, rotated_nw, rotated_sw], \n",
    "               color='red').add_to(m)\n",
    "\n",
    "# Show the map\n",
    "m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: panels, Value Type: <class 'list'>\n",
      "Key: height, Value Type: <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in data.items():\n",
    "    print(f\"Key: {key}, Value Type: {type(value)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'maxArrayPanelsCount': 'int', 'maxArrayAreaMeters2': 'float', 'maxSunshineHoursPerYear': 'int', 'carbonOffsetFactorKgPerMwh': 'float', 'wholeRoofStats': 'dict', 'roofSegmentStats': 'list', 'solarPanelConfigs': 'list', 'financialAnalyses': 'list', 'panelCapacityWatts': 'int', 'panelHeightMeters': 'float', 'panelWidthMeters': 'float', 'panelLifetimeYears': 'int', 'buildingStats': 'dict', 'solarPanels': 'list'}\n"
     ]
    }
   ],
   "source": [
    "solarPotential_data = data['solarPotential']\n",
    "solarPotential_structure = {key: type(value).__name__ for key, value in solarPotential_data.items()}\n",
    "print(solarPotential_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exploring 'solarPotential' Dictionary:\n",
      "Key: maxArrayPanelsCount, Type: <class 'int'>\n",
      "Key: maxArrayAreaMeters2, Type: <class 'float'>\n",
      "Key: maxSunshineHoursPerYear, Type: <class 'int'>\n",
      "Key: carbonOffsetFactorKgPerMwh, Type: <class 'float'>\n",
      "Key: wholeRoofStats, Type: <class 'dict'>\n",
      "  Key: areaMeters2, Type: <class 'float'>\n",
      "  Key: sunshineQuantiles, Type: <class 'list'>\n",
      "    List Length: 11\n",
      "    Sample 1: 357\n",
      "    Sample 2: 678\n",
      "    Sample 3: 796\n",
      "  Key: groundAreaMeters2, Type: <class 'float'>\n",
      "Key: roofSegmentStats, Type: <class 'list'>\n",
      "  List Length: 4\n",
      "  Sample 1: {'pitchDegrees': 45.6794, 'azimuthDegrees': 125.6181, 'stats': {'areaMeters2': 54.87549, 'sunshineQuantiles': [381, 854, 1022, 1120, 1190, 1254, 1288, 1329, 1353, 1390, 1493], 'groundAreaMeters2': 38.34}, 'center': {'latitude': 42.329844099999995, 'longitude': -71.0804842}, 'boundingBox': {'sw': {'latitude': 42.3297831, 'longitude': -71.080547}, 'ne': {'latitude': 42.329893399999996, 'longitude': -71.0804161}}, 'planeHeightAtCenterMeters': 12.534987}\n",
      "  Sample 2: {'pitchDegrees': 46.083378, 'azimuthDegrees': 306.53458, 'stats': {'areaMeters2': 41.449757, 'sunshineQuantiles': [357, 586, 750, 802, 831, 858, 874, 899, 936, 1041, 1457], 'groundAreaMeters2': 28.75}, 'center': {'latitude': 42.329856799999995, 'longitude': -71.0805295}, 'boundingBox': {'sw': {'latitude': 42.3298124, 'longitude': -71.080569}, 'ne': {'latitude': 42.329896999999995, 'longitude': -71.0804656}}, 'planeHeightAtCenterMeters': 12.635618}\n",
      "  Sample 3: {'pitchDegrees': 46.868793, 'azimuthDegrees': 215.66063, 'stats': {'areaMeters2': 19.395285, 'sunshineQuantiles': [737, 1023, 1142, 1194, 1224, 1257, 1275, 1300, 1338, 1379, 1465], 'groundAreaMeters2': 13.26}, 'center': {'latitude': 42.32981540000001, 'longitude': -71.08047839999999}, 'boundingBox': {'sw': {'latitude': 42.3297884, 'longitude': -71.0805005}, 'ne': {'latitude': 42.3298433, 'longitude': -71.0804502}}, 'planeHeightAtCenterMeters': 12.250702}\n",
      "Key: solarPanelConfigs, Type: <class 'list'>\n",
      "  List Length: 41\n",
      "  Sample 1: {'panelsCount': 4, 'yearlyEnergyDcKwh': 1374.8905, 'roofSegmentSummaries': [{'pitchDegrees': 45.6794, 'azimuthDegrees': 125.6181, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1031.4918, 'segmentIndex': 0}, {'pitchDegrees': 46.868793, 'azimuthDegrees': 215.66063, 'panelsCount': 1, 'yearlyEnergyDcKwh': 343.39868, 'segmentIndex': 2}]}\n",
      "  Sample 2: {'panelsCount': 5, 'yearlyEnergyDcKwh': 1713.9993, 'roofSegmentSummaries': [{'pitchDegrees': 45.6794, 'azimuthDegrees': 125.6181, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1370.6006, 'segmentIndex': 0}, {'pitchDegrees': 46.868793, 'azimuthDegrees': 215.66063, 'panelsCount': 1, 'yearlyEnergyDcKwh': 343.39868, 'segmentIndex': 2}]}\n",
      "  Sample 3: {'panelsCount': 6, 'yearlyEnergyDcKwh': 2052.0884, 'roofSegmentSummaries': [{'pitchDegrees': 45.6794, 'azimuthDegrees': 125.6181, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1708.6897, 'segmentIndex': 0}, {'pitchDegrees': 46.868793, 'azimuthDegrees': 215.66063, 'panelsCount': 1, 'yearlyEnergyDcKwh': 343.39868, 'segmentIndex': 2}]}\n",
      "Key: financialAnalyses, Type: <class 'list'>\n",
      "  List Length: 23\n",
      "  Sample 1: {'monthlyBill': {'currencyCode': 'USD', 'units': '20'}, 'panelConfigIndex': -1}\n",
      "  Sample 2: {'monthlyBill': {'currencyCode': 'USD', 'units': '25'}, 'panelConfigIndex': -1}\n",
      "  Sample 3: {'monthlyBill': {'currencyCode': 'USD', 'units': '30'}, 'panelConfigIndex': 0, 'financialDetails': {'initialAcKwhPerYear': 1168.657, 'remainingLifetimeUtilityBill': {'currencyCode': 'USD', 'units': '2160'}, 'federalIncentive': {'currencyCode': 'USD', 'units': '1459'}, 'stateIncentive': {'currencyCode': 'USD', 'units': '842'}, 'utilityIncentive': {'currencyCode': 'USD'}, 'lifetimeSrecTotal': {'currencyCode': 'USD', 'units': '615'}, 'costOfElectricityWithoutSolar': {'currencyCode': 'USD', 'units': '8590'}, 'netMeteringAllowed': True, 'solarPercentage': 98.085556, 'percentageExportedToGrid': 0}, 'leasingSavings': {'leasesAllowed': True, 'leasesSupported': True, 'annualLeasingCost': {'currencyCode': 'USD', 'units': '237', 'nanos': 835845947}, 'savings': {'savingsYear1': {'currencyCode': 'USD', 'units': '34'}, 'savingsYear20': {'currencyCode': 'USD', 'units': '1674'}, 'presentValueOfSavingsYear20': {'currencyCode': 'USD', 'units': '1019', 'nanos': 528076172}, 'financiallyViable': True, 'savingsLifetime': {'currencyCode': 'USD', 'units': '1674'}, 'presentValueOfSavingsLifetime': {'currencyCode': 'USD', 'units': '1019', 'nanos': 528076172}}}, 'cashPurchaseSavings': {'outOfPocketCost': {'currencyCode': 'USD', 'units': '5615'}, 'upfrontCost': {'currencyCode': 'USD', 'units': '3313'}, 'rebateValue': {'currencyCode': 'USD', 'units': '2302', 'nanos': 149902344}, 'paybackYears': 9.5, 'savings': {'savingsYear1': {'currencyCode': 'USD', 'units': '272'}, 'savingsYear20': {'currencyCode': 'USD', 'units': '7046'}, 'presentValueOfSavingsYear20': {'currencyCode': 'USD', 'units': '1560', 'nanos': 798828125}, 'financiallyViable': True, 'savingsLifetime': {'currencyCode': 'USD', 'units': '7046'}, 'presentValueOfSavingsLifetime': {'currencyCode': 'USD', 'units': '1560', 'nanos': 798828125}}}, 'financedPurchaseSavings': {'annualLoanPayment': {'currencyCode': 'USD', 'units': '225', 'nanos': 916595459}, 'rebateValue': {'currencyCode': 'USD'}, 'loanInterestRate': 0.0325, 'savings': {'savingsYear1': {'currencyCode': 'USD', 'units': '46'}, 'savingsYear20': {'currencyCode': 'USD', 'units': '2527'}, 'presentValueOfSavingsYear20': {'currencyCode': 'USD', 'units': '1680', 'nanos': 556640625}, 'financiallyViable': True, 'savingsLifetime': {'currencyCode': 'USD', 'units': '2527'}, 'presentValueOfSavingsLifetime': {'currencyCode': 'USD', 'units': '1680', 'nanos': 556640625}}}}\n",
      "Key: panelCapacityWatts, Type: <class 'int'>\n",
      "Key: panelHeightMeters, Type: <class 'float'>\n",
      "Key: panelWidthMeters, Type: <class 'float'>\n",
      "Key: panelLifetimeYears, Type: <class 'int'>\n",
      "Key: buildingStats, Type: <class 'dict'>\n",
      "  Key: areaMeters2, Type: <class 'float'>\n",
      "  Key: sunshineQuantiles, Type: <class 'list'>\n",
      "    List Length: 11\n",
      "    Sample 1: 346\n",
      "    Sample 2: 493\n",
      "    Sample 3: 642\n",
      "  Key: groundAreaMeters2, Type: <class 'float'>\n",
      "Key: solarPanels, Type: <class 'list'>\n",
      "  List Length: 44\n",
      "  Sample 1: {'center': {'latitude': 42.32982, 'longitude': -71.08052459999999}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 344.34518, 'segmentIndex': 0}\n",
      "  Sample 2: {'center': {'latitude': 42.3298304, 'longitude': -71.0804824}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 343.39868, 'segmentIndex': 2}\n",
      "  Sample 3: {'center': {'latitude': 42.3298274, 'longitude': -71.0805178}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 342.80313, 'segmentIndex': 0}\n"
     ]
    }
   ],
   "source": [
    "def explore_nested_dict(d, indent=0, max_samples=3):\n",
    "    \"\"\"Recursively explore nested dictionaries.\"\"\"\n",
    "    for key, value in d.items():\n",
    "        print('  ' * indent + f\"Key: {key}, Type: {type(value)}\")\n",
    "        \n",
    "        if isinstance(value, dict):\n",
    "            explore_nested_dict(value, indent + 1)\n",
    "            \n",
    "        elif isinstance(value, list):\n",
    "            print('  ' * (indent + 1) + f\"List Length: {len(value)}\")\n",
    "            \n",
    "            # Show a few sample values\n",
    "            samples = value[:max_samples]\n",
    "            for i, sample in enumerate(samples):\n",
    "                print('  ' * (indent + 1) + f\"Sample {i+1}: {sample}\")\n",
    "                \n",
    "\n",
    "\n",
    "# Start the exploration\n",
    "print(\"Exploring 'solarPotential' Dictionary:\")\n",
    "explore_nested_dict(solarPotential_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of items in 'solarPanels': 71\n",
      "\n",
      "Sample 1:\n",
      "  Key: center, Value: {'latitude': 42.254137199999995, 'longitude': -70.9627687}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 378.35434, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 2:\n",
      "  Key: center, Value: {'latitude': 42.254136599999995, 'longitude': -70.9627807}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 377.43433, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 3:\n",
      "  Key: center, Value: {'latitude': 42.2541247, 'longitude': -70.9627675}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 376.63217, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 4:\n",
      "  Key: center, Value: {'latitude': 42.254137899999996, 'longitude': -70.9627567}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 374.56656, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 5:\n",
      "  Key: center, Value: {'latitude': 42.2541241, 'longitude': -70.9627795}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 369.32916, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 6:\n",
      "  Key: center, Value: {'latitude': 42.2541253, 'longitude': -70.9627555}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 360.83643, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 7:\n",
      "  Key: center, Value: {'latitude': 42.254126, 'longitude': -70.96274350000002}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 357.3741, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 8:\n",
      "  Key: center, Value: {'latitude': 42.2541234, 'longitude': -70.9627915}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 348.06253, Type: float\n",
      "  Key: segmentIndex, Value: 3, Type: int\n",
      "\n",
      "Sample 9:\n",
      "  Key: center, Value: {'latitude': 42.254106199999995, 'longitude': -70.96282479999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 342.13666, Type: float\n",
      "  Key: segmentIndex, Value: 5, Type: int\n",
      "\n",
      "Sample 10:\n",
      "  Key: center, Value: {'latitude': 42.254177299999995, 'longitude': -70.96274629999999}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 339.16382, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 11:\n",
      "  Key: center, Value: {'latitude': 42.2541054, 'longitude': -70.9628048}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 330.19138, Type: float\n",
      "  Key: segmentIndex, Value: 5, Type: int\n",
      "\n",
      "Sample 12:\n",
      "  Key: center, Value: {'latitude': 42.254207, 'longitude': -70.96276569999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 327.02625, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 13:\n",
      "  Key: center, Value: {'latitude': 42.254257599999995, 'longitude': -70.9627738}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 323.6862, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 14:\n",
      "  Key: center, Value: {'latitude': 42.2542665, 'longitude': -70.9627751}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 321.6426, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 15:\n",
