Cal_Panel_Num.ipynb

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   "cell_type": "code",
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   "source": [
    "import json\n",
    "\n",
    "# Load data from the JSON file\n",
    "with open('51n.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",
    "solarPotential_structure = {key: type(value).__name__ for key, value in solarPotential_data.items()}\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 = 60\n",
    "panel_height = 70\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Common optimal angle for all segments: 62.11802161326996\n"
     ]
    },
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       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7ff2296e6bc0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize_scalar\n",
    "import math\n",
    "import pandas as pd\n",
    "import folium\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\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",
    "# Function to calculate rotated bounding box area\n",
    "def rotated_area(angle_degrees, original_area, aspect_ratio):\n",
    "    angle_radians = math.radians(float(angle_degrees))\n",
    "    return original_area * (math.cos(angle_radians) + aspect_ratio * math.sin(angle_radians))\n",
    "\n",
    "# Function to find the optimal rotation angle\n",
    "def find_common_optimal_rotation_angle(data):\n",
    "    def objective(angle_degrees):\n",
    "        total_diff = 0\n",
    "        for segment in data['solarPotential']['roofSegmentStats']:\n",
    "            sw = segment['boundingBox']['sw']\n",
    "            ne = segment['boundingBox']['ne']\n",
    "            original_area = abs((ne['latitude'] - sw['latitude']) * (ne['longitude'] - sw['longitude']))\n",
    "            aspect_ratio = abs((ne['longitude'] - sw['longitude']) / (ne['latitude'] - sw['latitude']))\n",
    "            target_area = segment['stats']['groundAreaMeters2']\n",
    "            diff = abs(rotated_area(angle_degrees, original_area, aspect_ratio) - target_area)\n",
    "            \n",
    "            # Weighted by the original area\n",
    "            weighted_diff = diff * original_area\n",
    "            \n",
    "            total_diff += weighted_diff\n",
    "        return total_diff\n",
    "    \n",
    "    result = minimize_scalar(objective, bounds=(0, 90), method='bounded', options={'maxiter': 100})\n",
    "    return result.x\n",
    "\n",
    "# Find the common optimal angle for all segments\n",
    "common_optimal_angle = find_common_optimal_rotation_angle(data)\n",
    "logging.info(f\"Common optimal angle for all segments: {common_optimal_angle}\")\n",
    "\n",
    "# Initialize the folium map centered around a sample location\n",
    "first_sample = data['solarPotential']['roofSegmentStats'][0]\n",
    "initial_ne = first_sample['boundingBox']['ne']\n",
    "initial_location = [initial_ne['latitude'], initial_ne['longitude']]\n",
    "m = folium.Map(location=initial_location, zoom_start=15)\n",
    "\n",
    "# Loop through each segment in 'roofSegmentStats'\n",
    "for i, segment in enumerate(data['solarPotential']['roofSegmentStats']):\n",
    "    center = segment['center']\n",
    "    center_lat, center_long = center['latitude'], center['longitude']\n",
    "    \n",
    "    sw = segment['boundingBox']['sw']\n",
    "    ne = segment['boundingBox']['ne']\n",
    "    width = abs(ne['longitude'] - sw['longitude'])\n",
    "    height = abs(ne['latitude'] - sw['latitude'])\n",
    "    \n",
    "    # Rotate the corners around the center\n",
    "    rotated_sw = rotate_point(sw['latitude'], sw['longitude'], common_optimal_angle, center_lat, center_long)\n",
    "    rotated_ne = rotate_point(ne['latitude'], ne['longitude'], common_optimal_angle, center_lat, center_long)\n",
    "    rotated_se = rotate_point(ne['latitude'], sw['longitude'], common_optimal_angle, center_lat, center_long)\n",
    "    rotated_nw = rotate_point(sw['latitude'], ne['longitude'], common_optimal_angle, center_lat, center_long)\n",
    "    \n",
    "    # Draw the rotated bounding box as a polygon on the map\n",
    "    folium.Polygon([rotated_sw, rotated_se, rotated_ne, rotated_nw, rotated_sw], color='red').add_to(m)\n",
    "    #logging.info(f\"Added bounding box with corners: {rotated_sw, rotated_se, rotated_ne, rotated_nw}\")\n",
    "\n",
    "# Show the map\n",
    "m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.0, 3.00000000000000) (2.88000000000000, -0.840000000000000)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from sympy import symbols, solve, Eq, sqrt\n",
    "# Given points A(x1, y1) and B(x2, y2) and area A\n",
    "x1, y1 = 0, 0\n",
    "A = (x1, y1)\n",
    "x2, y2 = 4, 3\n",
    "B = (x2, y2)\n",
    "Area = 15\n",
    "\n",
    "# Calculate distance d between A and B\n",
    "d = sqrt((x2 - x1)**2 + (y2 - y1)**2)\n",
    "\n",
    "# Calculate radius r of the circumcircle\n",
    "r = d / 2\n",
    "\n",
    "# Calculate the center O of the circumcircle\n",
    "x_center = (x1 + x2) / 2\n",
    "y_center = (y1 + y2) / 2\n",
    "\n",
    "# Calculate the other dimension (either width or height) using the area A\n",
    "other_dimension = Area / d\n",
    "\n",
    "# Use the equation of the circle to find point C(x3, y3)\n",
    "x3, y3 = symbols('x3 y3')\n",
    "\n",
    "# Equation of the circle\n",
    "eq1 = Eq((x3 - x_center)**2 + (y3 - y_center)**2, r**2)\n",
    "\n",
    "# Equation representing the distance from A to C equal to the other dimension\n",
    "eq2 = Eq(sqrt((x3 - x1)**2 + (y3 - y1)**2), other_dimension)\n",
    "\n",
    "# Equation for 90-degree angles using dot product of vectors AB and AC\n",
    "eq3 = Eq((x3 - x1) * (x2 - x3) + (y3 - y1) * (y2 - y3), 0)\n",
    "\n",
    "# Solve the equations to find point C(x3, y3)\n",
    "solution_with_dot_product = solve((eq1, eq2, eq3), (x3, y3))\n",
    "C1,C2 = solution_with_dot_product\n",
    "final_c= [C1,C2]\n",
    "print (C1,C2)\n",
    "\n",
    "# Function to calculate D1 and D2 algebraically without using the solve function\n",
    "def calculate_D_algebraic(x_center, y_center, C):\n",
    "    x3, y3 = C\n",
    "    x4 = 2 * x_center - x3\n",
    "    y4 = 2 * y_center - y3\n",
    "    return x4, y4\n",
    "\n",
    "# Initialize list to store the corresponding D points using the algebraic method\n",
    "D_points_algebraic = []\n",
    "\n",
    "# Calculate D for each C using the algebraic method\n",
    "for C in final_c:\n",
    "    D = calculate_D_algebraic(x_center, y_center, C)\n",
    "    D_points_algebraic.append(D)\n",
    "\n",
    "D1,D2 = D_points_algebraic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42.2711749272716, -71.0848913669754) (42.2712045142647, -71.0849148411415) (42.2712497727284, -71.0848176330246) (42.2712201857353, -71.0847941588585)\n"
     ]
    }
   ],
   "source": [
    "import sympy as sp\n",
    "import math\n",
    "import numpy as np\n",
    "from sympy import symbols, solve, Eq, sqrt\n",
    "# Average latitude for North America (example: 40 degrees North)\n",
    "average_latitude = 35\n",
    "\n",
    "# Constants for distance calculations\n",
    "METERS_PER_DEGREE_LAT = 111000  # Approximate meters per degree of latitude\n",
    "METERS_PER_DEGREE_LON = METERS_PER_DEGREE_LAT * math.cos(math.radians(average_latitude))\n",
    "\n",
    "#Sample 1: SW = (42.271187999999995, -71.0849068), NE = (42.271236699999996, -71.0848022)\n",
    "# Given points A(x1, y1) and B(x2, y2) and area A\n",
    "lat1, lon1 = (42.271187999999995, -71.0849068)\n",
    "A = (lat1, lon1)\n",
    "lat2, lon2 = (42.271236699999996, -71.0848022)\n",
    "B = (lat2, lon2)\n",
    "Area = 20\n",
    "\n",
    "# Calculate the center O of the circumcircle\n",
    "x_center = (lat1 + lat2) / 2\n",
    "y_center = (lon1 + lon2) / 2\n",
    "# Calculate distance d between A and B\n",
    "distance_lat = (lat2 - lat1) * METERS_PER_DEGREE_LAT\n",
    "distance_lon = (lon2 - lon1) * METERS_PER_DEGREE_LON\n",
    "\n",
    "# Calculate the actual distance d between A and B using Pythagoras theorem\n",
    "d = sp.sqrt(distance_lat**2 + distance_lon**2)\n",
    "\n",
    "# Define the symbols for width (w) and height (h)\n",