      "  Key: center, Value: {'latitude': 42.2541766, 'longitude': -70.9627637}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 320.125, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 16:\n",
      "  Key: center, Value: {'latitude': 42.2541855, 'longitude': -70.9627643}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 334.3714, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 17:\n",
      "  Key: center, Value: {'latitude': 42.254167699999996, 'longitude': -70.962763}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 320.64215, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 18:\n",
      "  Key: center, Value: {'latitude': 42.2541588, 'longitude': -70.96276240000002}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 318.5643, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 19:\n",
      "  Key: center, Value: {'latitude': 42.2541498, 'longitude': -70.9627618}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 321.9167, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 20:\n",
      "  Key: center, Value: {'latitude': 42.2541594, 'longitude': -70.96274509999999}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 320.6161, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 21:\n",
      "  Key: center, Value: {'latitude': 42.2542487, 'longitude': -70.9627726}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 318.49643, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 22:\n",
      "  Key: center, Value: {'latitude': 42.2542577, 'longitude': -70.9627903}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 318.28018, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 23:\n",
      "  Key: center, Value: {'latitude': 42.2541684, 'longitude': -70.9627457}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 318.2625, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 24:\n",
      "  Key: center, Value: {'latitude': 42.254259000000005, 'longitude': -70.96275659999999}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 316.80142, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 25:\n",
      "  Key: center, Value: {'latitude': 42.2542429, 'longitude': -70.9627886}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 315.69824, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 26:\n",
      "  Key: center, Value: {'latitude': 42.254207699999995, 'longitude': -70.96275539999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 314.67575, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 27:\n",
      "  Key: center, Value: {'latitude': 42.254208399999996, 'longitude': -70.9627452}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 318.2571, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 28:\n",
      "  Key: center, Value: {'latitude': 42.2542232, 'longitude': -70.962747}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 317.7464, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 29:\n",
      "  Key: center, Value: {'latitude': 42.2542225, 'longitude': -70.9627572}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 317.19583, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 30:\n",
      "  Key: center, Value: {'latitude': 42.2541936, 'longitude': -70.9627434}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 316.48743, Type: float\n",
      "  Key: segmentIndex, Value: 4, Type: int\n",
      "\n",
      "Sample 31:\n",
      "  Key: center, Value: {'latitude': 42.2542678, 'longitude': -70.96275779999999}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 313.60388, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 32:\n",
      "  Key: center, Value: {'latitude': 42.2542767, 'longitude': -70.9627591}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 314.97888, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 33:\n",
      "  Key: center, Value: {'latitude': 42.254285599999996, 'longitude': -70.9627603}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 313.15494, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 34:\n",
      "  Key: center, Value: {'latitude': 42.2542754, 'longitude': -70.9627763}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 311.88287, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 35:\n",
      "  Key: center, Value: {'latitude': 42.2542725, 'longitude': -70.9627919}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 311.4096, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 36:\n",
      "  Key: center, Value: {'latitude': 42.2542281, 'longitude': -70.96278699999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 310.75882, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 37:\n",
      "  Key: center, Value: {'latitude': 42.2542571, 'longitude': -70.9628006}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 310.32324, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 38:\n",
      "  Key: center, Value: {'latitude': 42.254256399999996, 'longitude': -70.9628109}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 311.4238, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 39:\n",
      "  Key: center, Value: {'latitude': 42.2542416, 'longitude': -70.9628092}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 310.37503, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 40:\n",
      "  Key: center, Value: {'latitude': 42.2542712, 'longitude': -70.9628125}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 310.22363, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 41:\n",
      "  Key: center, Value: {'latitude': 42.2542275, 'longitude': -70.96279729999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.6536, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 42:\n",
      "  Key: center, Value: {'latitude': 42.2542127, 'longitude': -70.96279559999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.46658, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 43:\n",
      "  Key: center, Value: {'latitude': 42.2541979, 'longitude': -70.962794}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 310.3388, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 44:\n",
      "  Key: center, Value: {'latitude': 42.2541985, 'longitude': -70.9627837}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 313.75702, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 45:\n",
      "  Key: center, Value: {'latitude': 42.2541837, 'longitude': -70.96278199999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 315.7283, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 46:\n",
      "  Key: center, Value: {'latitude': 42.254168899999996, 'longitude': -70.9627804}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 315.98962, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 47:\n",
      "  Key: center, Value: {'latitude': 42.2541541, 'longitude': -70.9627787}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 316.92337, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 48:\n",
      "  Key: center, Value: {'latitude': 42.2541972, 'longitude': -70.9628043}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 312.01053, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 49:\n",
      "  Key: center, Value: {'latitude': 42.254211999999995, 'longitude': -70.96280589999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 309.72324, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 50:\n",
      "  Key: center, Value: {'latitude': 42.2541824, 'longitude': -70.9628026}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 309.25546, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 51:\n",
      "  Key: center, Value: {'latitude': 42.254167599999995, 'longitude': -70.962801}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 309.03128, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 52:\n",
      "  Key: center, Value: {'latitude': 42.2541528, 'longitude': -70.9627993}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 313.53653, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 53:\n",
      "  Key: center, Value: {'latitude': 42.2541831, 'longitude': -70.9627923}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.3689, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 54:\n",
      "  Key: center, Value: {'latitude': 42.2542423, 'longitude': -70.9627989}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.2465, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 55:\n",
      "  Key: center, Value: {'latitude': 42.254213299999996, 'longitude': -70.9627853}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.0594, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 56:\n",
      "  Key: center, Value: {'latitude': 42.2542719, 'longitude': -70.9628022}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 308.05896, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 57:\n",
      "  Key: center, Value: {'latitude': 42.2541534, 'longitude': -70.962789}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 306.30002, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 58:\n",
      "  Key: center, Value: {'latitude': 42.2541505, 'longitude': -70.96274439999999}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 305.39966, Type: float\n",
      "  Key: segmentIndex, Value: 2, Type: int\n",
      "\n",
      "Sample 59:\n",
      "  Key: center, Value: {'latitude': 42.2542268, 'longitude': -70.96280759999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 305.39264, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 60:\n",
      "  Key: center, Value: {'latitude': 42.254241, 'longitude': -70.9628195}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 304.91315, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 61:\n",
      "  Key: center, Value: {'latitude': 42.254168199999995, 'longitude': -70.9627907}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 304.17938, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 62:\n",
      "  Key: center, Value: {'latitude': 42.2541818, 'longitude': -70.9628129}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 303.9878, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 63:\n",
      "  Key: center, Value: {'latitude': 42.2542706, 'longitude': -70.9628228}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 300.50537, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 64:\n",
      "  Key: center, Value: {'latitude': 42.2542399, 'longitude': -70.9627713}, Type: dict\n",
      "  Key: orientation, Value: PORTRAIT, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 299.98065, Type: float\n",
      "  Key: segmentIndex, Value: 1, Type: int\n",
      "\n",
      "Sample 65:\n",
      "  Key: center, Value: {'latitude': 42.254211399999996, 'longitude': -70.9628162}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 295.79227, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 66:\n",
      "  Key: center, Value: {'latitude': 42.2541522, 'longitude': -70.9628096}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 294.24655, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 67:\n",
      "  Key: center, Value: {'latitude': 42.2541374, 'longitude': -70.962808}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 297.6442, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 68:\n",
      "  Key: center, Value: {'latitude': 42.254255799999996, 'longitude': -70.9628212}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 291.78018, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 69:\n",
      "  Key: center, Value: {'latitude': 42.2542262, 'longitude': -70.96281789999999}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 290.38132, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 70:\n",
      "  Key: center, Value: {'latitude': 42.254166999999995, 'longitude': -70.9628113}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 289.7245, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n",
      "\n",
      "Sample 71:\n",
      "  Key: center, Value: {'latitude': 42.2541966, 'longitude': -70.9628146}, Type: dict\n",
      "  Key: orientation, Value: LANDSCAPE, Type: str\n",
      "  Key: yearlyEnergyDcKwh, Value: 283.88312, Type: float\n",
      "  Key: segmentIndex, Value: 0, Type: int\n"
     ]
    }
   ],
   "source": [
    "# Extract 'solarPanels' list from the main 'data' dictionary\n",
    "solar_panels_data = solarPotential_data.get('solarPanels', [])\n",
    "\n",
    "# Print the number of items in the list\n",
    "print(f\"Number of items in 'solarPanels': {len(solar_panels_data)}\")\n",
    "\n",
    "# Loop through a few samples in the list to understand its layout\n",