    "w, h = sp.symbols('w h', real=True)\n",
    "\n",
    "# Equation for the area and diagonal of the rectangle\n",
    "eq_area = sp.Eq(w * h, Area)\n",
    "eq_diag = sp.Eq(w**2 + h**2, d**2)\n",
    "\n",
    "# Solve the system of equations\n",
    "solutions = sp.solve((eq_area, eq_diag), (w, h))\n",
    "\n",
    "# Check if there are any solutions\n",
    "if solutions:\n",
    "    # Take the solution with the positive width and height\n",
    "    width, height = next((sol for sol in solutions if sol[0] > 0 and sol[1] > 0), (None, None))\n",
    "\n",
    "    # Define symbols for point C (x3, y3)\n",
    "    lat3, lon3 = sp.symbols('lat3 lon3', real=True)\n",
    "\n",
    "    # Equations based on the rectangle's sides and Pythagorean theorem\n",
    "    eq1 = Eq ((lat3 - lat1)**2 * METERS_PER_DEGREE_LAT**2 + (lon3 - lon1)**2 * METERS_PER_DEGREE_LON**2, height**2)\n",
    "    eq2 = Eq ((lat3 - lat2)**2 * METERS_PER_DEGREE_LAT**2 + (lon3 - lon2)**2 * METERS_PER_DEGREE_LON**2, width**2)\n",
    "    # Equation for 90-degree angles using dot product of vectors AB and AC\n",
    "    #eq3 = Eq ((lat3 - lat1) * (lat3 - lat2)* METERS_PER_DEGREE_LAT**2 + (lon3 - lon1) * (lon3 - lon2)* METERS_PER_DEGREE_LON**2, 0)\n",
    "\n",
    "    # Solve the equations to find point C\n",
    "    solution_for_C = sp.solve((eq1, eq2,eq3), (x3, y3))\n",
    "\n",
    "    # There may be two valid solutions for point C\n",
    "    if solution_for_C:\n",
    "        C1, C2 = solution_for_C[0], solution_for_C[1]\n",
    "\n",
    "        # Function to calculate D algebraically without using the solve function\n",
    "        def calculate_D_algebraic(C):\n",
    "            lat3, lon3 = C\n",
    "            lat4 = (2 * x_center - lat3) \n",
    "            lon4 = (2 * y_center - lon3)\n",
    "            return lat4, lon4\n",
    "        # Calculate D for each C using the algebraic method\n",
    "        D1 = calculate_D_algebraic(C1)\n",
    "        D2 = calculate_D_algebraic(C2)\n",
    "    else:\n",
    "        C1, C2, D1, D2 = (None, None), (None, None), (None, None), (None, None)\n",
    "else:\n",
    "    width, height, C1, C2, D1, D2 = (None,) * 6\n",
    "\n",
    "print(C1,C2,D1,D2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42.271187999999995, -71.0849068),\n",
       " (42.271236699999996, -71.0848022),\n",
       " (42.2711750813400, -71.0848938569970),\n",
       " (42.2712496186600, -71.0848151430030))"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sympy as sp\n",
    "\n",
    "def find_rectangle_vertices(NE, SW, Area):\n",
    "    # Average latitude for North America (example: 40 degrees North)\n",
    "    average_latitude = 35\n",
    "\n",
    "    # Constants for distance calculations\n",
    "    METERS_PER_DEGREE_LAT = 111000  # Approximate meters per degree of latitude\n",
    "    METERS_PER_DEGREE_LON = METERS_PER_DEGREE_LAT * math.cos(math.radians(average_latitude))\n",
    "\n",
    "    # Extract latitude and longitude from NE and SW\n",
    "    lat1, lon1 = SW\n",
    "    lat2, lon2 = NE\n",
    "\n",
    "    # Calculate the center of the line AB\n",
    "    lat_center = (lat1 + lat2) / 2\n",
    "    lon_center = (lon1 + lon2) / 2\n",
    "\n",
    "    # Calculate the distances between the latitudes and longitudes\n",
    "    distance_lat = (lat2 - lat1) * METERS_PER_DEGREE_LAT\n",
    "    distance_lon = (lon2 - lon1) * METERS_PER_DEGREE_LON\n",
    "\n",
    "    # Calculate the actual distance d between A and B using the Pythagorean theorem\n",
    "    d = sp.sqrt(distance_lat**2 + distance_lon**2)\n",
    "\n",
    "    # Define the symbols for width (w) and height (h)\n",
    "    w, h = sp.symbols('w h', real=True)\n",
    "\n",
    "    # Equation for the area and diagonal of the rectangle\n",
    "    eq_area = sp.Eq(w * h, Area)\n",
    "    eq_diag = sp.Eq(w**2 + h**2, d**2)\n",
    "\n",
    "    # Solve the system of equations\n",
    "    solutions = sp.solve((eq_area, eq_diag), (w, h))\n",
    "\n",
    "    # Initialize an empty list to store the rectangle vertices\n",
    "    rectangles = []\n",
    "\n",
    "    # Check if there are any solutions\n",
    "    if solutions:\n",
    "        # Take the solution with the positive width and height\n",
    "        width, height = next((sol for sol in solutions if sol[0] > 0 and sol[1] > 0), (None, None))\n",
    "\n",
    "        # Define symbols for point C (x3, y3)\n",
    "        lat3, lon3 = sp.symbols('lat3 lon3', real=True)\n",
    "\n",
    "         # Equations based on the rectangle's sides and Pythagorean theorem\n",
    "        eq1 = Eq ((lat3 - lat1)**2 * METERS_PER_DEGREE_LAT**2 + (lon3 - lon1)**2 * METERS_PER_DEGREE_LON**2, height**2)\n",
    "        eq2 = Eq ((lat3 - lat2)**2 * METERS_PER_DEGREE_LAT**2 + (lon3 - lon2)**2 * METERS_PER_DEGREE_LON**2, width**2)\n",
    "        # Equation for 90-degree angles using dot product of vectors AB and AC\n",
    "        #eq3 = Eq ((lat3 - lat1) * (lat3 - lat2)* METERS_PER_DEGREE_LAT**2 + (lon3 - lon1) * (lon3 - lon2)* METERS_PER_DEGREE_LON**2, 0)\n",
    "\n",
    "        # Solve the equations to find point C\n",
    "        solution_for_C = sp.solve((eq1, eq2,eq3), (lat3, lon3))\n",
    "\n",
    "        # Calculate points C and D for each solution\n",
    "        for C in solution_for_C:\n",
    "            lat3, lon3 = C\n",
    "            lat4 = 2 * lat_center - lat3\n",
    "            lon4 = 2 * lon_center - lon3\n",
    "            D = (lat4, lon4)\n",
    "            rectangles.append(((lat1, lon1), (lat2, lon2), C, D))\n",
    "\n",
    "    return rectangles\n",
    "\n",
    "# Example usage\n",
    "NE = (42.271236699999996, -71.0848022)\n",
    "SW = (42.271187999999995, -71.0849068)\n",
    "Area = 20\n",
    "\n",
    "results = find_rectangle_vertices(NE, SW, Area)\n",
    "results[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[((42.271184399999996, -71.08495789999999), (42.2713159, -71.0848022), (42.2711624447939, -71.0848979192382), (42.2713378552061, -71.0848621807618)), ((42.271184399999996, -71.08495789999999), (42.2713159, -71.0848022), (42.2712279941393, -71.0849922596064), (42.2712723058607, -71.0847678403936))]\n"
     ]
    }
   ],
   "source": [
    "# Extract the southwest and northeast points\n",
    "sw_lat = data['boundingBox']['sw']['latitude']\n",
    "sw_lon = data['boundingBox']['sw']['longitude']\n",
    "ne_lat = data['boundingBox']['ne']['latitude']\n",
    "ne_lon = data['boundingBox']['ne']['longitude']\n",
    "\n",
    "# Extract the area from the solarPotential\n",
    "area = data['solarPotential']['wholeRoofStats']['areaMeters2']\n",
    "\n",
    "# Convert these points into a format suitable for your function\n",
    "# Assuming find_rectangle_vertices accepts southwest and northeast points as tuples and the area as a float\n",
    "sw_point = (sw_lat, sw_lon)\n",
    "ne_point = (ne_lat, ne_lon)\n",
    "\n",
    "# Now you can call your find_rectangle_vertices function\n",
    "# Here's a placeholder for how the call might look, but it depends on the actual signature of your function\n",
    "vertices = find_rectangle_vertices(SW=sw_point, NE=ne_point, Area=area)\n",
    "\n",
    "# Print the results or process them as needed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16.634693, 16.832132, 16.988735, 16.505928, 10.415322, 16.681196]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_heights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Label                                         A  \\\n",
      "0      1         (42.271187999999995, -71.0849068)   \n",
      "1      2         (42.271187999999995, -71.0849068)   \n",
      "2      1         (42.271228099999995, -71.0849289)   \n",
      "3      2         (42.271228099999995, -71.0849289)   \n",
      "4      1  (42.271233699999996, -71.08487250000002)   \n",
      "5      2  (42.271233699999996, -71.08487250000002)   \n",
      "6      1                 (42.2712703, -71.0849361)   \n",
      "7      2                 (42.2712703, -71.0849361)   \n",
      "8      1                  (42.2711889, -71.084957)   \n",
      "9      2                  (42.2711889, -71.084957)   \n",
      "10     1          (42.2712104, -71.08491529999999)   \n",
      "11     2          (42.2712104, -71.08491529999999)   \n",
      "\n",
      "                                    B                                      C  \\\n",
      "0   (42.271236699999996, -71.0848022)  (42.2711731803469, -71.0848910023426)   \n",
      "1   (42.271236699999996, -71.0848022)  (42.2712067011198, -71.0849142608726)   \n",
      "2    (42.2712808, -71.08485449999999)  (42.2712162269834, -71.0849072328427)   \n",
      "3    (42.2712808, -71.08485449999999)  (42.2712473765841, -71.0849401150622)   \n",
      "4           (42.2712994, -71.0848183)  (42.2712270864742, -71.0848500564415)   \n",
      "5           (42.2712994, -71.0848183)  (42.2712482910415, -71.0848883624112)   \n",
      "6   (42.271315099999995, -71.0848474)  (42.2712574526824, -71.0849211301514)   \n",
      "7   (42.271315099999995, -71.0848474)  (42.2712870260879, -71.0849433902299)   \n",
      "8           (42.2712888, -71.0849053)  (42.2711866978064, -71.0849494019169)   \n",
      "9           (42.2712888, -71.0849053)  (42.2711918418865, -71.0849642152787)   \n",
      "10   (42.2712356, -71.08486359999999)  (42.2712015570220, -71.0849042683491)   \n",
      "11   (42.2712356, -71.08486359999999)  (42.2712225605034, -71.0849195254579)   \n",
      "\n",
      "                                        D  Sampleheight  \n",
      "0   (42.2712515196531, -71.0848179976574)     16.634693  \n",
      "1   (42.2712179988802, -71.0847947391274)     16.634693  \n",
      "2   (42.2712926730166, -71.0848761671573)     16.832132  \n",
      "3   (42.2712615234159, -71.0848432849378)     16.832132  \n",
      "4   (42.2713060135258, -71.0848407435585)     16.988735  \n",
      "5   (42.2712848089585, -71.0848024375888)     16.988735  \n",
      "6   (42.2713279473176, -71.0848623698486)     16.505928  \n",
      "7   (42.2712983739121, -71.0848401097701)     16.505928  \n",
      "8   (42.2712910021936, -71.0849128980831)     10.415322  \n",
      "9   (42.2712858581135, -71.0848980847213)     10.415322  \n",
      "10  (42.2712444429780, -71.0848746316509)     16.681196  \n",
      "11  (42.2712234394966, -71.0848593745420)     16.681196  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a list to hold dictionaries for each row\n",
    "data_rows = []\n",
    "\n",
    "# Loop through each sample in the JSON data\n",
    "for sample in roofSegmentStats_data:\n",
    "    # Extract the bounding box and the area from the sample\n",
    "    SW_lat = sample['boundingBox']['sw']['latitude']\n",
    "    SW_lon = sample['boundingBox']['sw']['longitude']\n",
    "    NE_lat = sample['boundingBox']['ne']['latitude']\n",
    "    NE_lon = sample['boundingBox']['ne']['longitude']\n",
    "    Area = sample['stats']['groundAreaMeters2']\n",
    "    sample_height = sample['planeHeightAtCenterMeters']\n",
    "    \n",
    "    # Convert SW and NE to tuples\n",
    "    SW = (SW_lat, SW_lon)\n",
    "    NE = (NE_lat, NE_lon)\n",
    "    \n",
    "    # Calculate the rectangle vertices using the function\n",
    "    rectangle_vertices = find_rectangle_vertices(NE, SW, Area)\n",
    "    \n",
    "    # Check if two results are found and append them to the list with labels\n",
    "    if rectangle_vertices and len(rectangle_vertices) == 2:\n",
    "        data_rows.append({\n",
    "            'Label': '1',\n",
    "            'A': rectangle_vertices[0][0],\n",
    "            'B': rectangle_vertices[0][1],\n",
    "            'C': rectangle_vertices[0][2],\n",
    "            'D': rectangle_vertices[0][3],\n",
    "            'Sampleheight': sample_height\n",
    "        })\n",
    "        data_rows.append({\n",
    "            'Label': '2',\n",
    "            'A': rectangle_vertices[1][0],\n",
    "            'B': rectangle_vertices[1][1],\n",
    "            'C': rectangle_vertices[1][2],\n",
    "            'D': rectangle_vertices[1][3],\n",
    "            'Sampleheight': sample_height\n",
    "        })\n",
    "\n",
    "# Create a DataFrame from the list of dictionaries\n",
    "df_results = pd.DataFrame(data_rows)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df_results)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Label</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Sampleheight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(42.271187999999995, -71.0849068)</td>\n",
       "      <td>(42.271236699999996, -71.0848022)</td>\n",
       "      <td>(42.2711731803469, -71.0848910023426)</td>\n",
       "      <td>(42.2712515196531, -71.0848179976574)</td>\n",
       "      <td>16.634693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>(42.271228099999995, -71.0849289)</td>\n",
       "      <td>(42.2712808, -71.08485449999999)</td>\n",
       "      <td>(42.2712162269834, -71.0849072328427)</td>\n",
       "      <td>(42.2712926730166, -71.0848761671573)</td>\n",
       "      <td>16.832132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>(42.271233699999996, -71.08487250000002)</td>\n",
       "      <td>(42.2712994, -71.0848183)</td>\n",
       "      <td>(42.2712270864742, -71.0848500564415)</td>\n",
       "      <td>(42.2713060135258, -71.0848407435585)</td>\n",
       "      <td>16.988735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>(42.2712703, -71.0849361)</td>\n",
       "      <td>(42.271315099999995, -71.0848474)</td>\n",
       "      <td>(42.2712574526824, -71.0849211301514)</td>\n",
       "      <td>(42.2713279473176, -71.0848623698486)</td>\n",
       "      <td>16.505928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>(42.2711889, -71.084957)</td>\n",
       "      <td>(42.2712888, -71.0849053)</td>\n",
       "      <td>(42.2711866978064, -71.0849494019169)</td>\n",
       "      <td>(42.2712910021936, -71.0849128980831)</td>\n",
       "      <td>10.415322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label                                         A  \\\n",
       "0     1         (42.271187999999995, -71.0849068)   \n",
       "2     1         (42.271228099999995, -71.0849289)   \n",
       "4     1  (42.271233699999996, -71.08487250000002)   \n",
       "6     1                 (42.2712703, -71.0849361)   \n",
       "8     1                  (42.2711889, -71.084957)   \n",
       "\n",
       "                                   B                                      C  \\\n",
       "0  (42.271236699999996, -71.0848022)  (42.2711731803469, -71.0848910023426)   \n",
       "2   (42.2712808, -71.08485449999999)  (42.2712162269834, -71.0849072328427)   \n",
       "4          (42.2712994, -71.0848183)  (42.2712270864742, -71.0848500564415)   \n",
       "6  (42.271315099999995, -71.0848474)  (42.2712574526824, -71.0849211301514)   \n",
       "8          (42.2712888, -71.0849053)  (42.2711866978064, -71.0849494019169)   \n",
       "\n",
       "                                       D  Sampleheight  \n",
       "0  (42.2712515196531, -71.0848179976574)     16.634693  \n",
       "2  (42.2712926730166, -71.0848761671573)     16.832132  \n",
       "4  (42.2713060135258, -71.0848407435585)     16.988735  \n",
       "6  (42.2713279473176, -71.0848623698486)     16.505928  \n",
       "8  (42.2712910021936, -71.0849128980831)     10.415322  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split into lable equal to 1 and label equal to 2\n",
    "df_results_1 = df_results[df_results['Label'] == '1']\n",
    "df_results_2 = df_results[df_results['Label'] == '2']\n",
    "\n",
    "df_results_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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.patches as patches\n",
    "\n",
    "def draw_rectangles(df):\n",
    "    # Create a new figure and axis for the plot\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    \n",
    "    # Define colors for each label\n",
    "    color_map = {1: 'blue', 2: 'green'}\n",
    "    \n",
    "    # Iterate over the DataFrame rows\n",
    "    for index, row in df.iterrows():\n",
    "        # Extract points A, B, C, D and label\n",
    "        A = row['A']\n",
    "        B = row['B']\n",
    "        C = row['C']\n",
    "        D = row['D']\n",
    "        label = row['Label']\n",
    "        \n",
    "        # Extract the x and y coordinates\n",