    "for i, sample in enumerate(solar_panels_data):  # Adjust the slice for more or fewer samples\n",
    "    print(f\"\\nSample {i+1}:\")\n",
    "    for key, value in sample.items():\n",
    "        print(f\"  Key: {key}, Value: {value}, Type: {type(value).__name__}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of items in 'solarPanels': 1128\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize an empty list to store the solar panels data\n",
    "solar_panels_data = []\n",
    "\n",
    "# Extract 'solarPanels' list from the main 'data' dictionary\n",
    "solar_panels_list = solarPotential_data.get('solarPanels', [])\n",
    "\n",
    "# Loop through the list to construct solar_panels_data\n",
    "for sample in solar_panels_list:\n",
    "    sample_dict = {\n",
    "        'center': sample.get('center', {}),\n",
    "        'orientation': sample.get('orientation', ''),\n",
    "        'yearlyEnergyDcKwh': sample.get('yearlyEnergyDcKwh', 0.0),\n",
    "        'segmentIndex': sample.get('segmentIndex', 0)\n",
    "    }\n",
    "    solar_panels_data.append(sample_dict)\n",
    "\n",
    "# Output the number of items in the list for verification\n",
    "print(f\"Number of items in 'solarPanels': {len(solar_panels_data)}\")\n",
    "# Prepare the coordinates for plotting\n",
    "latitudes = [sample['center']['latitude'] for sample in solar_panels_data]\n",
    "longitudes = [sample['center']['longitude'] for sample in solar_panels_data]\n",
    "colors = [sample['segmentIndex'] for sample in solar_panels_data]\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 10))\n",
    "sc = plt.scatter(longitudes, latitudes, c=colors, cmap='viridis')\n",
    "\n",
    "# Add color bar to indicate segmentIndex\n",
    "plt.colorbar(sc, label='Segment Index')\n",
    "\n",
    "# Labels and title\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Solar Panels')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: areaMeters2, Value: 172.86774\n",
      "Key: sunshineQuantiles, Value: [514, 1013, 1204, 1228, 1238, 1247, 1260, 1278, 1316, 1443, 1523]\n",
      "Key: groundAreaMeters2, Value: 149.85\n"
     ]
    }
   ],
   "source": [
    "# Extract 'wholeRoofStats' from the 'solarPotential_data'\n",
    "whole_roof_stats = solarPotential_data.get('wholeRoofStats', {})\n",
    "\n",
    "\n",
    "# If you also want to see the values, you can do so as follows:\n",
    "for key, value in whole_roof_stats.items():\n",
    "    print(f\"Key: {key}, Value: {value}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel Config 1: {'panelsCount': 4, 'yearlyEnergyDcKwh': 1506.9874, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1506.9873, 'segmentIndex': 3}]}\n",
      "Panel Config 2: {'panelsCount': 5, 'yearlyEnergyDcKwh': 1876.3165, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1876.3164, 'segmentIndex': 3}]}\n",
      "Panel Config 3: {'panelsCount': 6, 'yearlyEnergyDcKwh': 2237.153, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 6, 'yearlyEnergyDcKwh': 2237.1528, 'segmentIndex': 3}]}\n",
      "Panel Config 4: {'panelsCount': 7, 'yearlyEnergyDcKwh': 2594.527, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2594.5269, 'segmentIndex': 3}]}\n",
      "Panel Config 5: {'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5896, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}]}\n",
      "Panel Config 6: {'panelsCount': 9, 'yearlyEnergyDcKwh': 3284.7263, 'roofSegmentSummaries': [{'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 1, 'yearlyEnergyDcKwh': 342.13666, 'segmentIndex': 5}]}\n",
      "Panel Config 7: {'panelsCount': 10, 'yearlyEnergyDcKwh': 3623.8901, 'roofSegmentSummaries': [{'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 1, 'yearlyEnergyDcKwh': 339.16382, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 1, 'yearlyEnergyDcKwh': 342.13666, 'segmentIndex': 5}]}\n",
      "Panel Config 8: {'panelsCount': 11, 'yearlyEnergyDcKwh': 3954.0815, 'roofSegmentSummaries': [{'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 1, 'yearlyEnergyDcKwh': 339.16382, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 9: {'panelsCount': 12, 'yearlyEnergyDcKwh': 4281.108, 'roofSegmentSummaries': [{'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 1, 'yearlyEnergyDcKwh': 339.16382, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 10: {'panelsCount': 13, 'yearlyEnergyDcKwh': 4604.794, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 1, 'yearlyEnergyDcKwh': 323.6862, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 1, 'yearlyEnergyDcKwh': 339.16382, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 11: {'panelsCount': 14, 'yearlyEnergyDcKwh': 4926.4365, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 1, 'yearlyEnergyDcKwh': 339.16382, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 12: {'panelsCount': 15, 'yearlyEnergyDcKwh': 5246.5615, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 2, 'yearlyEnergyDcKwh': 659.2888, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 13: {'panelsCount': 16, 'yearlyEnergyDcKwh': 5580.933, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 3, 'yearlyEnergyDcKwh': 993.6602, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 14: {'panelsCount': 17, 'yearlyEnergyDcKwh': 5901.575, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1314.3024, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 15: {'panelsCount': 18, 'yearlyEnergyDcKwh': 6220.139, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1632.8667, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 16: {'panelsCount': 19, 'yearlyEnergyDcKwh': 6542.056, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1954.7833, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 17: {'panelsCount': 20, 'yearlyEnergyDcKwh': 6862.672, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 2, 'yearlyEnergyDcKwh': 645.3288, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2275.3994, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 18: {'panelsCount': 21, 'yearlyEnergyDcKwh': 7181.1685, 'roofSegmentSummaries': [{'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 3, 'yearlyEnergyDcKwh': 963.82526, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2275.3994, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 19: {'panelsCount': 22, 'yearlyEnergyDcKwh': 7499.4487, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 1, 'yearlyEnergyDcKwh': 318.28018, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 3, 'yearlyEnergyDcKwh': 963.82526, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2275.3994, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 20: {'panelsCount': 23, 'yearlyEnergyDcKwh': 7817.7114, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 1, 'yearlyEnergyDcKwh': 318.28018, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 3, 'yearlyEnergyDcKwh': 963.82526, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 21: {'panelsCount': 24, 'yearlyEnergyDcKwh': 8134.5127, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 1, 'yearlyEnergyDcKwh': 318.28018, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 22: {'panelsCount': 25, 'yearlyEnergyDcKwh': 8450.211, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 1, 'yearlyEnergyDcKwh': 327.02625, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 23: {'panelsCount': 26, 'yearlyEnergyDcKwh': 8764.887, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 2, 'yearlyEnergyDcKwh': 641.70197, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 24: {'panelsCount': 27, 'yearlyEnergyDcKwh': 9083.144, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 3, 'yearlyEnergyDcKwh': 959.95905, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 25: {'panelsCount': 28, 'yearlyEnergyDcKwh': 9400.89, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1277.7054, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 26: {'panelsCount': 29, 'yearlyEnergyDcKwh': 9718.086, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1594.9012, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 27: {'panelsCount': 30, 'yearlyEnergyDcKwh': 10034.573, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1280.6267, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 28: {'panelsCount': 31, 'yearlyEnergyDcKwh': 10348.178, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1594.2306, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 29: {'panelsCount': 32, 'yearlyEnergyDcKwh': 10663.156, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1909.2095, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 30: {'panelsCount': 33, 'yearlyEnergyDcKwh': 10976.312, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2222.3645, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 31: {'panelsCount': 34, 'yearlyEnergyDcKwh': 11288.194, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 2, 'yearlyEnergyDcKwh': 633.97845, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 32: {'panelsCount': 35, 'yearlyEnergyDcKwh': 11599.604, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 3, 'yearlyEnergyDcKwh': 945.38806, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 33: {'panelsCount': 36, 'yearlyEnergyDcKwh': 11910.362, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1256.1469, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 34: {'panelsCount': 37, 'yearlyEnergyDcKwh': 12220.686, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1566.4701, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 35: {'panelsCount': 38, 'yearlyEnergyDcKwh': 12532.109, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1877.8939, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 36: {'panelsCount': 39, 'yearlyEnergyDcKwh': 12842.484, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2188.269, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 37: {'panelsCount': 40, 'yearlyEnergyDcKwh': 13152.708, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2498.4927, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 38: {'panelsCount': 41, 'yearlyEnergyDcKwh': 13461.361, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2807.1462, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 39: {'panelsCount': 42, 'yearlyEnergyDcKwh': 13769.828, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 10, 'yearlyEnergyDcKwh': 3115.6128, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 40: {'panelsCount': 43, 'yearlyEnergyDcKwh': 14080.167, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 11, 'yearlyEnergyDcKwh': 3425.9517, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 41: {'panelsCount': 44, 'yearlyEnergyDcKwh': 14393.924, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 12, 'yearlyEnergyDcKwh': 3739.7087, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 42: {'panelsCount': 45, 'yearlyEnergyDcKwh': 14709.652, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 13, 'yearlyEnergyDcKwh': 4055.437, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 43: {'panelsCount': 46, 'yearlyEnergyDcKwh': 15025.642, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 14, 'yearlyEnergyDcKwh': 4371.427, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 44: {'panelsCount': 47, 'yearlyEnergyDcKwh': 15342.565, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 15, 'yearlyEnergyDcKwh': 4688.35, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 45: {'panelsCount': 48, 'yearlyEnergyDcKwh': 15654.576, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 16, 'yearlyEnergyDcKwh': 5000.361, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 46: {'panelsCount': 49, 'yearlyEnergyDcKwh': 15964.299, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 17, 'yearlyEnergyDcKwh': 5310.084, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 47: {'panelsCount': 50, 'yearlyEnergyDcKwh': 16273.555, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 18, 'yearlyEnergyDcKwh': 5619.3394, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 48: {'panelsCount': 51, 'yearlyEnergyDcKwh': 16582.586, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 19, 'yearlyEnergyDcKwh': 5928.3706, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 49: {'panelsCount': 52, 'yearlyEnergyDcKwh': 16896.123, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 20, 'yearlyEnergyDcKwh': 6241.907, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 50: {'panelsCount': 53, 'yearlyEnergyDcKwh': 17204.492, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 21, 'yearlyEnergyDcKwh': 6550.2764, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 