    "        x_coords = [A[1], C[1], B[1], D[1], A[1]]\n",
    "        y_coords = [A[0], C[0], B[0], D[0], A[0]]\n",
    "        \n",
    "        # Draw the rectangle\n",
    "        ax.plot(x_coords, y_coords, color=color_map[int(label)])\n",
    "        \n",
    "        # Add a patch to the plot with a semi-transparent fill\n",
    "        rect = patches.Polygon(xy=list(zip(x_coords, y_coords)), closed=True, fill=True, color=color_map[int(label)], alpha=0.3)\n",
    "        ax.add_patch(rect)\n",
    "    \n",
    "    # Set the aspect of the plot to be equal\n",
    "    ax.set_aspect('equal')\n",
    "    \n",
    "    # Set labels and title\n",
    "    ax.set_xlabel('Longitude')\n",
    "    ax.set_ylabel('Latitude')\n",
    "    ax.set_title('Rectangles Plot')\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "# Assuming 'df' is the DataFrame containing your data\n",
    "draw_rectangles(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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: 72\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "from math import radians, sin, cos, sqrt, atan2\n",
    "def find_smallest_bounding_box(df):\n",
    "    # Extract all latitudes and longitudes from the DataFrame\n",
    "    all_lats = []\n",
    "    all_lons = []\n",
    "    for column in ['A', 'B', 'C', 'D']:\n",
    "        all_lats.extend(df[column].apply(lambda x: x[0]))\n",
    "        all_lons.extend(df[column].apply(lambda x: x[1]))\n",
    "\n",
    "    # Find the min and max of latitudes and longitudes\n",
    "    min_lat = min(all_lats)\n",
    "    max_lat = max(all_lats)\n",
    "    min_lon = min(all_lons)\n",
    "    max_lon = max(all_lons)\n",
    "\n",
    "    # SW and NE corners\n",
    "    sw_corner = (min_lat, min_lon)\n",
    "    ne_corner = (max_lat, max_lon)\n",
    "\n",
    "    return sw_corner, ne_corner\n",
    "\n",
    "box = find_smallest_bounding_box(df_results_1)\n",
    "\n",
    "# Extract main bounding box\n",
    "main_box = [\n",
    "    box[0][0],\n",
    "    box[0][1],\n",
    "    box[1][0],\n",
    "    box[1][1]\n",
    "]\n",
    "    \n",
    "\n",
    "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",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum distance between neighboring panels: 62.68302449734502 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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'panel_centers' 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 51\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[39mreturn\u001b[39;00m panel_numbers, panel_colors\n\u001b[1;32m     49\u001b[0m \u001b[39m# Example usage\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[39m# Assuming 'df_results' is your DataFrame and 'panel_centers' is already defined\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m panel_numbers, panel_colors \u001b[39m=\u001b[39m assign_panel_numbers_and_colors(panel_centers, df_results)\n\u001b[1;32m     53\u001b[0m \u001b[39m# Access the global DataFrame for later use\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[39mprint\u001b[39m(panel_df)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'panel_centers' is not defined"
     ]
    }
   ],
   "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, df):\n",
    "    global panel_df  # Indicate that we are using the global variable\n",
    "    panel_info = []\n",
    "    panel_numbers = {}\n",
    "    panel_colors = {}\n",
    "    \n",
    "    # Iterate over each panel center\n",
    "    for i, panel_center in enumerate(panel_centers):\n",
    "        lat, lon = panel_center\n",
    "        assigned_sample = None\n",
    "        highest_sample_height = -1\n",
    "        \n",
    "        # Iterate over each row in the DataFrame instead of a dictionary\n",
    "        for index, row in df.iterrows():\n",
    "            # Extract bounding box coordinates and sample height\n",
    "            sw_lat, sw_lon = row['A'][0], row['A'][1]\n",
    "            ne_lat, ne_lon = row['C'][0], row['C'][1]\n",
    "            sample_height = row['Sampleheight']\n",
    "            \n",
    "            # Check if the panel center is within the bounding box\n",
    "            if sw_lat <= lat <= ne_lat and sw_lon <= lon <= ne_lon:\n",
    "                # Check if this is the highest sample so far\n",
    "                if sample_height > highest_sample_height:\n",
    "                    assigned_sample = index  # Use the DataFrame index as the sample name\n",
    "                    highest_sample_height = sample_height\n",
    "\n",
    "        # If a sample was assigned, record panel information\n",
    "        if assigned_sample is not None:\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",
    "# Example usage\n",
    "# Assuming 'df_results' is your DataFrame and 'panel_centers' is already defined\n",
    "panel_numbers, panel_colors = assign_panel_numbers_and_colors(panel_centers, df_results)\n",
    "\n",
    "# Access the global DataFrame for later use\n",
    "print(panel_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
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       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_6cb493f786a276c7e51340b270b909e3&quot; &gt;&lt;/div&gt;\n",
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       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
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       "<folium.folium.Map at 0x7fe98f7b99f0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# Function to plot a single rectangle on a Folium map\n",
    "def plot_rectangle_folium(vertices):\n",
    "    # Initialize the map centered around the first vertex\n",
    "    central_lat = vertices[0][0]\n",
    "    central_lon = vertices[0][1]\n",
    "    m = folium.Map(location=[central_lat, central_lon], zoom_start=18)\n",
    "\n",
    "    # Define the points of the rectangle ACBD\n",
    "    A, B, C, D = vertices\n",
    "\n",
    "    # Points list must start and end at the same point to close the polygon\n",
    "    points = [A, C, B, D, A]\n",
    "\n",
    "    # Create a Polygon for the rectangle and add it to the map\n",
    "    folium.Polygon(\n",
    "        locations=points,  # List of points defining the Polygon's shape\n",
    "        color='blue',  # Color of the rectangle\n",
    "        weight=1,\n",
    "        fill=True,\n",
    "        fill_opacity=0.5\n",
    "    ).add_to(m)\n",
    "\n",
    "    # Return the Folium map object\n",
    "    return m\n",
    "\n",
    "# Assume vertices is a list of tuples returned from find_rectangle_vertices\n",
    "# vertices = find_rectangle_vertices(SW=sw_point, NE=ne_point, Area=area)\n",
    "# For example, let's say vertices looks like this:\n",
    "# vertices = [(latA, lonA), (latC, lonC), (latB, lonB), (latD, lonD)]\n",
    "\n",
    "plot_rectangle_folium(vertices[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_614a5277ec212723e0b130780edccfdd {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_614a5277ec212723e0b130780edccfdd&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_614a5277ec212723e0b130780edccfdd = L.map(\n",
       "                &quot;map_614a5277ec212723e0b130780edccfdd&quot;,\n",
       "                {\n",
       "                    center: [42.271184399999996, -71.08495789999999],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 17,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_548122a5d3941480b693d4d8f40ff072 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_614a5277ec212723e0b130780edccfdd);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fe98de0ecb0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# Initialize the map centered around the first point\n",
    "first_point = vertices[0][0]\n",
    "\n",
    "m = folium.Map(location=first_point, zoom_start=17)\n",
    "def plot_rectangle(a, c, b, d, label, map):\n",
    "    # Define the points of the rectangle ACBD\n",
    "    points = [a, c, b, d, a]\n",
    "\n",
    "    # Create a Polygon for the rectangle and add it to the map\n",
    "    folium.Polygon(\n",
    "        locations=points,  # List of points defining the Polygon's shape\n",
    "        color='blue' if label == 1 else 'gray',  # Color of the rectangle blue or no color\n",
    "        weight=1,\n",
    "        fill=True,\n",
    "        fill_opacity=0.5\n",
    "    ).add_to(map)\n",
    "\n",
    "# Show the map\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42.2712703, -71.0849361) (42.271315099999995, -71.0848474) (42.2712878812111, -71.0849444984180) (42.2712975187888, -71.0848390015820)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A, B, C1, D1 = df_results.iloc[7][['A', 'B', 'C', 'D']]\n",
    "print (A , B , C1 , D1 )\n",
    "lat1, lon1 = A\n",
    "lat2, lon2 = B\n",
    "lat_center = (lat1 + lat2) / 2\n",
    "lon_center = (lon1 + lon2) / 2\n",
    "center = (lat_center, lon_center)\n",
    "d = sqrt((lat2 - lat1)**2 + (lon2 - lon1)**2)*METERS_PER_DEGREE_LAT\n",
    "r = d / 2\n",
    "\n",
    "# Convert the radius from meters to degrees\n",
    "r_lat = r / METERS_PER_DEGREE_LAT\n",
    "r_lon = r / METERS_PER_DEGREE_LON\n",
    "\n",
    "# Data for the circumcircle\n",
    "theta = np.linspace(0, 2 * np.pi, 100)\n",
    "x_circle = lat_center + r_lat * np.cos(theta)  # Latitude values\n",
    "y_circle = lon_center + r_lon * np.sin(theta)  # Longitude values\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(x_circle, y_circle, linestyle='--', label='Circumcircle')\n",
    "plt.scatter(*zip(*[A, B, C1, D1, center]), c=['red', 'green', 'blue', 'blue', 'black'])\n",
    "\n",
    "\n",
    "# Label points\n",
    "plt.text(A[0], A[1], 'A', fontsize=12, ha='right')\n",
    "plt.text(B[0], B[1], 'B', fontsize=12, ha='right')\n",
    "plt.text(C1[0], C1[1], 'C1', fontsize=12, ha='right')\n",
    "plt.text(D1[0], D1[1], 'D1', fontsize=12, ha='right')\n",
    "plt.text(center[0], center[1], 'Center', fontsize=12, ha='right')\n",
    "# Draw rectangles\n",
    "rectangle1 = np.array([A, C1, B, D1, A])\n",
    "plt.plot(rectangle1[:,0], rectangle1[:,1], 'r-')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Rectangles and Circumcircle')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
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       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
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       "                #map_64cd7eab8a99ed3990d5d30468bf90ca {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
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       "                }\n",
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       "            &lt;/style&gt;\n",
       "        \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_64cd7eab8a99ed3990d5d30468bf90ca&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_64cd7eab8a99ed3990d5d30468bf90ca = L.map(\n",
       "                &quot;map_64cd7eab8a99ed3990d5d30468bf90ca&quot;,\n",
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       "                    center: [42.27121235, -71.0848545],\n",
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       "            \n",
       "\n",
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       "    \n",
       "            var tile_layer_a77c2b9f8af96511eda903ba71340ce1 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_64cd7eab8a99ed3990d5d30468bf90ca);\n",
       "        \n",
       "    \n",
       "            var circle_e570fa03f86649cf4ec6c5ee8b4545ea = L.circle(\n",
       "                [42.271187999999995, -71.0849068],\n",
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       "            ).addTo(map_64cd7eab8a99ed3990d5d30468bf90ca);\n",
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       "            ).addTo(map_64cd7eab8a99ed3990d5d30468bf90ca);\n",
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       "            ).addTo(map_64cd7eab8a99ed3990d5d30468bf90ca);\n",
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       "            ).addTo(map_64cd7eab8a99ed3990d5d30468bf90ca);\n",
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fe9941f6b00>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# Define coordinates for the points\n",
    "points =  results[1]\n",
    "# Calculate center of the map\n",
    "lat_center = sum(p[0] for p in points) / len(points)\n",
    "lon_center = sum(p[1] for p in points) / len(points)\n",
    "\n",
    "# Create a map centered around the points\n",
    "m = folium.Map(location=[lat_center, lon_center], zoom_start=18)\n",
    "\n",
    "# Add points to the map\n",
    "for point in points:\n",
    "    folium.Circle(point, radius=1, color='red').add_to(m)\n",
    "\n",
    "# Display the map\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85030.93318620656\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Average latitude for North America (example: 40 degrees North)\n",
    "average_latitude = 40\n",
    "\n",
    "# Constants for distance calculations\n",
    "METERS_PER_DEGREE_LAT = 111000  # Approximate meters per degree of latitude\n",
    "METERS_PER_DEGREE_LON = METERS_PER_DEGREE_LAT * math.cos(math.radians(average_latitude))\n",
    "print(METERS_PER_DEGREE_LON)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: d = 0.000115381324312536\n",
      "Sample 1: area = 25.707256\n",
      "Sample 1: SW = (42.271187999999995, -71.0849068), NE = (42.271236699999996, -71.0848022), C = None, D = None\n",
      "Sample 2: d = 0.0000911737352579643\n",
      "Sample 2: area = 22.291653\n",
      "Sample 2: SW = (42.271228099999995, -71.0849289), NE = (42.2712808, -71.08485449999999), C = None, D = None\n",
      "Sample 3: d = 0.0000851711805863390\n",
      "Sample 3: area = 20.261522\n",
      "Sample 3: SW = (42.271233699999996, -71.08487250000002), NE = (42.2712994, -71.0848183), C = None, D = None\n",
      "Sample 4: d = 0.0000993716760369290\n",
      "Sample 4: area = 19.979897\n",
      "Sample 4: SW = (42.2712703, -71.0849361), NE = (42.271315099999995, -71.0848474), C = None, D = None\n",
      "Sample 5: d = 0.000112485110127748\n",
      "Sample 5: area = 10.486612\n",
      "Sample 5: SW = (42.2711889, -71.084957), NE = (42.2712888, -71.0849053), C = None, D = None\n",
      "Sample 6: d = 0.0000575146068388128\n",
      "Sample 6: area = 7.98301\n",
      "Sample 6: SW = (42.2712104, -71.08491529999999), NE = (42.2712356, -71.08486359999999), C = None, D = None\n"
     ]
    }
   ],
   "source": [
    "import sympy as sp\n",
    "\n",
    "import math\n",
    "\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "\n",
    "    # Convert decimal degrees to radians\n",
    "    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])\n",
    "\n",
    "    # Haversine formula\n",
    "    dlat = lat2 - lat1\n",
    "    dlon = lon2 - lon1\n",
    "    a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n",
    "    c = 2 * math.asin(math.sqrt(a))\n",
    "    \n",
    "    # Radius of earth in kilometers is approximately 6,371 km\n",
    "    r = 6371e3  # in meters\n",
    "    return c * r\n",
    "\n",
    "def diagonal_distance(ax, ay, bx, by):\n",
    "    return sp.sqrt((ax - bx)**2 + (ay - by)**2)\n",
    "\n",
    "def find_rectangle_sides(area, diagonal):\n",
    "    w, h = sp.symbols('w h')\n",
    "    equations = (w * h - area, w**2 + h**2 - diagonal**2)\n",
    "    solutions = sp.solve(equations, (w, h))\n",
    "    \n",
    "    # Check if solutions is a list of tuples (indicating multiple solutions)\n",
    "    if isinstance(solutions, list) and all(isinstance(sol, tuple) for sol in solutions):\n",
    "        return [(abs(sol[0]), abs(sol[1])) for sol in solutions if sol[0].is_real and sol[1].is_real]\n",
    "    # Check if solutions is a single tuple (indicating a single solution)\n",
    "    elif isinstance(solutions, tuple):\n",
    "        return [(abs(solutions[0]), abs(solutions[1]))] if solutions[0].is_real and solutions[1].is_real else []\n",
    "    # If solutions is not a list or tuple, it's an unexpected return type\n",
    "    else:\n",
    "        return []\n",
    "\n",
    "def find_rectangle_points(ax, ay, bx, by, area):\n",
    "    d = diagonal_distance(ax, ay, bx, by)\n",
    "    w_h_pairs = find_rectangle_sides(area, d)\n",
    "    if not w_h_pairs:\n",
    "        return None, None\n",
    "    w, h = w_h_pairs[0]\n",
    "    cx, cy = ax + w, ay\n",