51: {'panelsCount': 54, 'yearlyEnergyDcKwh': 17512.738, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 22, 'yearlyEnergyDcKwh': 6858.523, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 52: {'panelsCount': 55, 'yearlyEnergyDcKwh': 17820.797, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 23, 'yearlyEnergyDcKwh': 7166.5825, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 53: {'panelsCount': 56, 'yearlyEnergyDcKwh': 18128.855, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 24, 'yearlyEnergyDcKwh': 7474.6416, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 54: {'panelsCount': 57, 'yearlyEnergyDcKwh': 18435.156, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 25, 'yearlyEnergyDcKwh': 7780.9414, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2593.6619, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 55: {'panelsCount': 58, 'yearlyEnergyDcKwh': 18740.557, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 25, 'yearlyEnergyDcKwh': 7780.9414, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 56: {'panelsCount': 59, 'yearlyEnergyDcKwh': 19045.95, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 26, 'yearlyEnergyDcKwh': 8086.334, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 57: {'panelsCount': 60, 'yearlyEnergyDcKwh': 19350.861, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 27, 'yearlyEnergyDcKwh': 8391.247, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 58: {'panelsCount': 61, 'yearlyEnergyDcKwh': 19655.041, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 28, 'yearlyEnergyDcKwh': 8695.427, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 59: {'panelsCount': 62, 'yearlyEnergyDcKwh': 19959.03, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 29, 'yearlyEnergyDcKwh': 8999.414, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 60: {'panelsCount': 63, 'yearlyEnergyDcKwh': 20259.533, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 30, 'yearlyEnergyDcKwh': 9299.92, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2534.2473, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 61: {'panelsCount': 64, 'yearlyEnergyDcKwh': 20559.516, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 30, 'yearlyEnergyDcKwh': 9299.92, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 62: {'panelsCount': 65, 'yearlyEnergyDcKwh': 20855.307, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 31, 'yearlyEnergyDcKwh': 9595.712, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 63: {'panelsCount': 66, 'yearlyEnergyDcKwh': 21149.553, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 32, 'yearlyEnergyDcKwh': 9889.958, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 64: {'panelsCount': 67, 'yearlyEnergyDcKwh': 21447.197, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 33, 'yearlyEnergyDcKwh': 10187.603, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 65: {'panelsCount': 68, 'yearlyEnergyDcKwh': 21738.979, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 34, 'yearlyEnergyDcKwh': 10479.383, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 66: {'panelsCount': 69, 'yearlyEnergyDcKwh': 22029.36, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 35, 'yearlyEnergyDcKwh': 10769.764, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 67: {'panelsCount': 70, 'yearlyEnergyDcKwh': 22319.084, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 36, 'yearlyEnergyDcKwh': 11059.488, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n",
      "Panel Config 68: {'panelsCount': 71, 'yearlyEnergyDcKwh': 22602.967, 'roofSegmentSummaries': [{'pitchDegrees': 30.733656, 'azimuthDegrees': 266.58456, 'panelsCount': 37, 'yearlyEnergyDcKwh': 11343.371, 'segmentIndex': 0}, {'pitchDegrees': 30.035275, 'azimuthDegrees': 85.3547, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2834.228, 'segmentIndex': 1}, {'pitchDegrees': 29.773289, 'azimuthDegrees': 88.282814, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2899.0615, 'segmentIndex': 2}, {'pitchDegrees': 32.35102, 'azimuthDegrees': 177.24368, 'panelsCount': 8, 'yearlyEnergyDcKwh': 2942.5894, 'segmentIndex': 3}, {'pitchDegrees': 31.179035, 'azimuthDegrees': 86.184044, 'panelsCount': 6, 'yearlyEnergyDcKwh': 1911.3887, 'segmentIndex': 4}, {'pitchDegrees': 12.6894455, 'azimuthDegrees': 184.46716, 'panelsCount': 2, 'yearlyEnergyDcKwh': 672.328, 'segmentIndex': 5}]}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Access the 'solarPanelConfigs' list\n",
    "solar_panel_configs = solarPotential_data.get('solarPanelConfigs', [])\n",
    "\n",
    "# Loop through each configuration and print it\n",
    "for i, config in enumerate(solar_panel_configs):\n",
    "    print(f\"Panel Config {i+1}: {config}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'monthlyBill': 'dict', 'panelConfigIndex': 'int'},\n",
       " {'monthlyBill': 'dict', 'panelConfigIndex': 'int'},\n",
       " {'monthlyBill': 'dict', 'panelConfigIndex': 'int'},\n",
       " {'monthlyBill': 'dict',\n",
       "  'panelConfigIndex': 'int',\n",
       "  'financialDetails': 'dict',\n",
       "  'leasingSavings': 'dict',\n",
       "  'cashPurchaseSavings': 'dict',\n",
       "  'financedPurchaseSavings': 'dict'},\n",
       " {'monthlyBill': 'dict',\n",
       "  'panelConfigIndex': 'int',\n",
       "  'financialDetails': 'dict',\n",
       "  'leasingSavings': 'dict',\n",
       "  'cashPurchaseSavings': 'dict',\n",
       "  'financedPurchaseSavings': 'dict'}]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code to explore all entries under 'financialAnalyses'\n",
    "def explore_financialAnalyses(financialAnalyses_list):\n",
    "    # Initialize an empty list to store the structures of each entry\n",
    "    all_structures = []\n",
    "    \n",
    "    # Loop through each entry in 'financialAnalyses'\n",
    "    for i, entry in enumerate(financialAnalyses_list):\n",
    "        # Extract the structure of the current entry\n",
    "        current_structure = {key: type(value).__name__ for key, value in entry.items()}\n",
    "        \n",
    "        # Append the current structure to the list of all structures\n",
    "        all_structures.append(current_structure)\n",
    "    \n",
    "    return all_structures\n",
    "\n",
    "# Extract the 'financialAnalyses' list from the 'solarPotential' data\n",
    "financialAnalyses_list = solarPotential_data['financialAnalyses']\n",
    "\n",
    "# Get the structures of all entries under 'financialAnalyses'\n",
    "all_financialAnalyses_structures = explore_financialAnalyses(financialAnalyses_list)\n",
    "all_financialAnalyses_structures[:5]  # Displaying the first 5 entries for illustration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "financialAnalyses_data = solarPotential_data['financialAnalyses']\n",
    "# Convert the list of financial analyses to a DataFrame\n",
    "financial_df = pd.DataFrame(financialAnalyses_data)\n",
    "\n",
    "# Importing necessary libraries for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Extracting the 'units' from 'monthlyBill' and 'panelConfigIndex' for all entries\n",
    "monthlyBill_units = [float(item['monthlyBill']['units']) for item in financialAnalyses_data]\n",
    "panelConfigIndex_values = [item['panelConfigIndex'] for item in financialAnalyses_data]\n",
    "\n",
    "# Plotting the graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(panelConfigIndex_values, monthlyBill_units, marker='o', color='b')\n",
    "plt.title('Monthly Bill Units vs Panel Configuration Index')\n",
    "plt.xlabel('Panel Configuration Index')\n",
    "plt.ylabel('Monthly Bill Units (USD)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'solarPotential_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Accessing the 'roofSegmentStats' from the 'solarPotential_data' dictionary\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m roofSegmentStats_data \u001b[39m=\u001b[39m solarPotential_data\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mroofSegmentStats\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[39m# Printing the structure and a few samples for illustration\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[39mif\u001b[39;00m roofSegmentStats_data \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'solarPotential_data' is not defined"
     ]
    }
   ],
   "source": [
    "# Accessing the 'roofSegmentStats' from the 'solarPotential_data' dictionary\n",
    "roofSegmentStats_data = solarPotential_data.get('roofSegmentStats', None)\n",
    "\n",
    "# Printing the structure and a few samples for illustration\n",
    "if roofSegmentStats_data is not None:\n",
    "    print(f\"Type: {type(roofSegmentStats_data)}\")\n",
    "    print(f\"List Length: {len(roofSegmentStats_data)}\")\n",
    "    print(\"Sample Data:\")\n",
    "    for i, sample in enumerate(roofSegmentStats_data):  # Showing first 3 samples\n",
    "        print(f\"Sample {i+1}: {sample}\")\n",
    "else:\n",
    "    print(\"Key 'roofSegmentStats' not found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3762/1710955717.py:40: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  plt.scatter([sw[1], ne[1]], [sw[0], ne[0]], c=color, zorder=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "# Extract 'roofSegmentStats' list from 'solarPotential' dictionary\n",
    "roofSegmentStats = data['solarPotential']['roofSegmentStats']\n",
    "\n",
    "# Create a color map\n",
    "n_samples = len(roofSegmentStats)\n",
    "colors = cm.rainbow(np.linspace(0, 1, n_samples))\n",
    "\n",
    "# Extract main bounding box coordinates\n",
    "main_bounding_box = data['boundingBox']\n",
    "main_sw = (main_bounding_box['sw']['latitude'], main_bounding_box['sw']['longitude'])\n",
    "main_ne = (main_bounding_box['ne']['latitude'], main_bounding_box['ne']['longitude'])\n",
    "\n",
    "# Plot main bounding box\n",
    "plt.plot([main_sw[1], main_ne[1]], [main_sw[0], main_sw[0]], 'k-', label='Main Bounding Box')\n",
    "plt.plot([main_sw[1], main_ne[1]], [main_ne[0], main_ne[0]], 'k-')\n",
    "plt.plot([main_sw[1], main_sw[1]], [main_sw[0], main_ne[0]], 'k-')\n",
    "plt.plot([main_ne[1], main_ne[1]], [main_sw[0], main_ne[0]], 'k-')\n",
    "\n",
    "# Highlight main SW and NE points\n",
    "plt.scatter([main_sw[1], main_ne[1]], [main_sw[0], main_ne[0]], c='k', zorder=5)\n",
    "\n",
    "# Plot sample bounding boxes\n",
    "for i, segment in enumerate(roofSegmentStats):\n",
    "    sw = (segment['boundingBox']['sw']['latitude'], segment['boundingBox']['sw']['longitude'])\n",
    "    ne = (segment['boundingBox']['ne']['latitude'], segment['boundingBox']['ne']['longitude'])\n",
    "    color = colors[i]\n",
    "    \n",
    "    plt.plot([sw[1], ne[1]], [sw[0], sw[0]], color=color)\n",
    "    plt.plot([sw[1], ne[1]], [ne[0], ne[0]], color=color)\n",
    "    plt.plot([sw[1], sw[1]], [sw[0], ne[0]], color=color, label=f'Sample {i+1}')\n",
    "    plt.plot([ne[1], ne[1]], [sw[0], ne[0]], color=color)\n",
    "\n",
    "    # Highlight SW and NE points\n",
    "    plt.scatter([sw[1], ne[1]], [sw[0], ne[0]], c=color, zorder=5)\n",
    "\n",
    "# Add legend\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Height of Sample 1: 9.990049 meters\n",
      "Height of Sample 2: 10.234403 meters\n",
      "Height of Sample 3: 10.224159 meters\n",
      "Height of Sample 4: 9.479269 meters\n",
      "Height of Sample 5: 10.453384 meters\n",
      "Height of Sample 6: 3.233157 meters\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Extract heights of each sample and store in a list\n",
    "sample_heights = []\n",
    "for i, segment in enumerate(roofSegmentStats):\n",
    "    height = segment.get('planeHeightAtCenterMeters', None)\n",
    "    if height is not None:\n",
    "        sample_heights.append(height)\n",