    "    dx, dy = cx, cy + h\n",
    "    return (cx, cy), (dx, dy)\n",
    "\n",
    "# Function to process each sample box in the data\n",
    "def process_sample_boxes(roofSegmentStats_data):\n",
    "    for i, sample in enumerate(roofSegmentStats_data):\n",
    "        sw_lat = sample['boundingBox']['sw']['latitude']\n",
    "        sw_long = sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat = sample['boundingBox']['ne']['latitude']\n",
    "        ne_long = sample['boundingBox']['ne']['longitude']\n",
    "\n",
    "        # Calculate the diagonal distance\n",
    "        d = diagonal_distance(sw_lat, sw_long, ne_lat, ne_long)\n",
    "        # change d to meters\n",
    "        def convert_to_meters(d):\n",
    "            return d * 111000\n",
    "        print (f\"Sample {i+1}: d = {d}\")\n",
    "\n",
    "        # Get the area from the sample data\n",
    "        area = sample['stats']['areaMeters2']\n",
    "        print(f\"Sample {i+1}: area = {area}\")\n",
    "        \n",
    "        # Calculate points C and D for the rectangle\n",
    "        C, D = find_rectangle_points(sw_lat, sw_long, ne_lat, ne_long, area)\n",
    "        \n",
    "        # Store the results in the sample data\n",
    "        sample['boundingBox']['alternativeBox'] = {'C': C, 'D': D}\n",
    "\n",
    "        # Output the results\n",
    "        print(f\"Sample {i+1}: SW = ({sw_lat}, {sw_long}), NE = ({ne_lat}, {ne_long}), C = {C}, D = {D}\")\n",
    "\n",
    "# Call the function with your actual roofSegmentStats_data from the JSON file\n",
    "process_sample_boxes(roofSegmentStats_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: SW = (42.271187999999995, -71.0849068), NE = (42.271236699999996, -71.0848022), C = (-71.08480711982187, 42.27113651130026), D = (-71.08489405206389, 42.27120161332616)\n",
      "Sample 2: SW = (42.271228099999995, -71.0849289), NE = (42.2712808, -71.08485449999999), C = (-71.08484672620763, 42.271185654071246), D = (-71.08491549079878, 42.27124241947167)\n",
      "Sample 3: SW = (42.271233699999996, -71.08487250000002), NE = (42.2712994, -71.0848183), C = (-71.08478909733158, 42.27119061922276), D = (-71.08486049157206, 42.27124652358111)\n",
      "Sample 4: SW = (42.2712703, -71.0849361), NE = (42.271315099999995, -71.0848474), C = (-71.08484958007159, 42.27122560912674), D = (-71.08492468510437, 42.271282489770506)\n",
      "Sample 5: SW = (42.2711889, -71.084957), NE = (42.2712888, -71.0849053), C = (-71.08483886458247, 42.271127878537555), D = (-71.08495261218931, 42.271193585685474)\n",
      "Sample 6: SW = (42.2712104, -71.08491529999999), NE = (42.2712356, -71.08486359999999), C = (-71.08486584479459, 42.2711848544848), D = (-71.08490732100204, 42.2712189206473)\n"
     ]
    }
   ],
   "source": [
    "import sympy as sp\n",
    "\n",
    "def find_rectangle_sides(area, diagonal):\n",
    "    # Define symbols for width (w) and height (h)\n",
    "    w, h = sp.symbols('w h', real=True)\n",
    "\n",
    "    # Set up the system of equations based on the area and the length of the diagonal\n",
    "    equations = (w * h - area, w**2 + h**2 - diagonal**2)\n",
    "\n",
    "    # Solve the system of equations\n",
    "    solutions = sp.solve(equations, (w, h))\n",
    "\n",
    "    # Filter the solutions to find the pair where w and h are real and positive\n",
    "    real_solutions = []\n",
    "    for sol in solutions:\n",
    "        if isinstance(sol, tuple) and all(val.is_real and val > 0 for val in sol):\n",
    "            real_solutions.append((abs(sol[0]), abs(sol[1])))\n",
    "\n",
    "    # Ensure that the solutions indeed form a rectangle by checking the dot product\n",
    "    # In this context, since the sides are aligned with the coordinate axes, one side would be a vector along the x-axis and the other along the y-axis\n",
    "    # Hence, their dot product is inherently zero, and we don't need an explicit check here.\n",
    "\n",
    "    return real_solutions\n",
    "\n",
    "\n",
    "\n",
    "# Function to calculate points C and D in UTM\n",
    "def find_rectangle_points_utm(ax, ay, bx, by, area):\n",
    "    # Convert to UTM coordinates\n",
    "    ax, ay = latlon_to_utm(ax, ay)\n",
    "    bx, by = latlon_to_utm(bx, by)\n",
    "\n",
    "    # Calculate the diagonal distance in UTM\n",
    "    diagonal = ((bx - ax) ** 2 + (by - ay) ** 2) ** 0.5\n",
    "\n",
    "    # Use the function to find the sides of the rectangle\n",
    "    w_h_pairs = find_rectangle_sides(area, diagonal)\n",
    "    \n",
    "    # If there's no valid solution, return None\n",
    "    if not w_h_pairs:\n",
    "        return None, None\n",
    "    \n",
    "    # We choose the first pair of sides as a valid solution (width, height)\n",
    "    w, h = w_h_pairs[0]\n",
    "\n",
    "    # Calculate points C and D in UTM\n",
    "    # Assuming the width is along the x-axis\n",
    "    cx, cy = ax + w, ay\n",
    "    dx, dy = ax, ay + h\n",
    "\n",
    "    return (cx, cy), (dx, dy)\n",
    "\n",
    "# Function to process each sample box in the data\n",
    "def process_sample_boxes(roofSegmentStats_data):\n",
    "    for i, sample in enumerate(roofSegmentStats_data):\n",
    "        # Extract latitude and longitude from the sample\n",
    "        sw_lat = sample['boundingBox']['sw']['latitude']\n",
    "        sw_long = sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat = sample['boundingBox']['ne']['latitude']\n",
    "        ne_long = sample['boundingBox']['ne']['longitude']\n",
    "\n",
    "        # Get the area from the sample data\n",
    "        area = sample['stats']['areaMeters2']\n",
    "        \n",
    "        # Calculate points C and D for the rectangle in UTM\n",
    "        C_utm, D_utm = find_rectangle_points_utm(sw_lat, sw_long, ne_lat, ne_long, area)\n",
    "\n",
    "        # Only proceed if a valid solution was found\n",
    "        if C_utm and D_utm:\n",
    "            # Convert C and D back to latitude and longitude\n",
    "            C_lat, C_lon = utm_projection(C_utm[0], C_utm[1], inverse=True)\n",
    "            D_lat, D_lon = utm_projection(D_utm[0], D_utm[1], inverse=True)\n",
    "\n",
    "            # Store the results in the sample data\n",
    "            sample['boundingBox']['alternativeBox'] = {\n",
    "                'C': {'latitude': C_lat, 'longitude': C_lon},\n",
    "                'D': {'latitude': D_lat, 'longitude': D_lon}\n",
    "            }\n",
    "\n",
    "            # Output the results\n",
    "            print(f\"Sample {i+1}: SW = ({sw_lat}, {sw_long}), NE = ({ne_lat}, {ne_long}), C = ({C_lat}, {C_lon}), D = ({D_lat}, {D_lon})\")\n",
    "        else:\n",
    "            print(f\"Sample {i+1}: No valid rectangle can be formed with the given area and diagonal distance.\")\n",
    "\n",
    "process_sample_boxes(roofSegmentStats_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "either zone_letter or northern needs to be set",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[47], line 80\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39mDataFrame(data_for_df)\n\u001b[1;32m     75\u001b[0m \u001b[39m# Assuming roofSegmentStats_data is provided and contains the necessary information\u001b[39;00m\n\u001b[1;32m     76\u001b[0m \u001b[39m# Replace with the actual data\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[39m# roofSegmentStats_data = [...] \u001b[39;00m\n\u001b[1;32m     78\u001b[0m \n\u001b[1;32m     79\u001b[0m \u001b[39m# Uncomment the following line and replace with the actual data\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m df_results \u001b[39m=\u001b[39m process_sample_boxes(roofSegmentStats_data)\n\u001b[1;32m     81\u001b[0m df_results\u001b[39m.\u001b[39mhead()  \u001b[39m# Display the first few rows of the DataFrame\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[47], line 44\u001b[0m, in \u001b[0;36mprocess_sample_boxes\u001b[0;34m(roofSegmentStats_data)\u001b[0m\n\u001b[1;32m     42\u001b[0m C_utm, D_utm \u001b[39m=\u001b[39m find_rectangle_points_utm(sw_lat, sw_long, ne_lat, ne_long, area)\n\u001b[1;32m     43\u001b[0m \u001b[39mif\u001b[39;00m C_utm \u001b[39mand\u001b[39;00m D_utm:\n\u001b[0;32m---> 44\u001b[0m     C_lat, C_lon \u001b[39m=\u001b[39m utm\u001b[39m.\u001b[39;49mto_latlon(C_utm[\u001b[39m0\u001b[39;49m], C_utm[\u001b[39m1\u001b[39;49m], utm\u001b[39m.\u001b[39;49mlatlon_to_zone_number(sw_lat, sw_long))\n\u001b[1;32m     45\u001b[0m     D_lat, D_lon \u001b[39m=\u001b[39m utm\u001b[39m.\u001b[39mto_latlon(D_utm[\u001b[39m0\u001b[39m], D_utm[\u001b[39m1\u001b[39m], utm\u001b[39m.\u001b[39mlatlon_to_zone_number(sw_lat, sw_long))\n\u001b[1;32m     46\u001b[0m     data_for_df\u001b[39m.\u001b[39mappend({\n\u001b[1;32m     47\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mSample\u001b[39m\u001b[39m'\u001b[39m: i \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m     48\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mSW_lat\u001b[39m\u001b[39m'\u001b[39m: sw_lat,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mValid_Rectangle\u001b[39m\u001b[39m'\u001b[39m: \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m     58\u001b[0m     })\n",
      "File \u001b[0;32m/usr/local/python/3.10.8/lib/python3.10/site-packages/utm/conversion.py:117\u001b[0m, in \u001b[0;36mto_latlon\u001b[0;34m(easting, northing, zone_number, zone_letter, northern, strict)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"This function converts UTM coordinates to Latitude and Longitude\u001b[39;00m\n\u001b[1;32m     81\u001b[0m \n\u001b[1;32m     82\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    114\u001b[0m \n\u001b[1;32m    115\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m zone_letter \u001b[39mand\u001b[39;00m northern \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39meither zone_letter or northern needs to be set\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    119\u001b[0m \u001b[39melif\u001b[39;00m zone_letter \u001b[39mand\u001b[39;00m northern \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    120\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mset either zone_letter or northern, but not both\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: either zone_letter or northern needs to be set"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sympy as sp\n",
    "import utm\n",
    "\n",
    "def find_rectangle_sides(area, diagonal):\n",
    "    # Define symbols for width (w) and height (h)\n",
    "    w, h = sp.symbols('w h', real=True)\n",
    "    # Set up the system of equations based on the area and the length of the diagonal\n",
    "    equations = (w * h - area, w**2 + h**2 - diagonal**2)\n",
    "    # Solve the system of equations\n",
    "    solutions = sp.solve(equations, (w, h))\n",
    "    # Filter the solutions to find the pair where w and h are real and positive\n",
    "    real_solutions = [(abs(sp.re(w_val)), abs(sp.re(h_val))) for w_val, h_val in solutions if sp.re(w_val).is_real and sp.re(h_val).is_real]\n",
    "    return real_solutions\n",
    "\n",
    "def find_rectangle_points_utm(ax, ay, bx, by, area):\n",
    "    # Convert to UTM coordinates\n",
    "    ax, ay, _, _ = utm.from_latlon(ax, ay)\n",
    "    bx, by, _, _ = utm.from_latlon(bx, by)\n",
    "    # Calculate the diagonal distance in UTM\n",
    "    diagonal = ((bx - ax)**2 + (by - ay)**2)**0.5\n",
    "    # Use the function to find the sides of the rectangle\n",
    "    w_h_pairs = find_rectangle_sides(area, diagonal)\n",
    "    # If there's no valid solution, return None\n",
    "    if not w_h_pairs:\n",
    "        return None, None\n",
    "    # We choose the first pair of sides as a valid solution (width, height)\n",
    "    w, h = w_h_pairs[0]\n",
    "    # Calculate points C and D in UTM\n",
    "    cx, cy = ax + w, ay\n",
    "    dx, dy = ax, ay + h\n",
    "    return (cx, cy), (dx, dy)\n",
    "\n",
    "def process_sample_boxes(roofSegmentStats_data):\n",
    "    data_for_df = []\n",
    "    for i, sample in enumerate(roofSegmentStats_data):\n",
    "        sw_lat = sample['boundingBox']['sw']['latitude']\n",
    "        sw_long = sample['boundingBox']['sw']['longitude']\n",
    "        ne_lat = sample['boundingBox']['ne']['latitude']\n",
    "        ne_long = sample['boundingBox']['ne']['longitude']\n",
    "        area = sample['stats']['areaMeters2']\n",
    "        C_utm, D_utm = find_rectangle_points_utm(sw_lat, sw_long, ne_lat, ne_long, area)\n",
    "        if C_utm and D_utm:\n",
    "            C_lat, C_lon = utm.to_latlon(C_utm[0], C_utm[1], utm.latlon_to_zone_number(sw_lat, sw_long))\n",
    "            D_lat, D_lon = utm.to_latlon(D_utm[0], D_utm[1], utm.latlon_to_zone_number(sw_lat, sw_long))\n",
    "            data_for_df.append({\n",
    "                'Sample': i + 1,\n",
    "                'SW_lat': sw_lat,\n",
    "                'SW_long': sw_long,\n",
    "                'NE_lat': ne_lat,\n",
    "                'NE_long': ne_long,\n",
    "                'C_lat': C_lat,\n",
    "                'C_long': C_lon,\n",
    "                'D_lat': D_lat,\n",
    "                'D_long': D_lon,\n",
    "                'Area': area,\n",
    "                'Valid_Rectangle': True\n",
    "            })\n",
    "        else:\n",
    "            data_for_df.append({\n",
    "                'Sample': i + 1,\n",
    "                'SW_lat': sw_lat,\n",
    "                'SW_long': sw_long,\n",
    "                'NE_lat': ne_lat,\n",
    "                'NE_long': ne_long,\n",
    "                'C_lat': None,\n",
    "                'C_long': None,\n",
    "                'D_lat': None,\n",
    "                'D_long': None,\n",
    "                'Area': area,\n",
    "                'Valid_Rectangle': False\n",
    "            })\n",
    "    return pd.DataFrame(data_for_df)\n",
    "\n",
    "# Assuming roofSegmentStats_data is provided and contains the necessary information\n",
    "# Replace with the actual data\n",
    "# roofSegmentStats_data = [...] \n",
    "\n",
    "# Uncomment the following line and replace with the actual data\n",
    "df_results = process_sample_boxes(roofSegmentStats_data)\n",
    "df_results.head()  # Display the first few rows of the DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "# Function to plot a sample\n",
    "def plot_sample(sample, ax, label):\n",
    "    # Extract points\n",
    "    SW = sample['SW'][::-1]  # Reverse to (longitude, latitude)\n",
    "    NE = sample['NE'][::-1]\n",
    "    C = sample['C']\n",
    "    D = sample['D']\n",
    "    \n",
    "    # Calculate the NW and SE points to complete the rectangle\n",
    "    NW = (SW[0], NE[1])\n",
    "    SE = (NE[0], SW[1])\n",
    "    \n",
    "    # Draw the original box (SW, NW, NE, SE)\n",
    "    ax.plot([SW[0], NW[0]], [SW[1], NW[1]], 'ro-')  # West line\n",
    "    ax.plot([NW[0], NE[0]], [NW[1], NE[1]], 'ro-')  # North line\n",
    "    ax.plot([NE[0], SE[0]], [NE[1], SE[1]], 'ro-')  # East line\n",
    "    ax.plot([SE[0], SW[0]], [SE[1], SW[1]], 'ro-')  # South line\n",
    "    \n",
    "    # Draw the alternative box (SW, C, NE, D)\n",
    "    ax.plot([SW[0], C[0]], [SW[1], C[1]], 'bo--')  # South line\n",
    "    ax.plot([C[0], NE[0]], [C[1], NE[1]], 'bo--')  # East line\n",
    "    ax.plot([NE[0], D[0]], [NE[1], D[1]], 'bo--')  # North line\n",
    "    ax.plot([D[0], SW[0]], [D[1], SW[1]], 'bo--')  # West line\n",
    "    \n",
    "    # Label the sample\n",
    "    ax.text(SW[0], SW[1], label, fontsize=9, verticalalignment='bottom', horizontalalignment='right')\n",
    "\n",
    "# Set up the plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.set_title('Sample Boxes in color') \n",
    "\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.set_aspect('equal')\n",
    "ax.grid(True)\n",
    "ax.legend(['Original', 'Alternative'])\n",
    "\n",
    "# Plot each sample with a unique label\n",
    "for i, sample in enumerate(samples):\n",
    "    plot_sample(sample, ax, f'Sample {i+1}')\n",
    "\n",
    "# Set the aspect of the plot to be equal\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "# Show the plot with a grid\n",
    "ax.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First bounding box structure: {'sw': {'latitude': 32.750347, 'longitude': -117.16028879999999}, 'ne': {'latitude': 32.750606399999995, 'longitude': -117.15983460000001}, 'alternativeBox1': {'C': (25208743988795.5 - 64401116725.7851*I, 64401116608.625 + 25208743988762.8*I)}, 'alternativeBox2': {'C': (25208743988795.5 + 64401116725.7851*I, 64401116608.625 - 25208743988762.8*I)}}\n",