    "        print(f\"Height of Sample {i+1}: {height} meters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m     ax\u001b[39m.\u001b[39mscatter([xmin, xmax], [ymin, ymax], c\u001b[39m=\u001b[39medgecolor, zorder\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m)\n\u001b[1;32m     15\u001b[0m \u001b[39m# Extract main bounding box\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m main_box_data \u001b[39m=\u001b[39m data[\u001b[39m'\u001b[39m\u001b[39mboundingBox\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m     17\u001b[0m main_box \u001b[39m=\u001b[39m [main_box_data[\u001b[39m'\u001b[39m\u001b[39msw\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mlatitude\u001b[39m\u001b[39m'\u001b[39m], main_box_data[\u001b[39m'\u001b[39m\u001b[39msw\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mlongitude\u001b[39m\u001b[39m'\u001b[39m], \n\u001b[1;32m     18\u001b[0m             main_box_data[\u001b[39m'\u001b[39m\u001b[39mne\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mlatitude\u001b[39m\u001b[39m'\u001b[39m], main_box_data[\u001b[39m'\u001b[39m\u001b[39mne\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mlongitude\u001b[39m\u001b[39m'\u001b[39m]]\n\u001b[1;32m     20\u001b[0m \u001b[39m# Extract sample boxes\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "def draw_box(ax, box, label, edgecolor, facecolor='none'):\n",
    "    xmin, ymin, xmax, ymax = box\n",
    "    ax.add_patch(\n",
    "        patches.Rectangle(\n",
    "            (xmin, ymin), xmax - xmin, ymax - ymin,\n",
    "            linewidth=1, edgecolor=edgecolor, facecolor=facecolor, label=label\n",
    "        )\n",
    "    )\n",
    "    ax.scatter([xmin, xmax], [ymin, ymax], c=edgecolor, zorder=5)\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box_data = data['boundingBox']\n",
    "main_box = [main_box_data['sw']['latitude'], main_box_data['sw']['longitude'], \n",
    "            main_box_data['ne']['latitude'], main_box_data['ne']['longitude']]\n",
    "\n",
    "# Extract sample boxes\n",
    "sample_boxes_data = data['solarPotential']['roofSegmentStats']\n",
    "sample_boxes = []\n",
    "for sample in sample_boxes_data:\n",
    "    box = sample['boundingBox']\n",
    "    sample_boxes.append([box['sw']['latitude'], box['sw']['longitude'], \n",
    "                         box['ne']['latitude'], box['ne']['longitude']])\n",
    "\n",
    "# Create shapely polygons for sample boxes\n",
    "polygons = [Polygon([(box[0], box[1]), (box[0], box[3]), (box[2], box[3]), (box[2], box[1])]) for box in sample_boxes]\n",
    "\n",
    "# Initialize plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Draw main bounding box\n",
    "draw_box(ax, main_box, 'Main Box', 'black')\n",
    "\n",
    "# Draw sample bounding boxes and find intersections\n",
    "colors = ['red', 'green', 'blue', 'purple']\n",
    "for i, (box, poly1) in enumerate(zip(sample_boxes, polygons)):\n",
    "    draw_box(ax, box, f'Sample {i+1}', colors[i % len(colors)])\n",
    "    \n",
    "    # Find intersection with other sample boxes and add to plot\n",
    "    for j, poly2 in enumerate(polygons[i+1:], start=i+1):\n",
    "        intersection = poly1.intersection(poly2)\n",
    "        \n",
    "        if intersection.is_empty:\n",
    "            continue\n",
    "\n",
    "        if intersection.geom_type == 'Polygon':\n",
    "            x, y = intersection.exterior.xy\n",
    "            ax.fill(x, y, alpha=0.5, label=f'Overlap {i+1}-{j+1}')\n",
    "        elif intersection.geom_type in ['LineString', 'Point']:\n",
    "            print(f\"Sample {i+1} and Sample {j+1} are touching but not overlapping.\")\n",
    "\n",
    "# Annotations and Grid\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "plt.grid(True)\n",
    "plt.title('Bounding Boxes with Overlaps')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1\n",
      "Calculated Bounding Box Area: 103.73257340321358 m^2\n",
      "areaMeters2 from JSON: 80.44886 m^2\n",
      "groundAreaMeters2 from JSON: 69.15 m^2\n",
      "---\n",
      "Sample 2\n",
      "Calculated Bounding Box Area: 26.276470546530206 m^2\n",
      "areaMeters2 from JSON: 21.866259 m^2\n",
      "groundAreaMeters2 from JSON: 18.93 m^2\n",
      "---\n",
      "Sample 3\n",
      "Calculated Bounding Box Area: 30.1279071570338 m^2\n",
      "areaMeters2 from JSON: 20.933252 m^2\n",
      "groundAreaMeters2 from JSON: 18.17 m^2\n",
      "---\n",
      "Sample 4\n",
      "Calculated Bounding Box Area: 26.99931821069944 m^2\n",
      "areaMeters2 from JSON: 19.9814 m^2\n",
      "groundAreaMeters2 from JSON: 16.88 m^2\n",
      "---\n",
      "Sample 5\n",
      "Calculated Bounding Box Area: 23.932011603403737 m^2\n",
      "areaMeters2 from JSON: 18.28056 m^2\n",
      "groundAreaMeters2 from JSON: 15.64 m^2\n",
      "---\n",
      "Sample 6\n",
      "Calculated Bounding Box Area: 20.098764741518128 m^2\n",
      "areaMeters2 from JSON: 11.357405 m^2\n",
      "groundAreaMeters2 from JSON: 11.08 m^2\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Function to calculate distance between two latitude-longitude points\n",
    "def calculate_distance(coord1, coord2):\n",
    "    R = 6371000  # Radius of Earth in meters\n",
    "    lat1, lon1 = math.radians(coord1[0]), math.radians(coord1[1])\n",
    "    lat2, lon2 = math.radians(coord2[0]), math.radians(coord2[1])\n",
    "    \n",
    "    dlat = lat2 - lat1\n",
    "    dlon = lon2 - lon1\n",
    "    \n",
    "    a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n",
    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n",
    "    \n",
    "    return R * c\n",
    "\n",
    "# Extract sample boxes\n",
    "sample_boxes_data = data['solarPotential']['roofSegmentStats']\n",
    "\n",
    "# Calculate and compare areas\n",
    "for i, sample in enumerate(sample_boxes_data):\n",
    "    box = sample['boundingBox']\n",
    "    sw = (box['sw']['latitude'], box['sw']['longitude'])\n",
    "    ne = (box['ne']['latitude'], box['ne']['longitude'])\n",
    "    \n",
    "    # Calculate area of bounding box in meters^2\n",
    "    width = calculate_distance((sw[0], sw[1]), (ne[0], sw[1]))\n",
    "    height = calculate_distance((sw[0], sw[1]), (sw[0], ne[1]))\n",
    "    bounding_box_area = width * height\n",
    "    \n",
    "    # Extract areaMeters2 and groundAreaMeters2 from JSON\n",
    "    area_meters2 = sample['stats']['areaMeters2']\n",
    "    ground_area_meters2 = sample['stats']['groundAreaMeters2']\n",
    "    \n",
    "    print(f\"Sample {i+1}\")\n",
    "    print(f\"Calculated Bounding Box Area: {bounding_box_area} m^2\")\n",
    "    print(f\"areaMeters2 from JSON: {area_meters2} m^2\")\n",
    "    print(f\"groundAreaMeters2 from JSON: {ground_area_meters2} m^2\")\n",
    "    print(\"---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of panels that can fit in each sample: {'Sample_1': 76, 'Sample_2': 14, 'Sample_3': 21, 'Sample_4': 18, 'Sample_5': 14, 'Sample_6': 12}\n"
     ]
    }
   ],
   "source": [
    "def greedy_panel_fitting(samples):\n",
    "    \"\"\"Calculate the maximum number of panels that can fit in each sample using a Greedy Method.\"\"\"\n",
    "    \n",
    "    panel_count_per_sample = {}  # Dictionary to hold the number of panels that can fit in each sample\n",
    "    panel_width = 65  # Width of the panel in inches\n",
    "    panel_height = 39  # Height of the panel in inches\n",
    "\n",
    "    # Loop through each sample to calculate the number of panels that can fit\n",
    "    for i, sample in enumerate(samples):\n",
    "        # Extract the coordinates for the South-West and North-East corners of the bounding box\n",
    "        sw_lat, sw_lon = sample['boundingBox']['sw']['latitude'], sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat, ne_lon = sample['boundingBox']['ne']['latitude'], sample['boundingBox']['ne']['longitude']\n",
    "\n",
    "        # Convert latitude and longitude to a Cartesian coordinate system\n",
    "        # For simplicity, assume 1 degree latitude or longitude is approximately 69 miles,\n",
    "        # and 1 mile is 63360 inches\n",
    "        width = abs(ne_lon - sw_lon) * 69 * 63360  # Width of the bounding box in inches\n",
    "        height = abs(ne_lat - sw_lat) * 69 * 63360  # Height of the bounding box in inches\n",
    "\n",
    "        # Calculate how many panels can fit along the width and height of the bounding box\n",
    "        panels_in_width = int(width // panel_width)\n",
    "        panels_in_height = int(height // panel_height)\n",
    "\n",
    "        # Calculate the total number of panels that can fit in this bounding box\n",
    "        total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "        # Store the result in the dictionary\n",
    "        panel_count_per_sample[f\"Sample_{i+1}\"] = total_panels\n",
    "\n",
    "    return panel_count_per_sample\n",
    "\n",
    "# Extract 'roofSegmentStats' from the 'solarPotential_data'\n",
    "roof_segment_stats = solarPotential_data.get('roofSegmentStats', [])\n",
    "\n",
    "# Initialize an empty list to hold the samples\n",
    "samples = []\n",
    "\n",
    "# Loop through each segment to extract the bounding box information\n",
    "for segment in roof_segment_stats:\n",
    "    bounding_box = segment.get('boundingBox', {})\n",
    "    sample = {'boundingBox': bounding_box}\n",
    "    samples.append(sample)\n",
    "\n",
    "\n",
    "# Run the Greedy Method to find the number of panels that can fit in each sample\n",
    "result = greedy_panel_fitting(samples)\n",
    "print(\"Total number of panels that can fit in each sample:\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of panels that can fit in each sample: {'Sample_1': 57, 'Sample_2': 14, 'Sample_3': 14, 'Sample_4': 12, 'Sample_5': 7, 'Sample_6': 8}\n"
     ]
    }
   ],
   "source": [
    "from math import radians, sin, cos, sqrt, atan2\n",
    "\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    \"\"\"Calculate the Haversine distance between two points on the earth in inches.\"\"\"\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "def greedy_panel_fitting(samples):\n",
    "    panel_count_per_sample = {}\n",
    "    panel_width = 65  # in inches\n",
    "    panel_height = 39  # in inches\n",
    "\n",
    "    for i, sample in enumerate(samples):\n",
    "        sw_lat, sw_lon = sample['boundingBox']['sw']['latitude'], sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat, ne_lon = sample['boundingBox']['ne']['latitude'], sample['boundingBox']['ne']['longitude']\n",
    "\n",
    "        # Calculate the width and height of the bounding box in inches using Haversine formula\n",
    "        width = haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon)\n",
    "        height = haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon)\n",
    "\n",
    "        panels_in_width = int(width // panel_width)\n",
    "        panels_in_height = int(height // panel_height)\n",
    "        total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "        panel_count_per_sample[f\"Sample_{i+1}\"] = total_panels\n",
    "\n",
    "    return panel_count_per_sample\n",
    "\n",
    "# Your data extraction code here, such as:\n",
    "# roof_segment_stats = solarPotential_data.get('roofSegmentStats', [])\n",
    "# samples = [{'boundingBox': bounding_box} for segment in roof_segment_stats]\n",
    "\n",
    "result = greedy_panel_fitting(samples)\n",
    "print(\"Total number of panels that can fit in each sample:\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of panels that can fit in each sample: {'Sample_1': 57, 'Sample_2': 16, 'Sample_3': 16, 'Sample_4': 16, 'Sample_5': 16, 'Sample_6': 10}\n"
     ]
    }
   ],
   "source": [
    "from math import radians, sin, cos, sqrt, atan2\n",
    "\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2)**2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "def greedy_panel_fitting(samples):\n",
    "    panel_count_per_sample = {}\n",
    "    panel_width = 65  # in inches\n",
    "    panel_height = 39  # in inches\n",
    "\n",
    "    for i, sample in enumerate(samples):\n",
    "        sw_lat, sw_lon = sample['boundingBox']['sw']['latitude'], sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat, ne_lon = sample['boundingBox']['ne']['latitude'], sample['boundingBox']['ne']['longitude']\n",
    "\n",
    "        width = haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon)\n",
    "        height = haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon)\n",
    "        \n",
    "        # Calculate the number of panels for both orientations\n",
    "        panels_in_width = round(width / panel_width)\n",
    "        panels_in_height = round(height / panel_height)\n",
    "        total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "        panel_count_per_sample[f\"Sample_{i+1}\"] = total_panels\n",
    "\n",
    "    return panel_count_per_sample\n",
    "\n",