      "SW Latitude: 32.750347\n",
      "SW Longitude: -117.16028879999999\n",
      "NE Latitude: 32.750606399999995\n",
      "NE Longitude: -117.15983460000001\n"
     ]
    }
   ],
   "source": [
    "if roofSegmentStats_data:\n",
    "    # Print the structure of the first bounding box\n",
    "    first_sample = roofSegmentStats_data[0]\n",
    "    first_bounding_box = first_sample.get('boundingBox', None)\n",
    "    if first_bounding_box:\n",
    "        print(f\"First bounding box structure: {first_bounding_box}\")\n",
    "        print(f\"SW Latitude: {first_bounding_box['sw']['latitude']}\")\n",
    "        print(f\"SW Longitude: {first_bounding_box['sw']['longitude']}\")\n",
    "        print(f\"NE Latitude: {first_bounding_box['ne']['latitude']}\")\n",
    "        print(f\"NE Longitude: {first_bounding_box['ne']['longitude']}\")\n",
    "    else:\n",
    "        print(\"First sample does not contain a 'boundingBox'.\")\n",
    "else:\n",
    "    print(\"No data in 'roofSegmentStats'.\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0) (4, 3) (0.0, 3.00000000000000) (2.88000000000000, -0.840000000000000) (4.00000000000000, 0) (1.12000000000000, 3.84000000000000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (A , B , C1 , C2 , D1 , D2)\n",
    "center = (x_center, y_center)\n",
    "\n",
    "# Data for the circumcircle\n",
    "theta = np.linspace(0, 2*np.pi, 100)\n",
    "x_circle = center[0] + r * np.cos(theta)\n",
    "y_circle = center[1] + r * np.sin(theta)\n",
    "\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(x_circle, y_circle, linestyle='--', label='Circumcircle')\n",
    "plt.scatter(*zip(*[A, B, C1, C2, center,D1,D2]), c=['red', 'green', 'blue', 'blue', 'purple','black','black'])\n",
    "\n",
    "\n",
    "# Label points\n",
    "plt.text(A[0], A[1], 'A', fontsize=12, ha='right')\n",
    "plt.text(B[0], B[1], 'B', fontsize=12, ha='right')\n",
    "plt.text(C1[0], C1[1], 'C1', fontsize=12, ha='right')\n",
    "plt.text(C2[0], C2[1], 'C2', fontsize=12, ha='right')\n",
    "plt.text(D1[0], D1[1], 'D1', fontsize=12, ha='right')\n",
    "plt.text(D2[0], D2[1], 'D2', fontsize=12, ha='right')\n",
    "plt.text(center[0], center[1], 'Center', fontsize=12, ha='right')\n",
    "\n",
    "# Draw rectangles\n",
    "rectangle1 = np.array([A, C1, B, D1, A])\n",
    "rectangle2 = np.array([A, C2, B, D2, A])\n",
    "plt.plot(rectangle1[:,0], rectangle1[:,1], 'r-')\n",
    "plt.plot(rectangle2[:,0], rectangle2[:,1], 'g-')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Rectangles and Circumcircle')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 2, got 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 50\u001b[0m\n\u001b[1;32m     47\u001b[0m area \u001b[39m=\u001b[39m sample[\u001b[39m'\u001b[39m\u001b[39mstats\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mareaMeters2\u001b[39m\u001b[39m'\u001b[39m]  \u001b[39m# Assuming this is the area of the roof segment\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[39m# Find points C and D for the rectangle\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m C1, D1, C2, D2 \u001b[39m=\u001b[39m find_points_C_and_D(sw_lat, sw_long, ne_lat, ne_long, area)\n\u001b[1;32m     52\u001b[0m \u001b[39m# Store the alternative boxes\u001b[39;00m\n\u001b[1;32m     53\u001b[0m sample[\u001b[39m'\u001b[39m\u001b[39mboundingBox\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39malternativeBox1\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mC\u001b[39m\u001b[39m'\u001b[39m: C1, \u001b[39m'\u001b[39m\u001b[39mD\u001b[39m\u001b[39m'\u001b[39m: D1}\n",
      "Cell \u001b[0;32mIn[9], line 33\u001b[0m, in \u001b[0;36mfind_points_C_and_D\u001b[0;34m(sw_lat, sw_long, ne_lat, ne_long, area)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[39m# Solving the equations\u001b[39;00m\n\u001b[1;32m     32\u001b[0m solutions \u001b[39m=\u001b[39m solve((eq1, eq2, eq3), (x3, y3))\n\u001b[0;32m---> 33\u001b[0m C1, C2 \u001b[39m=\u001b[39m solutions\n\u001b[1;32m     35\u001b[0m \u001b[39m# Calculate corresponding D points by reflecting C points over the midpoint\u001b[39;00m\n\u001b[1;32m     36\u001b[0m D1 \u001b[39m=\u001b[39m (\u001b[39m2\u001b[39m \u001b[39m*\u001b[39m x_center \u001b[39m-\u001b[39m C1[\u001b[39m0\u001b[39m], \u001b[39m2\u001b[39m \u001b[39m*\u001b[39m y_center \u001b[39m-\u001b[39m C1[\u001b[39m1\u001b[39m])\n",
      "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
     ]
    }
   ],
   "source": [
    "from sympy import symbols, Eq, solve, sqrt\n",
    "\n",
    "# Function to calculate the distance between two points\n",
    "def calculate_distance(x1, y1, x2, y2):\n",
    "    # Calculate the distance using Pythagorean theorem\n",
    "    return sqrt((x2 - x1)**2 + (y2 - y1)**2)\n",
    "\n",
    "# Function to find points C and D given two diagonal points of a rectangle and its area\n",
    "def find_points_C_and_D(sw_lat, sw_long, ne_lat, ne_long, area):\n",
    "    # Define symbolic variables for C's coordinates\n",
    "    x3, y3 = symbols('x3 y3')\n",
    "\n",
    "    # Midpoint coordinates (center of the rectangle)\n",
    "    x_center = (sw_lat + ne_lat) / 2\n",
    "    y_center = (sw_long + ne_long) / 2\n",
    "\n",
    "    # Distance between the known SW and NE points\n",
    "    diagonal = calculate_distance(sw_lat, sw_long, ne_lat, ne_long)\n",
    "    r = diagonal / 2  # Radius of the circumcircle\n",
    "\n",
    "    # Other dimension (width or height) using the area and the diagonal\n",
    "    other_dimension = area / diagonal\n",
    "\n",
    "    # Equations to solve\n",
    "    eq1 = Eq((x3 - x_center)**2 + (y3 - y_center)**2, r**2)\n",
    "    eq2 = Eq(sqrt((x3 - sw_lat)**2 + (y3 - sw_long)**2), other_dimension)\n",
    "    eq3 = Eq((x3 - sw_lat) * (ne_lat - x3) + (y3 - sw_long) * (ne_long - y3), 0)\n",
    "\n",
    "    # Solving the equations\n",
    "    solutions = solve((eq1, eq2, eq3), (x3, y3))\n",
    "\n",
    "    # If no solution is found, return None\n",
    "    if not solutions:\n",
    "        return None, None, None, None\n",
    "\n",
    "    # Extract solutions for C1 and C2\n",
    "    C1, C2 = solutions\n",
    "\n",
    "    # Calculate corresponding D points by reflecting C points over the midpoint\n",
    "    D1 = (2 * x_center - C1[0], 2 * y_center - C1[1])\n",
    "    D2 = (2 * x_center - C2[0], 2 * y_center - C2[1])\n",
    "\n",
    "    # Return the coordinates of C and D\n",
    "    return C1, D1, C2, D2\n",
    "\n",
    "# Sample data (structure based on the user's JSON structure)\n",
    "roofSegmentStats_data = [\n",
    "    {'stats': {'areaMeters2': 80.44886},\n",
    "     'boundingBox': {'sw': {'latitude': 42.2541204, 'longitude': -70.96283369999999},\n",
    "                     'ne': {'latitude': 42.2542924, 'longitude': -70.9627678}}},\n",
    "    # ... (other samples would be listed here)\n",
    "]\n",
    "\n",
    "# Test the function with the provided data\n",
    "for sample in roofSegmentStats_data:\n",
    "    sw_lat = sample['boundingBox']['sw']['latitude']\n",
    "    sw_long = sample['boundingBox']['sw']['longitude']\n",
    "    ne_lat = sample['boundingBox']['ne']['latitude']\n",
    "    ne_long = sample['boundingBox']['ne']['longitude']\n",
    "    area = sample['stats']['areaMeters2']\n",
    "\n",
    "    # Find points C and D for the rectangle\n",
    "    C1, D1, C2, D2 = find_points_C_and_D(sw_lat, sw_long, ne_lat, ne_long, area)\n",
    "\n",
    "    # If solutions are found, store the alternative boxes\n",
    "    if C1 and D1 and C2 and D2:\n",
    "        sample['boundingBox']['alternativeBox1'] = {'C': C1, 'D': D1}\n",
    "        sample['boundingBox']['alternativeBox2'] = {'C': C2, 'D': D2}\n",
    "    else:\n",
    "        print(f\"No solutions found for sample with SW: ({sw_lat}, {sw_long}) and NE: ({ne_lat}, {ne_long})\")\n",
    "\n",
    "roofSegmentStats_data  # This should now include the alternative boxes for each sample\n",
    "\n"
   ]
  }
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