    "# Your data extraction code here, such as:\n",
    "# roof_segment_stats = solarPotential_data.get('roofSegmentStats', [])\n",
    "# samples = [{'boundingBox': bounding_box} for segment in roof_segment_stats]\n",
    "\n",
    "# Calculate panel fitting results\n",
    "result = greedy_panel_fitting(samples)\n",
    "print(\"Total number of panels that can fit in each sample:\", result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of panels that can fit inside the main bounding box: 126\n"
     ]
    }
   ],
   "source": [
    "from math import radians, sin, cos, sqrt, atan2\n",
    "\n",
    "# Function to calculate the Haversine distance between two points on the Earth's surface\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "# Function to calculate the number of panels that can fit inside a bounding box\n",
    "def panels_in_bounding_box(bounding_box, panel_width, panel_height):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "\n",
    "    # Calculate the width and height of the bounding box in inches using Haversine formula\n",
    "    width = haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon)\n",
    "    height = haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon)\n",
    "\n",
    "    # Calculate the number of panels that can fit\n",
    "    panels_in_width = int(width // panel_width)\n",
    "    panels_in_height = int(height // panel_height)\n",
    "    total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "    return total_panels\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box_data = data['boundingBox']\n",
    "main_box = [\n",
    "    main_box_data['sw']['latitude'],\n",
    "    main_box_data['sw']['longitude'],\n",
    "    main_box_data['ne']['latitude'],\n",
    "    main_box_data['ne']['longitude']\n",
    "]\n",
    "\n",
    "# Dimensions of a single solar panel in inches\n",
    "panel_width = 65\n",
    "panel_height = 39\n",
    "\n",
    "# Calculate the number of panels that can fit inside the main bounding box\n",
    "total_panels_in_main_box = panels_in_bounding_box(main_box, panel_width, panel_height)\n",
    "\n",
    "# Print the result\n",
    "print(\"Total number of panels that can fit inside the main bounding box:\", total_panels_in_main_box)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "# Function to calculate the Haversine distance between two points on the Earth's surface\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "# Function to calculate the number of panels that can fit inside a bounding box\n",
    "def panels_in_bounding_box(bounding_box, panel_width, panel_height):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "\n",
    "    # Calculate the width and height of the bounding box in inches using Haversine formula\n",
    "    width = haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon)\n",
    "    height = haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon)\n",
    "\n",
    "    # Calculate the number of panels that can fit\n",
    "    panels_in_width = int(width // panel_width)\n",
    "    panels_in_height = int(height // panel_height)\n",
    "    total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "    return total_panels\n",
    "\n",
    "# Function to calculate the panel centers\n",
    "def calculate_panel_centers(bounding_box, panel_width, panel_height):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "\n",
    "    # Calculate the number of panels in width and height\n",
    "    panels_in_width = int(haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon) // panel_width)\n",
    "    panels_in_height = int(haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon) // panel_height)\n",
    "\n",
    "    # Calculate the step size for panel centers\n",
    "    lat_step = (ne_lat - sw_lat) / panels_in_height\n",
    "    lon_step = (ne_lon - sw_lon) / panels_in_width\n",
    "\n",
    "    # Calculate and store panel centers\n",
    "    panel_centers = []\n",
    "    for i in range(panels_in_height):\n",
    "        for j in range(panels_in_width):\n",
    "            center_lat = sw_lat + i * lat_step + lat_step / 2\n",
    "            center_lon = sw_lon + j * lon_step + lon_step / 2\n",
    "            panel_centers.append((center_lat, center_lon))\n",
    "\n",
    "    return panel_centers\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box_data = data['boundingBox']\n",
    "main_box = [\n",
    "    main_box_data['sw']['latitude'],\n",
    "    main_box_data['sw']['longitude'],\n",
    "    main_box_data['ne']['latitude'],\n",
    "    main_box_data['ne']['longitude']\n",
    "]\n",
    "\n",
    "# Dimensions of a single solar panel in inches\n",
    "panel_width = 65\n",
    "panel_height = 39\n",
    "\n",
    "# Calculate the number of panels that can fit inside the main bounding box\n",
    "total_panels_in_main_box = panels_in_bounding_box(main_box, panel_width, panel_height)\n",
    "\n",
    "# Calculate panel centers\n",
    "panel_centers = calculate_panel_centers(main_box, panel_width, panel_height)\n",
    "\n",
    "# Plotting code\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot([main_box[1], main_box[3], main_box[3], main_box[1], main_box[1]], \n",
    "         [main_box[0], main_box[0], main_box[2], main_box[2], main_box[0]], 'k-', label='Main Bounding Box')\n",
    "\n",
    "# Plot panel centers\n",
    "for center in panel_centers:\n",
    "    plt.scatter(center[1], center[0], c='b', marker='.', s=10)\n",
    "\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Panel Centers Inside Main Bounding Box')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of panels that can fit inside the main bounding box: 36\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "# Function to calculate the Haversine distance between two points on the Earth's surface\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "# Function to calculate the number of panels that can fit inside a bounding box\n",
    "def panels_in_bounding_box(bounding_box, panel_width, panel_height):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "\n",
    "    # Calculate the width and height of the bounding box in inches using Haversine formula\n",
    "    width = haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon)\n",
    "    height = haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon)\n",
    "\n",
    "    # Calculate the number of panels that can fit\n",
    "    panels_in_width = int(width // panel_width)\n",
    "    panels_in_height = int(height // panel_height)\n",
    "    total_panels = panels_in_width * panels_in_height\n",
    "\n",
    "    return total_panels\n",
    "\n",
    "# Function to calculate the panel centers\n",
    "def calculate_panel_centers(bounding_box, panel_width, panel_height):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "\n",
    "    # Calculate the number of panels in width and height\n",
    "    panels_in_width = int(haversine_distance(sw_lat, sw_lon, sw_lat, ne_lon) // panel_width)\n",
    "    panels_in_height = int(haversine_distance(sw_lat, sw_lon, ne_lat, sw_lon) // panel_height)\n",
    "\n",
    "    # Calculate the step size for panel centers\n",
    "    lat_step = (ne_lat - sw_lat) / panels_in_height\n",
    "    lon_step = (ne_lon - sw_lon) / panels_in_width\n",
    "\n",
    "    # Calculate and store panel centers along with their latitudes and longitudes\n",
    "    panel_centers = []\n",
    "    for i in range(panels_in_height):\n",
    "        for j in range(panels_in_width):\n",
    "            center_lat = sw_lat + i * lat_step + lat_step / 2\n",
    "            center_lon = sw_lon + j * lon_step + lon_step / 2\n",
    "            panel_centers.append((center_lat, center_lon))\n",
    "            #print(f\"Panel Center {len(panel_centers)} - Latitude: {center_lat}, Longitude: {center_lon}\")\n",
    "\n",
    "    return panel_centers\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box_data = data['boundingBox']\n",
    "main_box = [\n",
    "    main_box_data['sw']['latitude'],\n",
    "    main_box_data['sw']['longitude'],\n",
    "    main_box_data['ne']['latitude'],\n",
    "    main_box_data['ne']['longitude']\n",
    "]\n",
    "\n",
    "\n",
    "# Calculate the number of panels that can fit inside the main bounding box\n",
    "total_panels_in_main_box = panels_in_bounding_box(main_box, panel_width, panel_height)\n",
    "\n",
    "# Calculate panel centers and print their latitudes and longitudes\n",
    "panel_centers = calculate_panel_centers(main_box, panel_width, panel_height)\n",
    "\n",
    "# Plotting code\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot([main_box[1], main_box[3], main_box[3], main_box[1], main_box[1]], \n",
    "         [main_box[0], main_box[0], main_box[2], main_box[2], main_box[0]], 'k-', label='Main Bounding Box')\n",
    "\n",
    "# Plot panel centers\n",
    "for center in panel_centers:\n",
    "    plt.scatter(center[1], center[0], c='b', marker='.', s=10)\n",
    "\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Panel Centers Inside Main Bounding Box')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "# Print the result\n",
    "print(\"Total number of panels that can fit inside the main bounding box:\", total_panels_in_main_box)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum distance between neighboring panels: 102.04258066106527 inches\n"
     ]
    }
   ],
   "source": [
    "from math import radians, sin, cos, sqrt, atan2\n",
    "\n",
    "# Function to calculate the Haversine distance between two points on the Earth's surface\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 3958.8  # Radius of Earth in miles\n",
    "    dlat = radians(lat2 - lat1)\n",
    "    dlon = radians(lon2 - lon1)\n",
    "    a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
    "    distance = R * c  # Distance in miles\n",
    "    return distance * 63360  # Convert to inches\n",
    "\n",
    "# Function to calculate distances between neighboring panel centers\n",
    "def calculate_distances(panel_centers):\n",
    "    num_centers = len(panel_centers)\n",
    "    min_distances = {}\n",
    "\n",
    "    for i in range(num_centers):\n",
    "        for j in range(i + 1, num_centers):\n",
    "            lat1, lon1 = panel_centers[i]\n",
    "            lat2, lon2 = panel_centers[j]\n",
    "            distance = haversine_distance(lat1, lon1, lat2, lon2)\n",
    "            min_distances[(i, j)] = distance\n",
    "\n",
    "    return min_distances\n",
    "\n",
    "\n",
    "# Calculate minimum distances between neighboring panel centers in inches\n",
    "distances = calculate_distances(panel_centers)\n",
    "\n",
    "# Find the minimum distance\n",
    "min_distance = min(distances.values())\n",
    "\n",
    "# Print the minimum distance in inches\n",
    "print(f\"Minimum distance between neighboring panels: {min_distance} inches\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1 - SW: (42.2507291, -70.9785879), NE: (42.2508397, -70.9784355)\n",
      "Sample 2 - SW: (42.2507266, -70.9785713), NE: (42.2508121, -70.97846930000001)\n",
      "Sample 3 - SW: (42.2507527, -70.9786257), NE: (42.250807599999995, -70.97856949999999)\n",
      "Sample 4 - SW: (42.2506938, -70.9785444), NE: (42.250728300000006, -70.9784707)\n",
      "Sample 5 - SW: (42.2507127, -70.97859369999999), NE: (42.250754099999995, -70.978544)\n"
     ]
    }
   ],
   "source": [
    " # Extract 'roofSegmentStats' from the 'solarPotential_data'\n",
    "roof_segment_stats = solarPotential_data.get('roofSegmentStats', [])\n",
    "\n",
    "# Initialize an empty list to hold the samples\n",
    "samples = []\n",
    "\n",
    "# Initialize a dictionary to store the bounding boxes for each sample\n",
    "sample_bounding_boxes = {}\n",
    "\n",
    "# Loop through each sample and extract the bounding box information\n",
    "for i, segment in enumerate(roof_segment_stats):\n",
    "    bounding_box = segment.get('boundingBox', {})\n",
    "    sw_lat = bounding_box.get('sw', {}).get('latitude', None)\n",
    "    sw_lon = bounding_box.get('sw', {}).get('longitude', None)\n",
    "    ne_lat = bounding_box.get('ne', {}).get('latitude', None)\n",
    "    ne_lon = bounding_box.get('ne', {}).get('longitude', None)\n",
    "\n",
    "    if sw_lat is not None and sw_lon is not None and ne_lat is not None and ne_lon is not None:\n",
    "        sample_name = f'Sample_{i+1}'\n",
    "        sample_bounding_boxes[sample_name] = (sw_lat, sw_lon, ne_lat, ne_lon)\n",
    "        #print(f\"Sample {i+1} - SW: ({sw_lat}, {sw_lon}), NE: ({ne_lat}, {ne_lon})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sw': {'latitude': 42.254099499999995, 'longitude': -70.9628376},\n",
       " 'ne': {'latitude': 42.2541436, 'longitude': -70.9627878}}"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel at 42.250704675, -70.97851448333334 is within bounding box (42.2506938, -70.9785444, 42.250728300000006, -70.9784707)\n",
      "Panel at 42.250704675, -70.97851448333334 is assigned to Sample_4\n",
      "the highest changed from -1 to 15.932819\n",
      "Panel at 42.250704675, -70.97848245 is within bounding box (42.2506938, -70.9785444, 42.250728300000006, -70.9784707)\n",
      "Panel at 42.250704675, -70.97848245 is assigned to Sample_4\n",
      "the highest changed from -1 to 15.932819\n",
      "Panel at 42.250729225, -70.97857855 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250729225, -70.97857855 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250729225, -70.97857855 is within bounding box (42.2507127, -70.97859369999999, 42.250754099999995, -70.978544)\n",
      "Panel at 42.250729225, -70.97857855 is assigned to Sample_5\n",
      "the highest changed from 8.720731 to 17.056068\n",
      "Panel at 42.250729225, -70.97854651666667 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250729225, -70.97854651666667 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250729225, -70.97854651666667 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250729225, -70.97854651666667 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250729225, -70.97854651666667 is within bounding box (42.2507127, -70.97859369999999, 42.250754099999995, -70.978544)\n",
      "Panel at 42.250729225, -70.97854651666667 is assigned to Sample_5\n",
      "the highest changed from 15.900317 to 17.056068\n",
      "Panel at 42.250729225, -70.97851448333334 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250729225, -70.97851448333334 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250729225, -70.97851448333334 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250729225, -70.97851448333334 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250729225, -70.97848245 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250729225, -70.97848245 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250729225, -70.97848245 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250729225, -70.97848245 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250729225, -70.97845041666666 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250729225, -70.97845041666666 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250753775, -70.97861058333334 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250753775, -70.97861058333334 is assigned to Sample_3\n",
      "the highest changed from -1 to 15.851688\n",
      "Panel at 42.250753775, -70.97857855 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250753775, -70.97857855 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250753775, -70.97857855 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250753775, -70.97857855 is assigned to Sample_3\n",
      "the highest changed from 8.720731 to 15.851688\n",
      "Panel at 42.250753775, -70.97857855 is within bounding box (42.2507127, -70.97859369999999, 42.250754099999995, -70.978544)\n",
      "Panel at 42.250753775, -70.97857855 is assigned to Sample_5\n",
      "the highest changed from 15.851688 to 17.056068\n",
      "Panel at 42.250753775, -70.97854651666667 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250753775, -70.97854651666667 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250753775, -70.97854651666667 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250753775, -70.97854651666667 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250753775, -70.97854651666667 is within bounding box (42.2507127, -70.97859369999999, 42.250754099999995, -70.978544)\n",
      "Panel at 42.250753775, -70.97854651666667 is assigned to Sample_5\n",
      "the highest changed from 15.900317 to 17.056068\n",
      "Panel at 42.250753775, -70.97851448333334 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250753775, -70.97851448333334 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250753775, -70.97851448333334 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250753775, -70.97851448333334 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250753775, -70.97848245 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250753775, -70.97848245 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250753775, -70.97848245 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250753775, -70.97848245 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250753775, -70.97845041666666 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250753775, -70.97845041666666 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250778325, -70.97861058333334 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250778325, -70.97861058333334 is assigned to Sample_3\n",
      "the highest changed from -1 to 15.851688\n",
      "Panel at 42.250778325, -70.97857855 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250778325, -70.97857855 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250778325, -70.97857855 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250778325, -70.97857855 is assigned to Sample_3\n",
      "the highest changed from 8.720731 to 15.851688\n",
      "Panel at 42.250778325, -70.97854651666667 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250778325, -70.97854651666667 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250778325, -70.97854651666667 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250778325, -70.97854651666667 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250778325, -70.97851448333334 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250778325, -70.97851448333334 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250778325, -70.97851448333334 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250778325, -70.97851448333334 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250778325, -70.97848245 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250778325, -70.97848245 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250778325, -70.97848245 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250778325, -70.97848245 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250778325, -70.97845041666666 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250778325, -70.97845041666666 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250802875000005, -70.97861058333334 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250802875000005, -70.97861058333334 is assigned to Sample_3\n",
      "the highest changed from -1 to 15.851688\n",
      "Panel at 42.250802875000005, -70.97857855 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250802875000005, -70.97857855 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250802875000005, -70.97857855 is within bounding box (42.2507527, -70.9786257, 42.250807599999995, -70.97856949999999)\n",
      "Panel at 42.250802875000005, -70.97857855 is assigned to Sample_3\n",
      "the highest changed from 8.720731 to 15.851688\n",
      "Panel at 42.250802875000005, -70.97854651666667 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250802875000005, -70.97854651666667 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250802875000005, -70.97854651666667 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250802875000005, -70.97854651666667 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250802875000005, -70.97851448333334 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250802875000005, -70.97851448333334 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250802875000005, -70.97851448333334 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250802875000005, -70.97851448333334 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250802875000005, -70.97848245 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250802875000005, -70.97848245 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250802875000005, -70.97848245 is within bounding box (42.2507266, -70.9785713, 42.2508121, -70.97846930000001)\n",
      "Panel at 42.250802875000005, -70.97848245 is assigned to Sample_2\n",
      "the highest changed from 8.720731 to 15.900317\n",
      "Panel at 42.250802875000005, -70.97845041666666 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250802875000005, -70.97845041666666 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250827425000004, -70.97857855 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250827425000004, -70.97857855 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250827425000004, -70.97854651666667 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250827425000004, -70.97854651666667 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250827425000004, -70.97851448333334 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250827425000004, -70.97851448333334 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250827425000004, -70.97848245 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250827425000004, -70.97848245 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "Panel at 42.250827425000004, -70.97845041666666 is within bounding box (42.2507291, -70.9785879, 42.2508397, -70.9784355)\n",
      "Panel at 42.250827425000004, -70.97845041666666 is assigned to Sample_1\n",
      "the highest changed from -1 to 8.720731\n",
      "    Panel Number Sample Assigned                              Panel Center  \\\n",
      "0              1        Sample_4        (42.250704675, -70.97851448333334)   \n",
      "1              2        Sample_4              (42.250704675, -70.97848245)   \n",
      "2              3        Sample_5              (42.250729225, -70.97857855)   \n",
      "3              4        Sample_5        (42.250729225, -70.97854651666667)   \n",
      "4              5        Sample_2        (42.250729225, -70.97851448333334)   \n",
      "5              6        Sample_2              (42.250729225, -70.97848245)   \n",
      "6              7        Sample_1        (42.250729225, -70.97845041666666)   \n",
      "7              8        Sample_3        (42.250753775, -70.97861058333334)   \n",
      "8              9        Sample_5              (42.250753775, -70.97857855)   \n",
      "9             10        Sample_5        (42.250753775, -70.97854651666667)   \n",
      "10            11        Sample_2        (42.250753775, -70.97851448333334)   \n",
      "11            12        Sample_2              (42.250753775, -70.97848245)   \n",
      "12            13        Sample_1        (42.250753775, -70.97845041666666)   \n",
      "13            14        Sample_3        (42.250778325, -70.97861058333334)   \n",
      "14            15        Sample_3              (42.250778325, -70.97857855)   \n",
      "15            16        Sample_2        (42.250778325, -70.97854651666667)   \n",
      "16            17        Sample_2        (42.250778325, -70.97851448333334)   \n",
      "17            18        Sample_2              (42.250778325, -70.97848245)   \n",
      "18            19        Sample_1        (42.250778325, -70.97845041666666)   \n",
      "19            20        Sample_3  (42.250802875000005, -70.97861058333334)   \n",
      "20            21        Sample_3        (42.250802875000005, -70.97857855)   \n",
      "21            22        Sample_2  (42.250802875000005, -70.97854651666667)   \n",
      "22            23        Sample_2  (42.250802875000005, -70.97851448333334)   \n",
      "23            24        Sample_2        (42.250802875000005, -70.97848245)   \n",
      "24            25        Sample_1  (42.250802875000005, -70.97845041666666)   \n",
      "25            26        Sample_1        (42.250827425000004, -70.97857855)   \n",
      "26            27        Sample_1  (42.250827425000004, -70.97854651666667)   \n",
      "27            28        Sample_1  (42.250827425000004, -70.97851448333334)   \n",
      "28            29        Sample_1        (42.250827425000004, -70.97848245)   \n",
      "29            30        Sample_1  (42.250827425000004, -70.97845041666666)   \n",
      "\n",
      "    Panel Height  \n",
      "0      15.932819  \n",
      "1      15.932819  \n",
      "2      17.056068  \n",
      "3      17.056068  \n",
      "4      15.900317  \n",
      "5      15.900317  \n",
      "6       8.720731  \n",
      "7      15.851688  \n",
      "8      17.056068  \n",
      "9      17.056068  \n",
      "10     15.900317  \n",
      "11     15.900317  \n",
      "12      8.720731  \n",
      "13     15.851688  \n",
      "14     15.851688  \n",
      "15     15.900317  \n",
      "16     15.900317  \n",
      "17     15.900317  \n",
      "18      8.720731  \n",
      "19     15.851688  \n",
      "20     15.851688  \n",
      "21     15.900317  \n",
      "22     15.900317  \n",
      "23     15.900317  \n",
      "24      8.720731  \n",
      "25      8.720731  \n",
      "26      8.720731  \n",
      "27      8.720731  \n",
      "28      8.720731  \n",
      "29      8.720731  \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Declare panel_df as global\n",
    "global panel_df\n",
    "\n",
    "def assign_panel_numbers_and_colors(panel_centers, sample_bounding_boxes, sample_heights):\n",
    "    global panel_df  # Indicate that we are using the global variable\n",
    "    panel_info = []\n",
    "    panel_numbers = {}\n",
    "    panel_colors = {}\n",
    "    \n",
    "    for i, panel_center in enumerate(panel_centers):\n",
    "        #print(f\"Checking panel at {panel_center}\")\n",
    "        lat, lon = panel_center\n",
    "        assigned_sample = None\n",
    "        highest_sample_height = -1\n",
    "\n",
    "        for j, (sample_name, bounding_box) in enumerate(sample_bounding_boxes.items()):\n",
    "            #=print(f\"Panel at {lat}, {lon} is being checked against {sample_name}\")\n",
    "            sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "            #print(f\"sw_lat: {sw_lat}, sw_lon: {sw_lon}, ne_lat: {ne_lat}, ne_lon: {ne_lon}, lat: {lat}, lon: {lon}\")\n",
    "            if sw_lat <= lat <= ne_lat and sw_lon <= lon <= ne_lon:\n",
    "                print(f\"Panel at {lat}, {lon} is within bounding box {bounding_box}\")\n",
    "                if sample_heights[j] > highest_sample_height:\n",
    "                    print(f\"Panel at {lat}, {lon} is assigned to {sample_name}\")\n",
    "                    assigned_sample = sample_name\n",
    "                    if sample_heights[j] > highest_sample_height:\n",
    "                        print(f\"the highest changed from {highest_sample_height} to {sample_heights[j]}\")\n",
    "                    highest_sample_height = sample_heights[j]\n",
    "\n",
    "        if assigned_sample:\n",
    "            #print( f\"Panel at {lat}, {lon} is assigned to {assigned_sample}\")\n",
    "            panel_number = len(panel_numbers) + 1\n",
    "            panel_numbers[panel_center] = panel_number\n",
    "            panel_colors[panel_center] = cm.tab20(i)\n",
    "            panel_info.append([panel_number, assigned_sample, panel_center, highest_sample_height])\n",
    "            \n",
    "    panel_df = pd.DataFrame(panel_info, columns=['Panel Number', 'Sample Assigned', 'Panel Center', 'Panel Height'])\n",
    "    \n",
    "    # Filter out unassigned panels\n",
    "    panel_df = panel_df[panel_df['Sample Assigned'].notna()]\n",
    "    \n",
    "    return panel_numbers, panel_colors\n",
    "\n",
    "# Call the function\n",
    "panel_numbers, panel_colors = assign_panel_numbers_and_colors(panel_centers, sample_bounding_boxes, sample_heights)\n",
    "\n",
    "# Access the global DataFrame for later use\n",
    "print(panel_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Assign panel numbers and colors\n",
    "def assign_panel_numbers_and_colors(panel_centers, sample_bounding_boxes, sample_heights):\n",
    "    panel_numbers = {}\n",
    "    panel_colors = {}\n",
    "    \n",
    "    for i, panel_center in enumerate(panel_centers):\n",
    "        lat, lon = panel_center\n",
    "        assigned_sample = None\n",
    "        highest_sample_height = -1\n",
    "        color_index = -1\n",
    "\n",
    "        for j, (sample_name, bounding_box) in enumerate(sample_bounding_boxes.items()):\n",
    "            sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "            if sw_lat <= lat <= ne_lat and sw_lon <= lon <= ne_lon:\n",
    "                if sample_heights[j] > highest_sample_height:\n",
    "                    assigned_sample = sample_name\n",
    "                    highest_sample_height = sample_heights[j]\n",
    "                    color_index = j\n",
    "\n",
    "        if assigned_sample:\n",
    "            panel_number = len(panel_numbers) + 1\n",
    "            panel_numbers[panel_center] = panel_number\n",
    "            color = cm.tab20(color_index)  \n",
    "            panel_colors[panel_center] = color\n",
    "    \n",
    "    return panel_numbers, panel_colors\n",
    "\n",
    "# Call the function\n",
    "panel_numbers, panel_colors = assign_panel_numbers_and_colors(panel_centers, sample_bounding_boxes, sample_heights)\n",
    "\n",
    "# Plotting\n",
    "for panel_center, panel_number in panel_numbers.items():\n",
    "    lat, lon = panel_center\n",
    "    color = panel_colors.get(panel_center, 'blue')\n",
    "    plt.scatter(lon, lat, c=[color], s=50)\n",
    "    plt.annotate(f'Panel {panel_number}', (lon, lat), fontsize=8, color='black', ha='center', va='center')\n",
    "\n",
    "# Plot sample bounding boxes with unique colors\n",
    "for i, (sample_name, bounding_box) in enumerate(sample_bounding_boxes.items()):\n",
    "    sw_lat, sw_lon, ne_lat, ne_lon = bounding_box\n",
    "    color = cm.tab20(i)\n",
    "    height = sample_heights[i]  # Extract the height for this sample\n",
    "    plt.plot([sw_lon, ne_lon, ne_lon, sw_lon, sw_lon], [sw_lat, sw_lat, ne_lat, ne_lat, sw_lat], color=color, label=f'{sample_name} (Height: {height} meters)')\n",
    "\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Sample_1': [544, 894, 1200, 1221, 1229, 1235, 1241, 1246, 1252, 1278, 1521], 'Sample_2': [684, 1051, 1199, 1233, 1249, 1259, 1267, 1273, 1283, 1297, 1424], 'Sample_3': [696, 1187, 1236, 1255, 1266, 1280, 1293, 1311, 1350, 1398, 1515], 'Sample_4': [757, 1072, 1094, 1469, 1503, 1512, 1516, 1517, 1518, 1519, 1523], 'Sample_5': [988, 1202, 1232, 1252, 1261, 1267, 1273, 1281, 1299, 1370, 1511], 'Sample_6': [514, 782, 960, 1158, 1235, 1299, 1319, 1342, 1366, 1377, 1413]}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3762/4075939581.py:22: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '1363.6363636363637' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  panel_df.loc[index, 'Average Sunshine Quantile'] = average_sunshine_quantile\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store sunshine quantiles for each sample\n",
    "sunshine_quantiles_dict = {}\n",
    "\n",
    "# Loop through each sample and extract sunshine quantile information\n",
    "for i, segment in enumerate(roofSegmentStats):\n",
    "    stats = segment.get('stats', {})\n",
    "    sunshine_quantiles = stats.get('sunshineQuantiles', [])\n",
    "    sample_name = f'Sample_{i+1}'\n",
    "    sunshine_quantiles_dict[sample_name] = sunshine_quantiles\n",
    "#print(sunshine_quantiles_dict)\n",
    "\n",
    "# Create a new column for average sunshine quantiles in panel_df\n",
    "panel_df['Average Sunshine Quantile'] = 0\n",
    "\n",
    "# Loop through each row in panel_df to assign the average sunshine quantile\n",
    "for index, row in panel_df.iterrows():\n",
    "    sample_name = row['Sample Assigned']\n",
    "    sunshine_quantiles = sunshine_quantiles_dict.get(sample_name, [])\n",
    "    \n",
    "    if sunshine_quantiles:\n",
    "        average_sunshine_quantile = sum(sunshine_quantiles) / len(sunshine_quantiles)\n",
    "        panel_df.loc[index, 'Average Sunshine Quantile'] = average_sunshine_quantile\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Panel Number</th>\n",
       "      <th>Sample Assigned</th>\n",
       "      <th>Panel Center</th>\n",
       "      <th>Panel Height</th>\n",
       "      <th>Average Sunshine Quantile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sample_4</td>\n",
       "      <td>(42.250704675, -70.97851448333334)</td>\n",
       "      <td>15.932819</td>\n",
       "      <td>1363.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Sample_4</td>\n",
       "      <td>(42.250704675, -70.97848245)</td>\n",
       "      <td>15.932819</td>\n",
       "      <td>1363.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Sample_5</td>\n",
       "      <td>(42.250729225, -70.97857855)</td>\n",
       "      <td>17.056068</td>\n",
       "      <td>1266.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sample_5</td>\n",
       "      <td>(42.250729225, -70.97854651666667)</td>\n",
       "      <td>17.056068</td>\n",
       "      <td>1266.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sample_5</td>\n",
       "      <td>(42.250753775, -70.97857855)</td>\n",
       "      <td>17.056068</td>\n",
       "      <td>1266.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Sample_5</td>\n",
       "      <td>(42.250753775, -70.97854651666667)</td>\n",
       "      <td>17.056068</td>\n",
       "      <td>1266.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Sample_3</td>\n",
       "      <td>(42.250753775, -70.97861058333334)</td>\n",
       "      <td>15.851688</td>\n",
       "      <td>1253.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Sample_3</td>\n",
       "      <td>(42.250802875000005, -70.97857855)</td>\n",
       "      <td>15.851688</td>\n",
       "      <td>1253.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Sample_3</td>\n",
       "      <td>(42.250802875000005, -70.97861058333334)</td>\n",
       "      <td>15.851688</td>\n",
       "      <td>1253.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Sample_3</td>\n",
       "      <td>(42.250778325, -70.97861058333334)</td>\n",
       "      <td>15.851688</td>\n",
       "      <td>1253.363636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Panel Number Sample Assigned                              Panel Center  \\\n",
       "0              1        Sample_4        (42.250704675, -70.97851448333334)   \n",
       "1              2        Sample_4              (42.250704675, -70.97848245)   \n",
       "2              3        Sample_5              (42.250729225, -70.97857855)   \n",
       "3              4        Sample_5        (42.250729225, -70.97854651666667)   \n",
       "8              9        Sample_5              (42.250753775, -70.97857855)   \n",
       "9             10        Sample_5        (42.250753775, -70.97854651666667)   \n",
       "7              8        Sample_3        (42.250753775, -70.97861058333334)   \n",
       "20            21        Sample_3        (42.250802875000005, -70.97857855)   \n",
       "19            20        Sample_3  (42.250802875000005, -70.97861058333334)   \n",
       "13            14        Sample_3        (42.250778325, -70.97861058333334)   \n",
       "\n",
       "    Panel Height  Average Sunshine Quantile  \n",
       "0      15.932819                1363.636364  \n",
       "1      15.932819                1363.636364  \n",
       "2      17.056068                1266.909091  \n",
       "3      17.056068                1266.909091  \n",
       "8      17.056068                1266.909091  \n",
       "9      17.056068                1266.909091  \n",
       "7      15.851688                1253.363636  \n",
       "20     15.851688                1253.363636  \n",
       "19     15.851688                1253.363636  \n",
       "13     15.851688                1253.363636  "
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel_df = panel_df.sort_values(by=['Average Sunshine Quantile'], ascending=False)\n",
    "panel_df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of average sunshine quantiles: 36601.63636363636\n",
      "total number of panels: 30\n"
     ]
    }
   ],
   "source": [
    "# sum of average sunshine quantiles\n",
    "sum(panel_df['Average Sunshine Quantile'])\n",
    "print (f\"sum of average sunshine quantiles: {sum(panel_df['Average Sunshine Quantile'])}\")\n",
    "print (f\"total number of panels: {len(panel_df)}\")"
   ]
  }
 ],
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